Solving on-shelf availability by a creative inference of data science imputation models. (20th August 2022)

• Record Type: Journal Article
• Authors: Mahapatra, Ashok; Patnaik, Srikanta; Dash, Manoranjan; Mahapatra, Ananya
• Is Part Of: International journal of grid and utility computing. Volume 13:Number 4(2022)
• Page Start: 425
• Page End: 446
• Publication Date: 2022-08-20
• Subjects: OOS -- OSA -- out of stock -- missing sales -- missing value imputation -- on shelf availability; Electronic data processing -- Distributed processing -- Periodicals; Electronic commerce -- Management -- Computer programs -- Periodicals; 004.605
• Languages: English
• ISSNs: 1741-847X

Abstract

Retailers' foremost objective is to ensure right product at right place at right price for the right customer, which predominantly hinges on its competencies to maximise On-Shelf-Availability (OSA). Although many studies are undertaken to identify and resolve root causes for diminished OSA, they always have centred around supply side inventory and replenishment. In this article we ventured out to explore OSA from a sell side perspective and discovered unidentified diminished OSA occurrence. Accordingly, systematic approach to identify and then estimate the value of diminished sales due to diminished OSA is proposed by means of modern data science mechanisms. Furthermore, a new nomenclature 'invisible missing data' is proposed by drawing a parallel to (1) Out-Of-Stock (OOS) as missing data and (2) estimation of OOS impact on sales as a missing value imputation problem. Finally, the solution made lucid enough to cater to a wider audience and encourage further future refinements in this field.