Thomas Friedli

Quality excellence is a key factor in pharmaceutical manufacturing that differentiates the high performers from the less successful companies. Quality Metrics research identified that ‘high performer’ manufacturing plants excel for most Pharmaceutical Quality System (PQS) effectiveness metrics, achieve this with lower inventory levels, all while handling more Stock Keeping Units (SKUs).6 On the other hand, the research also confirms that manufacturing plants with low PQS excellence, report a higher share of indirect QA/QC labor full time equivalents (FTEs), in response to underlying operational stability problems. Quality Excellence is described as an advanced approach to quality which goes beyond merely being compliant with regulations. Quality excellence is patient-driven, culturally embedded and built into the processes and behavior of an organization. This article provides an overview of quality metrics research undertaken by the authors on behalf of FDA and the development of a machine learning based, predictive risk surveillance model.

The objective of this paper is to examine the relationships between technical and social lean bundles as well as operational performance in the context of the pharmaceutical industry. We investigate the direct and mediating effects of the lean bundles Total Productive Maintenance (TPM), Total Quality Management (TQM), Just-In-Time (JIT), and Human Resource Management(HRM) on operational performance. Our analysis relies on 113 manufacturing facilities from the St.Gallen OPEX benchmarking database. The results show that HRM has a positive indirect effect on operational performance mediated by the technical lean bundles.

Purpose – The purpose of this study is to investigate how soft lean practices moderate the performance effects of hard lean practices. The authors provide new evidence from the pharmaceutical industry, which is characterized by a highly regulated and technical environment and has been largely uncharted in the lean literature. Design/methodology/approach – Based on a review of the literature, the authors define a set of soft and hard lean practices. The authors test the hypotheses using factor analysis and moderated hierarchical linear regression on a unique dataset containing survey data and real performance measures of 351 pharmaceutical plants. Findings – The results show that soft lean practices can be both enabling and constraining. When management engages in performance measurement, visualisation and employee empowerment the relationship between hard lean practices and performance is positively moderated. On the other hand, when managers emphasise goal setting and work standardisation the performance outcomes are reduced.

This article provides a comprehensive overview of outsourcing in pharmaceutical manufacturing. It discusses the motivation of the industry to leverage outsourcing advanteages, especially to China and India, but also related disadvantages. Key facts about quality issues related to outsourcing are outlined with examples. The increasing complexity of pharmaceutical manufacturing supply chains an an aggravated oversight of suppliers with the loose of control are just one explanation for that. Furthermore, the article describes how outsourcing impacts quality risks and draws on examples from the past. To mitigate these, practical advice for supplier due diligence, contractor selection and assessment process, and how to achieve operational excellence in outsourcing and internal manufacturing is drawn from a cost and risk perspective.

There exists a wide range of optimization models in the Operations Management (OM) community to solve complex problems such as lot sizing. However, their practical performance is often criticized due to the complexity of implementation and insufficient applicability in real-world decision processes. These theory-driven approaches are either simple to compute, but only focus on single aspects of the decision without being able to capture the practical problem comprehensively, or are complex computational models with limited practicability. We apply a Design Science Research approach to resolve this issue and show how lot size decision-making models should be designed to thoroughly support managers. Our innovative model combines Discrete Event Simulation (DES) with OM methods and is developed and tested in a case study in the metal processing industry. Results reveal that the model is suitable to provide transparency about effects and a range of efficient solutions.