Blog: Data analytics solutions to improve the OEE key figure

Data analytics solutions to improve the OEE key figure.


Overall Equipment Effectiveness (OEE) is the most important indicator for industrial companies. Many companies rely on data analytics solutions to optimize the overall equipment effectiveness. In this blog, we show how an improvement of the OEE key figure is possible with a digital Retrofit.



Optimizing effectiveness is one of the highest priorities for many companies. Overall Equipment Effectiveness (OEE) measures whether machines and systems are running effectively. Composed of the maximum availability, quality, and output, this determines the overall effectiveness of plants and machines.


With the help of data analytics solutions, the generated data from machines and systems can be recorded and evaluated by machine learning algorithms. This gives companies an overview of the status of plants and machines and enables them to predict malfunctions or plan maintenance work.


In the second part, you will get to know other data analytics solutions that contribute to improving the OEE key figure. Thanks to a digital retrofit of machines and systems, these solutions can also be used in legacy systems:


Predictive quality applications use the collected data to predict malfunctions in machines and systems that could have a negative impact on product quality. By intervening promptly and changing process parameters or machine settings, industrial companies can avoid the occurrence of scrap and reduce added costs.


Predictive performance applications draw on the detected anomalies of predictive maintenance and predictive quality. The data is used to continuously optimize and train the machine learning algorithms. This enables continuous improvement of performance.


Legacy systems pose a particular challenge in the realization and implementation of such data analytics solutions. With the help of a digital retrofit, machines as old as 20 or even 30 years, can be upgraded with sensors and data interfaces. In addition to conventional vibration, temperature and current measurement technology, optical and acoustic sensors can also be installed. Industrial companies can rely on proven technical frameworks to implement data analytics solutions such as predictive maintenance, predictive quality, and predictive performance to improve overall equipment effectiveness.


Have we aroused your interest?


We would be happy to advise you and show you various ways in which you can improve your overall equipment effectiveness with a retrofit of your machines.


Blog: Digital Retrofit and Overall Equipment Effectiveness

Digital Retrofit and Overall Equipment Effectiveness

To optimize overall equipment effectiveness, companies are turning to data analytics solutions. For this, they do not necessarily need modern machines with built-in sensors. In this blog, we show you how a digital retrofit of your machines can contribute to optimizing overall equipment effectiveness.



Industrial companies rely heavily on the Overall Equipment Effectiveness (OEE). Based on the metrics availability, quality, and output, this most important key figure measures the overall effectiveness of plants and machines. At the same time, conclusions can be drawn about the performance of the production processes. Optimizing this key figure is inevitably one of the main goals of companies.


Data analytics solutions offer excellent opportunities for this. The data from machines and systems can be recorded and evaluated with machine learning algorithms. In this way, predictions can be made that not only enable timely intervention in the event of faults, but at the same time contribute to the continuous improvement of OEE.


In this blog, we first focus on predictive maintenance applications. We will show you how all OEE key figures can be covered with the help of these applications:


Predictive maintenance applications can predict the wear of machine and plant parts from data generated by these machines and plants. This allows companies to detect the wear and tear of parts before planned maintenance work and take maintenance measures in suitable time. In this way, unplanned downtimes of machines and entire production lines can be prevented and fines due to missing goods are avoided.


One challenge in the realization of these applications are legacy systems. Compared to modern machines and systems, they do not have the necessary sensors. With a digital retrofit, these production systems can be retrofitted with sensors and the corresponding interfaces for data acquisition.


In the second part of our article “Data Analytics Solutions for Improving the OEE Key Figure”, you can read which other data analytics solutions can contribute to the optimization of the OEE key figures and with which sensors legacy systems can be equipped.

Blog: Predictive Quality: Edge or Cloud Solution?

Predictive Quality: Edge or Cloud Solution?


Predictive quality applications are becoming increasingly important in production.  The cloud offers a wide range of new possibilities, but when it comes to speed, it makes sense to rely on edge computing. In this blog, we show you how to use edge systems despite their lower computing power.



With predictive quality applications, companies can continuously optimize the quality of their products and processes. This also makes it possible to identify quality problems. The prerequisite for this is a machine learning process that can quickly predict problems such as jams on a machine or overheating of an electric motor. The analytics calculations of the algorithms required for this can be carried out both via edge computing and via a cloud platform. In the first part “Predictive Quality and Edge Computing“, we have shown you reasons why it is not advisable to use a cloud platform for time-critical use cases.


In this part, we explain how you can use edge systems despite lower computing power by optimizing machine learning models:

  • Pruning: By simplifying, shortening, and optimizing the decision trees, the complexity of the input parameters can be reduced, thus requiring less computing power.
  • Frameworks: The range of frameworks is wide and offers different advantages and disadvantages. With a framework such as Google’s Learn2Compress, the layers of deep learning models can be reduced in their breadth and depth, thus saving further computing power.
  • Programming language: Machine learning models are usually written in the Python programming language. However, Python requires high processor and computing power. Compilers can be used to translate the code into a language that requires less processing power, such as C#.

With these tips, machine learning models can be optimized, and edge systems can be used. However, the efficiency of the cloud cannot be made possible by an edge solution. This is a major disadvantage, because the cloud often requires less computing capacity due to load balancing and is therefore more cost-effective than the edge solution.


Whether you choose a cloud or edge solution should depend on each specific case. A combination of the two solutions is also conceivable and possible. For example, the data can be prepared and pre-filtered at the edge and further processing can be done in the cloud.


Do you have any questions about the different options? Please feel free to contact us.

Blog: Predictive Quality and Edge Computing

Predictive Quality and Edge Computing


Everyone is talking about the cloud, but especially when things need to move fast, it is good to rely on edge computing for predictive quality applications. In this blog, we will show you the advantages of edge computing over the cloud.



Predictive quality applications give industrial companies the opportunity to optimize both the quality of products and the process flows.  At the same time, quality problems that arise at short term can also be identified. For example, an imminent blockage of a  valve or a suddenly occurring unusual behavior of an electric motor of a machine can be detected. Immediate intervention, for example by changing the process parameters or machine settings, can therefore often prevent major damage, provided that the used machine learning method can predict these problems as quickly as possible.


We show you why, for time-critical use cases, outsourcing the analytical computations of algorithms to a cloud platform can be problematic:


  • Data transmission to the cloud platform: The sensor measurement data and process parameters recorded by the machines and systems must be transmitted to the cloud platform for the calculations. This results in time delays. In extreme cases, this delay can be too big and results in the inability to react and intervene in time.
  • Data exchange with the cloud platform: In contrast to edge computing, the exchange with a cloud requires a stable Internet connection. Companies may find it difficult to ensure this, as production and manufacturing halls are often located in remote locations. As a result, companies are constantly exposed to the risk of a connection failure, which in turn inhibits timely intervention.

With analytics calculation directly on the machines and systems, companies can avoid these problems. The hardware for an edge solution is increasingly available as there are more and more sensors with their own processing devices and industrial computers today are more often equipped with high-performance processing units.


The choice between an edge or cloud solution must be made individually, there is no general recommendation. Companies should consider important factors such as response time, network availability and costs when deciding. We are happy to support you in this.


Find out how edge solutions can be used despite their lower computing power and what major disadvantages still exist compared to the cloud, in our second blog „Predictive Quality: Edge- or Cloud-Solution“.

Publication: Computer vision in industrial companies

LeanBI in the media on the topic:


Computer vision in industrial companies


The quality of computer vision systems strongly depends on the recording technology used. Each industrial company has its own requirements and conditions to consider.


With our checklist you will find the right solution.
 Computer Vision in industrial companies



Blog: Digital retrofit for legacy systems – paving the way for Industry 4.0


Digital retrofit for legacy systems – paving the way for Industry 4.0

Machinery and plant parks are indispensable for many companies, but many of them are no longer state of the art. These so-called legacy systems have a shortcoming, they lack the connectivity to integrate them into digital workflows. In our blog, we show you the positive effects of a digital retrofit on three application scenarios.


Leidensdruck Retrofit I


As digitization progresses, many companies in the industrial environment are facing a major challenge: How can legacy systems be integrated? They lack two essential factors: the data interface and the sensors for collecting the data. A digital retrofit enables upgrading the legacy systems with the necessary sensor and control technology. This not only increases performance, but also improves the quality rate.


The application scenarios for a digital retrofit are broadly diversified. We have already given you a first insight in our blog «Integration of legacy systems through digital retrofit». Using three further examples, we will show you the practical use of the retrofit and its positive effects:

  1. Metal production: Detecting quality fluctuations in the manufacture of metal products such as tubes, components or body parts at an early stage is crucial. Thanks to a digital retrofit with which old systems are equipped with the appropriate sensors, fluctuations in production can be minimized. Production control can be designed to be reactive; adapting to fluctuations measured by the sensors. As a result, continuous traceability can also be ensured throughout the entire production process. The combination of decentralized data acquisition and centralized data analysis can also contribute to scrap reduction and more cost-effective production.
  2. Surface treatment: By combining digital retrofitting with predictive maintenance, wear times for machining tools can be better predicted. The use of additional sensors paired with the analysis optimizes the processes in several ways: on the one hand, highly stressed components can be used longer in surface treatment, on the other hand, this reduces material consumption and downtime. Productivity can thus be increased.
  3. Electronics production: With the upgrade of legacy systems through a digital retrofit, highly complex, AI-supported test methods can be used for the early detection of component failures in the production process. Not only does this increase productivity rates, but it also simultaneously helps to understand and learn to fix emerging anomalies.

The sustainable modernization technology retrofit is an important enabler for Industry 4.0. Without them, valuable and in some cases indispensable legacy systems and production facilities would be lost, which would be accompanied by an immense loss of value.


Have we aroused your interest? Then contact us and we will be happy to answer your questions.

Blog: Integration of legacy systems through digital retrofit


Integration of legacy systems through digital retrofit

To be able to digitize machinery and plant parks without carrying over any burden? Which company does not hope for this? Often it is not a matter of starting from scratch, but a question about the conversion and integration of various legacy systems into digital workflows. In this blog, we show you the positive effects of a digital retrofit using three exemplary application scenarios.


Leidensdruck Retrofit I


Digitalization is in full swing – one of the biggest stumbling blocks in the industrial environment is the integration of legacy systems. Two decisive prerequisites to digital operations are missing, thus hindering optimization of planning and changeover times, increase of performance and availability, improvement of the quality rate and implementation of predictive maintenance: The sensor technology for collecting data as well as an interface for forwarding it is missing. With a digital retrofit, legacy systems can be equipped with the necessary sensor and control technology.


In the first part, we show you three scenarios for practical use and the potential positive effects of a digital retrofit:

  1. Intralogistics: With the booming online trade, the shipping volume has also risen rapidly, accompanied by the high stress on logistics facilities, which cannot keep up with this growth due to their (partially) analog structures. In contrast to a retrofit of existing systems, the construction or acquisition of additional buildings and/or systems cannot be achieved at the same time. This means that a digital retrofit is not only the faster, but often also the more cost-effective and resource-saving option. As an icing on the cake, the service life of the existing systems is also increased.
  2. Assembly lines: Many product suppliers such as automotive suppliers or bicycle manufacturers are faced with major challenges: How can delivery times be met despite high demand and at the same time growing number of product variants? A failure in the production chain leads to complete plants coming to a standstill, delivery dates not being met and thus high penalty payments. Through predictive maintenance, possible failures can be detected at an early stage. Thanks to the retrofit of legacy systems, making this possible in the first place, delivery reliability can be ensured, production output increased, and customer satisfaction enhanced.
  3. Packaging industry: The fact that many machines are connected in series distinguishes the packaging industry, but also carries the risk that a complete line will come to a standstill in the event of a failure. Here, too, unplanned downtime and planned maintenance work can be reduced with the help of retrofitting and predictive maintenance. For example, legacy systems can use sensors to automatically check the packaging quality of different products and thus reduce the reject rate.

With a digital retrofit, not only the important prerequisites for the integration of legacy systems into digital workflows can be fulfilled, but also the way for Industry 4.0 can be prepared We’d be happy to support you.


Would you like to know more about the integration of legacy systems through a digital retrofit? In our second blog post «Digital retrofit for legacy systems – paving the way for Industry 4.0», we outlined further application examples.

LeanBI AG has won the InnoSuisse project “Automated Bridge Defect Recognition”

LeanBI AG has won the InnoSuisse project “Automated Bridge Defect Recognition” in August 2022.  



Innosuisse is the Swiss agency for innovation. Innosuisse’s mandate is to promote science-based innovation and start-ups in Switzerland. The agency promotes the transfer of knowledge from research to business.


The kick-off meeting in Zurich in October 2022 was excellent (and as you can see with a lot of outlook) and all of us at LeanBI are looking forward to working with the research partners OST St.Gallen and Hes-so Genève and the implementation partner Basler & Hofmann.  The future of bridge inspection and infrastructure asset management is digital! We professionalize and simplify automated AI (Artificial Intelligence) for damage detection on the digital 3D bridge twins.

Blog: Transparent air quality


Transparent air quality

Air quality is a sensitive issue, especially when many people come together in one room. With indoor air measurement technology, sensors can contribute to the transparency of air quality. We will show you more positive aspects of this technology in this blog.


Sensoren und Luftqualität II


Good or bad air quality is crucial for how effectively we work and learn, furthermore determines our job satisfaction. Good air quality monitoring also makes sense for energy reasons: it gives facility management another control option for their heating, ventilation and air conditioning (HVAC) strategy and can thus optimize energy costs. The installation and operation of a technology for indoor air measurement is simple, robust, and efficient in operation.


In our blog «Indoor Air Quality» we have already listed some reasons for the use of indoor air measurement technology. We now outline further points of how plug-and-play sensors and indoor air measurement can contribute to improved indoor air quality:

  • Reduce energy costs: A recurring topic of contention during the winter months: Heating and ventilating at the same time. Heating air is a major contributor to deteriorated indoor air quality. Thanks to room air measurement, the indoor air quality can be quantified independently of the subjective feeling of warmth and fresh air. The sensors can not only detect temperature values, but also CO2 content and humidity. This allows ventilation in a much more targeted and efficient manner. This is good for the wallet especially in times of drastically rising energy prices, where higher energy efficiency is achieved, and thus energy costs reduced.
  • Making facility management more efficient: Room air measurements are the basis for an efficient HVAC strategy. With the help of indoor air measurement technology, facility management can use the measured air quality values for efficient control of the HVAC equipment. This allows buildings to be heated sensibly and cost-effectively and thus increasing energy efficiency. Demand-based control of ventilation and heating technology also helps to reduce operating costs. The room sensors can also be connected to the alarm system. In this way, air pressure differences between the rooms can provide information about air flow in the building.

Transparent indoor air quality is not only highly useful but can save energy costs and contribute to efficient building operations.


If you have any questions or would like to know more about the technology for indoor air measurement, please feel free to contact us.

Blog: Indoor air quality


Indoor air quality

Closed rooms, stale air, and no possibility of ventilation – lack of air quality has not only been a major issue since the Covid pandemic. In this blog, we show you how clever sensor technology and cloud technology contribute to the transparency of air quality.


Work efficiency and satisfaction of employees, the utilization of office and conference rooms and energy costs, all this is influenced by the quality of indoor air. Even more important: clean air. Because wherever people meet in closed rooms, be it in schools , universities or in the office, the quality of the air is a sensitive issue. The technology for indoor air measurement is now easy to install and operate. The sensors, which are simply affixed into the rooms by plug-and-play, are connected to their own cloud by a network independent of normal Wi-Fi (LORA-WAN) and a gateway. There, the values are processed and can be read at any time by an online dashboard.



Sensoren und Luftqualität I


We show you some positive effects of this technology:

  • Well-being and performance increase: The quality of the ambient air has a measurable influence on our work performance during concentrated learning and working. Tiredness, lack of concentration or even headaches are the consequences of poor air quality. The sensors can detect the decisive parameters responsible for the poor air quality. In this way, carbon dioxide (CO2), temperature, humidity and volatile organic compounds (VOC) can be recorded, and conclusions can be drawn about the humidity, and even about the germ and aerosol content of the room air. If required, other parameters can also be recorded by the technology, such as particulate matter, radon, or carbon monoxide.
  • Measuring room layout: Exceedances of limit values are reliably recorded by this technology and can be displayed transparently in real time. This makes it possible to determine the number of people in a room anonymously and without the use of cameras. This can be used, for example, to determine a current overcrowding or a chronic over- or under-utilization of capacity limits. All of this can be done without collecting any personal data.
  • Take action measures: By providing the values directly, action instructions can be derived, which are made visible in the affected rooms with the help of light pulses, like a kind of traffic light. The measured values are also fed into the cloud via device management over a separate network. There, the data is processed, analyzed, compared, and made available via an online dashboard. This not only gives those directly affected an insight into the values, but also other authorized persons, such as managers or air conditioning technicians.

Measuring and controlling indoor air quality makes sense in many ways. With the currently available technologies, the systems can be installed quickly and cost-effectively. We’d be happy to support you.


You can read about further positive effects of a technology for indoor air measurement in our second article «Transparent air quality».