Techniques for Modelling and Optimisation in Industrial Management

10 credits

Syllabus, Master's level, 1TE732

A revised version of the syllabus is available.
Code
1TE732
Education cycle
Second cycle
Main field(s) of study and in-depth level
Industrial Engineering and Management A1N
Grading system
Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
Finalised by
The Faculty Board of Science and Technology, 4 May 2016
Responsible department
Department of Engineering Sciences

Entry requirements

120 credits, of which 20 credits in the area of mathematics. The courses Industrial management (or Business Studies A/B) and Industrial Project Management must be completed.

Learning outcomes

After the course the student should be able to:

  • collect and analyse complex customer and market data, such as "big data", using databases and computer-based tools,
  • describe and apply methods and techniques that can be used to study and analyse customer environments and user situations,
  • modelling and simulate innovation processes and analyse and evaluate market data as input into product development activities,
  • explain and describe the strategic importance of optimised supply chains with high efficiency,
  • explain and describe the strategic role of procurement for the efficiency of supply chains,
  • manage logistical issues such as purchasing, warehousing, queuing and production,
  • simulate and optimise the elements of the logistics flow,
  • develop policy options and perform decision analysis with regard to risk and forecasts regarding the supply and marketing strategies.

Content

Tools for collecting customer data, for data analysis and databases. Software for analysis of big data, and the use of multivariate analysis techniques to simulate and analyse market reactions during different phases of the product development process.

Cross-disciplinary analysis models and analytical tools for the analysis of very large databases, where data is characterised by great variation in data quality, data types and data sources and time-lapse. Innovation dispersion models and technology adoption forecasts, strategic business planning and product portfolio planning and product planning. Strategic planning and optimised design of the location, transportation, and internal and external flows (Supply Chain Management and Demand Chain Management). Models and methods of decision analysis in product development, sourcing and supplier strategies, outsourcing, strategic alliances, inventory management, and forecasting. Mathematical models for inventory optimisation, demand planning, project planning, optimisation in queues, general optimisation, simulation, risk, game theory, and optimisation of product selection.

Instruction

Lectures, exercises, seminars, laboratory work and tutorials.

Assessment

Written project report with oral presentation (2 credits). Written exam (3 credits). Written home exam (5 credits), where written assignments during the course could result in a higher grade.

Other directives

The course may not be included in a degree together with 1TE674 Business Development and Entrepreneurship.

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