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A framework for the application of Industry 4.0 in logistics and supply chains

  • Elly Philpott
  • , Yanqing Duan
  • , Ram Ramanathan
  • , Usha Ramanathan
  • , Elaine Philpott

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

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Abstract

This research aims to identify and understand the contemporary practice of using Business Analytics (BA) in improving the performance of logistics companies by conducting exploratory case studies. We present seven case studies using a within-case and cross-case analysis of the practice of BA use in UK logistics firms. We position our analysis under major BA application areas identified in previous third-party logistics surveys. Based on an in-depth analysis, we present a Value-Adding Input-Output (VAIO) framework to support an understanding of the use of Business Analytics in logistics companies. One of the main findings is the recognition of four antecedents (skills, systems, technology, and trust issues) before deriving value from business analytics investments. When the antecedents are in place, it is possible for logistics companies to derive value by engaging in BA application areas. The value dimensions ultimately help logistics firms to be competitive in the market place. The framework supports the applicability of the Resource-Based View of a firm for BA use in logistics. The framework developed in this chapter provides a practical basis for logistics companies to derive value from their investments in Business Analytics. The Value-Adding Process Framework is a new framework suggested in this chapter.
Original languageEnglish
Title of host publicationSupply Chain: Strategies, Issues and Models
Place of PublicationLondon
PublisherSpringer
Pages51-74
Edition1
ISBN (Electronic)9783030488765
ISBN (Print)9783030488758
DOIs
Publication statusPublished - 19 Jul 2020

Keywords

  • Big Data
  • logistics

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