Data Analysis and Intelligent Decision-Making in Industrial Production

Dear Colleagues,

Effective, responsive, cost-efficient industrial production is not only critical in maintaining companies’ competitive advantages, but is also essential to the sustainability of supply chains in a forever competitive market, with changing demand patterns and emerging products, unprecedented security threats, a limited supply of raw materials, and tighter net-zero emission goals. In this new global context, adaptive data-driven decision making that offers attractive profits and companies’ growth is timely and relevant.

In addition to the traditional challenges caused by floor shop scheduling, planning, design, machine reliability, supply chain logistics, routing, and ordering policies, the new global context imposes accounting for different objective functions, as well as accounting for forecasted demand and uncertainty. The diversity of markets, of their sizes, and of logistics’ characteristics constitute another challenge to production processes. In this new world, the effective planning of what, when, and how to produce, store, route, and ship requires the gathering, processing, and analysis of large amounts of data, both qualitative and quantitative, of different levels of reliability and granularity. These data include competitors, prices, shippers, costs, customers, demands, raw materials, suppliers, emissions, regulations, etc.

This Special Issue focuses on using data analysis as a driver for intelligent decision making that directly impacts production processes and companies’ sustainability. Topics include, but are not limited to, the following:

• Data-driven industrial production.
• Machine learning and data analysis for industrial production.
• New and innovative predictive modelling approaches for industrial production.
• Enhancement of classic optimisation0based approaches via learning from historical data for industrial production.
• Planning, scheduling, and re-scheduling in manufacturing processes.
• Forecasting and scheduling using exact algorithms, heuristics, metaheuristics, matheuristics, and reinforcement learning for industrial processes.
• Data collection and analysis tools for production management.
• Data-driven enterprise resource planning systems.
• Intelligent decision making for production processes.
• Computational challenges emanating from big data, pertinent to data-driven intelligent decision making.

Prof. Dr. Rym M'Hallah
Dr. Yacine Laalaoui
Guest Editors

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• industrial production
• responsive manufacturing
• machine and reinforcement learning
• data analysis
• intelligent data-driven decision making