Bjorn Madsen, Researcher at Lego - The future of supply chain


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Bjorn Madsen, Researcher at Lego spoke at the SCL Event UK 2013

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Bjorn Madsen, Researcher at Lego - The future of supply chain

  1. 1. BJORN MADSEN The Future of Supply ChainAll statements are the personal view of the presenter and should not be associated with any organisation or individual mentioned. SCL Event has not been asked for permission, nor will accept liability in any connection with the presented material. All digital art are the exclusive property of their respective owners. The presenter claims no ownership nor right hereof.
  2. 2. The Future of Supply ChainSource:
  3. 3. We Can Predict The Future of Supply Chain By Inventing ItSource:
  4. 4. MAIN PROBLEM: To make the MOST PRODUCTIVE INTERVENTION, we need to TRANSFORM INFORMATION into DECISIONS but we keep designing our processes with 4 fundamentals flaws that inhibit our ability to deliver results.Source:
  5. 5. We design business processes We assume more information for perfect machines, but use gives better decisions but dazzle humans that fail at 400ppm decision-makers who do not have time to attend to it TRANSFORM INFORMATION into DECISIONS We leave other decision-makers We spend trillions of $ on systems waiting for the information we & system integration but sendneed by queuing information to be spreadsheets with email to get processed in large batches data to justify decisions
  6. 6. PROBLEM 1: Design: Human Problem: Limited Management Attention Time
  7. 7. PROBLEM 1: PAST PRESENT FUTURESimple & Tacit Problems Complicated Problems Complex Explicit Problems Human Superior Human ≡ Computer Computer Superior
  8. 8. PROBLEM 2: Design: Human Centric Processes & Distributed Information Problem: Poor Utilisation of Data
  9. 9. “No single person, in anytransport office has an overviewof the demand, beyond our ownregion.”“Management DazzleBoards giveus no better coordination. Theyrather, distract us.” A B
  10. 10. “Office A would like tomake better decisions;And so would Office B.”“So we give them thesame tools to exploit“, A“...and cheat them bypulling their data to showthe joint case of ABcollaboration...” B
  11. 11. AAB B
  12. 12. profit profit 100% 100% AInternal market B
  13. 13. profit profit 100% 100% profit100% A Internal market B
  14. 14. For the UK Based Operator, the experiment resulted in an increase in vehicle utilisation from 43% to 64%.“The study makes it clear thatpolicy depends on facts.Making policy and hoping that itbecomes fact, is the wrong wayaround.”
  15. 15. PROBLEM 2: PAST PRESENT FUTURE Limited Information Abundant Information Noisy InformationHuman Centric Processes ? Information Centric Processes CONSUMER = PRODUCER
  16. 16. PROBLEM 3: Design: Batch-Processing Problem: Delay; Not Real-time
  17. 17. Data production rate ( Longest Queue Time + Run-Time ) * Chain Length = Total Delay Queue length Growing Queue Computer Run-time = larger problem: Time = longer runtime Assignment problem Time Data volume = queue length
  18. 18. “Play-back” using the business’ own dataFRAMEWORK We know today’s results We know X % of how today works Very complex noise pattern (humans involved) ...But we don’t know how big X actually is... We know today’s processes We mapped every process because we built their existing decision support tools Professional Judgment of “when good is good enough” We imitate today’s processes using our scheduler and adding real-world constraints This defines our Base Case We replicate today’s results Decision Making Trend line Stochastic Cheating* Model Forecasting Forecasting Base Case A B C 1 Real-time 2 Remove constraints that inhibit responsiveness Real-time + 3 Flexible Bus. Processes * We load future data to = 9+1 cases get the result of having a Improve decision making method perfect forecast
  19. 19. Profit Potential – And how to achieve it Ideal forecast 100% 88% 82% 81% (un-realistic)Upper Limit 90%for practice Probabilistic 81% 76% 66% forecast Trendline 76% 61% 56% adjustment Real-time Current Perfect scheduling Real-time Scheduling Scheduling (un-realistic) Scheduling Flex. Process Practice
  20. 20. Profit Service Lost Revenue Cost Case (of max) Level (of max Profit) (of min for all demands)Theoretical Ideal (un-realistic) 100% 100% 0% 100%A. Real-time scheduling w. flex. process1. Ideal forecast 88% 90% 10% 102%A. Real-time scheduling w. flex. process2. Probability oscillating forecast 81% 86% 16% 105% Achievable!A. Real-time scheduling w. flex. process3. Trend forecast 76% 86% 20% 105%B. Real-time scheduling1. Ideal forecast 82% 83% 17% 96%B. Real-time scheduling2. Probability oscillating forecast 76% 79% 22% 96%B. Real-time scheduling3. Trend forecast 61% 71% 35% 96%C. Regular scheduling1. Ideal forecast 81% 82% 17% 96%C. Regular scheduling2. Probability oscillating forecast 66% 69% 31% 95%C. Regular scheduling 56% 66% 40% 95% Current3. Trend forecast Practice!
  21. 21. PROBLEM 3: PAST PRESENT FUTUREWaiting for information Waiting for answers Real-time Response Larger Batches + Never Ending Batch Processing Exponential Runtime Stream of Updates
  22. 22. PROBLEM 4:Google image search: robot vs human Design: Process Dependency Problem: Data Exchange is Not Integration
  23. 23. SchedulesX B A C A BY C B A time X Y
  24. 24. SchedulesX B A C A BY C B A time X Y Change of order: Produce more “C”
  25. 25. SchedulesX B A C A BY C B A time X Y Change of order: Produce more “C” X/Y orders Scheduling Company
  26. 26. Schedules X B A C A BY C B A time X Y Change of order: Produce more “C” X/Y orders Scheduling CompanyCollective Optimization under Conditions of Pure Competition.91% of market driven disruptions are gone.Customer Experience: “...The Industry has improved its responsiveness...”
  27. 27. PROBLEM 4: PAST PRESENT FUTURE Isolated Actions Partner Alliances Virtual Marketplace in IndustryTier Based Competition Tier Based Collaboration Market-wide collaboration Google image search: robot vs human Google image search: networks
  28. 28. Google image search: robot vs human40 seconds recap
  29. 29. The Future of the Supply Chain... Impact......See’s management attention as a scarce resource! Human performance limited toHumans for tacit & simple problems, 78% - 82% of true potential inComputers for explicit & complex problems. your systems. Google image search: robot vs human...Shift’s towards Information Centric Processeswith of the shelf software. Human Centric Processes From 43% To 63% Utilisation.are evidently poorly coordinated...Needs information processing in a batch of one +25%point profit,everywhere. Delay of information is simply too +20%point service,expensive. +24%point sales....eliminates silo-planning through market wide +8% sales at no additional cost.system integration. Joint operational planning can From 3.1 to 4.4 of 5 in customerliterally change the market over night. rating
  30. 30. Google image search: robot vs humanBut that’s not all...
  31. 31. LESSON LEARNED: Pre-requisites: 1. Profound understanding of the situation. 2. Can conceive an experiment that increases productivity immediately.Google image search: robot vs human 3. Authorised & self-powered.
  32. 32. Research, Development & Deployment of a supply chain is a supply chain! Staff it! Invent it! Manage it! Thank You!
  33. 33. The Future of the Supply Chain... ...Impact... ...See’s management attention as a scarce resource! Human performance limited to 78% Humans for tacit & simple problems, - 82% of true potential in your Computers for explicit & complex problems. systems. ...Shift’s towards Information Centric ProcessesExecutive Resume with of the shelf software. Human Centric Processes are From 43% To 63% Utilisation. evidently poorly coordinated +25%point profit, ...Needs information processing in a batch of one +20%point service, everywhere. Delay of information is simply too expensive. +24%point sales. ...eliminates silo-planning through system integration. Joint +8% sales at no additional cost. operational planning can benefit the local industry in From 3.1 to 4.4 of 5 in customer competition on a global market. rating