Mixture distributions for modelling demand during lead time

A mixture distribution approach to modelling demand during lead time in a continuous-review inventory model is described. Using this approach, both lead time and demand per unit time can follow state-dependent distributions. By using mixtures of truncated exponentials functions to approximate these distributions, mixture distributions that can be easily manipulated in closed form can be constructed as the marginal distributions for lead time and demand per unit time. These are then used to approximate the mixture of compound distributions for demand during lead time. The technique is illustrated by first applying it to a ‘normal-gamma’ inventory problem, then by modelling a problem with empirical distributions for lead time and demand per unit time.

[1]  John E. Tyworth,et al.  Robustness of the normal approximation of lead‐time demand in a distribution setting , 1997 .

[2]  Serafín Moral,et al.  Mixtures of Truncated Exponentials in Hybrid Bayesian Networks , 2001, ECSQARU.

[3]  Søren Holbech Nielsen,et al.  Proceedings of the Second European Workshop on Probabilistic Graphical Models , 2004 .

[4]  C. R. Mitchell,et al.  An Analysis of Air Force EOQ Data with an Application to Reorder Point Calculation , 1983 .

[5]  Frank Witlox,et al.  Using the inventory-theoretic framework to determine cost-minimizing supply strategies in a stochastic setting , 2008 .

[6]  Arthur F. Veinott,et al.  Analysis of Inventory Systems , 1963 .

[7]  Amy Hing-Ling Lau,et al.  Nonrobustness of the normal approximation of lead‐time demand in a (Q, R) system , 2003 .

[8]  A. F. Smith,et al.  Statistical analysis of finite mixture distributions , 1986 .

[9]  Terry P. Harrison,et al.  Estimation in supply chain inventory management , 2006 .

[10]  Rob J. Hyndman,et al.  Exponential smoothing models: Means and variances for lead-time demand , 2004, Eur. J. Oper. Res..

[11]  Uttarayan Bagchi,et al.  MODELING DEMAND DURING LEAD TIME , 1984 .

[12]  Rommert Dekker,et al.  Inventory control based on advanced probability theory, an application , 2005, Eur. J. Oper. Res..

[13]  Yu-Jen Lin,et al.  Minimax distribution free procedure with backorder price discount , 2008 .

[14]  Rafael Rumí,et al.  Learning hybrid Bayesian networks using mixtures of truncated exponentials , 2006, Int. J. Approx. Reason..

[15]  L. Zurich,et al.  Operations Research in Production Planning, Scheduling, and Inventory Control , 1974 .

[16]  Jong-Wuu Wu,et al.  Mixture inventory model with back orders and lost sales for variable lead time demand with the mixtures of normal distribution , 2001, Int. J. Syst. Sci..

[17]  Prakash P. Shenoy,et al.  Approximating probability density functions in hybrid Bayesian networks with mixtures of truncated exponentials , 2006, Stat. Comput..

[18]  Terry P. Harrison,et al.  Exploring the structural properties of the ( D , 0) inventory model , 2009 .

[19]  K. Baken,et al.  An inventory model: What is the influence of the shape of the lead time demand distribution? , 1986, Z. Oper. Research.

[20]  Farzad Mahmoodi,et al.  Safety stock determination based on parametric lead time and demand information , 2010 .

[21]  Terry P. Harrison,et al.  A mirror-image lead time inventory model , 2010 .

[22]  Serafín Moral,et al.  Estimating Mixtures of Truncated Exponentials from Data , 2002, Probabilistic Graphical Models.

[23]  Alan Bundy,et al.  Symbolic and Quantitative Approaches to Reasoning and Uncertainty , 1993 .

[24]  Prakash P. Shenoy,et al.  Inference in hybrid Bayesian networks with mixtures of truncated exponentials , 2006, Int. J. Approx. Reason..

[25]  James H. Bookbinder,et al.  Estimation of Inventory Re-Order Levels Using the Bootstrap Statistical Procedure , 1989 .

[26]  J. Ord,et al.  The truncated normal–gamma mixture as a distribution for lead time demand , 1983 .

[27]  Uttarayan Bagchi,et al.  Demand during Lead Time for Normal Unit Demand and Erlang Lead Time , 1984 .

[28]  Fred R. McFadden ON LEAD TIME DEMAND DISTRIBUTIONS , 1972 .

[29]  B. Sarkar,et al.  Improved quality, setup cost reduction, and variable backorder costs in an imperfect production process , 2014 .

[30]  Antonio Fernández,et al.  Parameter learning in MTE networks using incomplete data , 2010 .

[31]  Teemu Roos,et al.  Proceedings of the Fifth European Workshop on Probabilistic Graphical Models , 2010 .

[32]  Robert Handfield,et al.  Production, Manufacturing and Logistics Ðq ; Rþ Inventory Policies in a Fuzzy Uncertain Supply Chain Environment , 2022 .

[33]  J. Wolfowitz,et al.  An Introduction to the Theory of Statistics , 1951, Nature.

[34]  Chao-Hsien Chu,et al.  The effect of lead-time variability: The case of independent demand , 1986 .