Abstract
In manufacturing companies, quality costs can be determined and reduced, typically, by increasing preventive costs that are compensated by a reduction in failure costs. However, there are barriers to the determination of quality costs, limiting the ability of companies to establish quality cost reduction programs to become more competitive. The paper's objective is to propose a simple approach to assess quality cost that encourages many companies to start or deepen (depending on the level of skills they have in this area) quality cost reduction processes. The proposed approach is grounded on models based on available data (more available in the industry 4.0 context) and it can lead to models of quality costs with different levels of granularity. Results show that modeling a specific situation with different granularity levels can influence the cost estimations. This approach should allow companies without any experience in determining quality costs to start this process and companies with skills in the area to improve these models, so that both can access the benefits resulting from the quality cost reduction plans mentioned in the bibliography, such as increased competitiveness.
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