Operationsmanagement 919slidespresentation-090928145353-phpapp01

Chapter 1-17
Operations Management

Roberta Russell & Bernard W. Taylor, III

Organization of This Text:
Part I – Operations Management
Intro. to Operations and
Supply Chain Management:
Quality Management:
Statistical Quality Control:
Product Design:
Service Design:
Processes and Technology:
Facilities:
Human Resources:
Project Management:

Chapter 1 (Slide 5)
Chapter 2 (Slide 67)
1 -2

Organization of This Text:
Part II – Supply Chain Management
Supply Chain
Strategy and Design:
Global Supply Chain
Procurement and Distribution:
Forecasting:
Inventory Management:
Sales and
Operations Planning:
Resource Planning:
Lean Systems:
Scheduling:

1 -3

Learning Objectives of
this Course
Gain an appreciation of strategic importance
of operations and supply chain management
in a global business environment
Understand how operations relates to other
business functions
Develop a working knowledge of concepts
and methods related to designing and
managing operations and supply chains
Develop a skill set for quality and process
improvement
1 -4

Chapter 1
Introduction to Operations and
Supply Chain Management

Lecture Outline
What Operations and Supply Chain
Managers Do
Operations Function
Evolution of Operations and Supply Chain
Management
Globalization and Competitiveness
Operations
Strategy and Organization of the Text
Learning Objectives for This Course
1 -6

What Operations and
Supply Chain Managers Do
What is Operations Management?
design, operation, and improvement of productive
systems

What is Operations?
a function or system that transforms inputs into outputs of
greater value

What is a Transformation Process?
a series of activities along a value chain extending from
supplier to customer
activities that do not add value are superfluous and
should be eliminated

1 -7

Transformation Process
Physical: as in manufacturing operations
Locational: as in transportation or
warehouse operations
Exchange: as in retail operations
Physiological: as in health care
Psychological: as in entertainment
Informational: as in communication
1 -8

Operations as a
Transformation Process
INPUT
•Material
•Machines
•Labor
•Management
•Capital

TRANSFORMATION
PROCESS

OUTPUT
•Goods
•Services

Feedback & Requirements
1 -9

Operations Function
Operations
Marketing
Finance and
Accounting
Human
Resources
Outside
Suppliers
1-10

How is Operations Relevant to my
Major?
Accounting
Information
Technology
Management

“As an auditor you must
understand the fundamentals of
operations management.”
“IT is a tool, and there’s no better
place to apply it than in
operations.”
“We use so many things you
learn in an operations class—
class—
scheduling, lean production,
theory of constraints, and tons of
quality tools.”
1-11

How is Operations Relevant to my
Major? (cont.)
Economics
Marketing

Finance

“It’s all about processes. I live
by flowcharts and Pareto
analysis.”
“How can you do a good job
marketing a product if you’re
unsure of its quality or delivery
status?”
“Most of our capital budgeting
requests are from operations,
and most of our cost savings,
too.”
1-12

Evolution of Operations and
Craft production
process of handcrafting products or
services for individual customers

Division of labor
dividing a job into a series of small tasks
each performed by a different worker

Interchangeable parts
standardization of parts initially as
replacement parts; enabled mass
production

1-13

Supply Chain Management (cont.)
Scientific management
systematic analysis of work methods

Mass production
highhigh-volume production of a standardized
product for a mass market

Lean production
adaptation of mass production that prizes
quality and flexibility
1-14

Historical Events in
Era
Industrial
Revolution

Events/Concepts

Dates

Originator

Steam engine
Division of labor
Interchangeable parts
Principles of scientific
management

1769
1776
1790

James Watt

1911

Frederick W. Taylor

Time and motion studies
Scientific
Management Activity scheduling chart
Moving assembly line

1911
1912
1913

Adam Smith
Eli Whitney

Frank and Lillian
Gilbreth
Henry Gantt
Henry Ford

1-15

Operations Management (cont.)
Era

Operations
Research

Dates

Originator

Hawthorne studies
Human
Relations

Events/Concepts

1930
1940s
1950s
1960s
1947
1951

Elton Mayo
Abraham Maslow
Frederick Herzberg
Douglas McGregor
George Dantzig
Remington Rand

1950s

Operations research
groups

1960s,
1970s

Joseph Orlicky, IBM
and others

Motivation theories
Linear programming
Digital computer
Simulation, waiting
line theory, decision
theory, PERT/CPM
MRP, EDI, EFT, CIM

1-16

Era

Events/Concepts Dates Originator

JIT (just-in-time)
TQM (total quality
management)
Strategy and
Quality
Revolution operations
Business process
reengineering
Six Sigma

1970s
1980s
1980s
1990s
1990s

Taiichi Ohno (Toyota)
W. Edwards Deming,
Joseph Juran
Wickham Skinner,
Robert Hayes
Michael Hammer,
James Champy
GE, Motorola

1-17

Era

Events/Concepts

Internet
Revolution

Internet, WWW, ERP,
1990s
supply chain management

E-commerce

Dates Originator

2000s

Globalization WTO, European Union,
1990s
and other trade
2000s
agreements, global supply
chains, outsourcing, BPO,
Services Science

ARPANET, Tim
Berners-Lee SAP,
i2 Technologies,
ORACLE
Amazon, Yahoo,
eBay, Google, and
others
Numerous countries
and companies

1-18

Supply Chain Management (cont.)
Supply chain management
management of the flow of information, products, and services across
a network of customers, enterprises, and supply chain partners

1-19

Globalization and
Competitiveness
Why “go global”?
favorable cost
access to international markets
response to changes in demand
reliable sources of supply
latest trends and technologies

Increased globalization
results from the Internet and falling trade
barriers
1-20

Globalization and
Competitiveness (cont.)

Hourly Compensation Costs for Production Workers
Source: U.S. Bureau of Labor Statistics, 2005.
1-21

Globalization and

World Population Distribution
Source: U.S. Census Bureau, 2006.
1-22

Globalization and

Trade in Goods as % of GDP
(sum of merchandise exports and imports divided by GDP, valued in U.S. dollars)
1-23

Productivity and
Competitiveness
Competitiveness
degree to which a nation can produce goods and
services that meet the test of international
markets

Productivity
ratio of output to input

Output
sales made, products produced, customers
served, meals delivered, or calls answered

Input
labor hours, investment in equipment, material
usage, or square footage

1-24

Productivity and

Measures of Productivity

1-25

Productivity and

Average Annual Growth Rates in Productivity, 1995-2005.
1995Source: Bureau of Labor Statistics. A Chartbook of
International Labor Comparisons. January 2007, p. 28.
1-26

Productivity and

Average Annual Growth Rates in Output and Input, 1995-2005
1995Source: Bureau of Labor Statistics. A Chartbook of International
Labor Comparisons, January 2007, p. 26.

Dramatic Increase in
Output w/ Decrease in
Labor Hours
1-27

Productivity and
Retrenching
productivity is increasing, but both output and input
decrease with input decreasing at a faster rate

Assumption that more input would cause
output to increase at the same rate
certain limits to the amount of output may not be
considered
output produced is emphasized, not output sold;
sold;
increased inventories
1-28

Strategy and Operations
Strategy
Provides direction for achieving a mission

Five Steps for Strategy Formulation
Defining a primary task
What is the firm in the business of doing?

Assessing core competencies
What does the firm do better than anyone else?

Determining order winners and order qualifiers
What qualifies an item to be considered for purchase?
What wins the order?

Positioning the firm
How will the firm compete?

Deploying the strategy
1-29

Strategic Planning
Mission
and Vision

Corporate
Strategy

Marketing
Strategy

Operations
Strategy

Financial
Strategy
1-30

Order Winners
and Order Qualifiers

Source: Adapted from Nigel Slack, Stuart Chambers, Robert Johnston, and Alan
Betts, Operations and Process Management, Prentice Hall, 2006, p. 47
Management,
1-31

Positioning the Firm
Cost
Speed
Quality
Flexibility

1-32

Positioning the Firm:
Cost
Waste elimination
relentlessly pursuing the removal of all waste

Examination of cost structure
looking at the entire cost structure for
reduction potential

Lean production
providing low costs through disciplined
operations

1-33

Speed
fast moves, fast adaptations, tight linkages
Internet
conditioned customers to expect immediate responses

Service organizations
always competed on speed (McDonald’s, LensCrafters, and
Federal Express)

Manufacturers
timetime-based competition: build-to-order production and
build-toefficient supply chains

Fashion industry
twotwo-week design-to-rack lead time of Spanish retailer, Zara
design-to-

1-34

Quality
Minimizing defect rates or conforming to
design specifications; please the customer
RitzRitz-Carlton - one customer at a time
Service system is designed to “move heaven
and earth” to satisfy customer
Every employee is empowered to satisfy a
guest’s wish
Teams at all levels set objectives and devise
quality action plans
Each hotel has a quality leader
1-35

Flexibility
ability to adjust to changes in product mix,
production volume, or design
National Bicycle Industrial Company
offers 11,231,862 variations
delivers within two weeks at costs only 10%
above standard models
mass customization: the mass production of
customization:
customized parts

1-36

Policy Deployment
Policy deployment
translates corporate strategy into measurable
objectives

Hoshins
action plans generated from the policy
deployment process

1-37

Policy Deployment

Derivation of an Action Plan Using Policy Deployment
1-38

Balanced Scorecard
Balanced scorecard
measuring more than financial performance
finances
customers
processes
learning and growing

Key performance indicators
a set of measures that help managers evaluate
performance in critical areas
1-39

Balanced Scorecard
Balanced Scorecard Worksheet

1-40

Balanced Scorecard

Radar Chart

Dashboard

1-41

Operations Strategy
Services
Products

Capacity

Facilities

Human
Resources

Sourcing

Process
and
Technology

Quality

Operating
Systems

1-42

Chapter 1 Supplement
Decision Analysis

Lecture Outline
Decision Analysis
Decision Making without Probabilities
Decision Analysis with Excel
Decision Analysis with OM Tools
Decision Making with Probabilities
Expected Value of Perfect Information
Sequential Decision Tree
Supplement 1-44
1-

Decision Analysis
Quantitative methods
a set of tools for operations manager

Decision analysis
a set of quantitative decision-making
decisiontechniques for decision situations in which
uncertainty exists
Example of an uncertain situation
demand for a product may vary between 0 and 200
units, depending on the state of market

Supplement 1-45
1-

Decision Making
Without Probabilities
States of nature
Events that may occur in the future
Examples of states of nature:
high or low demand for a product
good or bad economic conditions

Decision making under risk
probabilities can be assigned to the occurrence of
states of nature in the future

Decision making under uncertainty
probabilities can NOT be assigned to the
occurrence of states of nature in the future
Supplement 1-46
1-

Payoff Table
Payoff table
method for organizing and illustrating payoffs from different
decisions given various states of nature

Payoff
outcome of a decision

States Of Nature
Decision
a
b
1
Payoff 1a
Payoff 1b
2
Payoff 2a
Payoff 2b
Supplement 1-47
1-

Decision Making Criteria Under
Uncertainty
Maximax
choose decision with the maximum of the
maximum payoffs

Maximin
choose decision with the maximum of the
minimum payoffs

Minimax regret
choose decision with the minimum of the
maximum regrets for each alternative
Supplement 1-48
1-

Decision Making Criteria Under
Uncertainty (cont.)
Hurwicz
choose decision in which decision payoffs are
weighted by a coefficient of optimism, alpha
coefficient of optimism is a measure of a
decision maker’s optimism, from 0 (completely
pessimistic) to 1 (completely optimistic)

Equal likelihood (La Place)
choose decision in which each state of nature is
weighted equally
Supplement 1-49
1-

Southern Textile
Company
STATES OF NATURE
Good Foreign

DECISION
Expand
Maintain status quo
Sell now

Poor Foreign

Competitive Conditions


$ 800,000
1,300,000
320,000

$ 500,000
-150,000
320,000

Supplement 1-50
1-

Maximax Solution
STATES OF NATURE
Good Foreign

DECISION
Expand
Maintain status quo
Sell now
Expand:
Status quo:
Sell:

Poor Foreign



$ 800,000
1,300,000
320,000
$800,000
1,300,000
320,000

$ 500,000
-150,000
320,000

← Maximum
Decision: Maintain status quo
Supplement 1-51
1-

Maximin Solution
STATES OF NATURE
Good Foreign

DECISION
Expand
Maintain status quo
Sell now

Expand:
Status quo:
Sell:

Poor Foreign



$ 800,000
1,300,000
320,000

$500,000
-150,000
320,000

$ 500,000
-150,000
320,000

← Maximum

Decision: Expand
Supplement 1-52
1-

Minimax Regret Solution
Good Foreign

Poor Foreign

$1,300,000 - 800,000 = 500,000
$500,000 - 500,000 = 0
1,300,000 - 1,300,000 = 0
500,000 - (-150,000)= 650,000
1,300,000 - 320,000 = 980,000
500,000 - 320,000= 180,000

Expand:
Status quo:
Sell:

$500,000
650,000
980,000

← Minimum

Decision: Expand
Supplement 1-53
1-

Hurwicz Criteria
STATES OF NATURE
Good Foreign

DECISION
Expand
Maintain status quo
Sell now

α = 0.3

Poor Foreign



$ 800,000
1,300,000
320,000

$ 500,000
-150,000
320,000

1 - α = 0.7

Expand: $800,000(0.3) + 500,000(0.7) = $590,000 ← Maximum
Status quo: 1,300,000(0.3) -150,000(0.7) = 285,000
Sell: 320,000(0.3) + 320,000(0.7) = 320,000
Decision: Expand
Supplement 1-54
1-

Equal Likelihood Criteria
STATES OF NATURE
Good Foreign

DECISION
Expand
Maintain status quo
Sell now

Poor Foreign



$ 800,000
1,300,000
320,000

$ 500,000
-150,000
320,000

Two states of nature each weighted 0.50
Expand: $800,000(0.5) + 500,000(0.5) = $650,000 ← Maximum
Status quo: 1,300,000(0.5) -150,000(0.5) = 575,000
Sell: 320,000(0.5) + 320,000(0.5) = 320,000
Decision: Expand
Supplement 1-55
1-

Decision Analysis with
Excel

Supplement 1-56
1-

Decision Analysis with
OM Tools

Supplement 1-57
1-

Decision Making with
Probabilities
Risk involves assigning probabilities to
states of nature
Expected value
a weighted average of decision outcomes in
which each future state of nature is
assigned a probability of occurrence

Supplement 1-58
1-

Expected value
EV (x) =
(x
p(xi)xi

n

∑
i =1

where
xi = outcome i
p(xi) = probability of outcome i

Supplement 1-59
1-

Probabilities: Example
STATES OF NATURE
Good Foreign

DECISION
Expand
Maintain status quo
Sell now

Poor Foreign

$ 800,000
1,300,000
320,000

p(good) = 0.70

$ 500,000
-150,000
320,000
p(poor) = 0.30

EV(expand): $800,000(0.7) + 500,000(0.3) = $710,000
EV(status quo): 1,300,000(0.7) -150,000(0.3) = 865,000 ← Maximum
EV(sell):
320,000(0.7) + 320,000(0.3) = 320,000

Decision: Status quo
Supplement 1-60
1-

Probabilities: Excel

Supplement 1-61
1-

Expected Value of
Perfect Information
EVPI
maximum value of perfect information to
the decision maker
maximum amount that would be paid to
gain information that would result in a
decision better than the one made
without perfect information

Supplement 1-62
1-

EVPI Example
Good conditions will exist 70% of the time
choose maintain status quo with payoff of $1,300,000

Poor conditions will exist 30% of the time
choose expand with payoff of $500,000

Expected value given perfect information
= $1,300,000 (0.70) + 500,000 (0.30)
= $1,060,000
Recall that expected value without perfect
information was $865,000 (maintain status quo)
EVPI=
EVPI= $1,060,000 - 865,000 = $195,000

Supplement 1-63
1-

Sequential
Decision Trees
A graphical method for analyzing
decision situations that require a
sequence of decisions over time
Decision tree consists of
Square nodes - indicating decision points
Circles nodes - indicating states of nature
Arcs - connecting nodes

Supplement 1-64
1-

Evaluations at Nodes
Compute EV at nodes 6 & 7
EV(node 6)= 0.80($3,000,000) + 0.20($700,000) = $2,540,000
EV(
6)=
EV(node 7)= 0.30($2,300,000) + 0.70($1,000,000)= $1,390,000
EV(
7)=

Decision at node 4 is between
$2,540,000 for Expand and
$450,000 for Sell land

Choose Expand
Repeat expected value calculations and decisions at
remaining nodes

Supplement 1-65
1-

Decision Tree Analysis
$2,000,000

$1,290,000
0.60

Market growth

2
0.40
$225,000
$2,540,000

$3,000,000

0.80
$1,740,000

6
0.20

1

$700,000

4

$1,160,000

$450,000
0.60
3
$1,360,000

$1,390,000

0.40
$790,000

$2,300,000

0.30
7
0.70

$1,000,000

5
$210,000
Supplement 1-66
1-

Chapter 2
Quality Management

Lecture Outline
What Is Quality?
Evolution of Quality
Management
Quality Tools
TQM and QMS
Focus of Quality
Management—
Management—
Customers
Role of Employees in
Quality Improvement

Quality in Service
Companies
Six Sigma
Cost of Quality
Effect of Quality
Management on
Productivity
Quality Awards
ISO 9000
2-68

What Is Quality?
Oxford American Dictionary
a degree or level of excellence

American Society for Quality
totality of features and characteristics
that satisfy needs without deficiencies

Consumer’s and producer’s
perspective

2-69

What Is Quality:
Customer’s Perspective
Fitness for use
how well product or
service does what it is
supposed to

Quality of design
designing quality
characteristics into a
product or service
A Mercedes and a Ford are
equally “fit for use,” but with
different design dimensions.

2-70

Dimensions of Quality:
Manufactured Products
Performance
basic operating characteristics of a product; how
well a car handles or its gas mileage

Features
“extra” items added to basic features, such as a
stereo CD or a leather interior in a car

Reliability
probability that a product will operate properly
within an expected time frame; that is, a TV will
work without repair for about seven years

2-71

Manufactured Products (cont.)
Conformance
degree to which a product meets pre–established
pre–
standards

Durability
how long product lasts before replacement; with
care, L.L.Bean boots may last a lifetime

Serviceability
ease of getting repairs, speed of repairs, courtesy
and competence of repair person

2-72

Manufactured Products (cont.)
Aesthetics
how a product looks, feels, sounds,
smells, or tastes

Safety
assurance that customer will not suffer
injury or harm from a product; an
especially important consideration for
automobiles

Perceptions
subjective perceptions based on brand
name, advertising, and like
2-73

Services
Time and timeliness
how long must a customer wait for service,
and is it completed on time?
is an overnight package delivered overnight?

Completeness:
is everything customer asked for provided?
is a mail order from a catalogue company
complete when delivered?

2-74

Service (cont.)
Courtesy:
how are customers treated by employees?
are catalogue phone operators nice and are
their voices pleasant?

Consistency
is same level of service provided to each
customer each time?
is your newspaper delivered on time every
morning?

2-75

Service (cont.)
Accessibility and convenience
how easy is it to obtain service?
does service representative answer you calls quickly?

Accuracy
is service performed right every time?
is your bank or credit card statement correct every month?

Responsiveness
how well does company react to unusual situations?
how well is a telephone operator able to respond to a
customer’s questions?

2-76

What Is Quality:
Producer’s Perspective
Quality of conformance
making sure product or service is produced
according to design
if new tires do not conform to specifications, they
wobble
if a hotel room is not clean when a guest checks
in, hotel is not functioning according to
specifications of its design
2-77

What Is Quality:
A Final Perspective
Customer’s and producer’s perspectives
depend on each other
Producer’s perspective:
production process and COST

Customer’s perspective:
fitness for use and PRICE

Customer’s view must dominate

2-79

Evolution of Quality Management:
Quality Gurus
Walter Shewart
In 1920s, developed control charts
Introduced term “quality assurance”
“quality

W. Edwards Deming
Developed courses during World War II to teach
statistical quality-control techniques to engineers and
qualityexecutives of companies that were military suppliers
After war, began teaching statistical quality control to
Japanese companies

Joseph M. Juran
Followed Deming to Japan in 1954
Focused on strategic quality planning
Quality improvement achieved by focusing on projects
to solve problems and securing breakthrough solutions
2-80

Evolution of Quality Management:
Quality Gurus (cont.)
Armand V. Feigenbaum
In 1951, introduced concepts of total quality control
and continuous quality improvement

Philip Crosby
In 1979, emphasized that costs of poor quality far
outweigh cost of preventing poor quality
In 1984, defined absolutes of quality management—
management—
conformance to requirements, prevention, and “zero
defects”

Kaoru Ishikawa
Promoted use of quality circles
Developed “fishbone” diagram
Emphasized importance of internal customer
2-81

Deming’s 14 Points
1. Create constancy of purpose
2. Adopt philosophy of prevention
3. Cease mass inspection
4. Select a few suppliers based on
quality
5. Constantly improve system and
workers
2-82

Deming’s 14 Points (cont.)
6. Institute worker training
7. Instill leadership among
supervisors
8. Eliminate fear among employees
9. Eliminate barriers between
departments
10. Eliminate slogans
2-83

Deming’s 14 Points (cont.)
11. Remove numerical quotas
12. Enhance worker pride
13. Institute vigorous training and
education programs
14. Develop a commitment from top
management to implement
above 13 points
2-84

Deming Wheel: PDCA Cycle

2-85

Quality Tools

Process Flow
Chart
Cause-andCause-andEffect Diagram
Check Sheet
Pareto Analysis

Histogram
Scatter Diagram
Statistical Process
Control Chart

2-86

Cause-andCause-and-Effect Diagram
Cause-andCause-and-effect diagram (“fishbone” diagram)
chart showing different categories of problem causes

2-88

Cause-andCause-and-Effect Matrix
Cause-andCause-and-effect matrix
grid used to prioritize causes of quality problems

2-89

Check Sheets and Histograms

2-90

Pareto Analysis
Pareto analysis
most quality problems result from a few causes

2-91

TQM and QMS
Total Quality Management (TQM)
customercustomer-oriented, leadership, strategic
planning, employee responsibility,
continuous improvement, cooperation,
statistical methods, and training and
education

Quality Management System (QMS)
system to achieve customer satisfaction
that complements other company
systems
2-95

Focus of Quality Management—
Management—
Customers
TQM and QMSs
serve to achieve customer satisfaction

Partnering
a relationship between a company and
its supplier based on mutual quality
standards

Measuring customer satisfaction
important component of any QMS
customer surveys, telephone interviews
2-96

Role of Employees in
Quality Improvement
Participative
problem solving
employees involved in
qualityquality-management
every employee has
undergone extensive
training to provide quality
service to Disney’s guests

Kaizen
involves everyone in
process of continuous
improvement
2-97

Quality Circles
and QITs
Organization
8-10 members
Same area
Supervisor/moderator

Quality circle
group of workers
and supervisors
from same area
who address
quality problems

Implementation
Monitoring

Group processes
Data collection
Problem analysis

Process/Quality
improvement teams
(QITs)

Solution

Problem
Identification

focus attention on
business processes
rather than separate
company functions

Training

Presentation

Problem results

Problem
Analysis

List alternatives
Consensus
Brainstorming

Cause and effect
Data collection
and analysis

2-98

Quality in Services
Service defects are not always easy
to measure because service output
is not usually a tangible item
Services tend to be labor intensive
Services and manufacturing
companies have similar inputs but
different processes and outputs

2-99

Quality Attributes in
Services
Principles of TQM apply
equally well to services
and manufacturing
Timeliness
how quickly a service is
provided?

Benchmark
“best” level of quality
achievement in one
company that other
companies seek to achieve

“quickest, friendliest, most
accurate service
available.”

2-100

Six Sigma
A process for developing and delivering
virtually perfect products and services
Measure of how much a process
deviates from perfection
3.4 defects per million opportunities
Six Sigma Process
four basic steps of Six Sigma—align,
Sigma—
mobilize, accelerate, and govern

Champion
an executive responsible for project success
2-101

Six Sigma:
Breakthrough Strategy—DMAIC
Strategy—
DEFINE

MEASURE

ANALYZE

IMPROVE

CONTROL

3.4 DPMO

67,000 DPMO
cost = 25% of
sales
2-102

Six Sigma:
Black Belts and
Green Belts

Black Belt
project leader

Master Black Belt
a teacher and mentor
for Black Belts

Green Belts
project team
members

2-103

Six Sigma
Design for Six Sigma (DFSS)
a systematic approach to designing products and
processes that will achieve Six Sigma

Profitability
typical criterion for selection Six Sigma project
one of the factors distinguishing Six Sigma from
TQM
“Quality is not only free, it is an
honest-tohonest-to-everything profit maker.”
2-104

Cost of Quality
Cost of Achieving Good Quality
Prevention costs
costs incurred during product design

Appraisal costs
costs of measuring, testing, and analyzing

Cost of Poor Quality
Internal failure costs
include scrap, rework, process failure, downtime,
and price reductions

External failure costs
include complaints, returns, warranty claims,
liability, and lost sales
2-105

Prevention Costs
Quality planning costs
costs of developing and
implementing quality
management program

ProductProduct-design costs
costs of designing
products with quality
characteristics

Process costs
costs expended to make
sure productive process
conforms to quality
specifications

Training costs
costs of developing and
putting on quality training
programs for employees
and management

Information costs
costs of acquiring
and maintaining data
related to quality, and
development and
analysis of reports on
quality performance

2-106

Appraisal Costs
Inspection and testing
costs of testing and inspecting materials, parts, and
product at various stages and at end of process

Test equipment costs
costs of maintaining equipment used in testing
quality characteristics of products

Operator costs
costs of time spent by operators to gather data for
testing product quality, to make equipment
adjustments to maintain quality, and to stop work to
assess quality

2-107

Internal Failure Costs
Scrap costs
costs of poor-quality
poorproducts that must be
discarded, including labor,
material, and indirect costs

Rework costs
costs of fixing defective
products to conform to
quality specifications

Process failure costs
costs of determining why
production process is
producing poor-quality
poorproducts

Process downtime costs
costs of shutting down
productive process to fix
problem

PricePrice-downgrading costs
costs of discounting poorpoorquality products—that is,
products—
selling products as
“seconds”

2-108

External Failure Costs
Customer complaint costs
costs of investigating and
satisfactorily responding to a
customer complaint resulting
from a poor-quality product
poor-

Product return costs
costs of handling and replacing
poorpoor-quality products returned
by customer

Warranty claims costs
costs of complying with
product warranties

Product liability costs
litigation costs
resulting from product
liability and customer
injury

Lost sales costs
costs incurred
because customers
are dissatisfied with
poorpoor-quality products
and do not make
additional purchases

2-109

Measuring and
Reporting Quality Costs
Index numbers
ratios that measure quality costs against a
base value
labor index
ratio of quality cost to labor hours

cost index
ratio of quality cost to manufacturing cost

sales index
ratio of quality cost to sales

production index
ratio of quality cost to units of final product
2-110

Quality–
Quality–Cost Relationship
Cost of quality
difference between price of
nonconformance and conformance
cost of doing things wrong
20 to 35% of revenues

cost of doing things right
3 to 4% of revenues

2-111

Effect of Quality
Management on Productivity
Productivity
ratio of output to input

Quality impact on productivity
fewer defects increase output, and quality
improvement reduces inputs

Yield
a measure of productivity
Yield=(total input)(% good units) + (total input)(1-%good units)(% reworked)

or
Y=(I)(%G)+(I)(1Y=(I)(%G)+(I)(1-%G)(%R)
2-112

Computing Product
Cost per Unit
Product Cost

(Kd )(I) +(Kr )(R)
=
Y

where:
Kd = direct manufacturing cost per unit
I = input
Kr = rework cost per unit
R = reworked units
Y = yield

2-113

Computing Product Yield
for Multistage Processes
Y = (I)(%g1)(%g2) … (%gn)

where:
I = input of items to the production process that will
result in finished products
gi = good-quality, work-in-process products at stage i

2-114

Quality–
Quality–Productivity Ratio
QPR
productivity index that includes productivity and
quality costs

QPR =

(good-quality units)
(input) (processing cost) + (reworked units) (rework cost)

(100)

2-115

Malcolm Baldrige Award
Created in 1987 to stimulate growth of
quality management in United States
Categories
Leadership
Information and analysis
Strategic planning
Human resource focus
Process management
Business results
Customer and market focus
2-116

Other Awards for Quality
National individual
awards
Armand V. Feigenbaum
Medal
Deming Medal
E. Jack Lancaster Medal
Edwards Medal
Shewart Medal
Ishikawa Medal

International awards
European Quality Award
Canadian Quality Award
Australian Business
Excellence Award
Deming Prize from Japan

2-117

ISO 9000
A set of procedures and
policies for international
quality certification of
suppliers
Standards
ISO 9000:2000
Quality Management
Systems—
Systems—Fundamentals
and Vocabulary
defines fundamental
terms and definitions
used in ISO 9000 family

ISO 9001:2000
Quality Management
Systems—
Systems—Requirements
standard to assess ability to
achieve customer satisfaction

ISO 9004:2000
Quality Management
Systems—
Systems—Guidelines for
Performance Improvements
guidance to a company for
continual improvement of its
qualityquality-management system

2-118

ISO 9000 Certification,
Implications, and Registrars
ISO 9001:2000—only
9001:2000—
standard that carries thirdthirdparty certification
Many overseas companies
will not do business with a
supplier unless it has ISO
9000 certification
ISO 9000 accreditation
ISO registrars

2-119

Chapter 3
Statistical Process Control

Lecture Outline
Basics of Statistical Process Control
Control Charts
Control Charts for Attributes
Control Charts for Variables
Control Chart Patterns
SPC with Excel and OM Tools
Process Capability
3-121

Basics of Statistical
Process Control
Statistical Process Control
(SPC)
monitoring production process
to detect and prevent poor
quality

UCL

Sample
subset of items produced to
use for inspection

LCL

Control Charts
process is within statistical
control limits

3-122

Basics of Statistical
Process Control (cont.)
Random
inherent in a process
depends on equipment
and machinery,
engineering, operator,
and system of
measurement
natural occurrences

NonNon-Random
special causes
identifiable and
correctable
include equipment out of
adjustment, defective
materials, changes in
parts or materials, broken
machinery or equipment,
operator fatigue or poor
work methods, or errors
due to lack of training
3-123

SPC in Quality Management
SPC
tool for identifying problems in
order to make improvements
contributes to the TQM goal of
continuous improvements

3-124

Quality Measures:
Attributes and Variables
Attribute
a product characteristic that can be
evaluated with a discrete response
good – bad; yes - no

Variable measure
a product characteristic that is continuous
and can be measured
weight - length
3-125

SPC Applied to
Services
Nature of defect is different in services
Service defect is a failure to meet
customer requirements
Monitor time and customer satisfaction

3-126

SPC Applied to
Services (cont.)
Hospitals
timeliness and quickness of care, staff responses to requests,
accuracy of lab tests, cleanliness, courtesy, accuracy of
paperwork, speed of admittance and checkouts

Grocery stores
waiting time to check out, frequency of out-of-stock items,
out-ofquality of food items, cleanliness, customer complaints,
checkout register errors

Airlines
flight delays, lost luggage and luggage handling, waiting time
at ticket counters and check-in, agent and flight attendant
checkcourtesy, accurate flight information, passenger cabin
cleanliness and maintenance

3-127

SPC Applied to
Services (cont.)
FastFast-food restaurants
waiting time for service, customer complaints,
cleanliness, food quality, order accuracy, employee
courtesy

CatalogueCatalogue-order companies
order accuracy, operator knowledge and courtesy,
packaging, delivery time, phone order waiting time

Insurance companies
billing accuracy, timeliness of claims processing,
agent availability and response time

3-128

Where to Use Control Charts
Process has a tendency to go out of control
Process is particularly harmful and costly if it
goes out of control
Examples
at the beginning of a process because it is a waste of
time and money to begin production process with bad
supplies
before a costly or irreversible point, after which
product is difficult to rework or correct
before and after assembly or painting operations that
might cover defects
before the outgoing final product or service is
delivered
3-129

Control Charts
A graph that establishes
control limits of a
process
Control limits
upper and lower bands of
a control chart

Types of charts
Attributes
p-chart
c-chart

Variables
mean (x bar – chart)
range (R-chart)
(R-

3-130

Process Control Chart
Out of control
Upper
control
limit
Process
average
Lower
control
limit

1

2

3

4

5

6

7

8

9

10

Sample number
3-131

Normal Distribution

95%
99.74%
- 3σ

- 2σ

- 1σ

µ=0

1σ

2σ

3σ

3-132

A Process Is in
Control If …
1. … no sample points outside limits
2. … most points near process average
3. … about equal number of points above
and below centerline
4. … points appear randomly distributed

3-133

Control Charts for
Attributes
p-chart
uses portion defective in a sample

c-chart
uses number of defective items in
a sample

3-134

p-Chart
UCL = p + zσp
LCL = p - zσp
z = number of standard deviations from
process average
p = sample proportion defective; an estimate
of process average
σp = standard deviation of sample proportion

σp =

p(1 - p)
n
3-135

Construction of p-Chart
pSAMPLE

1
2
3
:
:
20

NUMBER OF
DEFECTIVES

PROPORTION
DEFECTIVE

6
0
4
:
:
18
200

.06
.00
.04
:
:
.18

20 samples of 100 pairs of jeans
3-136

Construction of p-Chart (cont.)
pp=

total defectives
total sample observations

UCL = p + z

p(1 - p)
n

= 200 / 20(100) = 0.10

= 0.10 + 3

0.10(1 - 0.10)
100

UCL = 0.190
LCL = p - z

p(1 - p)
n

= 0.10 - 3

0.10(1 - 0.10)
100

LCL = 0.010

3-137

0.20
UCL = 0.190

0.18

Construction
of p-Chart
p(cont.)

Proportion defective

0.16
0.14
0.12
0.10

p = 0.10

0.08
0.06
0.04
0.02

LCL = 0.010
2

4

6

8
10
12 14
Sample number

16

18

20

3-138

c-Chart

UCL = c + zσc
LCL = c - zσc

σc =

c

where
c = number of defects per sample

3-139

c-Chart (cont.)
Number of defects in 15 sample rooms

SAMPLE

1
2
3

NUMBER
OF
DEFECTS

:
:
15

c=

12
8
16

:
:
15
190

190
15

= 12.67

UCL = c + zσc
= 12.67 + 3
= 23.35

12.67

= c - zσ c
= 12.67 - 3
= 1.99

12.67

LCL

3-140

24
UCL = 23.35

c-Chart
(cont.)

Number of defects

21
18
c = 12.67
15
12
9
6
LCL = 1.99

3

2

4

6

8

10

12

14

16

Sample number

3-141

Control Charts for
Variables
Range chart ( R-Chart )
Ruses amount of dispersion in a
sample

Mean chart ( x -Chart )
uses process average of a
sample

3-142

x-bar Chart:
Standard Deviation Known
=
UCL = x + zσx

LCL = = - zσx
x

x1 + x2 + ... xn
n

=
x =
where
=

x = average of sample means
3-143

x-bar Chart Example:
Standard Deviation Known (cont.)

3-144

Standard Deviation Known (cont.)

3-145

Standard Deviation Unknown
=
UCL = x + A2R

=
LCL = x - A2R

where

x = average of sample means
3-146

Standard Deviation Unknown
OBSERVATIONS (SLIP- RING DIAMETER, CM)
(SLIPSAMPLE k

1

2

3

4

5

x

R

1

5.02

5.01

4.94

4.99

4.96

4.98

0.08

2

5.01

5.03

5.07

4.95

4.96

5.00

0.12

3

4.99

5.00

4.93

4.92

4.99

4.97

0.08

4

5.03

4.91

5.01

4.98

4.89

4.96

0.14

5

4.95

4.92

5.03

5.05

5.01

4.99

0.13

6

4.97

5.06

5.06

4.96

5.03

5.01

0.10

7

5.05

5.01

5.10

4.96

4.99

5.02

0.14

8

5.09

5.10

5.00

4.99

5.08

5.05

0.11

9

5.14

5.10

4.99

5.08

5.09

5.08

0.15

10

5.01

4.98

5.08

5.07

4.99

5.03

0.10

50.09

1.15

Example 15.4

3-148

Standard Deviation Unknown (cont.)
∑R

R=

=
x=

k

∑x
k

=

=

1.15
10

= 0.115

50.09 5.01 cm
=
10

=
UCL = x + A2R = 5.01 + (0.58)(0.115) = 5.08
LCL = x = A2R = 5.01 - (0.58)(0.115) = 4.94
Retrieve Factor Value A2
3-149

5.10 –
5.08 –

UCL = 5.08

5.06 –

Mean

5.04 –
=
x = 5.01

5.02 –
5.00 –

x- bar
Chart
Example
(cont.)

4.98 –
4.96 –

LCL = 4.94

4.94 –
4.92 –

|
1

|
2

|
3

|
|
|
|
4
5
6
7
Sample number

|
8

|
9

|
10

3-150

R- Chart
UCL = D4R
R=

LCL = D3R
∑R
k

where
R = range of each sample
k = number of samples
3-151

R-Chart Example
OBSERVATIONS (SLIP-RING DIAMETER, CM)
(SLIPSAMPLE k

1

2

3

4

5

x

R

1

5.02

5.01

4.94

4.99

4.96

4.98

0.08

2

5.01

5.03

5.07

4.95

4.96

5.00

0.12

3

4.99

5.00

4.93

4.92

4.99

4.97

0.08

4

5.03

4.91

5.01

4.98

4.89

4.96

0.14

5

4.95

4.92

5.03

5.05

5.01

4.99

0.13

6

4.97

5.06

5.06

4.96

5.03

5.01

0.10

7

5.05

5.01

5.10

4.96

4.99

5.02

0.14

8

5.09

5.10

5.00

4.99

5.08

5.05

0.11

9

5.14

5.10

4.99

5.08

5.09

5.08

0.15

10

5.01

4.98

5.08

5.07

4.99

5.03

0.10

50.09

1.15

Example 15.3

3-152

R-Chart Example (cont.)
UCL = D4R = 2.11(0.115) = 0.243
LCL = D3R = 0(0.115) = 0
Retrieve Factor Values D3 and D4

Example 15.3
3-153

R-Chart Example (cont.)
0.28 –
0.24 –

UCL = 0.243

Range

0.20 –
0.16 –

R = 0.115

0.12 –
0.08 –
0.04 –
0–

LCL = 0
|
|
|
1 2
3

|
|
|
|
4
5
6
7
Sample number

|
8

|
9

|
10

3-154

Using x- bar and R-Charts
xRTogether
Process average and process variability must be in control
It is possible for samples to have very narrow ranges, but
their averages might be beyond control limits
It is possible for sample averages to be in control, but
ranges might be very large
It is possible for an R-chart to exhibit a distinct downward
Rtrend, suggesting some nonrandom cause is reducing
variation

3-155

Control Chart Patterns
Run
sequence of sample values that display same characteristic
Pattern test
determines if observations within limits of a control chart display a
nonrandom pattern
To identify a pattern:
8 consecutive points on one side of the center line
8 consecutive points up or down
14 points alternating up or down
2 out of 3 consecutive points in zone A (on one side of center line)
4 out of 5 consecutive points in zone A or B (on one side of center
line)

3-156

Control Chart Patterns (cont.)
UCL

UCL
LCL
Sample observations
consistently below the
center line

LCL
Sample observations
consistently above the
center line
3-157

Control Chart Patterns (cont.)
UCL

UCL
LCL
Sample observations
consistently increasing

LCL
Sample observations
consistently decreasing
3-158

Zones for Pattern Tests
=
3 sigma = x + A2R

UCL
Zone A

= 2
2 sigma = x +
(A
(A2R)
3

Zone B
= 1
1 sigma = x +
(A
(A2R)
3

Zone C
=
x

Process
average

Zone C
= 1
1 sigma = x - (A2R)
3

Zone B
= 2
2 sigma = x - (A2R)
3

Zone A
=
3 sigma = x - A2R

LCL
|
1

|
2

|
3

|
4

|
5

|
6

|
7

|
8

|
9

|
10

|
11

|
12

|
13

Sample number
3-159

Performing a Pattern Test
SAMPLE
1
2
3
4
5
6
7
8
9
10

x

ABOVE/BELOW

UP/DOWN

ZONE

4.98
5.00
4.95
4.96
4.99
5.01
5.02
5.05
5.08
5.03

B
B
B
B
B
—
A
A
A
A

—
U
D
D
U
U
U
U
U
D

B
C
A
A
C
C
C
B
A
B

3-160

Sample Size Determination

Attribute charts require larger sample sizes
50 to 100 parts in a sample
Variable charts require smaller samples
2 to 10 parts in a sample

3-161

SPC with Excel and OM Tools

3-163

Process Capability
Tolerances
design specifications reflecting product
requirements

Process capability
range of natural variability in a process—
process—
what we measure with control charts

3-164

Process Capability (cont.)
Design
Specifications
(a) Natural variation
exceeds design
specifications; process
is not capable of
meeting specifications
all the time.
Process
Design
Specifications
(b) Design specifications
and natural variation the
same; process is capable
of meeting specifications
most of the time.
Process
3-165

Process Capability (cont.)
Design
Specifications
(c) Design specifications
greater than natural
variation; process is
capable of always
conforming to
specifications.
Process
Design
Specifications
(d) Specifications greater
than natural variation,
but process off center;
capable but some output
will not meet upper
specification.
Process
3-166

Process Capability Measures
Process Capability Ratio
Cp =

tolerance range
process range
upper specification limit lower specification limit

=

6σ
3-167

Computing Cp
Net weight specification = 9.0 oz ± 0.5 oz
Process mean = 8.80 oz
Process standard deviation = 0.12 oz

Cp =

=

upper specification limit lower specification limit
6σ
9.5 - 8.5 = 1.39
6(0.12)

3-168

Process Capability Measures
Process Capability Index
=
Cpk = minimum

x - lower specification limit
3σ

,

upper specification limit - x

=

3σ

3-169

Computing Cpk
Net weight specification = 9.0 oz ± 0.5 oz
Process mean = 8.80 oz
Process standard deviation = 0.12 oz
=
x - lower specification limit
Cpk = minimum

,

3σ
=
upper specification limit - x
3σ
8.80 - 8.50

= minimum

3(0.12) ,

9.50 - 8.80
3(0.12)

= 0.83

3-170

Process Capability
with Excel

3-171

Process Capability
with Excel and OM Tools

3-172

Acceptance Sampling

Lecture Outline
SingleSingle-Sample Attribute Plan
Operating Characteristic Curve
Developing a Sampling Plan with Excel
Average Outgoing Quality
Double - and Multiple-Sampling Plans
Multiple-

Supplement 3-174
3-

Acceptance Sampling
Accepting or rejecting a production lot based
on the number of defects in a sample
Not consistent with TQM or Zero Defects
philosophy
producer and customer agree on the number of
acceptable defects
a means of identifying not preventing poor quality
percent of defective parts versus PPM

Sampling plan
provides guidelines for accepting a lot
Supplement 3-175
3-

Single–
Single–Sample
Attribute Plan
Single sampling plan
N = lot size
n = sample size (random)
c = acceptance number
d = number of defective items in sample

If d ≤ c, accept lot; else reject

Supplement 3-176
3-

Producer’s and
Consumer’s Risk
AQL or acceptable quality level
proportion of defects consumer will accept in
a given lot

α or producer’s risk

probability of rejecting a good lot

LTPD or lot tolerance percent defective
limit on the number of defectives the
customer will accept

β or consumer’s risk
probability of accepting a bad lot
Supplement 3-177
3-

Producer’s and
Consumer’s Risk (cont.)
Good Lot

Reject

No Error

Type I Error
Producer’ Risk

Bad Lot

Accept

Type II Error
Consumer’s Risk

No Error

Sampling Errors

Supplement 3-178
3-

Operating Characteristic
(OC) Curve
shows probability of accepting lots of
different quality levels with a specific
sampling plan
assists management to discriminate
between good and bad lots
exact shape and location of the curve is
defined by the sample size (n) and
(n
acceptance level (c) for the sampling
(c
plan
Supplement 3-179
3-

OC Curve (cont.)
1.00 –
α = 0.05

Probability of acceptance, Pa

0.80 –

OC curve for n and c

0.60 –

0.40 –

0.20 –
β = 0.10

|

–

|

|

|

|

|

|

|

|

|

0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20

AQL

Proportion defective

LTPD
Supplement 3-180
3-

Developing a Sampling Plan
with OM Tools
ABC Company produces mugs
in lots of 10,000. Performance
measures for quality of mugs
sent to stores call for a
producer’s risk of 0.05 with an
AQL of 1% defective and a
consumer’s risk of 0.10 with a
LTPD of 5% defective. What
size sample and what
acceptance number should
ABC use to achieve
performance measures called
for in the sampling plan?

N = 10,000
α = 0.05
?
β = 0.10
AQL = 1%
LTPD = 5%

n=?
c=

Supplement 3-181
3-

Average Outgoing
Quality (AOQ)
Expected number of defective
items that will pass on to
customer with a sampling plan
Average outgoing quality limit
(AOQL)
maximum point on the curve
worst level of outgoing quality
Supplement 3-182
3-

AOQ Curve

Supplement 3-183
3-

DoubleDouble-Sampling Plans
Take small initial sample
If # defective ≤ lower limit, accept
If # defective > upper limit, reject
If # defective between limits, take second
sample

Accept or reject based on 2 samples
Less costly than single-sampling plans
single-

Supplement 3-184
3-

MultipleMultiple-Sampling Plans
Uses smaller sample sizes
Take initial sample
If # defective ≤ lower limit, accept
If # defective > upper limit, reject
If # defective between limits, resample

Continue sampling until accept or reject
lot based on all sample data
Supplement 3-185
3-

Chapter 4
Product Design

Lecture Outline
Design Process
Concurrent Design
Technology in Design
Design Reviews
Design for Environment
Design for Robustness
Quality Function Deployment
4-187

Design Process
Effective design can provide a competitive
edge
matches product or service characteristics with
customer requirements
ensures that customer requirements are met in the
simplest and least costly manner
reduces time required to design a new product or
service
minimizes revisions necessary to make a design
workable
Copyright 2009 John Wiley & Sons, Inc.

4-188

Design Process (cont.)
Product design
defines appearance of product
sets standards for performance
specifies which materials are to be used
determines dimensions and tolerances

4-189

Idea Generation
Company’s own
R&D department
Customer complaints
or suggestions
Marketing research
Suppliers

Salespersons in the
field
Factory workers
New technological
developments
Competitors

4-191

Idea Generation (cont.)
Perceptual Maps
Visual comparison of
customer perceptions

Benchmarking
Comparing product/process
against best-in-class
best-in-

Reverse engineering
Dismantling competitor’s product to
improve your own product

4-192

Perceptual Map of
Breakfast Cereals

4-193

Feasibility Study
Market analysis
Economic analysis
Technical/strategic analyses
Performance specifications

4-194

Rapid Prototyping
testing and revising a
preliminary design model
Build a prototype
form design
functional design
production design

Test prototype
Revise design
Retest
4-195

Form and Functional Design
Form Design
how product will
look?

Functional Design
how product will
perform?
reliability
maintainability
usability

4-196

Computing Reliability

Components in series
0.90

0.90

0.90 x 0.90 = 0.81

4-197

Computing Reliability (cont.)
Components in parallel
0.90
R2
0.95 + 0.90(1-0.95) = 0.995
0.90(1-

0.95
R1

4-198

System Reliability
0.90

0.98

0.98

0.92

0.98

0.92+(10.92+(1-0.92)(0.90)=0.99

0.98

0.98 x 0.99 x 0.98 = 0.951
4-199

System Availability (SA)

SA =

MTBF
MTBF + MTTR

where:
MTBF = mean time between failures
MTTR = mean time to repair

4-200

System Availability
(cont.)
PROVIDER

MTBF (HR)

MTTR (HR)

A
B
C

60
36
24

4.0
2.0
1.0

SAA = 60 / (60 + 4) = .9375 or 94%
SAB = 36 / (36 + 2) = .9473 or 95%
SAC = 24 / (24 + 1) = .96 or 96%

4-201

Usability
Ease of use of a product or service
ease of learning
ease of use
ease of remembering how to use
frequency and severity of errors
user satisfaction with experience

4-202

Production Design
How the product will be made
Simplification
reducing number of parts, assemblies, or options in a
product

Standardization
using commonly available and interchangeable parts

Modular Design
combining standardized building blocks, or modules, to
create unique finished products

Design for Manufacture (DFM)
• Designing a product so that it can be produced easily and
economically

4-203

Design
Simplification
(a) Original design

Assembly using
common fasteners

Source: Adapted from G. Boothroyd and
P. Dewhurst, “Product Design…. Key to
Successful Robotic Assembly.” Assembly
Engineering (September 1986), pp. 909093.

(b) Revised design

(c) Final design

OneOne-piece base &
elimination of
fasteners

Design for
push-andpush-and-snap
assembly
4-204

Final Design and Process Plans
Final design
detailed drawings
and specifications
for new product or
service

Process plans
workable instructions
necessary equipment
and tooling
component sourcing
recommendations
job descriptions and
procedures
computer programs for
automated machines

4-205

Concurrent Design
A new approach to
design that involves
simultaneous design of
products and processes
by design teams
Improves quality of early
design decisions

Involves suppliers
Incorporates production
process
Uses a price-minus
pricesystem
Scheduling and
management can be
complex as tasks are
done in parallel
Uses technology to aid
design

4-207

Technology in Design
Computer Aided Design (CAD)
assists in creation, modification, and analysis of
a design
computercomputer-aided engineering (CAE)
tests and analyzes designs on computer screen

computercomputer-aided manufacturing (CAD/CAM)
ultimate design-to-manufacture connection
design-to-

product life cycle management (PLM)
managing entire lifecycle of a product

collaborative product design (CPD)
4-208

Collaborative Product Design
(CPD)
A software system for collaborative design and
development among trading partners
With PML, manages product data, sets up project
workspaces, and follows life cycle of the product
Accelerates product development, helps to resolve
product launch issues, and improves quality of design
Designers can
conduct virtual review sessions
test “what if” scenarios
assign and track design issues
communicate with multiple tiers of suppliers
create, store, and manage project documents
4-209

Design Review
Review designs to prevent failures and
ensure value
Failure mode and effects analysis (FMEA)
a systematic method of analyzing product
failures

Fault tree analysis (FTA)
a visual method for analyzing interrelationships
among failures

Value analysis (VA)
helps eliminate unnecessary features and
functions
4-210

FMEA for Potato Chips
Failure
Mode

Cause of
Failure

Effect of
Failure

Corrective
Action

Stale

low moisture content
expired shelf life
poor packaging

tastes bad
won’t crunch
thrown out
lost sales

add moisture
cure longer
better package seal
shorter shelf life

Broken

too thin
too brittle
rough handling
rough use
poor packaging

can’t dip
poor display
injures mouth
chocking
perceived as old
lost sales

change recipe
change process
change packaging

Too Salty

outdated receipt
process not in control
uneven distribution of salt

eat less
drink more
health hazard
lost sales

experiment with recipe
experiment with process
introduce low salt version

4-211

Fault tree analysis (FTA)

4-212

Value analysis (VA)
Can we do without it?
Does it do more than is required?
Does it cost more than it is worth?
Can something else do a better job?
Can it be made by
a less costly method?
with less costly tooling?
with less costly material?

Can it be made cheaper, better, or faster by
someone else?
4-213

Value analysis (VA) (cont.)
Updated versions also include:
Is it recyclable or biodegradable?
Is the process sustainable?
Will it use more energy than it is worth?
Does the item or its by-product harm the
byenvironment?

4-214

Design for Environment and
Extended Producer Responsibility
Design for environment
designing a product from material that can be recycled
design from recycled material
design for ease of repair
minimize packaging
minimize material and energy used during manufacture,
consumption and disposal

Extended producer responsibility
holds companies responsible for their product even after its
useful life

4-215

Sustainability
Ability to meet present needs without compromising
those of future generations
Green product design
Use fewer materials
Use recycled materials or recovered components
Don’t assume natural materials are always better
Don’t forget energy consumption
Extend useful life of product
Involve entire supply chain
Change paradigm of design
Source: Adapted from the Business
Social Responsibility Web site,
www.bsr.org,
www.bsr.org, accessed April 1, 2007.

4-217

Quality Function
Deployment (QFD)
Translates voice of customer into technical
design requirements
Displays requirements in matrix diagrams
first matrix called “house of quality”
series of connected houses

4-218

House of Quality
Importance

5
TradeTrade-off matrix
3
Design
characteristics

1

4

2

Customer
requirements

Relationship
matrix

Competitive
assessment

6

Target values

4-219

Competitive Assessment
of Customer
Requirements
Competitive Assessment
Customer Requirements

1

2

3

9

Removes wrinkles

8

AB

Doesn’t stick to fabric

6

X

BA

8

AB

Doesn’t spot fabric

6

X AB

Doesn’t scorch fabric

9

A XB

Heats quickly

6

Automatic shut-off
shut-

3

Quick cool-down
cool-

3

X

Doesn’t break when dropped

5

AB

Doesn’t burn when touched

5

AB X

Not too heavy

8

X

5

X

Provides enough steam

Irons
well

Presses quickly

Easy and
safe to use

B A

4

X

X

B

X

A
ABX
ABX

A B
X
4-220

A

B

Presses quickly

-

+

Doesn’t stick to fabric

-

Provides enough steam

+

+

+ +
-

-

-

+ - +
+

-

Automatic shut-off
shut-

+

Quick cool-down
coolDoesn’t break when dropped

-

- +

+ + +

Doesn’t burn when touched
Not too heavy

+
+ -

-

- +

+
+
+ + +
-4-221

Automatic shutoff

Protective cover for soleplate

+ +

+ + +
+ -

Heats quickly

Time to go from 450º to 100º

+ + +

+

Doesn’t scorch fabric

Time required to reach 450º F

Flow of water from holes

Size of holes

Number of holes

-

+

Doesn’t spot fabric

Easy and
safe to use

Material used in soleplate

Thickness of soleplate

- + + +

Removes wrinkles
Irons
well

Size of soleplate

Weight of iron

Customer Requirements

Energy needed to press

From Customer
Requirements
to Design
Characteristics

4-222

Automatic shutoff



Time required to reach 450º


+

Size of holes

-

Number of holes



Size of soleplate

Weight of iron


Tradeoff Matrix
+

+

Units of measure

lb

in.

cm

ty

ea

Iron A

3

1.4

8x4

2

SS

27

15

0.5

45

500

N

Y

Iron B

4

1.2

8x4

1

MG

27

15

0.3

35

350

N

Y

Our Iron (X)

2

1.7

9x5

4

T

35

15

0.7

50

600

N

Y

Estimated impact

3

4

4

4

5

4

3

2

5

5

3

0

Estimated cost

3

3

3

3

4

3

3

3

4

4

5

2

1.2

8x5

3

SS

30

30

500

*

*

*

*

*

*

*

Objective
measures

ft-lb
ft-

Targets
Design changes

mm oz/s sec sec Y/N Y/N

4-223

Automatic shutoff



Time required to reach 450º


Size of holes

Number of holes



Size of soleplate

Weight of iron


Targeted Changes in
Design

Completed
House of Quality

SS = Silverstone
MG = Mirorrglide
T = Titanium

4-224

A Series of Connected
QFD Houses
Part
characteristics

Process
characteristics

A-2
Parts
deployment

Operations

A-3
Process
planning

Process
characteristics

House
of
quality

Part
characteristics

A-1
Product
characteristics

Customer
requirements

Product
characteristics

A-4

Operating
requirements
4-225

Benefits of QFD
Promotes better understanding of
customer demands
Promotes better understanding of
design interactions
Involves manufacturing in design
process
Provides documentation of design
process

4-226

Design for Robustness
Robust product
designed to withstand variations in environmental and
operating conditions

Robust design
yields a product or service designed to withstand
variations

Controllable factors
design parameters such as material used, dimensions,
and form of processing

Uncontrollable factors
user’s control (length of use, maintenance, settings, etc.)

4-227

Design for Robustness (cont.)
Tolerance
allowable ranges of variation in the dimension of a
part

Consistency
consistent errors are easier to correct than random
errors
parts within tolerances may yield assemblies that
are not within limits
consumers prefer product characteristics near their
ideal values
4-228

Quantifies customer
preferences toward
quality
Emphasizes that
customer preferences
are strongly oriented
toward consistently
Design for Six Sigma
(DFSS)

Quality Loss

Taguchi’s Quality Loss
Function

Lower
tolerance
limit

Target

Upper
tolerance
limit

4-229

Copyright 2009 John Wiley & Sons, Inc.
All rights reserved. Reproduction or translation of this work beyond
that permitted in section 117 of the 1976 United States Copyright
Act without express permission of the copyright owner is unlawful.
Request for further information should be addressed to the
Permission Department, John Wiley & Sons, Inc. The purchaser
may make back-up copies for his/her own use only and not for
backdistribution or resale. The Publisher assumes no responsibility for
errors, omissions, or damages caused by the use of these
programs or from the use of the information herein.

4-230

Chapter 5
Service Design

Lecture Outline
Service Economy
Characteristics of Services
Service Design Process
Tools for Service Design
Waiting Line Analysis for
Service Improvement
5-232

Service Economy

Source: U.S. Bureau of Labor Statistics, IBM Almaden Research Center
5-233

Characteristics of Services
Services
acts, deeds, or performances

Goods
tangible objects

Facilitating services
accompany almost all purchases of goods

Facilitating goods
accompany almost all service purchases
5-235

Continuum from
Goods to Services

Source: Adapted from Earl W. Sasser, R.P. Olsen, and D. Daryl Wyckoff,
Management of Service Operations (Boston: Allyn Bacon, 1978), p.11.
5-236

Characteristics
of Services (cont.)
Services are
intangible
Service output is
variable
Services have higher
customer contact
Services are
perishable

Service inseparable
from delivery
Services tend to be
decentralized and
dispersed
Services are
consumed more often
than products
Services can be easily
emulated

5-237

Service Design
Process (cont.)
Service concept
purpose of a service; it defines target
market and customer experience

Service package
mixture of physical items, sensual
benefits, and psychological benefits

Service specifications
performance specifications
design specifications
delivery specifications
5-239

High v. Low Contact
Services
Design
Decision
Facility
location
Facility
layout

High-Contact Service
Convenient to
customer
Must look presentable,
accommodate
customer needs, and
facilitate interaction
with customer

Low-Contact Service
Near labor or
transportation source
Designed for efficiency

Source: Adapted from R. Chase, N. Aquilano, and R. Jacobs, Operations Management for Compensative
Advantage (New York:McGraw-Hill, 2001), p. 210

5-241

High v. Low Contact
Services (cont.)
Design
Decision


Quality
control

More variable since
customer is involved in
process; customer
expectations and
perceptions of quality
may differ; customer
present when defects
occur

Capacity

Excess capacity required
to handle peaks in
demand

Low-Contact
Service
Measured against
established
standards; testing
and rework possible
to correct defects

Planned for average
demand


5-242

High v. Low Contact
Services (cont.)
Design
Decision


Low-Contact
Service

Worker skills

Must be able to
interact well with
customers and use
judgment in decision
making

Technical skills

Scheduling

Must accommodate
customer schedule

Customer
concerned only
with completion
date


5-243

High v. Low Contact
Services (cont.)
Design
Decision
Service
process

Service package


Low-Contact
Service

Mostly front-room
activities; service may
change during delivery
in response to
customer

Mostly back-room
activities;
planned and
executed with
minimal
interference

Varies with customer;
includes environment
as well as actual
service

Fixed, less
extensive


5-244

Tools for Service Design
Service blueprinting
line of influence
line of interaction
line of visibility
line of support

Front-office/BackFront-office/Backoffice activities

Servicescapes
space and function
ambient conditions
signs, symbols, and
artifacts

Quantitative
techniques

5-245

Service Blueprinting (Con’t)

5-247

Elements of
Waiting Line Analysis
Operating characteristics
average values for characteristics that describe
performance of waiting line system

Queue
a single waiting line

Waiting line system
consists of arrivals, servers, and waiting line
structure

Calling population
source of customers; infinite or finite
5-248

Elements of
Waiting Line Analysis (cont.)
Arrival rate (λ)
(λ
frequency at which customers arrive at a waiting line
according to a probability distribution, usually Poisson

Service time (µ)
(µ
time required to serve a customer, usually described by
negative exponential distribution

Service rate must be shorter than arrival rate (λ < µ)
(λ
Queue discipline
order in which customers are served

Infinite queue
can be of any length; length of a finite queue is limited

5-250

Elements of
Waiting Line Analysis (cont.)
Channels
number of
parallel
servers for
servicing
customers

Phases
number of
servers in
sequence a
customer
must go
through
5-251

Operating Characteristics
Operating characteristics are assumed to
approach a steady state

5-252

Traditional Cost Relationships
as service improves, cost increases

5-253

Psychology of Waiting
Waiting rooms
magazines and
newspapers
televisions

Bank of America

Disney
costumed characters
mobile vendors
accurate wait times
special passes

mirrors

Supermarkets
magazines
“impulse purchases”
5-254

Psychology of Waiting (cont.)
Preferential treatment
Grocery stores: express lanes for customers with
few purchases
Airlines/Car rental agencies: special cards
available to frequent-users or for an additional fee
frequentPhone retailers: route calls to more or less
experienced salespeople based on customer’s
sales history

Critical service providers
services of police department, fire department, etc.
waiting is unacceptable; cost is not important
5-255

Waiting Line Models
SingleSingle-server model
simplest, most basic waiting line structure

Frequent variations (all with Poisson arrival rate)
exponential service times
general (unknown) distribution of service times
constant service times
exponential service times with finite queue
exponential service times with finite calling population

5-256

Basic Single-Server Model
SingleAssumptions
Poisson arrival rate
exponential service
times
firstfirst-come, firstfirstserved queue
discipline
infinite queue length
infinite calling
population

Computations
λ = mean arrival rate
µ = mean service rate
n = number of
customers in line

5-257

Basic Single-Server Model (cont.)
Singleprobability that no customers
are in queuing system

( )

P0 =

λ
1–
µ

λ

L=
µ–λ

probability of n customers in
queuing system

average number of customers
in waiting line

( ) ( )( )

Pn =

λ

µ

n

· P0 =

λ

µ

average number of customers
in queuing system

n

1–

λ

µ

Lq =

λ2
µ ( µ – λ)

5-258

Basic Single-Server Model (cont.)
Singleaverage time customer
spends in queuing system
W=

1
µ–λ

=

L
λ

average time customer
spends waiting in line
λ
Wq =
µ ( µ – λ)

probability that server is busy
and a customer has to wait
(utilization factor)
λ
ρ=
µ
probability that server is idle
and customer can be served
I=1– ρ
=1–

λ
µ

= P0
5-259

SingleExample

5-260

SingleExample (cont.)

5-261

Service Improvement Analysis
waiting time (8 min.) is too long
hire assistant for cashier?
increased service rate

hire another cashier?
reduced arrival rate

Is improved service worth the cost?

5-262

SingleExample: Excel

5-263

Advanced Single-Server Models
SingleConstant service times
occur most often when automated equipment or
machinery performs service

Finite queue lengths
occur when there is a physical limitation to length of
waiting line

Finite calling population
number of “customers” that can arrive is limited

5-264

Advanced Single-Server
SingleModels (cont.)

5-265

Basic Multiple-Server Model
Multiplesingle waiting line and service facility with
several independent servers in parallel
same assumptions as single-server model
singlesµ > λ
s = number of servers
servers must be able to serve customers faster than
they arrive

5-266

Multiple(cont.)
probability that there are no customers in system
1
P0 = n = s – 1
1 λ n
1 λ s
sµ

∑

n=0

()
n!

+

µ

( )( )
s!

µ

sµ - λ

probability of n customers in system
1
λ n
P0, for n > s
n–s
s!s
µ
Pn =
1 λ n
P0, for n ≤ s
n! µ

{

()
()

5-267

Multiple(cont.)
probability that customer must wait
Pw =

L=

()

1
s!

λ

µ

s

sµ

sµ – λ

λµ (λ/µ)s
(s – 1)! (sµ – λ)2
(s
L
W=
λ

P0

P0 +

Lq = L –

λ
µ

λ
µ

Wq = W –

1
µ

=

Lq
λ

λ
ρ=
sµ
5-268

MultipleExample

5-269

MultipleExample (cont.)

5-270


5-271


5-272


5-273

To cut wait time, add another service
representative
now, s = 4

Therefore:

5-274

MultipleMultiple-Server Waiting Line
in Excel

5-275

Chapter 6
Processes and Technology

Lecture Outline
Process Planning
Process Analysis
Process Innovation
Technology Decisions

6-277

Process Planning
Process
a group of related tasks with specific inputs and outputs

Process design
what tasks need to be done and how they are
coordinated among functions, people, and
organizations

Process strategy
an organization’s overall approach for physically
producing goods and services

Process planning
converts designs into workable instructions for
manufacture or delivery
6-278

Process Strategy
Vertical integration
extent to which firm will produce inputs and control outputs of
each stage of production process

Capital intensity
mix of capital (i.e., equipment, automation) and labor
resources used in production process

Process flexibility
ease with which resources can be adjusted in response to
changes in demand, technology, products or services, and
resource availability

Customer involvement
role of customer in production process

6-279

Outsourcing
Cost
Capacity
Quality

Speed
Reliability
Expertise

6-280

Process Selection
Projects
one-of-a-kind production of a product to customer order

Batch production
processes many different jobs at the same time in groups or
batches

Mass production
produces large volumes of a standard product for a mass
market

Continuous production
used for very-high volume commodity products

6-281

ProductProduct-Process Matrix

Source: Adapted from Robert Hayes and Steven Wheelwright, Restoring the Competitive Edge
Competing through Manufacturing (New York, John Wiley & Sons, 1984), p. 209.

6-283

Types of Processes
PROJECT

Type of
product

Type of
customer

Product
demand

BATCH

MASS

CONT.
CONT.

Unique

Made-toMade-toorder

Made-toMade-tostock

Commodity

(customized)

(standardized )

Few
individual
customers

Mass
market

Mass
market

Fluctuates

Stable

Very stable

One-atOne-at-atime

Infrequent

Source: Adapted from R. Chase, N. Aquilano, and R. Jacobs, Operations Management for Competitive
York:McGraw-

6-284

Types of Processes (cont.)
PROJECT

BATCH

MASS

CONT.
CONT.

Demand
volume

Very low

Low to
medium

High

Very high

No. of
different
products

Infinite
variety

Many, varied

Few

Very few

Production
system

LongLong-term
project

Discrete, job
shops

Repetitive,
assembly
lines

Continuous,
process
industries

York:McGraw-

6-285

PROJECT

BATCH

MASS

CONT.
CONT.

Equipment

Varied

GeneralGeneralpurpose

SpecialSpecialpurpose

Highly
automated

Primary
type of
work

Specialized
contracts

Fabrication

Assembly

Mixing,
treating,
refining

Worker
skills

Experts,
craftscraftspersons

Wide range
of skills

Limited
range of
skills

Equipment
monitors

York:McGraw-

6-286

PROJECT

Advantages

DisDisadvantages

Examples

BATCH

MASS

CONT.
CONT.

Custom work,
latest technology

Flexibility,
quality

Efficiency,
speed,
low cost

Highly efficient,
large capacity,
ease of control

NonNon-repetitive,
small customer
base, expensive

Costly, slow,
difficult to
manage

Capital
investment;
lack of
responsiveness

Difficult to change,
far-reaching errors,
farlimited variety

Construction,
shipbuilding,
spacecraft

Machine shops,
print shops,
bakeries,
education

Automobiles,
televisions,
computers,
fast food

Paint, chemicals,
foodstuffs

Source: Adapted from R. Chase, N. Aquilano, and R. Jacobs, Operations Management for Competitive Advantage (New
York:McGrawYork:McGraw-Hill, 2001), p. 210

6-287

Process Selection with
BreakBreak-Even Analysis
examines cost trade-offs associated with demand volume
Cost
Fixed costs
constant regardless of the number of units produced

Variable costs
vary with the volume of units produced

Revenue
price at which an item is sold
Total revenue
is price times volume sold
Profit
difference between total revenue and total cost
6-288

BreakBreak-Even Analysis (cont.)
Total cost = fixed cost + total variable cost
TC = cf + vcv
Total revenue = volume x price
TR = vp
Profit = total revenue - total cost
Z = TR – TC = vp - (cf + vcv)

6-289

BreakBreak-Even Analysis (cont.)
TR = TC
vp = cf + vcv
vp - vcv = cf
v ( p - c v) = c f
v=

cf

p - cv

Solving for Break-Even Point (Volume)
Break6-290

BreakBreak-Even Analysis: Example
Fixed cost = cf = $2,000
Variable cost = cv = $5 per raft
Price = p = $10 per raft
BreakBreak-even point is
v=

cf
p - cv

=

2000

= 400 rafts

10 - 5
6-291

BreakBreak-Even Analysis: Graph
Dollars

Total
cost
line

$3,000 —

$2,000 —

$1,000 —
Total
revenue
line
400
BreakBreak-even point

Units

6-292

Process Plans
Set of documents that detail manufacturing
and service delivery specifications
assembly charts
operations sheets
quality-control check-sheets

6-293

Process Selection
Process A

Process B

$2,000 + $5v = $10,000 + $3v
$5v
$3v
$2v = $8,000
$2v
v = 4,000 rafts

Below or equal to 4,000, choose A
Above or equal to 4,000, choose B
6-294

Process Analysis

•
systematic
examinatio
n of all
aspects of
process to
improve
operation

6-295

An Operations Sheet for a Plastic Part
Part name

Crevice Tool

Part No.

52074

Usage

Hand-Vac
Hand-

Assembly No. 520
Oper. No.

Description

Dept.

Machine/Tools

Time

10

Pour in plastic bits

041

Injection molding

2 min

20

Insert mold

041

#076

2 min

30

Check settings
& start machine

041

113, 67, 650

20 min

40

Collect parts & lay flat

051

Plastics finishing

10 min

50

Remove & clean mold

042

Parts washer

15 min

60

Break off rough edges

051

Plastics finishing

10 min

6-296

Process Analysis
Building a flowchart
Determine objectives
Define process boundaries
Define units of flow
Choose type of chart
Observe process and collect data
Map out process
Validate chart
6-297

Process Flowcharts
look at manufacture of product or delivery
of service from broad perspective
Incorporate
nonproductive activities (inspection,
transportation, delay, storage)
productive activities (operations)

6-298

Process Flowchart
Symbols
Operations
Inspection
Transportation
Delay
Storage
6-299

Process
Flowchart
of Apple
Processin
g
6-300

Simple Value Chain Flowchart

6-302

Process Innovation
Continuous improvement
refines the breakthrough

Breakthrough
Improvement

Total redesign
of a process for
breakthrough
improvements

Continuous improvement activities
peak; time to reengineer process

6-303

From Function to Process

Sales

Manufacturing

Purchasing

Accounting

Product Development
Order Fulfillment
Customer Service
Function

Process

6-304

Process Innovation

Customer
Requirements

Strategic
Directives

Baseline Data
Benchmark
Data

Goals for Process
Performance
High - level
Process map

Innovative
Ideas

Detailed
Process Map

Model
Validation

Pilot Study
of New Design

No

Goals
Met?

Yes

Design
Principles
Key
Performance
Measures

Full Scale
Implementation

6-305

HighHigh-Level Process Map

6-306

Principles for Redesigning
Processes
Remove waste, simplify, and consolidate
similar activities
Link processes to create value
Let the swiftest and most capable enterprise
execute the process
Flex process for any time, any place, any way
Capture information digitally at the source and
propagate it through process
6-307

Principles for Redesigning
Processes (cont.)
Provide visibility through fresher and richer
information about process status
Fit process with sensors and feedback loops
that can prompt action
Add analytic capabilities to process
Connect, collect, and create knowledge around
process through all who touch it
Personalize process with preferences and
habits of participants

6-308

Techniques for Generating
Innovative Ideas
Vary the entry point to a problem
in trying to untangle fishing lines, it’s best to start
from the fish, not the poles

Draw analogies
a previous solution to an old problem might work

Change your perspective
think like a customer
bring in persons who have no knowledge of
process

6-309

Techniques for Generating
Innovative Ideas (cont.)
Try inverse brainstorming
what would increase cost
what would displease the customer

Chain forward as far as possible
if I solve this problem, what is the next problem

Use attribute brainstorming
how would this process operate if. . .
our workers were mobile and flexible
there were no monetary constraints
we had perfect knowledge

6-310

Technology Decisions
Financial justification of technology
Purchase cost
Operating Costs
Annual Savings
Revenue Enhancement
Replacement Analysis
Risk and Uncertainty
Piecemeal Analysis

6-311

Components of e-Manufacturing
e-

6-312

A Technology Primer
Product Technology
Computer-aided
design (CAD)
Group technology
(GT)
Computer-aided
engineering (CAE)
Collaborative
product commerce
(CPC)

Creates and communicates designs
electronically
Classifies designs into families for easy
retrieval and modification
Tests functionality of CAD designs
electronically
Facilitates electronic communication and
exchange of information among designers
and suppliers

6-313

A Technology Primer (cont.)
Product Technology
Product data
management
(PDM)
Product life cycle
management
(PLM)
Product
configuration

Keeps track of design specs and revisions
for the life of the product
Integrates decisions of those involved in
product development, manufacturing, sales,
customer service, recycling, and disposal
Defines products “configured” by customers
who have selected among various options,
usually from a Web site

6-314

Process Technology
Standard for
exchange of
product model data
(STEP)
Computer-aided
design and
manufacture
(CAD/CAM)
Computer aided
process (CAPP)
E-procurement

Set standards for communication among
different CAD vendors; translates CAD data
into requirements for automated inspection
and manufacture
Electronic link between automated design
(CAD) and automated manufacture (CAM)
Generates process plans based on
database of similar requirements
Electronic purchasing of items from eemarketplaces, auctions, or company
websites

6-315

Manufacturing Technology
Computer
numerically control
(CNC)
Flexible
manufacturing
system (FMS)
Robots
Conveyors

Machines controlled by software code to perform a
variety of operations with the help of automated
tool changers; also collects processing information
and quality data
A collection of CNC machines connected by an
automated material handling system to produce a
wide variety of parts
Manipulators that can be programmed to perform
repetitive tasks; more consistent than workers but
less flexible
FixedFixed-path material handling; moves items along a
belt or overhead chain; “reads” packages and
diverts them to different directions; can be very fast

6-316

Manufacturing Technology
Automatic guided
vehicle (AGV)

A driverless truck that moves material along a
specified path; directed by wire or tape embedded
in floor or by radio frequencies; very flexible

Automated storage
and retrieval system
(ASRS)

An automated warehouse—some 26 stores high—
warehouse—
high—
in which items are placed in a carousel-type
carouselstorage system and retrieved by fast-moving
faststacker cranes; controlled by computer

Process Control

Continuous monitoring of automated equipment;
makes real-time decisions on ongoing operation,
realmaintenance, and quality

Computer-integrated
manufacturing (CIM)

Automated manufacturing systems integrated
through computer technology; also called eemanufacturing

6-317

Information Technology
Business – to –
Business (B2B)
Business – to –
Consumer (B2C)
Internet

Electronic transactions between businesses
usually over the Internet

Intranet

Communication networks internal to an
organization; can be password (i.e., firewall)
protected sites on the Internet

Extranet

Electronic transactions between businesses and
their customers usually over the Internet
A global information system of computer networks
that facilitates communication and data transfer

Intranets connected to the Internet for shared
access with select suppliers, customers, and
trading partners
6-318

Bar Codes
Radio Frequency
Identification tags
(RFID)
Electronic data
interchange (EDI)
Extensive markup
language (XML)
Enterprise
resource planning
(ERP)

A series of vertical lines printed on most packages that
identifies item and other information when read by a
scanner
An integrated circuit embedded in a tag that can send
and receive information; a twenty-first century bar code
twentywith read/write capabilities
A computer-to-computer exchange of business
computer-todocuments over a proprietary network; very expensive
and inflexible
A programming language that enables computer – to computer communication over the Internet by tagging
data before its is sent
Software for managing basic requirements of an
enterprise, including sales & marketing, finance and
accounting, production & materials management, and
human resources

6-319

Supply chain
management (SCM)
Customer relationship
management (CRM)
Decision support
systems (DSS)
Expert systems (ES)
Artificial intelligence
(AI)

Software for managing flow of goods and information
among a network of suppliers, manufacturers and
distributors
Software for managing interactions with customers and
compiling and analyzing customer data
An information system that helps managers make
decisions; includes a quantitative modeling component
and an interactive component for what-if analysis
whatA computer system that uses an expert knowledge base
to diagnose or solve a problem
A field of study that attempts to replicate elements of
human thought in computer processes; includes expert
systems, genetic algorithms, neural networks, and fuzzy
logic

6-320

Chapter 7

Capacity and Facilities

Lecture Outline
Capacity Planning
Basic Layouts
Designing Process Layouts
Designing Service Layouts
Designing Product Layouts
Hybrid Layouts

Capacity
Maximum capability to produce
Capacity planning
establishes overall level of productive
resources for a firm

3 basic strategies for timing of capacity
expansion in relation to steady growth in
demand (lead, lag, and average)

Capacity (cont.)
Capacity increase depends on
volume and certainty of anticipated demand
strategic objectives
costs of expansion and operation

Best operating level
% of capacity utilization that minimizes unit costs

Capacity cushion
% of capacity held in reserve for unexpected
occurrences

Economies of Scale
it costs less per unit to produce high levels of
output
fixed costs can be spread over a larger number of
units
production or operating costs do not increase
linearly with output levels
quantity discounts are available for material
purchases
operating efficiency increases as workers gain
experience

Best Operating Level for a Hotel

Machine Objectives of
Facility Layout
Arrangement of areas within a facility to:
Minimize material-handling
costs
Utilize space efficiently
Utilize labor efficiently
Eliminate bottlenecks
Facilitate communication and
interaction
Reduce manufacturing cycle
time
Reduce customer service time
Eliminate wasted or redundant
movement
Increase capacity

Facilitate entry, exit, and
placement of material, products,
and people
Incorporate safety and security
measures
Promote product and service
quality
Encourage proper maintenance
activities
Provide a visual control of
activities
Provide flexibility to adapt to
changing conditions

BASIC LAYOUTS
Process layouts
group similar activities together
according to process or function they
perform

Product layouts
arrange activities in line according to
sequence of operations for a particular
product or service

Fixed-position layouts
are used for projects in which product
cannot be moved

Process Layout in Services
Women’s
lingerie

Shoes

Housewares

Women’s
dresses

Cosmetics
and jewelry

Children’s
department

Women’s
sportswear

Entry and
display area

Men’s
department

Comparison of Product
and Process Layouts
Product
Description

Type of process

Product
Demand
Volume
Equipment

Process

Sequential
arrangement of
activities
Continuous, mass
production, mainly
assembly

Functional
grouping of
activities
Intermittent, job
shop, batch
production, mainly
fabrication
Varied, made to
order
Fluctuating
Low
General purpose

Standardized, made
to stock
Stable
High
Special purpose

Comparison of Product
and Process Layouts
Product
Workers
Inventory
Storage space
Material handling
Aisles
Scheduling
Layout decision
Goal
Advantage

Limited skills
Low in-process, high
infinished goods
Small
Fixed path (conveyor)
Narrow
Part of balancing
Line balancing
Equalize work at each
station
Efficiency

Process
Varied skills
High in-process, low
infinished goods
Large
Variable path (forklift)
Wide
Dynamic
Machine location
Minimize material
handling cost
Flexibility

FixedFixed-Position Layouts
Typical of projects in
which product produced
is too fragile, bulky, or
heavy to move
Equipment, workers,
materials, other
resources brought to the
site
Low equipment utilization
Highly skilled labor
Typically low fixed cost
Often high variable costs
7-335

Designing Process Layouts
Goal: minimize material handling costs
Block Diagramming
minimize nonadjacent loads
use when quantitative data is available

Relationship Diagramming
based on location preference between areas
use when quantitative data is not available

Block Diagramming
Unit load
quantity in which
material is normally
moved

Nonadjacent load
distance farther
than the next block

STEPS
create load summary chart
calculate composite (two
way) movements
develop trial layouts
minimizing number of
nonadjacent loads

Block Diagramming: Example
Load Summary Chart

4

2

5

3

DEPARTMENT

Department 1

1

FROM/TO

2

3

100
—

50
200
—

1
2
3
4
5

—
60

100
50

4
50
40
—

5

50
60
—

Block Diagramming:
Example (cont.)
2
2
1
1
4
3
2
3
1
1

3
4
3
2
5
5
5
4
4
5

200 loads
150 loads
110 loads
100 loads
60 loads
50 loads
50 loads
40 loads
0 loads
0 loads

Nonadjacent Loads:
110+40=150
0
110

1

4
Grid 1
2

100

2

150
200

3
4

150 200 50
50 40 60
50
110
50
60

3
5

5

40

Block Diagramming:
Example (cont.)
Block Diagram
type of schematic layout diagram; includes space requirements
(a) Initial block diagram

1

(b) Final block diagram

2

4

3

5

1

4
2

3

5

Relationship Diagramming

Schematic diagram that
uses weighted lines to
denote location preference
Muther’s grid
format for displaying
manager preferences for
department locations

Relationship Diagramming: Excel

Relationship A Absolutely necessary
E Especially important
Diagramming: Example
I Important
O Okay
U Unimportant
X Undesirable

Production
O
A

Offices
U

I

A
Shipping and
receiving

U

U
O

O
O

A

X
U

Locker room
Toolroom

E

O

Stockroom

Relationship Diagrams: Example (cont.)
(a) Relationship diagram of original layout

Offices

Stockroom

Locker
room

Toolroom

Shipping
and
receiving
Key: A
E
I
Production
O
U
X

Relationship Diagrams: Example (cont.)
(b) Relationship diagram of revised layout

Stockroom

Shipping
and
receiving

Offices

Toolroom

Production

Locker
room

Key: A
E
I
O
U
X

Computerized layout
Solutions
CRAFT
Computerized Relative Allocation of Facilities
Technique

CORELAP
Computerized Relationship Layout Planning

PROMODEL and EXTEND
visual feedback
allow user to quickly test a variety of scenarios

Three-D modeling and CAD
integrated layout analysis
available in VisFactory and similar software

Designing Service
Layouts
Must be both attractive and functional
Types
Free flow layouts
encourage browsing, increase impulse purchasing, are flexible
and visually appealing

Grid layouts
encourage customer familiarity, are low cost, easy to clean and
secure, and good for repeat customers

Loop and Spine layouts
both increase customer sightlines and exposure to products,
while encouraging customer to circulate through the entire
store

Designing Product
Layouts
Objective
Balance the assembly line

Line balancing
tries to equalize the amount of work at each
workstation

Precedence requirements
physical restrictions on the order in which operations
are performed

Cycle time
maximum amount of time a product is allowed to
spend at each workstation

Cycle Time Example

Cd =
Cd =

production time available
desired units of output
(8 hours x 60 minutes / hour)
(120 units)

Cd =

480
120

= 4 minutes

Flow Time vs Cycle Time
Cycle time = max time spent at any station
Flow time = time to complete all stations
1

2

3

4 minutes

4 minutes

4 minutes

Flow time = 4 + 4 + 4 = 12 minutes
Cycle time = max (4, 4, 4) = 4 minutes

Efficiency of Line and Balance Delay
Efficiency

Minimum number of
workstations

i

∑
E=

i=1

nCa

i

∑

ti

N=

ti

i=1

Cd

where

ti
j
n
Ca
Cd

= completion time for element i
= number of work elements
= actual number of workstations
= actual cycle time
= desired cycle time

Balance
delay
total idle
time of line
calculated
as (1 efficiency)

Line Balancing Procedure
1. Draw and label a precedence diagram
2. Calculate desired cycle time required for line
3. Calculate theoretical minimum number of
workstations
4. Group elements into workstations, recognizing cycle
time and precedence constraints
5. Calculate efficiency of line
6. Determine if theoretical minimum number of
workstations or an acceptable efficiency level has
been reached. If not, go back to step 4.

Line Balancing: Example
WORK ELEMENT
A
B
C
D

PRECEDENCE

TIME (MIN)

—
A
A
B, C

0.1
0.2
0.4
0.3

Press out sheet of fruit
Cut into strips
Outline fun shapes
Roll up and package

B

0.2

0.1 A

D
C

0.4

0.3

Line Balancing: Example (cont.)
WORK ELEMENT
A
B
C
D

PRECEDENCE

TIME (MIN)

—
A
A
B, C

0.1
0.2
0.4
0.3

Press out sheet of fruit
Cut into strips
Outline fun shapes
Roll up and package

Cd =

N=

40 hours x 60 minutes / hour

=

6,000 units
0.1 + 0.2 + 0.3 + 0.4
0.4

2400

= 0.4 minute

6000
=

1.0
0.4

= 2.5

3 workstations

WORKSTATION
1
2
3

ELEMENT
A
B
C
D
B

REMAINING
TIME
0.3
0.1
0.0
0.1
0.2

0.1 A

Cd = 0.4
N = 2.5
D

C

0.4

REMAINING
ELEMENTS
B, C
C, D
D
none

0.3

Work
station 1

Work
station 3

A, B

C

D

0.3
minute

E=

Work
station 2

0.4
minute

0.3
minute

0.1 + 0.2 + 0.3 + 0.4
3(0.4)

=

1.0
1.2

Cd = 0.4
N = 2.5

= 0.833 = 83.3%

Computerized Line
Balancing
Use heuristics to assign tasks to
workstations
Longest operation time
Shortest operation time
Most number of following tasks
Least number of following tasks
Ranked positional weight

Hybrid Layouts
Cellular layouts
group dissimilar machines into work centers (called cells)
that process families of parts with similar shapes or
processing requirements

Production flow analysis (PFA)
reorders part routing matrices to identify families of parts
with similar processing requirements

Flexible manufacturing system
automated machining and material handling systems
which can produce an enormous variety of items

Mixed-model assembly line
processes more than one product model in one line

Cellular Layouts
1. Identify families of parts with similar
flow paths
2. Group machines into cells based on
part families
3. Arrange cells so material movement
is minimized
4. Locate large shared machines at
point of use

Parts Families

A family of
similar parts

A family of related
grocery items

Original Process Layout
Assembly

4

6

7

8

5
2

A

B

12

10
3

1

9

C

11

Raw materials

Part Routing Matrix
Parts

1

2

A
B
C
D
E
F
G
H

x

x

Figure 5.8

8

x

3

Machines
4 5 6 7

x
x

x
x

x

x
x

10 11 12
x

x

x

x
x
x
x

9

x

x
x

x

x

x
x
x

x
x

x

x
x

Revised Cellular Layout
Assembly

8

10

9

12
11

4

Cell 1

Cell 2

6

Cell 3
7

2

1

3

A B C
Raw materials

5

Reordered Routing Matrix
Parts

1

2

4

Machines
8 10 3 6

A
D
F
C
G
B
H
E

x
x
x

x
x

x
x
x

x
x
x

9

5

7

11 12

x
x

x
x
x
x

x
x
x
x

x
x

x
x
x

x

x

x
x

Operationsmanagement 919slidespresentation-090928145353-phpapp01

Advantages and Disadvantages
of Cellular Layouts
Advantages
Reduced material
handling and transit time
Reduced setup time
Reduced work-inwork-inprocess inventory
Better use of human
resources
Easier to control
Easier to automate

Disadvantages
Inadequate part families
Poorly balanced cells
Expanded training and
scheduling of workers
Increased capital
investment

Automated Manufacturing Cell

Source: J. T. Black, “Cellular
Manufacturing Systems Reduce
Setup Time, Make Small Lot
Production Economical.” Industrial
Engineering (November 1983)

Flexible Manufacturing
Systems (FMS)
FMS consists of numerous programmable
machine tools connected by an automated
material handling system and controlled by
a common computer network
FMS combines flexibility with efficiency
FMS layouts differ based on
variety of parts that the system can process
size of parts processed
average processing time required for part
completion

Mixed Model
Assembly Lines
Produce multiple models in any order
on one assembly line
Issues in mixed model lines
Line balancing
U-shaped lines
Flexible workforce
Model sequencing

Balancing U-Shaped Lines
UPrecedence diagram:
B

Cycle time = 12 min

C

D

A

E

(a) Balanced for a straight line

(b) Balanced for a U-shaped line
U-

A,B

C,D

E

9 min

12 min

3 min

Efficiency =

24
3(12)

=

24

A,B

= .6666 = 66.7 %

C,D

36
E

Efficiency =

24
2(12)

=

24
24

= 100 % 12 min

12 min

Facility Location Models

Lecture Outline
Types of Facilities
Site Selection: Where to Locate
Location Analysis Techniques

Supplement 7-374
7-

Types of Facilities
HeavyHeavy-manufacturing facilities
large, require a lot of space, and are
expensive

LightLight-industry facilities
smaller, cleaner plants and usually less
costly

Retail and service facilities
smallest and least costly
Supplement 7-375
7-

Factors in Heavy Manufacturing
Location
Construction costs
Land costs
Raw material and finished goods
shipment modes
Proximity to raw materials
Utilities
Means of waste disposal
Labor availability
Supplement 7-376
7-

Factors in Light Industry
Location
Land costs
Transportation costs
Proximity to markets
depending on delivery requirements
including frequency of delivery
required by customer

Supplement 7-377
7-

Factors in Retail Location

Proximity to customers
Location is everything

Supplement 7-378
7-

Site Selection: Where to Locate
Infrequent but important
being “in the right place at the
right time”

Must consider other factors,
especially financial
considerations
Location decisions made more
often for service operations
than manufacturing facilities
Location criteria for service
access to customers

Location criteria for
manufacturing facility
nature of labor force
labor costs
proximity to suppliers and
markets
distribution and
transportation costs
energy availability and cost
community infrastructure
quality of life in community
government regulations and
taxes

Supplement 7-379
7-

Global Location Factors
Government stability
Government regulations
Political and economic
systems
Economic stability and growth
Exchange rates
Culture
Climate
Export/import regulations,
duties and tariffs

Raw material availability
Number and proximity of
suppliers
Transportation and
distribution system
Labor cost and education
Available technology
Commercial travel
Technical expertise
CrossCross-border trade
regulations
Group trade agreements
Supplement 7-380
7-

Regional and Community
Location Factors in U.S.
Labor (availability,
education, cost, and
unions)
Proximity of customers
Number of customers
Construction/leasing
costs
Land cost

Modes and quality of
transportation
Transportation costs
Community government
Local business
regulations
Government services
(e.g., Chamber of
Commerce)

Supplement 7-381
7-

Regional and Community
Location Factors in U.S. (cont.)
Business climate
Community services
Incentive packages
Government regulations
Environmental
regulations
Raw material availability
Commercial travel
Climate

Infrastructure (e.g.,
roads, water, sewers)
Quality of life
Taxes
Availability of sites
Financial services
Community inducements
Proximity of suppliers
Education system

Supplement 7-382
7-

Location Incentives
Tax credits
Relaxed government regulation
Job training
Infrastructure improvement
Money

Supplement 7-383
7-

Geographic Information
Systems (GIS)
Computerized system for storing, managing,
creating, analyzing, integrating, and digitally
displaying geographic, i.e., spatial, data
Specifically used for site selection
enables users to integrate large quantities of
information about potential sites and analyze these
data with many different, powerful analytical tools

Supplement 7-384
7-

GIS Diagram

Supplement 7-385
7-

Location Analysis Techniques
Location factor rating
Center-ofCenter-of-gravity
LoadLoad-distance

Supplement 7-386
7-

Location Factor Rating
Identify important factors
Weight factors (0.00 - 1.00)
Subjectively score each factor (0 - 100)
Sum weighted scores

Supplement 7-387
7-

Location Factor Rating: Example
SCORES (0 TO 100)
LOCATION FACTOR
Labor pool and climate
Proximity to suppliers
Wage rates
Community environment
Proximity to customers
Shipping modes
Air service

WEIGHT

Site 1

Site 2

Site 3

.30
.20
.15
.15
.10
.05
.05

80
100
60
75
65
85
50

65
91
95
80
90
92
65

90
75
72
80
95
65
90

Weighted Score for “Labor pool and climate” for
Site 1 = (0.30)(80) = 24

Supplement 7-388
7-

Location Factor Rating: Example
(cont.)
WEIGHTED SCORES
Site 1

Site 2

Site 3

24.00
20.00
9.00
11.25
6.50
4.25
2.50
77.50

19.50
18.20
14.25
12.00
9.00
4.60
3.25
80.80

27.00
15.00
10.80
12.00
9.50
3.25
4.50
82.05

Site 3 has the
highest factor rating

Supplement 7-389
7-

Location Factor Rating

Supplement 7-390
7-

Center-ofCenter-of-Gravity
Technique
Locate facility at center of movement
in geographic area
Based on weight and distance
traveled; establishes grid-map of
gridarea
Identify coordinates and weights
shipped for each location

Supplement 7-391
7-

GridGrid-Map Coordinates
y

n

∑ xiWi
x=

n

∑ Wi
i=1

1 (x1, y1), W1
(x
3 (x3, y3), W3
(x

y3

x1

x2

x3

∑ yiWi

i=1

2 (x2, y2), W2
(x

y2

y1

n

i=1
y=

n

∑ Wi
i=1

where,
x, y = coordinates of new facility
at center of gravity
xi, yi = coordinates of existing
facility i
Wi = annual weight shipped from
facility i

x
Supplement 7-392
7-

Center-ofCenter-of-Gravity Technique:
Example
A

y
700
600

Miles

500

C
(135)

B

200

C

D

200
200
75

100
500
105

250
600
135

500
300
60

(105)

400
300

x
y
Wt

B

D
(60)

A
(75)

100
0

100 200 300 400 500 600 700 x
Miles
Supplement 7-393
7-

Example (cont.)
n

∑ xiWi

x=

i=1
n

=

∑ Wi

(200)(75) + (100)(105) + (250)(135) + (500)(60)
75 + 105 + 135 + 60

= 238

i=1
n

∑ yiWi

y=

i=1
n

∑ Wi

=

(200)(75) + (500)(105) + (600)(135) + (300)(60)
75 + 105 + 135 + 60

= 444

i=1

Supplement 7-394
7-

Example (cont.)
A

y
700
600

Miles

500

C
(135)

B
(105)

400
300
200

A

x
y
Wt

B

C

D

200
200
75

100
500
105

250
600
135

500
300
60

Center of gravity (238, 444)
D
(60)

(75)

100
0

100 200 300 400 500 600 700 x
Miles
Supplement 7-395
7-

Center-ofCenter-of-Gravity Technique

Supplement 7-396
7-

LoadLoad-Distance Technique

Compute (Load x Distance) for each site
Choose site with lowest (Load x Distance)
Distance can be actual or straight-line
straight-

Supplement 7-397
7-

LoadLoad-Distance Calculations
n

∑ ld

LD =

i

i

i=1
where,
LD =

loadload-distance value

li

load expressed as a weight, number of trips or units
being shipped from proposed site and location i
distance between proposed site and location i

=

di

=

di

=

(xi - x)2 + (yi - y)2
(y

where,
(x,y) = coordinates of proposed site
x,y)
(xi , yi) = coordinates of existing facility
Supplement 7-398
7-

LoadLoad-Distance: Example
Potential Sites
Site
X
1
360
2
420
3
250

Y
180
450
400

A
200
200
75

X
Y
Wt

Suppliers
B
C
100
250
500
600
105
135

D
500
300
60

Compute distance from each site to each supplier
Site 1 dA =
dB =

=

(200(200-360)2 + (200-180)2 = 161.2
(200-

(xB - x1)2 + (yB - y1)2 =

(100(100-360)2 + (500-180)2 = 412.3
(500-

(xA - x1)2 + (yA - y1)2

dC = 434.2

dD = 184.4

Supplement 7-399
7-

LoadLoad-Distance: Example (cont.)
Site 2 dA = 333

dB = 323.9 dC = 226.7 dD = 170

Site 3 dA = 206.2 dB = 180.3 dC = 200

dD = 269.3

Compute load-distance
load-

n

LD =

∑ ld
i

i

i=1
Site 1 = (75)(161.2) + (105)(412.3) + (135)(434.2) + (60)(434.4) = 125,063
Site 2 = (75)(333) + (105)(323.9) + (135)(226.7) + (60)(170) = 99,789
Site 3 = (75)(206.2) + (105)(180.3) + (135)(200) + (60)(269.3) = 77,555*

* Choose site 3
Supplement 7-400
7-

LoadLoad-Distance Technique

Supplement 7-401
7-

Chapter 8
Human Resources

Lecture Outline
Human Resources and Quality Management
Changing Nature of Human Resources
Management
Contemporary Trends in Human Resources
Management
Employee Compensation
Managing Diversity in Workplace
Job Design
Job Analysis
Learning Curves
8-403

Human Resources and Quality
Management
Employees play important
role in quality management
Malcolm Baldrige National
Quality Award winners have a
pervasive human resource
focus
Employee training and
education are recognized as
necessary long-term
investments

Employees have power to
make decisions that will
improve quality and customer
service
Strategic goals for quality and
customer satisfaction require
teamwork and group
participation

8-404

Changing Nature of Human
Resources Management
Scientific management
Breaking down jobs into
elemental activities and
simplifying job design

Jobs
Comprise a set of tasks,
elements, and job motions
(basic physical
movements)

In a piece-rate wage
system, pay is based on
output

Assembly-line
Production meshed with
principles of scientific
management

Advantages of task
specialization
High output, low costs,
and minimal training

Disadvantages of task
specialization
Boredom, lack of
motivation, and physical
and mental fatigue
8-405

Employee Motivation
Motivation
willingness to work hard because
that effort satisfies an employee
need

Improving Motivation
positive reinforcement and
feedback
effective organization and
discipline
fair treatment of people
satisfaction of employee needs
setting of work-related goals

Improving Motivation
(cont.)
design of jobs to fit employee
work responsibility
empowerment
restructuring of jobs when
necessary
rewards based on company as
well as individual performance
achievement of company goals

8-406

Evolution of Theories of
Employee Motivation
Abraham Maslow’s
Pyramid of Human
Needs

Douglas McGregor’s
Theory X and Theory Y
•Theory X Employee

SelfSelfactualization
Esteem
Social
Safety/Security
Physiological (financial)

• Dislikes work
• Must be coerced
• Shirks responsibility
• Little ambition
• Security top motivator

•Theory Y Employee
• Work is natural
• Self-directed
Self• Controlled
• Accepts responsibility
• Makes good decisions

Frederick Herzberg’s
Hygiene/Motivation
Theories
•Hygiene Factors
• Company policies
• Supervision
• Working conditions
• Interpersonal relations
• Salary, status, security
•Motivation Factors
• Achievement
• Recognition
• Job interest
• Responsibility
• Growth
• Advancement
8-407

Contemporary Trends in
Human Resources Management
Job training
extensive and varied
two of Deming’s 14 points
refer to employee
education and training

Cross Training
an employee learns more
than one job

Job rotation
horizontal movement
between two or more jobs
according to a plan

Empowerment
giving employees
authority to make
decisions

Teams
group of employees work
on problems in their
immediate work area

8-408

Contemporary Trends in Human
Resources Management (cont.)
Job enrichment
vertical enlargement
allows employees control
over their work

horizontal enlargement
an employee is assigned a
complete unit of work with
defined start and end

Flexible time
part of a daily work
schedule in which
employees can choose
time of arrival and
departure

Alternative workplace
nontraditional work location

Telecommuting
employees work
electronically from a
location they choose

Temporary and part-time
employees
mostly in fast-food and
restaurant chains, retail
companies, package delivery
services, and financial firms

8-409

Employee Compensation
Types of pay
hourly wage
the longer someone works, the more s/he is paid

individual incentive or piece rate
employees are paid for the number of units they produce
during the workday

straight salary
common form of payment for management

commissions
usually applied to sales and salespeople

8-410

Employee Compensation (cont.)
Gainsharing
an incentive plan joins employees
in a common effort to achieve
company goals in which they
share in the gains

Profit sharing
sets aside a portion of profits for
employees at year’s end

8-411

Managing Diversity in
Workplace
Workforce has become more diverse
4 out of every 10 people entering workforce during
the decade from 1998 to 2008 will be members of
minority groups
In 2000 U.S. Census showed that some minorities,
primarily Hispanic and Asian, are becoming
majorities

Companies must develop a strategic approach
to managing diversity
8-412

Affirmative Actions vs.
Managing Diversity
Affirmative action
an outgrowth of laws and
regulations
government initiated and
mandated
contains goals and
timetables designed to
increase level of
participation by women
and minorities to attain
parity levels in a
company’s workforce
not directly concerned
with increasing company
success or increasing
profits

Managing diversity
process of creating a work
environment in which all
employees can contribute
to their full potential in
order to achieve a
company’s goals
voluntary in nature, not
mandated
seeks to improve internal
communications and
interpersonal
relationships, resolve
conflict, and increase
product quality,
productivity, and efficiency
8-413

Diversity Management Programs

Education
Awareness
Communication
Fairness
Commitment

8-414

Global Diversity Issues
Cultural, language, geography
significant barriers to managing a globally diverse workforce

E-mails, faxes, Internet, phones, air travel
make managing a global workforce possible but not
necessarily effective

How to deal with diversity?
identify critical cultural elements
learn informal rules of communication
use a third party who is better able to bridge cultural gap
become culturally aware and learn foreign language
teach employees cultural norm of organization
8-415

Attributes of Good Job Design
An appropriate degree of
repetitiveness
An appropriate degree of
attention and mental
absorption
Some employee
responsibility for
decisions and discretion
Employee control over
their own job

Goals and achievement
feedback
A perceived contribution
to a useful product or
service
Opportunities for
personal relationships
and friendships
Some influence over the
way work is carried out
in groups
Use of skills
8-416

Factors in Job Design
Task analysis
how tasks fit together to form a job

Worker analysis
determining worker capabilities and responsibilities for a
job

Environment analysis
physical characteristics and location of a job

Ergonomics
fitting task to person in a work environment

Technology and automation
broadened scope of job design

8-417

Job Analysis
Method Analysis (work methods)
Study methods used in the work included in
the job to see how it should be done
Primary tools are a variety of charts that
illustrate in different ways how a job or work
process is done

8-419

Process Flowchart Symbols
Operation:
An activity directly contributing to product or service
Transportation:
Moving the product or service from one location to another
Inspection:
Examining the product or service for completeness,
irregularities, or quality
Delay:
Process having to wait
Storage:
Store of the product or service

8-420

Job Photo-Id Cards
PhotoTime
(min)

–1

WorkerWorkerMachine
Chart

Operator

Date
Time
(min)

10/14

Photo Machine

Key in customer data
on card

2.6

Idle

Feed data card in

0.4

Accept card

–2
–3

Idle

Take picture

0.6

Begin photo process

Idle

–4

Position customer for photo 1.0

3.4

Photo/card processed

Inspect card & trim edges

1.2

Idle

–5
–6
–7
–8
–9

8-422

WorkerWorker-Machine Chart: Summary

Summary
Operator Time

%

Photo Machine Time

%

Work

5.8

63

4.8

52

Idle

3.4

37

4.4

48

Total

9.2 min

100%

9.2 Min

100%

8-423

Motion Study
Used to ensure efficiency of motion in
a job
Frank & Lillian Gilbreth
Find one “best way” to do task
Use videotape to study motions

8-424

General Guidelines for
Motion Study
Efficient Use Of Human Body
Work
simplified, rhythmic and symmetric
Hand/arm motions
coordinated and simultaneous
Employ full extent of physical capabilities
Conserve energy
use machines, minimize distances, use momentum
Tasks
simple, minimal eye contact and muscular effort, no
unnecessary motions, delays or idleness
8-425

General Guidelines for
Motion Study
Efficient Arrangement of Workplace
Tools, material, equipment - designated, easily accessible
location
Comfortable and healthy seating and work area

Efficient Use of Equipment
Equipment and mechanized tools enhance worker abilities
Use foot-operated equipment to relieve hand/arm stress
footConstruct and arrange equipment to fit worker use

8-426

Illustrates
improvement rate of
workers as a job is
repeated
Processing time per
unit decreases by a
constant percentage
each time output
doubles

Processing time per unit

Learning Curves

Units produced

8-427

Learning Curves (cont.)
Time required for the nth unit =
tn = t1n b
where:
tn =
t1 =
n=
b=

time required for nth unit produced
time required for first unit produced
cumulative number of units produced
ln r where r is the learning curve percentage
ln 2 (decimal coefficient)

8-428

Learning Curve Effect
Contract to produce 36 computers.
t1 = 18 hours, learning rate = 80%
What is time for 9th, 18th, 36th units?
t9 = (18)(9)ln(0.8)/ln 2 = (18)(9)-0.322
= (18)/(9)0.322 = (18)(0.493) = 8.874hrs
t18 = (18)(18)ln(0.8)/ln 2 = (18)(0.394) = 7.092hrs
t36 = (18)(36)ln(0.8)/ln 2 = (18)(0.315) = 5.674hrs
8-429

Processing time per unit

Learning Curve for Mass
Production Job

End of improvement

Standard
time

Units produced

8-430

Learning Curves (cont.)
Advantages
planning labor
planning budget
determining
scheduling
requirements

Limitations
product modifications
negate learning curve
effect
improvement can derive
from sources besides
learning
industry-derived learning
curve rates may be
inappropriate

8-431

Work Measurement

Lecture Outline
Time Studies
Work Sampling

Supplement 8-433
8-

Work Measurement
Determining how long it takes to do a job
Growing importance in service sector
Services tend to be labor-intensive
laborService jobs are often repetitive

Time studies
Standard time
is time required by an average worker to perform a job once

Incentive piece-rate wage system based on time
piecestudy

Supplement 8-434
8-

Stopwatch Time
Study Basic Steps
1.
2.
3.
4.
5.

Establish standard job method
Break down job into elements
Study job
Rate worker’s performance (RF)
Compute average time (t)

Supplement 8-435
8-

Stopwatch Time Study
Basic Steps (cont.)
6. Compute normal time
Normal Time = (Elemental average) x (rating factor)
Nt = (t )(RF)
)(RF)
Normal Cycle Time = NT = ΣNt

7. Compute standard time
Standard Time = (normal cycle time) x (1 + allowance factor)
ST = (NT)(1 + AF)
Supplement 8-436
8-

Performing a Time Study
Time Study Observation Sheet
Identification of operation

Date

Sandwich Assembly
Operator
Smith

Approval
Jones

Observer
Russell

Cycles
1
Grasp and lay
1 out bread slices
2

Spread mayonnaise
on both slices

3

Place ham, cheese,
and lettuce on bread

4

t

2

3

4

5

6

5/17

Summary
7

8

9

10

.04 .05 .05 .04 .06 .05 .06 .06 .07 .05

R .04

.06

R .11

.11

RF

Nt

.53 .053 1.05 .056

.44 .79 1.13 1.47 1.83 2.21 2.60 2.98 3.37

t .12

t

.38 .72 1.05 1.40 1.76 2.13 2.50 2.89 3.29

t .07

Σt

R .23 .55

.07 .08 .07 .07

.14

.12

.13

.13

.08

.13

.10

.12

.09

.14

.08

.77 .077 1.00 .077

.14 1.28 1.28 1.10 .141

.93 1.25 1.60 1.96 2.34 2.72 3.12 3.51

Place top on sandwich, t .10 .12 .08 .09 .11 .11 .10 .10 .12 .10 1.03 1.03 1.10 .113
Slice, and stack
R .33 .67 1.01 1.34 1.71 2.07 2.44 2.82 3.24 3.61

Supplement 8-437
8-

Performing a Time
Study (cont.)
Average element time = t =

0.53
Σt
=
= 0.053
10
10

Normal time = (Elemental average)(rating factor)
Nt = ( t )(RF) = (0.053)(1.05) = 0.056
)(RF)
Normal Cycle Time = NT = Σ Nt = 0.387
ST = (NT) (1 + AF) = (0.387)(1+0.15) = 0.445 min

Supplement 8-438
8-

Performing a Time
Study (cont.)
How many sandwiches can be made in 2 hours?
120 min
0.445 min/sandwich

= 269.7 or 270 sandwiches

Example 17.3
Supplement 8-439
8-

Number of Cycles
To determine sample size:
zs
n=

2

eT

where
z = number of standard deviations from the mean in a
normal distribution reflecting a level of statistical
confidence
s=

Σ(xi - x)2 = sample standard deviation from sample
time study
n-1

T = average job cycle time from the sample time study
e = degree of error from true mean of distribution

Supplement 8-440
8-

Number of Cycles: Example
• Average cycle time = 0.361
• Computed standard deviation = 0.03
• Company wants to be 95% confident that computed time is
within 5% of true average time

zs
n=

eT

2

=

2

(1.96)(0.03)
= 10.61 or 11
(0.05)(0.361)

Supplement 8-441
8-

Number of Cycles: Example
(cont.)

Supplement 8-442
8-

Developing Time Standards
without a Time Study
Elemental standard time
files
predetermined job
element times

Predetermined motion
times
predetermined times for
basic micro-motions
micro-

Time measurement units
TMUs = 0.0006 minute
100,000 TMU = 1 hour

Advantages
worker cooperation
unnecessary
workplace uninterrupted
performance ratings
unnecessary
consistent

Disadvantages
ignores job context
may not reflect skills and
abilities of local workers

Supplement 8-443
8-

MTM Table for MOVE
TIME (TMU) WEIGHT ALLOWANCE
DISTANCE
MOVED
(INCHES)
A
3/4 or less
2.0
1
2.5
2
3.6
3
4.9
4
6.1
…
20
19.2

B
2.0
2.9
4.6
5.7
6.9

C
2.0
3.4
5.2
6.7
8.0

18.2

22.1

Hand in
motion
B

Weight
(lb)
up to:

Static
constant
TMU

Dynamic
factor

2.3
2.9
3.6
4.3

2.5

1.00

0

7.5

1.06

2.2

15.6

37.5

1.39

12.5

A. Move object to other hand or against stop
B. Move object to approximate or indefinite location
Source: MTM Association for Standards and Research.
C. Move object to exact location
Supplement 8-444
8-

Work Sampling
Determines the proportion of time a worker
spends on activities
Primary uses of work sampling are to
determine
ratio delay
percentage of time a worker or machine is delayed or idle

analyze jobs that have non-repetitive tasks
non-

Cheaper, easier approach to work
measurement
Supplement 8-445
8-

Steps of Work Sampling
1.
2.

Define job activities
Determine number of observations in work sample
2

n=

z
e p(1 - p)

where
n = sample size (number of sample observations)
z = number of standard deviations from mean for desired
level of confidence
e = degree of allowable error in sample estimate
p = proportion of time spent on a work activity estimated
prior to calculating work sample
Supplement 8-446
8-

Steps of Work Sampling
(cont.)
3. Determine length of sampling
period
4. Conduct work sampling study;
record observations
5. Periodically re-compute number
reof observations

Supplement 8-447
8-

Work Sampling: Example
What percent of time is spent looking up
information? Current estimate is p = 30%
Estimate within +/- 2%, with 95% confidence
+/-

n=

z
e

2

2

p(1 - p) =

1.96

(0.3)(0.7) = 2016.84 or 2017

0.02

After 280 observations, p = 38%

n=

z
e

2

2

p(1 - p) =

1.96

(0.38)(0.62) = 2263

0.02
Supplement 8-448
8-

Chapter 9
Project Management

Lecture Outline
Project Planning
Project Scheduling
Project Control
CPM/PERT
Probabilistic Activity Times
Microsoft Project
Project Crashing and Time-Cost
Trade-off
9-451

Project Management Process
Project
unique, one-time operational activity or effort

9-452

(cont.)

9-453

(cont.)

9-454

Project Elements
Objective
Scope
Contract requirements
Schedules
Resources
Personnel
Control
Risk and problem analysis
9-455

Project Team and Project Manager
Project team
made up of individuals from various areas and
departments within a company

Matrix organization
a team structure with members from functional
areas, depending on skills required

Project manager
most important member of project team

9-456

Scope Statement and Work
Breakdown Structure
Scope statement
a document that provides an understanding,
justification, and expected result of a project

Statement of work
written description of objectives of a project

Work breakdown structure (WBS)
breaks down a project into components,
subcomponents, activities, and tasks
9-457

Work Breakdown Structure for Computer Order
Processing System Project

9-458

Responsibility Assignment Matrix
Organizational
Breakdown
Structure (OBS)
a chart that
shows which
organizational
units are
responsible for
work items

Responsibility
Assignment
Matrix (RAM)
shows who is
responsible for
work in a
project
9-459

Global and Diversity Issues in
Project Management
In existing global business environment,
project teams are formed from different
genders, cultures, ethnicities, etc.
In global projects diversity among team
members can add an extra dimension to
project planning
Cultural research and communication are
important elements in planning process

9-460

Project Scheduling
Steps
Define activities
Sequence
activities
Estimate time
Develop schedule

Techniques
Gantt chart
CPM/PERT
Microsoft Project

9-461

Gantt Chart
Graph or bar chart with a bar for each
project activity that shows passage of
time
Provides visual display of project
schedule
Slack
amount of time an activity can be delayed
without delaying the project

9-462

Example of Gantt Chart
0

|

2

|

Month
4

|

6

|

8

|

10

Activity
Design house
and obtain
financing
Lay foundation
Order and
receive
materials
Build house

Select paint

Select carpet

1
Finish work

3

5

7

9

Month
9-463

Project Control
Time management
Cost management
Quality management
Performance management
Earned Value Analysis
a standard procedure for numerically measuring a
project’s progress, forecasting its completion date and
cost and measuring schedule and budget variation

Communication
Enterprise project management

9-464

CPM/PERT
Critical Path Method (CPM)
DuPont & Remington-Rand (1956)
RemingtonDeterministic task times
Activity-onActivity-on-node network construction

Project Evaluation and Review Technique
(PERT)
US Navy, Booz, Allen & Hamilton
Multiple task time estimates; probabilistic
Activity-onActivity-on-arrow network construction
9-465

Project Network
Activity-on-node (AON)
nodes represent activities,
and arrows show
precedence relationships

Node

Activity-on-arrow (AOA)
arrows represent activities
and nodes are events for
points in time

Event

1

2

3

Branch

completion or beginning
of an activity in a project

Dummy
two or more activities
cannot share same start
and end nodes
9-466

AOA Project Network for
a House
3

Lay
foundation
2

1

3
Design house
and obtain
financing

2

Dummy
Build
house

0
1

Order and
receive
materials

4
Select
paint

Finish
work

6

3
1

1

1

7

Select
carpet

5

9-467

Concurrent Activities
Lay foundation

2

3
Lay
foundation

3

Order material

(a) Incorrect precedence
relationship

2

Dummy
2

0
1

4

Order material
(b) Correct precedence
relationship

9-468

AON Network for House
Building Project
Lay foundations

Build house

4
3

2
2
Start

Finish work

7
1

1
3

Design house
and obtain
financing

3
1

Order and receive
materials

5
1

6
1
Select carpet

Select paint

9-469

Critical Path
4
3

2
2
Start

7
1

1
3
3
1

A:
B:
C:
D:

1-2-4-7
3 + 2 + 3 + 1 = 9 months
1-2-5-6-7
3 + 2 + 1 + 1 + 1 = 8 months
1-3-4-7
3 + 1 + 3 + 1 = 8 months
1-3-5-6-7
3 + 1 + 1 + 1 + 1 = 7 months

5
1

6
1

Critical path
Longest path
through a network
Minimum project
completion time
9-470

Activity Start Times
Start at 5 months

4
3

2
2
Start

Finish at 9 months

7
1

1
3
3
1
Start at 3 months

5
1

Finish

6
1
Start at 6 months

9-471

Node Configuration
Activity number

Earliest start

Earliest finish
1

0

3

3

0

3
Latest finish

Activity duration

Latest start

9-472

Activity Scheduling
Earliest start time (ES)
earliest time an activity can start
ES = maximum EF of immediate predecessors

Forward pass
starts at beginning of CPM/PERT network to
determine earliest activity times

Earliest finish time (EF)
earliest time an activity can finish
earliest start time plus activity time
EF= ES + t

9-473

Earliest Activity Start and
Finish Times
Lay foundations
Build house

2

Start

3

5
4

2

5

8

3
1

0

3

7

1
Design house
and obtain
financing

8

9

1
6
3

3

4

1
Order and receive
materials

6

7

Finish work

1
5

5

6

1

Select carpet

Select pain
9-474

Activity Scheduling (cont.)
Latest start time (LS)
Latest time an activity can start without delaying
critical path time
LS= LF - t

Latest finish time (LF)
latest time an activity can be completed without
delaying critical path time
LF = minimum LS of immediate predecessors

Backward pass
Determines latest activity times by starting at the end
of CPM/PERT network and working forward

9-475

Latest Activity Start and
Finish Times
Lay foundations
Build house

2

3

5

2

Start

3

5

4

5

8

3

5

8

1

0

3

7

8

9

1

0

3

1

8

9

Design house
and obtain
financing

6
3

3

1

4

5

Order and receive
materials

5

5
6

7

7

Finish work

8

6

1

7

1

4

6

Select carpet

Select pain

9-476

Activity Slack
Activity

LS

ES

LF

EF

Slack S

*1

0

0

3

3

0

*2

3

3

5

5

0

3

4

3

5

4

1

*4

5

5

8

8

0

5

6

5

7

6

1

6

7

6

8

7

1

*7

8

8

9

9

0

* Critical Path

9-477

Probabilistic Time Estimates
Beta distribution
a probability distribution traditionally used in
CPM/PERT
Mean (expected time):

Variance:

a + 4m + b
4m

t=

6

σ =
2

b-a

2

6

where
a = optimistic estimate
m = most likely time estimate
b = pessimistic time estimate
9-478

P(time)

P(time)

Examples of Beta Distributions

m

t

b

a

Time

t

m

b

Time

P(time)

a

a

m=t

b

Time
9-479

Project Network with Probabilistic
Time Estimates: Example
Equipment
installation

Equipment testing
and modification

1

4

6,8,10

2,4,12

System
development

Start

System
training

8

2

Manual
testing

3,6,9

5

Position
recruiting

2,3,4

3
1,3,5

3,7,11

1,4,7

Finish

11

9

Job Training

2,4,6

6

System
testing

3,4,5

Final
debugging
10

1,10,13
System
changeover

Orientation

7
2,2,2

9-480

Activity Time Estimates
TIME ESTIMATES (WKS)
ACTIVITY

1
2
3
4
5
6
7
8
9
10
11

MEAN TIME

VARIANCE

a

m

b

t

б2

6
3
1
2
2
3
2
3
2
1
1

8
6
3
4
3
4
2
7
4
4
10

10
9
5
12
4
5
2
11
6
7
13

8
6
3
5
3
4
2
7
4
4
9

0.44
1.00
0.44
2.78
0.11
0.11
0.00
1.78
0.44
1.00
4.00
9-481

Activity Early, Late Times,
and Slack
ACTIVITY

1
2
3
4
5
6
7
8
9
10
11

t

б2

ES

EF

LS

LF

S

8
6
3
5
3
4
2
7
4
4
9

0.44
1.00
0.44
2.78
0.11
0.11
0.00
1.78
0.44
1.00
4.00

0
0
0
8
6
3
3
9
9
13
16

8
6
3
13
9
7
5
16
13
17
25

1
0
2
16
6
5
14
9
12
21
16

9
6
5
21
9
9
16
16
16
25
25

1
0
2
8
0
2
11
0
3
8
0
9-482

Earliest, Latest, and Slack
1 0
8 1

Start

2 0
6 0

3 0
3 2

8

9

4 8
5 16 21

3

5

10 13 17

8 9
7 9

6

6

Critical Path

13

5 6
3 6
6 3
4 5

16

7

3
Finish

16

9

9

1 0

13

9 9
4 12 16

11 16 25

9 16 25

9

7 3 5
2 14 16

9-483

Total project variance
σ2 = б22 + б52 + б82 + б112
σ

= 1.00 + 0.11 + 1.78 + 4.00
= 6.89 weeks

9-484

Probabilistic Network Analysis
Determine probability that project is
completed within specified time
Z=
where

x-µ

σ

µ = tp = project mean time
σ = project standard deviation
x = proposed project time
Z = number of standard deviations x
is from mean
9-486

Normal Distribution of
Project Time
Probability

Zσ

µ = tp

x

Time

9-487

Southern Textile Example
What is the probability that the project is completed within 30
weeks?

P(x ≤ 30 weeks)

σ

2

= 6.89 weeks

σ
σ
µ = 25 x = 30

=

= 2.62 weeks

6.89

Z =
=

x-µ

σ

30 - 25
2.62

= 1.91

Time (weeks)

From Table A.1, (appendix A) a Z score of 1.91 corresponds to a
probability of 0.4719. Thus P(30) = 0.4719 + 0.5000 = 0.9719
9-488

Southern Textile Example
What is the probability that the project is completed within 22
weeks?
P(x ≤ 22 weeks)

σ

2

= 6.89 weeks

σ
σ

x = 22 µ = 25

=

= 2.62 weeks

6.89

Z =
=

x-µ

σ

22 - 25
2.62

= -1.14

Time
(weeks)

From Table A.1 (appendix A) a Z score of -1.14 corresponds to a
probability of 0.3729. Thus P(22) = 0.5000 - 0.3729 = 0.1271
9-489

Microsoft Project
Popular software package for project
management and CPM/PERT analysis
Relatively easy to use

9-490

Microsoft Project (cont.)

9-491


9-492


9-493


9-494


9-495


9-496

PERT Analysis with
Microsoft Project

9-497

PERT Analysis with

9-498

PERT Analysis with

9-499

Project Crashing
Crashing
reducing project time by expending additional
resources

Crash time
an amount of time an activity is reduced

Crash cost
cost of reducing activity time

Goal
reduce project duration at minimum cost
9-500

Project Network for Building
a House
4

2
8

12

7
4

1
12

3
4

5
4

6
4

9-501

Normal Time and Cost
vs. Crash Time and Cost
$7,000 –
$6,000 –

Crash cost
Crashed activity

$5,000 –

Slope = crash cost per week

$4,000 –
Normal activity

$3,000 –
Normal cost

$2,000 –

0
–

Normal time

Crash time

$1,000 –
|
2

|
4

|
6

|
8

|
10

|
12

|
14

Weeks
9-502

Project Crashing: Example
TOTAL
ALLOWABLE
CRASH TIME
(WEEKS)

NORMAL
TIME
(WEEKS)

CRASH
TIME
(WEEKS)

NORMAL
COST

1

12

7

$3,000

$5,000

5

$400

2

8

5

2,000

3,500

3

500

3

4

3

4,000

7,000

1

3,000

4

12

9

50,000

71,000

3

7,000

5

4

1

500

1,100

3

200

6

4

1

500

1,100

3

200

7

4

3

15,000

22,000

1

7,000

$75,000

$110,700

ACTIVITY

CRASH
COST

CRASH
COST PER
WEEK

9-503

$7000

$500

Project Duration:
36 weeks

4

2
8

$700

12

7
4

1

FROM …

12

$400

3
4

6
4

5
4

$3000

$200

$200
$7000

$500

4

2
8

TO…
Project Duration:
31 weeks
Additional Cost:
$2000

$700

12

7
4

1
7

$400

3
4
$3000

5
4

6
4
$200

$200
9-504

TimeTime-Cost Relationship
Crashing costs increase as project
duration decreases
Indirect costs increase as project
duration increases
Reduce project length as long as
crashing costs are less than indirect
costs

9-505

TimeTime-Cost Tradeoff
Minimum cost = optimal project time

Total project cost

Cost ($)

Indirect cost

Direct cost
Crashing

Time

Project duration
9-506

Chapter 10
Strategy and Design

Lecture Outline
The Management of Supply Chains
Information Technology: A Supply Chain
Enabler
Supply Chain Integration
Supply Chain Management (SCM)
Software
Measuring Supply Chain Performance
10-508
10-

Supply Chains
All facilities, functions, and activities
associated with flow and transformation
of goods and services from raw materials
to customer, as well as the associated
information flows
An integrated group of processes to
“source,” “make,” and “deliver” products

10-509
10-

Supply Chain Illustration
10-510
10-

Supply
Chain
for
Denim
Jeans

10-511
10-

Supply
Chain
for
Denim
Jeans
(cont.)
10-512
10-

Supply Chain Processes

10-513
10-

Supply Chain for Service
Providers
More difficult than manufacturing
Does not focus on the flow of physical goods
Focuses on human resources and support
services
More compact and less extended

10-514
10-

Value Chains
Value chain
every step from raw materials to the eventual end user
ultimate goal is delivery of maximum value to the end user

Supply chain
activities that get raw materials and subassemblies into
manufacturing operation
ultimate goal is same as that of value chain

Demand chain
increase value for any part or all of chain

Terms are used interchangeably
Value
creation of value for customer is important aspect of supply
chain management
10-515
10-

Supply Chain
Management (SCM)
Managing flow of information through supply
chain in order to attain the level of
synchronization that will make it more
responsive to customer needs while lowering
costs
Keys to effective SCM
information
communication
cooperation
trust

10-516
10-

Supply Chain
Uncertainty and Inventory
One goal in SCM:
respond to uncertainty in
customer demand
without creating costly
excess inventory

Negative effects of
uncertainty
lateness
incomplete orders

Inventory
insurance against supply
chain uncertainty

Factors that contribute to
uncertainty
inaccurate demand
forecasting
long variable lead times
late deliveries
incomplete shipments
product changes
batch ordering
price fluctuations and
discounts
inflated orders

10-517
10-

Bullwhip Effect
Occurs when slight demand variability is magnified as information
moves back upstream

10-518
10-

Risk Pooling
Risks are aggregated to reduce the
impact of individual risks
Combine inventories from multiple locations
into one
Reduce parts and product variability,
thereby reducing the number of product
components
Create flexible capacity

10-519
10-

Information Technology:
A Supply Chain Enabler
Information links all aspects of supply chain
E-business
replacement of physical business processes with electronic
ones

Electronic data interchange (EDI)
a computer-to-computer exchange of business documents

Bar code and point-of-sale
data creates an instantaneous computer record of a sale

10-520
10-

Information Technology:
A Supply Chain Enabler (cont.)
Radio frequency identification (RFID)
technology can send product data from an item to a reader
via radio waves

Internet
allows companies to communicate with suppliers,
customers, shippers and other businesses around the world
instantaneously

Build-to-order (BTO)
direct-sell-to-customers model via the Internet; extensive
communication with suppliers and customer

10-521
10-

Supply Chain Enablers

10-522
10-

RFID Capabilities (cont.)

10-524
10-

Supply Chain Integration
Information sharing among supply chain
members
Reduced bullwhip effect
Early problem detection
Faster response
Builds trust and confidence

Collaborative planning, forecasting,
replenishment, and design
Reduced bullwhip effect
Lower costs (material, logistics, operating, etc.)
Higher capacity utilization
Improved customer service levels

10-525
10-

Supply Chain Integration (cont.)
Coordinated workflow, production and
operations, procurement
Production efficiencies
Fast response
Improved service
Quicker to market

Adopt new business models and
technologies
Penetration of new markets
Creation of new products
Improved efficiency
Mass customization

10-526
10-

Collaborative Planning, Forecasting,
and Replenishment (CPFR)
Process for two or more companies in
a supply chain to synchronize their
demand forecasts into a single plan to
meet customer demand
Parties electronically exchange
past sales trends
point-of-sale data
on-hand inventory
scheduled promotions
forecasts
10-527
10-

(SCM) Software
Enterprise resource planning (ERP)
software that integrates the components of a
company by sharing and organizing
information and data

10-528
10-

Key Performance Indicators
Metrics used to measure supply chain performance
Inventory turnover
Inventory turns =

Cost of goods sold
Average aggregate value of inventory

Total value (at cost) of inventory
Average aggregate value of inventory = ∑ (average inventory for item i ) × (unit value item i )

Days of supply
Days of supply =

Average aggregate value of inventory
(Cost of goods sold)/(365 days)

Fill rate: fraction of orders filled by a distribution center within a
specific time period

10-529
10-

Computing
Key
Performance
Indicators
10-530
10-

Process Control and SCOR
Process Control
not only for manufacturing operations
can be used in any processes of supply chain

Supply Chain Operations Reference (SCOR)
a cross industry supply chain diagnostic tool
maintained by the Supply Chain Council

10-531
10-

Chapter 11
Global Supply Chain
Procurement and Distribution

Lecture Outline
Procurement
E-Procurement
Distribution
Transportation
The Global Supply Chain

11-535
11-

Procurement
The purchase of goods and services from suppliers
Cross enterprise teams
coordinate processes between a company and its supplier

OnOn-demand (direct-response) delivery
(directrequires the supplier to deliver goods when demanded by the
customer

Continuous replenishment
supplying orders in a short period of time according to a
predetermined schedule

11-536
11-

Outsourcing
Sourcing
selection of suppliers

Outsourcing
purchase of goods and services from an
outside supplier

Core competencies
what a company does best

Single sourcing
a company purchases goods and services
from only a few (or one) suppliers
11-537
11-

Categories of Goods and
Services...

11-538
11-

E-Procurement
Direct purchase from suppliers over the
Internet, by using software packages or
through e-marketplaces, e-hubs, and
eetrading exchanges
Can streamline and speed up the
purchase order and transaction process

11-539
11-

E-Procurement (cont.)
What can companies buy over the
Internet?
Manufacturing inputs
the raw materials and components that go
directly into the production process of the product

Operating inputs
maintenance, repair, and operation goods and
services
11-540
11-

E-Procurement (cont.)
E-marketplaces (e-hubs)
(eWebsites where companies and suppliers
conduct business-to-business activities
business-to-

Reverse auction
process used by e-marketplaces for buyers
eto purchase items; company posts orders on
the internet for suppliers to bid on

11-541
11-

Distribution
Encompasses all channels, processes, and functions,
including warehousing and transportation, that a
product passes on its way to final customer
Order fulfillment
process of ensuring on-time delivery of an order
onLogistics
transportation and distribution of goods and
services
Driving force today is speed
Particularly important for Internet dot-coms
dot-

11-542
11-

Distribution Centers (DC)
and Warehousing
DCs are some of the largest business
facilities in the United States
Trend is for more frequent orders in smaller
quantities
FlowFlow-through facilities and automated
material handling
Postponement
final assembly and product configuration
may be done at the DC
11-543
11-

Warehouse Management
Systems
Highly automated system that runs day-to-day
day-tooperations of a DC
Controls item putaway, picking, packing, and
shipping
Features
transportation management
order management
yard management
labor management
warehouse optimization

11-544
11-

VendorVendor-Managed Inventory
Manufacturers generate orders, not distributors or
retailers
Stocking information is accessed using EDI
A first step towards supply chain collaboration
Increased speed, reduced errors, and improved
service

11-546
11-

Collaborative Logistics and
Distribution Outsourcing
Collaborative planning, forecasting, and
replenishment create greater economies of
scale
InternetInternet-based exchange of data and
information
Significant decrease in inventory levels and
costs and more efficient logistics
Companies focus on core competencies
11-547
11-

Transportation
Rail
low-value, high-density, bulk
products, raw materials,
intermodal containers
not as economical for small
loads, slower, less flexible
than trucking

Trucking
main mode of freight
transport in U.S.
small loads, point-to-point
service, flexible
More reliable, less damage
than rails; more expensive
than rails for long distance

11-548
11-

Transportation (cont.)
Air
most expensive and fastest, mode of
freight transport
lightweight, small packages <500 lbs
high-value, perishable and critical
goods
less theft

Package Delivery
small packages
fast and reliable
increased with e-Business
primary shipping mode for Internet
companies
11-549
11-

Transportation (cont.)
Water
low-cost shipping mode
primary means of international shipping
U.S. waterways
slowest shipping mode

Intermodal
combines several modes of shippingtruck, water and rail
key component is containers

Pipeline
transport oil and products in liquid form
high capital cost, economical use
long life and low operating cost
11-550
11-

Internet Transportation
Exchanges
Bring together shippers and carriers
Initial contact, negotiations, auctions
Examples
www.nte.com
www.freightquote.com

11-551
11-

Global Supply Chain
International trade barriers have fallen
New trade agreements
To compete globally requires an effective supply chain
Information technology is an “enabler” of global trade

11-552
11-

Obstacles to Global Chain
Transactions
Increased documentation for invoices, cargo
insurance, letters of credit, ocean bills of lading or air
waybills, and inspections
Ever changing regulations that vary from country to
country that govern the import and export of goods
Trade groups, tariffs, duties, and landing costs
Limited shipping modes
Differences in communication technology and
availability

11-553
11-

Obstacles to Global Chain
Transactions (cont.)
Different business practices as well as language
barriers
Government codes and reporting requirements that
vary from country to country
Numerous players, including forwarding agents,
custom house brokers, financial institutions, insurance
providers, multiple transportation carriers, and
government agencies
Since 9/11, numerous security regulations and
requirements

11-554
11-

Duties and Tariffs
Proliferation of trade agreements
Nations form trading groups
no tariffs or duties within group
charge uniform tariffs to nonmembers

Member nations have a competitive advantage
within the group
Trade specialists
include freight forwarders, customs house brokers,
export packers, and export management and trading
companies

11-555
11-

Duties and Tariffs (cont.)

11-556
11-

Landed Cost
Total cost of producing, storing, and
transporting a product to the site of
consumption or another port
Value added tax (VAT)
an indirect tax assessed on the increase in value of
a good at any stage of production process from
raw material to final product

Clicker shock
occurs when an ordered is placed with a company
that does not have the capability to calculate landed
cost
11-557
11-

WebWeb-based International Trade
Logistic Systems
International trade logistics web-based software
systems reduce obstacles to global trade
convert language and currency
provide information on tariffs, duties, and customs processes
attach appropriate weights, measurements, and unit prices to
individual products ordered over the Web
incorporate transportation costs and conversion rates
calculate shipping costs online while a company enters an
order
track global shipments

11-558
11-

Recent Trends in Globalization for
U.S. Companies
Two significant changes
passage of NAFTA
admission of China in WTO

Mexico
cheap labor and relatively short shipping time

China
cheaper labor and longer work week, but lengthy
shipping time
Major supply chains have moved to China
11-559
11-

China’s Increasing Role
in the Global Supply Chain
World’s premier sources of supply
Abundance of low-wage labor
lowWorld’s fastest growing market
Regulatory changes have liberalized its
market
Increased exporting of higher technology
products
11-560
11-

Models in Doing Business in China
Employ local third-party trading agents
thirdWhollyWholly-owned foreign enterprise
Develop your own international
procurement offices

11-561
11-

Challenges Sourcing from China
Getting reliable information in more
difficult than in the U.S.
Information technology is much less
advanced and sophisticated than in the
U.S.
Work turnover rates among low-skilled
lowworkers is extremely high
11-562
11-

Effects of 9/11 on Global Chains
Increase security measures
added time to supply chain schedules
Increased supply chain costs

24 hours rules for “risk screening”
extended documentation
extend time by 3-4 days
3-

Inventory levels have increased 5%
Other costs include:
new people, technologies, equipment, surveillance,
communication, and security systems, and training necessary
for screening at airports and seaports around the world
11-563
11-

Transportation and
Transshipment Models

Lecture Outline
Transportation Model
Transshipment Model

Supplement 11-565
11-

Transportation Model
A transportation model is formulated for a class of
problems with the following characteristics
a product is transported from a number of sources to a
number of destinations at the minimum possible cost
each source is able to supply a fixed number of units of
product
each destination has a fixed demand for product

Solution Methods
steppingstepping-stone
modified distribution
Excel’s Solver

Supplement 11-566
11-

Transportation Method: Example

Supplement 11-567
11-

Transportation Method: Example

Supplement 11-568
11-

Problem
Formulation
Using Excel

Total Cost
Formula

Supplement 11-569
11-

Using Solver
from Tools
Menu

Supplement 11-570
11-

Solution

Supplement 11-571
11-

Modified
Problem
Solution

Supplement 11-572
11-

Transshipment
Model

Supplement 11-573
11-

Transshipment Model: Solution

Supplement 11-574
11-

Chapter 12
Forecasting

Lecture Outline
Strategic Role of Forecasting in Supply
Chain Management
Components of Forecasting Demand
Time Series Methods
Forecast Accuracy
Time Series Forecasting Using Excel
Regression Methods
12-576
12-

Forecasting
Predicting the future
Qualitative forecast methods
subjective

Quantitative forecast
methods
based on mathematical
formulas

12-577
12-

Forecasting and Supply Chain
Management
Accurate forecasting determines how much
inventory a company must keep at various points
along its supply chain
Continuous replenishment
supplier and customer share continuously updated data
typically managed by the supplier
reduces inventory for the company
speeds customer delivery

Variations of continuous replenishment
quick response
JIT (just-in-time)
(just-inVMI (vendor-managed inventory)
(vendorstockless inventory
12-578
12-

Forecasting
Quality Management
Accurately forecasting customer demand is
a key to providing good quality service

Strategic Planning
Successful strategic planning requires
accurate forecasts of future products and
markets

12-579
12-

Types of Forecasting Methods
Depend on
time frame
demand behavior
causes of behavior

12-580
12-

Time Frame
Indicates how far into the future is
forecast
Short- midShort- to mid-range forecast
typically encompasses the immediate future
daily up to two years

LongLong-range forecast
usually encompasses a period of time longer
than two years
12-581
12-

Demand Behavior
Trend
a gradual, long-term up or down movement of
longdemand

Random variations
movements in demand that do not follow a pattern

Cycle
an up-and-down repetitive movement in demand
up-and-

Seasonal pattern
an up-and-down repetitive movement in demand
up-andoccurring periodically
12-582
12-

Demand

Demand

Forms of Forecast Movement

Random
movement
Time
(b) Cycle

Demand

Demand

Time
(a) Trend

Time
(c) Seasonal pattern

Time
(d) Trend with seasonal pattern
12-583
12-

Forecasting Methods
Time series
statistical techniques that use historical demand data
to predict future demand

Regression methods
attempt to develop a mathematical relationship
between demand and factors that cause its behavior

Qualitative
use management judgment, expertise, and opinion to
predict future demand
12-584
12-

Qualitative Methods
Management, marketing, purchasing,
and engineering are sources for internal
qualitative forecasts
Delphi method
involves soliciting forecasts about
technological advances from experts

12-585
12-

Forecasting Process
1. Identify the
purpose of forecast

2. Collect historical
data

6. Check forecast
accuracy with one or
more measures

5. Develop/compute
forecast for period of
historical data

7.
Is accuracy of
forecast
acceptable?

No

3. Plot data and identify
patterns

4. Select a forecast
model that seems
appropriate for data

8b. Select new
forecast model or
adjust parameters of
existing model

Yes
8a. Forecast over
planning horizon

9. Adjust forecast based
on additional qualitative
information and insight

10. Monitor results
and measure forecast
accuracy

12-586
12-

Time Series
Assume that what has occurred in the past will
continue to occur in the future
Relate the forecast to only one factor - time
Include
moving average
exponential smoothing
linear trend line

12-587
12-

Moving Average
Naive forecast
demand in current period is used as next period’s
forecast

Simple moving average
uses average demand for a fixed sequence of
periods
stable demand with no pronounced behavioral
patterns

Weighted moving average
weights are assigned to most recent data

12-588
12-

Moving Average:
Naïve Approach
MONTH

ORDERS
PER MONTH
Jan
Feb
Mar
Apr
May
June
July
Aug
Sept
Oct

Nov

FORECAST
120 90
120
100
90
75
100
110
75
50
110
75
50
130
75
110
130
90
110
90

12-589
12-

Simple Moving Average
n

Σ D
i

MAn =

i=1

n

where
n = number of periods in
the moving average
Di = demand in period i

12-590
12-

3-month Simple Moving Average

MONTH
Jan
Feb
Mar
Apr
May
June
July
Aug
Sept
Oct
Nov

ORDERS
PER MONTH
120
90
100
75
110
50
75
130
110
90
-

3

Σ

MOVING
AVERAGE
–
–
–
103.3
88.3
95.0
78.3
78.3
85.0
105.0
110.0

MA3 =

=

Di

i=1

3
90 + 110 + 130
3

= 110 orders
for Nov

12-591
12-

5-month Simple Moving Average

MONTH
Jan
Feb
Mar
Apr
May
June
July
Aug
Sept
Oct
Nov

ORDERS
PER MONTH
120
90
100
75
110
50
75
130
110
90
-

MOVING
AVERAGE
–
–
–
–
–
99.0
85.0
82.0
88.0
95.0
91.0

5

Σ

MA5 =

=

Di

i=1

5

90 + 110 + 130+75+50
5
= 91 orders
for Nov

12-592
12-

Smoothing Effects
150 –
5-month

125 –

Orders

100 –
75 –
3-month

50 –
Actual

25 –
0–

|
Jan

|
Feb

|
Mar

|
|
|
|
Apr May June July
Month

|
|
Aug Sept

|
Oct

|
Nov

12-593
12-

Weighted Moving Average
Σ Wi Di
n

Adjusts moving average
method to more
closely reflect data
fluctuations

WMAn =

i=1

where

Wi = the weight for period i,
between 0 and 100
percent

Σ Wi = 1.00
12-594
12-

Weighted Moving Average Example
MONTH

WEIGHT

August
September
October

DATA

17%
33%
50%

130
110
90
3

November Forecast WMA3 =

Σ1 Wi Di
i=

= (0.50)(90) + (0.33)(110) + (0.17)(130)
= 103.4 orders
12-595
12-

Exponential Smoothing

Averaging method
Weights most recent data more strongly
Reacts more to recent changes
Widely used, accurate method

12-596
12-

Exponential Smoothing (cont.)
Ft +1 = α Dt + (1 - α)Ft

where:
Ft +1 = forecast for next period
Dt = actual demand for present period
Ft = previously determined forecast for
present period
α = weighting factor, smoothing constant

12-597
12-

Effect of Smoothing Constant
0.0 ≤ α ≤ 1.0
If α = 0.20, then Ft +1 = 0.20 Dt + 0.80 Ft
If α = 0, then Ft +1 = 0 Dt + 1 Ft = Ft

Forecast does not reflect recent data
If α = 1, then Ft +1 = 1 Dt + 0 Ft = Dt

Forecast based only on most recent data

12-598
12-

Exponential Smoothing (α=0.30)
(α
PERIOD

MONTH

DEMAND

1
2
3
4
5
6
7
8
9
10
11
12

Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec

37
40
41
37
45
50
43
47
56
52
55
54

F2 = αD1 + (1 - α)F1
= (0.30)(37) + (0.70)(37)
= 37
F3 = αD2 + (1 - α)F2
= (0.30)(40) + (0.70)(37)
= 37.9
F13 = αD12 + (1 - α)F12
= (0.30)(54) + (0.70)(50.84)
= 51.79

12-599
12-

PERIOD
1
2
3
4
5
6
7
8
9
10
11
12
13

MONTH
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Jan

DEMAND
37
40
41
37
45
50
43
47
56
52
55
54
–

FORECAST, Ft + 1
(α = 0.3)
(α = 0.5)
–
37.00
37.90
38.83
38.28
40.29
43.20
43.14
44.30
47.81
49.06
50.84
51.79

–
37.00
38.50
39.75
38.37
41.68
45.84
44.42
45.71
50.85
51.42
53.21
53.61
12-600
12-

70 –
α = 0.50

Actual

60 –

Orders

50 –
40 –
α = 0.30
30 –
20 –
10 –
0–

|
1

|
2

|
3

|
4

|
5

|
6

|
7

|
8

|
9

|
10

|
11

|
12

|
13

Month
12-601
12-

Adjusted Exponential Smoothing
AFt +1 = Ft +1 + Tt +1

where
T = an exponentially smoothed trend factor
Tt +1 = β(Ft +1 - Ft) + (1 - β) Tt
where
Tt = the last period trend factor
β = a smoothing constant for trend

12-602
12-

Adjusted Exponential
Smoothing (β=0.30)
(β
PERIOD

MONTH

DEMAND

1
2
3
4
5
6
7
8
9
10
11
12

Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec

37
40
41
37
45
50
43
47
56
52
55
54

T3 = β(F3 - F2) + (1 - β) T2
= (0.30)(38.5 - 37.0) + (0.70)(0)
= 0.45
AF3 = F3 + T3 = 38.5 + 0.45
= 38.95
T13 = β(F13 - F12) + (1 - β) T12
= (0.30)(53.61 - 53.21) + (0.70)(1.77)
= 1.36

AF13 = F13 + T13 = 53.61 + 1.36 = 54.97
12-603
12-

Adjusted Exponential Smoothing:
Example
PERIOD
1
2
3
4
5
6
7
8
9
10
11
12
13

MONTH
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Jan

DEMAND

FORECAST
Ft +1

37
40
41
37
45
50
43
47
56
52
55
54
–

37.00
37.00
38.50
39.75
38.37
38.37
45.84
44.42
45.71
50.85
51.42
53.21
53.61

TREND
ADJUSTED
Tt +1
FORECAST AFt +1
–
0.00
0.45
0.69
0.07
0.07
1.97
0.95
1.05
2.28
1.76
1.77
1.36

–
37.00
38.95
40.44
38.44
38.44
47.82
45.37
46.76
58.13
53.19
54.98
54.96
12-604
12-

Adjusted Exponential Smoothing
Forecasts
70 –
Adjusted forecast (β = 0.30)
(β
60 –
Actual

Demand

50 –
40 –
Forecast (α = 0.50)
(α

30 –
20 –
10 –
0–

|
1

|
2

|
3

|
4

|
5

|
|
6
7
Period

|
8

|
9

|
10

|
11

|
12

|
13
12-605
12-

Linear Trend Line
y = a + bx

where
a = intercept
b = slope of the line
x = time period
y = forecast for
demand for period x

Σ xy - nxy
b =
2 - nx2
Σx
a = y-bx
where
n = number of periods
Σx
x = n
= mean of the x values
Σy
y = n = mean of the y values
12-606
12-

Least Squares Example
x(PERIOD)

y(DEMAND)

1
2
3
4
5
6
8
9
10
11
12

47
56
52
55
54

37
80
123
148
225
300
301
376
504
520
605
648

78

557

3867

7

73
40
41
37
45
50

xy

43

x2
1
4
9
16
25
36
49
64
81
100
121
144
650

12-607
12-

Least Squares Example
(cont.)
78
12
557
12
∑xy - nxy
b = 2
∑x - nx2

x =
y =

= 6.5
= 46.42

3867 - (12)(6.5)(46.42)
=
650 - 12(6.5)2

=1.72

a = y - bx
= 46.42 - (1.72)(6.5) = 35.2

12-608
12-

Linear trend line y = 35.2 + 1.72x
Forecast for period 13 y = 35.2 + 1.72(13) = 57.56 units
70 –
60 –

Actual

Demand

50 –
40 –
Linear trend line
30 –
20 –
10 –
0–

|
1

|
2

|
3

|
4

|
5

|
|
6
7
Period

|
8

|
9

|
10

|
11

|
12

|
13

12-609
12-

Seasonal Adjustments
Repetitive increase/ decrease in demand
Use seasonal factor to adjust forecast

Seasonal factor = Si =

Di
∑D

12-610
12-

Seasonal Adjustment (cont.)
YEAR
2002
2003
2004
Total

DEMAND (1000’S PER QUARTER)
1
2
3
4
Total
12.6
14.1
15.3
42.0

8.6
10.3
10.6
29.5

6.3
7.5
8.1
21.9

17.5
18.2
19.6
55.3

45.0
50.1
53.6
148.7

D1
42.0
S1 =
=
= 0.28
∑D 148.7

D3
21.9
S3 =
=
= 0.15
∑D 148.7

D2
29.5
S2 =
=
= 0.20
∑D 148.7

D4
55.3
S4 =
=
= 0.37
∑D 148.7
12-611
12-

Seasonal Adjustment (cont.)

For 2005
y = 40.97 + 4.30x = 40.97 + 4.30(4) = 58.17
4.30x
SF1 = (S1) (F5) = (0.28)(58.17) = 16.28
(S (F
SF2 = (S2) (F5) = (0.20)(58.17) = 11.63
(S (F
SF3 = (S3) (F5) = (0.15)(58.17) = 8.73
(S (F
SF4 = (S4) (F5) = (0.37)(58.17) = 21.53
(S (F

12-612
12-

Forecast Accuracy
Forecast error
difference between forecast and actual demand
MAD
mean absolute deviation

MAPD
mean absolute percent deviation

Cumulative error
Average error or bias

12-613
12-

Mean Absolute Deviation
(MAD)
Σ| Dt - Ft |
MAD =
n
where
t = period number
Dt = demand in period t
Ft = forecast for period t
n = total number of periods
  = absolute value
12-614
12-

MAD Example
PERIOD
1
2
3
4
5
6
7
8
9
10
11
12

DEMAND, Dt

Ft (α =0.3)

(Dt - Ft)

|Dt - Ft|

37
37.00
–
40
37.00
3.00
41
Σ| D37.90 t | 3.10
t - F
37
38.83
1.83
MAD = 38.28 -6.72
n
45
50
40.29
9.69
53.39
= 43.20 -0.20
43
47
43.14
3.86
11
56
= 4.85 44.30 11.70
52
47.81
4.19
55
49.06
5.94
54
50.84
3.15

–
3.00
3.10
1.83
6.72
9.69
0.20
3.86
11.70
4.19
5.94
3.15

557

53.39

49.31

12-615
12-

Other Accuracy Measures
Mean absolute percent deviation (MAPD)

∑|Dt - Ft|
MAPD =
∑Dt
Cumulative error
E = ∑et
Average error
E=

∑et
n
12-616
12-

Comparison of Forecasts

FORECAST

MAD

MAPD

E

Exponential smoothing (α = 0.30) 4.85
(α
9.6% 49.31
Exponential smoothing (α = 0.50) 4.04
(α
8.5% 33.21
Adjusted exponential smoothing
3.81
7.5% 21.14
(α = 0.50, β = 0.30)
Linear trend line
2.29
4.9%
–

(E)
4.48
3.02
1.92
–

12-617
12-

Forecast Control
Tracking signal
monitors the forecast to see if it is biased
high or low
Tracking signal =

∑(Dt - Ft)
E
=
MAD
MAD

1 MAD ≈ 0.8 б
Control limits of 2 to 5 MADs are used most
frequently
12-618
12-

Tracking Signal Values
PERIOD

1
2
3
4
5
6
7
8
9
10
11
12

DEMAND
Dt

37
40
41
37
45
50
43
47
56
52
55
54

FORECAST,
Ft

ERROR
Dt - Ft

∑E =
∑(Dt - Ft)

37.00
–
–
37.00
3.00
3.00
37.90
3.10
6.10
38.83
-1.83
4.27
38.28
6.72
10.99
Tracking signal for period 3
40.29
9.69
20.68
43.20
-0.20
20.48
6.10
43.14 =
3.86 = 2.00
24.34
TS3
3.05
44.30
11.70
36.04
47.81
4.19
40.23
49.06
5.94
46.17
50.84
3.15
49.32

TRACKING
MAD SIGNAL

–
–
3.00 1.00
3.05 2.00
2.64 1.62
3.66 3.00
4.87 4.25
4.09 5.01
4.06 6.00
5.01 7.19
4.92 8.18
5.02 9.20
4.85 10.17

12-619
12-

Tracking Signal Plot
Tracking signal (MAD)

3σ –
2σ –

Exponential smoothing (α = 0.30)
α

1σ –
0σ –
-1σ –
-2σ –

Linear trend line

-3σ –
|
0

|
1

|
2

|
3

|
4

|
5

|
6
Period

|
7

|
8

|
9

|
10

|
11

|
12

12-620
12-

Statistical Control Charts
σ=

∑(Dt - Ft)2
n-1

Using σ we can calculate statistical control
limits for the forecast error
Control limits are typically set at ± 3σ

12-621
12-

Statistical Control Charts
18.39 –

UCL = +3σ
σ

12.24 –

Errors

6.12 –
0–
-6.12 –
-12.24 –
-18.39 –
|
0

LCL = -3σ
σ
|
1

|
2

|
3

|
4

|
5

|
6
Period

|
7

|
8

|
9

|
10

|
11

|
12

12-622
12-

Time Series Forecasting using Excel
Excel can be used to develop forecasts:
Moving average
Exponential smoothing
Adjusted exponential smoothing
Linear trend line

12-623
12-

Exponentially Smoothed and Adjusted
Exponentially Smoothed Forecasts

12-624
12-

Demand and exponentially
smoothed forecast

12-625
12-

Data Analysis option

12-626
12-

Computing a Forecast with
Seasonal Adjustment

12-627
12-

Regression Methods
Linear regression
a mathematical technique that relates a
dependent variable to an independent
variable in the form of a linear equation

Correlation
a measure of the strength of the relationship
between independent and dependent
variables

12-629
12-

Linear Regression
y = a + bx

a = y-bx
Σ xy - nxy
2 - nx2 b =
Σx
where
a = intercept
b = slope of the line
Σx
x =n
Σy
y =n

= mean of the x data
= mean of the y data
12-630
12-

Linear Regression Example
(WINS)

x
(ATTENDANCE)

4
6
6
8
6
7
5
7
49

y
xy

x2

36.3
40.1
41.2
53.0
44.0
45.6
39.0
47.5

145.2
240.6
247.2
424.0
264.0
319.2
195.0
332.5

16
36
36
64
36
49
25
49

346.7

2167.7

311

12-631
12-

Linear Regression Example (cont.)
49
x=
8
346.9y =
8

= 6.125
= 43.36

∑xy - nxy2 b =
∑x2 - nx2
=
(2,167.7) - (8)(6.125)(43.36)
(311) - (8)(6.125)2
= 4.06
a = y - bx
= 43.36 - (4.06)(6.125)
= 18.46

12-632
12-

Linear Regression Example (cont.)
Regression equation

Attendance forecast for 7 wins

y = 18.46 + 4.06x

y = 18.46 + 4.06(7)
= 46.88, or 46,880

60,000 –

Attendance, y

50,000 –
40,000 –
30,000 –

Linear regression line,
y = 18.46 + 4.06x
4.06x

20,000 –
10,000 –
|
0

|
1

|
2

|
3

|
4

|
|
5
6
Wins, x

|
7

|
8

|
9

|
10
12-633
12-

Correlation and Coefficient of
Determination
Correlation, r

Measure of strength of relationship
Varies between -1.00 and +1.00
Coefficient of determination, r2

Percentage of variation in dependent
variable resulting from changes in the
independent variable

12-634
12-

Computing Correlation
r=

r=

n∑ xy - ∑ x∑ y
[n∑ x2 - (∑ x)2] [n∑ y2 - (∑ y)2]
[n
(8)(2,167.7) - (49)(346.9)

[(8)(311) - (49)2] [(8)(15,224.7) - (346.9)2]
r = 0.947
Coefficient of determination
r2 = (0.947)2 = 0.897
12-635
12-

Regression Analysis with Excel

12-636
12-

(cont.)

12-637
12-

(cont.)

12-638
12-

Multiple Regression
Study the relationship of demand to two or more independent
variables

y = β0 + β1x1 + β2x2 … + βkxk
where
β0 = the intercept
β1, … , βk = parameters for the
independent variables
x1, … , xk = independent variables

12-639
12-

Multiple Regression with Excel

12-640
12-

Chapter 13
Inventory Management
Operations Management - 6th Edition

Beni Asllani
University of Tennessee at Chattanooga

Lecture Outline
Elements of Inventory Management
Inventory Control Systems
Economic Order Quantity Models
Quantity Discounts
Reorder Point
Order Quantity for a Periodic Inventory
System
13-642
13-

What Is Inventory?
Stock of items kept to meet future
demand
Purpose of inventory management
how many units to order
when to order

13-643
13-

Inventory and Supply Chain
Management
Bullwhip effect
demand information is distorted as it moves away
from the end-use customer
endhigher safety stock inventories to are stored to
compensate

Seasonal or cyclical demand
Inventory provides independence from vendors
Take advantage of price discounts
Inventory provides independence between
stages and avoids work stoppages
13-644
13-

Inventory and Quality
Management in the Supply Chain
Customers usually perceive quality
service as availability of goods they want
when they want them
Inventory must be sufficient to provide
high-quality customer service in QM

13-645
13-

Types of Inventory
Raw materials
Purchased parts and supplies
Work-in-process (partially completed)
products (WIP)
Items being transported
Tools and equipment

13-646
13-

Two Forms of Demand
Dependent
Demand for items used to produce final
products
Tires stored at a Goodyear plant are an
example of a dependent demand item

Independent
Demand for items used by external
customers
Cars, appliances, computers, and houses
are examples of independent demand
inventory

13-647
13-

Inventory Costs
Carrying cost

cost of holding an item in inventory
Ordering cost

cost of replenishing inventory
Shortage cost

temporary or permanent loss of sales
when demand cannot be met

13-648
13-

Inventory Control Systems
Continuous system (fixed-order(fixed-orderquantity)
constant amount ordered when
inventory declines to
predetermined level

Periodic system (fixed-time(fixed-timeperiod)
order placed for variable amount
after fixed passage of time

13-649
13-

ABC Classification
Class A
5 – 15 % of units
70 – 80 % of value

Class B
30 % of units
15 % of value

Class C
50 – 60 % of units
5 – 10 % of value

13-650
13-

ABC Classification: Example
PART
1
2
3
4
5
6
7
8
9
10

UNIT COST
$ 60
350
30
80
30
20
10
320
510
20

ANNUAL USAGE
90
40
130
60
100
180
170
50
60
120

13-651
13-

ABC Classification:
Example (cont.)
PART

9
8
2
1
4
3
6
5
10
7

PART
VALUE
$30,6001
16,0002
14,000
3
5,400
4,8004
3,9005
3,6006
3,000
CLASS 7
2,400
A 8
1,700
B 9
C 10

TOTAL
% OF TOTAL %
TOTAL
UNIT COSTQUANTITY OF% CUMMULATIVE
ANNUAL USAGE
VALUE

35.9 $ 60
6.0
18.7 350
5.0
16.4
4.0
30
6.3
9.0
80
5.6
6.0
4.6 30
10.0
4.2 % OF TOTAL
18.0
20
3.5 10VALUE
13.0
ITEMS
2.8
12.0
320 71.0
9, 8, 2
2.0
17.0
1, 4, 3 510 16.5
$85,400
6, 5, 10, 720 12.5

90 6.0
40 11.0
A
130 15.0
24.0
60 30.0
B 100 40.0
%180TOTAL
OF 58.0
170 71.0
QUANTITY
83.0
C 50 100.0
15.0
60 25.0
120 60.0
Example 10.1
13-652
13-

Economic Order Quantity
(EOQ) Models
EOQ
optimal order quantity that will
minimize total inventory costs

Basic EOQ model
Production quantity model

13-653
13-

Assumptions of Basic
EOQ Model
Demand is known with certainty and is constant over time
No shortages are allowed
Lead time for the receipt of orders is constant
Order quantity is received all at once

13-654
13-

Inventory Order Cycle
Inventory Level

Order quantity, Q

Demand
rate

Average
inventory

Q
2

Reorder point, R

0

Lead
time
Order Order
placed receipt

Lead
time
Order Order
placed receipt

Time

13-655
13-

EOQ Cost Model
Co - cost of placing order
Cc - annual per-unit carrying cost
per-

D - annual demand
Q - order quantity

Annual ordering cost =

CoD
Q

Annual carrying cost =

CcQ
2

Total cost =

CoD
+
Q

CcQ
2

13-656
13-

EOQ Cost Model
Deriving Qopt

Proving equality of
costs at optimal point

CcQ
CoD
TC =
+
Q
2
CoD
Cc
∂TC
=– 2 +
Q
2
∂Q
C0D
Cc
0=– 2 +
Q
2
Qopt =

2CoD
Cc

CoD
CcQ
=
Q
2
Q2

2CoD
=
Cc

Qopt =

2CoD
Cc

13-657
13-

EOQ Cost Model (cont.)
Annual
cost ($)

Total Cost
Slope = 0
CcQ
Carrying Cost =
2

Minimum
total cost

CoD
Ordering Cost = Q
Optimal order
Qopt

Order Quantity, Q

13-658
13-

EOQ Example
Cc = $0.75 per gallon
Qopt =

2CoD
Cc

Qopt =

Co = $150

2(150)(10,000)
(0.75)

Qopt = 2,000 gallons
Orders per year = D/Qopt
= 10,000/2,000
= 5 orders/year

D = 10,000 gallons

CcQ
CoD
TCmin =
+
Q
2
TCmin

(150)(10,000) (0.75)(2,000)
=
+
2
2,000

TCmin = $750 + $750 = $1,500
Order cycle time = 311 days/(D/Qopt)
days/(D
= 311/5
= 62.2 store days
13-659
13-

Production Quantity
Model
An inventory system in which an order is
received gradually, as inventory is
simultaneously being depleted
AKA non-instantaneous receipt model
assumption that Q is received all at once is relaxed

p - daily rate at which an order is received over
time, a.k.a. production rate
d - daily rate at which inventory is demanded
13-660
13-

Production Quantity Model
(cont.)
Inventory
level

Q(1-d/p)
(1-d/p)

Maximum
inventory
level

Q
(1-d/p)
(1-d/p)
2

Average
inventory
level

0
Order
receipt period

Begin
End
order order
receipt receipt

Time

13-661
13-

Production Quantity Model
(cont.)
p = production rate

d = demand rate

Q
Maximum inventory level = Q - p d
d
= Q 1 -p

2CoD
Qopt =

Q
d
Average inventory level =
1p
2

Cc 1 - d
p

CoD CcQ
d
TC = Q + 2 1 - p

13-662
13-

Production Quantity Model:
Example
Cc = $0.75 per gallon
Co = $150
d = 10,000/311 = 32.2 gallons per day
2C o D
Qopt =

Cc 1 - d
p

D = 10,000 gallons
p = 150 gallons per day

2(150)(10,000)
=

CoD CcQ
d
TC = Q + 2 1 - p

0.75 1 - 32.2
150

= 2,256.8 gallons

= $1,329

2,256.8
Q
Production run = p =
= 15.05 days per order
150
13-663
13-

Production Quantity Model:
Example (cont.)

10,000
D
Number of production runs = Q = 2,256.8 = 4.43 runs/year
d
Maximum inventory level = Q 1 - p

= 2,256.8 1 -

32.2
150

= 1,772 gallons

13-664
13-

Solution of EOQ Models with
Excel

13-665
13-

Solution of EOQ Models with
Excel (Con’t)

13-666
13-

Solution of EOQ Models with OM
Tools

13-667
13-

Quantity Discounts
Price per unit decreases as order
quantity increases
CcQ
CoD
TC =
+
+ PD
2
Q
where
P = per unit price of the item
D = annual demand
13-668
13-

Quantity Discount Model (cont.)
ORDER SIZE
0 - 99
100 – 199
200+

PRICE
$10
8 (d1)
6 (d2)

TC = ($10 )
TC (d1 = $8 )

Inventory cost ($)

TC (d2 = $6 )

Carrying cost

Ordering cost
Q(d1 ) = 100 Qopt

Q(d2 ) = 200
13-669
13-

Quantity Discount: Example
QUANTITY
1 - 49
50 - 89
90+
Qopt =
For Q = 72.5

For Q = 90

PRICE
$1,400
1,100
900
2C o D
=
Cc

Co = $2,500
Cc = $190 per TV
D = 200 TVs per year

2(2500)(200)
= 72.5 TVs
190

CcQopt
CoD
TC =
+
+ PD = $233,784
2
Qopt
CcQ
CoD
TC =
+
+ PD = $194,105
2
Q
13-670
13-

QuantityQuantity-Discount Model Solution
with Excel

13-671
13-

Reorder Point
Level of inventory at which a new order is placed

R = dL
where
d = demand rate per period
L = lead time

13-672
13-

Reorder Point: Example
Demand = 10,000 gallons/year
Store open 311 days/year
Daily demand = 10,000 / 311 = 32.154
gallons/day
Lead time = L = 10 days
R = dL = (32.154)(10) = 321.54 gallons

13-673
13-

Safety Stocks
Safety stock
buffer added to on hand inventory during lead
time

Stockout
an inventory shortage

Service level
probability that the inventory available during lead
time will meet demand

13-674
13-

Variable Demand with
a Reorder Point
Inventory level

Q

Reorder
point, R

0
LT

LT
Time

13-675
13-

Inventory level

Reorder Point with
a Safety Stock

Q
Reorder
point, R

Safety Stock

0
LT

LT
Time
13-676
13-

Reorder Point With
Variable Demand
R = dL + zσd L
where
d = average daily demand
L = lead time
σd = the standard deviation of daily demand
z = number of standard deviations
corresponding to the service level
probability
zσd L = safety stock

13-677
13-

Reorder Point for
a Service Level
Probability of
meeting demand during
lead time = service level

Probability of
a stockout

Safety stock
zσd L
σ
dL
Demand

R
13-678
13-

Reorder Point for
Variable Demand
The paint store wants a reorder point with a 95%
service level and a 5% stockout probability
d = 30 gallons per day
L = 10 days
σd = 5 gallons per day
For a 95% service level, z = 1.65
R = dL + z σd L

Safety stock = z σd L

= 30(10) + (1.65)(5)( 10)

= (1.65)(5)( 10)

= 326.1 gallons

= 26.1 gallons
13-679
13-

Determining Reorder Point with
Excel

13-680
13-

Order Quantity for a
Periodic Inventory System
Q = d(tb + L) + zσd

tb + L - I

where
d = average demand rate
tb = the fixed time between orders
L = lead time
σd = standard deviation of demand
zσd

tb + L = safety stock
I = inventory level
13-681
13-

Periodic Inventory System

13-682
13-

FixedFixed-Period Model with
Variable Demand
d = 6 packages per day
σd = 1.2 packages
tb = 60 days
L = 5 days
I = 8 packages
z = 1.65 (for a 95% service level)
Q = d(tb + L) + zσd

tb + L - I

= (6)(60 + 5) + (1.65)(1.2)

60 + 5 - 8

= 397.96 packages

13-683
13-

FixedFixed-Period Model with Excel

13-684
13-

Simulation

Lecture Outline

Monte Carlo Simulation
Computer Simulation with Excel
Areas of Simulation Application

Supplement 13-686
13-

Simulation
Mathematical and computer modeling technique for
replicating real-world problem situations
Modeling approach primarily used to analyze
probabilistic problems
It does not normally provide a solution; instead it provides
information that is used to make a decision

Physical simulation
Space flights, wind tunnels, treadmills for tires

Mathematical-computerized simulation
Computer-based replicated models

Supplement 13-687
13-

Monte Carlo Simulation
Select numbers randomly from a
probability distribution
Use these values to observe how a
model performs over time
Random numbers each have an equal
likelihood of being selected at random

Supplement 13-688
13-

Distribution of Demand
LAPTOPS DEMANDED
PER WEEK, x
0
1
2
3
4

FREQUENCY OF
DEMAND
20
40
20
10
10

PROBABILITY OF
DEMAND, P(x)
0.20
0.40
0.20
0.10
0.10

100

1.00

Supplement 13-689
13-

Roulette Wheel of Demand
0
90
x=4
x=0
80

20

x=3

x=2

x=1
60

Supplement 13-690
13-

Generating Demand
from Random Numbers
DEMAND,
x

RANGES OF RANDOM NUMBERS,
r

0
1
2
3
4

0-19
20-59
2060-79
6080-89
8090-99
90-

r = 39

Supplement 13-691
13-

Random Number Table

Supplement 13-692
13-

15 Weeks of Demand
WEEK

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

r

39
73
72
75
37
02
87
98
10
47
93
21
95
97
69

DEMAND (x)
(x

1
2
2
2
1
0
3
4
0
1
4
1
4
4
2
Σ = 31

REVENUE (S)

4,300
8,600
8,600
8,600
4,300
0
12,900
17,200
0
4,300
17,200 Average demand
4,300
= 31/15
17,200
= 2.07 laptops/week
17,200
8,600
$133,300
Supplement 13-693
13-

Computing Expected Demand
E(x) = (0.20)(0) + (0.40)(1) + (0.20)(2)
+ (0.10)(3) + (0.10)(4)
= 1.5 laptops per week
•Difference between 1.5 and 2.07 is due to small number of periods
analyzed (only 15 weeks)
•Steady-state result
Steady•an average result that remains constant after enough trials

Supplement 13-694
13-

Random Numbers in Excel

Supplement 13-695
13-

Simulation in Excel

Supplement 13-696
13-

Simulation in Excel (cont.)

Supplement 13-697
13-

Simulation

Supplement 13-698
13-

Simulation (cont.)

Supplement 13-699
13-

Waiting Lines/Service
Complex systems for which it is difficult to develop
analytical formulas
Determine how many registers and servers are
needed to meet customer demand

Inventory Management
Traditional models make the assumption that
customer demand is certain
Simulation is widely used to analyze JIT without
having to implement it physically

Supplement 13-700
13-

Areas of Simulation
Application (cont.)
Production and Manufacturing Systems
Examples: production scheduling, production sequencing,
assembly line balancing, plant layout, and plant location
analysis
Machine breakdowns typically occur according to some
probability distributions

Capital Investment and Budgeting
Capital budgeting problems require estimates of cash flows,
often resulting from many random variables
Simulation has been used to generate values of cash flows,
market size, selling price, growth rate, and market share

Supplement 13-701
13-

(cont.)
Logistics
Typically include numerous random variables, such as
distance, different modes of transport, shipping rates, and
schedules to analyze different distribution channels

Service Operations
Examples: police departments, fire departments, post offices,
hospitals, court systems, airports
Complex operations that no technique except simulation can
be employed

Environmental and Resource Analysis
Examples: impact of manufacturing plants, waste-disposal
facilities, nuclear power plants, waste and population
conditions, feasibility of alternative energy sources

Supplement 13-702
13-

Chapter 14
Sales and Operations Planning

Lecture Outline
The Sales and Operations Planning
Process
Strategies for Adjusting Capacity
Strategies for Managing Demand
Quantitative Techniques for Aggregate
Planning
Hierarchical Nature of Planning
Aggregate Planning for Services
14-704
14-

Determines the resource capacity needed to
meet demand over an intermediate time
horizon
Aggregate refers to sales and operations planning
for product lines or families
Sales and Operations planning (S&OP) matches
supply and demand

Objectives
Establish a company wide game plan for allocating
resources
Develop an economic strategy for meeting
demand

14-705
14-

Process

14-706
14-

The Monthly S&OP Planning
Process

14-707
14-

Meeting Demand Strategies
Adjusting capacity
Resources necessary to meet demand
are acquired and maintained over the
time horizon of the plan
Minor variations in demand are handled
with overtime or under-time
under-

Managing demand
Proactive demand management

14-708
14-

Strategies for Adjusting Capacity
Level production
Producing at a constant rate
and using inventory to
absorb fluctuations in
demand

Chase demand
Hiring and firing workers to
match demand

Peak demand
Maintaining resources for
highhigh-demand levels

Overtime and under-time
underIncreasing or decreasing
working hours

Subcontracting
Let outside companies
complete the work

PartPart-time workers
Hiring part time workers to
complete the work

Backordering
Providing the service or
product at a later time period

14-709
14-

Level Production
Demand

Units

Production

Time

14-710
14-

Chase Demand
Demand

Units

Production

Time

14-711
14-

Strategies for Managing Demand
Shifting demand into
other time periods
Incentives
Sales promotions
Advertising campaigns

Offering products or
services with countercyclical demand patterns
Partnering with suppliers
to reduce information
distortion along the
supply chain
14-712
14-

Quantitative Techniques For AP
Pure Strategies
Mixed Strategies
Linear Programming
Transportation Method
Other Quantitative
Techniques

14-713
14-

Pure Strategies
Example:

QUARTER
Spring
Summer
Fall
Winter

SALES FORECAST (LB)
80,000
50,000
120,000
150,000

Hiring cost = $100 per worker
Firing cost = $500 per worker
Inventory carrying cost = $0.50 pound per quarter
Regular production cost per pound = $2.00
Production per employee = 1,000 pounds per quarter
Beginning work force = 100 workers

14-714
14-

Level Production Strategy
Level production
(50,000 + 120,000 + 150,000 + 80,000)
= 100,000 pounds
4

QUARTER
Spring
Summer
Fall
Winter

SALES
FORECAST

PRODUCTION
PLAN
INVENTORY

80,000
50,000
120,000
150,000

100,000
20,000
100,000
70,000
100,000
50,000
100,000
0
400,000
140,000
Cost of Level Production Strategy
(400,000 X $2.00) + (140,00 X $.50) = $870,000
14-715
14-

Chase Demand Strategy
QUARTER

Spring
Summer
Fall
Winter

SALES PRODUCTION
FORECAST
PLAN

80,000
50,000
120,000
150,000

80,000
50,000
120,000
150,000

WORKERS WORKERS WORKERS
NEEDED
HIRED
FIRED

80
50
120
150

0
0
70
30

20
30
0
0

100

50

Cost of Chase Demand Strategy
(400,000 X $2.00) + (100 x $100) + (50 x $500) = $835,000

14-716
14-

Level Production with Excel

14-717
14-

Chase Demand with Excel

14-718
14-

Mixed Strategy
Combination of Level Production and
Chase Demand strategies
Examples of management policies
no more than x% of the workforce can be
laid off in one quarter
inventory levels cannot exceed x dollars

Many industries may simply shut down
manufacturing during the low demand
season and schedule employee
vacations during that time
14-719
14-

Mixed Strategies with Excel

14-720
14-

Mixed Strategies with Excel
(cont.)

14-721
14-

General Linear Programming (LP)
Model
LP gives an optimal solution, but demand
and costs must be linear
Let
Wt = workforce size for period t
Pt =units produced in period t
It =units in inventory at the end of period t
Ft =number of workers fired for period t
Ht = number of workers hired for period t
14-722
14-

LP MODEL
Minimize Z = $100 (H1 + H2 + H3 + H4)
+ $500 (F1 + F2 + F3 + F4)
+ $0.50 (I1 + I2 + I3 + I4)
+ $2 (P1 + P2 + P3 + P4)
Subject to

Demand
constraints
Production
constraints

Work force
constraints

P1 - I1
I1 + P2 - I2
I2 + P3 - I3
I3 + P4 - I4
1000 W1
1000 W2
1000 W3
1000 W4
100 + H1 - F1
W1 + H2 - F2
W2 + H3 - F3
W3 + H4 - F4

= 80,000
= 50,000
= 120,000
= 150,000
= P1
= P2
= P3
= P4
= W1
= W2
= W3
= W4

(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)

14-723
14-

Setting up the Spreadsheet

14-724
14-

Transportation Method
EXPECTED
QUARTER
DEMAND

1
2
3
4

900
1500
1600
3000

REGULAR
OVERTIME SUBCONTRACT
CAPACITY
CAPACITY
CAPACITY

1000
1200
1300
1300

100
150
200
200

500
500
500
500

Regular production cost per unit
$20
Overtime production cost per unit
$25
Subcontracting cost per unit
$28
Inventory holding cost per unit per period
$3
Beginning inventory
300 units
14-726
14-

Transportation Tableau
PERIOD OF USE

PERIOD OF PRODUCTION

1

2

Beginning

0

Inventory
1

3

300

Regular

600

3

—
20

300

6

—
23

100

—

29

1000

100

34

100

37

500

28

31

Subcontract

28

31

34

Subcontract

—

26

1200

28

150

31

150

28

1200

23

25

Regular
Overtime

3

31

Regular

—

1300

Overtime

200

20
25
28

Subcontract
4

Regular

250
—
—
500
1300

Overtime

200

Subcontract

500

Demand

300

26

25

20

9

—

Overtime

2

Unused
Capacity
Capacity

4

900

1500

1600

34

250

500

23

1300

28

200

31

500

20

1300

25

200

28
3000

500
250

14-727
14-

Burruss’ Production Plan
REGULAR
SUBSUBENDING
PERIOD DEMAND PRODUCTION OVERTIME CONTRACT INVENTORY

1
2
3
4
Total

900
1500
1600
3000
7000

1000
1200
1300
1300
4800

100
150
200
200
650

0
250
500
500
1250

500
600
1000
0
2100

14-728
14-

Using Excel for the Transportation
Method of Aggregate Planning

14-729
14-

Other Quantitative Techniques
Linear decision rule (LDR)
Search decision rule (SDR)
Management coefficients model

14-730
14-

Hierarchical Nature of Planning
Production
Planning

Capacity
Planning

Resource
Level

Product lines
or families

Sales and
Operations
Plan

Resource
requirements
plan

Plants

Individual
products

Master
production
schedule

Rough-cut
capacity
plan

Critical
work
centers

Components

Material
requirements
plan

Capacity
requirements
plan

All
work
centers

Manufacturing
operations

Shop
floor
schedule

Input/
output
control

Individual
machines

Items

Disaggregation: process of breaking an aggregate plan into more detailed plans
14-731
14-

Collaborative Planning
Sharing information and synchronizing
production across supply chain
Part of CPFR (collaborative planning,
forecasting, and replenishment)
involves selecting products to be jointly
managed, creating a single forecast of
customer demand, and synchronizing
production across supply chain

14-732
14-

Available-to-Promise (ATP)
Quantity of items that can be promised to customer
Difference between planned production and customer
orders already received
AT in period 1 = (On-hand quantity + MPS in period 1) –
(CO until the next period of planned production)
ATP in period n = (MPS in period n) –
(CO until the next period of planned production)

Capable-to-promise
quantity of items that can be produced and mad available at
a later date
14-733
14-

ATP: Example (cont.)

14-735
14-

ATP: Example (cont.)

Take excess units from April

ATP in April = (10+100) – 70 = 40
= 30
ATP in May = 100 – 110 = -10
=0
ATP in June = 100 – 50 = 50

14-736
14-

Rule Based ATP
Product
Request

Yes

Is the product
available at
this location?

No

Availableto-promise

Yes

Is an alternative
product available
at this location?

No

Allocate
inventory
Yes

Is this product
available at a
different
location?
No

Is an alternative
product available
at an alternate
location?

Yes

No

Allocate
inventory

Capable-topromise date

Is the customer
willing to wait for
the product?

No

Availableto-promise

Yes

Revise master
schedule

Trigger production

Lose sale

14-737
14-

Aggregate Planning for Services
1. Most services cannot be inventoried
2. Demand for services is difficult to predict
3. Capacity is also difficult to predict
4. Service capacity must be provided at the
appropriate place and time
5. Labor is usually the most constraining
resource for services

14-738
14-

Yield Management (cont.)

14-740
14-

Yield Management: Example
NO-SHOWS
NO-

PROBABILITY

0
1
2
3

P(N < X)

.15
.25
.30
.30

.00
.15
.40
.70

.517

Optimal probability of no-shows
noP(n < x) ≤
P(n

Cu
75
=
= .517
Cu + Co
75 + 70

Hotel should be overbooked by two rooms
14-741
14-

Linear Programming

Lecture Outline
Model Formulation
Graphical Solution Method
Linear Programming Model Solution
Solving Linear Programming Problems
with Excel
Sensitivity Analysis

Supplement 14-743
14-

Linear Programming (LP)
A model consisting of linear relationships
representing a firm’s objective and resource constraints

LP is a mathematical modeling technique used to determine a
level of operational activity in order to achieve an objective,
subject to restrictions called constraints

Supplement 14-744
14-

Types of LP

Supplement 14-745
14-

Types of LP (cont.)

Supplement 14-746
14-

Types of LP (cont.)

Supplement 14-747
14-

LP Model Formulation
Decision variables
mathematical symbols representing levels of activity of an
operation

Objective function
a linear relationship reflecting the objective of an operation
most frequent objective of business firms is to maximize profit
most frequent objective of individual operational units (such as
a production or packaging department) is to minimize cost

Constraint
a linear relationship representing a restriction on decision
making

Supplement 14-748
14-

LP Model Formulation (cont.)
Max/min

z = c1x1 + c2x2 + ... + cnxn

subject to:
a11x1 + a12x2 + ... + a1nxn (≤, =, ≥) b1
a21x1 + a22x2 + ... + a2nxn (≤, =, ≥) b2
:
an1x1 + an2x2 + ... + annxn (≤, =, ≥) bn
xj = decision variables
bi = constraint levels
cj = objective function coefficients
aij = constraint coefficients

Supplement 14-749
14-

LP Model: Example
RESOURCE REQUIREMENTS
PRODUCT
Bowl
Mug

Labor
(hr/unit)
1
2

Clay
(lb/unit)
4
3

Revenue
($/unit)
40
50

There are 40 hours of labor and 120 pounds of clay
available each day
Decision variables
x1 = number of bowls to produce
x2 = number of mugs to produce

Supplement 14-750
14-

LP Formulation: Example
Maximize Z = $40 x1 + 50 x2
Subject to
x1 +
4x1 +

2x2 ≤ 40 hr
3x2 ≤ 120 lb
x1 , x2 ≥ 0

Solution is x1 = 24 bowls
Revenue = $1,360

(labor constraint)
(clay constraint)
x2 = 8 mugs

Supplement 14-751
14-

Graphical Solution Method
1. Plot model constraint on a set of coordinates
in a plane
2. Identify the feasible solution space on the
graph where all constraints are satisfied
simultaneously
3. Plot objective function to find the point on
boundary of this space that maximizes (or
minimizes) value of objective function

Supplement 14-752
14-

Graphical Solution: Example
x2
50 –
40 –

4 x1 + 3 x2 ≤ 120 lb

30 –
Area common to
both constraints

20 –

x1 + 2 x2 ≤ 40 hr

10 –
0–

|
10

|
20

|
30

|
40

|
50

|
60

x1
Supplement 14-753
14-

Computing Optimal Values
x1 +
4x1 +

40 – 4 x1 + 3 x2 = 120 lb
30 –
20 –

x1 + 2 x2 = 40 hr

2x 2 =
3x 2 =

40
120

4x1 +
-4x1 -

8x 2 =
3x 2 =

160
-120

5x 2 =
x2 =

x2

40
8

x1 + 2(8) =
x1
=

40
24

10 – 8
0–

|
10

| 24 |
20
30

| x1
40
Z = $40(24) + $50(8) = $1,360
Supplement 14-754
14-

Extreme Corner Points
x1 = 0 bowls
x2 = 20 mugs
Z = $1,000

x2
40 –
30 –
20 –

A
B

10 –
0–

x1 = 224 bowls
x2 = 8 mugs
Z = $1,360
x1 = 30 bowls
x2 = 0 mugs
Z = $1,200

|
10

|
20

| C|
30 40

x1

Supplement 14-755
14-

Objective Function
x2
40 –

4x1 + 3x2 = 120 lb
3x
Z = 70x1 + 20x2
70x 20x

30 –

Optimal point:
x1 = 30 bowls
x2 = 0 mugs
Z = $2,100

A
20 –

B

10 –

0–

|
10

|
20

x1 + 2x2 = 40 hr
2x
| C
|
30
40
x
1

Supplement 14-756
14-

Minimization Problem
CHEMICAL CONTRIBUTION
Brand

Nitrogen (lb/bag)

GroGro-plus
CropCrop-fast

Phosphate (lb/bag)

2
4

4
3

Minimize Z = $6x1 + $3x2
subject to
2x1 + 4x2 ≥ 16 lb of nitrogen
4x1 + 3x2 ≥ 24 lb of phosphate
x 1, x 2 ≥ 0
Supplement 14-757
14-

Graphical Solution
x2
14 –

x1 = 0 bags of Gro-plus
GroCrop12 – x2 = 8 bags of Crop-fast
Z = $24
10 –
A

Z = 6x1 + 3x2
6x 3x

8–
6–
4–
2–
0–

B
|
2

|
4

|
6

|
8

C

|
10

|
12

|
14

x1
Supplement 14-758
14-

Simplex Method
A mathematical procedure for solving linear programming
problems according to a set of steps
Slack variables added to ≤ constraints to represent unused
resources
x1 + 2x2 + s1 =40 hours of labor
40
4x1 + 3x2 + s2 =120 lb of clay
120

Surplus variables subtracted from ≥ constraints to represent
excess above resource requirement. For example,
2x1 + 4x2 ≥ 16 is transformed into
4x
16
2x1 + 4x2 - s1 = 16
4x
16

Slack/surplus variables have a 0 coefficient in the objective
function
Z = $40x1 + $50x2 + 0s1 + 0s2

Supplement 14-759
14-

Solution
Points with
Slack
Variables

Supplement 14-760
14-

Solution
Points with
Surplus
Variables

Supplement 14-761
14-

Solving LP Problems with Excel

Supplement 14-762
14-

(cont.)

Supplement 14-763
14-

(cont.)

Supplement 14-764
14-

Sensitivity Range for Labor
Hours

Supplement 14-765
14-

Sensitivity Range for
Bowls

Supplement 14-766
14-

Chapter 15
Resource Planning

Lecture Outline
Material Requirements Planning (MRP)
Capacity Requirements Planning (CRP)
Enterprise Resource Planning (ERP)
Customer Relationship Management (CRM)
Supply Chain Management (SCM)
Product Lifecycle Management (PLM)

15-768
15-

Resource
Planning for
Manufacturing

15-769
15-

Material Requirements
Planning (MRP)
Computerized inventory control and
production planning system
When to use MRP?
Dependent demand items
Discrete demand items
Complex products
Job shop production
Assemble-to-order environments
15-770
15-

Demand Characteristics
Independent demand

Dependent demand

100 x 1 =
100 tabletops

100 x 4 = 400 table legs

100 tables

Continuous demand

Discrete demand

400 –
300 –
No. of tables

No. of tables

400 –

200 –
100 –
1

2

3

4
Week

300 –
200 –
100 –

5
M T W Th F

M T W Th F

15-771
15-

Material
Requirements
Planning
Product
structure
file

Master
production
schedule

Material
requirements
planning

Item
master
file

Planned
order
releases

Work
orders

Purchase
orders

Rescheduling
notices

15-772
15-

MRP Inputs and Outputs
Inputs
Master production
schedule
Product structure file
Item master file

Outputs
Planned order
releases
Work orders
Purchase orders
Rescheduling notices

15-773
15-

Master Production Schedule
Drives MRP process with a schedule of
finished products
Quantities represent production not demand
Quantities may consist of a combination of
customer orders and demand forecasts
Quantities represent what needs to be
produced, not what can be produced
Quantities represent end items that may or
may not be finished products

15-774
15-

(cont.)
MPS ITEM
Pencil Case
Clipboard
Lapboard
Lapdesk

1

2

125
85
75
0

125
95
120
50

PERIOD
3
4
125
120
47
0

125
100
20
50

5
125
100
17
0

15-775
15-

Product Structure File

15-776
15-

Product Structure
Clipboard

Top clip (1)

Pivot (1)

Bottom clip (1)

Spring (1)

Rivets (2)
Finished clipboard

Pressboard (1)

15-777
15-

Product Structure Tree
Level 0

Clipboard

Pressboard
(1)

Top Clip
(1)

Clip Ass’y
(1)

Bottom Clip
(1)

Rivets
(2)

Pivot
(1)

Level 1

Spring
(1)

Level 2

15-778
15-

Multilevel Indented BOM
LEVEL

0----1----2---2---2---2--1---1---

ITEM

Clipboard
Clip Assembly
Top Clip
Bottom Clip
Pivot
Spring
Rivet
Press Board

UNIT OF MEASURE

QUANTITY

ea
ea
ea
ea
ea
ea
ea
ea

1
1
1
1
1
1
2
1

15-779
15-

Specialized BOMs
Phantom bills
Transient subassemblies
Never stocked
Immediately consumed in next stage

K-bills
Group small, loose parts under pseudo-item
pseudonumber
Reduces paperwork, processing time, and file
space

15-780
15-

Specialized BOMs (cont.)
Modular bills
Product assembled from major subassemblies and
customer options
Modular bill kept for each major subassembly
Simplifies forecasting and planning
X10 automobile example
3 x 8 x 3 x 8 x 4 = 2,304 configurations
3 + 8 + 3 + 8 + 4 = 26 modular bills

15-781
15-

Modular BOMs
X10
Automobile

Engines
(1 of 3)

4-Cylinder (.40)

Exterior color
(1 of 8)

Interior
(1 of 3)

Bright red (.10)

Leather (.20)

Interior color
(1 of 8)

Body
(1 of 4)

Grey (.10)

Sports coupe (.20)

6-Cylinder (.50)

White linen (.10)

Tweed (.40)

Light blue (.10)

Two-door (.20)
Two-

8-Cylinder (.10)

Sulphur yellow (.10)

Plush (.40)

Rose (.10)

Four-door (.30)
Four-

Off-white (.20)
Off-

Station wagon (.30)

Neon orange (.10)
Metallic blue (.10)

Cool green (.10)

Emerald green (.10)

Black (.20)

Jet black (.20)

Brown (.10)

Champagne (.20)

B/W checked (.10)

15-782
15-

Time-phased Bills
an assembly chart shown against a time
scale

Forward scheduling: start at today‘s date and schedule forward to determine
the earliest date the job can be finished. If each item takes one period to
complete, the clipboards can be finished in three periods
Backward scheduling: start at the due date and schedule backwards to
determine when to begin work. If an order for clipboards is due by period three,
we should start production now
15-783
15-

Item Master File
DESCRIPTION
Item
Pressboard
Item no.
7341
Item type
Purch
Product/sales class
Comp
Value class
B
Buyer/planner
RSR
Vendor/drawing
07142
Phantom code
N
Unit price/cost
1.25
Pegging
Y
LLC
1

INVENTORY POLICY
Lead time
Annual demand
Holding cost
Ordering/setup cost
Safety stock
Reorder point
EOQ
Minimum order qty
Maximum order qty
Multiple order qty
Policy code

1
5000
1
50
0
39
316
100
500
1
3

15-784
15-

Item Master File (cont.)
PHYSICAL INVENTORY
On hand
Location
On order
Allocated
Cycle
Last count
Difference

150
W142
100
75
3
9/5
-2

USAGE/SALES
YTD usage/sales
MTD usage/sales
YTD receipts
MTD receipts
Last receipt
Last issue
CODES

Cost acct.
Routing
Engr

1100
75
1200
0
8/25
10/5

00754
00326
07142

15-785
15-

MRP Processes
Exploding the bill
of material
Netting out inventory
Lot sizing
TimeTime-phasing
requirements

Netting
process of subtracting ononhand quantities and
scheduled receipts from
gross requirements to
produce net requirements

Lot sizing
determining the quantities
in which items are usually
made or purchased

15-786
15-

MRP: Example
1
Clipboard
Lapdesk

85
0

2
95
60

3
120
0

4
100
60

5
100
0

Item Master File
On hand
On order
LLC
Lot size
Lead time

CLIPBOARD
LAPDESK
25
20
175 (Period 1)
0
(sch receipt)
0
0
L4L
Mult 50
1
1

PRESSBOARD
150
0
1
Min 100
1
15-788
15-

MRP: Example (cont.)
Product Structure Record

Level 0

Clipboard

Pressboard
(1)

Clip Ass’y
(1)

Rivets
(2)

Level 1

Level 0

Lapdesk

Pressboard
(2)

Trim
(3’)

Beanbag
(1)

Glue
(4 oz)

Level 1

15-789
15-

ITEM: CLIPBOARD
LOT SIZE: L4L

LLC: 0
LT: 1

Gross Requirements

PERIOD
1

2

3

4

5

85

95

120

100

100

Scheduled Receipts
Projected on Hand

175
25

Net Requirements
Planned Order Receipts
Planned Order Releases

15-790
15-

ITEM: CLIPBOARD
LOT SIZE: L4L

LLC: 0
LT: 1

Gross Requirements

PERIOD
1

2

3

4

5

85

95

120

100

100

Scheduled Receipts
Projected on Hand
Net Requirements

175
25

115
0


(25 + 175) = 200 units available
(200 - 85) = 115 on hand at the end of Period 1

15-791
15-

ITEM: CLIPBOARD
LOT SIZE: L4L

LLC: 0
LT: 1

PERIOD
1

3

4

5

85

Gross Requirements

2
95

120

100

100

Scheduled Receipts
Projected on Hand

25

Net Requirements

175
115

20

0

0


115 units available
(115 - 85) = 20 on hand at the end of Period 2

15-792
15-

ITEM: CLIPBOARD
LOT SIZE: L4L

LLC: 0
LT: 1

PERIOD
1

3

4

5

85

Gross Requirements

2
95

120

100

100

Scheduled Receipts
Projected on Hand

25

Net Requirements

175
115

20

0

0

0

100


100
100

20 units available
(20 - 120) = -100 — 100 additional Clipboards are required
Order must be placed in Period 2 to be received in Period 3
15-793
15-

ITEM: CLIPBOARD
LOT SIZE: L4L

LLC: 0
LT: 1

PERIOD
2

3

4

5

85

Gross Requirements

1

95

120

100

100

Scheduled Receipts
Projected on Hand

25

Net Requirements

175
115

20

0

0

0

0

0

100

100

100

100

100

100


100

100

100

Following the same logic Gross Requirements in Periods 4
and 5 develop Net Requirements, Planned Order Receipts, and
15-794
15-

ITEM: LAPDESK
LOT SIZE: MULT 50

LLC: 0
LT: 1

Gross Requirements

PERIOD
1

2

0

60

3
0

4
60

5
0

Scheduled Receipts
Projected on Hand

20

Net Requirements

15-795
15-

ITEM: LAPDESK
LOT SIZE: MULT 50

LLC: 0
LT: 1

Gross Requirements

PERIOD
1

2

3

4

5

0

60

0

60

0

20

10

10

0

0

Scheduled Receipts
Projected on Hand

20

Net Requirements

0


50

50

50


40

50
50

Following the same logic, the Lapdesk MRP matrix is
completed as shown

15-796
15-

ITEM: CLIPBOARD
LLC: 0
LOT SIZE: L4L
LT: 1

2
100

ITEM: LAPDESK
LOT SIZE: MULT 50

LLC: 0
LT: 1

ITEM: PRESSBOARD LLC: 0
LOT SIZE: MIN 100
LT: 1
Gross Requirements
Scheduled Receipts
Projected on Hand
Net Requirements

1

100

2

1

PERIOD
3
4
PERIOD
3
4

50
1

5

100
5

50
2

PERIOD
3
4

5

150

15-797
15-

ITEM: CLIPBOARD
LLC: 0
LOT SIZE: L4L
LT: 1
ITEM: LAPDESK
LOT SIZE: MULT 50

LLC: 0
LT: 1


2
100

1

PERIOD
3
4
100

2

PERIOD
x1
3
4

x1
1
50

x2
x2
LOT SIZE: MIN 100
LT: 1
1
2
Gross Requirements
100
100
Scheduled Receipts
Projected on Hand
150
Net Requirements

5

100

x1
5

50
PERIOD
3
4
200
100

5
0

15-798
15-

ITEM: CLIPBOARD
LLC: 0
LOT SIZE: L4L
LT: 1

2
100

ITEM: LAPDESK
LOT SIZE: MULT 50

LLC: 0
LT: 1

LOT SIZE: MIN 100
LT: 1
Gross Requirements
Scheduled Receipts
Projected on Hand
150
Net Requirements

1

100

2

1

PERIOD
3
4
PERIOD
3
4

50

100
5

50

1
100
50

PERIOD
3
4
200
100

2
100
50

100

5

50
100
150

0
150
150
100

0

5
0
0

100
100

15-799
15-

Planned Order Report
PERIOD
ITEM
Clipboard
Lapdesk
Pressboard

1

2

3

4

5

100 100 100
50
50
100 150 100

15-800
15-

Lot Sizing in MRP Systems
Lot-for-lot ordering policy
Fixed-size lot ordering policy
Minimum order quantities
Maximum order quantities
Multiple order quantities
Economic order quantity
Periodic order quantity
15-801
15-

Using Excel for MRP Calculations

15-802
15-

Advanced Lot Sizing Rules: L4L

Total cost of L4L = (4 X $60) + (0 X $1) = $240
15-803
15-

Advanced Lot Sizing Rules: EOQ
EO Q =

2(30)(60
= 60
1

minimum order quantity

Total cost of EOQ = (2 X $60) + [(10 + 50 + 40) X $1)] = $220
15-804
15-

Advanced Lot Sizing Rules: POQ
POQ = Q / d = 60 / 30 = 2 periods worth of requirements

Total cost of POQ = (2 X $60) + [(20 + 40) X $1] = $180

15-805
15-

Planned Order Report
Item
#2740
On hand
100
On order
200
Allocated
50
DATE

10-08
10-

10-27
10-

ORDER NO.
9-26
9-30
10-01
10SR 7542
10-10
1010-15
1010-23
10GR 6473

Date
9 - 25 - 05
Lead time
2 weeks
Lot size
200
Safety stock
50
GROSS REQS.

AL 4416
AL 4174
GR 6470

SCHEDULED PROJECTED
RECEIPTS
ON HAND
ACTION
25
25
50
200

CO 4471
GR 6471
GR 6471
50

Key: AL = allocated
CO = customer order
PO = purchase order

150

75
50
25
- 50

50
25
0
- 50
Expedite SR 10-01
1075
25
0
Release PO 10-13
10-

WO = work order
SR = scheduled receipt
GR = gross requirement
15-806
15-

MRP Action Report
Current date 9-25-08
9-25ITEM
#2740
#3616
#2412
#3427
#2516
#2740
#3666

DATE

10-08
1010-09
1010-10
1010-15
1010-20
1010-27
1010-31
10-

ORDER NO. QTY.
7542

200

7648

100
200
50

Expedite
Move forward
Move forward
Move backward
De-expedite
DeRelease
Release

ACTION
SR
PO
PO
PO
SR
PO
WO

10-01
1010-07
1010-05
1010-25
1010-30
1010-13
1010-24
10-

15-807
15-

Capacity Requirements
Planning (CRP)
Creates a load profile
Identifies under-loads and over-loads
Inputs
Planned order releases
Routing file
Open orders file

15-808
15-

CRP
MRP planned
order
releases

Routing
file

Capacity
requirements
planning

Open
orders
file

Load profile for
each process
15-809
15-

Calculating Capacity
Maximum capability to produce
Rated Capacity
Theoretical output that could be attained if a process were
operating at full speed without interruption, exceptions, or
downtime

Effective Capacity
Takes into account the efficiency with which a particular
product or customer can be processed and the utilization of
the scheduled hours or work
Effective Daily Capacity = (no. of machines or workers) x
(hours per shift) x (no. of shifts) x (utilization) x ( efficiency)
15-810
15-

Calculating Capacity (cont.)
Utilization
Percent of available time spent working

Efficiency
How well a machine or worker performs compared to a
standard output level

Load
Standard hours of work assigned to a facility

Load Percent
Ratio of load to capacity
load
Load Percent =
capacity

x 100%

15-811
15-

Load Profiles
graphical comparison of load versus
capacity
Leveling underloaded conditions:
Acquire more work
Pull work ahead that is scheduled for later
time periods
Reduce normal capacity
15-812
15-

Reducing Over-load Conditions
Over1. Eliminating unnecessary requirements
2. Rerouting jobs to alternative machines,
workers, or work centers
3. Splitting lots between two or more machines
4. Increasing normal capacity
5. Subcontracting
6. Increasing efficiency of the operation
7. Pushing work back to later time periods
8. Revising master schedule
15-813
15-

Hours of capacity

Initial Load Profile
120 –
110 –
100 –
90 –
80 –
70 –
60 –
50 –
40 –
30 –
20 –
10 –
0–

Normal
capacity

1

2

3

4

5

6

Time (weeks)

15-814
15-

Hours of capacity

Adjusted Load Profile
120 –
110 –
100 –
90 –
80 –
70 –
60 –
50 –
40 –
30 –
20 –
10 –
0–

Pull ahead
Overtime

1

2

Work
an
extra
shift

Push back
Push back

3

4

Normal
capacity

5

6

Time (weeks)

Load leveling
process of balancing underloads and overloads
15-815
15-

Relaxing MRP Assumptions
Material is not always the most constraining
resource
Lead times can vary
Not every transaction needs to be recorded
Shop floor may require a more sophisticated
scheduling system
Scheduling in advance may not be appropriate
for on-demand production.
15-816
15-

Enterprise Resource Planning
(ERP)
Software that organizes and manages
a company’s business processes by
sharing information across functional
areas
integrating business processes
facilitating customer interaction
providing benefit to global companies

15-817
15-

Organizational Data Flows

Source: Adapted from Joseph Brady, Ellen Monk, and Bret Wagner, Concepts in
Enterprise Resource Planning (Boston: Course Technology, 2001), pp. 7–12
15-818
15-

ERP’s Central Database

15-819
15-

Selected Enterprise Software
Vendors

15-820
15-

ERP Implementation
Analyze business processes
Choose modules to implement
Which processes have the biggest impact on
customer relations?
Which process would benefit the most from
integration?
Which processes should be standardized?

Align level of sophistication
Finalize delivery and access
Link with external partners
15-821
15-

Customer Relationship
Management (CRM)
Software that
Plans and executes business processes
Involves customer interaction
Changes focus from managing products to
managing customers
Analyzes point-of-sale data for patterns
point-ofused to predict future behavior

15-822
15-

Software that plans and executes business
processes related to supply chains
Includes
Supply chain planning
Supply chain execution
Supplier relationship management

Distinctions between ERP and SCM are
becoming increasingly blurred
15-823
15-

Product Lifecycle Management
(PLM)
Software that
Incorporates new product design and
development and product life cycle
management
Integrates customers and suppliers in the
design process though the entire product life
cycle

15-824
15-

ERP and Software Systems

15-825
15-

Connectivity
Application programming interfaces (APIs)
give other programs well-defined ways of speaking to
wellthem

Enterprise Application Integration (EAI) solutions
EDI is being replaced by XML, business
language of Internet
ServiceService-oriented architecture (SOA)
collection of “services” that communicate with each
other within software or between software

15-826
15-

Chapter 16
Lean Systems

Lecture Outline
Basic Elements of Lean Production
Benefits of Lean Production
Implementing Lean Production
Lean Services
Leaning the Supply Chain
Lean Six Sigma
Lean and the Environment
Lean Consumption

16-828
16-

Lean Production
Doing more with less inventory, fewer
workers, less space
Just-in-time (JIT)
smoothing the flow of material to arrive
just as it is needed
“JIT” and “Lean Production” are used
interchangeably

Muda
waste, anything other than that which
adds value to product or service

16-829
16-

Waste in Operations

16-830
16-

Waste in Operations (cont.)

16-831
16-

Waste in Operations (cont.)

16-832
16-

Basic Elements
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.

Flexible resources
Cellular layouts
Pull system
Kanbans
Small lots
Quick setups
Uniform production levels
Quality at the source
Total productive
maintenance
Supplier networks

16-833
16-

Flexible Resources
Multifunctional workers
perform more than one job
general-purpose machines perform
several basic functions

Cycle time
time required for the worker to complete
one pass through the operations
assigned

Takt time
paces production to customer demand

16-834
16-

Standard Operating
Routine for a Worker

16-835
16-

Cellular Layouts
Manufacturing cells
comprised of dissimilar machines brought
together to manufacture a family of parts

Cycle time is adjusted to match takt time
by changing worker paths

16-836
16-

Cells with Worker Routes

16-837
16-

Worker Routes Lengthen as
Volume Decreases

16-838
16-

Pull System
Material is pulled through the system when
needed
Reversal of traditional push system where
material is pushed according to a schedule
Forces cooperation
Prevent over and underproduction
While push systems rely on a predetermined
schedule, pull systems rely on customer
requests

16-839
16-

Kanbans
Card which indicates standard quantity
of production
Derived from two-bin inventory system
twoMaintain discipline of pull production
Authorize production and movement of
goods

16-840
16-

Origin of Kanban
a) Two-bin inventory system
Two-

b) Kanban inventory system

Bin 1
Kanban
Bin 2
Reorder
card

Q-R
R

R

Q = order quantity
R = reorder point - demand during lead time

16-842
16-

Types of Kanban
Production kanban
authorizes production of
goods

Withdrawal kanban
authorizes movement of
goods

Kanban square
a marked area designated
to hold items

Signal kanban
a triangular kanban
used to signal
production at the
previous workstation

Material kanban
used to order material in
advance of a process

Supplier kanban
rotates between the
factory and suppliers
16-843
16-

Determining Number of
Kanbans
No. of Kanbans =

average demand during lead time + safety stock
container size
dL + S
C
where

N =

N = number of kanbans or containers
d = average demand over some time period
L = lead time to replenish an order
S = safety stock
C = container size

16-847
16-

Determining Number of
Kanbans: Example
d = 150 bottles per hour
L = 30 minutes = 0.5 hours
S = 0.10(150 x 0.5) = 7.5
C = 25 bottles
(150 x 0.5) + 7.5
dL + S
N=
=
25
C
= 75 + 7.5 = 3.3 kanbans or containers
25
Round up to 4 (to allow some slack) or
down to 3 (to force improvement)
16-848
16-

Small Lots
Require less space and capital
investment
Move processes closer together
Make quality problems easier to
detect
Make processes more dependent
on each other
16-849
16-

Inventory Hides Problems

16-850
16-

Less Inventory Exposes Problems

16-851
16-

Components of Lead Time
Processing time
Reduce number of items or improve efficiency

Move time
Reduce distances, simplify movements, standardize
routings

Waiting time
Better scheduling, sufficient capacity

Setup time
Generally the biggest bottleneck
16-852
16-

Quick Setups
Internal setup
Can be performed
only when a
process is stopped

External setup
Can be performed
in advance

SMED Principles
Separate internal setup from
external setup
Convert internal setup to external
setup
Streamline all aspects of setup
Perform setup activities in
parallel or eliminate them entirely

16-853
16-

Common Techniques for Reducing
Setup Time

16-854
16-

Setup Time (cont.)

16-855
16-

Setup Time (cont.)

16-856
16-

Uniform Production Levels
Result from smoothing production
requirements on final assembly line
Kanban systems can handle +/- 10%
+/demand changes
Reduce variability with more accurate
forecasts
Smooth demand across planning
horizon
MixedMixed-model assembly steadies
component production
16-857
16-

MixedMixed-Model Sequencing

16-858
16-

Quality at the Source
Visual control
makes problems visible

Poka-yokes
prevent defects from
occurring

Kaizen
a system of continuous
improvement; “change for
the good of all”

Jidoka
authority to stop the
production line

Andons
call lights that signal
quality problems

Under-capacity
scheduling
leaves time for planning,
problem solving, and
maintenance

16-859
16-

Examples of Visual
Control

16-860
16-

Examples of Visual
Control (cont.)

16-861
16-

Examples of Visual
Control (cont.)

16-862
16-

5 Whys
One of the keys to an effective Kaizen is
finding the root cause of a problem and
eliminating it
A practice of asking “why?” repeatedly
until the underlying cause is identified
(usually requiring five questions)
Simple, yet powerful technique for finding
the root cause of a problem
16-863
16-

Total Productive
Maintenance (TPM)
Breakdown maintenance
Repairs to make failed machine operational

Preventive maintenance
System of periodic inspection and
maintenance to keep machines operating

TPM combines preventive maintenance
and total quality concepts
16-864
16-

TPM Requirements
Design products that can be easily produced
on existing machines
Design machines for easier operation,
changeover, maintenance
Train and retrain workers to operate machines
Purchase machines that maximize productive
potential
Design preventive maintenance plan spanning
life of machine

16-865
16-

5S Scan

Goal

Eliminate or Correct

Seiri(sort)

Keep only what you
need

Seiton(set in order)

A place for
everything and
everything in its
place
Cleaning, and looking
for ways to keep
clean and organized

Unneeded equipment, tools, furniture;
unneeded items on walls, bulletins; items
blocking aisles or stacked in corners;
unneeded inventory, supplies, parts; safety
hazards
Items not in their correct places; correct places
not obvious; aisles, workstations, & equipment
locations not indicated; items not put away
immediately after use
Floors, walls, stairs, equipment, & surfaces not
clean; cleaning materials not easily
accessible; lines, labels, signs broken or
unclean; other cleaning problems
Necessary information not visible; standards
not known; checklists missing; quantities and
limits not easily recognizable; items can’t be
located within 30 seconds
Number of workers without 5S training; number
of daily 5S inspections not performed; number
of personal items not stored; number of times
job aids not available or up-to-date

Seisou (shine)

Seiketsu
(standardize)
Shisuke (sustain)

Maintaining and
monitoring the first
three categories
Sticking to the rules

16-866
16-

Supplier Networks
Long-term supplier contracts
Synchronized production
Supplier certification
Mixed loads and frequent deliveries
Precise delivery schedules
Standardized, sequenced delivery
Locating in close proximity to the customer

16-867
16-

Benefits of Lean
Production
Reduced inventory
Improved quality
Lower costs
Reduced space requirements
Shorter lead time
Increased productivity

16-868
16-

Benefits of Lean
Production (cont.)
Greater flexibility
Better relations with suppliers
Simplified scheduling and control activities
Increased capacity
Better use of human resources
More product variety

16-869
16-

Implementing Lean Production
Use lean production to finely tune an
operating system
Somewhat different in USA than Japan
Lean production is still evolving
Lean production is not for everyone

16-870
16-

Lean Services
Basic elements of lean
production apply equally to
services
Most prevalent applications
lean retailing
lean banking
lean health care
16-871
16-

Leaning the Supply Chain
“pulling” a smooth flow of material through a
series of suppliers to support frequent
replenishment orders and changes in customer
demand
Firms need to share information and
coordinate demand forecasts, production
planning, and inventory replenishment with
suppliers and supplier’s suppliers throughout
supply chain
16-872
16-

Leaning the Supply Chain (cont.)
Steps in Leaning the Supply Chain:
Build a highly collaborative business
environment
Adopt the technology to support your
system

16-873
16-

Lean Six Sigma
Lean and Six Sigma are natural partners for
process improvement
Lean
Eliminates waste and creates flow
More continuous improvement

Six Sigma
Reduces variability and enhances process
capabilities
Requires breakthrough improvements
16-874
16-

Lean and the Environment
Lean’s mandate to eliminate waste and
operate only with those resources that
are absolutely necessary aligns well with
environmental initiatives
Environmental waste is often an indicator
of poor process design and inefficient
production
16-875
16-

EPA Recommendations
Commit to eliminate environmental waste through lean
implementation
Recognize new improvement opportunities by
incorporating environmental, heath and safety (EHS)
icons and data into value stream maps
Involve staff with EHS expertise in planning
Find and drive out environmental wastes in specific
process by using lean process-improvement tools
processEmpower and enable workers to eliminate
environmental wastes in their work areas

16-876
16-

Lean Consumption
Consumptions process involves locating,
buying, installing, using, maintaining, repairing,
and recycling.
Lean Consumption seeks to:
Provide customers what they want, where and
when they want it
Resolve customer problems quickly and completely
Reduce the number of problems customers need to
solve

16-877
16-

Chapter 17
Scheduling

Lecture Outline
Objectives in Scheduling
Loading
Sequencing
Monitoring
Advanced Planning and Scheduling Systems
Theory of Constraints
Employee Scheduling

17-879
17-

What is Scheduling?
Last stage of planning before production
occurs
Specifies when labor, equipment, and
facilities are needed to produce a
product or provide a service

17-880
17-

Scheduled Operations
Process Industry
Linear programming
EOQ with non-instantaneous
nonreplenishment

Mass Production
Assembly line balancing

Project
Project -scheduling
techniques (PERT, CPM)

Batch Production
Aggregate planning
Master scheduling
Material requirements
planning (MRP)
Capacity requirements
planning (CRP)

17-881
17-

Objectives in Scheduling
Meet customer due
dates
Minimize job lateness
Minimize response time
Minimize completion
time
Minimize time in the
system

Minimize overtime
Maximize machine or
labor utilization
Minimize idle time
Minimize work-inwork-inprocess inventory

17-882
17-

Shop Floor Control (SFC)
scheduling and monitoring of day-to-day production
day-toin a job shop
also called production control and production
activity control (PAC)
usually performed by production control department
Loading
Check availability of material, machines, and labor

Sequencing
Release work orders to shop and issue dispatch lists for
individual machines

Monitoring
Maintain progress reports on each job until it is complete
17-883
17-

Loading
Process of assigning work to limited
resources
Perform work with most efficient
resources
Use assignment method of linear
programming to determine allocation

17-884
17-

Assignment Method
1. Perform row reductions

4. If number of lines equals number
of rows in matrix, then optimum
subtract minimum value in each
solution has been found. Make
row from all other row values
assignments where zeros appear
2. Perform column reductions
subtract minimum value in each
column from all other column
values

3. Cross out all zeros in matrix
use minimum number of
horizontal and vertical lines

Else modify matrix
subtract minimum uncrossed value
from all uncrossed values
add it to all cells where two lines
intersect
other values in matrix remain
unchanged

5. Repeat steps 3 and 4 until
optimum solution is reached

17-885
17-

Assignment Method: Example
Initial
Matrix
Bryan
Kari
Noah
Chris
Row reduction
5
4
2
5

0
0
1
1

1
2
0
0

1
10
6
7
9

PROJECT
3
4
6
10
4
6
5
6
4
10

2
5
2
6
5

Column reduction
5
4
1
6

3
2
0
3

0
0
1
1

1
2
0
0

4
3
0
5

Cover all zeros
3
2
0
3

0
0
1
1

1
2
0
0

4
3
0
5

Number lines ≠ number of rows so modify matrix

17-886
17-

Assignment Method: Example (cont.)
Modify matrix
1
0
0
1

0
0
3
1

1
2
2
0

Cover all zeros
2
1
0
3

1
0
0
1

0
0
3
1

1
2
2
0

2
1
0
3

Number of lines = number of rows so at optimal solution
PROJECT
Bryan
Kari
Noah
Chris

1
1
0
0
1

2
0
0
3
1

3
1
2
2
0

PROJECT
4
2
1
0
3

Bryan
Kari
Noah
Chris

1
10
6
7
9

2
5
2
6
5

3
6
4
5
4

4
10
6
6
10

Project Cost = (5 + 6 + 4 + 6) X $100 = $2,100
17-887
17-

Sequencing
Prioritize jobs assigned to a resource
If no order specified use first-come first-served (FCFS)
Other Sequencing Rules
FCFS - first-come, first-served
LCFS - last come, first served
DDATE - earliest due date
CUSTPR - highest customer priority
SETUP - similar required setups
SLACK - smallest slack
CR - smallest critical ratio
SPT - shortest processing time
LPT - longest processing time
17-888
17-

Minimum Slack and
Smallest Critical Ratio
SLACK considers both work and time remaining
SLACK = (due date – today’s date) – (processing time)

CR recalculates sequence as processing
continues and arranges information in ratio form
time remaining
CR = remaining
work

due date - today’s date
=
remaining processing time

If CR > 1, job ahead of schedule
If CR < 1, job behind schedule
If CR = 1, job on schedule
17-889
17-

Sequencing Jobs through One Process
Flow time (completion time)
Time for a job to flow through system

Makespan
Time for a group of jobs to be completed

Tardiness
Difference between a late job’s due date
and its completion time
17-890
17-

Simple Sequencing Rules

JOB

PROCESSING
TIME

DUE
DATE

A
B
C
D
E

5
10
2
8
6

10
15
5
12
8

17-891
17-

Simple Sequencing
Rules: FCFS
FCFS
START PROCESSING COMPLETION
SEQUENCE TIME
TIME
TIME
DATE

A
B
C
D
E
Total
Average

0
5
15
17
25

5
10
2
8
6

5
15
17
25
31
93
93/5 = 18.60

10
15
5
12
8

DUE
TARDINESS

0
0
12
13
23
48
48/5 = 9.6

17-892
17-

Simple Sequencing
Rules: DDATE
DDATE
SEQUENCE TIME
TIME
TIME
DATE

C
E
A
D
B
Total
Average

0
2
8
13
21

2
6
5
8
10

2
8
13
21
31
75
75/5 = 15.00

5
8
10
12
15

DUE
TARDINESS

0
0
3
9
16
28
28/5 = 5.6

17-893
17-

Simple Sequencing
Rules: SLACK

A(10-0) – 5 = 5
B(15-0) – 10 = 5
C(5-0) – 2 = 3
D(12-0) – 8 = 4
E(8-0) – 6 = 2

SLACK
SEQUENCE TIME
TIME
TIME
DATE

E
C
D
A
B
Total
Average

0
6
8
16
21

6
2
8
5
10

6
8
16
21
31
82
82/5 = 16.40

8
5
12
10
15

DUE
TARDINESS

0
3
4
11
16
34
34/5 = 6.8

17-894
17-

Simple Sequencing
Rules: SPT
SPT
SEQUENCE TIME
TIME
TIME
DATE

C
A
E
D
B
Total
Average

0
2
7
13
21

2
5
6
8
10

2
7
13
21
31
74
74/5 = 14.80

5
10
8
12
15

DUE
TARDINESS

0
0
5
9
16
30
30/5 = 6

17-895
17-

Simple Sequencing
Rules: Summary
RULE

AVERAGE
COMPLETION TIME

FCFS
DDATE
SLACK
SPT

18.60
15.00
16.40
14.80

AVERAGE
TARDINESS

9.6
5.6
6.8
6.0

NO. OF
JOBS TARDY

3
3
4
3

MAXIMUM
TARDINESS

23
16
16
16

17-896
17-

Sequencing Jobs Through
Two Serial Process
Johnson’s Rule
1. List time required to process each job at each machine.
Set up a one-dimensional matrix to represent desired
onesequence with # of slots equal to # of jobs.
2. Select smallest processing time at either machine. If
that time is on machine 1, put the job as near to
beginning of sequence as possible.
3. If smallest time occurs on machine 2, put the job as
near to the end of the sequence as possible.
4. Remove job from list.
5. Repeat steps 2-4 until all slots in matrix are filled and all
2jobs are sequenced.

17-897
17-

Johnson’s Rule

JOB
A
B
C
D
E

PROCESS 1
6
11
7
9
5

PROCESS 2
8
6
3
7
10

E

A D

B

C

17-898
17-

Johnson’s Rule (cont.)
E
E

A
5

A

D

D
11

B

C

B

Process 1
(sanding)

C

20

31

38

Idle time
E
5

A
15

D
23

B
30

Process 2
(painting)

C
37

41

Completion time = 41
Idle time = 5+1+1+3=10
17-899
17-

Guidelines for Selecting a
Sequencing Rule
1.
2.
3.
4.
5.
6.

SPT most useful when shop is highly congested
Use SLACK for periods of normal activity
Use DDATE when only small tardiness values can
be tolerated
Use LPT if subcontracting is anticipated
Use FCFS when operating at low-capacity levels
lowDo not use SPT to sequence jobs that have to be
assembled with other jobs at a later date

17-900
17-

Monitoring
Work package
Shop paperwork that travels with a job

Gantt Chart
Shows both planned and completed
activities against a time scale

Input/Output Control
Monitors the input and output from each
work center
17-901
17-

Gantt Chart
Job 32B
Behind schedule

Facility

3
Job 23C

Ahead of schedule

2
Job 11C

Job 12A
On schedule

1

1
Key:

2

3

4

5

6
8
Today’s Date

9

10

11

12

Days

Planned activity
Completed activity
17-902
17-

Input/Output Control
Input/Output Report
PERIOD
Planned input
Actual input
Deviation
Planned output
Actual output
Deviation
Backlog

1

2

3

4

65

65

70

70

75

75

75

75

30

20

10

5

TOTAL
270
0
0
300
0
0
0

17-903
17-

Input/Output Control (cont.)
Input/Output Report
PERIOD
Planned input
Actual input
Deviation
Planned output
Actual output
Deviation
Backlog

1

2

3

4

65
60
-5
75
75
-0
30

65
60
-5
75
75
-0
15

70
65
-5
75
65
-10
0

70
65
-5
75
65
-10
0

TOTAL
270
250
-20
300
280
-20
0

17-904
17-

Advanced Planning and
Scheduling Systems
Infinite - assumes infinite capacity
Loads without regard to capacity
Then levels the load and sequences jobs

Finite - assumes finite (limited) capacity
Sequences jobs as part of the loading
decision
Resources are never loaded beyond
capacity
17-905
17-

Advanced Planning and
Scheduling Systems (cont.)
Advanced planning and scheduling (APS)
AddAdd-ins to ERP systems
ConstraintConstraint-based programming (CBP) identifies a
solution space and evaluates alternatives
Genetic algorithms based on natural selection
properties of genetics
Manufacturing execution system (MES) monitors
status, usage, availability, quality

17-906
17-

Theory of Constraints

Not all resources are used evenly
Concentrate on the” bottleneck” resource
Synchronize flow through the bottleneck
Use process and transfer batch sizes to
move product through facility

17-907
17-

Drum-Buffer-Rope
Drum
Bottleneck, beating to set the pace of production for
the rest of the system

Buffer
Inventory placed in front of the bottleneck to ensure
it is always kept busy
Determines output or throughput of the system

Rope
Communication signal; tells processes upstream
when they should begin production

17-908
17-

TOC Scheduling Procedure
Identify bottleneck
Schedule job first whose lead time to
bottleneck is less than or equal to
bottleneck processing time
Forward schedule bottleneck machine
Backward schedule other machines to
sustain bottleneck schedule
Transfer in batch sizes smaller than
process batch size

17-909
17-

A

B

D

B3 1 7

C3 2 15

D3 3 5

B2 2 3

C2 1 10

D2 2 8

B1 1 5

Synchronous
Manufacturing

C

C1 3 2

D1 3 10

Key:

i

ij k l

Item i
Operation j of item i performed at
machine center k takes l minutes
to process

17-910
17-

Synchronous
Manufacturing (cont.)
Demand = 100 A’s
Machine setup time = 60 minutes
MACHINE 1 MACHINE 2 MACHINE 3
B1
B3
C2
Sum

5
7
10
22

B2
C3
D2

3
15
8
26*

C1
D3
D1

2
5
10
17

* Bottleneck

17-911
17-

Synchronous Manufacturing (cont.)
Setup

Machine 1
C2

Setup
B1

2

B3
1562

1002

2322

Idle
Setup

Machine 2
C3

B2
1512

12
Machine 3
Setup
C1
0 200

Setup
D2
1872

2732

Setup
D1

Idle
1260

D3
1940

Completion
time

2737

17-912
17-

Employee Scheduling
Labor is very flexible
resource
Scheduling workforce is
complicated, repetitive
task
Assignment method can
be used
Heuristics are commonly
used

17-913
17-

Employee Scheduling Heuristic
1. Let N = no. of workers available
Di = demand for workers on day i
X = day working
O = day off
2. Assign the first N - D1 workers day 1 off. Assign the next N - D2
workers day 2 off. Continue in a similar manner until all days are
have been scheduled
3. If number of workdays for full time employee < 5, assign
remaining workdays so consecutive days off are possible
4. Assign any remaining work to part-time employees
part5. If consecutive days off are desired, consider switching schedules
among days with the same demand requirements

17-914
17-

Employee Scheduling
DAY OF WEEK
WORKERS REQUIRED

M

T
W
MIN NO. OF
3
3
4

TH

F

SA

SU

3

4

5

3

Taylor
Smith
Simpson
Allen
Dickerson

17-915
17-

Employee Scheduling (cont.)
DAY OF WEEK
WORKERS REQUIRED
Taylor
Smith
Simpson
Allen
Dickerson

M

T
W
MIN NO. OF
3
3
4

O
O
X
X
X

X
X
O
O
X

X
X
X
X
O

TH

F

SA

SU

3

4

5

3

O
O
X
X
X

X
X
O
X
X

X
X
X
X
X

X
X
X
O
O

Completed schedule satisfies requirements but has no
consecutive days off

17-916
17-

Employee Scheduling (cont.)
DAY OF WEEK
WORKERS REQUIRED
Taylor
Smith
Simpson
Allen
Dickerson

M

T
W
MIN NO. OF
3
3
4

O
O
X
X
X

O
O
X
X
X

X
X
O
X
X

TH

F

SA

SU

3

4

5

3

X
X
O
O
X

X
X
X
X
O

X
X
X
X
X

X
X
X
O
O

Revised schedule satisfies requirements with consecutive
days off for most employees

17-917
17-

Automated Scheduling Systems
Staff Scheduling
Schedule Bidding
Schedule
Optimization

17-918
17-

Thank You
www.bookfiesta4u.com

2-919

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