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Abstract
Advanced components assembly planning and related manufacturing production planning and scheduling (PPS) and supply planningare key elements responsible for deliveries and cost aspects as a resources workload and inventory driver. Industry 4.0 systems broaden science for improving system performance and decision making.Industry site environment because of material flow network, interrelated multi-variable, multilevel production becomes very complex what is challenged by a strong focus on operational excellence. Demand uncertainty requires additional attention and integration with Supply Chain. This paper presents an extended framework for analytics solutions in assembly, production and supply planning for manufacturing company. Risk related to violable customers demand is mitigated by buffer management. Buffer levels relay on a prediction from simulation model using computational methods based on machine learning algorithm using Neutral Networks to guarantee on-time deliveries and rational costs. Actual challenges and requirements for new use cases in data-driven intelligence are presented. The proposed models and the actual state will be comparably discussed with results analyses.
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