Qua2Pro Quality feature based quantification of production risks

• Applicant: Professor Dr.-Ing. Robert Schmitt
• Subject Area: Production Systems, Operations Management, Quality Management and Factory Planning
• Term: from 2014 to 2016
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 254946193

Due to shorter product development cycles, higher range of variants and increasing product complexity manufacturing companies are facing increasing technical risks that strongly affect the quality, reliability, and thus the success of a product as well as the competitiveness. A quantified and valid analysis and assessment of risk probability and potential monetary consequences of risks represents a key challenge to ensure the safety of products and processes. So far, quantifying approaches of risk management mainly concentrate on the financial sector and areas of a company, so that the expansion on operational risks, i. e. risks along the value chain is not established yet. Existing approaches for identification and assessment of technical risk management, such as FMEA, do not link the qualitative assessment. Besides the quantification of potential failures, the effect of Near-Misses, that represent high risks when only small changes in a process occur, are underestimated. Suitable approaches and methods for identification, analysis and systematic handling of these Near-Misses in production are missing.The overall objective of this research project is the development of methods to derive aggregated key performance indicators describing a failure risk in production, in the form of quantified assessment of the probability of occurrence and the associated monetary consequences. Starting point is the classification of requirements on product and process in form of quality features. Therefore, a procedure is developed to identify and characterize these quality features. As a secondary objective, models will be developed to determine the probability of risk and partial cost functions for determination of the monetary losses of a risk. For modeling, the use of statistical evaluation methods will be investigated, such as process capability indices and approaches for describing rare events (extreme value theory). Amongst others, through a simulation-based validation the transferability of the scientific result is assured. The validation of the developed methods is performed by means of a turbine blade.The quantified assessment of technical risks is one of the biggest scientific challenges of quality management. The investigation of the transferability of statistical approaches to the assessment of technical risks represents the main challenge within the research project. Through quantified predictability of a failure event and the connected monetary consequences, a better-grounded basis for the successful continuance of product and process innovations is provided. Preventive measures of risk treatment can be implemented more focused and their effect can be evaluated more precisely. The previously hard demonstration of the benefit of technical risk management, due to the lack of precise statements about the probability of occurrence and costs, is made possible by the results of the research project.

• DFG Programme: Research Grants