Sensitivity Analysis of the Optimal Inventory-Pooling Strategies According to Multivariate Demand Dependence

Abstract

Inventory-pooling (IP) is an effective tool to mitigate demand uncertainty and variability, to reduce operational costs, and consequently to increase the profit. The major assumptions of the previous works in literature on IP include the following: (1) Independents demand, which satisfy the typical normal independent and identically distributed (iid) random variables; (2) dependents (correlated) symmetric demands, which follows to a multivariate normal distribution. The effect of the dependent asymmetric demand is not yet studied. The aim of this paper is to consider this more realistic case. Indeed, the contribution of this paper is twofold. Firstly, it analyzes both the sensitivity of dependence structure and the levels of skewness of distributions on IP policies in terms of optimal total cost and demand satisfaction constraint. Secondly, both symmetric and asymmetric demand distributions are modeled using various beta distribution and the dependance between demands are modeled using various copulas. A newsvendor problem inspired by the literature, with two decentralized locations and two centralized locations, is considered the empirical study. For each dependance situation, three IP models are considered: inventory centralization, regular transshipments, and independent systems. The results suggest divergences in the decisions in about 9% of cases. Bad choice of marginal distributions given that the copula is appropriate can lead to divergences that vary between 2.2% and 4%, depending on whether the demand distributions are symmetric or asymmetric.

A Methodology for Consolidation Effects of Inventory Management with Serially Dependent Random Demand

“…In summary, in the previous research [11,14,[16][17][18][19]21] to our study, such as was warned in [22], there was a gap on inventory models under consolidation effects between those that incorporate temporal dependence and those that do not. In this context, one of the few works on consolidation effects in inventory models is [23], while in [24], optimal policies were proposed for inventories with temporally correlated demand in a similar setting, although not on the consolidation impact.…”

“…However, these works employed a continuous-review model with IID DPUT. Recent research has considered the dependence on DPUT [17]. Copulas methods have been the primary focus for modeling the joint DPUT distribution [11,18,19].…”

Prediction of Marshall Stability and Marshall Flow of Asphalt Pavements Using Supervised Machine Learning Algorithms

“…In this study, the sensitivity analysis (SA) and parametric analysis (PA) for the bestperforming model were performed based on performance assessment and comparison analysis of the developed models which was MEP. The parameters used in Equations ( 22) and (23) were utilized in SA and PA. To begin, the SA ranks the input parameters according to their significance to evaluate how sensitive the proposed developed model is, to a specific variation in certain input parameters, being considered for the relevant model [83,106,[132][133][134]. Input parameters are considered in the current research study, and their relative contribution to MS and MF is investigated by employing SA on MEP models using Equations ( 24) and (25).…”