Inventory management for new products with triangularly distributed demand and lead-time

Peter Wanke, Henrique Ewbank, VICTOR ELISEO LEIVA SANCHEZ, Fernando Rojas

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

This paper proposes a computational methodology to deal with the inventory management of new products by using the triangular distribution for both demand per unit time and lead-time. The distribution for demand during lead-time (or lead-time demand) corresponds to the sum of demands per unit time, which is difficult to obtain. We consider the triangular distribution because it is useful when a distribution is unknown due to data unavailability or problems to collect them. We provide an approach to estimate the probability density function of the unknown lead-time demand distribution and use it to establish the suitable inventory model for new products by optimizing the associated costs. We evaluate the performance of the proposed methodology with simulated and real-world demand data. This methodology may be a decision support tool for managers dealing with the measurement of demand uncertainty in new products.

Original languageEnglish
Pages (from-to)97-108
Number of pages12
JournalComputers and Operations Research
Volume69
DOIs
StatePublished - 1 May 2016

Keywords

  • (Q, r) model
  • Approximation of functions
  • Bisection method
  • Kernel method
  • Kullback-Leibler divergence
  • Monte Carlo method
  • R software
  • Statistical distributions

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