executing an uncertain schedule:

Executing an Uncertain Schedule:

Adapting to Reality to Maximize Target Completion Attainment

Annaka KaltonStottler Henke Associates, Inc.

www.stottlerhenke.com

PMI College of Scheduling“PMI” is a registered trade and service mark of the Project Management Institute, Inc.

2

Orientation

• Background• Executing a schedule – the problem• Possible approaches, CCPM overview• Simulation framework• Empirical simulation results• Conclusions• Questions


3

Executing to a Schedule: Challenges

• A schedule allows for planning, but is brittle

• The more the resource contentions impact a schedule, the more brittle it becomes

• It is easier to go over than under for most jobs

• Following a schedule minimizes the likelihood of catching up

• Ambitious schedules and high-variance jobs aggravate these attributes


4

Possible Solutions

• Reschedule frequently to maximize responsiveness– Pros: Highly responsive– Cons: Difficult to plan for

• Build contingency time into the schedule– Pros: Increases likelihood of meeting schedule– Cons: Potentially wasteful

• Consolidate contingency time (e.g. CCPM)– Pros: Flexible, dynamic, ambitious– Cons: Difficult to plan for, limited in handling of

high-density schedules


5

Topic for Exploration

• Empirically consider what combinations work best for a specific domain

• Posit extrapolations from that domain • Consider possible improvements based

on performance


6

Execution Methodologies

• Schedule-Based Methodologies– Work to the schedule or a schedule

derivative– Strong planning advantage

Psychological Practical

• Analysis-Based Methodologies– Work based on properties derived from the

schedule– Robust and flexible in the face of change

Abstraction reduces brittleness Abstraction allows broader comparisons

– Focus on CCPM methodologies


7

Introduction to CCPM

• Developed by Eliyahu M. Goldratt– Introduced in 1997

• Derived from the Theory of Constraints• Differs from CPM in focus on resource

requirements• Focuses on protecting the project’s

critical chain• Gathers contingency time into pooled

buffers protecting the critical chain


8

Critical Chain: Nature and Importance

• The shortest causal path through a schedule

• Takes resource requirements, contention into account

• Slippage in the critical chain causes slippage in the delivery date

1

2

3 4

56 7

8

PERT w ith critical path

1 2

3 4

56 7

8

Partial Order PERT w ith cp/cc


9

Critical Chain: Example

1

2

3 4

56 7

8

PERT

1 2

3 4

56 7

8

Partial Order Schedule

1 2

6

3 54

8

Schedule

R

R

7R

1 2

6

3 54

8

Schedule w ith Critical Chain

7


10

Buffering the Critical Chain

1 2

6

3 54

8 7

Contingency time

1 2

6

3 54

8 7

Combined contingency time

1 2

6

3 54

8 7

Buffer placement schedule

1 2

6

3 54

8 7

CCPM schedule


11

Execution Methodologies: Schedule-Based

• 1. Work to aggressive schedule– Each job duration set such that it can be

completed in the time specified 50% of the time

– Very aggressive, very brittle

• 2. Work to safe schedule– Each job duration set such that it can be

completed in the time specified 85% of the time

– Potentially wasteful, still somewhat brittle, people involved in later jobs will not be ready


12

Schedule-Based Prioritization: Detail

1 2

1

1 32

1

W orking to a schedule (best case)

R

R

2R

4 6

1

3 87

2

W orking to a schedule (standard)

R

R

5R

4 6

1

3 87

2

Result of slippage in job 4's start

R

5R

46

1

3 87

2

Preferred result (requires reschedule)

5R


13

Execution Methodologies: Analysis-Based

• 3. Prioritize based on scheduled slack• 4. Prioritize based on standard CCPM,

default buffer placement• 5. Prioritize based on standard CCPM,

fenced buffer placement• 6. Prioritize based on projected

completion CCPM


14

Slack-based Prioritization: Detail

• Slack reflects how much a job could shift without impacting the schedule

• Slack can consider temporal constraints, or actual schedule position (i.e. resource contention)

• For this test, we used schedule-based slack, rather than abstract slack (see below)

1 1

3

0 00

2

Prioritization based on slack

R

R

2R


15

CCPM-Based Prioritization: Detail

• Gives highest priority to jobs projected to cause buffer incursion

• Project buffer incursion trumps feeder buffer incursion

• Feeder buffer incursion is compared as a proportion of the buffer available

• The whole incurring chain may have the same priority, unless post-processed

1 1

4

3 13

2 2

CCPM prioritization


16

Projected Completion CCPM: Detail

• Traditional CCPM prioritization does not reflect how much work is left vs. how much buffer is left

• In some cases this can result in work that has sufficient buffer to finish in time being prioritized ahead of work that does not (see below)

• Projected completion CCPM recalculates the buffer needed for the remaining work

1 1

4

3 33

2 2

CCPM prioritization

2 2

4

3 33

1 1

Projected completion CCPM prioritization


17

Simulation Framework

1. Generate a schedule2. Derive a prioritization based on the schedule3. Assign current set of randomly generated

durations to all active activities 4. Using that prioritization, simulate the execution

for a given time span by scheduling in priority order, taking resource requirements into account.

5. Record resulting actuals for those activities within the current update time frame (e.g. 48 hours)

6. Remove the priorities and revert the durations to standard for all activities beyond the current update timeframe

7. If all of the activities have been completed, report results; otherwise, return to step 1 and repeat.


18

Simulation Detail

1 1

3

0 00

2

Produce a schedule and prioritize

R

R

2R

1 1

3

0 00

2

Assign durations and reschedule

R

R

2R

Record actuals, revert, schedule

01

0

Beta distribution for generation


19

File Details

• File 1 (Airplane assembly preparation):– 106 jobs– 525 resource requirements– 214 constraints– 2.1% resource-driven scheduling decisions

• File 2 (Final assembly):– 1,763 jobs– 7,451 resource requirements– 4,839 constraints– 36.4% resource-driven scheduling decisions– Dense: 21% of the jobs within three hours of

the critical chain, 42% within 6 hours and 59% are within ten hours


20

Empirical Results: File 1

File 1 Results

0.8

0.82

0.84

0.86

0.88

0.9

0.92

0.94

0.96

0.98

6 12 24

Hours Between Updates

% o

f S

afe

Du

rati

on

Sch

edu

le

model 1

model 2

model 3 aggressive

model 3 safe

model 4

model 5

model 6


21

File 1 Commentary

• Statistically insignificant differences between method 3 safe (slack determined in safe mode), method 4 (CCPM), and method 6 (projected completion)

• 10% variance in quality – all approaches did reasonably well

• Update period had minimal impact• All results reflect the fact that file 1 is a

fairly lean, temporally-driven model


22

Empirical Results: File 2

File 1 Results

0.95

1

1.05

1.1

1.15

1.2

1.25

1.3

6 12 24

Hours Between Updates

% o

f S

afe

Du

rati

on

Sch

edu

le

model 1

model 2

model 3 aggressive

model 3 safe

model 4

model 5

model 6

2 Results


23

File 2 Commentary

• Method 6 (projected completion) performed the best, although method 3 safe (slack determined in safe mode) determined quite well for such a simple methodology

• Much higher variation in quality based on method and update period

• All execution strategies produced results longer than the safe schedule


24

Results summaryMethod

Description File 1 Rank

File 2 Rank

1 Work to agg. schedule

2 4

2 Work to safe schedule

4 6

3 safe Prioritize by safe schedule slack

1 2

3 agg Prioritize by aggressive schedule slack

5 5

4 Prioritize by standard CCPM

1 4

5 Prioritize by fenced CCPM

3 3

6 Prioritize by projected completion CCPM

1 1


25

For Further Exploration

• Even the best performer did not meet the schedule for file 2– Dense schedules are a general execution challenge– Deeper buffering or buffering to reflect the

schedule’s density might produce better results

• Slack-based prioritization did surprisingly well– Explicitly taking job variance into account might

improve results further– Could be a useful approach in domains reluctant to

try CCPM methodologies

• All methodologies improved with higher period– Tradeoffs between dynamism and practical

considerations


26

Final Notes

• Schedule execution is a major problem• Appropriate execution strategies are

likely to vary across domains• Simulation gives a good option for

determining the best strategy and its weak points

• Estimated project completion appears to operate better than standard CCPM in at least some complex domains

• Slack-based prioritization gives a very simple but effective method, in spite of its lack of sophistication


27

Annaka KaltonStottler Henke Associates, Inc.

951 Mariner’s Island Blvd, Suite 360San Mateo, CA 94404

(650) [email protected]

Devin ClineStottler Henke Associates, Inc.

(650) [email protected]

The Authors

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