Recommender systems as an agility enabler in supply chain management

Camélia Dadouchi () and Bruno Agard ()
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Camélia Dadouchi: Laboratoire en Intelligence des Données, CIRRELT
Bruno Agard: Laboratoire en Intelligence des Données, CIRRELT

Journal of Intelligent Manufacturing, 2021, vol. 32, issue 5, No 1, 1229-1248

Abstract: Abstract In recent years, recommender systems have become necessary in overcoming the challenges related to the incredible growth of information. They are used in a wide range of contexts and applications, mainly as prediction tools for customer interest, designed to help customers decide, compare, discover and explore products (Meyer in Recommender systems in industrial contexts, Sciences et Technologies de l’Information, Grenoble, 2012). Therefore, research in the field has focused on improving the efficiency of data processing for instant and accurate recommendations. Recommendation of products, accordingly, does not take into consideration supply chain constraints for deliveries. This can lead to recommendations for products that can be costly or too long to ship to the customer, resulting in an avoidable increase in the stress on the supply chain. This paper addresses the problem of considering delivery constraints in product recommendations. The objective is to shift demand toward products that can be delivered using the current network state without additional resources in a given time window, perimeter and with a minimum acceptable profit, in the context of e-commerce. To achieve this goal, we propose a methodology to adjust product recommendations in order to shift customers’ interests towards particular products with consideration for remaining unit loads of scheduled deliveries. For this, quasireal-time information about the supply chain is taken into consideration to improve the number of shippable products in the recommendation list, resulting in a possible improvement in truck-load utilization, lower operation costs and reduced lead-times for delivery. This method works in two stages: the first stage is the computation of the recommendation with traditional recommendation systems, and the second stage is recommendation adjustments in four phases that consider the evaluation of active trucks, evaluation of physical constraints for transportation, evaluation of the profits associated with adding a pickup/delivery to a scheduled tour for each recommended item and adjustment of recommendation scores. A sensitivity analysis of the impact of the recommendation adjustment on the recommendation list has been conducted for each of the parameters considered in the proposed method: time window, perimeter radius and minimum acceptable profit. Various experimental results prove that the method permits increasing the number of recommended products that can be shipped using the available resources within a given perimeter radius, time window and minimum profit.

Keywords: E-commerce; Big data; Recommender systems; Supply chain; Agility (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10845-020-01619-5

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