A decision-making framework for determinants of an organisation’s readiness for smart warehouse

Abstract

Demanding, competitive business dynamics drive organisations to continually evaluate and adopt automation in their structural setups, particularly warehouses, for growth and profitability. Industry 4.0 technologies are revolutionising warehouse operations, resulting in cost savings, increased efficiency, and a more environmentally friendly approach for businesses. The readiness of organisations to adopt these technologies is evaluated with the help of hard and soft determinants selected through a theoretical framework of Technology–Organisation-Environment (TOE). In this study, a unique method is used that combines the Modified Delphi method, content validity for scale validation, expert opinion consensus tests for agreement, ranking determinants using the Best Worst Method (BWM), and rank validation through sensitivity analysis for warehousing industry experts and organisations. Warehouse service providers who convert their warehouses into smart modes to serve fast-moving consumer goods companies in Tier-2 cities are chosen for the structural framework. Results present the role of the ‘operational efficiency of technology’, ‘top management’, ‘employee’s education’, and ‘technical background’ as the most effective driving determinants. This research provides valuable information to researchers and managers trying to upgrade their warehouse into a smart warehouse.

Keywords:

• Intelligent logistics
• industrial development
• smart warehouse
• decision-making framework
• TOE
• emerging economy

Acknowledgements

The authors would like to thank the logistics industry experts who contributed to this study by providing support and valuable perspectives for the identification and comparison of enablers, as well as their subsequent validation of findings. The authors would like to thank the editor, co-editor, associate editor, and all reviewers who have provided us with constructive feedback to help improve the quality of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research work was funded by Institutional Fund Projects under grant no. [IFPHI-366-135-2020]. Therefore, authors gratefully acknowledge technical and financial support from the Ministry of Education and King Abdulaziz University, Jeddah, Saudi Arabia.

Notes on contributors

Sadia Samar Ali

Sadia Samar Ali (PhD in Operations Research) is a Full Professor in the Department of Industrial Engineering at King Abdulaziz University, Jeddah, Saudi Arabia. With a focus on Sustainable Supply Chain Management, she excels in utilizing Optimization and Quantitative Analysis in her research. Her expertise also includes Technology and Innovation in developing countries. Engaged with Association of European Operational Research Societies (EURO) and International Federation of Operational Research Societies groups (IFORS), She actively advocates for Sustainable and Optimization practices. Holding key editorial roles at eminent journals like CJOR (Springer); PLOS-One and GRETS (Elsevier), she contributes significantly to scholarly discourse. She has built a strong reputation in industrial engineering research, gaining over 5000 Google citations and consistently ranking 1st or 2nd in the field for four years in Saudi Arabia. Her portfolio includes 18 published papers, 13 of which are in prestigious ABDC (A) journals, with 8 in ABS 3/3* and 6 in ABS 2*/2 categories out of a selection of 90+ papers in ISI, SCI/SSCI, SCOPUS, and WOS indexed journals. She was privileged to be a keynote speaker at international forums focusing on data analytics, optimization, and industrial engineering. She is a recipient of numerous awards and accolades, most recently achieving the 3rd position for the best researcher in the Faculty of Engineering at the King Abdulaziz University for the year 2023. Her dedication to engineering education, research, and management is widely recognized.

Rajbir Kaur

Prof Rajbir Kaur holds dual master’s degrees. She has diverse teaching experience of 15 years in the field of managerial communications and Supply Chain management. She has published research papers in reputed journals and has presented at international conferences too.

Himanshu Gupta

Himanshu Gupta is an Assistant Professor of Operations and Supply Chain Management at Indian Institute of Technology (Indian School of Mines), Dhanbad, India. His research interest includes sustainability, green and sustainable supply chain management, green innovation, transportation management.

Zulfiqar Ahmad

Zulfiqar Ahmad association with King Abdulaziz University, Jeddah, Saudi Arabia and has research interests in innovative technologies such as AI, UAVs along with NLP and forensic linguistics. He has graduated from university of Sussex, United Kingdom.

Khaled Jebahi

Khaled Jebahi is associated with King Abdulaziz University, Jeddah, Saudi Arabia and received his Doctor of Philosophy (PhD), English Language and Applied Linguistics from University of Tunis, Tunis.