AI-assisted Production Planning
The challenge: The production scheduling in manufacturing has been done manually so far, and the complexity of considering all the constraints involved is constantly increasing.
The solution: Research and development of an AI application for optimized production scheduling using product and production data.
The added value: The operational efficiency is optimized, leading to increased value creation through time savings in the production process.
AI ensures intelligent product planning
Climate and ventilation technology, components, devices, and system engineering: Westaflex is a German family-owned company that has been specializing in architectural building technology and air ducts since 1933. They provide solutions for improved climate control and simplified construction using aluminum, stainless steel, and high-quality plastics. They offer ventilation systems in various shapes and materials, with different diameters and grooves, allowing for flexible combinations and customization based on specific building and piping requirements. As a result, their production process involves numerous setup operations, requiring precise planning. To ensure smooth and efficient operations, the order sequence must be carefully planned prior to production.
Order sequencing determines the production order of individual orders with the aim of optimizing the manufacturing process to be lean, time-saving, and efficient. Currently, order sequencing at Westaflex is done manually, using spreadsheets or analog planning boards, and not automatically. Factors such as processing time, setup time, delivery time, product specifications, materials, and workload reduction need to be individually considered. This manual process can be quite cumbersome: Orders and machine sequences are typically not easily adjustable without the need for manual adjustments to the rest of the planning.
Data analysis and AI for intelligent production planning
The goal is to optimize production order sequencing using artificial intelligence. Various data sources, such as ERP data and real-time production data, are analyzed to derive insights for optimal machine allocation and utilize these insights for order sequencing. To achieve this, a data platform is being developed as a web-independent on-premise solution. The data platform integrates order data, resource data, process data, tool/maintenance data, logistics data, and monetary data, preparing them for the AI application. The data platform serves as an IT infrastructure equipped with interfaces for internal factory data and data from the AI application. Subsequently, the solution will be implemented and tested using real data, optimized, and validated.
This project will contribute valuable solutions and components to the field of production system development for the AI Marketplace, benefiting other companies facing similar problems or goals. Westaflex expects this project to optimize their usable output and increase value creation through time savings.
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