It is known that lifting tasks are one of the risk factors of musculoskeletal disorders in the automotive industry.
Extensive research has been carried out over the years to develop guidelines and determine safe limits in which an
individual can lift. For this reason, the objective of this study is to determine the significant risk factors of
musculoskeletal discomfort among manual lifting task workers in the automotive industry, and propose a
methodological framework for future research on manual lifting tasks. The subjects of this study comprise 211
manual material handling workers from the automotive industry. The subjects completed a set of questionnaires
which are used to elicit information on their demographic characteristics, as well as physical factors and the
prevalence of musculoskeletal discomfort. The Chi-Square test was used to determine the relationship between the
risk factors and musculoskeletal discomfort. The findings of the study show that the following postures (trunk bent
slightly forwards, hands above the knee level (p < 0.05), trunk twisted (over 45o) and bent sideways (p < 0.05) are
the significant risk factors of musculoskeletal discomfort among manual lifting task workers in the automotive
industry. A methodological framework on manual lifting task in the automotive industry is proposed based on the
findings of this study. The framework is developed based on the need to model human lifting capabilities so that task
demands can be designed to fit the workers’ capacity when performing lifting tasks.
Most studies have examined the association of ergonomic risk factors and musculoskeletal discomfort in developed countries. Meanwhile the data are still lacking in developing countries such as Malaysia. The aim of this study was to determine the relation between risk factors and musculoskeletal discomfort among manual material handling workers in Malaysian automotive industries. A total of211 manual material handling workers from automotive industries completed a set of questionnaire on the individual, physical and environmental factors and the prevalence of musculoskeletal discomfort. The Chi-Square test and logistics regression analysis were used to determine the relationship of the risk factors and musculoskeletal. The findings highlighted that job tenure was significantly correlated with musculoskeletal discomfort among the workers (OR=2.33-5.56). The most significant physical risk factor that was associated with musculoskeletal discomfort was bending the trunk forward slightly, hands above knee level, which was significantly related to lower back discomfort (OR=5.13, 95%CI=1.56-16.8), thigh discomfort (OR=5.1, 95%CI=1.01-25.53) and wrist discomfort (OR=3.65, 95%CI=1.06-12.53). Twisting of the trunk (over 45o) and bending sideways were significantly associated to lower back discomfort (OR=4.04, 95%CI=1.44-14.44), and thigh discomfort (OR=4.3, 95%CI=1.29-8.50). The findings also highlighted that environmental factors was associated with musculoskeletal discomfort (p < 0.05. Musculoskeletal discomfort can be reduced by lowering work-related risk factors among automotive manual material handling workers, particularly by focusing on significant factors, including job tenure, bending or twisting postures and environmental factors.
Work productivity is one of the most important economic measures in the manufacturing industry. However, the physical, psychosocial and individual risk factors of an industrial work environment affect workers' physical or mental health, resulting in work productivity loss, absenteeism and presenteeism. Therefore, this study aims to identify the most critical risk factors and develop statistical models for predicting work productivity loss, absenteeism and presenteeism of garment industry workers. A sample of 224 sewing machine operators was taken for data collection through observation and self-reported studies. The results indicated that the average work productivity loss, absenteeism and presenteeism was 38.21, 2.35 and 37.23%, respectively. Finally, the statistical models of work productivity loss, absenteeism and presenteeism was developed using multiple linear regression with precision of 69.9, 53.7 and 84.0%, respectively. Hence, this study will help garment industries to improve their work productivity by taking initiatives based on the developed models.
Globalization of business and competitiveness in manufacturing has forced companies to improve their manufacturing facilities to respond to market requirements. Machine tool evaluation involves an essential decision using imprecise and vague information, and plays a major role to improve the productivity and flexibility in manufacturing. The aim of this study is to present an integrated approach for decision-making in machine tool selection. This paper is focused on the integration of a consistent fuzzy AHP (Analytic Hierarchy Process) and a fuzzy COmplex PRoportional ASsessment (COPRAS) for multi-attribute decision-making in selecting the most suitable machine tool. In this method, the fuzzy linguistic reference relation is integrated into AHP to handle the imprecise and vague information, and to simplify the data collection for the pair-wise comparison matrix of the AHP which determines the weights of attributes. The output of the fuzzy AHP is imported into the fuzzy COPRAS method for ranking alternatives through the closeness coefficient. Presentation of the proposed model application is provided by a numerical example based on the collection of data by questionnaire and from the literature. The results highlight the integration of the improved fuzzy AHP and the fuzzy COPRAS as a precise tool and provide effective multi-attribute decision-making for evaluating the machine tool in the uncertain environment.
The conveyor system plays a vital role in improving the performance of flexible manufacturing cells (FMCs). The conveyor selection problem involves the evaluation of a set of potential alternatives based on qualitative and quantitative criteria. This paper presents an integrated multi-criteria decision making (MCDM) model of a fuzzy AHP (analytic hierarchy process) and fuzzy ARAS (additive ratio assessment) for conveyor evaluation and selection. In this model, linguistic terms represented as triangular fuzzy numbers are used to quantify experts' uncertain assessments of alternatives with respect to the criteria. The fuzzy set is then integrated into the AHP to determine the weights of the criteria. Finally, a fuzzy ARAS is used to calculate the weights of the alternatives. To demonstrate the effectiveness of the proposed model, a case study is performed of a practical example, and the results obtained demonstrate practical potential for the implementation of FMCs.
This article aims to develop a novel Artificial Intelligence-powered Internet of Things (AI-powered IoT) system that can automatically monitor the conditions of the plant (crop) and apply the necessary action without human interaction. The system can remotely send a report on the plant conditions to the farmers through IoT, enabling them for tracking the healthiness of plants. Chili plant has been selected to test the proposed AI-powered IoT monitoring and actuating system as it is so sensitive to the soil moisture, weather changes and can be attacked by several types of diseases. The structure of the proposed system is passed through five main stages, namely, AI-powered IoT system design, prototype fabrication, signal and image processing, noise elimination and proposed system testing. The prototype for monitoring is equipped with multiple sensors, namely, soil moisture, carbon dioxide (CO2) detector, temperature, and camera sensors, which are utilized to continuously monitor the conditions of the plant. Several signal and image processing operations have been applied on the acquired sensors data to prepare them for further post-processing stage. In the post processing step, a new AI based noise elimination algorithm has been introduced to eliminate the noise in the images and take the right actions which are performed using actuators such as pumps, fans to make the necessary actions. The experimental results show that the prototype is functioning well with the proposed AI-powered IoT algorithm, where the water pump, exhausted fan and pesticide pump are actuated when the sensors detect a low moisture level, high CO2 concentration level, and video processing-based pests' detection, respectively. The results also show that the algorithm is capable to detect the pests on the leaves with 75% successful rate.