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Semi-Automatic Generation of a Fuzzy Inference System in a Reshoring Context
Jönköping University, Tekniska Högskolan, JTH, Datateknik och informatik.ORCID-id: 0000-0002-2342-3749
Jönköping University, Tekniska Högskolan, JTH, Industriell produktutveckling, produktion och design. Jönköping University, Tekniska Högskolan, JTH, Logistik och verksamhetsledning.ORCID-id: 0000-0002-8305-4412
2020 (Engelska)Ingår i: SPS2020: Proceedings of the Swedish Production Symposium, October 7–8, 2020 / [ed] K. Säfsten & F. Elgh, IOS Press, 2020, Vol. 13, s. 599-609Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Reshoring can be regarded as offshoring in reverse. While offshoring mainly has been driven by cost aspects, reshoring considers multiple aspects, such as higher quality demands, faster product delivery and product mass-customization. Where to locate manufacturing is usually a purely manual activity that relies on relocation experts, hence, an automated decision-support system would be extremely useful. This paper presents a decision-support system for reshoring decision-making building a fuzzy inference system. The construction and functionality of the fuzzy inference system is briefly outlined and evaluated within a high-cost environment considering six specific reshoring decision criteria, namely cost, quality, time, flexibility, innovation and sustainability. A challenge in fuzzy logic relates to the construction of the so called fuzzy inference rules. In the relocation domain, fuzzy inference rules represent the knowledge and competence of relocation experts and are usually generated manually by the same experts. This paper presents a solution where fuzzy inference rules are automatically generated applying one hundred reshoring scenarios as input data. Another important aspect in fuzzy logic relates to the membership functions. These are mostly manually defined but, in this paper, a semi-Automatic approach is presented. The reshoring decision recommendations produced by the semi-Automatically configured fuzzy inference system are shown to be as accurate as those of a manually configured fuzzy inference system. 

Ort, förlag, år, upplaga, sidor
IOS Press, 2020. Vol. 13, s. 599-609
Serie
Advances in Transdisciplinary Engineering, ISSN 2352-751X, E-ISSN 2352-7528 ; 13
Nyckelord [en]
Decision-support system, Fuzzy inference rule generation, Fuzzy inference system, Membership function generation, Reshoring, Automation, Computer circuits, Decision making, Decision support systems, Fuzzy logic, Fuzzy systems, Membership functions, Sustainable development, Automatically generated, Decision criterions, Fuzzy inference rules, Fuzzy inference systems, Manufacturing IS, Mass customization, Product delivery, Semi-automatic generation, Fuzzy inference
Nationell ämneskategori
Produktionsteknik, arbetsvetenskap och ergonomi
Identifikatorer
URN: urn:nbn:se:hj:diva-51484DOI: 10.3233/ATDE200199Scopus ID: 2-s2.0-85098638657ISBN: 978-1-64368-146-7 (tryckt)ISBN: 978-1-64368-147-4 (digital)OAI: oai:DiVA.org:hj-51484DiVA, id: diva2:1517688
Konferens
9th Swedish Production Symposium (SPS2020), 7-8 October 2020, Jönköping, Sweden
Tillgänglig från: 2021-01-14 Skapad: 2021-01-14 Senast uppdaterad: 2021-01-14Bibliografiskt granskad

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Adlemo, AndersHilletofth, Per

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JTH, Datateknik och informatikJTH, Industriell produktutveckling, produktion och designJTH, Logistik och verksamhetsledning
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