Inventory Management in the Footwear Industry: a Case Study of a Two-Seasons Business

Abstract

The purpose of this thesis is to understand, through a data analysis, the suitability of a Demand Driven Material Requirement Planning (DDMRP) based software in businesses characterized by seasonality and in particular the footwear industry, where the year is divided into two periods: spring/summer and fall/winter. To this end, a broad understanding of inventory management literature is needed, in order to comprehend why the DDMRP emerged and how it differs from others inventory monitoring models. Thus, in the first chapter a literature review is conducted to discover which methods can be used to handle stocks and which of them are efficient and why. A historical perspective is adopted, such that the basic lot size model (EOQ) – developed by F. Harris in 1913 – is explained, followed by reorder point methods (ROP), Material Requirement Planning (MRP) and finally DDMRP. Reorder-points methods are described in detail taking into consideration the periodic and continuous reviews systems, their extensions and the most important aspects that impact their performance, namely demand patterns, lead time and service level. Subsequently, the chapter presents the MRP, explaining the disruption it brought for inventory management in the 70s, its functioning and limits. Linked to the latter, the DDMRP is introduced as an answer to some issues of MRP and as a new system more suitable to contemporary manufacturing enterprises. After having discussed theoretically what inventory management is, the second chapter shows the methodology used in the empirical analysis conducted in this thesis. First of all, I try to identify which inventory strategies are used in seasonal businesses and how manufacturing planning techniques are applied in a make-to-order environment. The idea of this work was conceived during a curricular internship in a footwear distribution company located in the Riviera del Brenta footwear district. As a consequence, in this chapter I described the company and its business model, its supply chain agents and features, together with the data gathering process. Furthermore, I show the analytical model for the analysis, its assumptions and I explain the variables. In the final chapter the data analysis is conducted through different simulations run in Matlab using a script based on the model explained above. This returns a recommended stock strategy based on the lead-time demand in two different scenarios: when lead time is fixed and known and when it is stochastic. For every simulation, I explain the critical variables, the variations in costs and stock quantity and I try to identify which scenario is most suitable for the company. Additionally, the limits of the model are identified and a possible theoretical solution aiming at adapting the model for seasonal businesses is exposed. In the end, I explain the managerial implications of the different policies and finally, conclusions are drawn.