The manufacturing reshoring phenomenon has received more attention in the academic and business literature in recent years. Due to the newness of the phenomenon, there is a lack of knowledge about how these decisions were made. This research provides a theoretical framework by reviewing literature on possible criteria that are considered in a manufacturing reshoring decision. The criteria are categorized into six categories including competitive priority, resource, strategy, context, preference and global condition. A multiple case study methodology is used to identify the criteria and compare them with the theoretical framework. The findings indicate that total cost is the most common criteria considered and each case company has followed its own cost analysis techniques. Other criteria considered by all case companies were inventory cost, transportation cost, switching cost, delivery lead times, proximity to customer and availability of manufacturing technology. The research concludes that manufacturing reshoring is a holistic decision with criteria occurring at all categories in the theoretical framework. This contributes to the knowledge of reshoring decision-making and suggests that future research should investigate decision support tools for such decisions.

Reshoring decisions are complex due to multiple groups of criteria that have an impact on the decision involved. In order to support this manual decision-making process, more advanced tools need to be developed. The purpose of this paper is to explore a multi-stage fuzzy-logic model to support the reshoring decision-making process through a transdisciplinary approach. Three relevant stages pertaining to reshoring decisions were identified: production resources, production capabilities, and competitive priorities. For each stage, factor loading values were used to create fuzzy rules. The model provides a decision recommendation based on a set of input reshoring scenarios. The research contributes to the reshoring literature providing a multi-stage fuzzy-logic model that simultaneously handles different groups of criteria, and to practitioners by contemplating different key competencies within a company during the reshoring decision process.

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.

Manufacturing relocation decisions are complex because they involve combinations of location modes like offshoring or reshoring, and governance modes like insourcing or outsourcing. Furthermore, the uncertainty involved in the decision-making process makes it challenging to reach a right-shoring decision. This study presents a hybrid fuzzy-AHP-TOPSIS model to support generic relocation decisions. Industry experts were involved in a pairwise comparison of the competitive priorities’ decision criteria. A meta-synthesis of empirical studies is used to generate theoretical relocation scenarios. The presented hybrid model is used to rank the relocation scenarios in order to identify the most pertinent alternative. The resiliency of the solution is presented through a sensitivity analysis. The results indicate that the proposed hybrid model can simultaneously handle all the main relocation options involving governance modes. Based on the input data in this study, the competitive priorities criteria quality, time and cost are shown to have a strong impact, whereas the sustainability criterion has a weak impact on the choice of relocation option. The research presented in this paper contributes to the research field of manufacturing relocation by demonstrating the suitability of the hybrid fuzzy-AHP-TOPSIS model for relocation decisions and the resilience of the results. Furthermore, the research contributes to practice by providing managers with a generic relocation decision-support model that is capable of simultaneously handling and evaluating various relocation alternatives.