Inventory Optimization — A lot more than theory

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By Trevor Miles, VP of thought leadership, Kinaxis

Published in: Technology, Business

Inventory Optimization — A lot more than theory

  1. 1. Copyright © 2011 Kinaxis Inc. All Rights Reserved. 1
  2. 2. Inventory Optimization: A lot more than theory Trevor Miles director, thought leadership e: tmiles@kinaxis.com | m: +1.647.248.6269 | t: @milesahead Copyright © 2011 Kinaxis Inc. All Rights Reserved. 2Copyright © 2011 Kinaxis Inc. All Rights Reserved.
  3. 3. Agenda•  Intro to Kinaxis•  How are we doing?•  Demand and Supply Chain Segmentation•  Classical IO Methods•  Classical Example•  Multi-Echelon Inventory Optimization•  Conclusion Copyright © 2011 Kinaxis Inc. All Rights Reserved. 3
  4. 4. Copyright © 2011 Kinaxis Inc. All Rights Reserved. 4
  5. 5. Companies can’t predict thefuture,…build organizations that willsurvive and flourish under…any possible future.Source: McKinsey Quarterly, Dynamic management: Better decisions in uncertain times, December 2009 Copyright © 2011 Kinaxis Inc. All Rights Reserved. 5
  6. 6. Market Dynamics•  Top CPG companies forecast performance Terra Technologies –  MAPE for a one month lag was 31% + 12% •  Forecast Error Range: 19% - 43% –  Eight years ago: 36% + 10% MAPE•  High-Tech/Electronics – anecdotal –  Struggle to get better than 50% MAPE•  Where will breakthrough performance come from? –  Learning to forecast and plan better? –  Learning to respond profitably to plan variance? Copyright © 2011 Kinaxis Inc. All Rights Reserved. 6
  7. 7. How are we doing? Copyright © 2011 Kinaxis Inc. All Rights Reserved. 7Copyright © 2011 Kinaxis Inc. All Rights Reserved.
  8. 8. Inventory Performance – Computer Hardware ê $10BCopyright © 2011 Kinaxis Inc. All Rights Reserved. 8
  9. 9. Inventory Performance – Household CPGCopyright © 2011 Kinaxis Inc. All Rights Reserved. 9
  10. 10. Inventory Performance – Automotive OEM é $10BCopyright © 2011 Kinaxis Inc. All Rights Reserved. 10
  11. 11. Inventory Performance – PharmaceuticalCopyright © 2011 Kinaxis Inc. All Rights Reserved. 11
  12. 12. Demand Segmentation Copyright © 2011 Kinaxis Inc. All Rights Reserved. 12Copyright © 2011 Kinaxis Inc. All Rights Reserved.
  13. 13. Demand Segmentation 80% of Volume 80% of Variability•  Each point is a specific SKU•  Coefficient of variation = Std.Dev./Mean Copyright © 2011 Kinaxis Inc. All Rights Reserved. 13
  14. 14. Demand Segmentation 80% of Volume How would this graph look for: •  Revenue? •  Margin? 80% of Variability•  Each point is a specific SKU•  Coefficient of variation = Std.Dev./Mean Copyright © 2011 Kinaxis Inc. All Rights Reserved. 14
  15. 15. Supply Chain Segmentation Make-to-Stock Configure-to-Order Pull Make-to-Order Rationalize?•  Each point is a specific SKU•  Coefficient of variation = Std.Dev./Mean Copyright © 2011 Kinaxis Inc. All Rights Reserved. 15
  16. 16. Supply Chain Segmentation Make-to-Stock Configure-to-Order Pull Make-to-Order Rationalize?•  Each point is a specific SKU•  Coefficient of variation = Std.Dev./Mean Copyright © 2011 Kinaxis Inc. All Rights Reserved. 16
  17. 17. Supply Chain Segmentation for Product Family Demand forecast (units) Hi scenario 400 Uncommitted 300 Flexible quantity 200 Base scenario contract 100 Fixed quantity Lo Scenario contract 0 Time Venu Nagali , Procurement Risk Management (PRM) at Hewlett-Packard Company, Stanford Risk Management Roundtable, November 13, 2006 http://www.gsb.stanford.edu/scforum/login/pdfs/HP%20%20PRM%20Nov%2006%20Venu%20Nagali.pptCopyright © 2011 Kinaxis Inc. All Rights Reserved. 17
  18. 18. Supply Chain Segmentation for Product Family High-Tech/Electronics400 Hi Hi300 Hi Base200 Base Lo100 Lo Base Lo 0 Time Time Time •  Where will breakthrough performance come from? –  Learning to forecast and plan better? –  Learning to respond profitably to plan variance? Copyright © 2011 Kinaxis Inc. All Rights Reserved. 18
  19. 19. Product Life Cycle Segmentation Toolkit: Frameworks to Design and Enable Supply Chain Segmentation, Matthew Davis, Gartner, 19 May 2011Copyright © 2011 Kinaxis Inc. All Rights Reserved. 19
  20. 20. Classic IO Methods Copyright © 2011 Kinaxis Inc. All Rights Reserved. 20Copyright © 2011 Kinaxis Inc. All Rights Reserved.
  21. 21. Common Safety Stock Calculation•  Demand rate: –  the amount of items consumed by customers, on average, per unit time.•  Lead time: –  the delay between the time the reorder point (inventory level which initiates an order) is reached and renewed availability.•  Service level: –  the desired probability that a chosen level of safety stock will not lead to a stock out. Naturally, when the desired service level is increased, the required safety stock increases as well.•  Forecast error: –  an estimate of how far actual demand may be from forecasted demand. Expressed as the standard deviation of demand. Copyright © 2011 Kinaxis Inc. All Rights Reserved. 21
  22. 22. Common Safety Stock Calculations•  Service Factor * Forecast Error * √⁠    –  Where’s the representation of supply variability? –  How is demand variability associated with forecast error?•  (Demand Variability Factor) * (Service Factor) * (Lead-Time Factor) * (Order Cycle Factor) * (Forecast-to-Mean-Demand Factor) –  Where’s the representation of supply variability?•  Service Factor * √⁠​↓ ↓↑2 ∗  ​↓ ↓↑2 +​↓ ↓↑2 ∗  ​↓ ↓↑2   –  Includes supply variability•  What about Multi-Echelon Inventory Optimization? –  Have you looked at the math? Copyright © 2011 Kinaxis Inc. All Rights Reserved. 22
  23. 23. What about Supply Uncertainty?•  Types of supply uncertainty: –  Lead-time uncertainty –  Yield uncertainty –  Inspection failure –  Disruptions•  Strategies for dealing with supply uncertainty –  Safety stock inventory –  Dual sourcing –  Improved forecasts•  Has big effect on inventory levels Copyright © 2011 Kinaxis Inc. All Rights Reserved. 23
  24. 24. Dimensionless Analysis•  Safety Stock = Service Factor * √⁠​↓ ↓↑2 ∗  ​↓ ↓↑2 +​↓ ↓↑2  ∗  ​↓ ↓↑2  •  Safety Stock = Service Factor * ! * ​↓  * √⁠​↓ ↓↑2 +​ ↓ ↓↑2   –  CoV = ​⁄ •  Safety Stock / (! * ​↓ ) = Service Factor * √⁠​↓ ↓↑2 +​ ↓ ↓↑2   –  Safety Stock = Units –  ! = Periods –  ​↓  = Units/Period –  Safety Stock / (! * ​↓ ) = ​  /  ∗  ​⁄   = ​    ∗  /  ∗  = 1 Copyright © 2011 Kinaxis Inc. All Rights Reserved. 24
  25. 25. Copyright © 2011 Kinaxis Inc. All Rights Reserved. 25
  26. 26. Classical Example Copyright © 2011 Kinaxis Inc. All Rights Reserved. 26Copyright © 2011 Kinaxis Inc. All Rights Reserved.
  27. 27. Copyright © 2011 Kinaxis Inc. All Rights Reserved. 27
  28. 28. Copyright © 2011 Kinaxis Inc. All Rights Reserved. 28
  29. 29. Effect of Lead Time Variability on SSCopyright © 2011 Kinaxis Inc. All Rights Reserved. 29
  30. 30. Multi-Echelon Inventory Optimization Copyright © 2011 Kinaxis Inc. All Rights Reserved. 30Copyright © 2011 Kinaxis Inc. All Rights Reserved.
  31. 31. An Approximate Method•  Assume that each stage carries sufficient inventory to deliver product within S periods “most of the time” –  Definition of “most” depends on service level –  S is called the committed service time (CST)•  We simply ignore the times that the stage does not meet its CST –  For the purposes of the optimization –  Allows us to pretend LT is deterministic Copyright © 2011 Kinaxis Inc. All Rights Reserved. 31
  32. 32. Net Lead Time S3 S2 S1 3 2 1 T3 T2 T1•  Each stage has a processing time T and a CST S•  Net lead time at stage i = Si+1 + Ti – Si “bad” LT “good” LT Prof. Larry Snyder, Multi-Echelon Inventory, Lehigh University, June 15, 2006 Copyright © 2011 Kinaxis Inc. All Rights Reserved. 32
  33. 33. Net Lead Time vs. Inventory•  Suppose Si = Si+1 + Ti –  e.g., inbound CST = 4, proc time = 2, outbound CST = 6 –  Don’t need to hold any inventory –  Operate entirely as pull (make-to-order, JIT) system•  Suppose Si = 0 –  Promise immediate order fulfillment –  Make-to-stock system Prof. Larry Snyder, Multi-Echelon Inventory, Lehigh University, June 15, 2006 Copyright © 2011 Kinaxis Inc. All Rights Reserved. 33
  34. 34. Net Lead Time vs. Inventory•  Precise relationship between NLT and inventory: y* = µ × NLT + zα σ NLT•  NLT replaces LT in classical formula•  Ignores effect of supply variability•  Choose S (committed service level) at each stage•  Efficient algorithms exist for finding optimal S values –  Minimize holding cost while meeting customer service –  Optimal for only a few stages to hold inventory•  Essentially decomposes multi-echelon problem into multiple stages Prof. Larry Snyder, Multi-Echelon Inventory, Lehigh University, June 15, 2006 Copyright © 2011 Kinaxis Inc. All Rights Reserved. 34
  35. 35. A Hybrid Push-Pull System PART 2 CHARLESTON ($7) 7 8 14 PART 5 PART 3 6 PART 1 CHICAGO ($155) 45 9 30 AUSTIN ($2) DALLAS ($260) 5 45 14 15 7 PART 6 8 PART 4 CHARLESTON ($2) 32 BALTIMORE ($220) 8 32 5 8 PART 7 CHARLESTON ($30) 14 push/pull boundary 14•  Part of system operated produce-to-stock, part produce-to-order•  Moderate lead time to customer•  Influenced by postponement strategy Prof. Larry Snyder, Multi-Echelon Inventory, Lehigh University, June 15, 2006 Copyright © 2011 Kinaxis Inc. All Rights Reserved. 35
  36. 36. Practical Pragmatic, but …•  Ignores –  Multi-sourcing –  Alternate parts –  Alternate routing –  ECO/ECN –  Product life-cycle stage •  Especially new product introduction•  …and what about –  Changes in product mix? –  NPI effect on component requirements? –  Transportation costs? –  Labor costs? Copyright © 2011 Kinaxis Inc. All Rights Reserved. 36
  37. 37. Conclusion Copyright © 2011 Kinaxis Inc. All Rights Reserved. 37Copyright © 2011 Kinaxis Inc. All Rights Reserved.
  38. 38. Be Practical Pragmatic•  Some observations on Inventory Optimization –  More about SC design than Operations –  All ‘theories’ based upon rule-of-thumb –  Effort to maintain should be less than effort to deploy –  Demand changes more quickly than inventory policy•  Some practical suggestions –  Start with segmentation •  Customers •  Products –  Use theories to set ball park inventory levels –  Convert these to periods of supply (DOS, WOS, …) •  Adjusts automatically for seasonality and life-cycle stage Copyright © 2011 Kinaxis Inc. All Rights Reserved. 38
  39. 39. Thank you! Questions? Trevor Miles | e: tmiles@kinaxis.com | m: +1.647.248.6269 | t: @milesahead Copyright © 2011 Kinaxis Inc. All Rights Reserved. 39Copyright © 2011 Kinaxis Inc. All Rights Reserved.
  40. 40. Copyright © 2011 Kinaxis Inc. All Rights Reserved. 40

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