Modelling wholesale distribution operations: an artificial intelligence framework

• Published in: Industrial management + data systems Vol. 119; no. 4; pp. 698 - 718
• Main Authors: Bottani, Eleonora, Centobelli, Piera, Gallo, Mosé, Kaviani, Mohamad Amin, Jain, Vipul, Murino, Teresa
• Format: Journal Article
• Language: English
• Published: Wembley Emerald Publishing Limited 01.05.2019; Emerald Group Publishing Limited
• Subjects: Artificial intelligence; Artificial neural networks; Cost control; Decision making; Economic forecasting; Forecasting; Information sharing; Inventory; Inventory management; Neural networks; Purchase orders; Purchasing; Retail stores; Sales promotions; Suppliers; Supply chains; Time series; Wholesalers; Wholesaling; Price determination; Wholesale distribution; Supply chain management; Multiple neural networks (MNNs); Artificial neural networks (ANNs); Demand forecasting
• ISSN: 0263-5577; 1758-5783
• DOI: 10.1108/IMDS-04-2018-0164

Purpose The purpose of this paper is to propose an artificial intelligence-based framework to support decision making in wholesale distribution, with the aim to limit wholesaler out-of-stocks (OOSs) by jointly formulating price policies and forecasting retailer’s demand. Design/methodology/approach The framework is based on the cascade implementation of two artificial neural networks (ANNs) connected in series. The first ANN is used to derive the selling price of the products offered by the wholesaler. This represents one of the inputs of the second ANN that is used to anticipate the retailer’s demand. Both the ANNs make use of several other input parameters and are trained and tested on a real wholesale supply chain. Findings The application of the ANN framework to a real wholesale supply chain shows that the proposed methodology has the potential to decrease economic loss due to OOS occurrence by more than 56 percent. Originality/value The combined use of ANNs is a novelty in supply chain operation management. Moreover, this approach provides wholesalers with an effective tool to issue purchase orders according to more dependable demand forecasts.