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AHP-based support tools for initial screening of manufacturing reshoring decisions
Jönköping University, School of Engineering, JTH, Industrial Product Development, Production and Design.ORCID iD: 0000-0002-1318-1598
Jönköping University, School of Engineering, JTH, Supply Chain and Operations Management. Jönköping University, School of Engineering, JTH, Industrial Product Development, Production and Design.ORCID iD: 0000-0002-8305-4412
Jönköping University, School of Engineering, JTH, Department of Computer Science and Informatics.ORCID iD: 0000-0002-2342-3749
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 Group Publishing Limited, 2021. Vol. 14, no 3, p. 502-527
Keywords [en]
Quantitative, Decision-making, AHP, Fuzzy-AHP, Manufacturing relocation, Reshoring, Initial screening
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:hj:diva-52439DOI: 10.1108/JGOSS-07-2020-0037ISI: 000649030500001Scopus ID: 2-s2.0-85106234320Local ID: HOA;;52439OAI: oai:DiVA.org:hj-52439DiVA, id: diva2:1553844
Funder
Knowledge Foundation, 20200058Available from: 2021-05-11 Created: 2021-05-11 Last updated: 2023-10-05Bibliographically approved
In thesis
1. Decision support for multi-criteria evaluation of manufacturing reshoring decisions
Open this publication in new window or tab >>Decision support for multi-criteria evaluation of manufacturing reshoring decisions
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Reshoring decisions in the manufacturing sector have received increasing attention due to their potential to improve overall quality, flexibility, or reduce risk. Contemporary events such as the pandemic and the supply crisis have made manufacturing reshoring decisions a timely and important topic to study. Particularly, the decision support deserves attention as there is limited knowledge on tools for reshoring decisions. The purpose of this study is to investigate the feasibility of decision support for reshoring decisions. The purpose is addressed through three research questions (RQ). The first question (RQ1) is “What criteria should be considered in evaluating a manufacturing reshoring decision?” and should guide companies in identifying factors in a reshoring decision. The second one (RQ2) is “How do decision-makers reason with respect to criteria in manufacturing reshoring decisions?” and describes the importance of the identified criteria that come into play during the decision. The third one (RQ3) is “How can the criteria be modeled in decision-support tools for evaluating manufacturing reshoring decisions?” and explores the feasibility of decision support. The research methods involve systematic literature review, multiple case study, modeling, and archival research. The findings show that reshoring is a multi-criteria decision with cost, quality, and delivery time criteria are considered to be more important (high weight) than sustainability criteria (low weight). The reasoning is manifested by inference rules pertaining to the important criteria. Multi-criteria decision-making techniques such as fuzzy inference, AHP, fuzzy-AHP and fuzzy-TOPSIS are feasible for evaluating manufacturing reshoring decisions. While these techniques rely on decision-makers’ ability to specify weights and rules, rule mining is used to extract rules from large datasets. This research contributes through increased knowledge regarding the criteria, reasoning, and modeling of reshoring decisions. Furthermore, the research extrapolates on the theories that are required to move forward when investigating this topic. For practitioners, this research develops tools that can be used in different stages of the decision-making process. For society, the support tools mean making rational decisions on reshoring rather than emotional or politically motivated ones.

Abstract [sv]

Återflyttning av produktion har väckt mycket intresse. Fördelarna med att flytta hem produktion inkluderar ökad kvalitet, flexibilitet, marknadsföring och minskade risker. Dessutom har pandemin avslöjat riskerna med långa leveranskedjor, så det är lämpligt att studera möjligheterna med återflyttning. Det finns behov av mer kunskap om verktyg som kan hjälpa beslutsfattare att ta beslut om att flytta hem produktionen. Syftet med denna studie är att undersöka beslutsstöd för återflyttning av produktionen. Tre forskningsfrågor har formulerats enligt syftet. Den första frågan är “Vilka kriterier bör beslutsfattare överväga vid beslut om återflyttning av produktion?” Svaret för frågan kommer att vägleda beslutsfattaren och företag i med att identifiera kriterier. Den andra frågan är “Hur resonerar beslutsfattare i samband med kriterier vid beslut om återflyttning av produktion?” och den beskriver vikter för de identifierade kriterierna och reglerna som kommer in under beslutet. Den tredje frågan är “Hur kan kriterierna modelleras i ett verktyg för att utvärdera beslut om återflyttning av produktion?” Frågan undersöker olika beslutsmodeller. Studien använder sig av en blandning av metoder, inklusive litteraturstudier, fallstudier, modellering och arkivstudier. De kriterier som beaktas vid beslutet är kostnad, kvalitet och leveranstid, som betraktas som mycket viktiga, medan hållbarhetskriterier inte har samma vikt. Kriterierna är dock dynamiska och kan ändras över tiden. Beslutsreglerna tar hänsyn till de viktiga kriterierna. Fuzzy logic är en möjlig beslutsmodell som kan användas vid beslut om återflyttning. Forskningen bidrar till en ökad förståelse för kriterier, beslutsfattarens resonemang och modellering av återflyttningsbeslut. Dessutom reflekterar forskningen över de teorier som krävs för att gå vidare när man undersöker detta ämne. För praktiker och företag innebär det att utveckla verktyg som kan användas under olika steg i beslutsprocessen. För samhället innebär det att besluten hålls rationella och att undvika emotionella eller politiskt motiverade beslut. Avhandlingen är baserad på sex vetenskapliga artiklar.

Place, publisher, year, edition, pages
Jönköping: Jönköping University, School of Engineering, 2023. p. 80
Series
JTH Dissertation Series ; 076
Keywords
Reshoring decision, decision support, manufacturing, criteria, reasoning, multi-criteria decision-making, Återflyttningsbeslut, beslutsstöd, produktion, kriterier, resonemang, beslutsfattande
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:hj:diva-59923 (URN)978-91-87289-84-2 (ISBN)978-91-87289-85-9 (ISBN)
Public defence
2023-03-24, Gjuterisalen (E1405), School of Engineering, Jönköping, 10:00 (English)
Opponent
Supervisors
Available from: 2023-02-27 Created: 2023-02-27 Last updated: 2023-02-27Bibliographically approved

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Sequeira, MovinHilletofth, PerAdlemo, Anders

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