The starting point for the course is Lean Production, with an understanding of principles and concepts that can lead to higher efficiency. Then we introduce computer-based application for modelling and simulation of production systems. This provides the opportunity to simulate a production system, perform experiments on it according to different conditions, and optimize the system to increase efficiency.
This course is for professionals who work with production systems at different levels with a responsibility for individual production lines, departments, or has a role as production manager or production development. The course will mainly focus on the manufacturing industry in application examples, but principles that will be addressed will be applicable to a number of industries, including consulting companies working towards the manufacturing industry.
The course contains the following:
Production systems and concepts such as Industry 4.0 and Smart Industry
Lean Production and workshop with the Lean game
Programming exercises with event-based simulation of production systems
Analysis and optimization of production systems
The majority of the course will take place on Campus (Växjö), and at a distance according to the schedule below. Some of the exercises are preferably attend on Campus, but we plan for it to be possible to follow the full course at a distance due to the Covid-situation.
The emergence of artificial intelligence has created a new opportunity to apply machine learning (ML) in industry 4.0. In this era of the Internet of Things and Big data, processing of a large amount of data would not be possible without ML. Thus, ML made industrial production smarter than ever before. So, learn the course “Machine learning for Industry 4.0” to bring in new business models in your company and boost productivity using ML.
The course provides knowledge about basics of ML and data, describes ML algorithms and tools and also explains the concept of Industry 4.0 and digitalization in industry 4.0. The individual processes can be better understood and optimized with the help of the knowledge from the course. Also, it could have an important contribution in analysing the industrial data set, improving results, and making decision and/or predictions for failure, demand, sales, and production.
This course is a collaboration between Högskolan Väst, Linnéuniversitetet and Mälardalens Högskola. It consist of three course modules: Introduction to Machine Learning (MDH – 2 credits, Basic level), Industry Digitalization – Industry 4.0 (HV – 2,5 credits, Advanced level), Applied Machine Learning (LNU – 3 credits, Advanced level). Students who pass all three courses will receive a diploma from Learning for Professionals.
This course deals with model-based testing, a class of technologies shown to be effective and efficient in assessing the quality and correctness of large software systems. Throughout the course the participants will learn how to design and use model-based testing tools, how to create realistic models and how to use these models to automate the testing process in their organisation.
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.
In this course you will learn state-of-the-art statistical modelling for the purpose of analysing industrial data. The course also presents the basics of relational databases and data manipulation techniques needed to prepare the data for analysis.