Let’s take a look at the supply chain in manufacturing. The number of production machines, sensors, and parameters, as well as the amount of generated data, can no longer be effectively analyzed only by humans. Our intelligent algorithms process such data and translate it into more significant insights allowing you to predict an anomaly and optimize production. Thanks to that, you can notice anomalies before products enter quality assurance, reduce unplanned downtime, and prevent expensive breakdowns. Our solutions provide decision-makers with unprecedented insights, enabling them to make more informed choices.


In machine maintenance, anomaly detection is about how to predict abnormalities and optimize the process ahead of time instead of suffering losses. Anomaly detection aims to detect patterns deviating from the rest of the data, called anomalies. It can be an event or defective items mainly resulting from equipment malfunctions during the manufacturing process. Flawed manufacturing procedure – with quality control errors due to not noticing the gradual loss of the machines’ calibration – plays an essential role in the supply chain breakdown, as well as sales and market losses. However, this process is manageable. There are many data suitable for anomaly detection: all kinds of parameters such as temperature, vibrations, pressure, humidity, chemical reactions, spectroscopy, and so on. Our company deals with measuring data from machines and control stations.

What if we tell you that you can automatically detect defective products, significantly reduce unplanned downtime, prevent expensive breakdowns, and extend the lifetime of ageing assets? If traditional solutions are unsuccessful, it’s time to deploy software intelligence, such as machine learning, to analyze data and find non-obvious dependencies. It allows predicting anomalies, thus helping reduce costs and build a strong organization.

Anomaly Detection & Process Optimization | Industry 4.0

Many things can go wrong in the production process. Sometimes your team can quickly identify and fix them. But what if intuition and experience are not enough to solve all problems? How will you manage if you need data you do not even know you have, where to gather, and how to analyze?

The questions are what you can do to address significant problems and how you can find subtle dependencies, even if you cannot see them with the naked eye. The number of machines, parameters, and the amount of generated data can no longer be effectively analyzed only by humans. Intelligent algorithms are the means to predict an anomaly and optimize production.



Use case

Predicting production anomalies
Industry: AUTOMOTIVE, pre-assembling (international company)
Our goal: reducing the number of defective parts and detecting machinery anomalies


Exploratory Data Analysis
Proper analysis of data from machines and systems as well as the use of machine learning algorithms allow to build models and detect non-obvious factors that may generate production losses.

We started by asking the right questions. Out of the many production processes in this company, we focused first on the one that is prone to anomaly occurrence and is crucial to the company. Then, we carefully examined this production process from start to finish and found spots we wanted to investigate with the help of machine learning in order to find less apparent dependencies affecting losses. Then we pre-processed the data and performed explorative analysis via a number of visualizations.

What is a prototype?

Quick prototype creation enables fast verification of assumptions and focuses on where the greatest business potential is. The highly accurate machine learning prototype indicated the most important nonlinear correlations responsible for the anomalies. Thanks to the prototype, we saw the dependencies we had not seen before. Based on that, we were able to make the right decisions.

What is a production model?

After the prototype creation, we knew what had the greatest impact on achieving the goal.

We built a Machine Learning model that would remain stable throughout the production. It helps detect the factors causing anomalies at an early stage and take all precautions needed.

TOP 3 Benefits

1. Significantly reducing the number of defective products that generate losses.

2. Detecting anomalies at an early stage and saving production time.

3. Providing sustainability to the manufacturing process thanks to properly implemented data science and AI.

Machine Learning can be used in many different areas. Some of the most popular within the supply chain include AI-driven demand for forecasting based on location, category, brand, store, and SKU on a weekly or daily basis, forecast returns, reducing out-of-stock occurrences, new product forecasting, or price optimization.


We’re bringing the power of AI to your business, making the data work for you.

Our goal is to help companies implement machine learning and other data science techniques, such as time series analysis, deep learning, natural language processing, and many more.

We are a team of 5 experts who help you lead your Data Science & Machine Learning project from the beginning to the end. We provide you with specialist and practical knowledge related to data analysis, data science, and machine learning. We support you with our experience in the models’ implementation in production, understanding the business processes, and effective project management from start to finish.

Our strengths:

Helping companies achieve the greatest possible value with the least possible effort.

Partnering with the Tech Ecosystem of Rhenus Automation which enables end-to-end process automation in many industries, especially when combined with other methods and technologies.

Achievements in numbers:

500+ Machine Learning models deployed in production and massively influencing our customers’ bottom line.

Upskilling organization-wide data literacy. We trained 1000+ Machine Learning professionals as part of our educational activities.