Enhancing industrial decision-making through ML-integrated frameworks and multi-criteria decision-making approach

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Abstract Decision-making in contemporary industrial settings has evolved from intuition to data-driven methodologies, necessitating efficient handling of vast datasets. Conventional Multi-Criteria Decision Making (MCDM) approaches struggle with the complexities of big data. This study introduces an innovative decision-support system integrating multi-criteria methods with machine learning techniques as artificial neural network. The proposed six-step framework aims to optimize operational decisions by analyzing real-time performance data. The research contributes to the advancement of decision-making methodologies in the industrial field, offering dynamic responsiveness and enhanced recommendations compared to traditional MCDM methods. While promising, future work must focus on robustness testing, particularly in real-time data dependencies, to ensure sustained efficacy and mitigate potential biases in recommendations over time.

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