Towards an Optimized Industrial Decision-Making Model Powered by Artificial Neural Networks

Hala Mellouli (), Anwar Meddaoui and Abdelhamid Zaki
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Hala Mellouli: ENSAM, Hassan II University
Anwar Meddaoui: ENSAM, Hassan II University
Abdelhamid Zaki: ENSAM, Hassan II University

A chapter in Information Systems and Technological Advances for Sustainable Development, 2024, pp 85-92 from Springer

Abstract: Abstract In today's digital age, many different variables can affect industrial decision-making, challenging companies’ ability to optimize performance and achieve success in a competitive market. To stay ahead of the game, companies must find ways to improve their decision-making processes. One solution is to use artificial intelligence (AI) to manage large amounts of data quickly and efficiently. By learning from previous experiences, AI can help ensure that decisions are accurate and reliable. In this paper, we introduce a hybrid decision-making model that combines artificial neural networks with the Analytic Hierarchy Process and the balanced scorecard. This approach is designed for complex industrial problems and provides real-time recommendations for the most accurate and effective decisions.

Keywords: Industrial-decision-making; performance optimization; multicriteria decision-making; Artificial neural networks; machine learning (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-031-75329-9_10

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DOI: 10.1007/978-3-031-75329-9_10

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