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Pea Pods & Connecting the Upstream - Lean Kanban North America 2018
- 1. Pea Pods & Connecting the Upstream @martinaziz@fer_cuenca Fernando A. Cuenca, KCP Martin Aziz, KCP fernando.a.cuenca@gmail.com martinaziz@gmail.com
- 2. The Case for Organizational Flow Companies need to pay attention to end to end flow. Local optimization will only offer you temporary relief in solving your business challenges.
- 3. Punctuated Equilibriums. A multi-year journey of transformation and emerging maturity. Punctuation Points At Equilibrium Managed Projects Some consistent process Heroics Inconsistent Outcomes Scrum Consistent Outcomes Teams & Tribes Scrum All Teams No projects No managers Business needs unmet. 2016 ->2014-2015< - 2013 Equilibrium Time Need something more!
- 4. 5 to 7 Months Releasable FeaturesPea pods. From Years to Months – Highly Stable But still not fit.
- 5. Give up point. Committed demand with an expired opportunity Soup Kitchens. Overburdening & Over Servicing
- 6. Why is this happening to us?
- 7. Customers Products & Services A company responding to the environment
- 8. Dedicated ScrumMasters Dedicated Product Owners Cross- functional, small teams Decentralized Control & Capacity Management
- 9. Customers Product Owners Customers have expectations of performance & delivery.
- 10. Tribal Identifies, Inward-looking local optimization
- 11. Where is my stuff? What am I going to get at the end? Why does it take this long? Seems to work for me…. End to End Measurement for Fitness
- 12. Comes from different sources Comes in different types, requiring different processing It has different frequencies of arrival It has different levels of urgency, Importance, and cost of delay It has different perceived value Cost of Delay and Heterogeneous demand
- 13. Is the teams local agenda an issue? + Local Improvement Effort Complexity of Problem + +Number of Teams Tribal Behaviour
- 14. How does this impact Lead Time and Fitness for Purpose? ‐ Complexity of Problem +? Lead Time Fit for Purpose
- 16. What is the effect of a disconnect between commitments and capacity? Potential for overburdening Implicitness of Commitment Point Distance between Teams & Commitment Point Overburdening + +
- 17. White Spaces, Revealed Complexity & Batch Sizes Delivery “chains” Shared team members Internal “shared services” Person with very specialized skills “floating” around teams “External” team members
- 18. Overburdening and white spaces. Have we found the link to Fitness? + ‐ ‐ Over- burdening Coordination effort + White Space ‐ ‐ Number of Teams Flow Efficiency Lead Time Fit for Purpose
- 19. What about batch sizes? Reinforcing relationship to transaction costs. Feeds Dark Matter. Amplifies lead time. + + Transaction Cost +Lead Time Batch Size
- 20. Decomposition
- 21. Recognizability and Transaction Costs Some stay the same Some are re- aggregated and batchedSome describe business functionality Some are purely technical tasks Some have internal dependencies Some are sent to other team’s backlogs
- 22. Where does decomposition take us? Loss of Customer Recognizability. Losing your link to the customer. Number of Teams Tribal Behaviour + + Decomposition + Customer Recognizability Flow Efficiency ‐ Batch Size Local Improvement Effort + + Lead Time Fit for Purpose ‐ Implicitness of Commitment Point Distance between Teams & Commitment Point + Overburdening ‐ + Complexity of Problem Coordination effort + White Space + ‐ ‐ + + + + + + ‐ + Transaction Cost ‐ Decomposition Customer Recognizability
- 25. Number of Teams Tribal Behaviour + + Decomposition + Customer Recognizability Flow Efficiency ‐ Batch Size Local Improvement Effort + + Lead Time Fit for Purpose ‐ Implicitness of Commitment Point Distance between Teams & Commitment Point + Overburdening ‐ + Complexity of Problem Coordination effort + White Space + ‐ ‐ + + + + + ‐ + Transaction Cost ‐ Constraints ‐ ‐ Explicit Commitment ‐ Pull Policies Measurement ‐ ‐ Service Orientation + System level changes for the organization. Service Orientation Constraints Explicit Policies Pull Policies Measure- ment 1. Constraints 2. Service Orientation 3. Measuring 4. Pull 5. Policies Number of Teams Tribal Behaviour + + Decomposition + Customer Recognizability Flow Efficiency ‐ Batch Size Local Improvement Effort + + Lead Time Fit for Purpose ‐ Implicitness of Commitment Point Distance between Teams & Commitment Point + Overburdening ‐ + Complexity of Problem Coordination effort + White Space + ‐ ‐ + + + + + + ‐ + Transaction Cost ‐
- 26. Sense & Promise Push Stories Scrum Team Scrum Team XP Team Story Accumulation FeaturesIdeas Kanban Team Customers Delivery Customers Doing White spaces between teams sources of greatest delays. Work hard to recognize by customers Unconstrained demand Early Commitment without connection to capability Key challenges of the system
- 27. Linking Upstream Flow to Downstream Capacity Sense Pull Features Option Do Next Ideas CustomersDelivery Customers Options Do Options Good Constrained delivery pipeline Upstream Downstream Progress Customer Recognizable. No longer expressed as a team property. Delivery Improvements aligned to optimize for value delivery Work is pulled into delivery pipe automatically as capacity becomes available. Push is avoided to prevent overburdening. Shaping Demand
- 28. Board for Enterprise Flow. 3 levels of Constraints Shaping Uncommitted Options Maintaining Recognizability in the downstream. F4P Feedback Loop to improve selection and delivery Work Stream pull
- 29. Eliminating pea pods. Look beyond teams Constrain throughout the enterprise Maintain Customer Recognizability at all times Commit based on capacity signals
- 30. How to reach us to keep the conversation going @martinaziz@fer_cuenca fernando.a.cuenca@gmail.com martin.aziz@loyalty.com