The impact of unknown demand parameters on (R

Abstract For a given inventory control system and a known stationary demand pattern, it is relatively easy to calculate the safety factors needed to satisfy predetermined performance criteria. It is more or less customary to use these standard safety factors in practical situations as well. In practice, however, demand parameters are unknown, causing additional variation. Consequently, the performance of this standard approach generally stays below the desired level. Hence, the safety factors should be increased. This general phenomenon is studied here in some detail for an ( R , S )-inventory control system with normal demand, using the two best-known service criteria. Simple exponential smoothing is used to estimate the unknown demand parameters. Using large simulation runs, new (constant) safety factors are found that do satisfy the given service levels. They are not suitable for practical use, however: long series of past observations usually are not available, while stationary demand is rare. Therefore, time-varying safety factors are presented that seem to perform well for normal demand with unknown parameters.

[1]  G. D. Eppen,et al.  Determining Safety Stock in the Presence of Stochastic Lead Time and Demand , 1988 .

[2]  L.W.G. Strijbosch,et al.  Exact fill rates for (R, s, S) inventory control with gamma distributed demand , 2002, J. Oper. Res. Soc..

[3]  R. Brown,et al.  Smoothing, Forecasting, and Prediction of Discrete Time Series , 1965 .

[4]  Roberto Verganti,et al.  Measuring the impact of asymmetric demand distributions on inventories , 1999 .

[5]  Amy Z. Zeng,et al.  The performance of two popular service measures on management effectiveness in inventory control , 1999 .

[6]  T. W. Anderson,et al.  An Introduction to Multivariate Statistical Analysis , 1959 .

[7]  L.W.G. Strijbosch,et al.  Inventory Control : The Impact of Unknown Demand Distribution , 1998 .

[8]  Uday S. Karmarkar,et al.  A robust forecasting technique for inventory and leadtime management , 1994 .

[9]  L. Strijbosch,et al.  Assessing the Effects of Using Demand Parameters Estimates in Inventory Control , 2006 .

[10]  R. Watson,et al.  The Effects of Demand-Forecast Fluctuations on Customer Service and Inventory Cost When Demand is Lumpy , 1987 .

[11]  Ludo Gelders,et al.  The (R, Q) inventory policy subject to a compound Poisson demand pattern , 2000 .

[12]  T. W. Anderson.  An Introduction to Multivariate Statistical Analysis , 1959 .

[13]  L.W.G. Strijbosch,et al.  On the Interaction Between Forecasting and Inventory Control , 1997 .

[14]  David F. Pyke,et al.  Inventory management and production planning and scheduling , 1998 .

[15]  Edward A. Silver,et al.  Biased selection of the inventory reorder point when demand parameters are statistically estimated , 1987 .

[16]  Leo W. G. Strijbosch,et al.  A combined forecast—inventory control procedure for spare parts , 2000, J. Oper. Res. Soc..

[17]  Harvey M. Wagner,et al.  Reducing Inventory System Costs by Using Robust Demand Estimators , 1989 .

[18]  L. Strijbosch,et al.  Modified Normal Demand Distributions in (R,S) - Inventory Models , 2006 .

[19]  Poul Alstrøm,et al.  Numerical computation of inventory policies, based on the EOQ/σx value for order-point systems , 2001 .

[20]  Edward A. Silver,et al.  The Cost Effects of Statistical Sampling in Selecting the Reorder Point in a Common Inventory Model , 1986 .

[21]  Jr. Everette S. Gardner,et al.  Evaluating forecast performance in an inventory control system , 1990 .