Analysis of quality costs with the PAF model and design of a decision support system

Quality emerges as a concept that humanity continually pursues and strives to achieve, requiring constant development and transformations to ensure its attainment. Enterprises, in turn, must adapt to these transformations and developments. The primary objective and methodology of this study involve determining quality cost items using data obtained from a mining sector enterprise to conduct a detailed analysis of total quality costs. Subsequently, a Python-based decision support system will be developed to assist decision-makers in determining, monitoring, and improving quality costs. The designed decision support system should also incorporate hidden/unmeasurable/implicit costs often overlooked in applications. The distribution of enterprise costs reveals that 36% are production costs, 37% are quality costs, 21% are labor costs, and 6% are other costs. The high proportion of quality costs underscores the importance of quality. Proportional analysis found that quality costs account for 37.4% of total costs. The quality cost per ton of sales is calculated at 71.56 TL/ton. Additionally, internal failure cost per ton of sales amounts to 4.20 TL/ton. When the enterprise produces one unit, the quality cost is 1.05 units of cost. When the PAF model is applied, valuation costs emerge as the highest category at 71%. Maintenance and repair costs have been identified as the highest cost item. Greater emphasis on R&D and consulting categories is necessary to reduce maintenance, repair, and scrap expenses. The created interface facilitates cost calculation and enables enterprises to visualize hidden costs. This study contributes to the literature in three main ways. Firstly, it demonstrates the applicability of the PAF model in different sectors, specifically in mining. Secondly, it integrates the PAF model with Decision Support Systems (DSS) to create an interface. Thirdly, it includes the addition of hidden costs.