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Predictability: No Magic Required - LeanKit Webinar (June 2017)


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Knowledge work tends to be variable in nature and involves cross-functional teams collaborating on each step of the process. This makes project delivery hard to predict as work may be held up due to unforeseen blockers, hand-off delays, or approval cycles taking longer than expected.

In this webinar, I’ll provide guidance around choices you can make that impact your ability to meet your commitments with confidence.

You'll learn how to predict the cycle time of work before it's finished. I will also explain the basics of queuing theory, and the relationship between queue size, capacity utilization, and cycle times. Armed with this insight, you'll be able to:

* Monitor your workflow for leading predictability indicators.
* Make informed choices to maximize your value throughput.
* Forecast delivery with better accuracy using LeanKit data and analytics.

There's no magic required in achieving predictable delivery. The secret is using past performance data to predict future behavior.

Published in: Business
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Predictability: No Magic Required - LeanKit Webinar (June 2017)

  1. 1. Julia Wester Executive Consultant & Manager, Customer Education @everydaykanban Predictability No Magic Required
  2. 2. Adjective Expected, especially on the basis of previous or known behavior Predictable [pri-dik-tuh-buh l] @everydaykanban USUALLY ________!
  3. 3. You can be predictably bad… @everydaykanban USUALLY HORRIBLE!
  4. 4. @everydaykanban USUALLY GREAT! Or, you can be predictably good…
  5. 5. Predictability is driven by the range of outcomes @everydaykanban We deliver between 1 and 126 days
  6. 6. Smaller ranges mean more predictability @everydaykanban 10 to 50 days is more predictable
  7. 7. We usually only care about the upper limit @everydaykanban We deliver in <= 50 days
  8. 8. “In our zeal to improve the reliability of software development, we have institutionalized practices that decrease, rather than increase, the predictability of outcomes.” @everydaykanban Mary Poppendieck Lean Development & the Predictability Paradox (2003)
  9. 9. Ex: Over-Focus on Capacity Utilization @everydaykanban
  10. 10. Predict the # of circuits and operators needed to avoid blocked calls given:  Random arrivals  Random durations @everydaykanban This problem has existed for a while…
  11. 11. The mathematical study of waiting lines, or queues. Can quantify relationships between queue size, capacity utilization and cycle times Queueing Theory was devised to help @everydaykanban capacity utilization Queue size For a simple M/M/1/∞ queue
  12. 12. @everydaykanban Buildup starts well before 99.9% utilization A simple system that gets one request at a time Even for a simple M/M/1/∞ queue with random arrival and service times and a single ‘server’
  13. 13. Request size Utilization Cycle Time Single item requests 50% 2x Time in service 90% 10x Time in service Big Batch requests 50% 5x Time in Service 90% 22x Time in Service What about queues that aren’t so simple? @everydaykanban Big Batches with random arrivals and service times: M[x]/M/1/∞ queue
  14. 14. @everydaykanban How this affects planning accuracy What we assume What we know What is likely, given probabilities 80 weeks 100 weeks 5
  15. 15. @everydaykanban Queue size is a leading indicator Which lanes are going faster?
  16. 16. “100% of developers [that I surveyed] measured cycle time. 2% measured queues.” @everydaykanban Donald Reinertsen The Principles of Product Development Flow (2009)
  17. 17. If you only do one thing… make queues visible @everydaykanban
  18. 18. @everydaykanban Manage queues by, focus on improving flow of the work
  19. 19. Flow is about leveling out periods of inactivity and creating a smooth, consistent delivery of value @everydaykanban
  20. 20. A focus on keeping the worker busy Image: Todd A. Clarke -
  21. 21. What it looks like to focus on the worker @everydaykanban
  22. 22. A focus on the flow of the actual work Image: Todd A. Clarke -
  23. 23. What it looks like to focus on the work @everydaykanban
  24. 24. “Business units that embraced [process/queue management] reduced their average [product] development times by 30% to 50%.” @everydaykanban OnPoint - Getting the most out of your product development process (2003)
  25. 25. CHOICES YOU MAKE about managing the queue size and flow can make or break your ability to be predictable. @everydaykanban
  26. 26. Choice #1 How you assign work normal stopped Pre-Assign work More Predictable Slower, but consistent Workers pull work Less variation in queue times. More variation in queue times @everydaykanban
  27. 27. @everydaykanban Choice #2 The order that you process work FIFO Non- FIFO More variation in queue times Less variation in queue times. Feasible? More Predictable
  28. 28. @everydaykanban Choice #3 The amount of work you batch Once a week Once a month More variation Less variation
  29. 29. @everydaykanban Choice #4 No. of dependencies you create What are the odds you’ll finish on time with ‘n’ dependencies? 1 in 2n = 1 in 24 = 1 in 16 1 in 2n = 1 in 22 = 1 in 4 1 in 2n = 1 in 26 = 1 in 64 Troy Magennis Less More Variation
  30. 30. MONITORING YOUR PREDICTABILITY INDICATORS @everydaykanban Identify and monitor predictability indicators using LeanKit data and analytics
  31. 31. @everydaykanban Work-In-Process (hidden queues?) Queued work
  32. 32. @everydaykanban
  33. 33. Use LeanKit data to forecast delivery dates @everydaykanban with tools from Troy Magennis & Plug in LeanKit data
  34. 34. @everydaykanban Great references to check out