Publications

Um das Potenzial digitaler Technologien voll auszuschöpfen, sind Standortfähigkeiten zu analysieren. Notwendig ist auch ein strukturiertes Vorgehen, um Wissen auf andere Standorte zu übertragen.

Volltext

Wiech, M.; Walter, H.; Friedli, T. (2018): Management of international manufacturing networks – a site portfolio approach, in: 25th International EurOMA Conference, Budapest, Hungary

Wie gelingt Qualitätssicherung und Operational Excellence im täglichen Pharmabusiness?

Kenner der von Prof. Friedli herausgegeben Grundlagenwerke zur Operational Excellence lesen hier die Fortführung seiner Forschungen an der Universität St. Gallen (Schweiz): die Resultate einer Benchmark-Studie zum Qualitätsmanagement in der pharmazeutischen Industrie sowie eines Forschungsprojekts über Qualitätsmetriken.

Diese Studienergebnisse werden in praktische Handlungsempfehlungen für den Anwender umgesetzt. Neben dem ganzheitlichen Systemansatz bietet das Buch einen Blick in die Zukunft des Qualitätsmanagements in der pharmazeutischen Industrie.

Swiss Manufacturing Survey

Die jährlich stattfindende Erhebung hat das Ziel, die aktuelle Situation des produzierenden Gewerbes in der Schweiz aussagekräftig darzustellen und über ihre langfristige Ausrichtung einen möglichen Strukturwandel abzubilden. Der Fokus auf produzierende Unternehmen liefert neue Informationen, die durch andere Umfragen bisweilen nur unzureichend abgefragt werden. Durch diese Transparenz erhalten Unternehmen, Verbände, Wissenschaft und Politik die Möglichkeit, Entwicklungen sowie Trends zu identifizieren, zu analysieren und entsprechend zu agieren.

Quality culture as an enabler of high-quality performance and subsequently as a source of competitive advantage is increasingly discussed among operational excellence (OPEX) and quality executives. Research studies indicate an impact of quality culture on performance, especially on the success of quality improvement programs, such as Total Quality Management initiatives. A continual challenge in quality culture research, however, remains the lack of practical and accepted metrics to assess culture. In 2014, the Parental Drug Association (PDA) conducted a quality culture survey within the pharmaceutical industry. The results indicate a positive and significant correlation between quality (culture) behavior of a production site's employees and quality (system) maturity, which represents the maturity of the quality system in place. As the maturity of the quality system is more comfortable to assess by objective criteria, the positive correlation between quality (culture) behavior and quality (system) maturity may be exploited by using the latter as an indicator for quality culture. This paper confirms this positive relationship by exploring the comprehensive St. Gallen OPEX database for pharmaceutical production plants. Furthermore, data analysis shows that high-performing production sites, regarding timely provision of high-quality drugs, reveal a higher level of both quality (system) maturity and quality (culture) behavior than low-performing sites.

Friedli, T., Köhler, S., & Buess, P. (2018). Förderung von Operational-Excellence-Fähigkeiten als Basis für Effektivität des pharmazeutischen Qualitätssystems - Erkenntnisse aus der “Quality Metrics”-Forschung. Pharm. Ind., 80(6), 740–745

The FDA Quality Metrics initiative has emerged directly from the FDA Safety and Innovation Act (US Congress, 2012). It aims to ‘support the modernization of pharmaceutical manufacturing as part of the FDA’s mission to protect and promote public health’ (FDA, 2016), by encouraging both the industry and the regulators to implement modern, risk-based pharmaceutical quality assessment
systems to enable them to understand the current state of quality across the global pharmaceutical manufacturing sector.

The pharmaceutical industry invests enormous amounts of resources (>€1 billion and >10years) in the development of new products. External factors such as intensifying foreign competition and greater regulatory demands can negatively affect the profit margin, whereas the R&D productivity diminishes. To stay competitive and to maintain high R&D capabilities for developing new medicinal products, companies must make smart investment decisions to maximize their return on investment. Consequently, the entire lifecycle of a medicinal product must be effectively managed to ensure a sustained development through commercialization. This review critically assesses the current situation and the associated management strategies throughout the lifecycle of a medicinal product.

Lorenz, R.; Benninghaus, C.; Netland, T.; Friedli, T. (2018): Open Process Innovation and Digitalization of Manufacturing, in: 25th International EurOMA Conference, Budapest, Hungary.

Based on an international benchmarking and case study research, the influence of smart manufacturing (Industry 4.0) was analyzed in the context of strategic production management. Taking the existing barriers into account, an approach for the successful execution and roll-out in international manufacturing networks was designed and tested with industrial companies.

Friedli, T.; Benninghaus, C. (2017): Digitale Evolution mit Revolutionscharakter – Ganzheitlicher Ansatz der Digitalisierung, in: HSR Magazin, Nr. 01/2017.

Swiss Manufacturing Survey

Die jährlich stattfindende Erhebung hat das Ziel, die aktuelle Situation des produzierenden Gewerbes in der Schweiz aussagekräftig darzustellen und über ihre langfristige Ausrichtung einen möglichen Strukturwandel abzubilden. Der Fokus auf produzierende Unternehmen liefert neue Informationen, die durch andere Umfragen bisweilen nur unzureichend abgefragt werden. Durch diese Transparenz erhalten Unternehmen, Verbände, Wissenschaft und Politik die Möglichkeit, Entwicklungen sowie Trends zu identifizieren, zu analysieren und entsprechend zu agieren.

In pharmaceutical companies, it is universally understood that a robust Pharmaceutical Quality System (PQS) provides key elements of assurance and oversight for pharmaceutical manufacturing and quality control laboratory processes. However, despite recent advances in the manufacturing sector, quality issues remain a frequent occurrence. The research shows that a high degree of PQS Effectiveness is accompanied with a high level of Cultural Excellence

The FDA Quality Metrics initiative has emerged directly from the FDA Science and Innovation Act (US Congress, 2012) (FDASIA, 2012) and aims to provide both industry and regulators with better insight into the current state of quality across the global pharmaceutical manufacturing sector that serves the American publics’ healthcare needs.
As part of this initiative the FDA awarded a research grant to the University of St.Gallen to help establish the scientific base for relevant
performance metrics which might be useful in predicting risks of quality failures or drug shortages. An important factor in the
academic collaboration for this research was the availability of the St.Gallen Pharmaceutical OPEX Benchmarking database, consisting of key performance indicator and enabler data related to more than 330 pharmaceutical manufacturing sites.

Digitization is continuously increasing throughout all sections of manufacturing. Therefore, more data than ever before is accumulated along the value chain. The shop-floor data contains essential information for process optimizations. The correct exploitation of this data is expected to boost companies' competitiveness by supporting business-decision making. In an empirical study, 100 manufacturing companies - mostly located in Germany and Switzerland - were asked how they are handling this data, what problems they encounter and how they are dealing with them. Thus, the data analytics process was divided into four steps. The study shows that especially data processing and data exploitation are still challenging for most companies. Additionally, this study revealed that the current business performance impact of data analytics in manufacturing is not satisfactory for most companies due to problems across the data analytics process.

Complexity management methods in the field of strategic and operational complexity management are usually conceived for the discrete manufacturing industry and are hardly transferable to the process industry. In this paper, complexity management practices, as well as complexity drivers, in the discrete manufacturing industry and the process industry are identified and collected. Furthermore, an analysis is conducted regarding similarities and differences between the approaches in the two industries. In future research suitable evaluation criteria will be identified and combined in an evaluation model, which may help to show applicable complexity management methods from the discrete manufacturing industry for the process industry. Through a survey of 136 participating companies, the implementation degree of complexity management methods and the impact of complexity drivers within various industries were investigated. Based on these results, the process industry was identified as lagging behind in the field of complexity management. By conducting an interview study with five successful companies identified from the survey's sample, the researchers gained detailed information on the applied methods. Supported by a Delphi study with three companies of the process industry, specific complexity drivers and the main challenges to apply existing complexity management methods in their businesses were identified. To show the difference between the applied methods in the process industry and the discrete manufacturing industry, three cases are described. Two of them are from the process industry. The first one is representative for complexity management in the process industry, comes from a participating company from the Delphi study and is rated as less experienced in this field. On the other hand, the other case, rated as a successful practice case from the survey's sample, shows a high implementation degree of complexity management methods from the discrete manufacturing industry. In the third case, the approach of an automotive supplier with long experience in complexity management and a high implementation degree of complexity management methods is described. First results show, that the challenges in the process industry are mainly resulting from external complexity drivers, like legal and political factors. Whereas internal complexity drivers like product structure as well as the dynamic of technology, are rather influencing the discrete manufacturing industry. Hence methods for gaining transparency on materials and resources seem to be more valuable for companies in the process industry than others.

Köhler, S., & Buess, P. (2017). FDA/St.Gallen Quality Metrics Research. Dublin Institute of Technology Poster Session.

This article investigates a potential application of low cost computer hardware and open source software in machining areas of Learning Factories. Based on the implementation of an optical object detection to discover human errors in the setup process of a milling machine, the paper presents an example of how value stream improvements in Learning Factories can be achieved by student projects.

Therefore, the process of identifying possible solutions which are suitable for solving a problem in the value stream of a Learning Factory is described. In the presented case, this is an IT-solution to establish a Poka-Yoke system for depositing a work piece in a milling machine correctly. A solution, which uses a Raspberry Pi, is developed and integrated in the process. Furthermore, the ability of low cost hardware components and simple algorithms resting upon freely available software libraries to fulfil the requirements of modern manufacturing is demonstrated. Finally, this study illustrates that the implementation of in-house low cost digitalization rather rests on a profound understanding of the affected manufacturing process than on previous knowledge of programming or electronics.

Wenking, M.; Benninghaus, C.; Friedli, T. (2017): Impact of external contingency factors on the adoption of different configurations in international manufacturing networks, in: 28th Annual Production & Operations Management Conference (POMS), Seattle, Washington, USA

Im Zuge der 2017 veröffentlichten Studie „Manufacturing-Data-Analytics“ der Universität St. Gallen in Kooperation mit der RWTH Aachen wurden vielfältige Aspekte der industriellen Datennutzung untersucht. Hierbei wurden verschiedene Themenfelder wie beispielsweise technische Systeme, Implementierungsstatus und organisatorische Umsetzung analysiert. Während sich viele produzierende Unternehmen noch in einem rudimentären Einführungsstadium befinden, sind andere Unternehmen bereits heute in der Lage, durch umfassende Datenerhebung und auswertung Vorhersagen treffen zu können und so ihre Effizienz in der Produktion erheblich zu steigern. Dabei sind besonders die Datenqualität und eine systematische Evaluierung von Anwendungsfällen von entscheidender Bedeutung. Ebenso konnte gezeigt werden, dass strategische Partnerschaften sinnvoll sind, um die Implementierung zu beschleunigen und einfacher zu gestalten.

Im Rahmen des Benchmarkingprojekts „Industrie 4.0 - From a Management Perspective“ der Universität St.Gallen wurden vielfältige Aspekte der vierten industriellen Revolution aus der Managementperspektive untersucht. Dabei konnten diverse Merkmale wie beispielsweise der strategische Ansatz, Technologieeinsatz und Prozessbeherrschung identifiziert werden, die alle erfolgreichen Unternehmen in diesem Themengebiet gemeinsam haben. So ist es bereits heute vielen Unternehmen möglich Ihre Produktivität durch Effizienz- oder Effektivitätssteigerungen in der Produktion zu verbessern.

Benninghaus, C.; Wenking, M. (2016): Der richtige Umgang mit Industrie 4.0 – Aspekte der Digitalisierung und Vernetzung aus Managementsicht, Industrie-Anzeiger, Nr. 28.16, S. 26-27.

Das Benchmarkingprojekt „Industrie 4.0 - From a Management Perspective“, welches im Gegensatz zu anderen Studien, die ihren Schwerpunkt auf technische Fragen wie Sensortechnologien oder Robotik setzen, den Fokus auf die Managementperspektive legt, wurde zwischen Oktober 2015 und März 2016 durchgeführt. Mit über 115 Rückläufern und fünf durchgeführten eintägigen Unternehmensbesuchen hat das Institut für Technologiemanagement wertvolle Erkenntnisse zu Industrie 4.0 Themen wie Business Models, Stakeholder, Strategie, Performance und ähnlichen sammeln können. Dieses Benchmarking folgte einem effizienten und vielfach erprobten Benchmarkingprozess mit dem Ziel Successful Practice Unternehmen zu identifizieren und von diesen zu lernen. Dazu wurden nach der Screening-Phase zwölf Interviews mit Unternehmen geführt und von diesen Unternehmen fünf selektiert und besucht. Jedes dieser Unternehmen zeichnet sich auf Grund unterschiedlicher Einflussfaktoren wie beispielsweise Größe oder Branche, auf verschiedenen Gebieten im Bereich Industrie 4.0 aus.

Friedli, T.; Basu, P.; Calnan, N. (2016): Announcing FDA’s Pharmaceutical Manufacturing Quality Metrics Reserach, ISPE Pharmaceutical Engineering, 2016.

The Division of Production Management at the University of St.Gallen is working intensively on Industrie 4.0 topics and related issues. From a research perspective, new concepts and solutions in the field of production and complexity management as well as Smart Services are being developed. Furthermore, the institute supports industrial organizations in different industries and regions in their strategic planning, process optimization and the implementation of improvement initiatives. From process mapping to the (re-)formulation of a production strategy and recommendations for the use of Industrie 4.0 solutions, we offer a variety of research and transfer projects. Some of these projects are briefly presented below

Friedli, T.; Ponce, N.; Mänder, C. (2016): Towards the Operationalization of Production Systems: Simultaneously Increasing Effectiveness and Efficiency, Pharma Focus Asia, 2016.

Friedli, T.; Basu, P.; Calnan, N. (2016): Innovation in Quality Metrics, Pharmaceutical Manufacturing, 2016

Friedli, T.; Lützner, R.; Wenking, M. (2016): A holistic model for international manufacturing network management, 5th World Conference on Production and Operations Management P&OM Havana, 2016.

Friedli, T. ; Lützner, R.; Wenking, M. (2016): Für den Verbleib in der Schweiz gibt es gute Gründe, Die Volkswirtschaft, 2016

Lützner, R.; Friedli, T.; Budde, L. Noflatscher, S. (2016): Performance effects of factory-within-a-factory designs, 23rd EurOMA Conference, 17. - 22.07.2016, Trondheim, Norway, 2016.

Lützner, R.; Benninghaus, C. (2016): Wettbewerbsvorteile durch Industrie 4.0, Brugg-Windisch, Poster Session

Lützner, R.; Budde, L.; Friedli, T. (2016): Unfocused factories as outcome of deliberate strategic management decisions, 23rd EurOMA Conference, 17. - 22.07.2016, Trondheim, Norway, 2016.

Seghezzi, H.-D.; Friedli, T. (2016): Integriertes Qualitätsmanagement, der St. Galler Ansatz, Lexikon Qualitätsmanagement, Walter de Gruyter GmbH, Berlin/Boston, S.490-493, ISBN 978-3-486-58465-3.

Die Studie „Industrie 4.0 - From a Management Perspective“ der Universität St.Gallen beleuchtet unter anderem die größten Hindernisse für eine erfolgreiche Implementierung von Industrie 4.0, zeigt welche Probleme bereits gelöst werden konnten und erläutert an welchen Stellen nach wie vor akuter Handlungsbedarf besteht. Hierzu wurden insgesamt 116 Unternehmen der produzierenden Industrie befragt. Im Folgenden werden die signifikantesten Barrieren vorgestellt und Handlungsvorschläge präsentiert.

Industrie 4.0 bestimmt immer noch den Diskurs in Praxis und Forschung der industriellen Produktion. Mit der Vielfalt an möglichen Technologien und Einsatzmöglichkeiten habe insbesondere kleine und mittlere Unternehmen Schwierigkeiten, die für sie relevanten Anwen-dungsfälle zu identifizieren. In diesem Beitrag wird eine Methode vorgestellt, die eine Identifizierung der Anwendungsfälle anhand der strategischen Prioritäten des produzierenden Unter-nehmens ermöglicht. Die Methode wird anhand eines realen Beispiels eines metallverarbeitenden Unternehmens validiert.