Build the required knowledge and skills for efficient cross-disciplinary production development work. The course combines latest theories with practical cases from participating companies.
The course gives students the required knowledge and skills for efficient cross-disciplinary production development work. Assignments are based on theory and industrial needs, that will be further developed in practical cases selected in close collaboration with their respectively companies. The students will be trained in agile planning methods/principles and an iterative way of working in a structured manner. The aim is to meet challenges/deviations in production development projects by implementing agile feedback-loops and innovative methods and principles.
The course includes the following elements:
Knowledge Intensive Product Realisation
Challenges in industrial companies
Overview processes and change management
Organize for Information exchange and learning
Agile Project Management
Agile history and background, including methods and principles
Project management and decision making
Organization, collaboration and communication.
Iterative development methods
Planning, including Visible Planning (VP)
Production Concept Development
Requirement management, Product architecture and Production system
Innovative thinking and activities
Tools and methods for innovation and evaluation
Production Concept Selection and Decisions
Decision support and evaluation of alternatives
Tools and methods for concept presentation and selection
The rapid development of digital technologies and advances in communications have led to gigantic amounts of data with complex structures called ‘Big data’ being produced every day at exponential growth.
The aim of this course is to give the student insights in fundamental concepts of machine learning with big data as well as recent research trends in the domain. The student will learn about problems and industrial challenges through domain-based case studies. Furthermore, the student will learn to use tools to develop systems using machine-learning algorithms in big data.
The aim of this course is to provide participants with the principles behind model-driven development of software systems and the application of such a methodology in practice. Modelling is an effective solution to reduce problem complexity and, as a consequence, to enhance time-to-market and properties of the final product.
The course addresses the concept of circular economy, its use and limitations in the field of environmental engineering, methods for the assessment of resource efficiency, sustainability and environmental impact and practical examples related to waste management.
In this course, you will learn about common air pollutants and how to manage and control air pollution. The course gives you enhanced knowledge about relevant aspects of modern air quality management systems based on regulations and policies related to ambient air quality management in the EU and Sweden.
Statistics is vital in every field and in this course, you will learn the role it can play in the field of sustainable development. You will learn certain statistical tools, how to apply them, and ways of thinking about results that will aid you in your studies and future career.