Process Mining for Pharma, Biotech, and MedTech

In life sciences companies, precise control and monitoring of processes—especially in quality assurance, development, production, and across supply and distribution chains—is essential.

Process mining uses existing system data to visualize actual end-to-end workflows. This allows deviations from the process, bottlenecks, and rework loops to be identified and addressed in a structured manner.

Process mining uses data from your lab systems, production systems, and quality systems, such as LIMS, MES, or ERP. Each process step generates timestamped events, such as “CAPA opened,” “Investigation completed,” or “QA approval.”

The actual process paths are automatically reconstructed from this event data. In the process mining interface, you can:

• Track end-to-end workflows across systems and organizational units
• Evaluate throughput and wait times by step, role, or location
• Identify variations, rework, and deviations from the defined process

Unlike traditional reports and dashboards, process flows are visible in their entirety, down to the level of individual operations or batches.

In the batch release process, data and activities from production, quality control, and quality assurance interact. Process mining can reveal:

• at which process steps waiting times, retests, or additional inspections occur
• how lead times differ between products, lines, or locations

Based on these insights, release processes can be analyzed in a targeted manner and, where appropriate, shortened or stabilized.

In the deviation and CAPA environment, the focus is often on closure times, backlogs, and inspection findings. Process mining can support this by revealing:

• how often records are reopened
• at which points investigations or reviews are delayed

This provides a solid foundation for adjustments to roles, process steps, or work instructions.

In clinical development, the study startup and execution processes can be complex. Process mining can provide insights into:

• Workflows related to site activation (e.g., contracts, approvals, training)
• Processing times for query resolution

This allows the causes of delays to be identified and addressed in a targeted manner.

Additional use cases include supply chain processes such as order-to-cash and procure-to-pay, where process mining can make end-to-end workflows and manual interventions transparent.

In a deviation management project at a pharmaceutical company, data from the quality management system was analyzed using process mining. The analysis focused on:

• Lead times from opening to closure
• Number and reasons for reopenings
• Distribution of processing steps across roles and organizational units

The analysis revealed specific bottlenecks in individual process steps as well as recurring loops. Based on this, process steps, responsibilities, and templates were adjusted. In comparable projects, closure times were typically reduced by 20–40%, and backlogs were significantly reduced.

A typical workflow:

1. Scoping and use case selection

Joint selection of a suitable process (e.g., batch release, CAPA, Clinical, or Supply Chain) and definition of goals and metrics.

2. Data integration and preparation

Integration of relevant systems (e.g., SAP/ERP, MES, LIMS, QMS, CTMS), extraction of event data, and preparation for process mining analysis.

3. Analysis of process flows

Reconstruction of actual process paths, evaluation of variants, lead times, and bottlenecks, as well as comparison with defined target processes.

4. Identification of improvement actions

Identification of areas for improvement in processes, roles, work instructions, or system configurations.

5. Implementation and continuous monitoring

Support in implementing prioritized measures and, optionally, re-analysis to verify effects.