A Novel Approach to Ergonomic Risk Analyses

Abstract

The ergonomics and comfort of employee’s health and working conditions are reflected in the efficiency of the work. For this reason, the analysis and evaluation of ergonomic risks in the working environment is of great importance. A novel REBA-FMEA models-based Pythagorean Fuzzy-VIKOR integrated model approach is introduced to assess ergonomic risks. The proposed methodology incorporates PF-VIKOR methodologies based on integrated REBA-FMEA. The following 10 phases comprise the suggested method. A production line case study was conducted. An assessment is conducted on six distinct hazardous occupational positions. The REBA method is used to compute the risk ratings associated with these hazards. The most optimal outcome in the assessment of multi decision-makers with uncertainty in the risk analysis of ergonomic working positions with the novel technique was obtained by computing Pythagorean fuzzy. The novel model overcomes the limitations of traditional methods with the integration of reliability engineering approaches and Pythagorean fuzzy logic. Assessments of ergonomic risks often involve subjective judgments, especially when considering human factors. Different individuals may perceive risks differently, and this subjectivity can introduce variability into the assessment process. The novel method proposed in this study fills the gap in the literature on the subjectivity of decision makers in evaluations.

1. Introduction

The pursuit of occupational health and safety in workplaces has led to a heightened emphasis on ergonomic risk analysis. The growing recognition of the impact of poor ergonomics on employee well-being and productivity has prompted researchers to explore innovative methodologies. Among these, the integration of fuzzy logic into the Rapid Entire Body Assessment (REBA) method has emerged as a promising avenue for a nuanced and comprehensive understanding of ergonomic risks. Fuzzy logic, a mathematical approach that handles uncertainty and imprecision, brings a unique dimension to the conventional REBA method by allowing for more flexible and context-aware risk assessments.

In today’s workplace, occupational health and safety are top priorities, with a growing emphasis on reducing ergonomic hazards to improve employee well-being and output. A crucial method for determining and resolving risk factors for musculoskeletal injuries and diseases is ergonomic risk analysis. The Rapid Entire Body Assessment (REBA), one of the well-established methods, has become well-known for being a useful instrument for assessing the ergonomic risks connected to different employment. The REBA method, which was first presented by Hignett and McAtamney in 2000, offers a methodical framework for evaluating postural loading and related hazards in work environments [1].

REBA method categorizes tasks based on predefined postural and force criteria, assigning a risk score that correlates with potential musculoskeletal stress. The versatility of REBA has led to its widespread application across various industries, including manufacturing, healthcare, and office environments, underscoring its adaptability to diverse work contexts [1, 2].

Fuzzy logic systems are a prominent modeling approach because they allow for more effective processing of uncertain and complex ergonomic data [3, 4, 5]. Fuzzy logic can be used in modeling and analyzing ergonomic factors that involve uncertainty, helping to achieve results that are closer to real-world conditions.

Ergonomics, as a multidisciplinary science, plays a pivotal role in designing work environments that promote optimal human performance while minimizing the risk of musculoskeletal disorders and injuries. The REBA method, a widely utilized tool for ergonomic risk assessment, traditionally relies on precise categorizations and fixed parameters. However, the incorporation of fuzzy logic introduces a degree of adaptability, enabling the assessment of ergonomic risks in situations where conventional methods may fall short.

In the workplace, addressing ergonomic risks is imperative to enhance employee well-being and productivity. Ergonomics, the science of designing work environments to optimize human performance and well-being, has become increasingly crucial in mitigating the adverse effects of repetitive and strenuous tasks [6].

The motivation of this study to contribute valuable insights that can inform both researchers and practitioners in the field, fostering a safer and healthier work environment for diverse industries and occupations.

Conventional ergonomic risk assessment methods frequently concentrate on certain criteria or aspects. By addressing the shortcoming of considering several criteria at once, an integrated model such as Pythagorean Fuzzy-VIKOR may seek to provide a more comprehensive assessment of ergonomic risks. In most ergonomic assessments, dealing with ambiguous and inaccurate information is a requirement. The Pythagorean Fuzzy-VIKOR model may close a gap in the handling of ambiguous data by assisting in the management of uncertainty and vagueness in ergonomic risk assessments. The decision support gap for ergonomic interventions may be filled by the integrated model. REBA-FMEA based integration of PF-VIKOR method has the potential to offer a methodical approach to ergonomic risk management decision-making, facilitating the identification and implementation of interventions.

This study aims to provide a comprehensive overview of the strengths, limitations, and future directions in utilizing REBA for ergonomic risk assessment. The synthesis of existing knowledge will contribute to the ongoing efforts to create safer and healthier work environments across various industries.

2. Methodology

2.1 Rapid entire body assessment (REBA)

The body is divided into two main components using the REBA method, which was first presented by Hignett and McAtamney [1]. The first part is made up of the neck, trunk, and legs. Table A in the REBA worksheet is used to aggregate their scores into a single value. The scores from the upper arm, lower arm, and wrist in the second section are added together using Table B from the REBA worksheet. Table C is used to combine the scores from these tables once the coupling and force scores have been added. The score related to the kind of action is added last.

The final REBA score has a range from one to greater than eleven; the higher the final score is, the greater the hazards of Work-related Musculoskeletal Disorders will be.

Table 1 displays these scores together with the corresponding action levels.

REBA score	Risk level	Action level
1	Negligible	Not necessary
2–3	Low	It may be necessary
4–7	Middle	Necessary
8–10	High	Necessary soon
11–15	Very high	Needed immediately

Table 1.

REBA risk and action levels [1].

2.2 Failure mode and effects analysis (FMEA)

FMEA is an effective problem-prevention method that complements a variety of engineering and reliability approaches. FMEA has a broad impact on identifying possible product/process failures and planned actions to those failures/hazards, which enhances effective risk management [7].

Failure mode is defined in FMEA as the way in which a product or process can fail. Design faults, mistakes made by people, unpredictable processes, and other unforeseen reasons could be to blame for this. The effects of these failures are related to the possible repercussions of these failures [8].

Risk priority number (RPN) is calculated by multiplying the three parameters “Occurrence”, “Severity” and “Detectability” as follows.

$$\mathrm{RPN}={\left[\text{Occurrence}\right]}^{\ast }{\left[\text{Severity}\right]}^{\ast }\left[\text{Detectability}\right]$$E1

Decision makers/experts rate these three parameters on a scale of 1 to 10 based on assessment standards. As it is a measure of the risk of RPN, it can be used to rank errors/hazards and prioritize actions. Actions are taken by prioritizing the error/hazard with the highest RPN.

2.3 Pythagorean fuzzy sets and VIKOR (PF-VIKOR)

Among the various extensions of fuzzy set theory, Pythagorean Fuzzy Sets (PFS) have become a powerful tool because they allow for more flexibility and accuracy in capturing the subjective and imprecise information of decision-makers [9, 10].

PFS, initially put forth by Atanassov and then extended by Yager, provide a more expansive framework for handling ambiguity and vagueness by permitting a greater range of membership and non-membership degrees, so easing the limitations imposed by Intuitionistic Fuzzy Sets (IFS).

Opricovic’s VIKOR [11] technique offers a methodical and quantifiable approach to multi-criteria decision making, whereas PFS offers a framework for handling ambiguity. It arranges options in order of compromise.

2.3.1 Pythagorean fuzzy sets

First definition: X is a set inside a discourse universe. The shape of a Pythagorean fuzzy set P is as follows [12]:

$$P=\left\{<x,P\left({\mu }_P\left(x\right),v_P\left(x\right)\right)>|x\in X\right\}$$E2

where μP(x): X ›→ The membership level is indicated by [0, 1], and vP(x): X ›→ The degree of nonmembership of element x ∈ X to P is represented by [0, 1], and it takes for each x ∈ X.

$$0\le {\mu }_P{\left(x\right)}^2+v_P{\left(x\right)}^2\le 1$$E3

For every PF set P and x ∈ X, πP(x) = = $\sqrt{1-{\mu }^{2}P\left(x\right)-v^{2}P\left(x\right)}$ is the degree of x’s indeterminacy with respect to P.

Second definition: Considering two fuzzy Pythagorean numbers $P_1$ = P(${\mu }_{p1}$, $v_{p1}$) and $P_2$ = P(${\mu }_{p2}$, $v_{p2}$), and > 0, The operations listed below are defined [12]:

$$P_1\oplus P_2=\mathrm{P}\left(\sqrt{{\mu }_{p1}^2+{\mu }_{p2}^2-{\mu }_{p1}^2{\mu }_{p2}^2},v_{p1}{v}_{p2}\right)$$E4

$$P_1\oplus P_2=\mathrm{P}\left({\mu }_{p1}{\mu }_{p2},\sqrt{v_{p1}^2+v_{p2}^2-v_{p1}^2{v}_{p2}^2}\right)$$E5

$$\mathrm{\lambda }{P}_1=\mathrm{P}\left(\sqrt{1-{\left(1-{\mu }_{p1}^2\right)}^{\lambda }},{\left(v_{p1}\right)}^{\lambda }\right),\mathrm{\lambda }>0$$E6

$$P_1^{\lambda }=\mathrm{P}\left({\left({\mu }_{p1}\right)}^{\lambda },\sqrt{1-{\left(1-v_{p1}^2\right)}^{\lambda }}\right),\mathrm{\lambda }>0$$E7

Third definition: Considering two fuzzy Pythagorean numbers $P_1$ = P(${\mu }_{p1}$, $v_{p1}$) and $P_2$ = P(${\mu }_{p2}$, $v_{p2}$) the following is the determination of a natural quasi-ordering on the Pythagorean fuzzy numbers [12]:

$$P_1\ge P_2\text{only if}{\mu }_{p1}\ge {\mu }_{p2}\text{and}{v}_{p1}\le v_{p2}$$E8

Ref. [12] provide a scoring mechanism to compare the following two Pythagorean fuzzy numbers of magnitude:

$$\mathrm{s}\left(P_1\right)={\left({\mu }_{p1}\right)}^2-{\left(v_{p1}\right)}^2$$E9

Fourth definition: Using the recommended scoring functions, the following laws [12] are defined for the Pythagorean fuzzy numbers given above in order to compare two of them:

$$\begin{array}{c}\text{If}\mathrm{s}\left(P_1\right)<\mathrm{s}\left(P_2\right),\text{then P1}\prec \text{P2}\\ \text{If}\mathrm{s}\left(P_1\right)>\mathrm{s}\left(P_2\right),\text{then P1}\succ \text{P2}\\ \text{If}\mathrm{s}\left(P_1\right)=\mathrm{s}\left(P_2\right),\text{then P1}\sim \text{P2}\end{array}$$E10

2.4 The suggested integrated REBA-FMEA based PF-VIKOR method

Integrated REBA-FMEA based PF-VIKOR approaches are incorporated into the suggested methodology. The suggested method is broken down into the following ten steps:

Step 1: Define hazardous working positions and ergonomic risks.

Step 2: Calculate the REBA score of these risks.

Step 3: Matching the REBA score with the severity parameter of FMEA according to a seven-point Pythagorean fuzzy linguistic scale.

Step 4: Determine weight of occurrence and detectability parameters.

Step 5: Determine weight of decision makers.

Step 6: Evaluate the occurrence and detectability parameters by decision makers with 7 PF linguistic terms.

Step 7: The VIKOR S and R values should be calculated using VIKOR.

Step 8: Compute VIKOR Q values.

Step 9. Using the S, R, and Q values as a guide, order the risk priority numbers.

Step 10: Based on the risk priority number, decide on control and preventative measures.

3. Case study

A case study was carried out on the production line considered in the study of Tarakçı [6]. The study population, hazardous working positions and REBA scores were expanded with reference to this study [6].

For step 1 and step 2, the data in Tarakçı’s study [6] are taken as reference. Six different hazardous working positions are assessed. Risk scores of these hazards are calculated with the REBA method.

For step 3, the matching of the REBA score to the severity parameter of the FMEA according to the seven-point Pythagorean fuzzy linguistic scale is as shown in Table 2 below.

REBA score	Meaning at PF	Fuzzy numbers (u,v)
1	Very Low (VL)	(0.15,0.85)
2–3	Low (L)	(0.25,0.75)
4–5	Moderately Low (ML)	(0.35,0.65)
6–7	Medium (M)	(0.50,0.45)
8–9	Moderately High (MH)	(0.65,0.35)
10–12	High (H)	(0.75,0.25)
13–15	Very High (VH)	(0.85,0.15)

Table 2.

Matching table of REBA score and severity parameter according to PF linguistic scale.

For Step 4, the weights of the occurrence and detectability parameters are Wo = 0.3641, Ws = 0,3369, and Wd = 0.2989 obtained in Tarakçı’s PhD thesis [13].

For step 5, teams representing occupational health and safety (DM1), production line workers (DM2), and managers (DM3) made composed the decision-making committee.

For every decision maker, there were several weights assigned. Assigning decision makers is done by the computational method outlined in [14]. A weight (wdm > 0 and ∑wdm = 1) is assigned to each decision maker. The allocated weights are, in order, wDM1 = 0.5, wDM2 = 0.3, and wDM3 = 0.2.

For Step 6, decision makers rated the occurrence and detectability parameters with seven-point Pythagorean fuzzy logic linguistic terms as shown below (Table 3).

Meaning	Corresponding Pythagorean fuzzy number (u, v)
Very Low (VL)	(0.15,0.85)
Low (L)	(0.25,0.75)
Moderately Low (ML)	(0.35,0.65)
Medium (M)	(0.50,0.45)
Moderately High (MH)	(0.65,0.35)
High (H)	(0.75,0.25)
Very High (VH)	(0.85,0.15)

Table 3.

The seven-point Pythagorean fuzzy linguistic scale [15].

3.1 Case result

S, R, and Q values are found for Steps 7, 8, and 9. The formulation sets were computed using PyCharm Community program [16].

Table 4 shows the Q values and risk priority numbers for 6 hazardous working positions. Risk priority numbers are ranked according to Q values. The lowest Q value indicates the most important risk. The highest Q value is the risk with the lowest priority score.

Hazard working position	Rank	Q value
HWP1	6	0.986
HWP2	1	0.0
HWP3	3	0.804
HWP4	2	0.382
HWP5	4	0.845
HWP6	5	0.959

Table 4.

Q values and ranking orders for hazardous working positions.

As can be seen from Table 4 and Figure 1, the three most important and prioritized hazardous working positions are HWP2, HWP4 and HWP3 respectively. In addition, the least hazardous working positions are HWP1, HWP6 and HWP5 respectively.

Figure 1.

Sequencing on the Q value curve.

4. Conclusion

In conclusion, the integration of Pythagorean fuzzy with the REBA method has proven to be a powerful approach in the analysis and evaluation of ergonomic risks within various workplace settings. The amalgamation of these two methodologies addresses the inherent uncertainties and complexities associated with ergonomic data, providing a more nuanced and adaptable framework for risk assessment.

The application of fuzzy logic allows for a flexible representation of imprecise and uncertain ergonomic variables, which are prevalent in real-world occupational environments. By incorporating Pythagorean fuzzy into the REBA method, the analysis gains the capability to handle ambiguous data and account for the vagueness inherent in human postures and movements.

The findings of this study highlight the significance of utilizing a comprehensive approach that combines the precision of the REBA method with the adaptability of Pythagorean fuzzy logic. The integrated model not only facilitates a more accurate assessment of ergonomic risks but also enhances the ability to prioritize and manage these risks effectively.

Moreover, the outcomes of the analysis contribute to the broader field of ergonomics by providing insights into the dynamic nature of occupational risk factors. The adaptability of Pythagorean fuzzy logic-integrated REBA method ensures that it can be applied across diverse industries and work scenarios, making it a valuable tool for researchers, practitioners, and organizations seeking to optimize workplace conditions.

While this study showcases the potential of the integrated approach, it is essential to acknowledge its limitations. Further research could explore refinements in the fuzzy logic model and the validation of the integrated method in various industrial contexts.

This study demonstrates that the Pythagorean fuzzy-VIKOR-based novel scoring system was more sensitive to changes in input variables than the conventional approaches. When assessing MSDs, one might apply this proposed model.

The PF-VIKOR model could help handle ambiguity and vagueness in ergonomic risk assessments, which could fill a gap in the handling of confusing data. The integrated model may close the gap in decision support for ergonomic interventions. REBA-FMEA based integration PF-VIKOR method has the potential to provide a systematic approach to ergonomic risk management decision-making, making the identification and execution of interventions easier.

In conclusion, Pythagorean fuzzy-integrated REBA method offers a promising avenue for advancing ergonomic risk analysis. The synergy between the precision of REBA and the flexibility of fuzzy logic provides a robust foundation for future developments in optimizing workplace environments, promoting employee well-being, and fostering sustained productivity.

Conflict of interest

No conflict of interest was declared by the authors.

Research limitations

The research is limited to the opinions and assessments of experts (decision makers) and the hazardous working positions and space/time dimensions addressed in the study.