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ABSTRACT
This ar ticle evaluates the premise of demand adherence to normal distribution in inventor y management models, showing that this can lead to significant distortions, mainly to stock control of very low and low consumption items. The article thus proposes a framework to help managers determine the best stock policy to be adopted given product demand characteristics. The article also presents the use of such a framework in a case study, in an attempt to illustrate the benefits of adopting probability density functions that are more adequate to product demand characteristics, in terms of total costs of stocks.
Keywords: stock, lead-time demand, coefficient of variation, framework, costs
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1. Introduction
Inventory management permeates decision-making in countless firms and has been extensively studied in the academic and corporate spheres (Rosa et al. 2010). The key questions - usually influenced by a variety of circumstances - which inventory management seeks to answer are: when to order, how much to order and how much stock to keep as safety stock (Namit and Chen 1999; Silva 2009). According to Wanke (2011a), inventory management involves a set of decisions that aim at matching existing demand with the supply of products and materials over space and time in order to achieve specified cost and service level objectives, observing product, operation, and demand characteristics.
These diverse circumstances that should be taken into account for an appropriate selection of inventory management models have contributed to the development of research and production of articles on possible qualitative conceptual schemes - also known as classification approaches - aimed at supporting decision-making (Huiskonen 2001). There are several examples of this kind throughout the years.
Williams (1984), for example, developed an analytical method to classify demand as regular (high consumption), low consumption, or intermittent, by decomposing the variability of lead-time demand into three parts: variability of the number of occurrences per unit of time, variability of demand size, and lead-time variability. Botter and Fortuin (2000) based their classification of items on three criteria: lead time, price, and consumption level, which underpin the development of eight different inventory management models. Eaves and Kingsman (2004) revisited Williams' (1984) model, reclassifying spare parts into five categories: smooth, erratic, low turnover, slightly sporadic, and...