MoSCoW Rules: A quantitative expose

MoSCoW Rules: A quantitative exposé
Eduardo Miranda, PhD
Carnegie Mellon University

Agenda
• Brief introduction to MoSCoW Rules
• Importance of the quantitative analysis
• Assumptions made
• Quantitative (Monte Carlo) results
• Conclusions
• Q&A
2022 © Eduardo Miranda 2

MoSCoW Rules
• A feature development prioritization mechanism where each of them is assigned to one of
four categories: Must Have, Should Have, Could Have, Won’t Have (this time) based on user
preferences and technical dependencies. Typically a constraint of 60/20/20% of the available
budget is allocated to each of the first three categories
• By not starting work in a lower preference category until all the work in the more preferred
ones have been completed, the method effectively creates a buffer or management reserve
of 40% for the Must Have features, and of 20% for those in the Should Have category. These
buffers increase the confidence that all features in those categories will be delivered by the
project completion date
• In 1994, D. Clegg and R. Baker proposed the classification of requirements into Must Have,
Should Have, Could Have and Won’t Have. The classification was made on the basis of the
requirements’ own value and was unconstrained, i.e. all the requirements meeting the
criteria for “Must Have” could be classified as such
• In 2006, the DSDM Consortium, now the Agile Business Consortium, published the DSDM
Public Version 4.2 establishing the 60/20/20% recommendation. The current formulation of
the MoSCoW prioritization rules is documented in the DSDM Agile Project Framework

Must Have, Should Have, Could Have, Won’t Have
Must Have (MH)
Could Have (CH)
Should Have (MH)
•Fundamental to project success. Without any of these the project would be
considered a failure
•Features in this category might entail up to 60% of the development budget
•Important but not vital. May be painful to leave them out but the solution will
still be viable
•Nice to have. Will enhance a solution but will not undermine basic functionality
•There is not enough budget to develop them at this time
Won’t Have (WH)

How does it work?
Buffer (20%)
Buffer (40%)
Execution phase
Could
Planning phase
Buffer (40%)
Should have
(20%)
Buffer (20%)
Could have
(20%)
Fixed duration
FTEs
Time boxes
Must have (60%)
Should have
(20%)
Must have
FTEs
FTE: Full Time Equivalent

Importance of the quantitative analysis
• Under MoSCoW rules the project scope is variable
– Contracts will include price incentives/penalties contingent on deliveries
– Employee rewards must be aligned with releases’ completion

To calculate incentives or penalties, the supplier must take into consideration the
probability of successfully delivering (PoD) all the features in a category
• Must Have
– By definition the probability of delivering all features in this category must be close
to 1
– 𝑃𝑜𝐷 𝐸𝑓𝑓𝑜𝑟𝑡 𝐵𝑢𝑑𝑔𝑒𝑡 90%
• Should have
– Features in this category should have a fair chance of being delivered
– 𝑃𝑜𝐷 𝐸𝑓𝑓𝑜𝑟𝑡 𝐸𝑓𝑓𝑜𝑟𝑡 𝐵𝑢𝑑𝑔𝑒𝑡 50%
• Could have
– Features that the project could deliver within the defined time box if everything
went as expected, i.e. if there were no hiccups in the development of features
assigned higher priority
– 𝑃𝑜𝐷 𝐸𝑓𝑓𝑜𝑟𝑡 𝐸𝑓𝑓𝑜𝑟𝑡 𝐸𝑓𝑓𝑜𝑟𝑡 𝐵𝑢𝑑𝑔𝑒𝑡

Contracting example using MoSCoW rules
Budget = 4,000 hrs.; Must have = 2,400 hrs.; Should have = 800 hrs.; Could have = 800 hrs. Hourly cost = 100$, Desired
margin = 30%; Reserve & contingency for firm fix price contract = 20%, no margin on R&C
Project
delivers
Firm Fix Price Firm price with incentives
Client pays (K$)
(1)
Profit (%)
(2)
Firm (base)
price (K$)
(3)
Incentive (K$)
(4)
Probability
(5)
Client pays
(K$)
(6)
Expected
profit (%)
(7)
Must
have
600 [0, 50]% 400
0 1 400
32.5%
Should
have
160 .5 560
Could
have
200 .25 600
(1) 𝑃𝑟𝑖𝑐𝑒 𝐵𝑢𝑑𝑔𝑒𝑡 𝐷𝑒𝑠𝑖𝑟𝑒𝑑𝑃𝑟𝑜𝑓𝑖𝑡 𝐶𝑜𝑛𝑡𝑖𝑛𝑔𝑒𝑛𝑐𝑦
(3) Business decision covers fixed cost
(2)
𝑃𝑟𝑖𝑐𝑒
𝐴𝑐𝑡𝑢𝑎𝑙𝐶𝑜𝑠𝑡
1
(4) Business decision amounts must satisfy equation (7)
(5) Assumed probabilities. We’ll learn how to calculate them
on this presentation
𝐼 𝑃 𝐼 𝑃 𝐼 𝑃
𝐵𝑢𝑑𝑔𝑒𝑡
𝐷𝑒𝑠𝑖𝑟𝑒𝑑𝑀𝑎𝑟𝑔𝑖𝑛
(7)
(6) 𝑃𝑟𝑖𝑐𝑒 𝐵𝑎𝑠𝑒𝑃𝑟𝑖𝑐𝑒 𝐼𝑛𝑐𝑒𝑛𝑡𝑖𝑣𝑒

Confidence of the estimates
Probability
distributions for the
effort required by
each feature in the
low (uniform
distributions) and
typical (triangular
distributions)
confidence scenarios

Influence of confidence on the estimates: Low and typical
Cumulative
completion
probabilities for
“Must Have”
features under
increasing levels
of
underestimation,
independent
estimates, no
dominant
features, 15
features
‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐

How much certainty do the 60/20/20% allocation give us?
Underestimations of up to 50% Underestimations of up to 100% Underestimations of up to 200%
Independent
efforts
Correlated
efforts (r = 0.6)
Independent
efforts
Correlated
efforts (r = 0.6)
Independent
efforts
Correlated
efforts (r = 0.6)
Low
confidence
estimates
Typical
estimates
Low
confidence
estimates
Typical
estimates
Low
confidence
estimates
Typical
estimates
Low
confidence
estimates
Typical
estimates
Low
confidence
estimates
Typical
estimates
Low
confidence
estimates
Typical
estimates
Must
Have
100% 100% 100% 100% 98.9% 100% 74.0% 96.4% 1.3% 70.5% 26.9% 59.8%
Should
Have
50.2% 100% 49.9% 88.7% 0 39.7% 15.6% 50.3% 0 0 4.0% 20.5%
Could
Have
0 0 0 20.5% 0 0 0 8.6% 0 0 0 3%
Good protection Some protection Insufficient protection

Under what circumstances?
• Confidence on the estimates
– Low confidence to establish worst case, useful to calculate maximum exposure
– Typical estimate, allows for the possibility of overestimations, useful to calculate
nominal risks, penalties and incentives
• Correlated efforts
– Independent variations
– All the efforts tend to drift in the same direction as consequence of a common
driver
• Non dominant features
– All features require more or less the same effort
• Number of features
– There are at least 5 features in each category

Influence of correlation among features
Cumulative
completion
probabilities for
must have
features with up
to 100%
underestimation,
varying
correlation
levels, no
dominant
features, 15
features

Influence of dominant features
Borey-class
Cumulative
completion
probabilities for
“Must Have”
features with up
to 100%
underestimation,
independent
estimates,
varying level of
dominance, 15
features

Influence of the number of features
Cumulative
completion
probabilities for
“Must Have”
features with up
to 100%
underestimation,
independent
estimates, no
dominant
features, varying
number of
features

Why 60/20/20%?
• Probably based on experience
• Advantages
– It is simple
– Doesn’t look as bad as 50%
– Provides more protection than 70%

Why 60 and not 50, 70, or 80?
Cumulative
completion
probabilities for
“Must Have”
feature under
increasing levels
of
underestimation,
independent
estimates, no
dominant
features, number
of features
depend on
budget allocation

Summary
• MoSCoW Rules offer a simple, effective and efficient prioritization
mechanism capable of providing ample protection against
underestimations of up to 100% to the Must Have features and a fair
protection for the Should Have up to a 50% underestimation level
• To achieve the above mentioned level of protection a category must
consist of 5 or more, with no feature requiring more than 25% of the
effort allocated to the category
• The 60, 20, 20% allocation seems to be the “Goldilocks” solution,
balancing predictability with level of ambition

Questions?

Supplementary slides

Independent‐high correlation paradox
Independent elements: The minimum and
maximum corresponding to the sum of the
individual variables is seldom seem
Correlated elements: The minimum and
maximum corresponding to the sum of the
individual variables is more frequent

Probability of delivering all features in a category in the case of low confidence estimates under different
levels of underestimation when the efforts required by each feature are independent (r = 0)

Probability of delivering all features in a category in the case of low confidence estimates under different
levels of underestimation when the efforts required by each feature are highly correlated (r = 0.6)

Probability of delivering all features in a category in the case of typical estimates under different levels
of underestimation when the efforts required by each feature are independent (r = 0)

Probability of delivering all features in a category in the case of typical estimates under different levels
of underestimation when the efforts required by each feature are highly correlated (r = 0.6)

MoSCoW RULES: Example
Feature Estimate Dependencies
A 20 B, C
B 10
C 20
D 5 E
F 5
G 20
H 10 J, K
I 15
J 12
K 8
E 6
L 10
• Sponsor order of preference is: F, D, A,
G, K, E, L, J, H, I, B, C
• Total budget (time box) = 180hrs
• Project management, analysis, design &
technical work = 60hrs
• Development budget = 120hrs
• Find the:
– “Must Have” set
– “Should Have” set
– “Could Have” set
– “Won’t Have” set

MoSCoW RULES: Establish budget for each set
A 20 B, C
B 10
C 20
D 5 E
F 5
G 20
H 10 J, K
I 15
J 12
K 8
E 6
L 10
• Sponsor preference: F, D, A, G, K, E, L, J,
H, I, B, C
• Development budget = 120hrs
• Calculate the initial effort to be
devoted to each feature set:
– 𝐸𝑓𝑓𝑜𝑟𝑡 120 0.6 72
– 𝐸𝑓𝑓𝑜𝑟𝑡 120 0.2 24
– 𝐸𝑓𝑓𝑜𝑟𝑡 120 0.2 24

MoSCoW RULES: Must and Should Have
A 20 B, C
B 10
C 20
D 5 E
F 5
G 20
H 10 J, K
I 15
J 12
K 8
E 6
L 10
H, I, B, C
• Select Must Have features
– 𝐸𝑓𝑓𝑜𝑟𝑡 120 0.6 72
• Select Should Have features
– Update the ShouldHave effort with the
remnant of the MustHave effort
– 𝐸𝑓𝑓𝑜𝑟𝑡 24 6 30
1 Select F 72 ‐ 5 = 67
2 Select D, E 67 ‐ 11 = 56
3 Select A, B, C 56 ‐ 50 = 6
4 Select G 30 ‐ 20 = 10
5 Select K 10 ‐ 8 = 2

MoSCoW RULES: Could and Won’t Have
A 20 B, C
B 10
C 20
D 5 E
F 5
G 20
H 10 J, K
I 15
J 12
K 8
E 6
L 10
H, I, B, C
• Select Could Have features
– Update the CouldHave effort with the
remnant of the ShouldHave effort
– 𝐸𝑓𝑓𝑜𝑟𝑡 24 2 26
6 Select L 26 ‐ 10 = 16
• Won’t Have features: H, I
• Finally
– “Must Have” = {F, D, E, A, B, C}
– “Should Have” = {G, K}
– “Could Have” = {L, J}
– “Won’t Have” = {H, I}
7 Select J 16 ‐ 12 = 4

MoSCoW Rules: A quantitative expose

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