Illustrating scholar–practitioner collaboration for data-driven decision-making in the optimization of logistics facility location and implications for increasing the …
The TQM Journal, 2022•emerald.com
Purpose This study illustrates the experience of scholar–practitioner collaboration for data-
driven decision-making through the problematic of optimizing facility locations and
minimizing logistics costs for La Palette Rouge (LPR) of Portugal. Design/methodology/
approach The authors used a mixed mixed-method approach involving (1) a quantitative
exploratory analysis of big data, which applied analytics and mathematical modeling to
optimize LPR's logistics network, and (2) an illustrative case of scholar–practitioner …
driven decision-making through the problematic of optimizing facility locations and
minimizing logistics costs for La Palette Rouge (LPR) of Portugal. Design/methodology/
approach The authors used a mixed mixed-method approach involving (1) a quantitative
exploratory analysis of big data, which applied analytics and mathematical modeling to
optimize LPR's logistics network, and (2) an illustrative case of scholar–practitioner …
Purpose
This study illustrates the experience of scholar–practitioner collaboration for data-driven decision-making through the problematic of optimizing facility locations and minimizing logistics costs for La Palette Rouge (LPR) of Portugal.
Design/methodology/approach
The authors used a mixed mixed-method approach involving (1) a quantitative exploratory analysis of big data, which applied analytics and mathematical modeling to optimize LPR's logistics network, and (2) an illustrative case of scholar–practitioner collaboration for data-driven decision-making.
Findings
The quantitative analysis compared more than 20 million possible configurations and proposed the optimal logistics structures. The proposed optimization model minimizes the logistics costs by 22%. Another optimal configuration revealed that LPR can minimize logistics costs by 12% through closing one of its facilities. The illustrative description demonstrates that well-established resource-rich multinational enterprises do not necessarily have the in-house capabilities and competencies to handle and analyze big data.
Practical implications
The mathematical modeling for optimizing logistics networks demonstrates that outcomes are readily actionable for practitioners and can be extended to other country and industry contexts with logistics operations. The case illustrates that synergistic relationships can be created, and the opportunities exist between scholars and practitioners in the field of Logistics 4.0 and that scientific researcher is necessary for solving problems and issues that arise in practice while advancing knowledge.
Originality/value
The study illustrates that several Logistics 4.0 challenges highlighted in the literature can be collectively addressed through scholar–practitioner collaborations. The authors discuss the implications of such collaborations for adopting virtual and augmented reality (AR) technologies and to develop the capabilities for maximizing their benefits in mature low-medium technology industries, such as the food logistics industry.
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