DEVELOPMENT AND PERFORMANCE EVALUATION OF MULTI-CRITERIA INVENTORY CLASSIFICATION METHODS

Abstract

This thesis deals with the issue of inventory classification within supply chains. More specifically, it aims to provide new alternative classification methods to address the multi-criteria inventory classification (MCIC) problem. It is well known that the ABC inventory classification technique is widely used to streamline inventory systems composed of thousands of stock-keeping-units (SKUs). Single-criterion inventory classification (SCIC) methods are often used in practice and recently multi-criteria inventory ABC classification (MCIC) techniques have also attracted researchers and practitioners. With regard to the MCIC techniques, large number of studies have been developed that belong to three main approaches, namely: (1) the machine learning (ML) approach, (2) the mathematical programming (MP), and (3) the multi-criteria decision making (MCDM) approach. On the ML approach, many research methods belonging to the supervised ML type have been proposed as well as a number of hybrid methods. However, to the best of our knowledge, very few research studies have considered the unsupervised ML type. On the mathematical programming approach, a number of methods have been developed using linear and non-linear programming, such as the Ng and the ZF methods. Yet, most of these developed methods still can be granted more attentions for more improvements and shortcomings reduction. On the MCDM approach, several methods have been proposed to provide ABC classifications, including the TOPSIS (technique for order preference by similarity to ideal solution) method, which is well known for its wide attractiveness and utilization, as well as some hybrid TOPSIS methods. It is worth noting that most of the published studies have only focused on providing classification methods to rank the SKUs in an inventory system without any interest in the original and most important goal of this exercise, which is achieving a combined service-cost inventory performance, i.e. the maximization of service levels and the minimization of inventory costs. Moreover, most of the existing studies have not considered large and real-life datasets to recommend the run of MCIC technique for real life implementations. Thus, this thesis proposes first to evaluate the inventory performance (cost and service) of existing MCIC methods and to provide various alternative classification methods that lead to higher service and cost performance. More specifically, three unsupervised machine learning methods are proposed and analyzed: the Agglomerative hierarchical clustering (AHC), the Gaussian mixture model (GMM) and K-means. In addition, other hybrid methods within the MP and MCDM approaches are also developed. These proposed methods represent a hybridization of the TOPSIS and Ng methods with the triangular distribution, the Simple additive weighting (SAW) and the Multi-objective optimization method by ratio analysis (MOORA). To conduct our research, the thesis empirically analyzes the performance of the proposed methods by means of two datasets containing more than nine thousand SKUs. The first dataset is a benchmark dataset originating from a Hospital Respiratory Theory Unit, often used in the literature dealing with the MCIC methods, composed of 47 SKUs. The second dataset consists of 9,086 SKUs and coming from a retailer in the Netherlands that sells do-it-yourself products. The performances of the proposed methods are compared to that of existing MCIC classification methods in the literature. The empirical results reveal that the proposed methods can carry promising performances by leading to a higher combined service-cost efficiency.