**2. Research Background**

Ergonomic risks in manufacturing sectors can cause serious injuries and impact the health and quality of life of workers [10]. This can contribute to losses of quality and productivity. Different self-reporting methods such as rating scales, questionnaires, checklists, and interviews have been used in the past to study ergonomic risks [11]. An effective rapid-screening instrument was developed by Keyserling et al. [12] to identify the exposure of workers to risky postures in cyclical jobs. Shikdar et al. [1] developed the "ErgoTech" self-assessment software package to evaluate the ergonomic improvement potential of production systems in the manufacturing industry. The application of this tool enabled production managers to recognize ergonomic improvements in the workplace successfully. David [11] reported the use of several tools to assess the exposure to risk based on self-reports, observational methods, and direct measurements. Laring et al. [13] proposed the Ergo SAM tool, which can be used to optimize the workplace in terms of the production time and physical load on the operator. This tool facilitates the detection of high musculoskeletal loads early in the planning process.

The most widely used methods for ergonomic assessment are the Occupational Safety and Health Administration (OSHA) checklist and the standard Nordic MSD questionnaire. The standard Nordic MSD questionnaire has been used in applications such as furniture manufacturing [14] and LCD manufacturing [15]. The OSHA checklist has been used for the analysis of semiconductor manufacturing [16]. The main weakness of these self-reporting approaches is that the results are not always reliable, which can lead to biased interpretations.

Observational methods such as the OWAS and the Strain Index (SI) involve direct observation of the worker and the consequent tasks. Sonne et al. [17] devised an office risk assessment tool, Rapid Office Strain Assessment (ROSA), to measure risks related to computer work. This tool provides a report to the user detailing the need for modification of discomfort associated with office work. Poochada and Chaiklieng [18] demonstrated the use of the ROSA to evaluate the presence of risk elements for job-related MSD in a call center office. The RULA method has been used to assess the risk of work-related upper limb disorders [19,20]. The OCRA method has been used to evaluate upper limb disorders; the risk factors considered are repetition, strength, incorrect postures, and lack of rest intervals [21]. REBA has been effectively used to analyze the exposure related to the upper and lower limbs [22]. Chander and Cavatorta [23] proposed the postural ergonomic risk assessment (PERA) method to assess the postural ergonomic risk of short cyclic assembly jobs. The drawbacks of these observational methods are high intra- and inter-observer inconsistency due to the data collection, which is generally performed through subjective opinion or simple judgment from videos/pictures, and a lack of accuracy. To overcome these limitations, Maman et al. [24] and Plantard et al. [25] recommended the use of sensors attached to the worker's body to collect data directly; however, this is difficult to implement in real-world situations. Li et al. [26] proposed an improved physical demand analysis (PDA) by integrating risk assessment tools such as REBA, RULA, and NIOSH; the proposed method enables ergonomic risk identification and evaluation and proactively mitigates the risk to workers by providing modified work. The four main ergonomic risks identified in the case study were static whole-body posture, heavy material handling, sensory risks, and awkward body postures. Bortolini et al. [27] developed a motion analysis system (MAS) for the ergonomic analysis of operators during assembly tasks based on Motion Capture (MOCAP) technology with ad hoc software. The applicability of the MAS was discussed through a case study of a water pump assembly workstation. Using a deep learning algorithm to predict RULA scores, Nayak et al. [5] created an automated, RULAbased posture evaluation system. This will help to reduce the amount of time necessary for postural evaluation while also producing highly reliable RULA scores that are similar to the results obtained using the manual method.

In this study, an exhaustive literature review was conducted based on the factors considered for ergonomic evaluation and the area of study, and the results are summarized in Table 1. The literature review indicated that research on ergonomic assessments has often considered physiological and/or psychological factors. Parsons, K.C. [28] discussed the great deal of work on the effects of light, noise, vibration, and thermal environments on the health, comfort, and working efficiency. Health, safety, and environment (HSE) at the operational level will strive to eliminate injuries, adverse health effects, and damage to the environment; enhance worker productivity, provide improved worker safety (physical and mental), and job satisfaction [29]. However, significant factors such as environmental and safety factors that could influence "the ergonomic conducive level" of the industry have been neglected in most previous studies. Moreover, there appears to be a lack of amalgamation of ergonomic assessment tools for better prediction of ergonomic levels in any manufacturing industry. Hence, there is a need to develop a comprehensive evaluation model for workplace ergonomic assessments. This study proposes a conceptual model that facilitates the determination of a human factor index for workplace ergonomic measurements. The application of the model is demonstrated using analytical tools such as interpretive structural modeling (ISM), structural equation modeling (SEM), and a multigrade fuzzy approach to determine the ergonomic performance in the Indian automotive industry. ISM methodology typically helps to create a well-defined visible model from poorly articulated unclear mental model of experts. Since the factors cannot be directly measured, the structural equation modelling (SEM) methodology is typically used to analyze the structural relationship between factors for establishing either a theoretical or a predictive relationship.


**Table 1.**

Summary of the literature review.
