Predictive Analytics in R&D Controlling at Daimler Truck AG

  • Typ:Masterarbeit
  • Betreuung:

    Dominik Hammann

  • Zusatzfeld:


  • At present, controlling departments often rely on simple methods to predict their budgets, which
    often leads to large deviations from actual values. The spread of business intelligence and analytics along with
    machine learning offers the opportunity to improve forecasting accuracy and simplify the budget
    forecasting process. What is missing nowadays, however, is proof of the implementation of such
    methods and their applicability in practice. Hence, this thesis examines the role of predictive
    analytics and especially machine learning for the prediction of R&D budgets. The research in form
    of a case study takes place in the R&D controlling department of a large German utility vehicle
    manufacturer. I find that predictive analytics can help to improve the forecast quality and machine
    learning algorithms do not outperform parametric algorithms in general. Furthermore, employees have
    a positive, yet cautious attitude towards the application of predictive models and pay most
    attention to accuracy, integration of feedback, and acceptance by top level management.