A Cost–Benefit Analysis Simulation for the Digitalisation of Cold Supply Chains

Oliver Schiffmann*, Ben Hicks, Aydin Nassehi, James Gopsill, Maria Valero*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

2 Citations (Scopus)

Abstract

This paper investigates using simulation to predict the benefits and costs of digitalising cold distribution chains. The study focuses on the distribution of refrigerated beef in the UK, where digitalisation was implemented to re-route cargo carriers. By comparing simulations of both digitalised and non-digitalised supply chains, the study found that digitalisation can reduce beef waste and decrease the number of miles driven per successful delivery, leading to potential cost savings. Note that this work is not attempting to prove that digitalisation is appropriate for the chosen scenario, only to justify a simulation approach as a decision making tool. The proposed modelling approach provides decision-makers with more accurate predictions of the cost–benefit of increased sensorisation in supply chains. By accounting for stochastic and variable parameters, such as weather and demand fluctuations, simulation can be used to identify potential challenges and estimate the economic benefits of digitalisation. Moreover, qualitative assessments of the impact on customer satisfaction and product quality can help decision-makers consider the broader impacts of digitalisation. Overall, the study suggests that simulation can play a crucial role in facilitating informed decisions about the implementation of digital technologies in the food supply chain. By providing a better understanding of the potential costs and benefits of digitalisation, simulation can help organisations make more strategic and effective decisions.

Original languageEnglish
Article number4147
Number of pages23
JournalSensors
Volume23
Issue number8
DOIs
Publication statusPublished - 20 Apr 2023

Bibliographical note

Funding Information:
This research was funded by the EPSRC Doctoral Training Partnerships at the University of Bristol and an EPSRC grant—grant number EP/V05113X/1. The APC was funded by an MDPI invited voucher and an 10% IOAP discount for the University of Bristol.

Publisher Copyright:
© 2023 by the authors.

Keywords

  • digitalisation
  • Industry 4.0
  • optimisation
  • prediction
  • simulation

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