Three novel fuzzy logic concepts applied to reshoring decision-making

Hilletofth, Per; Sequeira, Movin; Adlemo, Anders

doi:10.1016/j.eswa.2019.02.018

Cited by 48 publications

(70 citation statements)

References 105 publications

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“… Journal of Business Research 2019 Fjellstrom, D., Fang, T., Chimenson, D. Journal of Asia Business Studies X 2019a Fratocchi, L., Di Stefano, C. World Review of Intermodal Transportation Research 2019b Fratocchi, L., Di Stefano, C. Journal of Global Operations and Strategic Sourcing 2019 Gadde, L.E., Jonsson, P. Journal of Purchasing and Supply Management 2019 Grappi, S., Romani, S, Bagozzi, R.P. Personnel Review 2019a Hilletofth, P, Eriksson, D., Tate, W., Kinkel, S. Journal of Purchasing and Supply Management X 2019b Hilletofth, P., Sequeira, M., Adlemo, A. Expert Systems with applications X 2019 Johansson, M., Olhager, J., Heikkilä, J., Stentoft, J.…”

Section: Appendixmentioning

confidence: 99%

Back-shoring vs near-shoring: a comparative exploratory study in the footwear industry

2021

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Section: Appendixmentioning

confidence: 99%

Back-shoring vs near-shoring: a comparative exploratory study in the footwear industry

2021

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“…There is a clear need for more advanced decision-support tools in the manufacturing reshoring domain (Barbieri et al, 2018;Hilletofth et al, 2019b;Stentoft et al, 2016). The complexity inherent in this kind of decision-making situation requires advanced decision- -2.27 -3.00 -1.21 -2.00 -2.95 -5.00 -2.45 -5.00 -1.49 -2.00 -1.22 -2.00 -3.04 -5.00 -3.04 -5.00 2 2.27 3.00 1.41 2.00 2.95 5.00 2.69 5.00 1.19 1.65 0.83 1.65 3.04 5.00 3.04 5.00 3 -2.27 -3.00 -1.37 -2.00 -2.95 -5.00 -2.42 -5.00 -1.89 -2.50 -1.45 -2.50 -3.04 -5.00 -3.03 -5.00 4 -2.85 -4.00 -1.96 -3.00 -3.01 -5.00 -2.84 -5.00 -2.44 -3.20 -2.08 -3.00 -3.04 -5.00 -3.03 -5.00 5 -2.12 -3.00 -1.32 -1.50 -2.71 -5.00 -2.13 -5.00 -1.84 -2.50 -1.38 -1.55 -2.86 -5.00 -2.81 -5.00 6 2.50 3.50 1.31 2.00 2.86 5.00 1.91 5.00 1.73 2.35 1.52 1.50 2.86 5.00 2.80 5.00 7 1.70 3.00 0.68 1.00 2.11 5.00 1.03 5.00 1.53 2.05 1.06 1.35 2.86 5.00 2.63 5.00 8 0.89 3.00 0.95 1.00 1.11 5.00 1.66 5.00 1.04 1.65 0.64 0.65 2.11 5.00 2.37 5.00 9 2.63 3.50 2.08 3.50 3.04 5.00 2.85 5.00 2.26 2.95 2.12 2.95 3.04 5.00 3.04 5.00 10 -2.60 -3.50 -1.73 -2.50 -2.99 -5.00 -2.62 -5.00 -1.89 -2.50 -1.71 -2.50 -3.04 -5.00 -3.03 -5.00 11 -2.17 -5.00 -0.14 0.00 -2.40 -5.00 -0.22 -5.00 -2.17 -5.00 -0.40 0.00 -2.40 -5.00 -0.82 -5.00…”

Section: Discussionmentioning

confidence: 99%

“…In the first support tool, a complete fuzzy rule base was created. A three-step process Fuzzy-logicbased support tools suggested previously by Hilletofth et al (2019b) was used (Figure 5). A weight was assigned to each linguistic variable, based on its relative importance.…”

Section: Define Fuzzy Rulesmentioning

confidence: 99%

Fuzzy-logic-based support tools for initial screening of manufacturing reshoring decisions

Hilletofth

Sequeira

Tate

2021

IMDS

Self Cite

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PurposeThis paper investigates the suitability of fuzzy-logic-based support tools for initial screening of manufacturing reshoring decisions.Design/methodology/approachTwo fuzzy-logic-based support tools are developed together with experts from a Swedish manufacturing firm. The first uses a complete rule base and the second a reduced rule base. Sixteen inference settings are used in both of the support tools.FindingsThe findings show that fuzzy-logic-based support tools are suitable for initial screening of manufacturing reshoring decisions. The developed support tools are capable of suggesting whether a reshoring decision should be further evaluated or not, based on six primary competitiveness criteria. In contrast to existing literature this research shows that it does not matter whether a complete or reduced rule base is used when it comes to accuracy. The developed support tools perform similarly with no statistically significant differences. However, since the interpretability is much higher when a reduced rule base is used and it require fewer resources to develop, the second tool is more preferable for initial screening purposes.Research limitations/implicationsThe developed support tools are implemented at a primary-criteria level and to make them more applicable, they should also include the sub-criteria level. The support tools should also be expanded to not only consider competitiveness criteria, but also other criteria related to availability of resources and strategic orientation of the firm. This requires further research with regard to multi-stage architecture and automatic generation of fuzzy rules in the manufacturing reshoring domain.Practical implicationsThe support tools help managers to invest their scarce time on the most promising reshoring projects and to make timely and resilient decisions by taking a holistic perspective on competitiveness. Practitioners are advised to choose the type of support tool based on the available data.Originality/valueThere is a general lack of decision support tools in the manufacturing reshoring domain. This paper addresses the gap by developing fuzzy-logic-based support tools for initial screening of manufacturing reshoring decisions.

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“…In addition, FLC exhibits strong performance in nonlinear controller systems without designing any mathematical model 61 – 64 . The fuzzy speed control has many parameters, such as the MF parameters, number of the rule base and number of the MFs 65 – 67 . The fuzzy speed control can be improved by optimising the value of these parameters.…”

Section: Methodsmentioning

confidence: 99%

Role of optimization algorithms based fuzzy controller in achieving induction motor performance enhancement

Hannan

Ali²,

Lipu

et al. 2020

Nat Commun

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Three-phase induction motors (TIMs) are widely used for machines in industrial operations. As an accurate and robust controller, fuzzy logic controller (FLC) is crucial in designing TIMs control systems. The performance of FLC highly depends on the membership function (MF) variables, which are evaluated by heuristic approaches, leading to a high processing time. To address these issues, optimisation algorithms for TIMs have received increasing interest among researchers and industrialists. Here, we present an advanced and efficient quantum-inspired lightning search algorithm (QLSA) to avoid exhaustive conventional heuristic procedures when obtaining MFs. The accuracy of the QLSA based FLC (QLSAF) speed control is superior to other controllers in terms of transient response, damping capability and minimisation of statistical errors under diverse speeds and loads. The performance of the proposed QLSAF speed controller is validated through experiments. Test results under different conditions show consistent speed responses and stator currents with the simulation results.

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Three novel fuzzy logic concepts applied to reshoring decision-making

Cited by 48 publications

References 105 publications

Back-shoring vs near-shoring: a comparative exploratory study in the footwear industry

Back-shoring vs near-shoring: a comparative exploratory study in the footwear industry

Fuzzy-logic-based support tools for initial screening of manufacturing reshoring decisions

Role of optimization algorithms based fuzzy controller in achieving induction motor performance enhancement

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