The Swiss engineering master's degree

A proper understanding of the current state and probable future development of a market is key to any successful business development. The module Market Analysis and Forecasting provides the foundations of analysis of complex socio-economic systems. It puts students in place to autonomously plan, design and execute their own qualitative and quantitative analysis. Development of well-founded forecasts and scenarios completes the understanding of customer data, markets and the socio-economic environment. Tools for the definition and the analysis of company reactions to potential future market scenarios will complete the module, allowing for transformation of market inputs into strategic choices.

Prerequisites

Good knowledge of English.
Bachelor degree in Business Administration or Engineering.

Learning Objectives

Students possess the knowledge and ability to understand and analyze markets as complex socio-economic systems. They can identify the most relevant factors determining the market behavior, to establish causal relations among these factors, and to describe techno-socio-economic systems by means of qualitative and quantitative modelling. 

Students understand and apply key concepts of the theory of complex systems such as observability, controllability, time variance or invariance, randomness or determinacy of factors, linear or nonlinear, static or dynamic behavior, and they assess how these properties influence the overall system behavior. 

Students apply qualitative and quantitative methods for model development and validation, including statistical analysis. Through practical examples, they learn to analyze, forecast, and control such systems. Finally, students are able to present the analytical results using different visualization techniques.

Contents of Module

The module includes the following topics:

1. Market Modelling

• Understanding the market as a complex, socio-economic system
• Outlook: system modelling in a broader context
• Understanding the role of critical success factors in the theory of complex systems
• Identification of key factors determining the dynamic, time variant and stochastic behavior of a market
• Systemic market analysis
• Experiencing complex market behavior, steering complex systems
• From qualitative to quantitative models
• Model validation
• Developing scenarios describing the market future
• Prospects and limits of modelling

2. Applications of Quantitative Methods for Market Analysis

Applications that cover topics in market analysis, for instance:

• Customer segmentation for, e.g., marketing campaign planning
• Customer feedback analysis, e.g., for service improvement planning
• Demand prediction for, e.g., electricity production planning and agricultural planning
• Credit card default prediction
• Customer life-time value consideration

Using basic quantitative methods such as:

• Data structuring, cleaning, and management
• k-Means clustering, RFM segmentation
• Decision trees
• Multiple linear- and non-linear regression, Lasso and Ridge regression
• Time series forecasting using ARIMA and LSTM models

The relevance and practical benefits of each topic will be illustrated through examples and applications. Analytical methods for problem solving will be introduced in a conceptually accessible yet methodologically rigorous manner. Particular emphasis will be placed on assessing the validity, robustness, and generalizability of analytical results, as well as on interpreting their implications for evidence-based decision making.

Teaching and Learning Methods

The module is taught by theory inputs, case studies and a software tool.

Literature

[1] Sterman, J. D. (2000). Business Dynamics. Systems Thinking and Modeling for a Complex World. Boston: McGraw-Hill. ISBN 978-0071241076. (Recommended.)

[2] Rob J. Hyndman, George Athanasopoulos, Forecasting: principles and practice, OTexts, 2013. The book is freely available as an online book at www.otexts.org/fpp. Alternatively, a print version is available: ISBN # 0987507109. (Required.)