Inventory management for new products with triangularly distributed demand and lead-time. (May 2016)

• Record Type: Journal Article
• Authors: Wanke, Peter; Ewbank, Henrique; Leiva, Víctor; Rojas, Fernando
• Is Part Of: Computers & operations research. Volume 69(2016)
• Page Start: 97
• Page End: 108
• Publication Date: 2016-05
• Subjects: Approximation of functions -- Bisection method -- Kernel method -- Kullback–Leibler divergence -- Monte Carlo method -- (Q, r) model -- R software -- Statistical distributions; Operations research -- Periodicals; Electronic digital computers -- Periodicals; 004.05
• DOI: 10.1016/j.cor.2015.10.017
• Languages: English
• ISSNs: 0305-0548

Abstract

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. Abstract : Highlights: We propose a computational methodology to deal with the inventory management of new products. We consider triangular distributions for both demand per unit time and lead-time. We provide an approach to derive the probability density function of the unknown lead-time demand distribution. We use this approach to establish inventory levels via optimization of the total cost. We exemplify the proposed methodology using simulated and real-world demand data.