AI-based Predictive Dynamic Production Planner

The Experiment's primary objective is to bolster the efficiency of sequencing and planning tasks in a manufacturing plant, by conceptualizing and deploying intelligent tools that can aid the production manager and inventory manager in their day-to-day operations.

In order to accurately reflect the dynamic nature of the shop floor's daily activities, the integration of machine learning and optimization techniques is crucial for the development of the anticipated troubleshooting features:

1. Demand Forecasting Tool: Leveraging advanced Machine Learning methods to assimilate market trends, annual growth rates, seasonal influences, and other pertinent factors. This tool will serve as the cornerstone for precise demand forecasting, thus facilitating supply, planning, and overall optimization processes.
2. Sequencing Tool: Real-time optimization achieved through a fusion of Constraint Programming and metaheuristics. This approach enables efficient sequencing while considering the duality between machines and products.

The primary challenge lies in implementing real-time optimization that aligns seamlessly with the practical demands of the shop floor. This entails the effective utilization of both structured and unstructured production data (including order details, operations, quantities, operation duration, and assets), alongside continuous machine data. Such an approach aims to revolutionize the management of production processes, leading to substantial advancements in overall efficiency.