Batch Performance Optimization

Reduce inefficiencies and equipment downtime in batch process manufacturing with ML-driven outcome prediction and root cause analysis.

The goal of this adapt and apply solution is to show how Dataiku can be used to reduce inefficiencies and equipment downtime in batch process manufacturing. More details on the specifics of the solution can be found in the knowledge base. This Solution is only available on installed instances.


Business Overview

Be it to produce bulk chemicals, packaged goods or to perform critical cleaning processes in food and drug production, batch processes form a critical part of the manufacturing value chain where inefficiencies cost billions of dollars each year. At a time when supply chains are stressed and raw material prices are increasingly volatile, the need to maximize equipment utilization by reducing downtime and to improve yield by reducing unnecessary waste becomes even more critical. The proliferation of IoT devices and centralized data collection systems for plant automation networks has led to unprecedented opportunities for enterprise manufacturers, with a potential for up to +15% in throughput increase(1). 

 The challenge ahead is now to turn the mountain of data produced by automation networks into insights actionable by Engineers and other professionals running batch manufacturing processes.  With this solution, organizations can quickly enhance their capacity to dissect vast volumes of production process data. They easily develop actionable insights for technicians,  operators as well as reliability and process engineers to understand root cause of failures and to predict batch outcomes – accelerating the move from reaction to anticipation in batch manufacturing.


  • Easily ingest production process data from Historians or other IoT sources and transactional batch data through an intuitive Dataiku App.
  • Visualize insights on batch process historical performance, key failure patterns, and their impact on future probability of success.
  • Perform root cause analysis by analyzing sensor data per batch and recipe or product.
  • For each recipe or product, predict risk of failure via an explainable and transparent machine learning model.
  • Deploy a web application to deliver insights and provide decision support to production and operators.