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Business Analytics & Datengetriebene Organisationen

Übersicht

Wie müssen Unternehmen ihre Prozesse, Entscheidungsstrukturen und Geschäftsmodelle anpassen, um mit Business Analytics, Data Science und Machine Learning Wettbewerbsvorteile aufzubauen? Wir untersuchen wie Unternehmen die datengetriebene Transformation bewerkstelligen und wie dieser Prozess erfolgreich gemanaged werden kann.


Schwerpunkte

  • Aufbau und Gestaltung datengetriebener Organisationen
  • Gestaltungen datenbasierter Innovationen und Geschäftsprozesse und -modelle
  • Implementierung von Business Analytics in Unternehmen
  • Datenkompetenzen für Manager
  • Akzeptanz und wirtschaftlicher Nutzen von Business Analytics, Data Science, und künstlicher Intelligenz
  • Validität und Fairness algorithmischer Entscheidungen
  • Daten- und API-basierte Geschäftsmodelle zur Monetarisierung von Daten
  • Messung des Wertes von Daten

Projekte

Learning Algorithms for Discrimination Free Innovation Funding Activities

The goal of the project is to evaluate the extent to which investment algorithms in new venture funding discriminate women entrepreneurs as well as the effectiveness of approaches to debiasing algorithms.

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Procurement Intelligence: Data-driven total cost and resilience optimization for purchasing

Efforts to contain the spread of COVID-19 have kickstarted an economic crisis throwing off the balance of international supply chains. Swiss companies seeking to remain globally competitive will find themselves between conflicting priorities of resilience enhancement and cost reduction. Purchasers from various industries face increasingly complex decisions (e.g. supplier selection, make-or-buy, etc.) under aspects of value contributions, incl. risk, compliance, and sustainability issues.

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Publikationen

With looming uncertainties and disruptions in today’s global supply chains, such as lockdown measures to contain COVID-19, supply chain resilience has gained considerable attention recently. While decision-makers in procurement have emphasized the importance of traditional risk assessment, its shortcomings can be complemented by resilience. However, while most resilience studies are too qualitative in nature and abstract to inform supplier decisions, many quantitative resilience studies frequently rely on complex and impractical operations research models fed with simulated supplier data. Thus there is the need for an integrative, intermediate way for the practical and automated prediction of resilience with real-world data. We therefore propose a random forest-based supervised learning method to predict supplier resilience, outperforming the current human benchmark evaluation by 139 percent. The model is trained on both internal ERP data and publicly available secondary data to help assess suppliers in a pre-screening step, before deciding which supplier to select for a specific product. The results of this study are to be integrated into a software tool developed for measuring and tracking the total cost of supply chain resilience from the perspective of purchasing decisions.

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While many firms in recent years have started to offer public Application Programming In-terfaces (APIs), firms struggle with shaping digital platform strategies that align API design with aspired business goals and the demands of external developers. We address the lack of theory that explains the performance impacts of three API archetypes (professional, media-tion, and open asset services). We couple survey data from 152 API product managers with manually coded API design classifications. With this data, we conduct cluster and regression analyses that reveal moderating effects of two value creation strategies (economies of scope in production and innovation) on the relationships between API archetype similarity and two API performance outcomes: return on investment and diffusion. We contribute to IS litera-ture by developing a unifying theory that consolidates different theoretical perspectives on API design, by extending current knowledge on the performance effects of API design, and by empirically studying the distinct circumstances under which digital platforms facilitate economies of scope in production or in innovation. Our results provide practical implica-tions on how API providers can align API archetype choice with the value creation strategy and the API’s business objective.

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Telemedicine services may improve the quality of life of individuals while also reducing the costs of service provisioning. They represent an important but as yet understudied type of complex services that integrates many stakeholders acting in service value networks. These complex services typically comprise a combination of information technology (IT) services and highly person-oriented, non-IT services, and are characterized by long service delivery periods. In such an environment, it is particularly difficult to generate successful and sustainable business models, which are necessary for the widespread provision of telemedicine services. Following a design research approach, we develop and evaluate the CompBizMod framework, a morphological box allowing for: (1) the analysis, description, and classification of telemedicine business models, (2) the identification of white spots for future business opportunities, (3) and the identification of patterns for successful business models. We contribute to the literature by presenting a specific business model framework and identifying three business model patterns in the telemedicine industry. We exhibit how business models for complex services can be decomposed into their constituent elements and present an easy and replicable approach for identifying business model patterns in a given industry.

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