Supply chain resilience is a growing concern as risk becomes increasingly challenging to interpret and anticipate due to sudden global events that disrupt the core of global supply chains. This paper discusses the use of advanced technologies to enhance supply chain resilience, proposing a two-step hybrid fuzzy analytic hierarchy process (FAHP) and the technique for order of preference by similarity to ideal solution (TOPSIS) approach that evaluates a set of different supply chain KPIs or criteria that trigger possible supply chain risks, with a focus on transportation disruptions. Using FAHP, the highest potential risks from disasters are identified, and TOPSIS is used to rank alternative solutions that enhance supply chain resilience. The approach is tested on real-world applications across multiple supply chain systems involving various companies and experts to demonstrate its validity, feasibility, and applicability. Based on five criteria and six alternatives per case study, the findings showed that for manufacturing supply chains, the highest risk was attributed to travel time (46%), and the most effective solution to mitigate it was found to be strengthening highway networks (0.72). For transportation, delivery time (56%) was the primary risk, addressed by green logistics and sustainability (0.89).

Purpose: To achieve a more concrete measurement of service quality, this paper proposes a prioritization model of key performance indicators (KPIs) that monitors the service quality of IT service management processes. Design/methodology/approach: Phase one establishes the baseline of process performance and enables the acquisition of customer perceptions via the ITIL framework’s “Maturity Self-Assessment Questionnaire.” Phase two defines (KPIs) for each process activity and their adherence to the SMART criteria, then, the fuzzy analytical hierarchy process (FAHP) assigns weights to the KPIs, enabling a hierarchical performance system. Finally, the FAHP approach is implemented in a case study. Findings: The proposed prioritization model produced a set of KPIs with different weights, where those with the highest weights are selected to represent the “Process KPIs.” Integrating fuzzy logic into AHP eliminates both the uncertainty and the reduced reliability associated with the traditional form of the AHP’s evaluation of attribute importance through pairwise comparison. Originality/value: The study aspires to investigate the possibility of deploying a multi-criteria decision-making model for prioritizing implemented KPIs. The evaluation system for index development uses the analytical hierarchy process method combined with the deployment of fuzzy logic sets, which define human knowledge using mathematical expressions. Fuzzy sets and logic express satisfaction preferences of personal evaluation amongst other information analysis concerns. This can effectively assist IT service managers in devising high-level KPIs to monitor and improve service quality continuously.

To evaluate and improve supply chain resilience across industries, including food manufacturing, this paper investigates the use of adaptive neuro-fuzzy inference systems (ANFIS) to assess supply chain resilience, with a focus on transportation and logistics. ANFIS models were developed and validated using two real industrial cases: a local plastic manufacturer and a global food manufacturer to capture and analyze relationships within supply chains across key attributes—security, survivability, recovery, technological capabilities, and sustainability. This research highlights the effectiveness of the fuzzy inference system (FIS), including membership function types within the ANFIS framework, in achieving computational efficiency and precision for modeling dynamic supply chain systems. The models were validated using two standard metrics: Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE), ensuring accurate predictions. Results showed that the global food company had lower average errors, indicating a more stable and reliable supply chain with a testing error of 5%. In contrast, the local plastic company exhibited higher errors in some areas, with an average error of 8%, suggesting areas for improvement. Overall, this study underscores the potential of ANFIS as a powerful tool for providing valuable insights into the strengths and weaknesses of supply chains, thereby offering a robust approach to enhancing their resilience. The findings suggest that companies can significantly benefit from adopting this neuro-fuzzy model (i.e., ANFIS), particularly when combined with continuous data refinement and targeted improvements to address specific vulnerabilities in their supply chains.