Business ML for Predicting Chemical Manufacturing Costs

A practical machine learning (ML) approach for predicting batch manufacturing costs can help empower engineers and managers to anticipate and optimize process profitability.

Today’s manufacturers face growing pressure to boost the efficiency and profitability of their production processes. Traditional cost accounting methods offer valuable insights, but they are often retrospective and unable to respond dynamically to variability in process conditions, resource usage, and market pricing. This delay in cost visibility can limit a company’s ability to make proactive decisions that improve efficiency and profitability (1).

Machine learning (ML) provides a transformative solution by enabling predictive modeling of process costs based on real-time or anticipated operational parameters. By correlating process times, resource consumption, and other key variables with cost outcomes, ML models can support faster and more accurate financial decision-making (2, 3).

In industries where batch and semi-batch processes are prevalent, such as pharmaceuticals and specialty chemicals, this approach is particularly relevant. Significant variability in process steps, raw material costs, and product pricing can greatly impact profitability within these sectors.

This article presents a practical business ML (BML) framework developed to predict manufacturing process costs based on time and resource variability. BML refers to machine learning models designed to connect process parameters with business outcomes such as cost, profitability, and resource optimization. This framework is demonstrated through a case study inspired by paracetamol (acetaminophen) production, a well-established pharmaceutical process (4). Although the case study uses synthetic data to illustrate the methodology, the underlying principles can be applied broadly across chemical manufacturing processes.

The framework provides a blueprint for companies to transition from reactive cost tracking to predictive cost management. This shift enables better resource planning, real-time profitability analysis, and improved competitiveness in rapidly changing markets...