Maintenance management is crucial for ensuring the availability and efficiency of machines and systems. Data-driven strategies such as predictive maintenance enable potential damage to be detected and rectified at an early stage.
It is essential to establish a solid data and knowledge base and develop a clear roadmap for sustainable maintenance processes. This increases cost-effectiveness and minimizes downtime.
Maintenance management is central to ensuring the functionality of technical systems throughout their entire life cycle. It encompasses the planning, implementation, and monitoring of preventive and corrective measures such as maintenance, inspection, repair, and optimization. The aim is to maximize the availability of equipment, minimize downtime, and comply with safety and efficiency standards. Modern technologies such as software-supported analyses and condition-based monitoring play a key role in this. Effective maintenance management also takes ecological requirements into account and makes a decisive contribution to a company's profitability.
We combine practice and research for robust, data-based maintenance. Our range of services includes maintenance planning and predictive maintenance, spare parts and master data management, system selection and implementation, operational excellence and TPM, organization and strategy development, knowledge management and AI chatbots, as well as safety and sustainability. With measurable results, practical tools, and methodological rigor, we ensure the availability of your production—today and tomorrow.
We support you in designing your maintenance management system so that you achieve exactly the plant availability you currently need.
Find out about the possibilities for your company!
The demands placed on manufacturing companies are becoming ever greater due to competitive pressure, energy and raw material costs, and regulations. Production facilities must therefore be highly available, operated sustainably, and continuously improved.
We offer you our expertise in maintenance management – from optimizing your maintenance organization to strategic decisions and technology selection to implementation.
A maturity assessment is a structured approach to assessing the current state of your maintenance across various dimensions and deriving targeted measures and a strategic development roadmap. Typical content includes:
By comparing the actual and target states, we develop prioritized packages of measures that will improve your maintenance with future viability and measurable results.
We provide structured support from the analysis of the status quo to the implementation of maintenance with sustainable future performance. In compact and proven workshops, we develop concrete measures and a robust roadmap based on a maturity analysis:
We guide your maintenance in a structured manner from the status quo to the target vision. The result is an integrated implementation plan that ensures the future readiness of your maintenance and delivers measurable impact.
"Maintenance is a key factor for digitalization and efficiency in production. With the Fraunhofer IML maturity model, you can find out what changes are necessary in the relevant areas of action to improve your company in the long term. And, of course, where you are already well positioned and how your maintenance management compares to other companies."
Dr. Thomas Heller, heads the Plant and Service Management Department at Fraunhofer IML
In modern maintenance management, digital technologies play a key role in optimizing processes and increasing efficiency. Smart tools and AI-supported software support solutions such as predictive maintenance to detect failures early and plan resources better.
Artificial intelligence evaluates historical data and creates real-time forecasts that make it easier to take preventive measures. Networked systems such as the internet of Things (IoT) enable continuous monitoring of plant conditions and seamless integration into existing planning software.
Digital assistance systems and AI chatbots support employees in troubleshooting and planning maintenance measures, accelerating processes and improving quality. Cloud-based software also enables flexible expansion and minimizes implementation costs. These technologies underscore how important data-driven and sustainable solutions are today for competitiveness and resilience.
Market overview and questionnaire-based selection process
Do you need suitable software for your maintenance and spare parts management? Fraunhofer IML can help you with this – impartially and based on a comprehensive market overview. You will benefit from Fraunhofer's many years of experience in the field of maintenance and its exchange with leading software manufacturers.
We support you in analyzing, selecting, and implementing the right software and technologies for your maintenance needs!
Efficient maintenance strategies help minimize resource consumption, reduce companies' carbon footprints, and promote sustainability. Regularly inspecting and maintaining technical systems and equipment can significantly extend their service life. In addition, preventive maintenance ensures that breakdowns are minimized and resources are used optimally. This means fewer replacements and thus a reduction in material and energy consumption.
Maintenance plays a particularly important role in the context of the circular economy and recycling: components that are maintained can be reused or upcycled, further reducing the ecological footprint. Modern technologies such as IoT and predictive maintenance also contribute to sustainability by providing data-driven insights for optimized maintenance and preventing unnecessary energy consumption, scrap, and waste. In addition, sustainable maintenance also evaluates processes according to social aspects such as working conditions and corporate responsibility, thereby addressing holistic sustainability goals.
The "Maintenance Free Factory (M2F)" research project aimed to transform maintenance processes in factories so that production time is maximized through digital and data-driven approaches without being affected by maintenance interruptions. It combines technological advances, such as AI and data-driven measures, with sustainable and human-centered principles to enable resilient and resource-efficient production.
Together with its project partner Nienstedt GmbH, Fraunhofer IML developed a roadmap for a selected machine type to establish a data and knowledge base for a predictive maintenance strategy. The findings from the project support the introduction of predictive maintenance by using machine data to initiate maintenance measures at an early stage, thereby increasing availability and cost-effectiveness.
The Fraunhofer-Gesellschaft's Smart Maintenance Community, an association of various Fraunhofer institutes in the field of maintenance, can help you to sustainably improve value creation in your production. Thanks to the cross-institutional network and as managing director of the community, we are able to contribute the necessary expertise in all areas.
A holistic approach to maintenance management: After the conceptual design phase, we are happy to assist you in implementing the strategies, concepts, and design measures developed according to your needs.
Maintenance management encompasses the planning, organization, implementation, and monitoring of all measures necessary to keep plants, machines, and buildings in a functional condition. The aim is to minimize downtime, extend the service life of operating resources, and ensure operational safety.
The four areas of maintenance are inspection, maintenance, repair, and improvement. They include activities such as checking the actual condition, maintaining the target condition, maintenance and replacement, and measures to increase the reliability and efficiency of equipment.
Maintenance according to DIN 31051 comprises all measures for preserving, restoring, and determining the functionality of a technical system. It is divided into four areas: inspection (determining the actual condition), maintenance (preserving the target condition), repair (restoring the target condition), and improvement (optimizing the technical system). The aim is to ensure the reliability and service life of the systems.
Preventive maintenance comprises measures aimed at preventing the failure of equipment or machines through planned interventions. A distinction is made between time-based measures, which are carried out at regular intervals, and condition-based measures, which are based on the monitoring of specific indicators. The aim is to ensure the reliability of the equipment and minimize unforeseen downtime.
A maintenance management system includes structures, processes, and software to coordinate all activities for planning, controlling, and optimizing maintenance processes. It encompasses the management of maintenance measures, work orders, inventories, and resources with the aim of minimizing downtime and maximizing the service life and utilization of equipment.
Fraunhofer Institute for Material Flow and Logistics IML