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Revisiting cost of change(and other things on my mind in 2009)
David J. [email protected]
USC CSSE Annual Research Review 2009
Lifecycle throughput & capacity
In this example, the developers have 2.5x greater capacity than the analysts. Analysis and customer acceptance (same resources) is the bottleneck. System testing has 1.5x greater capacity than the system throughput.
Example lifecycle with throughput capacity (approx) Function Points per unit of time (e.g. month)
Development is seldom the bottleneck
• Teaching Agile Management class over 3 years <10% of participants report development as their lifecycle/workflow bottleneck
• Alistair Cockburn has pointed out that rework in non-bottleneck stations is free and can be used as a process strategy
Cockburn, Alistair, “Spending efficiency to go faster”, Humans & Technology Report, March 2008, http://alistair.cockburn.us/%22Spending%22+Efficiency+to+Go+Faster
Cockburn, Alistair, “Two case studies motivating efficiency as a ‘spendable’ resource”, ICAM 2005 International Conference on Agility, Helsinki July 27–28, 2005http://alistair.cockburn.us/Two+Case+Studies+Motivating+Efficiency+as+a+%22Spendable%22+Quantity
Possible Conclusions
• The cost of change is unique to each project and value-stream and its resource allocation
• Research is required to discover the cost of change / rework framework for calculating cost of change in a specific workflow
• Question: How does throughput capacity change over project lifetime? Does the bottleneck move?
CYCLE TIME RELATIONSHIP TO QUALITY
Development cycle time has a non-linear relationship to quality
WIP is directly related to Lead Time and Quality
Device Management Ike II Cumulative Flow
020406080
100120140160180200220240
Time
Fe
atu
res
Inventory Started Designed Coded Complete
WIP
Lead Time
3 month lead time led to a >30x reduction in quality compared to 1 week lead time
Project B Cumulative Flow
0
25
50
75
100
125
150
175
9-O
ct
23-O
ct
6-Nov
20-Nov
4-Dec
18-Dec
1-Jan
15-Jan
29-Jan
12-Feb
26-Feb
11-M
ar
Time
Fe
atu
res
Inventory Started Designed Coded Complete
Lead Time
What I believe?...
time
Def
ect i
nser
tion
rate
linear
~5-10 days
Research Opportunities
• Is there existing data that can be analyzed to compare with this empirical result?
• Does the data correlate?• Can guidance be derived from the shape of
the resultant curve?...– Iteration lengths should not exceed 2 weeks– WIP and multi-tasking should be limited to
manage cycle time below 10 (working) days
REAL OPTION THEORYReal Options is Dead! Long live real options!
Real Options are not very useful
• Huchzermeier & Loch 1998 [INSEAD, Project Management under Risk]
• 5 types of uncertainty– Market payoff– Quality– Budget– Requirements– Schedule
Observations derived from Huchzermeier & Loch 1998
• Market payoff & budget uncertainty increase option valuations
• Quality uncertainty decreases option valuations. => Options are more valuable in high quality conditions
• Requirements uncertainty reduces options value. => In uncertain requirements environments lots of cheap/free options are desirable. With higher certainty, fewer higher value options are optimal
• Option value under schedule uncertainty depends on market payoff function
Conclusion
• Real Options valuation is only useful and possible in low uncertainty projects undertaken by high maturity (CMMI ML4/5 equiv.) organizations
• => Real Options are of no practical use in software engineering applications
SALVAGING VALUE IN THE WRECKAGE OF REAL OPTIONS
Real Options is Dead! Long live real options!
Abandon analogous mapping financial options but leverage some concepts
• Expiry Date• Liquidity• Optimal Exercise point?• Underlying asset?• Broker/market maker?• Valuation – determined as not useful
There is value in having options and making optimal decisions under uncertainty
• Understand goals• Create options• Define “last responsible moment” (decision
point at option expiry date)• Push back decision point as late as possible• Maximize information gathering before
decisionRef: Matts, Chris, “Last Responsible Moment”, Agile Journal, Jan 2009, http://www.agilejournal.com/component/content/article/884-last-responsible-moment-
Ref: Matts, Chris, “Last Responsible Moment”, Agile Journal, Jan 2009, http://www.agilejournal.com/component/content/article/884-last-responsible-moment-
Liquidity
• Liquidity is ability to transfer assets from one form to another – ability to exercise an option
• Liquidity is an aggregate of– Temporal elements– Work-in-progress (inventory)– Personnel (resources)
• Constraint personnel liquidity is ability to learn– (investment in) training will increase option liquidity
Liquidity guidance
• Generalists provide options liquidity (in comparison to specialists)– Rule: assign a specialist to a suitable task, hold generalists
in reserve• Resource availability affects liquidity
– Rule: assign a non-instant availability resource to non-urgent tasks, hold instant availability resources in reserve
• Cross-training, resource rotation generates option liquidity– E.g. Pair programming good, promiscuous pairing better
Ref: Belshee, Arlo, “Promiscuous Pairing and the Beginner’s Mind”, Proceedings of Agile Development Conference 2005, IEEE Computer Society
Liquidity & Task AllocationSkill A Skill B Skill C Skill D
Resource 1(Generalist)
Resource 2
Resource 3
Resource 4(Specialist)
Task requiring skill A
Who should we allocate the task in order to maintain option
liquidity?
If we assign a generalist…Skill A Skill B Skill C Skill D
Resource 1(Generalist)
Resource 2
Resource 3
Resource 4(Specialist)
3 Options
Liquidity 6
But if we assign a specialist…Skill A Skill B Skill C Skill D
Resource 1(Generalist)
Resource 2
Resource 3
Resource 4(Specialist) 4 Options
Liquidity 9
Some General Observations• Options liquidity is a key tool in project risk management• IID more liquidity than Waterfall (iterations)• Agile generally better than IID (generalist workforce)• 2 week Sprint Scrum than 4• 1 week iteration XP better than 2 weeks• Kanban/Pull system better than XP/Scrum (lower WIP, risk
based pull prioritization)• Marginal utility supplied by specialists can be traded
against liquidity from generalists• Efficiency (waste versus value-add) can be traded for
liquidity• Slack provides liquidity => line should not be balanced (i.e.
Lean manufacturing “balance the line” reduces liquidity)Ref: http://finance.groups.yahoo.com/group/real_options_discussion/message/369
Research questions?
• Can we measure/quantify liquidity?• Can we match required liquidity level to risk
profile (and/or uncertainty/variability in domain)?
• Can we provide (objective) guidance to managers on how much liquidity they need and how to maintain it?
Other research questions?
• Can we calculate an optimal exercise point (decision point) without calculating a value (and cost of exercising) for an option?
• Can this be useful if options are not free (or near free) to acquire and easy/low risk to exercise?
• Can these techniques be applied to architecture and design options and decisions[Alternative Architecture Assessment, Decision Analysis and Resolution (DAR)]?
Contact Details
David J. [email protected]://www.agilemanagement.net/Tel: 206.201.2717Skype: agilemanagerhttp://www.linkedin.com/in/agilemanagement