In this lecture the focus lies on the design of Management Control Systems (MCS) and Decision Support Systems (DSS) surrounded by an uncertain business environment predominantly by 1) considering Double Closed-Loop Management Control Systems (e.g. Balanced Scorecard), 2) using predictive analytics methods in the corporate planning, forecasting and budgeting domain (e.g. ROC-based forecasting) and in the risk management (e.g. scorecard modeling in predictive maintenance domain), and 3) applying optimization procedure in the decision supporting systems e.g. Minimum Exceedance Probability (MEP) approach in the investment decision domain and the stochastic dynamic control theory in the Sales & Operations Planning (S&OP) domain.
Imagine, your are hired by a company as an assistant of the executive board with the following responsibilities: 1) You assist the Chief Financial Officer (CFO) in setting up a Management Control System (MCS) in form of a double closed-loop planning and control system that is adequate for the firm’s uncertain business environment. 2) You are supporting the executive board in the corporate planning, forecasting and budgeting tasks by promoting predictive analytics methods. 3) You support the Chief Production Officer (CPO) in the Sales & Operations Planning (S&OP) for deriving stochastic optimal production plans. 4) With individual responsibility you design meta-model for the resource consumption-based planning and the variance-based control of direct material cost and indirect overhead costs in the production domain that allows the development of a software application with the Model Driven Software Engineering (MDE) approach.
In this course you acquire the needed knowledge, skills and competences for performing these tasks by discussing the key concepts according to the scholarly literature, by performing group work in break out sessions, by solving programming problems in the statistics language R and by performing a project assignment as group work.
