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Conference Paper
Advanced Planning and Scheduling Framework for Manufacturing Operation with Demand Forecasting
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- Authors
- Dohun Kim, Jinho Park, Wonjong Kim
- Issue Date
- 2026-02
- Citation
- International Conference on Consumer Electronics (ICCE) 2026, pp.1-4
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Advanced Planning and Scheduling (APS) has been adopted as a systematic decision-support tool to address global supply chain instability and increasing demand volatility. This study introduces an AI-based APS framework that integrates demand-order forecasting models and constraint-based scheduling algorithms to enhance responsiveness, efficiency, and flexibility in manufacturing operations. The system integrates forecasting, planning, scheduling, and execution feedback based on data linkage with external systems such as ERP, PLM, and MES. By applying deep learning models such as LSTM and PatchTST, the system improved demand forecasting accuracy, while the CP-based scheduler optimized production plans under resource and delivery constraints. The results demonstrate that forecast-driven scheduling enhances responsiveness and reliability in manufacturing operation.
- Keyword
- Advanced Planning and Scheduling (APS), Artificial Intelligence (AI), Demand Forecasting, Production Scheduling, Deep Learning, Constraint Programming, Manufacturing Data Analytics, Smart Factory
- KSP Keywords
- Advanced planning and scheduling, Data Analytics, Data linkage, Decision support tool, Demand Forecasting, Forecasting accuracy, Forecasting model, Manufacturing data, Manufacturing operations, Optimized Production, Scheduling Framework