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: Chapter 1 (Slide 5)
Quality Management: Chapter 2 (Slide 67)
Statistical Quality Control: Chapter 3 (Slide 120)
Product Design: Chapter 4 (Slide 186)
Service Design: Chapter 5 (Slide 231)
Processes and Technology: Chapter 6 (Slide 276)
Facilities: Chapter 7 (Slide 321)
Human Resources: Chapter 8 (Slide 402)
Project Management: Chapter 9 (Slide 450)

1 -2

Organization of This Text:
Part II – Supply Chain Management
Supply Chain
Strategy and Design: Chapter 10 (Slide 507)
Global Supply Chain
Procurement and Distribution: Chapter 11 (Slide 534)
Forecasting: Chapter 12 (Slide 575)
Inventory Management: Chapter 13 (Slide 641)
Sales and
Operations Planning: Chapter 14 (Slide 703)
Resource Planning: Chapter 15 (Slide 767)
Lean Systems: Chapter 16 (Slide 827)
Scheduling: Chapter 17 (Slide 878)
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
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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
TRANSFORMATION OUTPUT
•Machines
PROCESS •Goods
•Labor
•Services
•Management
•Capital

Feedback & Requirements

1 -9

Operations Function

Operations
Marketing
Finance and
Accounting
Human
Resources
Outside
Suppliers

1-10

How is Operations Relevant to my
Major?
“As an auditor you must
Accounting understand the fundamentals of
operations management.”
Information “IT is a tool, and there’s no better
Technology place to apply it than in
operations.”
“We use so many things you
Management 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.)
“It’s all about processes. I live
Economics by flowcharts and Pareto
analysis.”
Marketing “How can you do a good job
marketing a product if you’re
unsure of its quality or delivery
status?”
Finance “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
high-
high-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 Events/Concepts Dates Originator
Steam engine 1769 James Watt
Industrial
Division of labor 1776 Adam Smith
Revolution
Interchangeable parts 1790 Eli Whitney
Principles of scientific
1911 Frederick W. Taylor
management
Frank and Lillian
Scientific Time and motion studies 1911 Gilbreth
Management Activity scheduling chart 1912 Henry Gantt
Moving assembly line 1913 Henry Ford

1-15

Operations Management (cont.)
Hawthorne studies 1930 Elton Mayo
Human 1940s Abraham Maslow
Relations Motivation theories 1950s Frederick Herzberg
1960s Douglas McGregor
Linear programming 1947 George Dantzig
Digital computer 1951 Remington Rand
Simulation, waiting
Operations Operations research
line theory, decision 1950s
Research groups
theory, PERT/CPM
1960s, Joseph Orlicky, IBM
MRP, EDI, EFT, CIM
1970s and others

1-16

JIT (just-in-time) 1970s Taiichi Ohno (Toyota)
TQM (total quality W. Edwards Deming,
1980s
management) Joseph Juran
Quality Strategy and Wickham Skinner,
1980s
Revolution operations Robert Hayes
Business process Michael Hammer,
1990s
reengineering James Champy
Six Sigma 1990s GE, Motorola

1-17

Internet Internet, WWW, ERP, 1990s ARPANET, Tim
Revolution supply chain management Berners-Lee SAP,
i2 Technologies,
ORACLE
E-commerce 2000s Amazon, Yahoo,
eBay, Google, and
others
Globalization WTO, European Union, 1990s Numerous countries
and other trade 2000s and companies
agreements, global supply
chains, outsourcing, BPO,
Services Science
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.
1995-
Source: 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
1995- Dramatic Increase in
Source: Bureau of Labor Statistics. A Chartbook of International Output w/ Decrease in
Labor Comparisons, January 2007, p. 26.
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 Operations Financial
Strategy Strategy 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
time-
time-based competition: build-to-order production and
build-to-
efficient supply chains
Fashion industry
two-
two-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
Ritz-
Ritz-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 Process
and
Products
Technology

Human
Resources Quality
Capacity

Facilities Sourcing 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
decision-
techniques 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 Poor Foreign
DECISION Competitive Conditions Competitive Conditions

Expand $ 800,000 $ 500,000
Maintain status quo 1,300,000 -150,000
Sell now 320,000 320,000

Supplement 1-50
1-

Maximax Solution
STATES OF NATURE

Expand $ 800,000 $ 500,000
Sell now 320,000 320,000

Expand: $800,000
Status quo: 1,300,000 ← Maximum
Sell: 320,000
Decision: Maintain status quo

Supplement 1-51
1-

Maximin Solution
STATES OF NATURE

Expand $ 800,000 $ 500,000
Sell now 320,000 320,000

Expand: $500,000 ← Maximum
Status quo: -150,000
Sell: 320,000
Decision: Expand

Supplement 1-52
1-

Minimax Regret Solution
Competitive Conditions Competitive Conditions

$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: $500,000 ← Minimum
Status quo: 650,000
Sell: 980,000
Decision: Expand

Supplement 1-53
1-

Hurwicz Criteria
STATES OF NATURE

Expand $ 800,000 $ 500,000
Sell now 320,000 320,000
α = 0.3 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

Expand $ 800,000 $ 500,000
Sell now 320,000 320,000

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

n
EV (x) =
(x
p(xi)xi ∑
i =1

where

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

Supplement 1-59
1-

Probabilities: Example
STATES OF NATURE

Expand $ 800,000 $ 500,000
Sell now 320,000 320,000

p(good) = 0.70 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
$1,290,000 $2,000,000
0.60 Market growth
2
0.40
$225,000
$3,000,000
$2,540,000
0.80
$1,740,000 6
$700,000
0.20
1 $1,160,000 4

$450,000
0.60 $2,300,000
$1,390,000
3
0.40
0.30
$790,000 7
$1,360,000
0.70 $1,000,000
5

$210,000
Supplement 1-66
1-

Chapter 2

Quality Management



Lecture Outline

What Is Quality? Quality in Service
Evolution of Quality Companies
Management Six Sigma
Quality Tools Cost of Quality
TQM and QMS Effect of Quality
Focus of Quality Management on
Management—
Management— Productivity
Customers Quality Awards
Role of Employees in ISO 9000
Quality Improvement
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

Meaning of Quality

2-78

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
quality-
executives 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 Histogram
Chart Scatter Diagram
Cause-and-
Cause-and- Statistical Process
Effect Diagram Control Chart
Check Sheet
Pareto Analysis

2-86

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

2-88

Cause-and-
Cause-and-Effect Matrix
Cause-and-
Cause-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

Scatter Diagram

2-93

TQM and QMS

Total Quality Management (TQM)
customer-
customer-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
quality-
quality-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
Quality circle Supervisor/moderator

group of workers
Training
and supervisors Presentation Group processes
Implementation Data collection
from same area Monitoring Problem analysis
who address
quality problems
Process/Quality Problem
improvement teams Solution
Problem results
Identification
List alternatives
(QITs) Consensus
Brainstorming
Problem
focus attention on Analysis
business processes Cause and effect
Data collection
rather than separate and analysis

company functions
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 “quickest, friendliest, most
achievement in one accurate service
company that other available.”
companies seek to achieve

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-to-
honest-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 Training costs
costs of developing and costs of developing and
implementing quality putting on quality training
management program
programs for employees
Product-
Product-design costs and management
costs of designing Information costs
products with quality
characteristics costs of acquiring
Process costs and maintaining data
related to quality, and
costs expended to make
sure productive process development and
conforms to quality analysis of reports on
specifications 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 Process downtime costs
costs of poor-quality
poor-
products that must be costs of shutting down
discarded, including labor, productive process to fix
material, and indirect costs problem
Rework costs Price-
Price-downgrading costs
costs of fixing defective
products to conform to costs of discounting poor-
poor-
quality specifications quality products—that is,
products—
Process failure costs selling products as
costs of determining why “seconds”
production process is
producing poor-quality
poor-
products

2-108

External Failure Costs

Customer complaint costs Product liability costs
costs of investigating and litigation costs
satisfactorily responding to a resulting from product
customer complaint resulting
from a poor-quality product
poor- liability and customer
injury
Product return costs
costs of handling and replacing Lost sales costs
poor-
poor-quality products returned costs incurred
by customer because customers
Warranty claims costs are dissatisfied with
costs of complying with poor-
poor-quality products
product warranties 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)(1-
Y=(I)(%G)+(I)(1-%G)(%R)

2-112

Computing Product
Cost per Unit

(Kd )(I) +(Kr )(R)
Product Cost
=
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

(good-quality units)
QPR = (100)
(input) (processing cost) + (reworked units) (rework cost)

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 International awards
awards European Quality Award
Armand V. Feigenbaum Canadian Quality Award
Medal Australian Business
Deming Medal Excellence Award
E. Jack Lancaster Medal Deming Prize from Japan
Edwards Medal
Shewart Medal
Ishikawa Medal

2-117

ISO 9000

A set of procedures and ISO 9001:2000
policies for international Quality Management
quality certification of Systems—
Systems—Requirements
suppliers standard to assess ability to
Standards achieve customer satisfaction
ISO 9000:2000 ISO 9004:2000
Quality Management Quality Management
Systems—Fundamentals
Systems— Systems—
Systems—Guidelines for
and Vocabulary Performance Improvements
defines fundamental guidance to a company for
terms and definitions continual improvement of its
used in ISO 9000 family quality-
quality-management system

2-118

ISO 9000 Certification,
Implications, and Registrars

ISO 9001:2000—only
9001:2000—
standard that carries third-
third-
party 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 UCL
quality
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 Non-
Non-Random
inherent in a process special causes
depends on equipment identifiable and
and machinery, correctable
engineering, operator, include equipment out of
and system of adjustment, defective
measurement materials, changes in
natural occurrences 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-of-
quality 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
check-
courtesy, accurate flight information, passenger cabin
cleanliness and maintenance

3-127

SPC Applied to
Services (cont.)
Fast-
Fast-food restaurants
waiting time for service, customer complaints,
cleanliness, food quality, order accuracy, employee
courtesy
Catalogue-
Catalogue-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 Types of charts
control limits of a
process Attributes
Control limits p-chart
upper and lower bands of c-chart
a control 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

p(1 - p)
σp =
n
3-135

Construction of p-Chart
p-

NUMBER OF PROPORTION
SAMPLE DEFECTIVES DEFECTIVE
1 6 .06
2 0 .00
3 4 .04
: : :
: : :
20 18 .18
200

20 samples of 100 pairs of jeans

3-136

Construction of p-Chart (cont.)
p-

total defectives
p= = 200 / 20(100) = 0.10
total sample observations

p(1 - p) 0.10(1 - 0.10)
UCL = p + z = 0.10 + 3
n 100
UCL = 0.190

p(1 - p) 0.10(1 - 0.10)
LCL = p - z = 0.10 - 3
n 100
LCL = 0.010

3-137

0.20

0.18 UCL = 0.190

0.16

Construction 0.14

Proportion defective
of p-Chart
p- 0.12
p = 0.10

(cont.) 0.10

0.08

0.06

0.04

0.02 LCL = 0.010

2 4 6 8 10 12 14 16 18 20
Sample number

3-138

c-Chart

UCL = c + zσc
σc = c
LCL = c - zσc

where

c = number of defects per sample

3-139

c-Chart (cont.)
Number of defects in 15 sample rooms
NUMBER
OF
SAMPLE
DEFECTS 190
1 12 c= = 12.67
15
2 8
3 16 UCL = c + zσc
= 12.67 + 3 12.67
: : = 23.35
: :
LCL = c - zσ c
15 15
= 12.67 - 3 12.67
190
= 1.99

3-140

24
UCL = 23.35
21

18

Number of defects
c = 12.67

15
c-Chart 12
(cont.) 9

6

3 LCL = 1.99

2 4 6 8 10 12 14 16
Sample number

3-141

Control Charts for
Variables
Range chart ( R-Chart )
R-
uses 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
x = n

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

Control
Limits

3-147

Standard Deviation Unknown
OBSERVATIONS (SLIP- RING DIAMETER, CM)
(SLIP-
SAMPLE 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
Example 15.4 50.09 1.15
3-148

Standard Deviation Unknown (cont.)
∑R 1.15
R= k = 10 = 0.115

= ∑x 50.09 5.01 cm
x= = =
k 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 –

5.04 –
=
Mean 5.02 – x = 5.01

5.00 –

x- bar 4.98 –
Chart
4.96 – LCL = 4.94
Example
(cont.) 4.94 –

4.92 – | | | | | | | | | |
1 2 3 4 5 6 7 8 9 10
Sample number

3-150

R- Chart

UCL = D4R LCL = D3R

∑R
R=
k
where
R = range of each sample
k = number of samples

3-151

R-Chart Example
OBSERVATIONS (SLIP-RING DIAMETER, CM)
(SLIP-
SAMPLE 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
Example 15.3 50.09 1.15
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
0.20 –
Range

0.16 – R = 0.115

0.12 –

0.08 –
LCL = 0
0.04 – | | | | | | | | | |
1 2 3 4 5 6 7 8 9 10
0– Sample number

3-154

Using x- bar and R-Charts
x- R-
Together

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
R-
trend, 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
LCL
consistently below the
center line
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
UCL =
3 sigma = x + A2R
Zone A
= 2
2 sigma = x + (A2R)
(A
3
Zone B
= 1
1 sigma = x + (A2R)
(A
3
Zone C
Process =
x
average
Zone C
= 1
1 sigma = x - (A2R)
3
Zone B
= 2
2 sigma = x - (A2R)
3
Zone A
=
LCL 3 sigma = x - A2R
| | | | | | | | | | | | |
1 2 3 4 5 6 7 8 9 10 11 12 13
Sample number
3-159

Performing a Pattern Test
SAMPLE x ABOVE/BELOW UP/DOWN ZONE

1 4.98 B — B
2 5.00 B U C
3 4.95 B D A
4 4.96 B D A
5 4.99 B U C
6 5.01 — U C
7 5.02 A U C
8 5.05 A U B
9 5.08 A U A
10 5.03 A D 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

3-162

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

tolerance range
Cp =
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

upper specification limit -
lower specification limit
Cp =
6σ

= 9.5 - 8.5 = 1.39
6(0.12)

3-168

Process Capability Measures

Process Capability Index

=
x - lower specification limit
3σ
,
Cpk = minimum
=
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 9.50 - 8.80
= minimum 3(0.12) , 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

Single-
Single-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.)
Accept Reject
Good Lot

Type I Error
No Error
Producer’ Risk
Bad Lot

Type II Error
No Error
Consumer’s Risk

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

0.80 –

Probability of acceptance, Pa

0.60 – OC curve for n and c

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
Proportion defective
AQL 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 N = 10,000 n=?
LTPD of 5% defective. What α = 0.05 c=
size sample and what ?
acceptance number should
ABC use to achieve β = 0.10
performance measures called AQL = 1%
for in the sampling plan? LTPD = 5%

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-

Double-
Double-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-

Multiple-
Multiple-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

Design Process (cont.)

4-190

Idea Generation

Company’s own Salespersons in the
R&D department field
Customer complaints Factory workers
or suggestions New technological
Marketing research developments
Suppliers 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 0.95 + 0.90(1-0.95) = 0.995
0.90(1-
R1

4-198

System Reliability

0.90

0.98 0.92 0.98

0.98 0.92+(1-
0.92+(1-0.92)(0.90)=0.99 0.98

0.98 x 0.99 x 0.98 = 0.951

4-199

System Availability (SA)

MTBF
SA =
MTBF + MTTR

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

4-200

System Availability
(cont.)

PROVIDER MTBF (HR) MTTR (HR)
A 60 4.0
B 36 2.0
C 24 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 Source: Adapted from G. Boothroyd and
P. Dewhurst, “Product Design…. Key to
Successful Robotic Assembly.” Assembly
Simplification Engineering (September 1986), pp. 90-
90-
93.

(a) Original design (b) Revised design (c) Final design

Assembly using One-
One-piece base & Design for
common fasteners elimination of push-and-
push-and-snap
fasteners assembly

4-204

Final Design and Process Plans

Final design Process plans
detailed drawings workable instructions
and specifications necessary equipment
for new product or and tooling
service component sourcing
recommendations
job descriptions and
procedures
computer programs for
automated machines

4-205

Concurrent Design

A new approach to Involves suppliers
design that involves Incorporates production
simultaneous design of process
products and processes Uses a price-minus
price-
by design teams system
Scheduling and
Improves quality of early management can be
design decisions 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
computer-
computer-aided engineering (CAE)
tests and analyzes designs on computer screen
computer-
computer-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 Cause of Effect of Corrective
Mode Failure Failure Action
Stale low moisture content tastes bad add moisture
expired shelf life won’t crunch cure longer
poor packaging thrown out better package seal
lost sales shorter shelf life
Broken too thin can’t dip change recipe
too brittle poor display change process
rough handling injures mouth change packaging
rough use chocking
poor packaging perceived as old
lost sales
Too Salty outdated receipt eat less experiment with recipe
process not in control drink more experiment with process
uneven distribution of salt health hazard introduce low salt version
lost sales

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
by-
environment?

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

Design for Environment

4-216

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
5

Importance
Trade-
Trade-off matrix

3
Design
characteristics

1 4 2

Customer Relationship Competitive
requirements matrix assessment

6 Target values

4-219

Competitive Assessment
of Customer
Requirements
Competitive Assessment
Customer Requirements 1 2 3 4 5
Presses quickly 9 B A X
Removes wrinkles 8 AB X
Irons
well

Doesn’t stick to fabric 6 X BA
Provides enough steam 8 AB X
Doesn’t spot fabric 6 X AB
Doesn’t scorch fabric 9 A XB
safe to use

Heats quickly 6 X B A
Easy and

Automatic shut-off
shut- 3 ABX
ABX
Quick cool-down
cool- 3 X A B
Doesn’t break when dropped 5 AB X
Doesn’t burn when touched 5 AB X
4-220
Not too heavy 8 X A B

Protective cover for soleplate
Time required to reach 450º F
From Customer

Time to go from 450º to 100º
Material used in soleplate

Flow of water from holes
Energy needed to press
Requirements

Thickness of soleplate

Automatic shutoff
to Design

Number of holes
Size of soleplate
Weight of iron

Size of holes
Characteristics
Customer Requirements
Presses quickly - - + + + -
Removes wrinkles + + + + +
Irons
well

Doesn’t stick to fabric - + + + +
Provides enough steam + + + +
Doesn’t spot fabric + - - -
Doesn’t scorch fabric + + + - +
safe to use

Heats quickly - - + -
Easy and

Automatic shut-off
shut- +
Quick cool-down
cool- - - + +
Doesn’t break when dropped + + + +
Doesn’t burn when touched + + + +
Not too heavy + - - - + -4-221

Tradeoff Matrix

Weight of iron
-
+

Size of soleplate
-
+

Number of holes
+

Size of holes
Time required to reach 450º
4-222

Automatic shutoff

Targeted Changes in
Design

Time required to reach 450º



Automatic shutoff
Number of holes
Size of soleplate
Weight of iron

Size of holes
Units of measure ft-lb
ft- lb in. cm ty ea mm oz/s sec sec Y/N Y/N
measures
Objective

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

Targets 1.2 8x5 3 SS 30 30 500

Design changes * * * * * * *
4-223

Completed
House of Quality

SS = Silverstone
MG = Mirorrglide
T = Titanium

4-224

A Series of Connected
QFD Houses
Product
characteristics
requirements
Customer

Part
A-1 characteristics

characteristics
Product

Process
House A-2 characteristics
of

characteristics
quality
Parts Operations

Part
A-3
deployment

characteristics
Process
Process A-4
planning

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

Taguchi’s Quality Loss
Function
Quantifies customer
preferences toward
quality

Quality Loss
Emphasizes that
customer preferences
are strongly oriented
toward consistently Lower Target Upper
tolerance tolerance
Design for Six Sigma limit limit
(DFSS)

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
back-
distribution 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 Service inseparable
intangible from delivery
Service output is Services tend to be
variable decentralized and
Services have higher dispersed
customer contact Services are
Services are consumed more often
perishable than products
Services can be easily
emulated

5-237

Service
Design
Process

5-238

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

Service Process Matrix

5-240

High v. Low Contact
Services
Design High-Contact Service Low-Contact Service
Decision
Facility Convenient to Near labor or
location customer transportation source

Facility Must look presentable, Designed for efficiency
layout accommodate
customer needs, and
facilitate interaction
with customer

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 High-Contact Service Low-Contact
Decision Service

Quality More variable since Measured against
control customer is involved in established
process; customer standards; testing
expectations and and rework possible
perceptions of quality
may differ; customer to correct defects
present when defects
occur
Capacity Excess capacity required Planned for average
to handle peaks in demand
demand

5-242

High v. Low Contact
Services (cont.)
Decision Service
Worker skills Must be able to Technical skills
interact well with
customers and use
judgment in decision
making
Scheduling Must accommodate Customer
customer schedule concerned only
with completion
date


5-243

High v. Low Contact
Services (cont.)
Decision Service
Service Mostly front-room Mostly back-room
process activities; service may activities;
change during delivery planned and
in response to executed with
customer
minimal
interference

Service package Varies with customer; Fixed, less
includes environment extensive
as well as actual
service


5-244

Tools for Service Design

Service blueprinting Servicescapes
line of influence space and function
line of interaction ambient conditions
line of visibility signs, symbols, and
line of support artifacts
Front-office/Back-
Front-office/Back- Quantitative
office activities techniques

5-245

Service Blueprinting

5-246

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 Disney
magazines and costumed characters
newspapers mobile vendors
televisions accurate wait times
Bank of America 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
frequent-
Phone 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

Single-
Single-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
Single-

Assumptions Computations
Poisson arrival rate λ = mean arrival rate
exponential service µ = mean service rate
times n = number of
first-
first-come, first-
first- customers in line
served queue
discipline
infinite queue length
infinite calling
population

5-257

Basic Single-Server Model (cont.)
Single-

probability that no customers average number of customers
are in queuing system in queuing system

P0 = ( ) λ
1–
µ
L=
µ–λ
λ

probability of n customers in average number of customers
queuing system in waiting line

( ) ( )( )
λ n λ n λ λ2
Pn = · P0 = 1– Lq =
µ µ µ µ ( µ – λ)

5-258

Basic Single-Server Model (cont.)
Single-

average time customer probability that server is busy
spends in queuing system and a customer has to wait
1 L (utilization factor)
W= = λ
µ–λ λ ρ=
µ

average time customer probability that server is idle
spends waiting in line and customer can be served
λ I=1– ρ
Wq =
µ ( µ – λ) λ
=1– = P0
µ

5-259

Single-
Example

5-260

Single-
Example (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

Single-
Example: Excel

5-263

Advanced Single-Server Models
Single-

Constant 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
Single-
Models (cont.)

5-265

Basic Multiple-Server Model
Multiple-

single waiting line and service facility with
several independent servers in parallel
same assumptions as single-server model
single-
sµ > 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
∑
n=0
() n!
+
µ ( )( )
s! µ
sµ
sµ - λ

probability of n customers in system
λ n
()
1

Pn = { ()
s!sn–s µ

1 λ n
P0, for n > s

P0, for n ≤ s
n! µ
5-267

Multiple-
(cont.)
probability that customer must wait

()
1 λ s sµ λ
Pw = P0 Lq = L –
s! µ sµ – λ µ

λµ (λ/µ)s λ 1 Lq
L= P0 + Wq = W – =
(s – 1)! (sµ – λ)2
(s µ µ λ

L λ
W= ρ=
λ sµ

5-268

Multiple-
Example

5-269

Multiple-
Example (cont.)

5-270

Multiple-
Example (cont.)

5-271

Multiple-
Example (cont.)

5-272

Multiple-
Example (cont.)

5-273

Multiple-
Example (cont.)
To cut wait time, add another service
representative
now, s = 4
Therefore:

5-274

Multiple-
Multiple-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 Speed
Capacity Reliability
Quality 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

Sourcing Continuum

6-282

Product-
Product-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 BATCH MASS CONT.
CONT.

Made-to-
Made-to- Made-to-
Made-to-
Type of
Unique order stock Commodity
product
(customized) (standardized )

One-at-
One-at-a- Few
Type of Mass Mass
time individual
customer market market
customers

Product
demand Infrequent Fluctuates Stable Very stable

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

6-284

Types of Processes (cont.)
CONT.

Demand Low to
Very low High Very high
volume medium

No. of Infinite
different Many, varied Few Very few
products variety

Repetitive, Continuous,
Production Long-
Long-term Discrete, job
system assembly process
project shops
lines industries
York:McGraw-

6-285

CONT.

Varied General-
General- Special-
Special- Highly
Equipment
purpose purpose automated

Primary Mixing,
type of Specialized
Fabrication Assembly treating,
work contracts
refining

Experts, Limited
Worker Wide range Equipment
skills crafts-
crafts- range of
of skills monitors
persons skills
York:McGraw-

6-286

CONT.

Efficiency, Highly efficient,
Custom work, Flexibility,
Advantages latest technology quality
speed, large capacity,
low cost ease of control

Capital
Non-
Non-repetitive, Costly, slow, Difficult to change,
Dis-
Dis- small customer difficult to
investment;
far-reaching errors,
far-
advantages base, expensive lack of
manage limited variety
responsiveness

Machine shops, Automobiles,
Construction, print shops, televisions, Paint, chemicals,
Examples shipbuilding,
foodstuffs
spacecraft bakeries, computers,
education fast food
Source: Adapted from R. Chase, N. Aquilano, and R. Jacobs, Operations Management for Competitive Advantage (New
York:McGraw-
York:McGraw-Hill, 2001), p. 210

6-287

Process Selection with
Break-
Break-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

Break-
Break-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

Break-
Break-Even Analysis (cont.)

TR = TC
vp = cf + vcv
vp - vcv = cf
v ( p - c v) = c f
cf
p - cv
v=
Solving for Break-Even Point (Volume)
Break-

6-290

Break-
Break-Even Analysis: Example

Fixed cost = cf = $2,000
Variable cost = cv = $5 per raft
Price = p = $10 per raft

Break-
Break-even point is
cf 2000
v= = = 400 rafts
p - cv 10 - 5

6-291

Break-
Break-Even Analysis: Graph
Dollars

$3,000 — Total
cost
line

$2,000 —

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

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 041 113, 67, 650 20 min
& start machine
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 Continuous improvement activities
peak; time to reengineer process
of a process for
breakthrough
improvements

6-303

From Function to Process

Product Development
Manufacturing
Purchasing
Accounting

Order Fulfillment

Sales

Customer Service

Function Process

6-304

Process Innovation
Strategic
Directives

Baseline Data
Customer Goals for Process Benchmark
Performance
Requirements Data

High - level Innovative
Process map Design
Ideas
Principles
Detailed Model
Process Map Key
Validation
Performance
Measures
Pilot Study
of New Design

Goals Full Scale
No Met? Yes Implementation

6-305

High-
High-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 Creates and communicates designs
design (CAD) electronically
Group technology Classifies designs into families for easy
(GT) retrieval and modification
Computer-aided Tests functionality of CAD designs
engineering (CAE) electronically
Collaborative
product commerce Facilitates electronic communication and
(CPC) exchange of information among designers
and suppliers

6-313

A Technology Primer (cont.)
Product Technology
Product data Keeps track of design specs and revisions
management for the life of the product
(PDM)
Integrates decisions of those involved in
Product life cycle product development, manufacturing, sales,
management customer service, recycling, and disposal
(PLM)
Product Defines products “configured” by customers
configuration who have selected among various options,
usually from a Web site

6-314

Process Technology
Standard for Set standards for communication among
exchange of different CAD vendors; translates CAD data
product model data into requirements for automated inspection
(STEP) and manufacture
Computer-aided Electronic link between automated design
design and (CAD) and automated manufacture (CAM)
manufacture
(CAD/CAM)
Computer aided Generates process plans based on
process (CAPP) database of similar requirements
E-procurement Electronic purchasing of items from e-
e-
marketplaces, auctions, or company
websites

6-315

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

6-316

Manufacturing Technology
Automatic guided A driverless truck that moves material along a
vehicle (AGV) specified path; directed by wire or tape embedded
in floor or by radio frequencies; very flexible
An automated warehouse—some 26 stores high—
warehouse— high—
Automated storage in which items are placed in a carousel-type
carousel-
and retrieval system storage system and retrieved by fast-moving
fast-
(ASRS) stacker cranes; controlled by computer
Continuous monitoring of automated equipment;
makes real-time decisions on ongoing operation,
real-
Process Control maintenance, and quality
Automated manufacturing systems integrated
Computer-integrated through computer technology; also called e-
e-
manufacturing
manufacturing (CIM)

6-317

Information Technology
Business – to – Electronic transactions between businesses
Business (B2B) usually over the Internet
Business – to – Electronic transactions between businesses and
Consumer (B2C) their customers usually over the Internet
Internet A global information system of computer networks
that facilitates communication and data transfer

Intranet Communication networks internal to an
organization; can be password (i.e., firewall)
protected sites on the Internet
Intranets connected to the Internet for shared
Extranet access with select suppliers, customers, and
trading partners

6-318

Bar Codes A series of vertical lines printed on most packages that
identifies item and other information when read by a
scanner
Radio Frequency An integrated circuit embedded in a tag that can send
Identification tags and receive information; a twenty-first century bar code
twenty-
(RFID) with read/write capabilities
A computer-to-computer exchange of business
computer-to-
Electronic data documents over a proprietary network; very expensive
and inflexible
interchange (EDI)
A programming language that enables computer – to -
computer communication over the Internet by tagging
Extensive markup data before its is sent
language (XML) Software for managing basic requirements of an
enterprise, including sales & marketing, finance and
accounting, production & materials management, and
Enterprise human resources
resource planning
(ERP)
6-319

Software for managing flow of goods and information
Supply chain among a network of suppliers, manufacturers and
management (SCM) distributors
Software for managing interactions with customers and
Customer relationship compiling and analyzing customer data
management (CRM) An information system that helps managers make
decisions; includes a quantitative modeling component
Decision support and an interactive component for what-if analysis
what-
systems (DSS)
A computer system that uses an expert knowledge base
to diagnose or solve a problem
Expert systems (ES)
A field of study that attempts to replicate elements of
human thought in computer processes; includes expert
Artificial intelligence systems, genetic algorithms, neural networks, and fuzzy
(AI) 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 Facilitate entry, exit, and
costs placement of material, products,
Utilize space efficiently and people
Utilize labor efficiently Incorporate safety and security
Eliminate bottlenecks measures
Facilitate communication and Promote product and service
interaction quality
Reduce manufacturing cycle Encourage proper maintenance
time activities
Reduce customer service time Provide a visual control of
activities
Eliminate wasted or redundant Provide flexibility to adapt to
movement changing conditions
Increase capacity

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
Shoes Housewares
lingerie

Women’s Cosmetics Children’s
dresses and jewelry department

Women’s Entry and Men’s
sportswear display area department

A Product Layout
In

Out

Comparison of Product
and Process Layouts
Product Process
Description Sequential Functional
arrangement of grouping of
activities activities
Continuous, mass Intermittent, job
Type of process production, mainly shop, batch
assembly production, mainly
fabrication
Standardized, made Varied, made to
Product order
to stock
Demand Stable Fluctuating
Volume High Low
Equipment Special purpose General purpose

Comparison of Product
and Process Layouts
Product Process
Workers Limited skills Varied skills
Inventory Low in-process, high
in- High in-process, low
in-
finished goods finished goods
Storage space Small Large
Material handling Fixed path (conveyor) Variable path (forklift)
Aisles Narrow Wide
Scheduling Part of balancing Dynamic
Layout decision Line balancing Machine location
Goal
Equalize work at each Minimize material
station handling cost
Advantage
Efficiency Flexibility

Fixed-
Fixed-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 STEPS
quantity in which create load summary chart
material is normally calculate composite (two
moved way) movements
Nonadjacent load develop trial layouts
distance farther minimizing number of
than the next block nonadjacent loads

Block Diagramming: Example
Load Summary Chart
FROM/TO DEPARTMENT
1 2 3
Department 1 2 3 4 5

1 — 100 50
2 — 200 50
4 5 3 60 — 40 50
4 100 — 60
5 50 —

Block Diagramming:
Example (cont.)
Nonadjacent Loads:
2 3 200 loads
110+40=150
0
2 4 150 loads
1 3 110 loads
110
1 2 100 loads
4 5 60 loads
100 150
200
3 5 50 loads 1 2 3
4
2 5 50 loads
150 200 50
50 50 40 60
3 4 40 loads 110
1 4 0 loads 60 50
4 3
5 5
1 5 0 loads
40
Grid 1
2

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

1 4
1 2 4 2

3 5 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
Production X Undesirable
O
Offices A
U I
Stockroom O E
A X A
Shipping and U U
receiving
U O
Locker room O
O
Toolroom

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

Offices Locker Shipping
room and
receiving

Key: A
E
Stockroom Toolroom Production I
O
U
X

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

Stockroom

Offices Shipping
and
receiving

Locker Key: A
Toolroom Production
room 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

production time available
Cd = desired units of output

(8 hours x 60 minutes / hour)
Cd = (120 units)

480
Cd = 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
Minimum number of
Efficiency workstations

i i

∑ ti
∑ ti Balance
delay
i=1 i=1
E= nCa
N= Cd
total idle
time of line
calculated
where
as (1 -
ti = completion time for element i efficiency)
j = number of work elements
n = actual number of workstations
Ca = actual cycle time
Cd = desired cycle time

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 PRECEDENCE TIME (MIN)
A Press out sheet of fruit — 0.1
B Cut into strips A 0.2
C Outline fun shapes A 0.4
D Roll up and package B, C 0.3

0.2
B

0.1 A D 0.3

C
0.4

Line Balancing: Example (cont.)
WORK ELEMENT PRECEDENCE TIME (MIN)
A Press out sheet of fruit — 0.1
B Cut into strips A 0.2
C Outline fun shapes A 0.4
D Roll up and package B, C 0.3

40 hours x 60 minutes / hour 2400
Cd = = = 0.4 minute
6,000 units 6000

0.1 + 0.2 + 0.3 + 0.4 1.0
N= = = 2.5 3 workstations
0.4 0.4

REMAINING REMAINING
WORKSTATION ELEMENT TIME ELEMENTS
1 A 0.3 B, C
B 0.1 C, D
2 C 0.0 D
3 D 0.1 none
0.2 Cd = 0.4
B
N = 2.5

0.1 A D 0.3

C
0.4


Work Work Work
station 1 station 2 station 3
Cd = 0.4
N = 2.5
A, B C D
0.3 0.4 0.3
minute minute minute

0.1 + 0.2 + 0.3 + 0.4 1.0
E= = = 0.833 = 83.3%
3(0.4) 1.2

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 A family of related
similar parts grocery items

Original Process Layout
Assembly

4 6 7 9

5 8

2 10 12

1 3 11

A B C Raw materials

Part Routing Matrix
Machines
Parts 1 2 3 4 5 6 7 8 9 10 11 12
A x x x x x
B x x x x
C x x x
D x x x x x
E x x x
F x x x
G x x x x
H x x x

Figure 5.8

Revised Cellular Layout
Assembly

8 10 9 12

11
4 Cell 1 Cell 2 6 Cell 3
7

2 1 3 5

A B C
Raw materials

Reordered Routing Matrix
Machines
Parts 1 2 4 8 10 3 6 9 5 7 11 12
A x x x x x
D x x x x x
F x x x
C x x x
G x x x x
B x x x x
H x x x
E x x x

Operationsmanagement 919slidespresentation-090928145353-phpapp01

Advantages and Disadvantages
of Cellular Layouts
Advantages Disadvantages
Reduced material Inadequate part families
handling and transit time Poorly balanced cells
Reduced setup time Expanded training and
Reduced work-in-
work-in- scheduling of workers
process inventory Increased capital
Better use of human investment
resources
Easier to control
Easier to automate

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
U-
Precedence diagram:

A B C

Cycle time = 12 min
D E

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

A,B C,D E
A,B
9 min 12 min 3 min
24 24
Efficiency = = = .6666 = 66.7 % C,D
3(12) 36

E

24 24
Efficiency = = = 100 % 12 min 12 min
2(12) 24


Facility Location Models



Lecture Outline

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

Supplement 7-374
7-

Types of Facilities

Heavy-
Heavy-manufacturing facilities
large, require a lot of space, and are
expensive
Light-
Light-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 Location criteria for
being “in the right place at the manufacturing facility
right time” nature of labor force
Must consider other factors, labor costs
especially financial proximity to suppliers and
considerations markets
Location decisions made more distribution and
often for service operations transportation costs
than manufacturing facilities energy availability and cost
Location criteria for service community infrastructure
access to customers quality of life in community
government regulations and
taxes

Supplement 7-379
7-

Global Location Factors

Government stability Raw material availability
Government regulations Number and proximity of
suppliers
Political and economic
systems Transportation and
distribution system
Economic stability and growth Labor cost and education
Exchange rates Available technology
Culture Commercial travel
Climate Technical expertise
Export/import regulations, Cross-
Cross-border trade
duties and tariffs regulations
Group trade agreements

Supplement 7-380
7-

Regional and Community
Location Factors in U.S.
Labor (availability, Modes and quality of
education, cost, and transportation
unions) Transportation costs
Proximity of customers Community government
Number of customers Local business
Construction/leasing regulations
costs Government services
Land cost (e.g., Chamber of
Commerce)

Supplement 7-381
7-

Regional and Community
Location Factors in U.S. (cont.)
Business climate Infrastructure (e.g.,
Community services roads, water, sewers)
Incentive packages Quality of life
Government regulations Taxes
Environmental Availability of sites
regulations Financial services
Raw material availability Community inducements
Commercial travel Proximity of suppliers
Climate 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-of-
Center-of-gravity

Load-
Load-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 WEIGHT Site 1 Site 2 Site 3
Labor pool and climate .30 80 65 90
Proximity to suppliers .20 100 91 75
Wage rates .15 60 95 72
Community environment .15 75 80 80
Proximity to customers .10 65 90 95
Shipping modes .05 85 92 65
Air service .05 50 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 19.50 27.00
Site 3 has the
20.00 18.20 15.00 highest factor rating
9.00 14.25 10.80
11.25 12.00 12.00
6.50 9.00 9.50
4.25 4.60 3.25
2.50 3.25 4.50
77.50 80.80 82.05

Supplement 7-389
7-

Location Factor Rating

Supplement 7-390
7-

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

Supplement 7-391
7-

Grid-
Grid-Map Coordinates
y n n
∑ xiWi ∑ yiWi
2 (x2, y2), W2
(x i=1 i=1
y2 x= n y= n
∑ Wi ∑ Wi
1 (x1, y1), W1
(x i=1 i=1
y1
where,
x, y = coordinates of new facility
3 (x3, y3), W3
(x at center of gravity
y3
xi, yi = coordinates of existing
facility i
Wi = annual weight shipped from
facility i

x1 x2 x3 x

Supplement 7-392
7-

Center-of-
Center-of-Gravity Technique:
Example
y A B C D
700 x 200 100 250 500
C
600
y 200 500 600 300
(135)
B Wt 75 105 135 60
500 (105)
Miles

400
D
300
A (60)
200 (75)
100

0 100 200 300 400 500 600 700 x
Miles

Supplement 7-393
7-

Center-of-
Example (cont.)
n
∑ xiWi
i=1 (200)(75) + (100)(105) + (250)(135) + (500)(60)
x= = = 238
n 75 + 105 + 135 + 60
∑ Wi
i=1

n
∑ yiWi
i=1 (200)(75) + (500)(105) + (600)(135) + (300)(60)
y= = = 444
n 75 + 105 + 135 + 60
∑ Wi
i=1

Supplement 7-394
7-

Center-of-
Example (cont.)
y A B C D
700 x 200 100 250 500
C
600
y 200 500 600 300
(135)
B Wt 75 105 135 60
500 (105)
Center of gravity (238, 444)
Miles

400
D
300
A (60)
200 (75)
100

0 100 200 300 400 500 600 700 x
Miles

Supplement 7-395
7-

Center-of-
Center-of-Gravity Technique

Supplement 7-396
7-

Load-
Load-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-

Load-
Load-Distance Calculations
n
LD = ∑ ld i i

i=1
where,
LD = load-
load-distance value
li = load expressed as a weight, number of trips or units
being shipped from proposed site and location i
di = distance between proposed site and location i
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-

Load-
Load-Distance: Example
Potential Sites Suppliers
Site X Y A B C D
1 360 180 X 200 100 250 500
2 420 450 Y 200 500 600 300
3 250 400 Wt 75 105 135 60

Compute distance from each site to each supplier

Site 1 dA = (xA - x1)2 + (yA - y1)2 = (200-360)2 + (200-180)2 = 161.2
(200- (200-

dB = (xB - x1)2 + (yB - y1)2 = (100-360)2 + (500-180)2 = 412.3
(100- (500-

dC = 434.2 dD = 184.4

Supplement 7-399
7-

Load-
Load-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-

Load-
Load-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 Employees have power to
role in quality management make decisions that will
Malcolm Baldrige National improve quality and customer
Quality Award winners have a service
pervasive human resource Strategic goals for quality and
focus customer satisfaction require
Employee training and teamwork and group
education are recognized as participation
necessary long-term
investments

8-404

Changing Nature of Human
Resources Management
Scientific management Assembly-line
Breaking down jobs into Production meshed with
elemental activities and principles of scientific
simplifying job design management
Jobs Advantages of task
Comprise a set of tasks, specialization
elements, and job motions High output, low costs,
(basic physical and minimal training
movements)
Disadvantages of task
In a piece-rate wage specialization
system, pay is based on Boredom, lack of
output motivation, and physical
and mental fatigue

8-405

Employee Motivation
Motivation Improving Motivation
willingness to work hard because (cont.)
that effort satisfies an employee design of jobs to fit employee
need work responsibility
Improving Motivation empowerment
positive reinforcement and restructuring of jobs when
feedback necessary
effective organization and rewards based on company as
discipline well as individual performance
fair treatment of people achievement of company goals
satisfaction of employee needs
setting of work-related goals

8-406

Evolution of Theories of
Employee Motivation
Abraham Maslow’s Douglas McGregor’s Frederick Herzberg’s
Pyramid of Human Theory X and Theory Y Hygiene/Motivation
Needs Theories
•Theory X Employee •Hygiene Factors
• Dislikes work • Company policies
• Must be coerced • Supervision
• Shirks responsibility • Working conditions
Self-
Self- • Little ambition • Interpersonal relations
actualization • Security top motivator • Salary, status, security
•Theory Y Employee •Motivation Factors
Esteem • Achievement
• Work is natural
Social • Self-directed
Self- • Recognition
• Controlled • Job interest
Safety/Security • Responsibility
• Accepts responsibility
Physiological (financial) • Makes good decisions • Growth
• Advancement

8-407

Contemporary Trends in
Human Resources Management
Job training Empowerment
extensive and varied
giving employees
two of Deming’s 14 points
refer to employee authority to make
education and training decisions
Cross Training Teams
an employee learns more group of employees work
than one job
on problems in their
Job rotation immediate work area
horizontal movement
between two or more jobs
according to a plan

8-408

Contemporary Trends in Human
Resources Management (cont.)
Job enrichment Alternative workplace
vertical enlargement nontraditional work location
allows employees control
over their work Telecommuting
horizontal enlargement employees work
an employee is assigned a
electronically from a
complete unit of work with location they choose
defined start and end Temporary and part-time
Flexible time employees
part of a daily work mostly in fast-food and
schedule in which restaurant chains, retail
employees can choose companies, package delivery
time of arrival and services, and financial firms
departure

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 Managing diversity
an outgrowth of laws and process of creating a work
regulations environment in which all
government initiated and employees can contribute
mandated to their full potential in
contains goals and order to achieve a
timetables designed to company’s goals
increase level of voluntary in nature, not
participation by women mandated
and minorities to attain seeks to improve internal
parity levels in a communications and
company’s workforce interpersonal
not directly concerned relationships, resolve
with increasing company conflict, and increase
success or increasing product quality,
profits 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 Goals and achievement
repetitiveness feedback
An appropriate degree of A perceived contribution
attention and mental to a useful product or
absorption service
Some employee Opportunities for
responsibility for personal relationships
decisions and discretion and friendships
Employee control over Some influence over the
their own job 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

Elements of Job Design

8-418

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

Process Flowchart

8-421

Job Photo-Id Cards
Photo- Date 10/14
Time Time
(min) Operator (min) Photo Machine

–1
Key in customer data 2.6 Idle
on card
–2
Worker-
Worker- Feed data card in 0.4 Accept card
Machine –3 Position customer for photo 1.0 Idle
Chart
Take picture 0.6 Begin photo process
–4

–5
Idle 3.4 Photo/card processed

–6

–7
Inspect card & trim edges 1.2 Idle

–8

8-422
–9

Worker-
Worker-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
foot-
Construct and arrange equipment to fit worker use

8-426

Learning Curves

Illustrates

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

8-427

Learning Curves (cont.)

Time required for the nth unit =

tn = t1n b
where:
tn = time required for nth unit produced
t1 = time required for first unit produced
n= cumulative number of units produced
b= 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

Learning Curve for Mass
Production Job
Processing time per unit

End of improvement

Standard
time

Units produced

8-430

Learning Curves (cont.)

Advantages Limitations
planning labor product modifications
planning budget negate learning curve
determining effect
scheduling improvement can derive
requirements 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
labor-
Service 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
piece-
study

Supplement 8-434
8-

Stopwatch Time
Study Basic Steps

1. Establish standard job method
2. Break down job into elements
3. Study job
4. Rate worker’s performance (RF)
5. 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 Sandwich Assembly Date 5/17
Operator Approval Observer
Smith Jones Russell
Cycles Summary
1 2 3 4 5 6 7 8 9 10 Σt t RF Nt
Grasp and lay t .04 .05 .05 .04 .06 .05 .06 .06 .07 .05 .53 .053 1.05 .056
1 out bread slices
R .04 .38 .72 1.05 1.40 1.76 2.13 2.50 2.89 3.29
Spread mayonnaise t .07 .06 .07 .08 .07 .07 .08 .10 .09 .08 .77 .077 1.00 .077
2
on both slices R .11 .44 .79 1.13 1.47 1.83 2.21 2.60 2.98 3.37

Place ham, cheese, t .12 .11 .14 .12 .13 .13 .13 .12 .14 .14 1.28 1.28 1.10 .141
3
and lettuce on bread R .23 .55 .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
4
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.)
Σt 0.53
Average element time = t = = = 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
= 269.7 or 270 sandwiches
0.445 min/sandwich

Example 17.3
Supplement 8-439
8-

Number of Cycles
To determine sample size:
2
zs
n=
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

2 2
zs (1.96)(0.03)
n= = = 10.61 or 11
eT (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 Advantages
files worker cooperation
predetermined job unnecessary
element times
workplace uninterrupted
Predetermined motion performance ratings
times unnecessary
predetermined times for consistent
basic micro-motions
micro-
Time measurement units Disadvantages
TMUs = 0.0006 minute ignores job context
100,000 TMU = 1 hour may not reflect skills and
abilities of local workers

Supplement 8-443
8-

MTM Table for MOVE
TIME (TMU) WEIGHT ALLOWANCE
DISTANCE Hand in Weight Static
MOVED motion (lb) Dynamic constant
(INCHES) A B C B up to: factor TMU
3/4 or less 2.0 2.0 2.0
1 2.5 2.9 3.4 2.3 2.5 1.00 0
2 3.6 4.6 5.2 2.9
3 4.9 5.7 6.7 3.6 7.5 1.06 2.2
4 6.1 6.9 8.0 4.3
…
20 19.2 18.2 22.1 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
C. Move object to exact location Source: MTM Association for Standards and Research.

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. Define job activities
2. Determine number of observations in work sample
2
z
n= 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
re-
of 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
+/-

2 2
z 1.96
n= p(1 - p) = (0.3)(0.7) = 2016.84 or 2017
e 0.02

After 280 observations, p = 38%

2 2
z 1.96
n= p(1 - p) = (0.38)(0.62) = 2263
e 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 Techniques
Define activities Gantt chart
Sequence CPM/PERT
activities Microsoft Project
Estimate time
Develop schedule

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
Month
0 | 2 | 4 | 6 | 8 | 10
Activity

Design house
and obtain
financing

Lay foundation

Order and
receive
materials

Build house

Select paint

Select carpet
1 3 5 7 9
Month
Finish work
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)
Remington-
Deterministic task times
Activity-on-
Activity-on-node network construction
Project Evaluation and Review Technique
(PERT)
US Navy, Booz, Allen & Hamilton
Multiple task time estimates; probabilistic
Activity-on-
Activity-on-arrow network construction

9-465

Project Network
Activity-on-node (AON)
nodes represent activities,
and arrows show Node
precedence relationships
Activity-on-arrow (AOA)
arrows represent activities 1 2 3
and nodes are events for
points in time
Event 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 Dummy
foundation
2 0 Build Finish
3 1 house work
1 2 4 3
6 1
7
Design house Order and
and obtain receive 1 1
Select Select
financing materials paint carpet
5

9-467

Concurrent Activities

3
Lay foundation Lay
Dummy
foundation
2 0
2 3
1
Order material 2 4
Order material

(a) Incorrect precedence (b) Correct precedence
relationship relationship

9-468

AON Network for House
Building Project
Lay foundations Build house

2 4
Finish work
2 3
7
Start 1 1
3
Design house 6
and obtain 3
financing 1 5 1
1 Select carpet
Order and receive
materials Select paint

9-469

Critical Path
2 4
2 3
7
Start 1 1
3

3 6
1 5 1
1

A: 1-2-4-7
3 + 2 + 3 + 1 = 9 months Critical path
B: 1-2-5-6-7 Longest path
3 + 2 + 1 + 1 + 1 = 8 months through a network
C: 1-3-4-7 Minimum project
3 + 1 + 3 + 1 = 8 months
D: 1-3-5-6-7
completion time
3 + 1 + 1 + 1 + 1 = 7 months
9-470

Activity Start Times

Start at 5 months

2 4
Finish at 9 months
2 3
7 Finish
Start 1 1
3

3 6
1 5 1
1 Start at 6 months
Start at 3 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 3 5
Start 4 5 8
2
3

1 0 3 7 8 9
1 1
Design house
and obtain 6 6 7 Finish work
financing 3 3 4
1
1 5 5 6
Select carpet
Order and receive 1
materials 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
Start 4 5 8
2 3 5
3 5 8

1 0 3 7 8 9
1 0 3 1 8 9
Design house
and obtain 6 6 7 Finish work
financing 3 3 4
1 7 8
1 4 5 5 5 6
Select carpet
Order and receive 1 6 7
materials 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
a + 4m + b
4m
Mean (expected time): t=
6
2
b-a
Variance: σ =
2
6
where
a = optimistic estimate
m = most likely time estimate
b = pessimistic time estimate

9-478

P(time)
Examples of Beta Distributions

P(time)
a m t b a t m b
Time Time
P(time)

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 Final
training debugging
System 10
development 8
Manual 3,7,11 1,4,7
Start 2 testing Finish
3,6,9
5 11
Position 2,3,4 9 1,10,13
recruiting 2,4,6
Job Training System
3 6 System changeover
1,3,5 3,4,5 testing

Orientation
7
2,2,2

9-480

Activity Time Estimates
TIME ESTIMATES (WKS) MEAN TIME VARIANCE

ACTIVITY a m b t б2
1 6 8 10 8 0.44
2 3 6 9 6 1.00
3 1 3 5 3 0.44
4 2 4 12 5 2.78
5 2 3 4 3 0.11
6 3 4 5 4 0.11
7 2 2 2 2 0.00
8 3 7 11 7 1.78
9 2 4 6 4 0.44
10 1 4 7 4 1.00
11 1 10 13 9 4.00

9-481

Activity Early, Late Times,
and Slack
ACTIVITY t б2 ES EF LS LF S
1 8 0.44 0 8 1 9 1
2 6 1.00 0 6 0 6 0
3 3 0.44 0 3 2 5 2
4 5 2.78 8 13 16 21 8
5 3 0.11 6 9 6 9 0
6 4 0.11 3 7 5 9 2
7 2 0.00 3 5 14 16 11
8 7 1.78 9 16 9 16 0
9 4 0.44 9 13 12 16 3
10 4 1.00 13 17 21 25 8
11 9 4.00 16 25 16 25 0

9-482

Earliest, Latest, and Slack
Critical Path
1 0 8 4 8 13
8 1 9 5 16 21
10 13 17

16
1 0 3
8 9
Start 2 0 6 Finish
7 9 16
6 0 6 9
5 6 11 16 25
3 6 9 9 9 13
9 16 25
4 12 16
3 0 3 6 3 7
3 2 5 4 5 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
x-µ
Z=
σ
where
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) x-µ
σ 2 = 6.89 weeks Z =
σ
σ = 6.89 = 30 - 25
2.62
σ = 2.62 weeks
= 1.91
µ = 25 x = 30 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?
x-µ
P(x ≤ 22 weeks)
σ 2 = 6.89 weeks Z =
σ
σ = 6.89 = 22 - 25
2.62
σ = 2.62 weeks
= -1.14

x = 22 µ = 25 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

2 4
12
8
7
1 4
12

3 6
4 5 4
4

9-501

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

$6,000 –
Crash cost

$5,000 – Crashed activity

Slope = crash cost per week
$4,000 –

Normal activity
$3,000 –
Normal cost

$2,000 –

Crash time Normal time
$1,000 –
| | | | | | |
0 2 4 6 8 10 12 14 Weeks
–

9-502

Project Crashing: Example

TOTAL
NORMAL CRASH ALLOWABLE CRASH
TIME TIME NORMAL CRASH CRASH TIME COST PER
ACTIVITY (WEEKS) (WEEKS) COST COST (WEEKS) WEEK

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
9-503

$500 $7000
Project Duration:
2 4
$700 36 weeks
8 12
7
1 4 FROM …
12

$400 3 6
4 5 4
4 $200
$3000
$200

$500 $7000

2 4
8 12 $700
7
TO… 1 4
7

Project Duration: $400 3 6
31 weeks 4 5 4
Additional Cost: 4 $200
$3000
$2000 $200

9-504

Time-
Time-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

Time-
Time-Cost Tradeoff
Minimum cost = optimal project time
Total project cost

Indirect cost
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: Factors that contribute to
respond to uncertainty in uncertainty
customer demand inaccurate demand
without creating costly forecasting
excess inventory long variable lead times
Negative effects of late deliveries
uncertainty incomplete shipments
lateness product changes
incomplete orders batch ordering
Inventory price fluctuations and
discounts
insurance against supply
chain uncertainty 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

10-523
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
Cost of goods sold
Inventory turns =
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
Average aggregate value of inventory
Days of supply =
(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-

SCOR
(cont.)

10-533
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
On-
On-demand (direct-response) delivery
(direct-
requires 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
e- e-
trading 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)
(e-
Websites where companies and suppliers
conduct business-to-business activities
business-to-
Reverse auction
process used by e-marketplaces for buyers
e-
to 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
on-
Logistics
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
Flow-
Flow-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-to-
operations of a DC
Controls item putaway, picking, packing, and
shipping
Features
transportation management
order management
yard management
labor management
warehouse optimization

11-544
11-

Vendor-
Vendor-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
Internet-
Internet-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 shipping-
truck, 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-

Web-
Web-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
low-
World’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
third-
Wholly-
Wholly-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
low-
workers 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
stepping-
stepping-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-in-
VMI (vendor-managed inventory)
(vendor-
stockless 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- mid-
Short- to mid-range forecast
typically encompasses the immediate future
daily up to two years
Long-
Long-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
long-
demand
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-and-
occurring periodically

12-582
12-

Forms of Forecast Movement

Demand
Demand

Random
movement

Time Time
(a) Trend (b) Cycle

Demand
Demand

Time Time
(c) Seasonal pattern (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 2. Collect historical 3. Plot data and identify
purpose of forecast data patterns

6. Check forecast 5. Develop/compute 4. Select a forecast
accuracy with one or forecast for period of model that seems
more measures historical data appropriate for data

7.
Is accuracy of No 8b. Select new
forecast
forecast model or
acceptable?
adjust parameters of
existing model

Yes
9. Adjust forecast based 10. Monitor results
8a. Forecast over
on additional qualitative and measure forecast
planning horizon
information and insight 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
ORDERS
MONTH PER MONTH FORECAST

Jan 120 -
Feb 90
120
Mar 100
90
Apr 75
100
May 110
75
June 50
110
July 75
50
Aug 130
75
Sept 110
130
Oct 90
110
90
Nov -
12-589
12-

Simple Moving Average

n
Σ D
i
i=1
MAn =
n
where

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

12-590
12-

3-month Simple Moving Average

3

MONTH
ORDERS
PER MONTH
MOVING
AVERAGE
Σ Di
i=1
MA3 =
Jan 120 – 3
Feb 90 –
Mar 100 – 90 + 110 + 130
Apr 75 103.3 = 3
May 110 88.3
June 50 95.0
July 75 78.3 = 110 orders
Aug 130 78.3 for Nov
Sept 110 85.0
Oct 90 105.0
Nov - 110.0

12-591
12-

5-month Simple Moving Average

ORDERS MOVING
MONTH PER MONTH AVERAGE 5
Σ Di
Jan 120 – i=1
Feb 90 – MA5 =
Mar 100 – 5
Apr 75 –
May 110 – 90 + 110 + 130+75+50
June 50 99.0 = 5
July 75 85.0
Aug 130 82.0
Sept 110 88.0 = 91 orders
Oct 90 95.0 for Nov
Nov - 91.0

12-592
12-

Smoothing Effects
150 –

5-month
125 –

100 –
Orders

75 –

50 – 3-month

Actual
25 –

0– | | | | | | | | | | |
Jan Feb Mar Apr May June July Aug Sept Oct Nov
Month

12-593
12-

Weighted Moving Average

Σ Wi Di
n
Adjusts moving average WMAn =
method to more i=1
closely reflect data
fluctuations
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 DATA
August 17% 130
September 33% 110
October 50% 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 F2 = αD1 + (1 - α)F1
1 Jan 37 = (0.30)(37) + (0.70)(37)
2 Feb 40 = 37
3 Mar 41
4 Apr 37 F3 = αD2 + (1 - α)F2
5 May 45 = (0.30)(40) + (0.70)(37)
6 Jun 50 = 37.9
7 Jul 43
8 Aug 47
F13 = αD12 + (1 - α)F12
9 Sep 56
= (0.30)(54) + (0.70)(50.84)
10 Oct 52
11 Nov 55 = 51.79
12 Dec 54

12-599
12-

FORECAST, Ft + 1
PERIOD MONTH DEMAND (α = 0.3) (α = 0.5)
1 Jan 37 – –
2 Feb 40 37.00 37.00
3 Mar 41 37.90 38.50
4 Apr 37 38.83 39.75
5 May 45 38.28 38.37
6 Jun 50 40.29 41.68
7 Jul 43 43.20 45.84
8 Aug 47 43.14 44.42
9 Sep 56 44.30 45.71
10 Oct 52 47.81 50.85
11 Nov 55 49.06 51.42
12 Dec 54 50.84 53.21
13 Jan – 51.79 53.61

12-600
12-

70 –

60 – Actual α = 0.50

50 –

40 –
Orders

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 T3 = β(F3 - F2) + (1 - β) T2
= (0.30)(38.5 - 37.0) + (0.70)(0)
1 Jan 37
2 Feb 40 = 0.45
3 Mar 41
4 Apr 37 AF3 = F3 + T3 = 38.5 + 0.45
5 May 45 = 38.95
6 Jun 50
7 Jul 43 T13 = β(F13 - F12) + (1 - β) T12
8 Aug 47 = (0.30)(53.61 - 53.21) + (0.70)(1.77)
9 Sep 56 = 1.36
10 Oct 52
11 Nov 55
12 Dec 54 AF13 = F13 + T13 = 53.61 + 1.36 = 54.97

12-603
12-

Adjusted Exponential Smoothing:
Example
FORECAST TREND ADJUSTED
PERIOD MONTH DEMAND Ft +1 Tt +1 FORECAST AFt +1

1 Jan 37 37.00 – –
2 Feb 40 37.00 0.00 37.00
3 Mar 41 38.50 0.45 38.95
4 Apr 37 39.75 0.69 40.44
5 May 45 38.37 0.07 38.44
6 Jun 50 38.37 0.07 38.44
7 Jul 43 45.84 1.97 47.82
8 Aug 47 44.42 0.95 45.37
9 Sep 56 45.71 1.05 46.76
10 Oct 52 50.85 2.28 58.13
11 Nov 55 51.42 1.76 53.19
12 Dec 54 53.21 1.77 54.98
13 Jan – 53.61 1.36 54.96
12-604
12-

Adjusted Exponential Smoothing
Forecasts
70 –
Adjusted forecast (β = 0.30)
(β
60 –
Actual
50 –
Demand

40 –

30 – Forecast (α = 0.50)
(α

20 –

10 –

0– | | | | | | | | | | | | |
1 2 3 4 5 6 7 8 9 10 11 12 13
Period

12-605
12-

Linear Trend Line

Σ xy - nxy
y = a + bx b =
Σx 2 - nx2

where a = y-bx
a = intercept
b = slope of the line where
n = number of periods
x = time period
y = forecast for Σx
demand for period x 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) xy x2
1 73 37 1
2 40 80 4
3 41 123 9
4 37 148 16
5 45 225 25
6 50 300 36
7 43 301 49
8 47 376 64
9 56 504 81
10 52 520 100
11 55 605 121
12 54 648 144
78 557 3867 650

12-607
12-

Least Squares Example
(cont.)
78
12 x = = 6.5
557
12 y = = 46.42
∑xy - nxy 3867 - (12)(6.5)(46.42)
b = 2 = =1.72
∑x - nx2 650 - 12(6.5)2

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

50 –
Demand

40 –
Linear trend line
30 –

20 –

10 – | | | | | | | | | | | | |
1 2 3 4 5 6 7 8 9 10 11 12 13
0– Period

12-609
12-

Seasonal Adjustments

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

Di
Seasonal factor = Si =
∑D

12-610
12-

Seasonal Adjustment (cont.)
DEMAND (1000’S PER QUARTER)
YEAR 1 2 3 4 Total
2002 12.6 8.6 6.3 17.5 45.0
2003 14.1 10.3 7.5 18.2 50.1
2004 15.3 10.6 8.1 19.6 53.6
Total 42.0 29.5 21.9 55.3 148.7

D1 42.0 D3 21.9
S1 = = = 0.28 S3 = = = 0.15
∑D 148.7 ∑D 148.7
D2 29.5 D4 55.3
S2 = = = 0.20 S4 = = = 0.37
∑D 148.7 ∑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 DEMAND, Dt Ft (α =0.3) (Dt - Ft) |Dt - Ft|
1 37 37.00 – –
2 40 37.00 3.00 3.00
3 41
Σ| D37.90 t | 3.10
t - F
3.10
4
5
MAD = 38.28 -6.72
37
45
38.83
n
1.83 1.83
6.72
6 50 40.29 9.69 9.69
7 43
53.39
= 43.20 -0.20 0.20
8 47 11
43.14 3.86 3.86
9 56
10 52
= 4.85 44.30 11.70
47.81 4.19
11.70
4.19
11 55 49.06 5.94 5.94
12 54 50.84 3.15 3.15
557 49.31 53.39

12-615
12-

Other Accuracy Measures

Mean absolute percent deviation (MAPD)
∑|Dt - Ft|
MAPD =
∑Dt
Cumulative error
E = ∑et
Average error
∑et
E=
n
12-616
12-

Comparison of Forecasts

FORECAST MAD MAPD E (E)
Exponential smoothing (α = 0.30) 4.85
(α 9.6% 49.31 4.48
Exponential smoothing (α = 0.50) 4.04
(α 8.5% 33.21 3.02
Adjusted exponential smoothing 3.81 7.5% 21.14 1.92
(α = 0.50, β = 0.30)
Linear trend line 2.29 4.9% – –

12-617
12-

Forecast Control

Tracking signal
monitors the forecast to see if it is biased
high or low
∑(Dt - Ft) E
Tracking signal = =
MAD MAD
1 MAD ≈ 0.8 б
Control limits of 2 to 5 MADs are used most
frequently

12-618
12-

Tracking Signal Values
DEMAND FORECAST, ERROR ∑E = TRACKING
PERIOD Dt Ft Dt - Ft ∑(Dt - Ft) MAD SIGNAL

1 37 37.00 – – – –
2 40 37.00 3.00 3.00 3.00 1.00
3 41 37.90 3.10 6.10 3.05 2.00
4 37 38.83 -1.83 4.27 2.64 1.62
5 45 38.28 6.72 10.99
Tracking signal for period 3 3.66 3.00
6 50 40.29 9.69 20.68 4.87 4.25
7 43 43.20 -0.20 20.48 4.09 5.01
6.10
8 47 43.14 =
TS3 3.86 = 2.00
24.34 4.06 6.00
9 56 44.30 3.05
11.70 36.04 5.01 7.19
10 52 47.81 4.19 40.23 4.92 8.18
11 55 49.06 5.94 46.17 5.02 9.20
12 54 50.84 3.15 49.32 4.85 10.17

12-619
12-

Tracking Signal Plot
3σ –
Tracking signal (MAD)

2σ –
α
Exponential smoothing (α = 0.30)
1σ –

0σ –

-1σ –

-2σ – Linear trend line

-3σ –
| | | | | | | | | | | | |
0 1 2 3 4 5 6 7 8 9 10 11 12
Period

12-620
12-

Statistical Control Charts

∑(Dt - Ft)2
σ= 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 –
σ
UCL = +3σ
12.24 –

6.12 –
Errors

0–

-6.12 –

-12.24 –
σ
LCL = -3σ
-18.39 –

| | | | | | | | | | | | |
0 1 2 3 4 5 6 7 8 9 10 11 12
Period

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-

OM Tools

12-628
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
Σx 2 - nx2 b =

where
a = intercept
b = slope of the line
Σx
x =n = mean of the x data
Σy
y =n = mean of the y data

12-630
12-

Linear Regression Example
x y
(WINS) (ATTENDANCE) xy x2
4 36.3 145.2 16
6 40.1 240.6 36
6 41.2 247.2 36
8 53.0 424.0 64
6 44.0 264.0 36
7 45.6 319.2 49
5 39.0 195.0 25
7 47.5 332.5 49
49 346.7 2167.7 311

12-631
12-

Linear Regression Example (cont.)
49
8 x= = 6.125
346.9y = = 43.36
8

∑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)
60,000 – = 46.88, or 46,880

50,000 –

40,000 –
Attendance, y

30,000 –

Linear regression line,
20,000 – y = 18.46 + 4.06x
4.06x

10,000 –

| | | | | | | | | | |
0 1 2 3 4 5 6 7 8 9 10
Wins, x
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
n∑ xy - ∑ x∑ y
r=
[n∑ x2 - (∑ x)2] [n∑ y2 - (∑ y)2]
[n

(8)(2,167.7) - (49)(346.9)
r=
[(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
end-
higher 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-order-
quantity)
constant amount ordered when
inventory declines to
predetermined level
Periodic system (fixed-time-
(fixed-time-
period)
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 UNIT COST ANNUAL USAGE
1 $ 60 90
2 350 40
3 30 130
4 80 60
5 30 100
6 20 180
7 10 170
8 320 50
9 510 60
10 20 120

13-651
13-

ABC Classification:
Example (cont.)
TOTAL
PART
PART
VALUE
UNIT COSTQUANTITY OF% CUMMULATIVE
VALUE
% OF TOTAL % TOTAL
ANNUAL USAGE
9 $30,6001 35.9 $ 60 6.0 90 6.0
8 16,0002 18.7 350 5.0 40 11.0
2 14,000 16.4 4.0 A
1 5,400
3 6.3
30 9.0
130 15.0
24.0
4 4,8004 5.6 80 6.0 60 30.0
B 100 40.0
3 3,9005 4.6 30 10.0
6 3,6006 20
4.2 % OF TOTAL
18.0 %180TOTAL
OF 58.0
5 3,000
CLASS 7 ITEMS3.5 10VALUE 13.0 170 71.0
QUANTITY
10 2,400 2.8 12.0 83.0
7 A 8
1,700 9, 8, 2
2.0
320 71.017.0
C 50 100.0
15.0
B 9 1, 4, 3 510 16.5
$85,400
60 25.0
C 10 6, 5, 10, 720 12.5 120 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
Order quantity, Q
Demand Average
rate inventory
Inventory Level

Q
2

Reorder point, R

0 Lead Lead Time
time time
Order Order Order Order
placed receipt placed receipt

13-655
13-

EOQ Cost Model
Co - cost of placing order D - annual demand
Cc - annual per-unit carrying cost
per- Q - order quantity

CoD
Annual ordering cost =
Q
CcQ
Annual carrying cost =
2
CoD CcQ
Total cost = +
Q 2

13-656
13-

EOQ Cost Model

Deriving Qopt Proving equality of
costs at optimal point
CoD CcQ
TC = +
Q 2 CoD CcQ
=
∂TC CoD Cc Q 2
=– 2 +
∂Q Q 2
2CoD
C0D Q2 =
Cc Cc
0=– 2 +
Q 2
2CoD
2CoD Qopt =
Qopt = Cc
Cc

13-657
13-

EOQ Cost Model (cont.)
Annual
cost ($) Total Cost
Slope = 0
CcQ
Minimum Carrying Cost =
2
total cost

CoD
Ordering Cost = Q

Optimal order Order Quantity, Q
Qopt

13-658
13-

EOQ Example
Cc = $0.75 per gallon Co = $150 D = 10,000 gallons

2CoD CoD CcQ
Qopt = TCmin = +
Cc Q 2
2(150)(10,000) (150)(10,000) (0.75)(2,000)
Qopt = TCmin = +
(0.75) 2,000 2

Qopt = 2,000 gallons TCmin = $750 + $750 = $1,500

Orders per year = D/Qopt Order cycle time = 311 days/(D/Qopt)
days/(D
= 10,000/2,000 = 311/5
= 5 orders/year = 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

Maximum
Q(1-d/p)
(1-d/p) inventory
level

Average
Q inventory
(1-d/p)
(1-d/p)
2 level

0
Begin End Time
order order
Order receipt receipt
receipt period

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 Cc 1 - d
Average inventory level = 1- p
2 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 gallons
d = 10,000/311 = 32.2 gallons per day p = 150 gallons per day

2C o D 2(150)(10,000)
Qopt = = = 2,256.8 gallons
Cc 1 - d 0.75 1 - 32.2
p 150

CoD CcQ d
TC = Q + 2 1 - p = $1,329

Q 2,256.8
Production run = p = = 15.05 days per order
150

13-663
13-

Production Quantity Model:
Example (cont.)

D 10,000
Number of production runs = Q = 2,256.8 = 4.43 runs/year

d 32.2
Maximum inventory level = Q 1 - p = 2,256.8 1 -
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

CoD CcQ
TC = + + PD
Q 2

where

P = per unit price of the item
D = annual demand

13-668
13-

Quantity Discount Model (cont.)
ORDER SIZE PRICE
0 - 99 $10 TC = ($10 )
100 – 199 8 (d1)
200+ 6 (d2) TC (d1 = $8 )

TC (d2 = $6 )
Inventory cost ($)

Carrying cost

Ordering cost

Q(d1 ) = 100 Qopt Q(d2 ) = 200
13-669
13-

Quantity Discount: Example
QUANTITY PRICE
Co = $2,500
1 - 49 $1,400 Cc = $190 per TV
50 - 89 1,100 D = 200 TVs per year
90+ 900

2C o D 2(2500)(200)
Qopt = = = 72.5 TVs
Cc 190

For Q = 72.5
CoD CcQopt
TC = + + PD = $233,784
Qopt 2

For Q = 90
CoD CcQ
TC = + + PD = $194,105
Q 2

13-670
13-

Quantity-
Quantity-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
Q
Inventory level

Reorder
point, R

0
LT LT
Time

13-675
13-

Reorder Point with
a Safety Stock
Inventory level

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
σ
zσd L

dL R
Demand

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-

Fixed-
Fixed-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-

Fixed-
Fixed-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 FREQUENCY OF PROBABILITY OF
PER WEEK, x DEMAND DEMAND, P(x)
0 20 0.20
1 40 0.40
2 20 0.20
3 10 0.10
4 10 0.10
100 1.00

Supplement 13-689
13-

Roulette Wheel of Demand
0
90

x=4
x=0
80 x=3 20

x=2

x=1

60

Supplement 13-690
13-

Generating Demand
from Random Numbers

DEMAND, RANGES OF RANDOM NUMBERS,
x r
0 0-19
1 20-59
20- r = 39
2 60-79
60-
3 80-89
80-
4 90-99
90-

Supplement 13-691
13-

Random Number Table

Supplement 13-692
13-

15 Weeks of Demand
WEEK r DEMAND (x)
(x REVENUE (S)
1 39 1 4,300
2 73 2 8,600
3 72 2 8,600
4 75 2 8,600
5 37 1 4,300
6 02 0 0
7 87 3 12,900
8 98 4 17,200
9 10 0 0
10 47 1 4,300
11 93 4 17,200 Average demand
12 21 1 4,300 = 31/15
13 95 4 17,200 = 2.07 laptops/week
14 97 4 17,200
15 69 2 8,600
Σ = 31 $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 Overtime and under-time
under-
Producing at a constant rate Increasing or decreasing
and using inventory to working hours
absorb fluctuations in Subcontracting
demand
Let outside companies
Chase demand complete the work
Hiring and firing workers to Part-
Part-time workers
match demand
Hiring part time workers to
Peak demand complete the work
Maintaining resources for Backordering
high-
high-demand levels
Providing the service or
product at a later time period

14-709
14-

Level Production

Demand

Production
Units

Time

14-710
14-

Chase Demand
Demand

Production
Units

Time

14-711
14-

Strategies for Managing Demand

Shifting demand into
other time periods
Incentives
Sales promotions
Advertising campaigns
Offering products or
services with counter-
cyclical 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 SALES FORECAST (LB)
Spring 80,000
Summer 50,000
Fall 120,000
Winter 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

SALES PRODUCTION
QUARTER FORECAST PLAN INVENTORY
Spring 80,000 100,000 20,000
Summer 50,000 100,000 70,000
Fall 120,000 100,000 50,000
Winter 150,000 100,000 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
SALES PRODUCTION WORKERS WORKERS WORKERS
QUARTER FORECAST PLAN NEEDED HIRED FIRED
Spring 80,000 80,000 80 0 20
Summer 50,000 50,000 50 0 30
Fall 120,000 120,000 120 70 0
Winter 150,000 150,000 150 30 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
P1 - I1 = 80,000 (1)
Demand I1 + P2 - I2 = 50,000 (2)
constraints I2 + P3 - I3 = 120,000 (3)
I3 + P4 - I4 = 150,000 (4)
Production 1000 W1 = P1 (5)
constraints 1000 W2 = P2 (6)
1000 W3 = P3 (7)
1000 W4 = P4 (8)
100 + H1 - F1 = W1 (9)
Work force W1 + H2 - F2 = W2 (10)
constraints W2 + H3 - F3 = W3 (11)
W3 + H4 - F4 = W4 (12)

14-723
14-

Setting up the Spreadsheet

14-724
14-

The LP Solution

14-725
14-

Transportation Method

EXPECTED REGULAR OVERTIME SUBCONTRACT
QUARTER DEMAND CAPACITY CAPACITY CAPACITY
1 900 1000 100 500
2 1500 1200 150 500
3 1600 1300 200 500
4 3000 1300 200 500

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

Unused
PERIOD OF PRODUCTION 1 2 3 4 Capacity Capacity

Beginning 0 3 6 9
Inventory 300 — — — 300
1 Regular 600 20 300 23 100 26 — 29 1000

Overtime 25 28 31 100 34 100

Subcontract 28 31 34 37 500

2 Regular 1200 20 — 23 — 26 1200

Overtime 25 28 150 31 150

28 31 34
Subcontract 250 250 500

3 20 23
Regular 1300 — 1300
25 28
Overtime 200 — 200
28 31
Subcontract 500 500

4 20
Regular 1300 1300
25
Overtime 200 200
28
Subcontract 500 500

Demand 900 1500 1600 3000 250

14-727
14-

Burruss’ Production Plan

REGULAR SUB-
SUB- ENDING
PERIOD DEMAND PRODUCTION OVERTIME CONTRACT INVENTORY

1 900 1000 100 0 500
2 1500 1200 150 250 600
3 1600 1300 200 500 1000
4 3000 1300 200 500 0
Total 7000 4800 650 1250 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 Capacity Resource
Items Planning Planning Level

Product lines Sales and Resource
Operations requirements Plants
or families
Plan plan

Master Rough-cut Critical
Individual
production capacity work
products
schedule plan centers

Material Capacity All
Components requirements requirements work
plan plan centers

Shop Input/
Manufacturing Individual
floor output
operations machines
schedule control

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

14-734
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 Is an alternative Yes
product available Available-
available at to-promise
this location? at an alternate
location?

No No
Allocate
inventory
Capable-to-
Yes Is an alternative promise date
Available-
to-promise product available
at this location?

No Yes
Allocate Is the customer Revise master
inventory willing to wait for schedule
the product?
Yes Is this product
available at a
different
location? No Trigger production

Lose sale
No

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

14-739
14-

Yield Management (cont.)

14-740
14-

Yield Management: Example
NO-SHOWS
NO- PROBABILITY P(N < X)
0 .15 .00
1 .25 .15
2 .30 .40 .517
3 .30 .70

Optimal probability of no-shows
no-

P(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
Labor Clay Revenue
PRODUCT (hr/unit) (lb/unit) ($/unit)
Bowl 1 4 40
Mug 2 3 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 + 2x2 ≤ 40 hr (labor constraint)
4x1 + 3x2 ≤ 120 lb (clay constraint)
x1 , x2 ≥ 0
Solution is x1 = 24 bowls x2 = 8 mugs
Revenue = $1,360

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
20 – both constraints

10 – x1 + 2 x2 ≤ 40 hr

| | | | | |
0– 10 20 30 40 50 60 x1

Supplement 14-753
14-

Computing Optimal Values
x1 + 2x 2 = 40
x2
4x1 + 3x 2 = 120
40 – 4 x1 + 3 x2 = 120 lb 4x1 + 8x 2 = 160
-4x1 - 3x 2 = -120
30 – 5x 2 = 40
x2 = 8

20 – x1 + 2 x2 = 40 hr x1 + 2(8) = 40
x1 = 24
10 – 8
| | 24 | | x1
0– 10 20 30 40
Z = $40(24) + $50(8) = $1,360

Supplement 14-754
14-

Extreme Corner Points
x1 = 0 bowls
x2 x2 = 20 mugs
Z = $1,000 x1 = 224 bowls
x2 = 8 mugs
40 –
Z = $1,360 x1 = 30 bowls
30 – x2 = 0 mugs
Z = $1,200
A
20 –

10 – B
| | | C|
0– 10 20 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
A x2 = 0 mugs
20 – Z = $2,100

B
10 –
x1 + 2x2 = 40 hr
2x
| | | C |
0– 10 20 30 40 x1
Supplement 14-756
14-

Minimization Problem

CHEMICAL CONTRIBUTION
Brand Nitrogen (lb/bag) Phosphate (lb/bag)
Gro-
Gro-plus 2 4
Crop-
Crop-fast 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
Gro-
12 – x2 = 8 bags of Crop-fast
Crop-
Z = $24
10 –
A
8– Z = 6x1 + 3x2
6x 3x

6–

4– B

2– C
| | | | | | |
2 4 6 8 10 12 14 x1
0–
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 tables 100 x 4 = 400 table legs

Continuous demand
Discrete demand
400 –
400 –
300 –
No. of tables

300 –

No. of tables
200 –
200 –
100 –
100 –

1 2 3 4 5
Week M T W Th F M T W Th F

15-771
15-

Material Master
production
Requirements schedule

Planning
Product Material Item
structure requirements master
file planning file

Planned
order
releases

Work Purchase Rescheduling
orders orders notices

15-772
15-

MRP Inputs and Outputs

Inputs Outputs
Master production Planned order
schedule releases
Product structure file Work orders
Purchase orders
Item master file
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.)
PERIOD
MPS ITEM 1 2 3 4 5
Pencil Case 125 125 125 125 125
Clipboard 85 95 120 100 100
Lapboard 75 120 47 20 17
Lapdesk 0 50 0 50 0

15-775
15-

Product Structure File

15-776
15-

Product Structure
Clipboard

Top clip (1) Bottom clip (1)

Pivot (1) Spring (1)

Rivets (2)
Finished clipboard Pressboard (1)

15-777
15-

Product Structure Tree
Clipboard Level 0

Pressboard Clip Ass’y Rivets Level 1
(1) (1) (2)

Top Clip Bottom Clip Pivot Spring Level 2
(1) (1) (1) (1)

15-778
15-

Multilevel Indented BOM

LEVEL ITEM UNIT OF MEASURE QUANTITY
0---- Clipboard ea 1
-1--- Clip Assembly ea 1
--2-- Top Clip ea 1
--2-- Bottom Clip ea 1
--2-- Pivot ea 1
--2-- Spring ea 1
-1--- Rivet ea 2
-1--- Press Board ea 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
pseudo-
number
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 Exterior color Interior Interior color Body
(1 of 3) (1 of 8) (1 of 3) (1 of 8) (1 of 4)

4-Cylinder (.40) Bright red (.10) Leather (.20) 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-
Neon orange (.10) Off-white (.20)
Off- Station wagon (.30)
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 INVENTORY POLICY

Item Pressboard Lead time 1
Item no. 7341 Annual demand 5000
Item type Purch Holding cost 1
Product/sales class Comp Ordering/setup cost 50
Value class B Safety stock 0
Buyer/planner RSR Reorder point 39
Vendor/drawing 07142 EOQ 316
Phantom code N Minimum order qty 100
Unit price/cost 1.25 Maximum order qty 500
Pegging Y Multiple order qty 1
LLC 1 Policy code 3

15-784
15-

Item Master File (cont.)
PHYSICAL INVENTORY USAGE/SALES
On hand 150 YTD usage/sales 1100
Location W142 MTD usage/sales 75
On order 100 YTD receipts 1200
Allocated 75 MTD receipts 0
Cycle 3 Last receipt 8/25
Last count 9/5 Last issue 10/5
Difference -2 CODES

Cost acct. 00754
Routing 00326
Engr 07142

15-785
15-

MRP Processes
Exploding the bill Netting
of material process of subtracting on-
on-
Netting out inventory hand quantities and
scheduled receipts from
Lot sizing gross requirements to
Time-
Time-phasing produce net requirements
requirements Lot sizing
determining the quantities
in which items are usually
made or purchased

15-786
15-

MRP Matrix

15-787
15-

MRP: Example
1 2 3 4 5

Clipboard 85 95 120 100 100
Lapdesk 0 60 0 60 0

Item Master File
CLIPBOARD LAPDESK PRESSBOARD
On hand 25 20 150
On order 175 (Period 1) 0 0
(sch receipt)
LLC 0 0 1
Lot size L4L Mult 50 Min 100
Lead time 1 1 1

15-788
15-

MRP: Example (cont.)
Product Structure Record

Clipboard Level 0

Pressboard Clip Ass’y Rivets Level 1
(1) (1) (2)

Lapdesk Level 0

Pressboard Trim Beanbag Glue Level 1
(2) (3’) (1) (4 oz)

15-789
15-


ITEM: CLIPBOARD LLC: 0 PERIOD
LOT SIZE: L4L LT: 1 1 2 3 4 5

Gross Requirements 85 95 120 100 100
Scheduled Receipts 175
Projected on Hand 25
Net Requirements
Planned Order Receipts
Planned Order Releases

15-790
15-


LOT SIZE: L4L LT: 1 1 2 3 4 5

Projected on Hand 25 115
Net Requirements 0

(25 + 175) = 200 units available
(200 - 85) = 115 on hand at the end of Period 1

15-791
15-


LOT SIZE: L4L LT: 1 1 2 3 4 5

Projected on Hand 25 115 20
Net Requirements 0 0

115 units available
(115 - 85) = 20 on hand at the end of Period 2

15-792
15-


LOT SIZE: L4L LT: 1 1 2 3 4 5

Projected on Hand 25 115 20 0
Net Requirements 0 0 100
Planned Order Receipts 100
Planned Order Releases 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-


LOT SIZE: L4L LT: 1 1 2 3 4 5

Projected on Hand 25 115 20 0 0 0
Net Requirements 0 0 100 100 100
Planned Order Receipts 100 100 100
Planned Order Releases 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 LLC: 0 PERIOD
LOT SIZE: MULT 50 LT: 1 1 2 3 4 5

Scheduled Receipts
Net Requirements

15-795
15-


LOT SIZE: MULT 50 LT: 1 1 2 3 4 5

Scheduled Receipts
Planned Order Receipts 50 50
Planned Order Releases 50 50

Following the same logic, the Lapdesk MRP matrix is
completed as shown

15-796
15-

LOT SIZE: L4L LT: 1 1 2 3 4 5
LOT SIZE: MULT 50 LT: 1 1 2 3 4 5
ITEM: PRESSBOARD LLC: 0 PERIOD
LOT SIZE: MIN 100 LT: 1 1 2 3 4 5
Gross Requirements
Scheduled Receipts
Net Requirements

15-797
15-

LOT SIZE: L4L LT: 1 1 2 3 4 5
ITEM: LAPDESK LLC: 0 x1 x1
PERIOD x1
LOT SIZE: MULT 50 LT: 1 1 2 3 4 5
x2
ITEM: PRESSBOARD LLC: 0 x2 PERIOD
LOT SIZE: MIN 100 LT: 1 1 2 3 4 5
Scheduled Receipts
Net Requirements

15-798
15-

LOT SIZE: L4L LT: 1 1 2 3 4 5
LOT SIZE: MULT 50 LT: 1 1 2 3 4 5
ITEM: PRESSBOARD LLC: 0 PERIOD
LOT SIZE: MIN 100 LT: 1 1 2 3 4 5
Scheduled Receipts
Planned Order Receipts 100 150 100

15-799
15-


Planned Order Report
PERIOD
ITEM 1 2 3 4 5

Clipboard 100 100 100
Lapdesk 50 50
Pressboard 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

2(30)(60
EO Q = = 60 minimum order quantity
1

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 Date 9 - 25 - 05
On hand 100 Lead time 2 weeks
On order 200 Lot size 200
Allocated 50 Safety stock 50

SCHEDULED PROJECTED
DATE ORDER NO. GROSS REQS. RECEIPTS ON HAND ACTION
50
9-26 AL 4416 25 25
9-30 AL 4174 25 0
10-01
10- GR 6470 50 - 50
10-08
10- SR 7542 200 150 Expedite SR 10-01
10-
10-10
10- CO 4471 75 75
10-15
10- GR 6471 50 25
10-23
10- GR 6471 25 0
10-27
10- GR 6473 50 - 50 Release PO 10-13
10-

Key: AL = allocated WO = work order
CO = customer order SR = scheduled receipt
PO = purchase order GR = gross requirement

15-806
15-

MRP Action Report

Current date 9-25-08
9-25-

ITEM DATE ORDER NO. QTY. ACTION
#2740 10-08
10- 7542 200 Expedite SR 10-01
10-
#3616 10-09
10- Move forward PO 10-07
10-
#2412 10-10
10- Move forward PO 10-05
10-
#3427 10-15
10- Move backward PO 10-25
10-
#2516 10-20
10- 7648 100 De-expedite
De- SR 10-30
10-
#2740 10-27
10- 200 Release PO 10-13
10-
#3666 10-31
10- 50 Release WO 10-24
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

Capacity Open
Routing
requirements orders
file
planning 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 = x 100%
capacity

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
Over-

1. 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-

Initial Load Profile
120 –
110 –
100 –
Hours of capacity

90 –
80 –
70 –
60 –
50 –
40 – Normal
capacity
30 –
20 –
10 –
0–
1 2 3 4 5 6
Time (weeks)

15-814
15-

Adjusted Load Profile
120 –
110 –
100 –
Hours of capacity

90 –
80 –
70 – Work
an
60 – extra Push back
Pull ahead
50 – shift
Overtime Push back Normal
40 –
capacity
30 –
20 –
10 –
0–
1 2 3 4 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-of-
used 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
well-
them
Enterprise Application Integration (EAI) solutions
EDI is being replaced by XML, business
language of Internet
Service-
Service-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. Flexible resources
2. Cellular layouts
3. Pull system
4. Kanbans
5. Small lots
6. Quick setups
7. Uniform production levels
8. Quality at the source
9. Total productive
maintenance
10. 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
two-
Maintain discipline of pull production
Authorize production and movement of
goods

16-840
16-

Sample Kanban

16-841
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 Signal kanban
authorizes production of a triangular kanban
goods used to signal
Withdrawal kanban production at the
previous workstation
authorizes movement of
goods Material kanban
Kanban square used to order material in
a marked area designated advance of a process
to hold items Supplier kanban
rotates between the
factory and suppliers
16-843
16-

Determining Number of
Kanbans
average demand during lead time + safety stock
No. of Kanbans =
container size

dL + S
N =
C
where

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

dL + S (150 x 0.5) + 7.5
N= =
C 25
= 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 SMED Principles
Separate internal setup from
Can be performed
external setup
only when a
process is stopped Convert internal setup to external
setup
External setup
Streamline all aspects of setup
Can be performed
Perform setup activities in
in advance
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
Mixed-
Mixed-model assembly steadies
component production

16-857
16-

Mixed-
Mixed-Model Sequencing

16-858
16-

Quality at the Source

Visual control Jidoka
makes problems visible authority to stop the
production line

Poka-yokes Andons
call lights that signal
prevent defects from
quality problems
occurring
Kaizen Under-capacity
a system of continuous
scheduling
improvement; “change for leaves time for planning,
the good of all” 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 Unneeded equipment, tools, furniture;
need unneeded items on walls, bulletins; items
blocking aisles or stacked in corners;
unneeded inventory, supplies, parts; safety
hazards
A place for Items not in their correct places; correct places
Seiton(set in order) everything and not obvious; aisles, workstations, & equipment
everything in its locations not indicated; items not put away
place immediately after use
Seisou (shine) Cleaning, and looking Floors, walls, stairs, equipment, & surfaces not
for ways to keep clean; cleaning materials not easily
clean and organized accessible; lines, labels, signs broken or
unclean; other cleaning problems
Seiketsu Maintaining and Necessary information not visible; standards
monitoring the first not known; checklists missing; quantities and
(standardize) three categories limits not easily recognizable; items can’t be
Sticking to the rules located within 30 seconds
Number of workers without 5S training; number
Shisuke (sustain) of daily 5S inspections not performed; number
of personal items not stored; number of times
job aids not available or up-to-date

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
process-
Empower 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 Batch Production
Linear programming
Aggregate planning
EOQ with non-instantaneous
non-
replenishment Master scheduling
Mass Production Material requirements
Assembly line balancing planning (MRP)
Project Capacity requirements
Project -scheduling planning (CRP)
techniques (PERT, CPM)

17-881
17-

Objectives in Scheduling

Meet customer due Minimize overtime
dates Maximize machine or
Minimize job lateness labor utilization
Minimize response time Minimize idle time
Minimize completion Minimize work-in-
work-in-
time process inventory
Minimize time in the
system

17-882
17-

Shop Floor Control (SFC)

scheduling and monitoring of day-to-day production
day-to-
in 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
subtract minimum value in each of rows in matrix, then optimum
row from all other row values solution has been found. Make
2. Perform column reductions assignments where zeros appear
subtract minimum value in each Else modify matrix
column from all other column subtract minimum uncrossed value
values from all uncrossed values
add it to all cells where two lines
3. Cross out all zeros in matrix intersect
use minimum number of other values in matrix remain
horizontal and vertical lines unchanged
5. Repeat steps 3 and 4 until
optimum solution is reached

17-885
17-

Assignment Method: Example
Initial PROJECT
Matrix 1 2 3 4
Bryan 10 5 6 10
Kari 6 2 4 6
Noah 7 6 5 6
Chris 9 5 4 10

Row reduction Column reduction Cover all zeros
5 0 1 5 3 0 1 4 3 0 1 4
4 0 2 4 2 0 2 3 2 0 2 3
2 1 0 1 0 1 0 0 0 1 0 0
5 1 0 6 3 1 0 5 3 1 0 5

Number lines ≠ number of rows so modify matrix

17-886
17-

Assignment Method: Example (cont.)
Modify matrix Cover all zeros
1 0 1 2 1 0 1 2
0 0 2 1 0 0 2 1
0 3 2 0 0 3 2 0
1 1 0 3 1 1 0 3
Number of lines = number of rows so at optimal solution
PROJECT PROJECT
1 2 3 4 1 2 3 4
Bryan 1 0 1 2 Bryan 10 5 6 10
Kari 0 0 2 1 Kari 6 2 4 6
Noah 0 3 2 0 Noah 7 6 5 6
Chris 1 1 0 3 Chris 9 5 4 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 due date - today’s date
CR = remaining
work =
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

PROCESSING DUE
JOB TIME DATE

A 5 10
B 10 15
C 2 5
D 8 12
E 6 8

17-891
17-

Simple Sequencing
Rules: FCFS

FCFS START PROCESSING COMPLETION DUE
SEQUENCE TIME TIME TIME DATE TARDINESS
A 0 5 5 10 0
B 5 10 15 15 0
C 15 2 17 5 12
D 17 8 25 12 13
E 25 6 31 8 23
Total 93 48
Average 93/5 = 18.60 48/5 = 9.6

17-892
17-

Simple Sequencing
Rules: DDATE

DDATE START PROCESSING COMPLETION DUE
C 0 2 2 5 0
E 2 6 8 8 0
A 8 5 13 10 3
D 13 8 21 12 9
B 21 10 31 15 16
Total 75 28
Average 75/5 = 15.00 28/5 = 5.6

17-893
17-

Simple Sequencing A(10-0) – 5 = 5
B(15-0) – 10 = 5
Rules: SLACK C(5-0) – 2 = 3
D(12-0) – 8 = 4
E(8-0) – 6 = 2

SLACK START PROCESSING COMPLETION DUE
E 0 6 6 8 0
C 6 2 8 5 3
D 8 8 16 12 4
A 16 5 21 10 11
B 21 10 31 15 16
Total 82 34
Average 82/5 = 16.40 34/5 = 6.8

17-894
17-

Simple Sequencing
Rules: SPT

SPT START PROCESSING COMPLETION DUE
C 0 2 2 5 0
A 2 5 7 10 0
E 7 6 13 8 5
D 13 8 21 12 9
B 21 10 31 15 16
Total 74 30
Average 74/5 = 14.80 30/5 = 6

17-895
17-

Simple Sequencing
Rules: Summary

AVERAGE AVERAGE NO. OF MAXIMUM
RULE COMPLETION TIME TARDINESS JOBS TARDY TARDINESS

FCFS 18.60 9.6 3 23
DDATE 15.00 5.6 3 16
SLACK 16.40 6.8 4 16
SPT 14.80 6.0 3 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
one-
sequence 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
2-
jobs are sequenced.

17-897
17-

Johnson’s Rule

JOB PROCESS 1 PROCESS 2
A 6 8
B 11 6
C 7 3
D 9 7
E 5 10

E A D B C

17-898
17-

Johnson’s Rule (cont.)

E A D B C

E A D B C Process 1
(sanding)
5 11 20 31 38
Idle time

E A D B C Process 2
(painting)
5 15 23 30 37 41

Completion time = 41
Idle time = 5+1+1+3=10

17-899
17-

Guidelines for Selecting a
Sequencing Rule

1. SPT most useful when shop is highly congested
2. Use SLACK for periods of normal activity
3. Use DDATE when only small tardiness values can
be tolerated
4. Use LPT if subcontracting is anticipated
5. Use FCFS when operating at low-capacity levels
low-
6. Do 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
3 Behind schedule

Job 23C
Facility

2 Ahead of schedule

Job 11C Job 12A
1 On schedule

1 2 3 4 5 6 8 9 10 11 12 Days
Today’s Date
Key: Planned activity
Completed activity

17-902
17-

Input/Output Control
Input/Output Report

PERIOD 1 2 3 4 TOTAL
Planned input 65 65 70 70 270
Actual input 0
Deviation 0
Planned output 75 75 75 75 300
Actual output 0
Deviation 0
Backlog 30
20 10 5 0

17-903
17-

Input/Output Control (cont.)
Input/Output Report

PERIOD 1 2 3 4 TOTAL
Planned input 65 65 70 70 270
Actual input 60 60 65 65 250
Deviation -5 -5 -5 -5 -20
Planned output 75 75 75 75 300
Actual output 75 75 65 65 280
Deviation -0 -0 -10 -10 -20
Backlog 30 15 0 0 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)
Add-ins to ERP systems
Add-
Constraint-
Constraint-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 C D

B3 1 7 C3 2 15 D3 3 5

B2 2 3 C2 1 10 D2 2 8

B1 1 5 C1 3 2 D1 3 10

Key: i Item i
Synchronous ij k l Operation j of item i performed at
machine center k takes l minutes
Manufacturing to process

17-910
17-

Synchronous
Manufacturing (cont.)

Demand = 100 A’s
Machine setup time = 60 minutes

MACHINE 1 MACHINE 2 MACHINE 3
B1 5 B2 3 C1 2
B3 7 C3 15 D3 5
C2 10 D2 8 D1 10
Sum 22 26* 17

* Bottleneck

17-911
17-

Synchronous Manufacturing (cont.)
Machine 1 Setup Setup

C2 B1 B3
2 1002 1562 2322
Idle
Machine 2 Setup Setup

C3 B2 D2
12 1512 1872 2732

Machine 3
Setup Setup

C1 D1 Idle D3
0 200 1260 1940
Completion 2737
time

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
part-
5. If consecutive days off are desired, consider switching schedules
among days with the same demand requirements

17-914
17-

Employee Scheduling
DAY OF WEEK M T W TH F SA SU
MIN NO. OF
WORKERS REQUIRED 3 3 4 3 4 5 3
Taylor
Smith
Simpson
Allen
Dickerson

17-915
17-

Employee Scheduling (cont.)
MIN NO. OF
Taylor O X X O X X X
Smith O X X O X X X
Simpson X O X X O X X
Allen X O X X X X O
Dickerson X X O X X X O

Completed schedule satisfies requirements but has no
consecutive days off

17-916
17-

Employee Scheduling (cont.)
MIN NO. OF
Taylor O O X X X X X
Smith O O X X X X X
Simpson X X O O X X X
Allen X X X O X X O
Dickerson X X X X O X 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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