Analysis of a Forecasting-Production-Inventory System with Stationary Demand

References

• Aviv Y.The effect of forecasting capabilities on supply chain coordination (1998) (John M. Olin School of Business, Washington University, St. Louis, MO) . Working paperGoogle Scholar
• Badinelli R. D. The inventory costs of common misspecifications of demand forecasting models. Internat. J. Production Res. (1990) 28:2321–2340Crossref, Google Scholar
• Billingsley P.Convergence of Probability Measures (1968) (John Wiley … Sons, New York) Google Scholar
• Box G. E. P., Jenkins G. M.Times Series Analysis: Forecasting and Control (1970) (Holden-Day, San Francisco, California-London-Amsterdam) Google Scholar
• Buzacott J. A., Shanthikumar J. G.Stochastic Models of Manufacturing Systems (1993) (Prentice Hall, Englewood Cliffs, NJ) Google Scholar
• Buzacott J. A., Shanthikumar J. G. Safety stock versus safety time in MRP controlled production systems. Management Sci. (1994) 40:1678–1689Link, Google Scholar
• Chen F., Drezner Z., Ryan J. K., Simchi-Levi D. Quantifying the bullwhip effect in a simple supply chain: the impact of forecasting, lead times and information. Management Sci. (2000a) 46:436–443Link, Google Scholar
• Chen R. Y., Ryan J. K., Simchi-Levi D. The impact of exponential smoothing forecasts on the bullwhip effect. Naval Res. Logist. (2000b) 47:269–286Crossref, Google Scholar
• Chen V. C. P., Ruppert D., Shoemaker C. A. Applying experimental design and regression splines to high-dimensional continuous-state stochastic dynamic programming. Oper. Res. (1999) 47:38–53Link, Google Scholar
• Clark E. C. The greatest of a finite set of random variables. Oper. Res. (1961) 9:145–162Link, Google Scholar
• Federgruen A., Zipkin P. An inventory model with limited production capacity and uncertain demands II. The discounted-cost criterion. Math. Oper. Res. (1986) 11(2):208–215Link, Google Scholar
• Glasserman P. Bounds and asymptotics for planning critical safety stocks. Oper. Res. (1997) 45:244–257Link, Google Scholar
• Glasserman P., Liu T.-W. Corrected diffusion approximations for a multistage production-inventory system. Math. Oper. Res. (1997) 22:186–201Link, Google Scholar
• Graves S. C. A tactical planning model for a job shop. Oper. Res. (1986) 34:522–533Link, Google Scholar
• Graves S. C., Meal H. C., Dasu S., Qiu Y., Axsäter S., Schneeweiss C., Silver E. Two-stage production planning in a dynamic environment. Multi-Stage Production Planning and Control (1986) 266(Lecture Notes in Economics and Mathematical Systems, Springer-Verlag, Berlin) 9–43Crossref, Google Scholar
• Graves S. C., Kletter D. B., Hetzel W. B. A dynamic model for requirements planning with application to supply chain optimization. Oper. Res. (1998) 46:S35–S49Link, Google Scholar
• Güllü R. On the value of information in dynamic production/inventory problems under forecast evolution. Naval Res. Logist. (1996) 43:289–303Crossref, Google Scholar
• Harrison J. M.Brownian Motion and Stochastic Flow Systems (1985) (John Wiley, New York) Google Scholar
• Heath D. C., Jackson P. L. Modeling the evolution of demand forecasts with application to safety stock analysis in production/distribution systems. IIE Trans. (1994) 26(3):17–30Crossref, Google Scholar
• Johnson G. D., Thompson H. E. Optimality of myopic inventory policies for certain dependent demand processes. Management Sci. (1975) 21:1303–1307Link, Google Scholar
• Karaesmen F., Buzacott J. A., Dallery Y.Integrating advance order information in make-to-stock production systems (2001) (Laboratoire Productique Logistique, Ecole Centrale Paris, Châtenay-Malabry, France) . Working paperGoogle Scholar
• Lovejoy W. S. Stopped myopic policies in some inventory models with generalized demand processes. Management Sci. (1992) 38:688–707Link, Google Scholar
• Markowitz D. M., Wein L. M.Heavy traffic analysis of dynamic cyclic policies: a unified treatment of the single machine scheduling problem (1998) (Sloan School of Management, MIT, Cambridge, MA) Google Scholar
• Miller B. L. Scarf's state reduction method, flexibility, and a dependent demand inventory model. Oper. Res. (1986) 34:83–90Link, Google Scholar
• Rubio R., Wein L. M. Setting base stock levels using product-form queueing networks. Management Sci. (1996) 42:259–268Link, Google Scholar
• Siegmund D. Corrected diffusion approximations for certain random walk problems. Adv. Appl. Prob. (1979) 11:701–719Crossref, Google Scholar
• Siegmund D.Sequential Analysis: Tests and Confidence Intervals (1985) (Springer, New York) Crossref, Google Scholar
• Toktay L. B.Analysis of a production-inventory system under a stationary demand process and forecast updates (1998) (Operations Research Center, MIT, Cambridge, MA) . Unpublished Ph.D. dissertationGoogle Scholar
• Veatch M., Wein L. M. Optimal control of a two-station tandem production/inventory system. Oper. Res. (1994) 42:337–350Link, Google Scholar
• Veinott A. F. Optimal policy for a multi-product, dynamic, nonstationary inventory problem. Management Sci. (1965) 12:206–222Link, Google Scholar