Use case: ThiaperProcess in discrete manufacturing

Process Mining for manufacturing: why?

The prerequisite to improving your process flows is to know your process flows. 

  • EXPLORE: a digital picture of the “as-is” state of your processes
  • MONITOR: processes as they occur
  • CHECK: compliance/ expectations violations
  • IMPROVE: automate wherever possible. reduce time delays. decrease cost.

Process mining is the perfect partner for robotic process automation (RPA), as it can identify the best places to automate processes in manufacturing, and then facilitates the quantification of pre/post-automation performance.  

Some numbers:

13% Process Manufacturing

30% Discrete Manufacturing

expected distribution of financial investments in Artificial Intelligence systems, Robotics and Drones in Western Europe 2019

55% of all GDP gains from AI over the period 2017-2030

expected labour productivity improvements


expected productivity gains from smart factories


companies that embrace AI over the next 5-7 years may double their cash flow


expected increase in worker production


expected increase in demand for products and services based on AI

71% forecast for 2025, as opposed to 29% in 2019

tasks completed by machines


For our use case, we  are using an open dataset, illustrating process data from a production process, including data on cases, activities, resources, timestamps and more data fields. 

The process map shows process flow structural changes (re-work and transfers in-between pieces of manufacturing equipment). These are each quantifiable in terms of frequency and time delay. It is immediately evident in the pathways view, that the average delay on some of the pathways is 10 times smaller than the average delay on others. In-house domain knowledge sheds light on whether this difference of order is to be expected or not.