Augmenting Process Failure Mode and Effects Analysis with Fuzzy Cognitive Mapping for Dynamic Manufacturing Risk Assessment
Year: 2026
Type: article
Abstract: Conventional Process Failure Mode and Effects Analysis (PFMEA) is linear and does not explicitly include a systems-based perspective that accounts for the complexity in which all manufacturing systems operate. In an illustrative case study using hypothetical inputs, this study augments traditional PFMEA methods with a Fuzzy Cognitive Mapping (FCM) approach. We suggest a Leverage Impact Number (LIN) that quantifies modeled system-wide risk reduction to include the impacts of individual intervention options available to decision-makers to better quantify risk, but also the relative contribution of management intervention to reduce those risks. Rank comparisons examine changes in priorities and an analysis of concepts covered only by the FCM considers organizational factors. Monte Carlo perturbations assess sensitivity to causal weights, and one-way analyses examine risk weighting, mitigation levels, driver baselines, transfer-function steepness, and the update rule. When traditional PFMEA is compared with our FCM-augmented approach, our results indicate that LIN and Risk Priority Number (RPN) rankings are moderately correlated in this illustrative case, with the augmented approach retaining key PFMEA priorities while extending the analysis to modeled system-wide intervention effects. Under reference assumptions, combined stressors increase modeled risk by approximately the sum of their separate effects. These results demonstrate additional decision support information under the same stated assumptions when using PFMEA and our FCM-augmented method.
Source: Systems
Authors: Amin Sehati, Steven A. Gray
Institution: Michigan State University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7222763323