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Movecat GmbH
Nufringen, Germany
A
51-100 Employees
1995
Key takeaway
The company emphasizes its commitment to quality and innovation, highlighting its predictive maintenance services through their partnership programs and service level agreements. Their offerings include special maintenance and inspection deals, ensuring products remain up-to-date and perform optimally throughout their lifespan.
Reference
Product
LP – PREDICTIVE MAINTENANCE – Movecat
Quantum-Factory
Munich, Germany
A
1-10 Employees
2018
Key takeaway
The text highlights the advantages of predictive maintenance over reactive maintenance, emphasizing that it helps prevent operational disruptions and reduces costs. The company's focus on networking and software integration further supports the implementation of predictive maintenance strategies.
Reference
Core business
Institute for Predictive Maintenance ? Industrie 4.0
ifm statmath gmbh
Germany
A
1001-5000 Employees
2011
Key takeaway
The company highlights that the significant increase in large data volumes, often seen as a downside of digitization, actually holds great potential.
Reference
Product
s.maintenance
Thanks to our data-driven software s.maintenance, we can not only make precise predictions about the remaining service life of wear components, but also maximise it and initiate maintenance activities early. Thus, s.maintenance helps achieve a more efficient use of resources and cut maintenance costs. Our predictive maintenance software takes into account the actual wear and predicts component wear before any machine failure can occur. This allows you to keep a permanent eye on the current functional state of your plant or machine, contributes to a more effective spare parts management and significantly reduces and optimises your service/maintenance intervals and activities. The wear and tear of parts mainly depends on factors such as process parameters and intensity of use. Applying a machine learning approach, the algorithm of s.maintenance uses this information to create a precise model describing the wear-out dynamics of a specific component. On this basis, it can make precise predictions as to when critical usage or failure limits are reached. The result is a continuous, intelligent and automatic monitoring system for relevant wear components that is used to plan and create a predictive maintenance schedule for the machine or an entire production line.
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Prognost Systems
Rheine, Germany
A
101-250 Employees
-
Key takeaway
PROGNOST Systems specializes in predictive maintenance through its advanced online condition monitoring solutions for rotating equipment. Their systems provide actionable insights that enable early detection of faults, such as rider band and piston ring failures, ensuring enhanced machinery protection and operational efficiency.
Reference
Core business
PROGNOST Systems - Online condition monitoring of Rotating Equipment
Online condition monitoring, machinery diagnostics, and SIL3 protection. Solutions to detect fault patterns and operational issues of critical machinery.
ai-omatic solutions GmbH
Germany
A
11-50 Employees
2020
Key takeaway
ai-omatic solutions GmbH offers a robust predictive maintenance solution that integrates engineering expertise into a powerful AI system, providing precise insights into machine conditions through a proprietary Health Score. This approach allows companies to identify potential issues before they escalate, minimizing downtime and optimizing maintenance schedules, which is essential for enhancing operational efficiency and sustainability.
Reference
Core business
Predictive Maintenance | ai-omatic solutions GmbH | Hamburg
Überwachung von Maschinen und der Produktion durch Condition Monitoring, Predictive Maintenace & Predictive Quality mithilfe von KI
WAKU Robotics
Berlin, Germany
A
1-10 Employees
2019
Key takeaway
WAKU Robotics GmbH offers a software solution called WAKU Care that facilitates predictive maintenance for AGV and AMR fleets. Their platform provides advanced diagnostics and insights, ensuring system availability and optimizing efficiency throughout the lifecycle of mobile robots.
Reference
Product
Predictive Maintenance for AGV and AMR - WAKU Robotics
HPC-CAS Solutions
Mannheim, Germany
A
51-100 Employees
2008
Key takeaway
HPC specializes in predictive maintenance and service, offering seamless integration of sensor data into SAP® systems, which is crucial for optimizing operational processes. Their commitment to automated intralogistics and Industry 4.0 solutions positions them as a forward-thinking partner for businesses looking to enhance their maintenance strategies.
Reference
Service
HPC.Predictive Maintenance & Service - HPC
DALOG Diagnosesysteme GmbH
Neusäß, Germany
A
11-50 Employees
1998
Key takeaway
DALOG Diagnosesysteme GmbH specializes in online condition monitoring solutions that enable proactive and predictive machine maintenance. Their innovative technologies and expert insights help clients minimize downtime and optimize machine life cycles.
Reference
Core business
DALOG – Online Condition Monitoring and Predictive Analytics
compacer GmbH
Gärtringen, Germany
A
251-500 Employees
1997
Key takeaway
The company offers a robust IoT solution that facilitates predictive maintenance through advanced condition monitoring. Their approach enhances decision-making and supports the transition towards Smart Factory and Industry 4.0.
Reference
Product
Condition monitoring and predictive maintenance solutions
Use condition monitoring and predictive maintenance as first steps towards Smart Factory and Industry 4.0. Use the newest software with old machines.
Proemion
Fulda, Germany
A
101-250 Employees
1995
Key takeaway
Proemion emphasizes its commitment to providing advanced connectivity solutions that support predictive maintenance by processing billions of data points annually. This capability allows machine owners and OEMs to extend the lifespan of equipment and minimize costly downtime through a digitized maintenance process.
Reference
Product
Maintenance Tasks Feature - DataPortal - Web Services - Products - Proemion
Machine owners have a huge interest in extending the lifetime of each machine and avoiding unnecessary, costly repairs and business-interrupting machine downtime. For OEMs, it's also essential to have a transparent overview of maintenance work to ...
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When exploring the predictive maintenance industry in Germany, several key considerations should be taken into account. First, the regulatory landscape is critical; compliance with strict EU and German regulations on data privacy and industrial standards is paramount. Companies must navigate frameworks such as the General Data Protection Regulation (GDPR) and industry-specific guidelines to ensure conformity. The competitive landscape is robust, with numerous established players and startups innovating in machine learning, IoT, and data analytics to enhance predictive capabilities. Challenges include integrating legacy systems with modern predictive maintenance technologies, which can be complex and costly. Moreover, ensuring a skilled workforce that understands both the technological and operational aspects of predictive maintenance is essential. Opportunities abound, particularly in industries such as manufacturing, automotive, and energy, where predictive maintenance can significantly reduce downtime and maintenance costs. Environmental considerations are also increasingly relevant, as companies in Germany strive to meet sustainability goals. Predictive maintenance can contribute to reduced energy consumption and lower emissions by optimizing equipment performance. Lastly, the global market relevance of predictive maintenance continues to grow, driven by digital transformation across industries. Understanding these factors will provide a comprehensive foundation for anyone interested in the predictive maintenance sector in Germany.
Some interesting numbers and facts about your company results for Predictive Maintenance
| Country with most fitting companies | Germany |
| Amount of fitting manufacturers | 229 |
| Amount of suitable service providers | 276 |
| Average amount of employees | 51-100 |
| Oldest suiting company | 1995 |
| Youngest suiting company | 2020 |
Some interesting questions that has been asked about the results you have just received for Predictive Maintenance
What are related technologies to Predictive Maintenance?
Based on our calculations related technologies to Predictive Maintenance are Big Data, E-Health, Retail Tech, Artificial Intelligence & Machine Learning, E-Commerce
Which industries are mostly working on Predictive Maintenance?
The most represented industries which are working in Predictive Maintenance are IT, Software and Services, Other, Electronics and Electrical engineering, Manufacturing, Automation
How does ensun find these Predictive Maintenance Companies?
ensun uses an advanced search and ranking system capable of sifting through millions of companies and hundreds of millions of products and services to identify suitable matches. This is achieved by leveraging cutting-edge technologies, including Artificial Intelligence.
