Green sourcing in the era of industry 4.0: towards green and digitalized competitive advantages

Alireza Fallahpour, Morteza Yazdani, Ahmed Mohammed*, Kuan Yew Wong

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)

Abstract

Purpose: In the last decade, sustainable sourcing decision has gained tremendous attention due to the increasing governmental restrictions and public attentiveness. This decision involves diverse sets of classical and environmental parameters, which are originated from a complex, ambiguous and inconsistent decision-making environment. Arguably, supply chain management is fronting the next industrial revolution, which is named industry 4.0, due to the fast advance of digitalization. Considering the latter's rapid growth, current supplier selection models are, or it will, inefficient to assign the level of priority of each supplier among a set of suppliers, and therefore, more advanced models merging “recipes” of sustainability and industry 4.0 ingenuities are required. Yet, no research work found towards a digitalized, along with sustainability's target, sourcing.

Design/methodology/approach: A new framework for green and digitalized sourcing is developed. Thereafter, a hybrid decision-making approach is developed that utilizes (1) fuzzy preference programming (FPP) to decide the importance of one supplier attribute over another and (2) multi-objective optimization on the basis of ratio analysis (MOORA) to prioritize suppliers based on fuzzy performance rating. The proposed approach is implemented in consultation with the procurement department of a food processing company willing to develop a greener supply chain in the era of industry 4.0.

Findings: The proposed approach is capable to recognize the most important evaluation criteria, explain the ambiguity of experts' expressions and having better discrimination power to assess suppliers on operational efficiency and environmental and digitalization criteria, and henceforth enhances the quality of the sourcing process. Sensitivity analysis is performed to help managers for model approval. Moreover, this work presents the first attempt towards green and digitalized supplier selection. It paves the way towards further development in the modelling and optimization of sourcing in the era of industry 4.0.

Originality/value: Competitive supply chain management needs efficient purchasing and production activities since they represent its core, and this arises the necessity for a strategic adaptation and alignment with the requirement of industry 4.0. The latter implies alterations in the avenue firms operate and shape their activities and processes. In the context of supplier selection, this would involve the way supplier assessed and selected. This work is originally initiated based on a joint collaboration with a food company. A hybrid decision-making approach is proposed to evaluate and select suppliers considering operational efficiency, environmental criteria and digitalization initiatives towards digitalized and green supplier selection (DG-SS). To this end, supply chain management in the era of sustainability and digitalization are discussed.

Original languageEnglish
Pages (from-to)1997-2025
Number of pages29
JournalIndustrial Management and Data Systems
Volume121
Issue number9
Early online date2 Sept 2021
DOIs
Publication statusPublished - 23 Sept 2021

Bibliographical note

Funding Information:
The research leading to these results has received Research Project Funding from The Minister of Higher Education, Research and Innovation of the Sultanate of Oman, under Commissioned Research Program, Contract NO. TRC/CRP/MU/COVID-19/20/15. The authors would like to thank the Guest editors and the anonymous referees whose thorough reviews and insightful comments made a valuable contribution to this work.

Publisher Copyright:
© 2021, Emerald Publishing Limited.

Keywords

  • Decision-making
  • Digitalization
  • Fuzzy sets
  • Industry 4.0
  • Supply chain

ASJC Scopus subject areas

  • Management Information Systems
  • Industrial relations
  • Computer Science Applications
  • Strategy and Management
  • Industrial and Manufacturing Engineering

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