June 26, 2025

12 MIN.

Smarter production with predictive analytics – challenges and opportunities

A familiar sight in many production plants are machines that have been performing reliably for decades – at least at first glance. Yet risks lurk beneath the surface. Machine maintenance is becoming increasingly complex, and potential downtime can only be identified through visual inspection. As production becomes more digitalized, subjective experience alone is no longer enough. To optimize production processes and proactively eliminate unplanned downtime, more data is needed. To be precise – we need the right data at the right time.
That is easier said than done, because there is often a significant gap between modern IT and legacy OT. Machines use proprietary, often insecure, protocols, interfaces are not designed for secure network connections, and the data formats feel like ancient history for today’s data scientists.

The challenge is clear – how do we bring these old treasures safely into the digital age to take advantage of the benefits of predictive technologies? 

Predictive Analytics – our main topic at #RETHINK! Visit us!

The challenge – making data from the OT world work for you

Though older control systems provide data, it is often without context, structure, or modern interfaces. To use predictive analytics meaningfully, organizations must be able to capture and process this data securely. Two key requirements must be met: 

1. Secure data connectivity 

he connection of OT systems with IT infrastructure must be done without creating vulnerabilities. Specialized security solutions such as edge.SHIELDOR can help. For instance, by converting outdated protocols such as SMBv1 into new, more secure SMBv3 protocols – without the need to update the firmware of the old machines (which is often not possible). 

Additionally, the solution scans all data traffic to detect malware and isolate any threats that are identified – Getting IT/OT convergence right. 

2. Standardization and harmonization

In many cases, various data formats must first be harmonized, and unstructured machine data converted before it can be analyzed. In complex environments, customized middleware solutions can be used to preprocess data and synchronize it with the downstream systems. 

Once these foundations have been laid, there is further added value: the data, tested for security, can be used for data-driven production, in addition to simple machine monitoring.

The next step – from reactive to predictive 

Predictive analytics is more than a buzzword. It involves using historical production data, statistical models, and machine learning to identify potential production disruptions at an early stage – ideally, before they even occur. The advantages are clear: 

Reduced downtime due to timely maintenance recommendations 

Optimized resource planning based on precise demand forecasts

Improved product quality through early anomaly detection

Lower energy and resource consumption due to continual process optimization 

Use case: Predicting failures with neural networks 

The reliability of the production facilities is crucial to an organization’s success. A leading manufacturer wanted to reduce unplanned machine downtime and optimize maintenance costs. Traditional maintenance strategies were too reactive and inefficient, as certain components, such as bellows for pressure regulation, only showed signs of wear shortly before failure. Although the drop in pressure could theoretically be observed a few days in advance, the manual detection was impractical and unreliable.  

The solution involved training a neural network using historical and current sensor data to continuously identify wear patterns and predict impending failures. The system analyzes the pressure values in real time and identifies significant deviations well before they become critical. 

The result is that critical drops in pressure can now be forecast up to a month in advance. The maintenance team receives automated notifications, enabling them to take early and targeted preventative measures, without the unnecessary replacement of working components. In addition to significantly reducing unplanned downtime, the solution also increased the efficiency of the entire production.  

In the long term, this approach laid the foundation for comprehensive, data-based production monitoring, and delivered further valuable insights for process optimization. 

Book a consultation today to discover the targeted measures that can boost your production to peak performance. 

Autor: Dr. Matthias Zahn

Dr. Matthias Zahn is a Senior Data Scientist at Triovega GmbH. He is responsible for the statistical analysis of customers’ production data and the development of predictive models for use in industrial production — enabling customers to increase their production efficiency permanently by leveraging data-based process improvement.

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