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Modeling Kanban Scheduling in Systems of Systems

Modeling Kanban Scheduling in Systems of Systems. Alexey Tregubov, Jo Ann Lane. Outline. Problem statement Research approach: Kanban process Overview of KSS Network Benefits of KSS Simulation model Simulation results Health care example Complex example Future work.

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Modeling Kanban Scheduling in Systems of Systems

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  1. Modeling KanbanScheduling in Systems of Systems Alexey Tregubov, Jo Ann Lane

  2. Outline • Problem statement • Research approach: Kanban process • Overview of KSS Network • Benefits of KSS • Simulation model • Simulation results • Health care example • Complex example • Future work

  3. Problem statement In SoS environments the following problems observed: • Lack of visibility: • What is going on at the project level? • What is the current status of SoS capabilities? • How to expedite certain urgent capabilities? • Everything is critical but nothing is done • Every constituent team establish their own priorities

  4. Research approach: Kanban process • Kanban in manufacturing industry: • It is a system to control the logistical supply/demand chain. Toyota,1953. • Kanban as part of lean concept: • “Pull” instead of “push” in demand • Kanban is part of Just in time (JIT) delivery approach • Kanban in a software engineering (as a process): • Visualized work flow – kanban boards • Limit WIP • Feedback and collaboration to improve the flow • Kanban in System of System environments: • Kanban scheduling in work prioritization • Value based work prioritization based on SoS capabilities, balanced with single system needs • Visualization

  5. Overview of KSS Network

  6. Benefits of using KSS • Eliminate waste • Minimize context switching • Limit work in progress • Make process more visible and transparent • Kanban boards • Increased value delivered earlier • Value-based work prioritization • Reduce governance overhead

  7. Cost context switching in multitasking

  8. Simulation model Discrete event simulation: • Inputs: • Event scenario: a sequence of events that describes how network evolves over course of their execution • Team configuration: structure of teams, resource/specialties allocation • Simulation configuration: stop condition • Outputs: • Sequence of network states • Analysis: various indicators of effectiveness

  9. Simulation model: Kanban scheduling • All work items (WI) prioritized according to their business value • Every WI has a class of service: Standard, Important, Date Certain, Critical Expedite • Limiting work in progress: work in progress is never interrupted unless • new work has a Critical class of service • work is suspended by prerequisites • Visualization: kanban boards for each team.

  10. Health care example of KSS Network

  11. Example: capabilities to requirements to products

  12. Example: capabilities to requirements to products Value = 60 Value = 20 Value = 10 Value = 10

  13. Example: capabilities to requirements to products Value = 60 Value = 20 Value = 10 Value = 10 10 10 40 20 20 50

  14. Example: network structure & scenario

  15. Example: outputs

  16. Example: simple scenario Value: Time

  17. Example 2: complex scenario • 10 teams (20 members each) + system engineering team. • 20 new capabilities at start. • Each capability unfolds into 30 requirements on average • Eachrequirement unfolds into 9 tasks on average. • Eachtasks takes 3-15 days. • There are 5 expedite tasks that cause blocked work (blocked tasks)

  18. Example 2: value comparison

  19. Example 2: number of suspended tasks

  20. Example 2: work items in progress

  21. Example 2: total time spent (schedule)

  22. Example 2: total effort Effort required if there are interruptions

  23. Example 2: context switching

  24. Conclusion: future work • Pilot the Kanban scheduling with several organizations • Fine-tune the simulator using empirical data and organizations’ feedback • Scale up the cases we run through the simulator • Refine and calibrate cost models

  25. Questions & answers

  26. References • Waldner, Jean-Baptiste (September 1992). Principles of Computer-Integrated Manufacturing. London: John Wiley. pp. 128–132. ISBN 0-471-93450-X. • "Kanban". Random House Dictionary. Dictionary.com. 2011. Retrieved April 12, 2011. • Ohno, Taiichi (June 1988). Toyota Production System - beyond large-scale production. Productivity Press. p. 29. ISBN 0-915299-14-3. • Richard Turner, Jo Ann Lane, Goal-question-Kanban: Applying Lean Concepts to Coordinate Multi-level Systems Engineering in Large Enterprises, Procedia Computer Science, Volume 16, 2013, Pages 512-521, ISSN 1877-0509, (http://www.sciencedirect.com/science/article/pii/S1877050913000550) • TorgeirDingsøyr, Sridhar Nerur, VenuGopalBalijepally, Nils Brede Moe, A decade of agile methodologies: Towards explaining agile software development, Journal of Systems and Software, Volume 85, Issue 6, June 2012, Pages 1213-1221, ISSN 0164-1212, (http://www.sciencedirect.com/science/article/pii/S0164121212000532) • Woods D. Why Lean And Agile Go Together : [Digital document], (http://www.forbes.com/2010/01/11/software-lean-manufacturing-technology-cio-network-agile.html). Verified on 7/7/2013. • Gerald Weinberg, Quality Software Management: Vol1. Systems Thinking. 1991

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