IND418 Continuous Improvement and Lean (Autumn 2026)

• Upon successful completion of the course, students will achieve the following learning outcomes in terms of knowledge, skills, and general competence:
  Knowledge
  The student will be able to:
  analyse and apply Lean principles and philosophy in modern organisations, and assess how they can be operationalised in environments characterised by artificial intelligence, automation, and data-driven improvement
• critically reflect on the relationships between Lean, innovation, and sustainability, and evaluate how these perspectives influence development and improvement work
• explain fundamental concepts within artificial intelligence, machine learning, robotics, and automation, and assess how such technologies influence improvement work in practice
• understand and apply process mapping techniques to identify flow, waste, and improvement opportunities, including digital and AI-supported methods
  

Skills
The student will be able to:

• carry out improvement work in an organisation or establish an AI- or automation-based improvement case
• integrate theory, empirical data, and AI-supported analysis in a reflective, research-based improvement project
• communicate improvement findings and recommendations in a structured, precise, and research-based manner
  

General Competence
The student will be able to:

• critically reflect on ethical, organisational, and societal implications of AI, automation, and continuous improvement
• work independently and in teams on complex problems at the intersection of people, technology, and organisation

The course provides a comprehensive and practice-oriented introduction to continuous improvement and Lean, situated within a modern context where artificial intelligence, digitalization, robotics, and automation play an increasingly important role.
Emphasis is placed on the interaction between people, technology, and organizations, and on how improvement work can be conducted in a sustainable and future-oriented manner.
Core Topics

• Lean philosophy, value creation, flow, and waste
• Continuous improvement in technology-intensive environments
• Artificial intelligence, machine learning, predictive analytics, and automation in improvement work
• Variation, process understanding, and data-driven decision-making
• Process mapping and digital mapping techniques
• Organisational learning, change management, and interdisciplinary improvement teams
• Ethics, risk, and societal consequences of AI and automation
• Future scenarios for manufacturing, the public sector, and service industries

The course is delivered as blended learning, combining short lectures, video-based micro-modules, asynchronous academic content, and active learning approaches. The pedagogical approach is inspired by flipped classroom, project-based learning, and problem-based learning.
Learning activities may include:

• short lectures and video summaries
• workshops and practical exercises
• game-based learning
• group-based case work
• analyses and simulations, including the use of AI tools
• discussions and reflection assignments

The total workload for the course is approximately 200 hours.