Grain supply chain optimisation: A qualitative perspective

Abstract

Grain supply chains (GSC) are complex systems with numerous stakeholders, dynamic interactions, and uncertainties, requiring sophisticated modelling and simulation for understanding and predicting decision-making (Behzadi et al., 2018). This abstract presents a systematic literature review using content analysis (using Leximancer) to identify gaps and interconnectedness in GSC research. A Boolean search string (agri* AND supply chain* AND (simulation* OR optimi?ation*)) in all fields (title, keywords, abstracts) published in English since 2000)) was used to obtain 1,681 records from SCOPUS and Web of Science databases, with 75 relevant studies (13 review articles, 7 conference proceedings, 49 Q1, and 6 Q2 articles) selected following PRISMA guidelines. Existing systematic literature reviews (SLRs) were separately examined to avoid duplication. The Leximancer analysis based on machine learning methods for understanding text data through word frequency relations of 62 selected studies revealed six primary themes: Supply Chain, Model, Farmers, Agents, Simulation, and Transport and Logistics.