| Summary: | Production efficiency is a critical determinant of growth and competitiveness in assessing the success of Malaysia's manufacturing sector. The daily and weekly production reports including information such as working time, output, production efficiency and machine utilization. However, there is no information related to the parameters contributed to the production activities. In meantime, to address production costs, industrial practitioners apply activity-based costing (ABC) to determine the cost per unit of finished products. Obviously, the existing method presents challenges in accurately determining capacity utilization and unused capacity. Regrettably, the quality and costing tools often operate independently, thus the impact of factors to the industrial capacity is less appreciated. This research aims to develop a framework that integrates the degree of contribution and cost driver in production environment. MTS is employed to predict and diagnose system performance using multivariate data for quantitative decision-making. TDABC is utilized as a costing model, enabling companies to allocate costs by calculating the time spent on activities. Data collection is involved 25 workstations, 51 parameters, and 59 activities. As a result, in April 2023, the normal sample has the average MD of 1.000001, while the abnormal sample has the average MD of 53.401398. Increasing the number of parameters which are exceed the normal range will increase the MD value. There are 34% parameters are classified in positive degree of contribution, whereas 66% parameters are classified in negative degree of contribution. For the sub-activity of prepare printing inspection equipment has -22,757.63 minutes and MYR -4,323.95 of unused capacity of time and cost respectively. It was found that there are three types of unused capacity have been identified such as Type I which is the workstation is over-utilized, Type II which is the workstation is small-utilized, and Type III which is the workstation is largely-utilized the resources and cost of apportionment. The proposed framework is great because the degree of contribution reflected the increment or decrement to the cost driver in high production complexity for better product cost.
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