Demand Forecasting in Discrete Manufacturing Using Artificial Intelligence
It is a fact that discrete manufacturers invariably face the problem of planning, where demand is highly uncertain, the diversity of product is wide, the bill of materials (BOM) has a high complexity level, and there are long lead times involved. Statistical demand forecast models like ARIMA, exponential smoothing, and moving average models may not be appropriate in representing multidimensional demand dynamics. In this paper, we present a design for developing demand forecast models using artificial intelligence, based on open-source software only and the customer's ERP system data alone.
Through this article, I present a practical design for implementing the replacement of rule-based or statistic-based forecasting models using a state-of-the-art AI stack based only on open source components and the customer's ERP data.
Case for AI in Discrete Manufacturing Planning
Statistics works well if demand is constant and seasonal. The nature of discrete manufacturing makes such scenarios rare. An average...
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