The Pitfalls of Failed Digitalization

Many companies invest heavily in digitalization projects that ultimately fail:

• €200,000 Invested, Zero Benefit: An automotive supplier implements an MES. The result: 40% faulty data, parallel use of Excel sheets, and frustrated management.
• €500,000 for False Alarms: An electronics manufacturer implements Predictive Maintenance, after one year: 80% false positives, system ignored.
• €2 Million Stagnation: A medical technology manufacturer develops an AI optimization that is technically perfect, but blocked by regulatory and cultural barriers.

These examples show that an IT system alone does not guarantee successful digitalization. The key lies in systematically integrating people, processes, and technology. 

Our Recipe for Success: Compass, Prioritization, Technology

We guide you through digitalization with a clear compass, ensure quick wins through consistent prioritization on business impact, and make sure that technology is truly adopted within the process.

The Four Stages of Your Shopfloor Digitalization (Your Compass): 

Level 1: Is Your Reactive Mode: Data Collection and Transparency

The first step of any successful digitalization begins with transparency. Many companies still struggle with fragmented data. At this level, we establish a unified data foundation:

• Systematic collection of machine and process data.
• Integration of existing systems (ERP, MES, SCADA).
• Real-time visualization through intuitive dashboards.

The Value: You gain a complete overview of your production processes. Downtimes become visible, bottlenecks identifiable, thus laying the foundation for data-driven decisions.

From Practice: An IT project manager was desperate: "Everything works perfectly technically. But people just don't use it correctly!" Olaf solved this by early involvement of users and 

Level 2: Is Your Preventive Mode – From Data to Insights

Collecting data is just the beginning. Level 2 focuses on gaining actionable insights from the collected information (Prioritization):

• Trend analysis and pattern recognition functions.
• Identification of improvement potentials.
• Root cause analysis for quality issues.

The Value: You understand not only what is happening, but also why it is happening. Recurring problems are tackled at their root.

From Practice: The Machine Builder with Too Many Dashboards

A machine manufacturing company with three plants had 47 different dashboards, however  no one knew which ones were truly important. Olaf eliminated the "dashboard chaos" with the 3-Dashboard Rule, which focuses on the ONE crucial metric:

• Machine Dashboard: Only output, scrap, downtime, and changeover.
• Production Manager Dashboard: Only OEE, quality, and lead time.
• Management Dashboard: Only KPIs with direct business impact.

Every report had to include a clear recommendation for action. The result: Decision-making time reduced from days to hours, 15% improvement in Overall Equipment Effectiveness (OEE).

Level 3: Proactive Optimization Mode – Predictive Maintenance and Quality

Level 3 marks the transition from reactive to proactive action (Technology). The data foundation built in previous stages pays off:

• Predictive Maintenance for proactive servicing.
• Anomaly detection for early identification of deviations.
• Quality forecasts based on process parameters.
• Automatic alerts for critical trends.

The Value: Unplanned downtimes are drastically reduced, maintenance costs optimized, and product quality stabilized. Your operating mode transforms from reactive to preventive.

From Practice: How do you overcome the distrust created by systems with false alarms? Olaf tackled this by prioritizing gradual trust-building and practical validation. He initiated a predictive maintenance system on a single production line, achieving an impressive >90% accuracy rate for its predictions before a wider rollout. This success was rooted in the daily, collaborative work between maintenance experts and data scientists. The measurable impact was clear: a 25-35% reduction in unplanned downtime and a 20% optimization of maintenance costs.

Level 4: Is Your Prescriptive Mode – AI-driven Self-Regulation

The highest stage of shopfloor digitalization uses Artificial Intelligence (Technology) for continuous, autonomous improvements:

• AI-powered real-time process optimization.
• Automatic adjustment of parameters.
• Self-learning systems for Quality Control.
• Predictive scheduling and resource planning.

The Value: Your production becomes a self-optimizing system that continuously learns and adapts to changing conditions.