Forecasting/Stock Control Interactions III

1. Forecasting/Stock Control Interactions III Tarkan Tan Technische Universiteit Eindhoven October 23, 2007 Forecasting and Inventory Management: Bridging the Gap EPSRC project Meeting - London

2. Outline • General Observations: The Role of Forecasting in Production/Inventory Systems • Research Interests • Past Research • Ongoing Research • Future Research

3. General Observations: The Role of Forecasting in Production/Inventory Systems • Deterministic Demand • Point Estimate for Future Demand • Mathematical Programming Models • E.g., aggregate production planning • Lot sizing / EOQ models • Materials requirement planning • Coordinated replensihment • Stochastic Models for other uncertainties • supply • price • capacity • etc • etc

4. General Observations (contd) • Stochastic Demand • Demand Distribution • Stationary • A wide variety of models and optimality results • Non-stationary • State-dependent policies

5. The Gap between Forecasting and Inventory Control • Demand distributions based on forecasting, time series models, Bayesian models, etc. do not capture the dynamic nature of the forecasting component(s) of the problem. • The effects of not only the forecast of the most imminent period, but also the forecasts for the following periods could be taken into account on the production/inventory strategy. • When forecasting the demand of a number of items, there may exist correlations among the forecasts. • Models that take these aspects into account are can/do improve system performance.

6. Some attempts of using data / information / forecast directly in inventory planning models: • Martingale Model of Forecast Evolution (Heath and Jackson 1991, Güllü 1997) • Advance Demand Information • Perfect (Hariharan and Zipkin 1995, Gallego and Özer 2001, Karaesmen et al. 2002) • Imperfect (Van Donselaar et al. 2001, Zhu and Thonemann 2004, Tan et al. 2007) • Other methods • Demand modelled as an autoregressive moving average process (Johnson and Thompson 1975, Erkip, Hausman, and Nahmias 1990, Gilbert 2005) • E[demand] follows an exponential smoothing formula (Miller 1986) • Bayesian model for evolving estimates of the demand distribution (Scarf 1959, Azoury and Miller 1984, Azoury 1985)

7. Multi-Echelon Inventory Systems • Sharing forecast information: forecasts communicated between supply chain members • Additional concerns • Revealing forecast updates (before firm orders) • Forecast volatility: too frequent or large updates => manufacturer ignores revisions • Truthful reveal? • Forecast inflation: to ensure sufficient supply => manufacturer penalizes the retailer for unreliable forecasts by providing lower service levels => retailers penalize suppliers that have a history of poor service by providing them with overly inflated forecasts • Lose-lose situation! (Terwiesch et al. 2005) • Mostly analyzed by game-theoretic models • Price or capacity as decision variable • Contracting issues

8. Spare Parts Inventory Control Systems (Service Logistics) • Growing Interest (increasing revenues, much higher profitability) • Differences with "regular" inventory control • Low, sporadic, and highly non-stationary demand rates, strong dependencies • Statistical forecasting is much harder • More of "risk management" than inventory control • Machine up-time: multi-item approach • Further complicating factors • High service requirements • Various service level aggreements • Commonalities • Transshipment issues (lateral, multiple-mode, etc.) • etc

9. Research Interests - Past Research • Advance Demand Information • Capacity Management • Service Logistics / Spare Parts Management

10. Advance Demand Information • Tan, T., Güllü, A. R., and Erkip, N. (2007), “Modelling Imperfect Advance Demand Information and Analysis of Optimal Inventory Policies”, European Journal of Operational Research, 177, 897-923. ADI-1.ppt

11. Advance Demand Information • Tan, T., Güllü, A. R., and Erkip, N., “Employing Imperfect Advance Demand Information in Ordering and Inventory Rationing Decisions”, WP 2004. ADI-2.pdf

12. Advance Demand Information • Tan, T., “Using Imperfect Advance Demand Information in Forecasting”, WP 2007.ADI-3.ppt

13. Capacity Management • Tan, T. and Alp, O., “An Integrated Approach to Inventory and Flexible Capacity Management under Non-stationary Stochastic Demand and Set-up Costs”, WP 2005. CM-1.ppt

14. Capacity Management • Alp, O. and Tan, T. (2007), “Tactical Capacity Management under Capacity Flexibility”, IIE Transactions (to appear). CM-2.ppt

15. Capacity Management • Mincsovics G., Tan T., and Alp, O., “Integrated Capacity and Inventory Management with Capacity Acquisition Lead Times”, WP 2006.CM-3.ppt

16. Capacity Management • Pac, M. F., Alp, O., and Tan, T., “Integrated Workforce Capacity and Inventory Management Under Temporary Labor Supply Uncertainty”, WP 2007.CM-4.ppt

17. Service Logistics / Spare Parts Management • Van Kooten, J. P. J. and Tan, T. “The Final Order Problem for Repairable Spare Parts under Condemnation”, WP 2007.SL.ppt

18. Ongoing Research • Revisions on past research • Minimizing maximum hazard risk in HazMat transportation (with Osman Alp) • Production/inventory models with stepwise production costs (with Osman Alp) • Deciding on RFID tagging levels (with Evsen Korkmaz) • Capacity management under supply uncertainty (with Refik Güllü and Simme Douwe Flapper) • A simple heuristic for integrated capacity and inventory management (with Osman Alp and Ton de Kok) • Multi-echelon spare parts management under batch ordering in the central warehouse (with Engin Topan and Pelin Bayindir)

19. Future Research • Health Care Operations Management • Service Logistics • Forecasting and Inventory Management: Bridging the Gap