Program Management using the Triple constraints

Scope
Time
Quality
Cost
Seminar by: Drew Harris Managing the Critical Constraints 2
Budget
Control
Program Goals
Cutting Edge
Technologies
Fab Schedules
New Developments
Employee Performance
Training & Development
Program Schedules
Component
cost-optimization
Fabrication
techniques
Productivity

 Clearly define what is in Scope and out of
scope, as scope creep impacts the budget
allocations.
 Ensure that new developments receive
budget prior to being included in scope.

 To control costs you must efficiently
manage the cost drivers
 Resources
 Machinery and Technology
 Materials
 Quality
 Consultants, Contractors, and specialty shops
 Delivery and expediting costs
 Schedule slips and changes.

 It is imperative that processes be measured to determine
performance, productivity, quality and repeatability.
 Equipment capabilities must be known and maintained
through scheduled maintenance.
 Materials must meet specifications and adhere to tight
delivery schedules.
 CAD/CAE/CAM technology should follow QA standards and
adhere to thorough documentation and file storage.
 Inventory turns and supply stocks must be fine tuned.
 Inputs and Outputs must be agreed upon between upstream
and downstream customers. A high satisfaction level should
be achieved and maintained.

 Develop an all encompassing Quality
Assurance plan that covers;
 Design and Engineering
 In-house Fabrication
 Technical services
 Ensure that all suppliers follow the QA plan
▪ Outside fabrication
▪ Assembly
▪ Materials
▪ Post processing

TIME SCOPE COST
CONSTRAINT
(Non-Flexible)
ENHANCE
(Somewhat
Flexible)
ACCEPT
(Flexible)
The Critical Constraints are anchored together and set, based on the
objective of the program. In this example, Scope is basically fixed and
non-flexible. This scope anchor allows for a somewhat flexible cost
constraint and a flexible time constraint (scheduling).
So let us now proceed to look at the Time Constraint of Scheduling for
the remainder of this talk.

 Scheduling has been defined as "the art of assigning
resources to tasks in order to insure the termination of
these tasks in a reasonable amount of time" (1).
According to French (2), the general problem is to find a
sequence, in which the jobs (e.g., a basic task) pass
between the resources (e.g., machines), which is a
feasible schedule, and optimal with respect to some
performance criterion.
References
1. M. Dempster, J. Lenstra, and R. Kan, Deterministic and stochastic scheduling:
introduction. Proceedings of the NATO Advanced Study and Research Institute on
Theoretical Approaches to Scheduling Problems, D. Reidel Publishing Company: 3-14,
1981.
2. S. French, Sequencing and Scheduling. New York: Halsted Press, 1982.

 Underestimating planned tasks
 Creative designs requiring additional learning curve
time.
 Capacity imbalances
 Unavailable critical resources, technology or
equipment
 Unusually low resource or equipment utilization
 “Hot” Tasks interjected into the plan.
 Conflicting priorities
 Lack of an integrated schedule
 Delays
 Absenteeism
 Rework

 Form a team to get to the root
cause of each impact. Go down 5
levels w/Why?
 Verify the root causes with data
 Complete the countermeasure
table with specific actions.
 For each root cause, identify up
to 3 broad countermeasures
(what to do).
 Rank the effectiveness of each
countermeasure.
 Identify the specific
implementation actions (how?)for
each countermeasure.
 Rank the feasibility (time, cost) of
each specific action and decide
which to implement.

Worse Case Scenario Std. Use of time (Mins)
Delays & Interferences 120 20
2-15 min Breaks 30 30
Daily Meetings 110 60
Focused Time 220 370
220
370
110
60
30
30
120
20
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Daily Utilization
Comparison for an 8 hr (480 Mins.) day
Focused Time Daily Meetings 2-15 min Breaks Delays & Interferences
46%
77%

As we push the
upper limit of
technology in our
development, work
centers, and
processes …we
must dig deeper
and utilize advance
scheduling theories
and optimization to
accurately plan
these events.

 Develop an Integrated schedule showing material
shipments and processing flowing into the schedule for
design, engineering, machining and assembly. It is
imperative to include the people, equipment, and material
that are used to complete tasks .
 Manage the critical path The series of tasks that must be
completed on schedule for a project to finish on schedule.
 Understand the network logic associated with each
schedule.
 Improve the duration estimates and reduce variation.
 Status and update the schedule frequently.
 Document the key learning of each schedule
performance.

MEASURE AND TRACK ESTIMATING
ACCURACY TO IMPROVE ESTIMATES
INDIVIDUAL
EST.
TEAM
ESTIMATE
ACTUAL
DURATION
ACCURACY
(INDIV)
ACCURACY
(TEAM)
TASK 1
6.0 7.6 8.0 75% 95%
TASK 2
2.7 3.1 2.8 98% 113%
TASK 3
1.75 2.3 1.8 97% 128%
TASK 4
10.2 13.0 12.6 81% 104%
EXPECTED TOLERANCE OF +/- 5%

 Use and integrated schedule and manage the critical path
Seminar by: Drew Harris Managing the Critical Constraints
15

 Develop Key metrics to assess deliveries and productivity.
 Determine and eliminate the delays that affect utilization
 Plan in “Critical and Hot” tasks and priorities
 Utilize PERT and mathematical algorithms to improve shop
loading.
 Deploy a rigorous scheduling process and include backward
and forward scheduling techniques
 Perform capacity analysis on resources, work centers,
equipment and technology.
 Improve team dynamics to gain synergy to function at a
higher level
 Optimize the integrated schedule through an advanced
scheduling and simulation modeling.

17
Metrics to aid in Scheduling accuracy!
Do the following Results Metrics exist?
On Time Delivery (OTD)
Order-Fulfillment Lead Time (OFLT)
Dock to Dock (DTD)
First Time Through quality (FTT)
Health and Safety metrics
Days lost due to accidents
Absenteeism
Employee Turnover
Do the following Productivity Metrics exist?
Build to Schedule (BTS)
Overall Equipment effectiveness (OEE)
Value added to Non-value added ratio (VA/NVA)

 Since scheduling problems fall into the class of NP-
complete problems, they are among the most difficult to
formulate and solve.
 Thus, I have adopted ideas from the paper Job Shop Scheduling
Techniques published by; Albert Jones, PhD National Institute of
Standards and Technology and Luis C. Rabelo, Ph.D., Professor
Industrial and Manufacturing Engineering Dept. California
Polytechnic State University
 It is necessary to adopt additional parameters to
categorize the variance that impacts the job shop
schedule.
 These additional parameters will allow us to;
 Develop algorithms to improve duration estimates through
regression analysis and predictions.
 Develop a knowledgebase of known machining center
processing paths.
 Construct a Discrete Scheduling Model to Optimize solutions

 Consider these additional parameters to categorize schedule problems
as proposed by Graves
1. Processing complexity,
2. Scheduling criteria,
3. Parameter variability,
4. Scheduling environment.
 Processing complexity, refers to the number of processing steps and
workstations associated with the production process. This dimension
can be decomposed further as follows:
1. One stage, one processor
2. One stage, multiple processors,
3. Multistage, flow shop,
4. Multistage, job shop.

 The second dimension, scheduling criteria,
states the desired objectives to be met. "They are
numerous, complex, and often conflicting”.
Some commonly used scheduling criteria
include the following:
1. Minimize total schedule slippage,
2. Minimize the number of late jobs,
3. Maximize system/resource utilization,
4. Minimize in-process inventory,
5. Balance resource usage,
6. Maximize production rate.

 The third dimension, parameters variability, indicates
the degree of uncertainty of the various parameters of
the scheduling problem.
 If the degree of uncertainty is insignificant, the
scheduling problem could be called deterministic.
 The last dimension, scheduling environment, defined
the scheduling problem as static or dynamic.
Scheduling problems in which the number of jobs to
be considered and their ready times are available are
called static. On the other hand, scheduling problems
in which the number of jobs and related
characteristics change over time are called dynamic.

 Improve the basic scheduling parameters
Critical Path analysis
Integration
Visibility, Delays and Prioritizations
Learning Curves
Capacity and Utilization
 Build Knowledgebase for processing
paths, estimates and critical parameters
 Use simulation modeling as an inference
engine to manage the variables, build
processing rules, input algorithms and
distributions, all to optimize our schedule.

References
1. M. Dempster, J. Lenstra, and R. Kan, Deterministic and stochastic
scheduling: introduction. Proceedings of the NATO Advanced Study
and Research Institute on Theoretical Approaches to Scheduling Problems,
D. Reidel Publishing Company: 3-14, 1981.
2. S. French, Sequencing and Scheduling. New York: Halsted Press,
1982.
3. S. Graves, A Review of Production Scheduling. Operations
Research, 29: 646-675, 1981.
4. S. Gershwin, Hierarchical flow control: a framework for
scheduling and planning discrete events in manufacturing
systems. Proceedings of IEEE Special Issue on Discrete Event Systems,
77: 195-209, 1989.

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Program Management using the Triple constraints

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