Resource modelling and allocation for stochastic retailer demand

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Resource modelling and allocation for stochastic retailer demand

 

Author: Ning, Andrew
Title: Resource modelling and allocation for stochastic retailer demand
Degree: Ph.D.
Year: 2013
Subject: Business logistics -- Management.
Hong Kong Polytechnic University -- Dissertations
Department: Dept. of Industrial and Systems Engineering
Pages: xi, 159 leaves : col. ill. ; 30 cm.
InnoPac Record: http://library.polyu.edu.hk/record=b2652777
URI: http://theses.lib.polyu.edu.hk/handle/200/7248
Abstract: In this study the supply chain of an enterprise with a wholesaler and retailer section is considered whereby retailers make demand requests to wholesalers. Each wholesaler is serviced by a number of suppliers of goods that have varying purchase prices and varying delivery lead times. In addition to their suppliers, wholesalers are also allowed to replenish their supplies from other wholesalers (which belong to the same cost centre) at mutually agreed fixed prices. These kinds of shipment are called trans-shipments and are assumed to be immediately available. When a demand for goods arrives at a wholesaler, one important decision that must be taken is whether or not a trans-shipment should be used to meet the demand. The scope for this study is to minimize the total operational cost of a wholesaler (i.e. the sum of product cost, total backorder, and holding cost related to lead time) by selection of a combination of suppliers, possible trans-shipment and minimizing shortage cost related to unfulfilled retailer demand. In order to assist calculation in minimizing total operation cost (of a wholesaler), an Intelligent Supply Chain System (ISCS) that aims to understand the underlying pattern and prediction of future retailer demand has been developed. The ISCS consists of three modules; these comprise of a Predictability Module (PM), an Optimized Forecast Module (OFM) and a Decision Rule Module (DRM). The PM is responsible for collecting the historical demand from each retailer at the end of the supply chain, and to evaluate the predictability of these demands. The OFM module is responsible for providing an optimized forecast by using a hybrid of artificial intelligence technologies. The DRM module is responsible for providing advisory options under different inventory replenishment policies. To validate the feasibility of the ISCS, a case study has been conducted in a local company using the approach explained above. The optimal prediction policy exhibits a significant advantage of reducing the wholesaler’s inventory cost in comparison with the normal cost without any analysis of the predicted demand. The significance of this research study includes an innovative approach that formulates decision rules which can be used for trans-shipment decision-making in supply chains made up of suppliers, wholesalers, and retailers, in any local region. One advantage of the model presented includes the inclusion of multiple suppliers for wholesalers as well as variable goods delivery lead times, both of which are important issues of the properties of previous models that neglect the main features of real operations in inventory systems. Another advantage is that its implementation is straight-forward and only requires direct calculations and a comparison of total costs.

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