Die Finanzorganisation von Infineon will sich vom Data Provider zum Business Advisor entwickeln. Ein treiberbasierter Forecast ist ein Instrument, um den Controller in der Routinearbeit zu entlasten. Der Top-Level Ergebnisforecast wird weitgehend ohne manuelle Planung erstellt und bietet Simulationsfähigkeit auf Knopfdruck. Die Geschäftstreiber stehen stärker im Mittelpunkt der Diskussion.
Innovations through the "business process as a service" (BPaaS) concept have shaped new business opportunities for service providers. Technological progress allows business process service providers (BPSPs) to offer a wide range of digitized and standardized services to business clients. Within this business model, capacity planning is a major challenge for BPSPs, as costs are the decisive factor in the competitive business environment of digital service provision. Accordingly, BPSPs must tackle inefficiencies in capacity planning resulting from both idle capacity and lost revenue caused by volatile demand. However, recent technological developments offering dynamic integration and information capabilities may help, as they enable the exchange of excess capacity between business partners. We examine the corresponding potential of IT-enabled excess capacity markets to create competitive advantage in e-business value chains by analyzing a BPSP's capacity-related optimization problem. We build an analytical model based on queuing theory and evaluate it through a discreteevent simulation applying a possible application scenario. By solving the optimization problem, we identified a remarkable cost advantage in using excess capacity as a first competitive advantage. Building on this cost advantage, we furthermore identified differentiation advantages realizable without raising prices. Both findings highlight the relevance of further research on this topic.
Driven by the increased relevance of digitalised and hypercompetitive business environments, companies need to focus on IT-related innovation projects (ITIPs) to guarantee long-term success. Although prior research has illustrated that an appropriate team design can increase project performance, an approach for determining the economically optimal team design from an ex ante perspective is missing. Against this backdrop, we follow analytical modelling research and develop a model that determines the optimal team design for an ITIP by transferring central findings of previous research regarding relevant influencing factors, e.g., team size and academic background diversity, into an ex ante economic evaluation. Thereby, our model allows the comparison of different team designs (i.e., team compositions) with regard to the prospective monetary project performance. Generally, the results show that only about a fifth of the random team designs resulted in a positive profit. In contrast, the well-founded, optimal team designs proposed by our model led to a positive profit in almost 90% of all cases. Regarding the influencing parameters, we observe that team size is the most important factor since a deviation from the optimum has a much more significant effect on the expected profit than do other factors such as work experience. To ensure the real-world fidelity and applicability of our model, we discuss the underlying assumptions with two practitioners. Our contribution is manifold: Inter alia, from an academic perspective, we enhance existing research on team design by converting well-accepted qualitative findings from a frequently investigated field outside business administration (i.e., [social] psychology) into a quantitative model that allows for the ex ante economic evaluation of team design parameters. For practitioners, we provide a model that assists managers in designing ITIP teams that are more likely to deliver desired results. This model enables managers to avoid relying only on gut feeling when designing ITIP teams, as is currently often the case due to a lack of alternative approaches.
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