6 Best Practices of Process Mining in Automotive Industry in '24

Hazal Şimşek

Manufacturing Process Mining

Updated on Jan 4

4 min read

Share on Linkedin Share on Twitter

6 Best Practices of Process Mining in Automotive Industry in '24

Table of contents

Discover automation opportunities Increase operational efficiency Improve after-sales services Detect inefficiencies in IT systems to maintain production Design and modify the manufacturing logistics processes Improve design and production process by generating digital twin Further Reading

Digital transformation has shifted the understanding of car manufacturing towards a more software-oriented approach, resulting in disruptive changes (e.g. smart mobility and autonomous driving) in the automotive industry. For example, latest DX trends in automotive predict that autonomous cars will hold 15% market share of vehicles in 2030, leading to partnerships among digital giants and car manufacturers.

The trends and challenges in the automotive industry increase the necessity to understand and transform the operational processes. Business leaders can leverage process mining to enable and facilitate the automotive industry’s transformation projects.

In that light, this research goes in-depth into process mining use cases in the automotive industry.

Discover automation opportunities

Automotive manufacturers can transform their business by automating their processes to maintain high quality while reducing production costs.

Process mining can indicate the areas that should be automated primarily. Car manufacturers can automate their back-end tasks with process mining, such as invoicing or billing of materials (BOM). Manufacturers can also automate their inventory management to place orders, track the flow of materials in the process cycle and generate receipts using RPA bots.

Read the top 8 use cases & benefits of RPA in manufacturing for more insights.

Increase operational efficiency

The entire process of a vehicle’s production, sale, and after-sale maintenance are long and complex. It usually involves various sub-processes, different parties, and interdepartmental interactions. Therefore, vehicle manufacturers often lack an understanding of their organizational processes. As a result, they cannot discover areas for improvement and automation.

Process mining provides an end-to-end overview of the entire workflow, including steps, tasks, and sub-processes required, visualizing them as simplified diagrams. Thus, some process mining tools generate digital twins of an organization (DTO) to offer a better visual replica of the car manufacturing processes.

Essmann Automotive (Germany) utilized process mining to analyze their entire production cycle, including interactions with partners, such as tier or metal suppliers, to increase production efficiency. The business analysts found that they could improve production competitiveness by integrating suppliers earlier in the production process, which had shifted to a team-oriented partnership.

Improve after-sales services

The automotive industry offers after-sales services, including technical support, customer support, and product service. Manufacturers use after-sales services to obtain information, from the customers, about the product, and the services, to assess the performance.

Process mining can be helpful to discover all the existing tasks and operations in the after-sales services to drive insights from them.

For example, an automotive company in Italy deployed process mining to discover and analyze after-sales car maintenance. The company was able to view its operations through process mining, with all details and visualized comparison between the reference model and the data-driven model for these services. The firm predicted that it could decrease its costs by 70 % after automating the manual tasks in the car maintenance service department.

Detect inefficiencies in IT systems to maintain production

Planning and producing a car relies on IT systems and cloud apps. So it is essential to monitor and improve these systems periodically.

Process mining extracts the process data from these systems and analyzes them. Therefore, users are constantly updated on the functioning of these systems and applications. That enables the users to identify issues early before affecting the entire production cycle.

In a process mining case study, Dräxlmaier (Germany) utilized process mining to detect inefficiencies in their SAP (an Enterprise Resource Planning) and SRM (Supplier Relationship Management) systems, such as insufficient database space, risky authorization, slow performance or errors occurring while importing requests. The insights allowed the company to identify the optimum way for the order dispatch process.

Design and modify the manufacturing logistics processes

The automotive industry’s supply chain or logistics refers to managing thousands of incoming and outgoing materials, products, and services to sustain vehicle manufacturing operations. Therefore, the manufacturing supply chain must constantly collect data and generate accurate plans.

Process mining enables manufacturers to analyze the tasks and operations involved in manufacturing logistics. With process mining, manufacturers can assess the performance of their supply chain management and identify and modify the processes that suffer from deviations and errors. The manufacturers can also discover areas where a new process is required to design one based on the insights they obtained by process mining.

For instance, Automotive & Terberg (Netherlands) employed process mining to design a new chain process into their supply chain operations. The company also discovered that it had to improve its communication across departments before implementing the changes.

Improve design and production process by generating digital twin

The automotive design process includes several steps until the product is ready to launch, such as product planning, designing, concept generation, theme selection, 3D and computer model generation, and product testing, making the entire design process complicated. As the designing process, the vehicle production process is complex, with several steps to identify bottlenecks.

Process mining gathers data produced at each stage of the vehicle design and production and measures the performance of the machinery and programs used in designing or building the vehicles. As a result, producers can assess the performance level for each step in these processes and estimate the performance for each machine or the program used.

For example, Daimler in Germany used process mining to generate a digital twin of their shop floor where the car production occurs to separate sub-processes and gain user-specific information. The company created a digital replica of a manufacturing plant, spanning several locations, to develop a throughput rate analysis, which measures the number of cars produced in a certain period and explores production bottlenecks.