Hybrid decision‑support model enhances risk prioritisation in sustainable supply chains

Researchers Abdel‑Basset and Mohamed develop a hybrid approach combining plithogenic set theory with TOPSIS and CRITIC tools to improve risk assessment and strategy ranking in sustainability-focused supply chains amidst uncertainty and conflicting data.

Abdel‑Basset and Mohamed (2020) propose a hybrid decision‑support approach that fuses plithogenic set theory with two established multi‑criteria tools , TOPSIS (Technique for Order Preference by Similarity to Ide...

al Solution) and CRITIC (Criteria Importance Through Inter‑criteria Correlation) , to tackle risk appraisal in sustainable supply chains. The model is offered as a structured way to capture uncertainty and heterogeneous expert judgement while producing a ranked set of risk‑management strategies for supply chains pursuing environmental and social objectives.

Plithogenic sets extend fuzzy and intuitionistic fuzzy frameworks by explicitly modelling contradictory, indeterminate and neutral information across attributes; integrating that formalism with CRITIC enables objective weighting that reflects both contrast intensity and information content among criteria. TOPSIS is then applied to derive closeness to an ideal solution, giving practitioners an ordered preference of mitigation options under the quantified uncertainty. According to Abdel‑Basset and Mohamed, this combination preserves the advantages of objective weighting and distance‑based ranking while accommodating the complex, ambiguous data commonly encountered in sustainability assessments.

The authors demonstrate the approach through a case study of a telecommunications equipment firm. In that application the method is used to identify and prioritise supply‑chain risks and to evaluate alternative mitigation strategies, with the results presented as evidence of the model’s practical applicability. The case highlights how the plithogenic TOPSIS‑CRITIC workflow can translate disparate expert inputs into actionable rankings that reflect both the relative importance of criteria and the uncertainty surrounding them.

Industry and academic literature referenced alongside the work underline two broader points that frame the model’s relevance. First, sustainable supply chains commonly face multi‑dimensional risks , environmental, regulatory, social and technological , that require trade‑offs across competing objectives; objective inter‑criteria weighting methods such as CRITIC are therefore valuable for reducing subjective bias in prioritisation. Second, decision frameworks that explicitly model uncertainty and heterogeneity in expert judgement (for example through fuzzy, intuitionistic or plithogenic extensions) are increasingly recommended for circular economy and reverse‑logistics contexts where data gaps and conflicting stakeholder views are frequent.

While the case study demonstrates feasibility, the authors’ claims are inherently method‑driven rather than empirically generalised. The model’s performance will depend on selection of criteria, quality of expert input and the specific supply‑chain context; practitioners should therefore treat results as decision guidance rather than definitive prescriptions. Future empirical work across different industries and larger datasets would be needed to validate robustness, compare the approach to alternative fuzzy multi‑criteria methods and assess sensitivity to varying membership function specifications.

In sum, Abdel‑Basset and Mohamed offer a technically rigorous hybrid framework that aims to improve prioritisation of risk‑management actions in sustainability‑oriented supply chains by combining objective criterion weighting with a distance‑based ranking method under a plithogenic uncertainty representation. The approach expands the toolkit available to supply‑chain managers seeking to reconcile multiple sustainability objectives under ambiguous and conflicting information, while leaving open the need for broader empirical validation.

Source: Noah Wire Services