Virtual warehousing through digitalized inventory and on-demand manufacturing: a case study

Sharifi, Elham; Chaudhuri, Atanu; Farahani, Saeed Davoudavadi; Staal, Lasse Guldborg; Waehrens, Brian Vejrum

Abstract

Novel digital on-demand manufacturing technologies provide a significant opportunity to support development of virtual warehousing and in turn improve supply chain performance. However, the implementation of virtual warehouse comes with a set of challenges, especially where the objective is to virtually warehouse standard or legacy parts that have been developed and verified initially by means of conventional (non-digital) manufacturing. In this paper, we explore the key elements required for successful implementation of a virtual warehouse for standard / legacy parts based on a combination of part digitalization, on-demand manufacturing and part validation. Our proposed framework for adoption of virtual warehouse includes development of a digital inventory which includes supply chain and manufacturability data, identification, and selection of suitable parts for on-demand manufacturing, selection of on-demand manufacturing technology, fit-for-purpose validation of the parts. Our framework is exemplified through a case study, and we conclude that the building of an effective VW requires several enablers, including availability of digital data about parts’ technical and supply chain characteristics, a suitable part identification tool which is flexible to include comparison with reference parts which were already produced by different on-demand manufacturing technologies and such technologies which can produce the parts to the fit-for-purpose quality.

Citation

Sharifi, E., Chaudhuri, A., Farahani, S. D., Staal, L. G., & Waehrens, B. V. (2025). Virtual warehousing through digitalized inventory and on-demand manufacturing: a case study. Computers in Industry, 164, Article 104184. https://doi.org/10.1016/j.compind.2024.104184

• Journal Article Type: Article
• Acceptance Date: Sep 9, 2024
• Online Publication Date: Sep 11, 2024
• Publication Date: Jan 1, 2025
• Deposit Date: Sep 10, 2024
• Publicly Available Date: Sep 20, 2024
• Journal: Computers in Industry
• Print ISSN: 0166-3615
• Electronic ISSN: 1872-6194
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 164
• Article Number: 104184
• DOI: https://doi.org/10.1016/j.compind.2024.104184
• Public URL: https://durham-repository.worktribe.com/output/2853052