Digital twin based framework to enhance productivity processes in retail industry

Rushan Arshad*, Salem Chakhar, Simon Hedaux, Nigel Leroy Williams, Edward Smart, Chris Simms, James Bolle

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents an innovative model, the Productivity Twin, a digital twin of the productivity process that can significantly enhance productivity measurement and help in decision making process for all stakeholders. In this paper, we focus on the application of this model in the retail industry where the model uses data collected from retail outlets, incorporates sophisticated data analysis techniques and AI algorithms to develop bespoke insights from the data to enhance the overall productivity of a company and its processes. A case study from a leading productivity consulting firm in the UK is used for validation.
Original languageEnglish
Title of host publicationThe 2024 International Conference on Decision Aid Sciences and Applications (DASA'24)
PublisherIEEE Computer Society
Publication statusAccepted for publication - 23 Nov 2024
Event2024 International Conference on Decision Aid Sciences and Applications: DASA'24 - , Bahrain
Duration: 11 Dec 202412 Dec 2024

Conference

Conference2024 International Conference on Decision Aid Sciences and Applications
Country/TerritoryBahrain
Period11/12/2412/12/24

Keywords

  • Productivity
  • Digital Twin
  • Artificial intelligence
  • Retail industry

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