A Fuzzy Multicriteria Decision-Making Approach for Assessing the Preparedness Level for the Implementation of Logistics 4.0: A Case Study in the Food Industry
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 14056 LNCS, Page: 32-46
2023
- 27Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Captures27
- Readers27
- 27
Conference Paper Description
Industries must prepare diagnoses that indicate their actual state in using Industry 4.0 technologies, especially in Logistics 4.0. So then, focused on the food sector industries, designing and testing a model to carry out this diagnosis is necessary. This project aims to present a Diagnostic and Characterization Model for the Food Sector Industries of the city of Barranquilla based on multifactorial strategies that help them evaluate and make decisions to increase the possibilities of implementing and adopting technologies established under the Logistics 4.0 in your supply chain. In this context, designing and testing a Diagnostic and Characterization Model for the Food Sector Industries will be carried out, applying a multicriteria methodology as AHP-TOPSIS and thus generating a tool that allows its applicability in the medium and long term.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85178507568&origin=inward; http://dx.doi.org/10.1007/978-3-031-48044-7_3; https://link.springer.com/10.1007/978-3-031-48044-7_3; https://dx.doi.org/10.1007/978-3-031-48044-7_3; https://link.springer.com/chapter/10.1007/978-3-031-48044-7_3
Springer Science and Business Media LLC
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