Supply chain energy sustainability with artificial intelligence

Michael Ntokozo Sishi, Arnesh Telukdarie

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Supply chain (SC) processes are complex and have been the subject of many optimization studies. The optimization of the SC is pivotal for the success of a business. optimizing SC usually requires rapid responses and additional resources, resulting in increased energy demand that in turn yields augmented CO2 emission. The advent of the Artificial Intelligence (AI) has delivered alternative optimization opportunities when adopted as a digital tool. This research provides insights into AI application for optimizing SC processes in order to decrease energy demand and CO2 emission. The AI engine is based on a Cyber Physical System (CPS) developed using business activities as defined by business processes. A Monte Carlo simulation is adopted to ensure that the baseline (CPS) model is statistically representative. The probability functions and results of2400 runs are extracted and inputted into Python code to generate ordinary least square (OLS) multiple linear regression model. The results of the OLS are used to create energy optimization formula to reduce CO2 emission and yield sustainable energy in the SC.

Original languageEnglish
Title of host publication2021 IEEE Technology and Engineering Management Conference - Europe, TEMSCON-EUR 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665440912
DOIs
Publication statusPublished - 17 May 2021
Event2021 IEEE Technology and Engineering Management Conference - Europe, TEMSCON-EUR 2021 - Virtual, Online, Croatia
Duration: 17 May 202120 May 2021

Publication series

Name2021 IEEE Technology and Engineering Management Conference - Europe, TEMSCON-EUR 2021

Conference

Conference2021 IEEE Technology and Engineering Management Conference - Europe, TEMSCON-EUR 2021
Country/TerritoryCroatia
CityVirtual, Online
Period17/05/2120/05/21

Keywords

  • Artificial Intelligence
  • Machine Learning
  • Optimization
  • Supply Chain

ASJC Scopus subject areas

  • Management Information Systems
  • Strategy and Management
  • Engineering (miscellaneous)
  • Civil and Structural Engineering
  • Safety, Risk, Reliability and Quality

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