Steffen Eich

Quality risk in the domain of drug production is a key factor contributing to patient safety and is critical for business success. Today, there is a lack of guidelines for its management at the industrial level. In recent years the limelight has been on the quality metrics draft guidance created by the United States Food and Drug Administration (FDA), as well as the industry-wide efforts on gathering, and analyzing operational data. Both trends have incited increased interest in the quantitative assessment of quality risk. Several scholars contributed to the discussion on two levels of abstraction. Guidance on Quality Risk Management in the pharmaceutical industry is typically focused on the micro-level of designing products and processes. On the other hand, Operations Management scholars provided research on the econometrical level. To the best of the authors knowledge, there is no published research yet that examines site internal operational characteristics, and their relationship to quality risk in terms of quality compliance evaluations. The main goal of this research is to develop a data-driven management approach for Quality Risk Management at the level of the manufacturing site. In order to achieve the research objective, four major steps were followed. First, a literature review of the known interlinks of inspection outcomes and characteristics of manufacturing operations was performed. Secondly, the relationship between Operational Excellence and quality risk is evaluated. Thirdly, the operational performance indicators were analyzed with regard to their possible link to inspection outcomes. And lastly, the applicability of predictive modeling in this Quality Risk Management context is tested and discussed. This research project is designed to follow a mixed-method approach combining qualitative and quantitative research, as well as the solution prototyping phase. An iterative process enables establishing a first-hand understanding, preparing credible analysis results, and improving upon it to get a thorough understanding of the issue. Employing a systems theory view enables the assessment of quality risk in manufacturing sites as an analytically measured systems state. Following the design science research methodology allows developing analytics methodologies, which will in turn facilitate and provide support to the decision-making process of the industrial managers.

This report presents the findings from three years of Quality Metrics Research and builds on seminal outcomes from earlier operations and quality management research, e.g. Voss et al. (Voss, Blackmon, Hanson, & Oak, 1995), Ferdows and De Meyer (Ferdows & De Meyer, 1990), Deming (Deming, 1986). The work undertaken in year 3 has deepened the insights and enhanced the models developed in the first two years of Quality Metrics Research by the University of St.Gallen (Friedli, Köhler, Buess, Basu, & Calnan, 2017, 2018). The following main results are highlighted below and are further described in more detail in this report in the relevant chapters noted.

Steffen Eich

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