time-cost tradeoffs time-cost tradeoff example time-cost tradeoff example (cont’d) project...
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Time-Cost Tradeoffs
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Time-Cost Tradeoff Example
TaskNormal
Duration Normal Cost
Marginal Cost to Crash One
Week
A 7 $60 $8B 6 $85 $5C 15 $55 $10D 10 $120 $4
A
B
C
D
Start End
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Time-Cost Tradeoff Example (cont’d)
Project
Duration
(weeks) Critical Path(s) Task(s) Reduced
Total Direct
Cost
22 Start-A-C-End - $320
21 Start-A-C-End A $328
Start-A-B-End
20 Start-A-C-End C $338
Start-A-B-End
19 Start-A-C-End C $348
Start-A-B-End
18 Start-A-C-End A, B $361
Start-A-B-End
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Linear Time-Cost TradeoffIn theory, the normal or expected duration of a task can be reduced by assigning additional resources to the task
Time
Cost
Crash Point
Normal Point
Slope (bj) = Increase in cost by reducing task by one time unit
Normal time =Crash time =
Normal cost =
Crash cost =
tjNtj
c
Cjc
CjN
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Balancing Overhead & Direct Costs
Project Duration
Cost
Indirect (overhead)
Costs
Direct Costs
Total Cost
Crash Time
Normal TimeMinimum Cost Solution
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Time-Cost Tradeoff (Direct Costs Only)Given Normal point with cost and time
and Crash point with cost and time
Assume constant marginal cost of crashing task j =
Decision Variables: Sj = Starting time of task j
END = End time of project
tj = Duration of task j
Minimize Total Direct Cost =
Sj ≥ Si + ti for all tasks i Pj
for all tasks in project
END = Tmax
tj, Sj ≥ 0
CjN
Cjc tj
c
tjN
bj = Cj
c - CjN
tjc - tj
N
bj tjj
tjc Š tj Š tj
N
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General Time-Cost Tradeoffs
where
I = indirect (overhead) cost/time period
P = penalty cost/time period if END is delayed beyonddeadline Tmax
L = number of time periods project is delayed beyond deadline Tmax
Minimize Total Costs = + I (END) + P L
bj tjj
QUESTION: HOW TO DEFINE L?
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Software Project Schedules
“Observe that for the programmer, as for the chef, the urgency of the patron may govern the scheduled completion of the task, but it cannot govern the actual completion. An omelet, promised in ten minutes, may appear to be progressing nicely. But when it has not set in ten minutes, the customer has two choices--wait or eat it raw. Software customers have the same choices.
The cook has another choice; he can turn up the heat. The result is often an omelet nothing can save--burned in one part, raw in another.”
F.P. Brooks, “The Mythical Man-Month”, Datamation, Vol 20, No 12 (Dec, 1974),
pp. 44-52.
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Coordination Costs (Software Development Project)
n Assume you want to develop program that will require (approximately) 50,000 lines of PERL code
n A typical programmer can write approximately 1500 lines of code per weekn Coordination time is M (M-1)/2 weeks
No. of Programmers
No. of Weeks Coding
No. of Coordination
Weeks
Total Number of
Weeks
1 33.33 0 33.332 16.67 1 17.673 11.11 3 14.114 8.33 6 14.335 6.67 10 16.676 5.56 15 20.567 4.76 21 25.768 4.17 28 32.179 3.70 36 39.7010 3.33 45 48.3311 3.03 55 58.03
0
2
4
6
8
10
12
0 10 20 30 40 50 60 70
Total Number of Weeks
No. of
Pro
gra
mm
ers
(Cost)
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Brook’s Law
“Adding manpower to a late software project makes it later.”
n
F.P. Brooks, “The Mythical Man-Month”, Datamation, Vol 20, No 12 (Dec, 1974), pp. 44-52.
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Compressing New Product Development Projects
Traditional Method
Design follows a sequential pattern where information about the new product is slowly accumulated in consecutive stages
Stage 0 Stage 1 Stage N
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New Product Development Process
Overlapped Product Design
Allows downstream design stages to start before preceding upstream stages have finalized their specifications….
Stage 0
Stage 1
Stage N
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Issues and Tradeoffs
What are the tradeoffs when moving from a traditional sequential product design process to an overlapped product design process?
• Increased uncertainty (that leads to additional work)
• Can add additional resources to tasks to reduce duration--but costs are increased
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Classic PERT Model Defined
• Since task durations are now random variables, time of any milestone (e.g., end of project) is now RV
• Assume all tasks are statistically independent
• Use values of j to identify expected critical path
• Since time of event (e.g., ESk) is now sum of independent RV’s, central limit theorem specifies that ESk is approximately normally distributed with mean E[ESk] and variance Var[ESk]
where there exists s paths to task k
Expected early start time of task k = EESk = maxs
jtasks j on path s
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Classic PERT Model (cont’d)
Expect Project Duration = E[ESEND] = jtasks j on CP
Variance of Project Duration = Var[ESEND] = j2
tasks j on CP
Thus, expected project duration is defined as:
Using central limit theorem and standard normal distribution:
P ESEND Š Tmax = P z Š Tmax - E ESEND
Var ESEND
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PERT Example #1
Duration Estimates ExpectedTask Description Predecessors Optimistic Pessimistic Likely Duration Variance
A Requirements Analysis none 2 14 6 6.67 4.00B Programming A 4 12 7 7.33 1.78C Hardware acquisition A 2 13 8 7.83 3.36D User training A 12 18 14 14.33 1.00E Implementation B, C 3 7 5 5.00 0.44F Testing E 3 7 4 4.33 0.44
END End of project D, F 0 0 0 0.00 0.00
StartTask A
Requirements Analysis
Task C Hardware
Acquisition
Task B Programming
Task F Testing
Task D User
Training
Task E Implementation
End
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PERT Example #1 (cont’d)
ExpectedTask Path Early Start Variance Due Date Zi Pr(zi)
B,C,D Start-A 6.67 4.00 6 -0.33 0.37E Start-A-C 14.50 7.36 15 0.18 0.57F Start-A-C-E 19.50 7.81 20 0.18 0.57
End Start-A-C-E-F-End 23.83 8.25 25 0.41 0.66
PERT Expected Duration = 23.83 Expected CP = {Start, A, C, E, F, End}PERT Variance = 8.250
StartTask A
Requirements Analysis
Task C Hardware
Acquisition
Task B Programming
Task F Testing
Task D User
Training
Task E Implementation
End
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PERT Example #2
Task B
B = 12
B2 = 4
Task D
D = 3
D2 = 1
Task A
A = 4
A2 = 2
Task C
C = 10
C2 = 5
ENDSTART
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Example #3: Discrete Probabilities
Task A Task B Task C Task DValue Prob Value Prob Value Prob Value Prob
7 0.333 2 0.2 5 0.2 3 0.3
8 0.333 12 0.8 15 0.2 12 0.7
9 0.333 25 0.6
START END
Task A(8.0)
Task B(10.0)
Task C(19.0)
Task D(9.3)
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Example #3 (cont’d) Task A Task B Task C Task D Critical Prob of Length PATHS
Combination Value Prob Value Prob Value Prob Value Prob Path CP of CP A,D B, D C1 7 0.333 2 0.2 5 0.2 3 0.3 A, D 0.004 10 0.004 0.000 0.0002 7 0.333 2 0.2 5 0.2 12 0.7 A, D 0.009 19 0.009 0.000 0.0003 7 0.333 2 0.2 15 0.2 3 0.3 C 0.004 15 0.000 0.000 0.0044 7 0.333 2 0.2 15 0.2 12 0.7 A, D 0.009 19 0.009 0.000 0.0005 7 0.333 2 0.2 25 0.6 3 0.3 C 0.012 25 0.000 0.000 0.0126 7 0.333 2 0.2 25 0.6 12 0.7 C 0.028 25 0.000 0.000 0.0287 7 0.333 12 0.8 5 0.2 3 0.3 B, D 0.016 15 0.000 0.016 0.0008 7 0.333 12 0.8 5 0.2 12 0.7 B, D 0.037 24 0.000 0.037 0.0009 7 0.333 12 0.8 15 0.2 3 0.3 B, D 0.016 15 0.000 0.016 0.000
10 7 0.333 12 0.8 15 0.2 12 0.7 B, D 0.037 24 0.000 0.037 0.00011 7 0.333 12 0.8 25 0.6 3 0.3 C 0.048 25 0.000 0.000 0.04812 7 0.333 12 0.8 25 0.6 12 0.7 C 0.112 25 0.000 0.000 0.11213 8 0.333 2 0.2 5 0.2 3 0.3 A, D 0.004 11 0.004 0.000 0.00014 8 0.333 2 0.2 5 0.2 12 0.7 A, D 0.009 20 0.009 0.000 0.00015 8 0.333 2 0.2 15 0.2 3 0.3 C 0.004 15 0.000 0.000 0.00416 8 0.333 2 0.2 15 0.2 12 0.7 A, D 0.009 20 0.009 0.000 0.00017 8 0.333 2 0.2 25 0.6 3 0.3 C 0.012 25 0.000 0.000 0.01218 8 0.333 2 0.2 25 0.6 12 0.7 C 0.028 25 0.000 0.000 0.02819 8 0.333 12 0.8 5 0.2 3 0.3 B, D 0.016 15 0.000 0.016 0.00020 8 0.333 12 0.8 5 0.2 12 0.7 B, D 0.037 24 0.000 0.037 0.00021 8 0.333 12 0.8 15 0.2 3 0.3 B, D 0.016 15 0.000 0.016 0.00022 8 0.333 12 0.8 15 0.2 12 0.7 B, D 0.037 24 0.000 0.037 0.00023 8 0.333 12 0.8 25 0.6 3 0.3 C 0.048 25 0.000 0.000 0.04824 8 0.333 12 0.8 25 0.6 12 0.7 C 0.112 25 0.000 0.000 0.11225 9 0.333 2 0.2 5 0.2 3 0.3 A, D 0.004 12 0.004 0.000 0.00026 9 0.333 2 0.2 5 0.2 12 0.7 A, D 0.009 21 0.009 0.000 0.00027 9 0.333 2 0.2 15 0.2 3 0.3 C 0.004 15 0.000 0.000 0.00428 9 0.333 2 0.2 15 0.2 12 0.7 A, D 0.009 21 0.009 0.000 0.00029 9 0.333 2 0.2 25 0.6 3 0.3 C 0.012 25 0.000 0.000 0.01230 9 0.333 2 0.2 25 0.6 12 0.7 C 0.028 25 0.000 0.000 0.02831 9 0.333 12 0.8 5 0.2 3 0.3 B, D 0.016 15 0.000 0.016 0.00032 9 0.333 12 0.8 5 0.2 12 0.7 B, D 0.037 24 0.000 0.037 0.00033 9 0.333 12 0.8 15 0.2 3 0.3 B, D 0.016 15 0.000 0.016 0.00034 9 0.333 12 0.8 15 0.2 12 0.7 B, D 0.037 24 0.000 0.037 0.00035 9 0.333 12 0.8 25 0.6 3 0.3 C 0.048 25 0.000 0.000 0.04836 9 0.333 12 0.8 25 0.6 12 0.7 C 0.112 25 0.000 0.000 0.112
6.8% 32.0% 61.1%
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Example #3 (cont’d)
Length of Cumulative
CP's Prob Prob10 0.004 0.0011 0.004 0.0112 0.004 0.0115 0.108 0.1219 0.019 0.1420 0.019 0.1621 0.019 0.1824 0.224 0.4025 0.599 1.00
Task A Task B Task C Task D
6.8% 32.0% 61.1% 38.8%
Criticality Indices
Expected Project Duration = 23.22
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Monte-Carlo Simulation (PERT Example 1)Task Duration Early Latest Total Expected
Task (Uniform Dist) Start Finish Slack Duration Variance
A 4.99 0 4.99 0.00 6.67 4.00B 4.75 4.99 9.74 0.00 7.33 1.78C 3.38 4.99 9.74 1.36 7.83 3.36D 12.20 4.99 21.02 3.83 14.33 1.00E 5.94 9.74 15.68 0.00 5.00 0.44F 5.34 15.68 21.02 0.00 4.33 0.44
END 0.00 21.02 21.02 0.00 0.00 0.00
Run Project Duration t(B) t(C) t(D) t(E) t(F)1 31.07 1 0 0 1 12 27.41 0 1 0 1 13 23.97 1 0 0 1 14 28.93 0 1 0 1 15 26.85 1 0 0 1 16 28.82 0 0 1 0 07 28.77 0 1 0 1 1
197 30.37 0 1 0 1 1198 29.78 1 0 0 1 1199 25.33 1 0 0 1 1200 29.70 0 1 0 1 1
Ave 27.13 48.5% 42.0% 9.5% 90.5% 90.5%Var 16.777
Project Makespan Lower Limit Upper Limit
95% Confidence interval 26.56 27.7299% Confidence interval 26.37 27.90
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Calculating Confidence Intervals
For a confidence interval, we can use the sample mean and the estimated standard error of the mean
where s is the sample standard deviation and n is the number of trials
Using a normal approximation, a (1- ) two-sided confidence interval is given by
sX = sn
X
X -+ z/2 sX
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New Product Development Projects
START
Lease Mfg/Office
Space
Identify/hire staff
Design of physical unit
Electronics design Software
Assemble prototype Beta test prototype
END
Beta test fails (with probability of 0.25)
and rework is needed
Beta test fails (with probability of 0.25)
and rework is needed
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New Product Development Projects (cont’d)
START
Lease Mfg/Office
Space
Identify/hire staff
Design of physical unit
Electronics design Software
Assemble prototype Beta test prototype
END
Beta test fails and rework is needed
Prob = .25
Prob = .75
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Critical Chain and the Theory of Constraints (TOC)
• Use deterministic CPM model with buffers to deal with any uncertainties,
• Place project buffer after last task to protect the customer’scompletion schedule,
• Exploit constraining resources (make certain that resources are fully utilized),
• Avoid wasting time slack time by encouraging early task completions,
• Carefully monitor the status of the buffer(s) and communicate this status to other project team members on a regular basis, and
• Make certain that the project team is 100 percent focused on critical chain tasks
Project “Goal” (according to Goldratt): Meet Project Due Date
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Project Buffer Defined
• Project Buffer is placed at the end of the project to protect the customer’s promised due date
PERT Example #1 Revisited with Project Buffer
Start
Task BProgramming
User Task D
Usertraining
Task EImplementation
End Project
Buffer
Task Arequirements
analysis
Task CHardwareacquisition
Task F
Testing
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Calculating Project Buffer Size
For tasks k on critical chain, we can calculate project buffer using following formula that project will be completed within worst-case duration estimates around 90 percent of the time:
For those “who want a scientific approach to sizing buffers....”
Buffer = tasks k on critical chain
tkp - k
2
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Implications of Project Uncertainty
Assume that the duration of both tasks A and B are described by a normal distribution with a mean of 30 days
START END
Task A
Task B
What is the probability that the project will be completed within 30 days?
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Uncertainty and Worker Behavior
Consider a project with two tasks that must be completed serially
The duration of each task is described by a RV with values Ti (i = 1, 2)
Values of T 1 Prob Values of T 2 Prob
7 0.3 14 0.58 0.4 18 0.59 0.3
8.0 16
Start Task 1 Task 2 End
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Parkinson’s Law (Expanding Work)“Work expands so as to fill the time available for its completion”
Professor C.N. Parkinson (1957)
Set a deadline D = 24 days
So T(D) = project makespan (function of D) where
E[T(D)] = E(T1) + E(T2) + E[max(0, D - T1 - T2)]
Values of T 1 Prob Values of T 2 Prob
Project Makespan Prob
7 0.3 14 0.5 24 0.157 0.3 18 0.5 25 0.158 0.4 14 0.5 24 0.28 0.4 18 0.5 26 0.29 0.3 14 0.5 24 0.159 0.3 18 0.5 27 0.15
E[T(D)] = 25 days
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Procrastinating Worker
Set a deadline D = 24 days
E’[T(D)] = E(T1) + E(T2) + E{max[0, D - T1 - E(T2)]}
Can show that E[T(D)] ≥ E’[T(D)] ≥ D
What are the implications for project managers?
Values of T 1 Prob
E[Delay] = max[0, D - T1 - E(T2)] E[Makespan]
7 0.3 1 248 0.4 0 249 0.3 0 25
8 0.3 24.30
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Schoenberger’s Hypothesis
An increase in the variability of task durations will increase the expected project duration….
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Schoenberger’s Hypothesis Illustrated
START END
Task A
Task B
Duration of Task A Probability
Duration of Task B Probability
12 0.1 10 0.514 0.8 15 0.516 0.1
14.0 12.5
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Schoenberger’s Hypothesis Illustrated
RealizationTask A
DurationTask B
Duration Probability Max (A, B)
1 12 10 0.05 122 14 10 0.4 143 16 10 0.05 164 12 15 0.05 155 14 15 0.4 156 16 15 0.05 16
Duration of Task A Probability
Duration of Task B Probability
12 0.3 10 0.514 0.4 15 0.516 0.3
14.0 12.5
Increasing the variance of Task A:
Results in an increased expected duration = 14.65 days
Expected duration equals 14.55 days
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Risk Management
• All projects involve some degree of risk
• Need to identify all possible risks and outcomes
• Need to identify person(s) responsible for managing project risks
• Identify actions to reduce likelihood that adverse events will occur
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Risk Analysis
Risk Exposure (RE) or Risk Impact =
(Probability of unexpected loss) x (size of loss)
Example: Additional features required by clientLoss: 3 weeksProbability: 20 percentRisk Exposure = (.20) (3 weeks) = .6 week
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How to Manage Project Risks?
Preventive Actions• Actions taken in anticipation of adverse events• May require action before project actually begins• Examples?
Contingency Planning• What will you do if an adverse event does occur?• “Trigger point” invokes contingency plan• Frequently requires additional costs
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Risk and Contracts
High Low Low High
Degree of Risk
Contractor ClientFixed Price Contract Cost Plus Contract
Firm price
Elements can be
renegotiated IncentivesT&M
with limits
Cost Plus with
IncentivesTime &
materials
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Tornado Diagram
Wage Rate
Direct Labor Hours
Material Units Needed
Early Completion Bonus
Material Unit Cost
Interest rates
Energy costs
Overhead
Project Cost ($000's)
$1290
$1265
$1260
$13101310$1350
$1350
$1380
$1400
$1700
$1720
$1680
$1690
$1640
$1620
$1625
$17601760
$1500 $1600 $1800$1700$1400$1300$1200
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Sensitivity Chart
Wage Rate 0.85Direct Labor Hours 0.73
Material Units Needed 0.62Early Completion Bonus -0.45
Material Unit Cost 0.42Interest rates 0.28Energy costs 0.19
Overhead 0.10
0 0.5 1.0-0.5
Rank Order Correlation with Total Project Cost
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Van Allen Company
Task
Immediate Predecessors Time Cost Time Cost
Start - 0 - 0 -A Start 3 $60 5 $40B Start 1 $50 5 $30C B 5 $70 10 $40D A 2 $60 7 $40E A 2 $50 6 $30F C, D 5 $90 11 $60G C, D 4 $60 6 $30H G 1 $40 5 $20I E, G 1 $50 4 $20
End F, H, I 0 - 0 -
Strike Expected(wks) Prob Duration
3 0.45 1.354 0.3 1.205 0.25 1.25
E[Strike Duration] 3.80