cycle time mathematics presented at klrat 2013 [email protected]
TRANSCRIPT
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Conclusions
• Forecasting using cycle time is proving useful• Cycle time follows a Weibull/Lognormal shape
– We can estimate the actual distribution with just a minimum and a maximum guess (initially)
• Controlling the shape of the distribution (narrowing) improves predictability
• Proposing a way of identifying what risks and delays will have the greatest impact…
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Prediction Intervals
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Actual Maximum
Actual Minimum
1
3
2
Q. What is the chance of the 4th sample being between the range seen after the first three samples? (no duplicates, uniform distribution, picked at random)
4
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Actual Maximum
Actual Minimum
1
3
2
4
Highest sample
Lowest sample
Q. What is the chance of the 4th sample being between the range seen after the first three samples? (no duplicates, uniform distribution, picked at random)
?
?
?
?
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Actual Maximum
Actual Minimum
1
3
2
4
25% chance higher than highest seen
25% lower than highest and higher than second highest
25% higher than lowest and lower than second lowest
25% lower than lowest seen
Highest sample
Lowest sample
Q. What is the chance of the 4th sample being between the range seen after the first three samples? (no duplicates, uniform distribution, picked at random)
A. 50%
% = (1 - (1 / n – 1)) * 100
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Actual Maximum
Actual Minimum
1
3
2
12
5% chance higher than highest seen
5% lower than lowest seen
Highest sample
Lowest sample
Q. What is the chance of the 12th sample being between the range seen after the first three samples? (no duplicates, uniform distribution, picked at random)
?
?
A. 90%
% = (1 - (1 / n – 1)) * 100
4
5
6
7
8
9
10
11
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# Prior Samples Prediction Next Sample Within Prior Sample Range
3 50%4 67%5 75%6 80%7 83%8 86%9 88%
10 89%11 90%12 91%13 92%15 93%17 94%20 95%
Halved the risk with 3 samples
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Monte Carlo
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If a measurement changes over time or is different each time you measure it, it is a
DISTRIBUTION
Can’t apply normal mathematical operators….
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Cycle Time Forecasting
1. Easy to Capture Metric
3. Follows Known Distribution Pattern
2. Forecasting using Historical Data
(even a little)
4. Forecast at Project or Feature
or Story Level
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Sum Random Numbers
25112943342631452227
31436545
87
34735448
1912242721
39
202329
187410295Sum:
…..
Historical Story Lead Time Trend
Days To Complete
Basic Cycle Time Forecast Monte Carlo Process
1. Gather historical story lead-times2. Build a set of random numbers based on pattern3. Sum a random number for each remaining story to build a single outcome4. Repeat many times to find the likelihood (odds) to build a pattern of likelihood outcomes
𝑇𝑜𝑡𝑎𝑙𝐷𝑎𝑦𝑠❑=𝑆𝑢𝑚(𝑆𝑡𝑜𝑟𝑦𝑛×𝑅𝑎𝑛𝑑𝑜𝑚𝑛)
𝐸𝑓𝑓𝑜𝑟𝑡
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How I Quantify Lead Time Reduction
Lead Time # Stories / Year Throughput Benefit
Current 6 0%
10% Decrease 7 17% More
20% Decrease 8 33% More
When no ROI is easily discerned (maintenance teams?)
Even a small decrease in Cycle Time has a increased impact on throughput over a year..
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Cycle Time
Forecast Date Forecast Cost
Cash flow to EOY14(cost saving + revenue)
Benefit
Current 15-Jul-2014 $1,000,000 $0 + $60,000 = $60,000 0%10% Decrease 27-May-2014 $912,500 $87,500 + $90,000 =
$177,500296% Better
20% Decrease 04-Apr-2014 $820,000 $120,000 + $145,000 =
$265,000442% Better
Month Revenue
April $30,000
May $25,000
June $20,000
July $20,000
Aug-Dec $10,000
Revenue Estimates for product:
How I Quantify Cycle Time Reduction
Even a small decrease in Cycle Time can have huge a benefit on
cash flow over time.
This example shows the cost of missing a seasonal uptick in summer sales (revenue estimates shown to the left). Cost per work day is calculated at $2,500 day.
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Shape and Impact of Cycle-Time & Scope
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Probability Density Function
Histogram Gamma (3P) Lognormal Rayleigh Weibull
x1301201101009080706050403020100-10
f(x)
0.32
0.28
0.24
0.2
0.16
0.12
0.08
0.04
0
Note: Histogram from actual data
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Probability Density Function
Histogram Weibull
x1301201101009080706050403020100-10
f(x)
0.32
0.28
0.24
0.2
0.16
0.12
0.08
0.04
0
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Likelihood…
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Total Story Lead Time
30 days
Story / Feature Inception5 Days
Waiting in Backlog25 days
System Regression Testing & Staging 5 Days
Waiting for Release Window5 Days
“Active Development”30 days
Pre Work
30 days
Post Work
10 days
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Binary PermutationsRisk1
Risk2
Risk3
Risk 4
Risk 5
Risk 6
Risk 7
Risk 8
Risk 9
Risk 10
No No No No No No No No No No
No No No No No No No No No Yes
No No No No No No No No Yes No
No No No No No No No No Yes Yes
No No No No No No No Yes No No
… … … … … …
Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
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Why Weibull
• Now for some Math – I know, I’m excited too!
• Simple Model• All units of work between 1 and 3 days• A unit of work can be a task, story, feature, project• Base Scope of 50 units of work – Always Normal• 5 Delays / Risks, each with
– 25% Likelihood of occurring– 10 units of work (same as 20% scope increase each)
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Normal, or it will be after a few
thousand more simulations
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Base + 1 Delay
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Base + 2 Delays
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Base + 3 Delays
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Base + 4 Delays
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Base + 5 Delays
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Probability Density Function
Histogram Weibull
x1201101009080706050403020100
f(x)
0.28
0.24
0.2
0.16
0.12
0.08
0.04
0
Scale – How Wide in Range. Related to the
Upper Bound. *Rough* Guess: (High – Low) / 4
Shape – How Fat the distribution. 1.5 is a good starting point.
Location – The Lower Bound
KEY POINT: WITH JUST MIN AND MAX THE CURVE CAN BE INFERRED
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Changing Cycle Time Shape
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Probability Density Function
Weibull (1.2; 25)
x8880726456484032241680
f(x)
0.028
0.024
0.02
0.016
0.012
0.008
0.004
0
Probability Density Function
Weibull (1.5; 30)
x8880726456484032241680
f(x)
0.026
0.024
0.022
0.02
0.018
0.016
0.014
0.012
0.01
0.008
0.006
0.004
0.002
0
Teams following this curve WILL be able to predict more
predictability because forecast range will be tighter
When forecasting, the wider the curve, the MORE higher
value numbers will occur
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Binary PermutationsRisk1
Risk2
Risk3
Risk 4
Risk 5
Risk 6
Risk 7
Risk 8
Risk 9
Risk 10
No No No No No No No No No No
No No No No No No No No No Yes
No No No No No No No No Yes No
No No No No No No No No Yes Yes
No No No No No No No Yes No No
… … … … … …
Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
EVERY RISK OR DELAY YOU CAN REMOVE REDUCE COMBINATIONS BY 2n
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Order of Priority for Improvement
• Prioritized list of blockers and delay states• Balanced to include most frequent & biggest delay• Order Risks and Delays by weighted impact
Impact = Frequency Risk or Delay “Type” x Duration
• This will remove the most combinations of delay and shrink the area of our distribution giving biggest benefit of predictability
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Mining Cycle Time Data
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Mining / Testing Cycle Time Data
Capture Data
Scatter Plot
Histogram
Fit Shape
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Cycle Time Capture Practices
• Clearly understand from where and to where• Capture begin and end date; compute the
number of days in-between• Does cycle time include defect fixing?• Are there multiple types of work in the same
cycle time data– Stories– Defects– Classes of Service
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Things that go wrong…
• Zero values• Repetitive (erroneous) values• Batching of updates• Include/exclude weekends• Project team A staff raided impacting their
cycle time (Team A up, team B’s down)• Work complexity changes• Team skill changes
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Probability Density Function
Histogram Weibull
x120100806040200
f(x)
0.36
0.32
0.28
0.24
0.2
0.16
0.12
0.08
0.04
0
Probability Density Function
Histogram Weibull
x160140120100806040200
f(x)
0.22
0.2
0.18
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
Many low values. Often zero or values below what makes sense. Check the most frequent low values.
Multiple modes. In this case two overlapping Weibulls. Often due to multiple classes of service, or most often - defects versus stories.
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Probability Density Function
Histogram Weibull
x1201101009080706050403020100
f(x)
0.28
0.24
0.2
0.16
0.12
0.08
0.04
0
Scale – How Wide in Range. Related to the
Upper Bound
Shape – How Fat the distribution.
Location – The Lower Bound
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Estimating Distributions using
Historical Data
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What Distribution To Use...
• No Data at All, or Less than < 11 Samples (why 11?)– Uniform Range with Boundaries Guessed (safest)– Weibull Range with Boundaries Guessed (likely)
• 11 to 50 Samples– Uniform Range with Boundaries at 5th and 95th CI– Weibull Range with Boundaries at 5th and 95th CI– Bootstapping (Random Sampling with Replacement)
• More than 100 Samples– Use historical data at random without replacement– Curve Fitting
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Sampling at Random Strategies
• If you pick what samples to use, you bias the prediction…
• Strategies for proper random sampling –– Use something you know is random (dice, darts)– Pick two groups using your chosen technique and
compute your prediction separately and compare– Don’t pre-filter to remove “outliers”– Don’t sort the data, in fact randomize more if
possible
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Concurrent WIP Sample : Find the smallest and the biggest or take at least 11 samples to be 90% sure of range
Estimating Concurrent Effort from Cumulative Flow Chart
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20% 40%
60% 80%
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Scope Creep Over TimeLook at the rate new scope is added over time