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AHP-based support tools for initial screening of manufacturing reshoring decisions
Jönköping University.
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Management, Industrial Design and Mechanical Engineering, Industrial Management. University of Gävle, Center for Logistics and Innovative Production. Jönköping University.ORCID iD: 0000-0002-8305-4412
Jönköping University.
2021 (English)In: Journal of Global Operations and Strategic Sourcing, ISSN 2398-5364, E-ISSN 2398-5372, Vol. 14, no 3, p. 502-527Article in journal (Refereed) Published
Abstract [en]

Purpose

The existing literature expresses a strong need to develop tools that support the manufacturing reshoring decision-making process. This paper aims to examine the suitability of analytical hierarchy process (AHP)-based tools for initial screening of manufacturing reshoring decisions.

Design/methodology/approach

Two AHP-based tools for the initial screening of manufacturing reshoring decisions are developed. The first tool is based on traditional AHP, while the second is based on fuzzy-AHP. Six high-level and holistic reshoring criteria based on competitive priorities were identified through a literature review. Next, a panel of experts from a Swedish manufacturing company was involved in the overall comparison of the criteria. Based on this comparison, priority weights of the criteria were obtained through a pairwise analysis. Subsequently, the priority weights were used in a weighted-sum manner to evaluate 20 reshoring scenarios. Afterwards, the outputs from the traditional AHP and fuzzy-AHP tools were compared to the opinions of the experts. Finally, a sensitivity analysis was performed to evaluate the stability of the developed decision support tools.

Findings

The research demonstrates that AHP-based support tools are suitable for the initial screening of manufacturing reshoring decisions. With regard to the presented set of criteria and reshoring scenarios, both traditional AHP and fuzzy-AHP are shown to be consistent with the experts' decisions. Moreover, fuzzy-AHP is shown to be marginally more reliable than traditional AHP. According to the sensitivity analysis, the order of importance of the six criteria is stable for high values of weights of cost and quality criteria.

Research limitations/implications

The limitation of the developed AHP-based tools is that they currently only include a limited number of high-level decision criteria. Therefore, future research should focus on adding low-level criteria to the tools using a multi-level architecture. The current research contributes to the body of literature on the manufacturing reshoring decision-making process by addressing decision-making issues in general and by demonstrating the suitability of two decision support tools applied to the manufacturing reshoring field in particular.

Practical implications

This research provides practitioners with two decision support tools for the initial screening of manufacturing reshoring decisions, which will help managers optimize their time and resources on the most promising reshoring alternatives. Given the complex nature of reshoring decisions, the results from the fuzzy-AHP are shown to be slightly closer to those of the experts than traditional AHP for initial screening of manufacturing relocation decisions.

Originality/value

This paper describes two decision support tools that can be applied for the initial screening of manufacturing reshoring decisions while considering six high-level and holistic criteria. Both support tools are applied to evaluate 20 identical manufacturing reshoring scenarios, allowing a comparison of their output. The sensitivity analysis demonstrates the relative importance of the reshoring criteria.

Place, publisher, year, edition, pages
Emerald , 2021. Vol. 14, no 3, p. 502-527
Keywords [en]
quantitative; decision-making; AHP; fuzzy-AHP; manufacturing relocation; reshoring; initial screening
National Category
Other Mechanical Engineering Economics and Business
Research subject
Intelligent Industry
Identifiers
URN: urn:nbn:se:hig:diva-35426DOI: 10.1108/JGOSS-07-2020-0037ISI: 000649030500001Scopus ID: 2-s2.0-85106234320OAI: oai:DiVA.org:hig-35426DiVA, id: diva2:1536715
Funder
Knowledge Foundation, 20200058Available from: 2021-03-11 Created: 2021-03-11 Last updated: 2026-08-28Bibliographically approved

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Hilletofth, Per

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CiteExportLink to record
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