Evaluation of the agri-food supply chain risks: the circular economy context

Abstract

PurposeCircular supply chains (CSC) are particularly important for the agri-food sector, which faces strict requirements generated by increased food consumption as a consequence of world population growth, changes in lifestyle, development of consumer society and increasing health awareness. Recent disruptive factors have placed the vulnerability of agri-food supply chains in the spotlight. Therefore, the purpose of this paper was to identify the most manageable groups of risks in order to ensure the smooth operation of agri-food circular supply chains.Design/methodology/approachSeven main risk groups were evaluated in relation to nine criteria. To solve this multi-criteria decision making (MCDM) problem, a novel MCDM model, which integrates the best-worst method (BWM) and the COmprehensive distance-Based RAnking (COBRA) method in a grey environment, was developed.FindingsThree risks were singled out, namely, product features risks, logistics risks and managerial risks. The obtained risks are those whose management would create the most positive effects for the stakeholders and help them achieve their primary goals regarding the circularity of agri-food supply chains.Originality/valueThis study investigates the main characteristics of the CSC in the agri-food sector, identifies, simultaneously explores and ranks all main risk groups associated with them and expands the possibilities for solving these kinds of problems by developing a novel MCDM model. It also identifies the most significant risks, both for individual stakeholders and for all stakeholder groups together.

Guest editorial: Circular economy in the agri-food, tourism and hospitality industries in the post-pandemic era

“…Under a holistic perspective on the entire agri-food supply chain, Krstić et al . (2024) have identified the most manageable groups of risks in the agri-food sector, as to ensure the smooth operation toward circular supply chains.…”

A Novel Hybrid Gray MCDM Model for Resilient Supplier Selection Problem

“…Step 1: First, the linguistic values (shown in Table 1) assigned by the decision makers are converted into gray numbers. Table 1 is taken from [49]. These are combined with Equation ( 10) to obtain a gray decision matrix (⊗B).…”

Evaluating Governance Models in Intermodal Terminal Operations: A Hybrid Grey MCDM Approach

“…In contrast, the COBRA method, as conceptualized by Krstić et al [30], is based on the principle of calculating comprehensive distances from a spectrum of solutions, integrating both Euclidean and taxicab distances. COBRA is preferred over other distance-based methods like TOPSIS and VIKOR due to its comprehensive nature, which enhances the reliability of solutions and aids in differentiating between closely ranked alternatives [31]. Despite being relatively new, COBRA has quickly gained prominence and has been applied in various research fields (e.g., Krstić et al [31], Tadić et al [32], Popović et al [33].…”

“…COBRA is preferred over other distance-based methods like TOPSIS and VIKOR due to its comprehensive nature, which enhances the reliability of solutions and aids in differentiating between closely ranked alternatives [31]. Despite being relatively new, COBRA has quickly gained prominence and has been applied in various research fields (e.g., Krstić et al [31], Tadić et al [32], Popović et al [33]. Both BWM and COBRA have been adapted to overcome the limitations of conventional methods, particularly in grey environments where ambiguity and uncertainty are prevalent.…”