Forecasting & Inventory Management Of Short Life-Cycle Products - Guillaume Roels

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Forecasting & Inventory Management Of Short Life-Cycle Products - Guillaume Roels

  1. 1. Forecasting and Inventory Management of Short Life-Cycle Products A. Kurawarwala and H. Matuso Presented by Guillaume Roels Operations Research Center This summary presentation is based on: Kurawarwala, A., and H. Matsuo. quot;Forecasting and Inventory Management of Short Life-Cycle Products.quot; Operations Research 44, no.1 (1996).
  2. 2. Short Life-Cycle Products (Screenshot of the home page from Dell Inc.: http://www.dell.com – last accessed June 29, 2004.)
  3. 3. Short Life-Cycles Causes Fast Changing Consumer Preferences Rapid Rate of Innovation Procurement Issues Forecasting with no historical data Long lead-times Perishable Inventory Introduction Time
  4. 4. Outline Forecasting new product introduction Procurement issues Model Formulation Optimal Control Solution Discussion Case Study
  5. 5. New Product Introduction No past sales data Time-series useless But multiple-product environment Some level of predictability Independence (serve different needs)
  6. 6. Diffusion Theory “[Innovation] is communicated through certain channels over time among members of a social system” - Rogers, Everett. Diffusion of Innovations. Simon & Schuster, 1982. ISBN: 0-02-926650-5. Marketing application: Mass Media Word of Mouth
  7. 7. The Bass Model Noncumulative adoption Internal Influence External Influence time
  8. 8. The Bass Model: Assumptions Market potential remains consistent over time Independence of other innovations Product and Market characteristics do not influence diffusion patterns Competition?
  9. 9. The Bass Model: Sales Evolution Remaining market Current Sales potential ⎛ Nt ⎞ dN t ⎟ (m − N t ) α t = ⎜p+q ⎝ m⎠ dt Mass Media Word-of-Mouth Seasonality Coeff. Many applications at Eastman Kodak, IBM, Sears, AT&T…
  10. 10. Case-Study: PC Manufacturer Monopolist (strongly differentiated) Life-cycle: 1-2 years * Peak sales timing is predictable T Christmas peak Typical seasonal variation in demand α t End-of-quarter effect Information on total life-cycle sales m
  11. 11. Numerical Example (M2) Sales Evolution of High-End Computers (M2) 800 700 600 Sales (units) 500 400 300 200 100 0 n n c g g r b r. ct ct Ap Ju Ju Au De Au Fe Ap O O Time - Data source: Kurawarwala, A., and H. Matsuo. quot;Forecasting and Inventory Management of Short Life-Cycle Products.quot; Operations Research 44, no.1 (1996).
  12. 12. Parameters Estimation Estimation of p, q, m, α t Nonlinear Least Squares R-squared above .9
  13. 13. Numerical Example (M2) Bass Model (M2) 800 700 600 500 M2 sales 400 Bass Model 300 200 100 0 Apr. Apr Aug Aug Oct Oct Dec Jun Feb Jun - Data source: Kurawarwala, A., and H. Matsuo. quot;Forecasting and Inventory Management of Short Life-Cycle Products.quot; Operations Research 44, no.1 (1996).
  14. 14. Outline Forecasting new product introduction Procurement issues Model Formulation Optimal Control Solution Discussion Case Study
  15. 15. Procurement Issues Need to place orders in advance Long lead-times Cost advantage, timely delivery Inventory/Backorder costs Schedule the procurement to meet the (random) demand, evolving according to Bass’ Model
  16. 16. Model Description State: Cumulative Procurement Vt Control: Instantaneous Procurement Transition function Vt = ut V0 = 0 Finite-Horizon T Optimization Discounted cost at rate r
  17. 17. Cost Parameters Instantaneous trade-off between Inventory holding costs h(Vt − N t ) + + Backorder costs p ( N t − Vt ) Pt (Vt − N t ) Terminal trade-off between Salvage inventory loss l (VT − NT ) + s ( NT − VT ) + Shortage costs QT ( NT − VT )
  18. 18. Optimal Control Model T min J = ∫ e − rt ∫ Pt (Vt − N t )ψ ( N t )dN t dt + e − rT ∫ Q (V − NT )ψ ( NT )dNT T T u 0 Nt NT such that: Vt = ut and ut ≥ 0 Timely delivery of customer orders? What if we do not want to serve all the demand? Why no chance constraints instead?
  19. 19. Hamiltonian function Define λt = ∇V J (t , Vt ) * * Hamiltonian H (V , u , λ ) = Pt (V , u ) + λu
  20. 20. Pontryagin Minimum Principle Adjoint Equation 1. ∂H (Vt * , ut* , λt ) λt = − ∂Vt Boundary Condition 2. ⎡ ⎤ d ⎢ ∫ QT ( NT − VT )ψ T ( NT )dNT ⎥ λT = ⎢ NT ⎥ dVT ⎣ ⎦ Optimality of Control 3. ut* = arg min H (Vt * , u , λt ) u ≥0
  21. 21. b s ≤ Case I: b+h s+l Maintain the same service level b Ψ t (Vt ) = b+h Impulse at the end of horizon s Ψ T (VT ) = s+l
  22. 22. Procurement Policy Cumulative Cumulative units Inventory Expected Cumulative Demand Time
  23. 23. b s > b+h s+l Case II: ^ For 0 ≤ t ≤ t , keep the same service level b Ψ t (Vt ) = b+h ^ For t ≤ t ≤ T , Do not purchase anymore Decrease gradually the service level down to s s+l
  24. 24. Desired/Effective Service Level In practice, backorder costs are hard to evaluate… b Instead, evaluate the desired SL b+h Terminal service level: switch the customers to an upgraded model Loss of goodwill Higher cost Case II is typical in practice Terminal SL < Lifetime SL
  25. 25. Procurement Policy Cumulative Cumulative units Inventory ^ V Phase-Out Period Expected Cumulative Demand ^ Time t
  26. 26. Revised Multiple-Period Implementation Update the estimation of p, q, m Units Model Update Cumulative Procurement Expected Cumulative Demand Lead-time Time
  27. 27. Time-varying costs Time-varying costs Decreasing purchase costs 30% in less than 6 months Underage Costs Backorder penalty b Save the cost decrease ct Save from the cost of capital − rct Decreasing over time Hence, increasing service level
  28. 28. Outline Forecasting new product introduction Procurement issues Model Formulation Optimal Control Solution Discussion Case Study
  29. 29. PC Manufacturer: New Product Introduction When should it be launched? 1. May or August? How much and when should we 2. order? Sensitivity Analysis on the Lifetime Service Level
  30. 30. Parameters Estimation Randomness summarized in m, p, q Estimation of the size of the market m * Estimation of the peak time T p and q Relation between Past Product Introductions (M1-M4) Estimation of the distribution of q Sensitivity Analysis on variance
  31. 31. Demand Estimation (May) 700 600 500 400 Demand 300 200 100 0 ay ly ch y r r be be ar Ju M ar nu em em M Ja pt ov Se N - Data source: Kurawarwala, A., and H. Matsuo. quot;Forecasting and Inventory Management of Short Life-Cycle Products.quot; Operations Research 44, no.1 (1996).
  32. 32. Service Levels Lifetime service level 95% vs. 99%? Terminal service level: 33% Hence, Case II, i.e. Purchase period Phase-out period
  33. 33. Procurement Decisions (May) Longer Phase-Out with low SL Reduce Procurement after peak season 1000 900 800 Demand 700 600 95%- 500 Procurement 400 300 99%- 200 Procurement 100 0 r ay r y y h ul ar be be rc M J a u m em an M te ov J ep N S - Data source: Kurawarwala, A., and H. Matsuo. quot;Forecasting and Inventory Management of Short Life-Cycle Products.quot; Operations Research 44, no.1 (1996).
  34. 34. Safety Stock Evolution Deplete SS in the last quarter Avg SS=5 or 8 weeks of demand 1200 1000 Demand 800 95%-Safety 600 Stock 400 99%-Safety Stock 200 0 ay r r y y h ul ar be be rc M J a u m em an M te ov J ep N - Data source: Kurawarwala, A., and H. Matsuo. quot;Forecasting and Inventory S Management of Short Life-Cycle Products.quot; Operations Research 44, no.1 (1996).
  35. 35. Additional Insights Launching the product early requires less inventories With decreasing costs, Reduced service levels (but increasing over time); hence, less inventory Delayed procurement cutoff time
  36. 36. Conclusions Application-driven research Adapt Bass’ Model Optimal Control Additional issues: Effectiveness of Bass’ Model? Backorder costs vs. Service Level? Terminal shortage penalty vs. Stopping time?
  37. 37. Thank you Questions?

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