Keywords

1 Introduction

Any company intending to compete entirely in their respective industries should prioritize employee wellness [1, 2]. The workplace has a direct impact on employee health. The productivity and efficiency of work organizations have an impact on employee wellness as well. Because of the critical role of humans in industry, these facts demonstrate that workplace ergonomics plays an essential part in long-term sustainability. Workplace ergonomics, linked to safety and health concerns, demand an organizational strategic direction to attain long-term viability [3]. Furthermore, a better working environment for workers is linked to long-term development [4]. Workers will be endangered by work-related musculoskeletal diseases (WMSDs) if their physical abilities do not match the job’s physical requirements due to inadequate workplace ergonomics. WMSDs are painful muscle, tendon, and nerve disorders. Because of their widespread use and detrimental impact on job productivity, WMSDs have resulted in economic losses worldwide [5]. As a result, holistic systems and strategies linking workplace ergonomics management to long-term organisation growth are required.

The importance of researching workplace ergonomics risk factors and creating musculoskeletal disorders (MSD) prevention techniques has grown. The MSD hazard and risk factors can be decreased early in developing a new product and process employing risk management in the organization [6]. In addition, the majority of MSD disorders are caused by a combination of risk factors [7]. To recognise critical risks and eliminate crucial risk factors, occupational safety and health (OSH) practitioners need a decision instrument that includes a systematic, participative ergonomics and risk-based method. As a result, this study employs a systematic approach using the analytic hierarchy process (AHP), one of the multi-criteria decision analysis techniques (MCDA), in a decision-making aid for multi-factorial investigations. The AHP is a structured approach based on mathematics and psychology for preparation and analysing complex decisions. AHP also is an effective and powerful technique for decision making [8]. Moreover, AHP can measure and synthesise many criteria [9] and helpful in complex issues [10].

In the ergonomics field, knowledge-based systems (KBS) have been used in a variety of ways. KBS is a software program that generates and employs a knowledge base to undertake complicated issues. KBS can collaborate with or take the place of human experts in workplace ergonomics assessments [11]. KBS, or computer-based information systems, can represent expert knowledge. KBS can achieve the level of skill required to resolve workplace hazard situations at an expert level [12]. Moreover, using KBS in ergonomics assessments can help workers rapidly and properly discard various risk concerns [13]. Employees and employers must have ergonomic knowledge and be informed of workplace ergonomics assessments to avoid the risk. Hence, KBS is critical in promoting proactive ergonomics to improve an organization’s long-term sustainability in work activities, workplaces, and working environments.

Many academics have worked on KBS to evaluate the ergonomic risk associated with WMSD [14,15,16]. Most KBSs for ergonomics assessments operate on a stand-alone mode that performs its function without a network link. Consequently, this study combines the AHP approach, KBS, and a web-server application to construct a web-based ergonomics assessment system (W-BEAS). Web-based apps are applications that use a web browser to communicate with a remote server. W-BEAS is a computer software tool that uses the internet to imitate ergonomics experts’ critical thinking abilities.

This study, which contributes to the ergonomics risk assessment literature and practice, demonstrates the use of a W-BEAS in deciding the essential workplace risk factors connected to WMSD.

Table 1 The AHP uses a pair-by-pair comparison scale
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Table 2 W-BEAS validation members’ demographic information
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2 AHP and KBS are Integrated to Create a W-BEAS

2.1 AHP Technique for Critical Risk Factor Prioritization

Workplace risk factors connected to physical, organizational, and psychosocial components have been linked to WMSDs, which reduce workers’ wellness and well-being [2, 17,18,19]. A macro-ergonomic assessment approach includes individual (IF), organizational (OF), physical (PhyF), and psychosocial (PsyF) variables to assess the critical WMSD risk factors. There are four primary factors and 26 sub-factors in the AHP (refer to Fig. 1 and Table 3). Figure 1 describes the formation of the AHP. Procedures to detecting ergonomic risk factors and sub-factors and a pairwise comparison to determine priority weight are included in the AHP structure.

Fig. 1
An illustration depicts the hierarchy for critical risk factors correlated to W M S D. There are four primary factors and 26 sub factors. It describes the formation of the A H P.

The hierarchy for critical risk factors correlated to WMSD

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Table 3a and b The RPN technique and the W-BEAS prioritized risk factors are compared
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The AHP model includes procedures for identifying ergonomic risk factors and sub-factors and a pairwise comparison to determine weight. The AHP process included the following steps:

  1. 1.

    Designing a decision form and conducting pair-wise comparisons, users were required to determine the significance of risk factors between certain ergonomic factors and sub-factors. Table 1 presents a mathematical scale for pairwise comparisons. This method included the formation of the square matrix \(A_{n\, \times \,n}\). Equation 1 represents the \(A_{n \, \times \, n}\).

    $$A_{n\, \times \,n} = \left[ {a_{11} { }a_{21} { }a_{12} { }a_{22} { } \cdots { }a_{1n} { }a_{2n} { }\begin{array}{*{20}c} \vdots & \ddots & \vdots \\ \end{array} { }a_{n1} { }a_{n2} { } \cdots { }a_{nn} { }} \right]$$
    (1)

where \(a_{ij}\) was the factor in the pair-wise comparison matrix. It delivered the comparative importance of criterion i concerning criterion j. Matrix \(A_{{n{ }\, \times { }\,n}}\), \(a_{ij} = 1\) when i = j and \(a_{ij} = \frac{1}{{a_{ij} }}\) when i ≠ j.

  1. 2.

    Combining the results. The method outputs a vector of local weights or priorities for every risk factor based on the overall goal. The Geometric mean (\(GM_{i}\)) was used to calculate the aggregate expert judgments.

    $$GM_{i} = \left[ {\mathop \prod \limits_{j}^{n} a_{ij} } \right]^{\frac{1}{n}} ,$$
    (2)

where n = number of members.

  1. 3.

    Determining the local weights. The Eqs. (3) and (4) can define the principal Eigenvector and Eigenvalues individually.

    $$w_{i} = \frac{{GM_{i} }}{{\mathop \sum \nolimits_{i = 1}^{n} GM_{i} }},$$
    (3)
    $$\lambda_{{{\text{max}}}} = \frac{{\mathop \sum \nolimits_{i}^{n} w_{i} }}{n},$$
    (4)

where n = number of factors.

  1. 4.

    Confirming the pair-wise comparison’s consistency. Equations (5)–(7) can be used to describe the consistency index (CI) and consistency ratio (CR).

    $$CI_{{}} = \frac{{\lambda_{{{\text{max}}}} - n}}{n - 1},$$
    (5)
    $$\lambda_{{{\text{max}}}} = \mathop \sum \limits_{i = 1}^{n} \left[ {\left( {\mathop \sum \limits_{i = 1}^{n} GM_{i} } \right)\left( {w_{j} } \right)} \right],$$
    (6)

where \(\lambda_{{{\text{max}}}}\) is the maximum eigenvalue and n is the number of factors

$$CR_{{}} = \frac{{Consistency{ }\,index{ }\left( {CI} \right)}}{{Random{ }\,index{ }\left( {RI} \right)}}\, \le \,0.{1}0,$$
(7)

If the CR value is less or equal to 0.1, it is acceptable. It must be replaced if the subjective judgment is more than 0.1 or 10%.

2.2 Integrated W-BEAS Design

W-BEAS’ core structure consists of a user interface (UI), an AHP inference engine (IE), and a knowledge base. W-BEAS was served with XAMPP as the AHP IE, and the database used MySQL. W-BEAS contains three parts: UI, a web server (WBS), and a KBS database, as presented in Fig. 2.

Fig. 2
An illustration depicts the structure of W- B E A S. It contains three parts. 1. Mode of comparing attributes. 2. Interference engine A H p. 3. Knowledge -based: ergonomics risk factors and control options.

The structure of W-BEAS

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The integrated web-based system is divided into five main components, as shown in Fig. 3:

Fig. 3
An illustration depicts the parts of the W-B E A S integrated system and their functionalities. Three main components are given. User, User interface and database or My S Q L. Four functions are described with factors. 1. Process of input. 2. Processing of data. 3. Process of output. 4. Results.

Parts of the W-BEAS integrated system and their functionalities

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  1. a.

    Database—after user retrieval, all factor information is recorded and stored in the knowledge database.

  2. b.

    Input process—refers to the user’s preference for studying data retrieved from a database.

  3. c.

    Processing of data—the server performs the consistency test and calculates each component and sub-factor weights using the AHP method.

  4. d.

    Process of output—the ergonomic risk factors and sub-factors connected to WMSD are prioritized.

  5. e.

    Analyze and report the results of the ergonomics evaluation in the form of charts and tables.

2.3 W-BEAS User Interface

Figures 4, 5 and 6 depict the W-BEAS interface. Before logging into the system, users were required to register (see Fig. 4). The comparison module page opened after logging in. Every questionnaire was addressed in this module under different potential threats, and the scale was determined.

Fig. 4
A screenshot depicts a page for registering system. The title reads, Ergonomic risk assessment system. For registration, username, password full name, and department information fields are given below. Buttons labeled, register and back to login are at the bottom.

A page for registering system

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Fig. 5
A screenshot depicts the system home page. The Icon reads, Ergonomics risk factor assessment index, consistency validation, graphs and manage user. Edit risk factors and questions options are listed.

System home page

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Fig. 6
A screenshot depicts the ergonomics risk factors comparison module in system. It has three components. 1. First pair: Individual factors, organizational factors and physical factors. 2. Values: 9, 7, 8, 3, 1, 3, 5, 7, 9. 3. Second pair: organizational factors, physical factors, psychosocial factors.

Ergonomics risk factors comparison module in system

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3 Validation of W-BEAS

3.1 Validation Method

The primary goal of the W-BEAS was to demonstrate the efficacy and logic of the proposed web-based expert system in practice. The W-BEAS was validated using real-world data. The validation method compares the results collected by the W-BEAS and the existing assessment method, risk priority number (RPN).

The W-BEAS validation was carried out at a local automotive component manufacturer, with twenty senior personnel chose based on expertise, abilities, and work experience. The demographic data of the W-BEAS validation respondents is presented in Table 2.

This study employed the RPN method to determine the weights of factors and sub-factors for validation reasons. RPN is the current failure mode and effect analysis to rank each failure mode. The W-BEAS was validated using the RPN procedures listed below:

  • Step 1: The workplace risk factors were assessed by assessing the Likelihood (L) of the risk happening, using a scale of 1 = rare, 2 = unlikely, 3 = likely, and 4 = almost certain.

  • Step 2: The workers were required to examine the severity (S) of risk factors if any relevant incident occurred, using a scale of 1 = minor/negligible, 2 = moderate, 3 = major, and 4 = severe/catastrophic.

  • Step 3: The following equation was adopted to measure the risk priority number (RPN).

    $$RPN_{{}} = L_{{}} \times S_{{}}$$
    (8)

Each risk factor’s priority weight percentage was determined.

3.2 Validation Results

The findings of the W-BEAS and RPN are compared in Table 3a and b. Refer to Table 3, the first-place rank of risk factors estimated by the W-BEAS and RPN methods is comparable. The rank arrangement reveals a slight variance for the sub-risk variables of individual, organization, and physical-job task. On the other hand, ranked risk factors differently by only one position deemed unimportant. ES must usually exhibit reasonable efficiency at some stage during development [20].

These results show that the W-BEAS can produce results that are comparable to the current assessment method. These validation results are similar to a previous study done by Falamarzi et al. [21] in that the developed web-based system percentages of answers were equivalent to those produced by the previous system. W-BEAS, on the other hand, produced more precise results [22]. Also, it improved the effectiveness of the ergonomics assessment method [23, 24]. Besides, the W-BEAS provides a more thorough indicator of WMSD risk variables than other techniques by employing a macro-ergonomics approach.

These results show that the W-BEAS can produce results that are comparable to the current assessment method. These validation results are similar to a previous study done by Falamarzi et al. [21] in that the developed web-based system percentages of answers were equivalent to those produced by the previous system. W-BEAS, on the other hand, produced more precise results [22]. Also, it improved the effectiveness of the ergonomics assessment method [23, 24]. Besides, the W-BEAS provides a more thorough indicator of WMSD risk variables than other techniques by employing a macro-ergonomics approach.

4 Conclusions

A web-based expert system was produced and validated for this study, using practical online ergonomics assessment advantages. This W-BEAS is valid and reliable in prioritizing the critical workplace risk factors. W-BEAS will help the OSH practitioners identify the critical risk factors needed to resolve the hazards at the workplace. This study indicates that workers are provided with a flexible ergonomics assessment system through a web-based approach. The worker can assess their ergonomics workplace individually at anywhere and anytime.

Workers must be informed of the critical risk factor of their workplaces and make plans to prevent the WMSD. W-BEAS support the organization to protect and promote the worker’s well-being and workplace sustainability. Thus, the results of this validation proof of concept suggest that a W-BEAS appears to be a promising straightforward alternative to the partly expensive and time-consuming expert training. We intend to apply the procedure illustrated in this paper to some other classification problems arising in different sectors for further research.