Quantitative Methods for Management

Aims of the course

- At the interdisciplinary level connect economic contents, mathematical modelling, (accounting)information systems and modern IT tools.
- Use practical examples to prepare students for challenges of real life; the implementation phase is by necessity replaced by a relevant theoretical analysis of possible implementation-adverse situations.

Course syllabus

1. Basic characteristics of decision support systems (DSS). Definition of DSS. Personal, organisational, institutional, and ad hoc DSS. Structure and technology. DSS for managerial decision-making. Organisation of an appropriate information (sub)system. Group DSS.
2. Quantitative modelling in management. Qualitative versus quantitative modelling and "blending". Phases in the problem-solving process. Problem identification, model building, data acquisition, model solving, formal testing, analysis of results. Practical implementation. deterministic and stochastic models, differences in approach. Information support issues.
3. Data sources for modelling in management and data quality issues. Internal (in-company) data sources and data quality issues. External data sources and data quality issues. Customer data collection and analysis.
4. DSS tools. Complex tools (software) for specific tasks (data manipulation support, strategic decision-making, project management, short-term financial decision-making and investing, ...). Spreadsheets in business decision-making. Optimisation software. Role and scope of expert-systems.
5. Practical examples. Inventories valuation methods. Optimal stock size (deterministic and stochastic demand). Blending problems (cost minimisation). Optimal portfolio (fixed income assets with investments constraints). Optimal location problems (common warehouse). Optimal allocation of advertising budget. Optimal structure of production (linear and non-linear objective functions). Own production versus outsourcing, "buy" versus "lease". Decision-making in uncertainty or risky conditions (new plant size decision). Investment decisions with stochastic income flows. Optimisation of queuing systems (optimal number of service channels). Expected cost of complaints caused by random failures. Renewable resources management. Optimal project management (CPM, PERT).

Course director(s)