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

doi:10.1109/DASA63652.2024.10836574

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

ReThink Productivity Consulting

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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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 language	English
Title of host publication	2024 International Conference on Decision Aid Sciences and Applications (DASA)
Publisher	IEEE Computer Society
Pages	1-5
Number of pages	5
ISBN (Electronic)	9798350369106
ISBN (Print)	9798350369113
DOIs	https://doi.org/10.1109/DASA63652.2024.10836574
Publication status	Published - 17 Jan 2025
Event	2024 International Conference on Decision Aid Sciences and Applications: DASA'24 - , Bahrain Duration: 11 Dec 2024 → 12 Dec 2024

Conference

Conference	2024 International Conference on Decision Aid Sciences and Applications
Country/Territory	Bahrain
Period	11/12/24 → 12/12/24

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Productivity
Industries
Ethics
Analytical models
Data models
Digital twins
Stakeholders
Artificial intelligence
Monitoring
Testing

Access to Document

10.1109/DASA63652.2024.10836574

Rushan_et_al_2024_Productivity_Twin_DASA 2024
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Accepted author manuscript (Post-print), 173 KB

Cite this

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title = "Digital twin based framework to enhance productivity processes in retail industry",

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.",

keywords = "Productivity, Industries, Ethics, Analytical models, Data models, Digital twins, Stakeholders, Artificial intelligence, Monitoring, Testing",

author = "Rushan Arshad and Salem Chakhar and Simon Hedaux and Williams, \{Nigel Leroy\} and Edward Smart and Chris Simms and James Bolle",

year = "2025",

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day = "17",

doi = "10.1109/DASA63652.2024.10836574",

language = "English",

isbn = "9798350369113",

pages = "1--5",

booktitle = "2024 International Conference on Decision Aid Sciences and Applications (DASA)",

publisher = "IEEE Computer Society",

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note = "2024 International Conference on Decision Aid Sciences and Applications : DASA'24 ; Conference date: 11-12-2024 Through 12-12-2024",

}

Arshad, R, Chakhar, S, Hedaux, S, Williams, NL , Smart, E , Simms, C & Bolle, J 2025, Digital twin based framework to enhance productivity processes in retail industry. in 2024 International Conference on Decision Aid Sciences and Applications (DASA). IEEE Computer Society, pp. 1-5, 2024 International Conference on Decision Aid Sciences and Applications, Bahrain, 11/12/24. https://doi.org/10.1109/DASA63652.2024.10836574

TY - GEN

T1 - Digital twin based framework to enhance productivity processes in retail industry

AU - Arshad, Rushan

AU - Chakhar, Salem

AU - Hedaux, Simon

AU - Williams, Nigel Leroy

AU - Smart, Edward

AU - Simms, Chris

AU - Bolle, James

PY - 2025/1/17

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N2 - 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.

AB - 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.

KW - Productivity

KW - Industries

KW - Ethics

KW - Analytical models

KW - Data models

KW - Digital twins

KW - Stakeholders

KW - Artificial intelligence

KW - Monitoring

KW - Testing

U2 - 10.1109/DASA63652.2024.10836574

DO - 10.1109/DASA63652.2024.10836574

M3 - Conference contribution

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BT - 2024 International Conference on Decision Aid Sciences and Applications (DASA)

PB - IEEE Computer Society

T2 - 2024 International Conference on Decision Aid Sciences and Applications

Y2 - 11 December 2024 through 12 December 2024

ER -

Digital twin based framework to enhance productivity processes in retail industry

Abstract

Conference

UN SDGs

Keywords

Access to Document

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