Production‐inventory systems with imperfect advance demand information and updating

We consider a supplier with finite production capacity and stochastic production times. Customers provide advance demand information (ADI) to the supplier by announcing orders ahead of their due dates. However, this information is not perfect, and customers may request an order be fulfilled prior to or later than the expected due date. Customers update the status of their orders, but the time between consecutive updates is random. We formulate the production-control problem as a continuous-time Markov decision process and prove there is an optimal state-dependent base-stock policy, where the base-stock levels depend upon the numbers of orders at various stages of update. In addition, we derive results on the sensitivity of the state-dependent base-stock levels to the number of orders in each stage of update. In a numerical study, we examine the benefit of ADI, and find that it is most valuable to the supplier when the time between updates is moderate. We also consider the impact of holding and backorder costs, numbers of updates, and the fraction of customers that provide ADI. In addition, we find that while ADI is always beneficial to the supplier, this may not be the case for the customers who provide the ADI. © 2011 Wiley Periodicals, Inc. Naval Research Logistics 58: 88-106, 2011

[1]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Optimal Control, Two Volume Set , 1995 .

[2]  Yves Dallery,et al.  The Value of Advance Demand Information in Production/Inventory Systems , 2004, Ann. Oper. Res..

[3]  M. D. Wilkinson,et al.  Management science , 1989, British Dental Journal.

[4]  John A. Buzacott,et al.  Stochastic models of manufacturing systems , 1993 .

[5]  Özalp Özer,et al.  Replenishment Strategies for Distribution Systems Under Advance Demand Information , 2003, Manag. Sci..

[6]  Paul H. Zipkin,et al.  Foundations of Inventory Management , 2000 .

[7]  Suresh P. Sethi,et al.  Peeling Layers of an Onion: Inventory Model with Multiple Delivery Modes and Forecast Updates , 2001 .

[8]  Ulrich Wilhelm Thonemann,et al.  Modeling the Benefits of Sharing Future Demand Information , 2004, Oper. Res..

[9]  Eugene A. Feinberg,et al.  Continuous Time Discounted Jump Markov Decision Processes: A Discrete-Event Approach , 2004, Math. Oper. Res..

[10]  Jean-Philippe Gayon,et al.  Using Imperfect Advance Demand Information in Production-Inventory Systems with Multiple Customer Classes , 2009, Manuf. Serv. Oper. Manag..

[11]  宮沢 政清,et al.  P. Bremaud 著, Markov Chains, (Gibbs fields, Monte Carlo simulation and Queues), Springer-Verlag, 1999年 , 2000 .

[12]  Wallace J. Hopp,et al.  A Simple, Robust Leadtime-Quoting Policy , 2001, Manuf. Serv. Oper. Manag..

[13]  Özalp Özer,et al.  Integrating Replenishment Decisions with Advance Demand Information , 2001, Manag. Sci..

[14]  K. Hinderer The optimality equation , 1970 .

[15]  L. Sennott Stochastic Dynamic Programming and the Control of Queueing Systems , 1998 .

[16]  David D. Yao,et al.  Supply chain structures : coordination, information and optimization , 2002 .

[17]  John Odentrantz,et al.  Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues , 2000, Technometrics.

[18]  Yves Dallery,et al.  Integrating advance order information in make-to-stock production systems , 2002 .

[19]  James E. Smith,et al.  Structural Properties of Stochastic Dynamic Programs , 2002, Oper. Res..

[20]  Martin L. Puterman,et al.  Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .

[21]  Paul H. Zipkin,et al.  Customer-order information, leadtimes, and inventories , 1995 .

[22]  D. Heath,et al.  Modelling the evolution of demand forecasts with application to safety stock analysis in production distribution systems , 1994 .

[23]  Rolando Cavazos-Cadena,et al.  Comparing recent assumptions for the existence of average optimal stationary policies , 1992, Oper. Res. Lett..

[24]  Özalp Özer,et al.  Inventory Control with Limited Capacity and Advance Demand Information , 2004, Oper. Res..

[25]  Ulrich Wilhelm Thonemann,et al.  Production, Manufacturing and Logistics Improving supply-chain performance by sharing advance demand information , 2002 .

[26]  Özalp Özer,et al.  Optimal Replenishment Policies for Multiechelon Inventory Problems Under Advance Demand Information , 2003, Manuf. Serv. Oper. Manag..

[27]  Richard F. Serfozo,et al.  Monotone optimal policies for Markov decision processes , 1976 .

[28]  M. Chial,et al.  in simple , 2003 .

[29]  Vijay S. Mookerjee,et al.  Purchasing demand information in a stochastic-demand inventory system , 1997 .

[30]  Weiping Zhu,et al.  Denumerable-state continuous-time Markov decision processes with unbounded transition and reward rates under the discounted criterion , 2002, Journal of Applied Probability.

[31]  Alfred Müller,et al.  How Does the Value Function of a Markov Decision Process Depend on the Transition Probabilities? , 1997, Math. Oper. Res..

[32]  Lawrence M. Wein,et al.  Monotone control of queueing networks , 1992, Queueing Syst. Theory Appl..

[33]  W. Hopp,et al.  Quoting Customer Lead Times , 1995 .

[34]  John A. Buzacott,et al.  Safety stock versus safety time in MRP controlled production systems , 1994 .

[35]  Yves Dallery,et al.  Optimal Stock Allocation for a Capacitated Supply System , 2002, Manag. Sci..

[36]  Steven A. Lippman,et al.  Applying a New Device in the Optimization of Exponential Queuing Systems , 1975, Oper. Res..

[37]  Shaler Stidham,et al.  Analysis, Design, and Control of Queueing Systems , 2002, Oper. Res..

[38]  Jean-Philippe Gayon,et al.  Using Imperfect Demand Information in Production-Inventory Systems with Multiple Customer Classes , 2004 .

[39]  Xianping Guo,et al.  Continuous-Time Controlled Markov Chains with Discounted Rewards , 2003 .

[40]  R. Güllü On the value of information in dynamic production/inventory problems under forecast evolution , 1996 .

[41]  Evan L. Porteus Conditions for characterizing the structure of optimal strategies in infinite-horizon dynamic programs , 1982 .

[42]  G. Liberopoulos,et al.  Base stock policies with some unreliable advance demand information , 2003 .

[43]  Jing-Sheng Song,et al.  Optimal Policies for Multiechelon Inventory Problems with Markov-Modulated Demand , 2001, Oper. Res..

[44]  Marc Wouters,et al.  The use of advance demand information in a project-based supply chain , 2001, Eur. J. Oper. Res..

[45]  Albert Y. Ha Inventory rationing in a make-to-stock production system with several demand classes and lost sales , 1997 .

[46]  Tong Wang,et al.  Inventory Management with Advance Demand Information and Flexible Delivery , 2008, Manag. Sci..

[47]  R. Kapuściński,et al.  Value of Information in Capacitated Supply Chains , 1999 .

[48]  Lawrence M. Wein,et al.  Scheduling a Make-To-Stock Queue: Index Policies and Hedging Points , 1996, Oper. Res..

[49]  Özalp Özer,et al.  Optimal Use of Demand Information in Supply Chain Management , 2002 .

[50]  Ming-Deh A. Huang,et al.  Proof of proposition 2 , 1992 .

[51]  Stephen C. Graves,et al.  TWO-STAGE PRODUCTION PLANNING IN A DYNAMIC ENVIRONMENT , 1985 .