Development of decision-support algorithms for commodity management

Abstract

The purpose of this paper is to improve the commodity management of retail chains by developing theoretical provisions of decision support system of retail chain commodity management (DSS RCM). Trading networks buy goods directly from manufacturers or large wholesalers, place these goods in warehouses, organize their distribution to stores. Commodity managers should decide how to provide all stores of the network in timely manner with the most profitable product every day. The objective of the DSS RCM is to maximize the daily trading margin, per each euro invested in commodities taking into account restrictions on commodity resources and shelf space. The DSS RCM have to prepare bills of lading and orders for inbound logistics, distribution and re-distribution of commodities within the network. Decision support uses data available in any retail software. The calculation algorithms are simple and effective to ensure the necessary and sufficient accuracy with the time and hardware limitations and without significant investments in hardware and staff qualifications update. Algorithms calculate the optimal solution based on the objective function (for example, profit maximization) and restrictions (for example, inventory level, store sales, profit from sales of a given product). The paper systematized the approaches of other scientists to solve the problem of product management in retail. Author analyze the strengths and weaknesses of those approaches for purpose to find the optimal approach for retail chains. So the aim of this study is to develop theoretical basis of decision support system of retail chain commodity management (DSS RCM). The proposed method of optimization of commodity assets in retail based on their mathematical modeling and calculating of the consolidated profitability ratio. Research results are limited to homogeneous product retail chains (e.g., clothing or footwear). By using proposed algorithms calculations could be in real-time mode in the database for tens (hundreds) of thousands items in the product range (for example, when selling clothes and shoes, a unique combination of model, color and size forms a separate item in the product range) in hundreds (thousands) of stores. Therefore, it could be hundreds of millions of combinations items-stores (Big Data).