MTM and Motion-Mining®: An ideal Duo

How can the best of proven methods and modern technologies be combined to ensure maximum efficiency and future security?

This central question is particularly relevant as companies face the challenge of continuously improving their processes in order to remain competitive. Accurate analysis tools are critical to success, but it's not just about using the right methods. The real progress lies in combining proven processes with modern technologies to exploit the full potential of work processes. Only this integrated approach leads to maximum efficiency and future security.

Two such tools that play an important role in this interplay are Methods-Time Measurement (MTM) and Motion-Mining®. With decades of expertise, the MTM method has established itself as a proven standard for analyzing and optimizing work processes. But Motion-Mining® brings a novel, digital approach to process analysis. This technology uses cutting-edge sensors and machine learning to automatically record and evaluate anonymized movement data at the workplace. Instead of rigid guidelines, Motion-Mining® analyses the real movements and actions of employees in order to precisely identify optimization potential. In this way, work processes can be improved in a targeted manner and productivity increased.

By systematically breaking down work processes into their basic movements, MTM provides exact data on how long individual tasks should take. These target data serve as a benchmark to identify deviations from the current situation and to correct them in a targeted manner. Motion-Mining®, in turn, provides precise actual data that reflect actual working conditions.

Together, both methods form a comprehensive basis for process understanding: MTM defines how processes should ideally run, while Motion-Mining® shows how they actually work. The combination not only makes it possible to precisely identify discrepancies between target and actual conditions, but also lays the foundation for well-founded decisions that lead to real improvements. Tradition and innovation go hand in hand here, as traditional procedures blend seamlessly with modern approaches. Together, they provide the necessary tools not only to develop a deep understanding of one's own work processes, but also to improve/optimize them efficiently and in the long term.

MTM: An Overview of Methods-Time Measurement

MTM is an internationally recognized method for analyzing and optimizing work processes. MTM was used in the 1940s develops and serves to break down manual work processes into their elementary movements and assign standard times to them. The aim is to increase the efficiency of work processes while taking ergonomic aspects into account.

The method is based on the assumption that every manual activity can be carried out optimally in a specific unit of time. These time units, also known as time measurement units (TMU), enable companies to define precise target data for various work processes.

The broad applicability is a major advantage of MTM. In the manufacturing industry, e.g. in automotive production or mechanical engineering, MTM is used to standardize assembly processes and increase efficiency. MTM is also used in aerospace to make complex assembly processes more precise and efficient.

In the area of logistics and distribution, MTM helps to optimize processes such as order picking, packaging and shipping while ensuring ergonomic working conditions. Through this versatility, MTM not only contributes to increasing productivity in all of these industries, but also to the continuous improvement of working conditions.

MTM's core principles:

- Standardised time measurement

MTM provides an objective basis for evaluating workflows by setting precise timelines for every fundamental movement.

- Detailed analysis

The method analyses the time required for each activity and thus identifies inefficiencies that can be remedied through targeted improvement measures.

- Optimization potential

By eliminating unnecessary movements and improving working methods, MTM helps to increase efficiency and reduce waste.

MotionMiners: How IST data is collected and provided by Motion-Mining®

Motion-Mining® is a technology for automated collection and analysis of movement data. It gives companies a precise insight into manual work processes by recording the movements of employees and vehicles in real time. The technology combines specialized wearables and sensors with advanced data analysis to provide detailed information about work processes.

It consists of two main components: the hardware and software, the MotionMiners PROCESSINTELLIGENCE (MPI). The sensors provide precise data on activities and movements. The MPI software processes this data using AI-based algorithms and visualizes the results in an understandable way. This enables a detailed analysis of work processes and helps to recognize patterns and inefficiencies. With the collected and analyzed data, companies can take targeted measures to optimize processes and improve ergonomics.

Benefits of MotionMiners

- Holistic analysis

Motion-Mining® enables a comprehensive analysis of work processes using precise actual data. This data provides insights into efficiency and ergonomics potential and helps to make targeted improvements. You can find further details in our Technology fact sheet.

- Data-based decisions

The automated collection and processing of movement data continuously delivers precise results that contribute to well-founded decision-making to increase efficiency. Find out more about the Data analysis method on our website.

- Integration and flexibility

The technology can be quickly and easily integrated into existing work processes without the need for complex IT integration. In addition, Motion-Mining® can be flexibly adapted to different work environments and scaled for different processes and locations.

Linking actual and target data: MTM and MotionMiners working together

Looking at the question of how proven methods and modern technologies can be combined, it is worth looking at the combination of MTM and Motion-Mining®. As described, both methods represent different approaches for analyzing and improving work processes.

MTM is based on the systematic measurement and standardization of working hours by defining precise timelines for basic movement processes and focuses on the TARGET side by defining benchmarks and standards that serve as objectives for efficient work processes.

Motion-Mining®, on the other hand, aims to collect and analyze actual data. Using modern sensors and AI-based analysis, Motion-Mining® provides precise real-time data about real work processes, which show how the work is actually carried out and thus enables precise evaluation and optimization.

By using Motion-Mining® to record actual movement data in combination with machine learning algorithms and the MTM method (target times), it is possible to analyse work processes in detail. Through machine learning, patterns and trends in the data can be identified and compared with theoretical target times. This combination of methods creates a solid basis for evaluating actual loads and efficiency by taking into account both real working conditions and theoretical standards.

The use of machine learning methods to analyze actual data makes it possible to precisely identify deviating patterns and inefficiencies. This not only helps to evaluate current work processes, but also to develop tailor-made optimization strategies. The combination of precise actual data from Motion-Mining® with the exact target times from MTM offers a holistic view of work processes and enables companies to take targeted measures to increase efficiency and improve processes.

The combination of MTM and Motion-Mining® offers significant advantages as it combines the strengths of both methods. While MTM offers a well-founded theoretical basis with standardized time limits, Motion-Mining® provides concrete insights into actual practice through precise actual data. This synergy enables a comprehensive and in-depth analysis of work processes.

Synergies

The combination of MTM and Motion-Mining® is particularly useful in the area of ergonomics, as the data required for an evaluation with the EAWS (Ergonomic Assessment Worksheet) can be significantly clarified using Motion-Mining® technology. While MTM provides basic ergonomic data, Motion-Mining® enables the detailed recording of specific movement patterns, such as the frequency of bending movements or the time spent at ergonomically unfavorable working heights. This precise IS data can then be incorporated into the EAWS assessment to ensure a well-founded and more comprehensive ergonomic analysis.

In addition, Motion-Mining® provides valuable support for MTM target analysis. While MTM traditionally uses data from limited observation periods, Motion-Mining® enables continuous collection over longer periods of time. This comprehensive actual data, e.g. on routes and movement frequencies, can be used to refine target times. This not only depicts processes more realistically, but also allows the derived target times to be adjusted more precisely to support effective and data-based process optimization.

Overall, the combination of MTM and Motion-Mining® represents a powerful approach for holistic process optimization that covers both theoretical and practical aspects of work analysis. It offers companies the opportunity to closely examine, evaluate and specifically improve their work processes in order to optimize both efficiency and working conditions.

Practical example

A current example of the successful integration of digital movement data, artificial intelligence (AI) and MTM methods can be found in the current project of MTM ASSOCIATION e.V. and MotionMiners GmbH. As part of a comprehensive preliminary study commissioned by the Federal Ministry of Labour and Social Affairs, the “last mile” of parcel and network delivery was examined in detail.

The focus is on the critical phase of the supply chain, in which parcels, some weighing over 30 kg, are loaded into the delivery vehicle and delivered to the end customer. The aim of the study is to create an objective database that makes it possible to define performance corridors for deliverers and to sustainably improve working conditions while taking occupational safety into account.

This is how the project was carried out:

By using Motion-Mining®, the movement data of deliverers is recorded and analyzed. Machine learning algorithms process this IS data to provide accurate insight into the actual workload. With the help of the MTM method, this data is compared with defined target times, which enables an accurate assessment of workload and efficiency. This combination of real-time data and standardized timelines provides a sound basis for optimizing work processes and defining realistic performance corridors.

Relevance and transferability

The project has not only opened up new perspectives on the working conditions of parcel delivery companies, but has also shown how effective the combination of actual data and target times can be. The methodology provides valuable insights that go beyond the area of package and network delivery. It can be transferred to other industries and industrial applications in order to create transparency and improve processes there as well.

On the MTM SUMMIT 2024 Dr. Sascha Feldhorst and Prof. Dr. Peter Kuhlang will present the results and methodology of the project and explain how the innovative combination of movement data and MTM models can also benefit other companies.

Hear more about the advantages of the method and in-depth insights into practice in the interview podcast ahead of the event and the joint presentation. Click here to listen to the podcast!

Conclusion

The combination of MTM and Motion-Mining® offers companies powerful tools for comprehensive analysis and optimization of work processes. Through the precise collection and evaluation of actual data in conjunction with the standardized target data from MTM, companies can not only continuously improve their efficiency but also their working conditions. This integrated methodology makes it possible to develop targeted measures for process optimization and to achieve sustainable improvements in the long term.

‍