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Dimitar Bakardzhiev
Managing DirectorTaller Technologies Bulgaria
@dimiterbak
#NoEstimates Project Planning using Monte Carlo
simulation
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Clients come to us with an idea for a new
product and they always ask the questions -
how long will it take and how much will it cost
us to deliver? They need a delivery date
and a budget estimate.
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Reality is uncertain, yet we as software developers are
expected to deliver new products with certainty.
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To increase the chances of project success we need to incorporate the uncertainty in our planning and exploit it.
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WE CAN’T CONTROL THE WAVES OF
UNCERTAINTY, BUT WE CAN LEARN
HOW TO SURF!
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TO ME #NOESTIMATES MEANS
No effort estimates
Effortless estimates
No estimates of effort
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Deterministic planning used these days forces certainty on uncertain situations and
masks the uncertainty instead of highlighting it.
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Project management paradigm is based on 1st principle of
Scientific Management namely “In principle it is possible to
know all you need to know to be able to plan what to do”.
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Project management paradigm believes uncertainty play a role
in project management uncertainty could be eliminated
by a more detailed planning.
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We challenge the project management paradigm and suggest that for
planning purposes it is better to model projects as a flow of work items
through a system.
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A project is a batch of work items each one representing
independent customer value that must be delivered on or before
due date.
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We don’t try to estimate the size of the work items. There are only two "sizes" - “Small Enough" and
“Too Big". "Too big" should be split and not allowed to enter the
backlog.
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A Project in a Kanban System
Input QueueDEPLOYED!
Project Backlog
Development Test QA
WIP 5 WIP 4 NO WIPWIP 2
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High-level probabilistic planning
• The initial budget and the range of the time frame• Does not include detailed project plans• The plan is created with the appropriate buffers• Schedules are the execution of the high-level plan• Keep focus on the project intent
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Reference class forecasting
Reference class forecasting promises more accuracy in forecasts by taking an "outside view" on the project being forecasted based on knowledge about actual performance in a reference class of comparable projects.
Daniel Kahneman
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Reference class forecasting
• Identification of a relevant reference class of past, similar projects. The class must be broad enough to be statistically meaningful but narrow enough to be comparable with the specific project.
• Establishing a probability distribution for the selected reference class.
• Comparing the new project with the reference class distribution, in order to establish the most likely outcome for the new project.
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IDENTIFICATION OF A REFERENCE CLASS OF
SIMILAR PROJECTS
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Are the Team structures comparable?
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Are the Technologies used comparable?
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Are the Development processes comparable?
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Are the Client types comparable?
http://blog.7geese.com/2013/07/04/7-reasons-why-i-decided-to-work-for-a-startup/
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Are the Business domains comparable?
http://www.mindoceantech.com/
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ESTABLISHING A PROBABILITY
DISTRIBUTION FOR THE SELECTED
REFERENCE CLASS
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What metric will be used in the forecast?
The metric should allow us:
• to take an “outside view” on the development system that worked on the project
• calculating delivery time
• make sense from client’s perspective.
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Takt Time!
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Takt Time is the average time between two successive
deliveries
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How manufacturing measure Takt Time?
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How knowledge workers measure Takt Time?
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Inter-Departure Time (IDT) is the time between two successive deliveries
Start 5 days 7 days 2 days 2 days 1 day 5 days Finish
IDT = 0 days
IDT = 0 days
IDT = 5 daysIDT = 7 days
Project delivery time (T) = 5 + 7 + 2 + 2 + 1 + 5 = 22 days
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• T is the time period over which the project was delivered• IDT is the inter-departure time or the time between two
successive deliveries
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Takt Time
𝑇𝑇= 𝑇𝑁
=∑𝑖=1
𝑁
𝐼𝐷𝑇 𝑖
𝑁
• T is the time period over which the project was delivered• N is the number of items to be delivered in period [0,T]• is the Takt Time for period [0,T]
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TT calculation
𝑇𝑇=𝑇𝑁
=22𝑑𝑎𝑦𝑠10𝑠𝑡𝑜𝑟𝑖𝑒𝑠
=2.2𝑑𝑎𝑦𝑠 /𝑠𝑡𝑜𝑟𝑦
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Project Delivery time
𝑇=𝑁𝑇 𝑇• T is the time period over which the project will be delivered
N is the number of items to be delivered in period [0,T]• is the Takt Time for period [0,T]
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Project Delivery time
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We should NOT use the Takt Time as a single number but a distribution of
the Takt Time instead!
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Bootstrapping• Introduced by Bradley Efron in 1979
• Based on the assumption that a random sample is a good representation of the unknown population.
• Does not replace or add to the original data.
• Bootstrap distributions usually approximate the shape, spread, and bias of the actual sampling distribution.
• Bootstrap is based on the assumption of independence.
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1. Have Inter-Departure Time (IDT) sample of size n 2. Have the number of work items delivered (N)3. Draw n number of observation with replacement out
of the sample from step 14. Calculate Project Delivery time (T) for the sample from
step 2 using 5. Calculate Takt Time () by using T from step 4 and N
from step 26. Repeat many times7. Prepare distribution for Takt Time ()
Bootstrapping the distribution of Takt Time
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Example: Bootstrapping Takt Time ()
Sampled IDT data=(0,0,1,1,1,2,2,2,5,7)
= 2.1 days/story
Another 998 draws with replacement
Historical IDT data=(0,0,0,0,1,2,2,5,5,7)
= 2.2 days/storySampled IDT data=(0,1,1,1,1,2,5,5,5,7)
= 2.8 days/story
1st draw with replacement
1000th draw with replacement
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Result: Takt Time () distribution
Median 2,2STD 0,788833Average T 2,194385 Perc 395 Perc 3,5Mode(s) 2,4SIP size 1000
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Stochastic Information Packet (SIP)• Comprised of a list of trials of some uncertain
parameter or metric generated from historical data using Monte Carlo simulation (resampling)
• Represents an uncertainty as an array of possible outcomes (distribution)
• It is unique per context (business domain, team, delivery process used etc.)
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COMPARING THE NEW PROJECT WITH
THE REFERENCE CLASS
DISTRIBUTION
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assumes linear delivery rate
Project Delivery Time (T)
Project Delivery Time (T)
Completed Work (N)
22 days
10 work items
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Most projects have non-linear delivery rate
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Z-curve
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Each leg of the Z-curve is characterized by:
• Different work type• Different level of variation• Different staffing in terms of headcount and level of
expertise
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1st leg – Setup time
• climbing the learning curve• conducting experiments to cover the riskiest work
items• Innovation!• setting up environments• adapting to client’s culture and procedures• understanding new business domain• mastering new technology
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2nd leg – Productivity period
If the project is scheduled properly the system should be like a clockwork – sustainable pace, no stress, no surprises…
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3rd leg – Cleaning up
• Clean up the battlefield• Fix some outstanding defects • Support the transition of the project deliverable into
operation
https://www.ocoos.com/me/professional-dog-training-in-home/
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Project delivery time T
Where:
– is the duration of the 1st leg of the Z-curve – is the duration of the 2nd leg of the Z-curve – is the duration of the 3rd leg of the Z-curve
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Project delivery time
Where: is the Takt Time for the 1st leg of the Z-curve
is the Takt Time for the 2nd leg of the Z-curve
is the Takt Time for the 3rd leg of the Z-curve
is the number of items delivered during the 1st leg of the Z-curve
is the number of items delivered during the 2nd leg of the Z-curve
is the number of items delivered during the 3rd leg of the Z-curve
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Monte Carlo simulation of Project Delivery Time (T) based on Z-curve
1. Have three Takt Time SIPs (, , ) each one of size n for each of the three legs of the Z-curve
2. Have the number of work items to be delivered for each of the three legs of the Z-curve (, , )
3. Draw one observation out of the n, with replacement (bootstrap) from each of (, , )
4. Calculate Project Delivery time (T) for the sample from step 3 using
5. Repeat many times6. Prepare Delivery time (T) probability distribution
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EXAMPLE: MONTE CARLO SIMULATION OF PROJECT DELIVERY TIME (T)
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The New Project to be delivered
• THE SAME Fortune 500 Staffing company
• THE SAME development organization
• THE SAME technology – Java; Spring; Oracle;
• Delivery time TO BE PREDICTED
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Takt Time distributions for each of the three legs of Z-curve for the reference
class
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Project scope
After some analysis the team have broken down the requirements into user stories, accounting for Cost of Delay, added work items for Dark matter and Failure load and decided that:
• 12 stories TO BE delivered in the 1st leg of Z-curve
• 70 stories TO BE delivered in the 2nd leg of Z-curve
• 18 stories TO BE delivered in the 3rd leg of Z-curve
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Monte Carlo simulated summation of…
…will give us the time needed to deliver the project!
12 work items 70 work items 18 work items
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Monte Carlo simulation of Project Delivery Time (T)
Simulated one Project Delivery Time value
49998 draws with replacement from each of (, , )
Takt Time SIPs:, , Work items:, ,
1st draw with replacement from each of (, , )
50000th draw with replacement from each of (, , )
Simulated one Project Delivery Time value
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Mode = 76 days; Median = 77 days; Mean = 78 days; 85th perc = 90 days
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By taking an outside view when forecasting a new project we will
produce more accurate results faster than using the deterministic inside
view.
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ReferencesHere are the distributions for the baseline project SIPs_MonteCarlo_FVR.xlsxHere is the planning simulation in Excel High_Level_Project_Planning.xlsx
What is SIP?
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Dimitar Bakardzhiev is the Managing Director of Taller Technologies Bulgaria and an expert in driving successful and cost-effective technology development. As a Lean-Kanban University (LKU)-Accredited Kanban Trainer (AKT) and avid, expert Kanban practitioner, Dimitar puts lean principles to work every day when managing complex software projects with a special focus on building innovative, powerful mobile CRM solutions. Dimitar has been one of the leading proponents and evangelists of Kanban in his native Bulgaria and has published David Anderson’s Kanban book as well as books by Eli Goldratt and W. Edwards Deming in the local language.
@dimiterbak