AHP-based support tools for initial screening of manufacturing reshoring decisions

Abstract

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.

Multi-Stage Fuzzy-Logic Model for Evaluation of Reshoring Decisions

“…These criteria correspond to the competitive priorities of the company, as the main objective of any reshoring is to increase the overall competitiveness [3]. Of these criteria, cost and quality are considered most important for reshoring, while sustainability is often considered to be the least important criteria [20].…”

“…A multi-stage fuzzy-logic model is developed based on weights of criteria found in previous research [18][19][20][21]. The first stage focus on production resources, the second on production capabilities, and the third one on competitive priorities.…”

A hybrid fuzzy-AHP-TOPSIS model for evaluation of manufacturing relocation decisions

“…Several existing MCDM models are promising for certain types of relocation decisions, for example, AHP (Sequeira et al 2021), DEMATEL (Chakraborty et al 2018), ELEC-TRE (Ray et al 2015), TOPSIS (Xu et al 2017), VIKOR (Huang et al 2020), among others. Thus, it becomes crucial to choose the MCDM best suited for the application at hand.…”

“…Considering the complexity in making decisions, the trade-offs between decision criteria can be managed by assigning weights to the respective decision criteria. AHP includes several features that make it an appropriate model, and its usefulness has been demonstrated for the initial screening purpose of reshoring decisions (Sequeira et al 2021). Next, considering the uncertainty in making decisions, fuzzy logic-based models are appropriate because they incorporate the mathematical concept of fuzzy sets to cope with the uncertainties.…”

“…Next, considering the uncertainty in making decisions, fuzzy logic-based models are appropriate because they incorporate the mathematical concept of fuzzy sets to cope with the uncertainties. Such models are useful in group decision-making situations, where several management executives are charged with making the final relocation decision (Hilletofth et al 2021;Sequeira et al 2021). When too many alternatives are available in a decision-making situation, TOPSIS could be considered an appropriate MCDM model.…”