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System Architectures for Sensor-Based Dynamic Remaining Shelf-life Prediction
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).ORCID iD: 0000-0002-6019-1182
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).ORCID iD: 0000-0003-0998-6585
2019 (English)In: International Journal of Operations Research and Information Systems (IJORIS), ISSN 1947-9328, Vol. 10, no 4, p. 21-38, article id 2Article in journal (Refereed)
Abstract [en]

Different storage and handling conditions in cold supply chains often cause variations in the remaining shelf life of perishable foods. In particular, the actual shelf life may differ from the expiration date printed on the primary package. Based on temperature sensors placed on or close to the food products, a remaining shelf-life prediction (RSLP) service can be developed, which estimates the remaining shelf life of individual products, in real-time. This type of service may lead to decreased food waste and is used for discovering supply chain inefficiencies and ensuring food quality. Depending on the system architecture, different service qualities can be obtained in terms of usability, accuracy, security, etc. This article presents a novel approach for how to identify and select the most suitable system architectures for RSLP services. The approach is illustrated by ranking different architectures for a RSLP service directed towards the supply chain managers. As a proof of concept, some of the most highly ranked architectures have been implemented and tested in food cold supply chains.

Place, publisher, year, edition, pages
IGI Global, 2019. Vol. 10, no 4, p. 21-38, article id 2
Keywords [en]
Remaining Shelf-life Prediction, Information System Architecture, Sensor-based System, Cold Supply Chain, AHP
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:mau:diva-2575DOI: 10.4018/IJORIS.2019100102Scopus ID: 2-s2.0-85126390043Local ID: 30040OAI: oai:DiVA.org:mau-2575DiVA, id: diva2:1399338
Available from: 2020-02-27 Created: 2020-02-27 Last updated: 2024-02-05Bibliographically approved

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Publisher's full textScopushttps://www.igi-global.com/article/system-architectures-for-sensor-based-dynamic-remaining-shelf-life-prediction/236644

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Jevinger, ÅseDavidsson, Paul

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