Extended entropy method for risk inspection and effect analysis in optical cable industry

Advanced manufacturing stands as an essential component of the fourth industrial era, where innovative machinery can sense and act autonomously in the production process to give higher and more convenient efficiency. However, there could be certain manufacturing-related possible failures. Identifying the possible failures and assessing their risk are essential. At the same time, global consensus has emerged on the need for assets and environmental defense. To ensure their continued sustainable growth, firms must take measures to mitigate the negative impacts of industrial production on the environment. Failure mode and effects analysis proficiently identifies failures and assesses their risks within a manufacturing process, design and system. Nevertheless, the ecological factors of the identified failure modes are frequently overlooked in failure mode and effects analysis techniques. Additionally, the risk priority number is typically used in conventional failure mode and effects analysis, which has numerous drawbacks in industrial sector, to determine the risk of failures. Considering the two previously mentioned primary issues, this research introduces an extended failure mode and effect analysis that addresses ambiguous information by using an entropy method & Combinative Distance based ASsesment method based on picture fuzzy rough number and adding environmental consequences as one of the risk variables. Firstly, an extended entropy method based on picture fuzzy rough number is utilized for the findings of risk factor’s weights then an extended Combinative Distance based ASsesment method based on picture fuzzy rough number is applied for the risk examination of possible failure modes. Consequently, the combination of the entropy method with the Combinative Distance-based Assessment method effectively manages uncertainty to a significant extent. An industrial case study of robotics optical cable sorting system is implemented to approve the usefulness of the recommended failure source and effect analysis. Upon meticulous implementation and comparison with others decision-making methodologies, this approach exhibits high reliability in providing perceptive conclusions. Sensitivity analysis is also used in the present research to improve the depth of our results.