Process optimisation in production: approaches & tools

Process optimization in production: with Process Mining

Industrial manufacturing is at a turning point: global supply chains, rising energy prices, and increasing competitive pressure are demanding a high degree of efficiency, flexibility, and transparency from manufacturing companies. Digitalization (Industry 4.0) in particular is opening up new ways for manufacturing companies to design smarter, networked production processes. At the same time, this transformation is accompanied by challenges in many respects. The often fragmented system landscape in manufacturing companies means that information remains isolated – there is a lack of approaches to use existing data specifically for process improvements. We provide an overview of proven methods for process optimization in manufacturing and show the role Process Mining plays in implementing these approaches.

How can production processes be optimized?

First of all: When we talk about “production,” we are not just referring to the physical manufacturing process itself. Production processes encompass all the steps necessary to manufacture a product. This includes material procurement, manufacturing, assembly, quality control, packaging, and, if applicable, intralogistics.

Good production processes are characterized by repeatability, consistent quality, and short throughput times. They are transparent, efficient, and flexible—even when demand fluctuates or product requirements vary. Process optimization in production can be achieved in various ways:

Reduced throughput time: Identification and elimination of bottlenecks and waiting times
Resource efficiency: Optimal utilization of machines and personnel
Quality improvement: Reduction of scrap and rework
Increased flexibility: Faster response to production changes
Cost reduction: Reduction of production and logistics costs
Process standardization: Standardization of processes across different production lines

Process optimization in production – methods and approaches at a glance

There are various methods that support companies in implementing process optimization in production. We present three approaches that play an important role in modern manufacturing.

Lean production: Lean aims to consistently eliminate all non-value-adding activities in the production process. The focus is on seven types of waste: overproduction, waiting times, unnecessary transport, inefficient movements, over processing, high inventory levels, and scrap. Tools such as value stream analysis, 5S, SMED, and Kanban help to record processes, identify bottlenecks, and implement improvements iteratively.
Total Productive Maintenance (TPM): TPM is a comprehensive approach to optimizing plant efficiency and availability. It aims to eliminate unplanned downtime and maximize plant performance. The goal is to increase overall equipment effectiveness (OEE), which is composed of availability, performance, and quality.
Six Sigma: Six Sigma is a systematic approach to production optimization. The aim here is to design processes in such a way that they have as few deviations and errors as possible. The term “Six Sigma” refers to a statistical quality standard in which processes run so stably that virtually no errors occur. The method follows the DMAIC cycle (Define, Measure, Analyze, Improve, Control), which helps companies to systematically identify and resolve problems.

These methods offer valuable approaches to process optimisation in production, but quickly reach their limits in practice due to the increasingly complex production environment. Process Mining provides a remedy here – this innovative method enables an objective, data-based view of processes and supports the implementation and effectiveness of the methods presented. Process Mining is now used in production in many places, but the technology is also used, for example, for process optimization in the energy industry.

Implement process optimisation in production with Process Mining

Process Mining is a data-based analysis method that makes real process flows visible. It is based on so-called event logs – digital traces that are created when processes are executed in IT systems. These event logs contain valuable information such as time stamps, executed activities, and process instances, which provide detailed insights into the actual course of business processes. Process Mining tools transform this raw data into meaningful, easy-to-understand process models, creating an objective, data-driven picture of real-world processes.

This objective view is particularly important in manufacturing, where a wide variety of different systems such as ERP, MES, SCADA, etc. are used. Process Mining bridges this fragmented system landscape and brings together the data scattered across these systems. The resulting holistic view of the process chain enables companies to identify bottlenecks in production, analyze throughput times, uncover deviations from the target process, and thus bring about process optimization in production.

The methods presented are all based on precise knowledge of the actual process events, which is why Process Mining represents an invaluable added value for their implementation. In the context of lean production, it helps to precisely locate waste by making unnecessary waiting times or overproduction visible on the basis of real data. For TPM, Process Mining provides valuable insights into actual plant utilization and supports the calculation of overall equipment effectiveness (OEE) by analyzing availability, performance, and quality data. Within the framework of Six Sigma, Process Mining supports the identification of process deviations and allows the targeted analysis of critical influencing factors.

Achieve process optimisation in production – with Process.Science

The implementation of Process Mining is still considered challenging in many companies. Projects often fail due to insufficient data quality, lack of support, or unrealistic expectations. At Process.Science, we make it easy for you. The key difference: our solutions are integrated directly into existing business intelligence platforms such as Microsoft Power BI and Qlik Sense. Integration requires minimal effort, has no impact on ongoing operations, and can be implemented both on-premise and directly in popular cloud environments such as Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Services. With us, you get a powerful, practical solution for process optimization in production.