Abstract
Problem statement: Production of automotive parts is among the largest contributor to economic earnings in Malaysia. The dominant work involve in producing automotive part were manual assembly process. Where it is definitely used a manpower capability. Thus the quality of the product heavily depends on worker's comfort in the working condition. Temperature is one of the environmental factors that give significant effect on the worker performance. Approach: Temperature level and productivity rate were observed in automotive factory. An automotive manufacturing firm was chosen to observe the temperature level and worker's productivity rate. The data were analyzed using Artificial Neural Network's analysis (ANN). ANN analysis technique is usual analysis method used to form the best linear relationship from the collected data. Results: It is apparent from the linear relationship, that the optimum value of production (value≈1) attained when temperature value (WBGT) is 24.5°C. Conclusion: Optimum value production rate (value≈1) for one manual production line in that particular company is successfully achieved. Through ANN method, the optimum temperature level for the optimum manual workers' performance manage to be predicted.
Highlights
Automotive Industry in Malaysia contributes large programs
After the WBGT level and production rates were obtained, the data were evaluated using Artificial Neural Network (ANN) analysis to determine a linear relationship between the WBGT level and the production rate
With the result for both proportion factor value, R which exceeded 0.5, this observation can concluded that the production rate after ANN analysis versus WBGT is proportional quite linear
Summary
Automotive Industry in Malaysia contributes large programs. It means that the comfort level for workers profit and investment. Productivity is relationship between goods output factors were recorded at 10 min time intervals one or service with employee input or non-human resource working shift. After the data were measured and recorded, the data were analyzed using the ANN’s process to obtain a linear regression of the production rate versus the temperature level.
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