An inventory-aware and revenue-based itemset placement framework for retail stores. (15th April 2023)

• Record Type: Journal Article
• Authors: Mondal, Anirban; Mittal, Raghav; Saurabh, Samant; Chaudhary, Parul; Reddy, Polepalli Krishna
• Is Part Of: Expert systems with applications. Volume 216(2023)
• Publication Date: 2023-04-15
• Subjects: Data mining -- Mining methods and algorithm -- Indexing methods -- Business intelligence -- Utility mining -- Retail; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.119404
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: Retailer revenue is significantly impacted by item placement. Given the prevalence and popularity of medium-to-large-sized retail stores, several research efforts have been made towards facilitating item/itemset placement for improving retailer revenue. However, they fail to consider the issue of inventory of the items w.r.t. itemset placement. Notably, the inventory of a given item refers to the number of instances of that item that are available to the retailer for sales purposes. Moreover, efficient retrieval and placement of top-revenue itemsets in the retail store slots cannot be performed by existing approaches. Our key contributions are summarized as follows. First, we introduce the notion of inventory in retail itemset placement. Second, we propose an inventory-aware indexing scheme, designated as IRIS, for efficiently retrieving high-revenue itemsets. Moreover, we propose the IRPS inventory-aware itemset placement scheme, which exploits the IRIS indexing scheme, for facilitating improved retailer revenue. Third, we conduct a performance study with two real datasets to demonstrate the effectiveness of our proposed itemset indexing and placement schemes in improving retailer revenue. Highlights: We introduced the notion of inventory in retail itemset placement. Inventory-aware itemset placement can significantly improve retailer revenue. We have proposed an inventory-aware itemset retrieval and placement framework. We conducted experiments with real datasets to demonstrate revenue improvement.