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Queueing theory in software development - ALEBathtub 2011-06-30

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Queueing theory in software development - ALEBathtub 2011-06-30

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This is the slides from the ALEBathtub.

In this session you will learn about queuing theory and Theory of Constrains. By using real world examples of different traffic situations in Stockholm, illustrations and examples from Kanban boards you will see the similarities between them. You will lean how you can apply Theory of Constrains to find the bottleneck in your development process and how you can use this to continuously improve your development process.

This is the slides from the ALEBathtub.

In this session you will learn about queuing theory and Theory of Constrains. By using real world examples of different traffic situations in Stockholm, illustrations and examples from Kanban boards you will see the similarities between them. You will lean how you can apply Theory of Constrains to find the bottleneck in your development process and how you can use this to continuously improve your development process.

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Queueing theory in software development - ALEBathtub 2011-06-30

  1. 1. Queueingtheoryinsoftware development<br />Håkan Forss - hakan.forss@avegagroup.se - @hakanforss<br />
  2. 2. Or<br />
  3. 3. What can traffic inteach you about yourdevelopment process<br />Håkan Forss - hakan.forss@avegagroup.se - @hakanforss<br />
  4. 4.
  5. 5.
  6. 6.
  7. 7.
  8. 8.
  9. 9. Little’s Law<br />Work-in-Process<br />Throughput<br />Cycle Time =<br />
  10. 10. Little’s Law<br />12<br />12 / min<br />1 min =<br />
  11. 11. Little’s Law<br />6<br />12 / min<br />0,5 min=<br />
  12. 12. Little’s Law<br />24<br />12 / min<br />2 min =<br />
  13. 13. 8 cars / min<br />4 cars / min<br />With less work-in-progress<br />Shorter cycle time<br />Faster feedback<br />Makes problems visible faster<br />
  14. 14.
  15. 15. TheoryofConstraints<br />
  16. 16.
  17. 17.
  18. 18. 5<br />
  19. 19. Don’t allow inertia to cause a system constraint.<br />
  20. 20. Capacity = 4<br />Capacity = 6<br />Capacity = 6<br />Throughput = 4<br />Bottlenecks<br />You can never go faster than your bottleneck<br />
  21. 21. Bottlenecks<br />Throughput = 2<br />You can never go faster than your bottleneck<br />Do whatever you can to make your bottleneck 100% utilized <br />Try your hardest to avoid problems at you bottleneck <br />You can’t make up for lost capacity at you bottleneck<br />
  22. 22. Throughput = 4<br />You can never go faster than your bottleneck<br />As long as capacity in front of the bottleneck is equal to or grater than the bottleneck you will go as fast as your bottleneck<br />Full use of a higher capacity in front of the bottleneck will make cycle time go up<br />Bottlenecks<br />
  23. 23. Bottlenecks<br />Throughput = 4<br />You can never go faster than your bottleneck<br />As long as capacity is equal to or grater after the bottleneck you will go as fast as your bottleneck<br />Higher capacity after the bottleneck than at the bottleneck will not improve throughput<br />
  24. 24.
  25. 25.
  26. 26.
  27. 27.
  28. 28.
  29. 29.
  30. 30.
  31. 31.
  32. 32. Non-instantavailabilityresource<br />A resource that is not available all the time<br />
  33. 33. Non-instantavailabilityresource & bottleneck<br />
  34. 34.
  35. 35.
  36. 36.
  37. 37.
  38. 38. You can never go faster than your bottleneck<br />Balance demand against throughput to keep work-in-progress low<br />Low work-in-progress<br />Keeps cycle time down <br />Makes bottlenecks visible faster<br />
  39. 39.
  40. 40. Slow down to go faster <br />Slowing down can stabilize the process flow<br />A stable process can go faster<br />
  41. 41.
  42. 42.
  43. 43. Håkan Forss<br />Mail: hakan.forss@avegagroup.se<br />Twitter: @hakanforss<br />Blog: http://hakanforss.wordpress.com/<br />

Hinweis der Redaktion

  • There are two different types of bottlenecks: - Capacity contraint resource - a resource limited by capacity, like a bottle - non instantly available resource - a resource that is not available all the time
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