Why Process Mining in manufacturing?
Process Mining is an emerging technology that leverages event logs from IT systems to provide an understanding of the as-is processes. As data transparency is a prerequisite for the digital transformation in manufacturing, Process Mining is a key enabler to create a data-driven shop floor. The effects can be dramatic and typically include higher throughput, improved machine utilization and reduced non-conformance costs. With Process Mining, production bottlenecks can be recognized and unnecessary process steps can be reduced. Process Mining allows manufacturing companies to identify process inefficiencies, understand their root causes and continuously improve production processes based on data-driven insights. The three main tasks performed by Process Mining software are process discovery, conformance checking and process model enhancement. To learn more about what Process Mining is and how it works, feel free to read our introductory article "Process Mining for Manufacturing".
With Process Mining, Manufacturing companies get full transparency about the status of their production. The resulting benefits are vast and include:
To get a comprehensive overview about the usage of Process Mining in manufacturing, we have conducted a systematic literature review of research publications about Process Mining in industrial settings. Process Mining in manufacturing is an emerging discipline, and the number of publications with application examples is constantly growing. We haved used the following publications as a starting point for our research:
Dreher et al. use the SCOR-model to cluster the available case studies along the process types Plan, Source, Make, Deliver, Return and Enable. The Make process is the core process for production including all activities that transform material to finished products. Accordingly, the most amount of publications from the study were assigned to the Make process (13 publications in total). With our research, we found additional publications related to the Make process and have analyzed 22 case study publications in total. Dependent on the production type and industry, the expected benefits and improvement opportunities of Process Mining are different, which led us to a clustering according to the production type. As the level of use case details and application domains provided by the authors vary, we have chosen the rather generic and high-level production type categories Job shop / Low volume production, Batch / Serial / Mass production and Continuous production. In total, we identified 4 case studies in the category Job shop / Low volume production, 15 case studies in the category Batch / Serial / Mass production and 3 case studies in the category Continuous production (Table 1).
In addition, we analyzed the case studies according to the three main tasks performed by Process Mining software Process discovery, Conformance checking and Process enhancement and identified typical goals of the Process Mining activities per production type category.
In the Batch / Serial / Mass production category, most case studies focussed on Process discovery (86%) and Process enhancement (53%). The majority of case studies were conducted in the Automotive sector. Typical goals included the detection of bottlenecks and outliers, the identification of anomalies and root causes of poor quality. The overall targets for process enhancements included the reduction of lead times, the optimization of OEE, the ability to implement traceability and gain production flexibility.
In the Continuous production category, all case studies targeted conformance checking. The business goals included the monitoring of workflow conformance to identify operational problems associated with human decisions and ensuring safety and procedural compliance to reduce non-conformance costs.
In the Job-shop / Low volume production category, all case studies focussed on process enhancement. Here, examples for business targets include the analysis of workload in make-to-order manufacturing characterized by very long lead times and the rearrangement of process steps to reduce delays.
In this section, we will summarize case study examples to provide a better understanding of application areas, challenges and benefits for Process Mining in manufacturing.
We focus on the following selected case studies, in case you are interested to discuss additional examples, please just let us know:
Want to get access to the full case study list? Just let us know and we will contact you soon.
In 2017, the automotive manufacturer BMW Group introduced Process Mining into their production processes with the aim to enable best production with the highest quality possible. Prior to this, production errors and their root causes were analyzed through direct observations, requiring long-term experiences, and testing. More information from sensors was already available, but not used to monitor entire processes. BMW's IT landscape was fortunately similar in each plant, the understanding of a process in the production environment was however rather difficult to reach based on the collected data only. One sub process in manufacturing could easily have more than 1,000 sensors collecting data. This differs from all non-production processes, such as procurement processes. The initial application of process mining at BMW was the optimization of a new paint shop, but soon the usage was extended along the value chain. Current plans include further scaling and extension with complementing technologies like RPA and AI.
e.Go was founded in 2015 with the aim to produce electric vehicles for short-distance routes. The production is semi-automated and consists of human staff at the stations and Autonomous Guided Vehicles carrying the cars through the production process automatically, without any buffering in between. e.Go applied process mining to improve its production, based on a dataset that includes recordings of every production step.
Geberit is a Swiss producer of sanitary products distributed in 50 countries and is considered as leading in its market. For the process mining use case, the factory in Switzerland which produces a plastic actuator with 18 different variants was chosen. The production process consists of molding, assembling (1&2), sorting, and packing, and was semi-automated. The unique ID is the serial number and gets scanned through DMC readers at the machines and stored with the scanning timestamp. The collected dataset therefore already reached 700,000 event logs.
Our research shows that all manufacturing companies that implemented Process Mining were able to realize substantial business benefits. But as Process Mining is a relatively new technology, shortcomings and challenges for the implementation of the technology still exist. We have summarized a few project management lessons learned from the publications below.
One of the particular challenges for Process Mining in production environments is the quality of the event logs. Very often, production processes are unstructured and non-linear, involving a high degree of manual activities. IT-system landscapes tend to be fragmented and the integration of heterogeneous data sources is challenging. We have identified the following challenges related to data quality:
To be able to get valuable insights from Process Mining in production environments, it is important to focus on data quality. On of the key drivers for better data quality in Process Mining is to include as much data from manual activities as possible. A solution such as the WORKERBASE Connected Worker Platform extends Process Mining software with event logs from manual activities and can thus drastically improve data quality and overall results. If you are interested in more information about how companies established Process Mining in production environments, please do not hesitate to contact us!
Sabrina Joos, Solution Consultant WORKERBASE
Thorsten Krüger, Co-Founder WORKERBASE