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We are the Motion-Miners GmbH from Dortmund. Making easier for companies to perform analyses regarding efficiency and ergonomics for process optimization.

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Key Indicator Method

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EAWS and the digital key indicator method: differences

Introduction: the digital leading feature method and EAWS

The digital key indicator method determines the instantaneous physical stress during manual work. At present, the (digital) Leitmerkmalmethode can be divided into the six variants: 1. manual lifting, holding and carrying of loads, 2. manual pulling and pushing of loads , 3. manual work processes, 4. exercise of whole-body forces, 5. body movement and 6. forced postures.  The key indicator method is available in both English and German. These methods, can now be performed and used in a digital manner (digital master feature method). In addition to the digital key indicator method, physical loads on the body can also be determined with the aid of EAWS. EAWS can also be performed in digital form. In the following, the origins, procedures, and reasons for the digital reference trait method and EAWS will be explained in more detail.

EAWS and the key indicator method: origin

Basics and necessity for the digital key indicator method method

The guidelines for the digital key indicator method are based on the Council Convention of 1990. The decision includes the minimum requirements for the protection of employees during manual handling of loads, in particular through the manual load back injuries can not be excluded.

The digital key indicator method is based on joint developments by the Federal Institute for Occupational Safety and Health (BAuA) and various scientific partners in coordination with the State Committee for Occupational Safety and Safety Technology (LASI).

The (digital) key indicator method LMM-HHT was first published in 2001, and the (digital) Leitmerkmalmethode LMM-ZS and LMM-MA appeared over the next 11 years. In October 2019, the BAuA last published the three extended leading characteristic methods “LMM-HHT”, “LMM-ZS” and “LMM-MA” and additionally the three new leading characteristic methods “LMM-GK”, “LMM-KB” and “LMM-KH”.

The digital key indicator method was developed to meet the requirements of the load handling regulation and to be able to organize the activities of the employees in a way that is suitable for people.

Zwei Männer in einem hellen Raum, die sich unterhalten und sich etwas erzählen.
Zwei Frauen sitzen in einer Halle und unterhalten sich. Eine hat einen Laptop auf ihrem Schoß.

EAWS and the Key Indicator Method: Fixed Guidelines

Development of the various methods and the digital lead feature method using Motion-Mining®.

The digital key indicator method enables an evaluation of the risk during a physical stress while manual movements such as lifting or carrying loads, pulling and pushing loads or similar are carried out in the work process.

Finally, the evaluation of the individual sub-activities is carried out from the evaluation of the determined key characteristics on the basis of a characteristic value and the determination of the weightings of the key characteristics, after the final multiplication with the time weighting.

Within the scope of Motion-Mining® ergonomics analyses, hidden optimization potentials are uncovered. The solution approach allows an evaluation of manual work, specifically the posture and movement of employees, using wearables and a deep learning algorithm, better known as artificial intelligence. Work processes are automatically and anonymously recorded, processed by artificial intelligence and converted into key performance indicators. Currently, we distinguish between more than 60 different movement sequences in our ergonomics analyses. Critical movements such as bending from the back, carrying, lifting, holding, overhead activities are considered in the ergonomics analysis. These movements are recorded in movement intervals, during the ergonomics analysis. In addition to the typical movements, vibrations and repetitions in particular can also be detected. Based on the data from the ergonomics analysis, overloads and permanent stresses can be identified and measures to avoid them can be derived. Methods such as EAWS are used as part of these analyses.

 

EAWS and the digital key indicator methode: gender differences.

Different approaches to the calculation

On average, the physical resilience of men is about 2/3 that of women. This difference between women and men is primarily due to a wide variety of factors, including different body proportions. In order that, for example, these factors can also be included in physically demanding activities for preventive health reasons, the weighting of loads in the manual lifting, holding and carrying of loads key indicator method is surveyed separately for men and women according to the corresponding table; with the same load weighting, women receive a higher rating. On the other hand, different multipliers apply to the various leading characteristic methods to compensate for the given differences. The intermediate score for men, is multiplied by a factor of 1.3 for women.

Ein Mann hat einen Block vor sich liegen und überträgt in diesen manuell einige Daten.
Drei Männer an einem Schreibtisch, die vor einem Laptop sitzen. Im Vordergrund steht eine Docking-Station. 01

EAWS and the digital master feature method: Digitization of Methods (EAWS).

Introduction of the method: EAWS

The digital key indicator method for the ergonomic assessment and evaluation of workplaces and processes can be used to provide a precise, generally understandable explanation of the strain on employees at specific workplaces with comparatively little effort. The digital key indicator method offers improvement approaches that can be derived directly from the collected data in order to integrate them into one’s own work planning at an early stage.

These methods, although initially conceived as an analog version, can now also be carried out and used in digital form (digital key indicator method). Implemented with the latest software development tools, EAWS-digital, for example, can easily and intuitively record and evaluate all the information required for the process.

Human simulation (digital human models) is also used in planning for the digital factory to clarify and question manual activities in 3D already in the planning phase. In planning, simulation is used to evaluate manual tasks both in terms of time economy (MTM process modules) and ergonomics (EAWS process). Human simulation for the investigation and optimization of assembly processes, time expenditure and ergonomics is thus increasingly finding its way into everyday operations.

For example, the increasing economic competition worldwide and the declining population growth in Europe are forcing an economic design of workplaces and process flows that takes human performance into account. A corresponding ergonomic instrument is EAWS (Ergonomic Assessment Worksheet), which includes all stages of the life cycle of goods (developed by MTM and IAD). In cooperation with well-known European companies as users of EAWS, a continuous improvement of the ergonomic assessment takes place. EAWS can be used to optimize ergonomics assessment with regard to posture, handling forces, manual load handling, load frequencies for the upper limb areas, e.g. in the form of the EAWS quick analysis or the automatic determination of movement data and forces (motion capturing). EAWS ensures a precise determination of the holistic stress at the workplace to achieve an ergonomic design quality for permanently healthy and productive work.

The demands on the industry are increasing due to advancing globalization and increasing digitalization. In order for the industry to keep up and hold its own in global and national competition in the future, it needs innovative approaches to solutions, such as the EAWS method, and continuous adaptation.

Through the use of EAWS, the physical loads on the body as well as the upper extremities can be determined at the respective workplace. Based on the understanding gained through the EAWS method, workplaces can be ergonomically designed as a whole.