PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
February 14, 2026Benchmarking An International Journal0 citations

Harnessing Industry 4.0 technologies in minimizing food loss and waste across the food supply chain: a hybrid study

View Full Paper
KSKamaldeep Kaur SarnaDDDebadyuti DasRSRajesh Kumar Singh

Key Points

  • The research aims to explore the applicability of Industry 4.0 technologies in minimizing food loss and waste throughout the food supply chain.
  • Conducted a systematic literature review of 122 research papers to identify trends and barriers.
  • Administered a structured questionnaire to 34 experts to rank six Industry 4.0 technologies.
  • Applied Kendall's Coefficient of Concordance to assess expert consensus on technology suitability across food supply chain stages.
  • Developed a decision support framework to guide the selection of technologies.
  • Consensus identified technology suitability across Consumption, Distribution, and Post-Harvest stages.
  • Blockchain and Big Data Analytics ranked highest for Production; Digital Twin and IoT for Post-Harvest.
  • AI and Digital Twin emerged as top choices for Processing; IoT and Blockchain for Distribution; Big Data Analytics and AI for Consumption.
  • Technologies mapped to specific quadrants based on capital, technology, and skills requirements.

Abstract

Purpose Minimizing food loss and waste (FLW) throughout the food supply chain (FSC) is imperative for achieving global sustainability. While Industry 4.0 technologies offer innovative solutions, a practical understanding of their stage-specific suitability in mitigating FLW remains inadequate. This study examines research trends, applicability and impediments in leveraging these technologies and empirically validates their suitability in advancing SDG 12.3. Design/methodology/approach A multi-stage systematic literature review (SLR) of 122 research papers was employed to identify emerging themes. Subsequently, a structured questionnaire was administered to 34 domain experts, who ranked six technologies: Artificial Intelligence (AI), Big Data Analytics (BDA), Blockchain, Cloud Computing (CC), Internet of Things (IoT) and Digital Twin (DT), across five FSC stages. Kendall's Coefficient of Concordance (W) was utilized to assess expert consensus, followed by the determination of suitable technologies across FSC stages. Finally, a decision support framework (DSF) was developed to guide the selection of technologies. Findings A significant consensus was observed across all FSC stages, with the strongest being in the Consumption, Distribution and Post-Harvest stages. Stage-wise rankings identified Blockchain and BDA as most suitable for Production; DT and IoT for Post-Harvest; AI and DT for Processing; IoT and Blockchain for Distribution; and BDA and AI for Consumption. The DSF demonstrates that each technology occupies a distinct resource quadrant. For example, Blockchain falls in the high capital, moderately high technology and low skills quadrant, etc. This precise mapping aids decision-makers in technology adoption under varying organizational constraints. Originality/value Integrating systematic synthesis with empirical validation, the study develops a novel DSF to guide managers and policymakers in adopting stage-appropriate Industry 4.0 technologies for sustainable operations.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Sarna et al. (2026) studied this question.

synapsesocial.com/papers/699012032ccff479cfe58b61https://doi.org/10.1108/bij-10-2024-0900
Ask AI
Helpful
Bookmark
Share
View Full Paper