Learn to apply the knowledge gained in this course through a game using Excel as well as R for simple forecasting methods.
This course is part of
Transforming Logistics with Analytics
Robert Goedegebuure & Henri Grolleman

About this course

Process Mining can roughly be defined as a data-driven approach to process management. The basic idea of process mining is to automatically distill and to visualize business processes using event logs from company IT-systems (e.g. ERP, WMS, CRM etc.) to identify specific areas for improvement at an operational level. An event log can be described as a database entry that signifies a specific action in a software application at a specific time. Simple examples of these actions are customer order entries, scanning an item in a warehouse, and registration of a patient for a hospital check-up.

Process mining has gained popularity in the logistics domain in recent years because of three main reasons. Firstly, the logistics IT-systems' large and exponentially growing amounts of event data are being stored and provide detailed information on the history of logistics processes. Secondly, to outperform competitors, most organizations are searching for (new) ways to improve their logistics processes such as reducing costs and lead time. Thirdly, since the 1970s, the power of computers has grown at an astonishing rate. As such, the use of advance algorithms for business purposes, which requires a certain amount of computational power, have become more accessible. 

Before diving into Process Mining, this course will first discuss some basic concepts, theories, and methods regarding the visualization and improvement of business processes.

Course subjects

More about the authors

Robert Goedegebuure

Robert Goedegebuure is, among other positions, researcher at the research groups Logistics & Alliances and Center of International Business Research at HAN University of Applied Sciences.

Henri Grolleman

Henri Grolleman is a lecturer in data and logistics in the Logistics Management program at Windesheim University. With extensive practical experience at major logistics service providers and manufacturers, he has insight into logistics and how data-driven work can assist and enhance logistical processes. In this digital publication, he has been primarily involved in the module focusing on forecasting.

This publication is part of the project ‘small projects 2022 route transport and logistics' with project number NWA.1418.22.023 which is financed by the Dutch Research Council (NWO).