Linnaeus University

Industry leaders can greatly benefit from learning about agile working methods as it can contribute to increased productivity, customer focus, flexibility, engaged employees and innovativeness. By using agile working methods, an organization can become more efficient and adaptable, which can increase its success in the market. Target groupThe course is aimed at people in a management function (managers, middle managers, team leaders, project managers, etc.) mainly in industry who are interested in learning more about agile working methods. ContentTraditional organizations tend to get clogged over time and inefficient. To mitigate this there is a need for agile organization that rely less on legacy structures and processes and much more on updating and optimizing processes to facilitate better work. In this manner agile organizations become responsive and adaptable to the changes accruing around them.  For achieving the agile transition organizations need agile methods.  This course is targeted for managers within the digitalized industry sector. The course covers the following areas: Foundations of agile methods. Case studies of practical examples of agile adoption in industrial settings. What industry managers should know about agile approach. Assessment methods of agile readiness of the digitalized industrial organization. Strategic thinking and roadmap development for agile transition. The goal of the course is that the participants should be able to: Describe and understand the core principles of agile methods. Understand the possibilities, challenges, and issues while adopting agile methods in industrial organizations. Identify the areas with the digitalized industry sector where agile approaches can be utilized and how can be adopted. Assess and develop a strategy and roadmap for implementing agile ways of working in the digitalized industry. The course is offered in collaboration with Combitech through Stefan Aleborg who has long experience of working with agile methods in different type of organizations. Practical informationThe course is primarily based on the flipped classroom approach with three on-site workshops. Materials will be provided in a form of pre-recorded and online lectures, online guest lectures, reading materials, and scientific articles. The course is assessed through 3 mandatory assignments, mandatory seminars, and active participation in discussions forum and workshops. Teaching language: lectures and materials can be in English, but the physical meetings will be held in Swedish. Entry requirementsThe basic eligibility for this course is a bachelor’s degree. Candidates with corresponding work experience are also invited to apply. Two years of relevant work experience is considered equivalent to one year of university studies at bachelor level. The course is free and gives 3 ECTS course credits  

The purpose of the course “Artificial Intelligence for Managers” is to give managers and decision makers a principle understanding of AI and to increase their understanding of opportunities, difficulties, benefits, and risks connected to AI. It is neither an “Introduction to AI” nor an “AI for dummies” course. Instead, it is set to demystify AI and to transform it into an actionable tool for manages and decision makers. Target groupThis course is for product managers, project managers, executives, and engineering managers in organizations that have already made, or are about to make, the transition to working with AI. ContentThe course is organized in three modules. The initial module will focus an introduction to AI, giving an understanding of what type of cases can be addressed with AI and what managers need to know about AI technology. Module two will cover tools and concrete on how to set up an AI strategy and roadmap, how to get started on AI projects, how to integrate AI and IT development, how to (self) evaluate AI in use, and, not to forget, the ethical and legal aspects of AI. The third module will give the participants the chance to use their new knowledge and tools and work with their own practical cases and how they could be addressed using AI. The goal of the course is to empower the participants to:  Describe the principal concept of AI, its strengths, and shortcomings Understand opportunities, myths, and pitfalls of AI Identify problem areas in industry, society, and in management where AI could be utilized Analyze how AI can be applied in a particular problem area Manage an AI strategy and get started: implement a strategy and a roadmap to apply AI in a particular problem area Understand how to integrate AI with IT development Assess the maturity of AI utilization in an organization Reflect on applications of AI from an ethical and legal perspective as well as the future challenges (technical, organizational, social, etc.) Practical informationAll materials will be accessible and include reading material, lecturer slides etc. The lectures can either be attended live via Zoom or later using the recordings at a time that is convenient for the participants. There will be 3 onsite workshops with a focus on interaction with the teacher and the co-participants of sharing real-life experiences and insights. The course will be delivered in a flexible manner to facilitate the combination of course work with your ongoing professional commitments. The total effort to pass this course is typically around 200 hours. Teaching language: English Entry requirementsThe basic eligibility for this course is a bachelor’s degree. Candidates with corresponding work experience are also invited to apply. Two years of relevant work experience is considered equivalent to one year of university studies at bachelor level. The course is free 

In the modern IT world, businesses often have access to large amounts of data collected from customer management systems, web services, customer interaction, etc. The data in itself does not bring value to the business; we must bring meaning to the data to create value. Data mining and machine learning is an area within computer science with the goal of bringing meaning to and learning from data. This course will focus on applied machine learning, where we learn what algorithms and approaches to apply on different types of data.This course is for experienced developers working in the industry. The course includes the following: Supervised learning, different types of data and data processing, Algorithms for handling text documents, Algorithms for handling data with numerical and categorical attributes, Neural Networks and Deep Learning for image recognition

Applied robotics deals with industrial robots and their use in industry. Different kinds of robots are simulated and presented in the course, namely, mobile robots, logistic robots, collaborative robots and additive manufacturing robots. The course covers the structure and properties of robots, as well as principles for use in industry based on different principles. The course introduces Industry 5.0, which is a human-centered vision of industry that complements the existing Industry 4.0 approach.Based on the requirements placed on a robot system, these can be configured based on different technical starting points, how they should be used and methods to create efficiency. Commonly occurring equipment is taken up as adaptations to different work processes and applications. This is where grippers and sensors come in, as well as process equipment of various kinds. The course covers factors for investment, as well as labs on how programming can be handled. Target group This course is for professionals who work with production systems, automation and robotization at various levels as responsible for individual production lines, departments, or role as production manager or production development. The course will mainly focus on the manufacturing industry in application examples, but the principles covered will be applicable to a number of industries including consulting companies working towards the manufacturing industry. Content The course includes the following: Structure and characteristics of industrial robots in automationGuide and factors when investing in robot systemsLabs in programming with industrial robotsDevelopment trends – a global perspectivePractical information The majority of the parts of the course will take place on Campus (Växjö), and remotely. During the course, participants will be asked to contribute an automation-based study case from the companies where they are active. Study cases are reported during the course. These, together with performed laboratories, constitute the examination of the course. We will as far as possible be flexible with times for the various course elements. Teaching language: Swedish. Literature and certain elements may be in English. The course is free of charge and gives 3 higher education credits, which normally includes approx. 80 hours of work. Course material will be distributed in connection with the course. Entry requirements Basic qualification at advanced level in mechanical engineering or equivalent. Candidates with relevant work experience are also welcome to apply. Two years of relevant work experience is considered equivalent to one year of university studies at bachelor's level. We can validate your competence if necessary. Registration: Registration can be done continuously until the start of the course.

Increased knowledge of condition monitoring and predictive maintenance can help companies and organizations increase their efficiency, reduce costs, improve reliability and sustainability, and increase their competitiveness in the market. It is an important part of modern technical and industrial activities. Target groupThe course is aimed at professionals who work in various ways with condition monitoring and predictive maintenance, such as maintenance engineers, maintenance technicians, maintenance managers and production managers or similar. ContentThe course consists of four parts. In the first part, we focus on the central concepts within maintenance strategies such as condition monitoring and predictive maintenance. How can these strategies contribute to the company's sustainability work and how to establish programs for condition monitoring and predictive maintenance. The second part focuses on building the theoretical base around condition monitoring. Various techniques in condition monitoring will be investigated. It will be practiced theoretically on the identification of problems and deviations in condition monitoring signals. In the third part of the course, practical work takes place. Different techniques will be used to detect and diagnose different problems. We will work on how to choose the appropriate specifications and requirements for sensors and data acquisition systems. The course ends with the fourth part where the project work that involves establishing a condition monitoring system in a maintenance organization is reported and discussed. The course includes the following elements: Maintenance strategies such as condition monitoring, condition-based maintenance and predictive maintenance. Different techniques for condition monitoring. How condition monitoring can affect the company's sustainability work and profitability. How to identify faults and damage by analyzing condition monitoring signals. Specifications of sensors and data acquisition systems. How to establish a condition-based/condition monitoring system. Practical informationThe course consists of lectures, exercises and seminars, these will be offered either online or onsite (see the schedule for more information). Assessment of the students' performance takes place through written assignments and participation in mandatory seminars. All the parts must be approved to be pass the course. The course is given in English. Entry requirementsBasic qualification at advanced level in mechanical engineering or equivalent. Applicants who do not meet this requirement can, by showing corresponding prior knowledge through work experience, be validated as qualified. Two years of relevant work experience then corresponds to one year of college or university studies at basic level.