Dynamic Ergonomic Risk Assessment with REBA and Fuzzy Multi-Criteria Decision Making: Addressing Repetitive Movements

Safiye Turgay; Samet Özyurt

doi:10.31181/sdmap4157

Authors

Safiye Turgay Department of Industrial Engineering, Faculty of Engineering, Sakarya University, Turkiye Author https://orcid.org/0000-0002-8706-0801
Samet Özyurt Department of Industrial Engineering, Faculty of Engineering, Sakarya University, Turkiye Author https://orcid.org/0009-0004-0919-550X

DOI:

https://doi.org/10.31181/sdmap4157

Keywords:

REBA (Rapid Entire Body Assessment); Fuzzy Multi-Criteria Decision Making (FMCDM); Repetitive Movements; Musculoskeletal Disorders (MSDs), Occupational Health and Safety, Risk Assessment, Human Factors Engineering, Dynamic Decision-Making

Abstract

Repetitive work tasks are a prominent cause of musculoskeletal disorders (MSDs) that generate losses in productivity, absenteeism, and extended health risks. Static ergonomic evaluation tools such as the Rapid Entire Body Assessment (REBA) are very informative but insensitive to varying and dynamic working conditions. With the incorporation of linguistic uncertainty and expert judgment through fuzzy logic, the new model enables more sophisticated risk factor prioritization for various work operating conditions. A case study in a production environment demonstrates the effectiveness of this hybrid model in identifying high-risk postures and guiding proactive ergonomic countermeasures. Repetitive work activities are responsible for a considerable percentage of MSDs, which adversely impact worker health, production levels, and organizational efficiency. Conventional ergonomic analysis instruments like REBA are useful in ascertaining posture-related risks but are generally lacking in depicting the dynamic and hazardous nature of real working conditions. This study aims to develop an advanced ergonomic risk assessment model by integrating REBA and Fuzzy Multi-Criteria Decision Making (FMCDM) to provide a more flexible and comprehensive evaluation of hazards in repetitive tasks within an industrial setting. The model combines traditional REBA scoring with fuzzy logic to handle linguistic imprecision and expert judgment. Major ergonomic parameters such as posture risk, repetition frequency, and applied force are examined in fuzzy settings to better replicate real-time variability. A case study was conducted in a factory on three repeated tasks—lifting boxes, sorting products, and labeling goods—to test the efficiency of the model. The integrated REBA-FMCDM methodology successfully identified high-risk postures and tasks, allowing for more detailed prioritization of ergonomic interventions. The fuzzy logic model enabled the use of expert opinion and context variation, providing superior and adaptive decision-making compared to static assessments. The dynamic REBA-FMCDM model offers a straightforward yet robust ergonomic risk assessment tool for environments with variability and subjectivity. By adding fuzzy logic to traditional evaluation methods, the model improves risk prioritization and enables timely and effective implementation of occupational health and safety interventions.

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