The philosophy of total quality management is based on meeting quality requirements in all processes and meeting customer needs quickly and accurately through the contribution of all employees. This concept means that all the processes in an enterprise, all the technology used, and all the workforce employed represent the total quality of the enterprise, with the necessary controls and corrections made to ensure that the quality is sustainable. In this study, a detailed literature review and classification study regarding Industry 4.0, Industry 4.0 technologies, and quality has been carried out. The place and importance of quality in Industry 4.0 applications have been revealed by this classification study. In previous studies in the literature, the relationship between Industry 4.0 technologies and quality has not been examined. With this classification study, the importance of quality in Industry 4.0 has emerged, and an analysis has been conducted regarding which quality criteria are used and how often.

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Some factors in the product development process can increase success. Evaluating the risks and success factors is necessary for a more successful product development process. Some inadequacies arise in classical risk assessment methods due to the subjective nature of likelihood and severity ratings. Different probability and impact values can give the same risk size. Due to these inadequacies, doubts about the accuracy of risk prioritization may arise. In this study, a new risk prioritization model is proposed to eliminate these doubts and to consider their contribution to the success of the process they affect while prioritizing the risks, with a detailed literature review and the support of the experts of the applied company, the risks affecting the product development process. The importance levels of risks and success factors were calculated using the analytical hierarchy process. With the proposed model, unlike the classical method, when calculating the risk size, the risk weight and the total score from the success factors are added to the likelihood and severity values of the risk. Thus, companies will obtain more detailed and objective results, considering success factors and risk importance levels, and use the resources they allocate for risk reduction activities more efficiently.

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