Process Mining in the BI ecosystem: Process.Science presents pathways to more transparent logistics processes at LogiMAT 2026

Process.Science GmbH & Co. KG is a German software company headquartered in Hamburg that specializes in process mining and data-driven process analysis. Founded seven years ago, the company employs around 15 people and supports organizations in systematically analysing and further developing business and logistics processes based on existing data. Process.Science is led by Lucas M. Schroth, Founder and CEO, who, together with an interdisciplinary team of IT, data, and logistics experts, pursues a practice-oriented approach to process analysis. Today, more than 40 customers from industry, manufacturing, and logistics work with the company’s solutions. At the same time, Process.Science is currently undergoing a rebranding initiative with a sharpened product structure and the guiding principle “Insights Drive Performance.”

The topic is particularly relevant for visitors to LogiMAT 2026. In many logistics organizations, large volumes of process data are available in ERP, WMS, or TMS systems, but they are often only analysed in a fragmented way. Traditional reports provide key figures but offer little insight into actual process flows, bottlenecks, or deviations. Process mining addresses this gap by reconstructing, visualizing, and analysing real processes based on event logs. This makes it possible to identify inefficiencies, media disruptions, or delays across systems and evaluate them in a structured manner.

A key feature of the Process.Science approach is its native integration into existing BI systems such as Power BI, Qlik Sense, or Tableau. Process analyses are carried out directly where companies already analyse their data. Additional platforms or extensive IT implementation projects are not required. This reduces implementation effort and enables business units to put analyses into productive use more quickly. For logistics managers, this translates into greater transparency across the entire supply chain, a sound basis for identifying optimization and automation potential, and the foundation for shorter lead times and more stable supply chains.

Beyond the analysis of existing system data, Process.Science is working on new capabilities to address future requirements. With the so-called IoT Miner, machine, sensor, and scan data are incorporated into process analysis. This makes it possible to link processes from production, warehousing, and transportation with real-time information from industrial automation and map them as end-to-end processes. In addition, the Process.Science Intelligence module extends analysis with AI-based methods, such as the automated detection of anomalies, early identification of process risks, or the derivation of recommended actions. The aim is to gradually develop logistics processes toward real-time transparency and data-driven, partially autonomous control.

At LogiMAT 2026, Process.Science will showcase practical demonstrations illustrating how process mining can be applied directly within BI environments and what insights companies can gain from it. The focus will be on real-world use cases, short time-to-value, and the question of how data-driven process analysis can sustainably improve efficiency, transparency, and resilience in logistics