Social Sciences and Humanities Open (Jan 2024)

Enhancement of the distribution process on light logistics SMEs in times post-pandemic Covid-19 with Ukraine-Russia conflict by lean logistics and big data

  • José Antonio Rojas-García,
  • Cynthia Elias-Giordano,
  • S. Nallusamy,
  • Juan Carlos Quiroz-Flores

Journal volume & issue
Vol. 10
p. 100945

Abstract

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The objective of the work was to enhance the competitiveness of e-commerce and to mitigate the disadvantages associated with it, such as the untimely delivery of purchased products. This was to be achieved through the implementation of a proposed methodology in the processes related to the ‘Last Mile’, leveraging Big Data and Lean Logistics to boost the productivity of light logistics SMEs (small and medium-sized enterprises). To identify the conditions impacting the distribution processes, a study was conducted on a population of 750 S MEs, utilizing a sample of 255 companies through stratified probabilistic sampling. The research spanned the years 2022 and 2023. The methodology advocated in this study combines Lean Logistics and Big Data to enhance the supply chain's efficiency and profitability for SMEs engaged in light logistics, amidst the post-pandemic landscape and the conflict between Ukraine and Russia. This methodology was structured into three stages: firstly, the organization of the customer shipment database; secondly, the cleaning of this database to pinpoint records scheduled for distribution; and thirdly, the assignment of delivery rates and probabilities using historical delivery outcomes. The findings suggested that the integration of Lean Logistics and Big Data offers a viable solution for improving the supply chain efficiency and profitability for light logistics SMEs during the post-pandemic period and amidst the Ukraine-Russia conflict. This research's originality is underscored by its novel approach of merging Lean Logistics and Big Data to fortify the supply chain efficiency and profitability for SMEs in light logistics, a synergy not previously identified in existing literature.

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