Smart Products & AI-driven Development

• Programming languages (classification, principles, history)
• Detailed consideration of a modern programming language (e.g. Phyton)
• Overview and selection of a coding assistant
• Structure of programs
• Data types, operators, flow structures
• Development environment
• Typical work steps
• Setting up the development environment
• AI enabled Programming (input, debugging, execution)
• Independent planning and programming based on the programming languages taught in the lecture
• Development of AI-enhanced programs

• Fundamentals of data transmission
• Wired data transmission
• Wireless communication
• Technologies and applications of modern networks
• OSI, hybrid and TCP/IP reference model
• Wired data transmission (I2C communication, serial communication)
• Wireless networks
• Sensor networks and approaches (e.g. MANET, WMN)
• End-to-end protocols: UDP, TCP
• Selected protocols of the application layer (e.g. FTP, HTTP, HTTPS, SMTP, MQTT)
• Security concepts and access procedures
• Release updates for networks
• Elements of a measurement report
• Selection of transmission technologies and protocols

• Introduction (data, information, knowledge, temporal components, objectives)
• Data process (collection, preparation, analysis, presentation)
• Data preparation (cleansing, transformation, rescaling, storage)
• Approaches for the analysis of data
• Presentation/visualization of results
• Software (open source and proprietary software)
• Machine Learning - process, approaches, implementation
• Introduction to the software used e.g. Python
• Collecting and preparing data using software
• Analysis and presentation of sample data using various approaches (e.g. regression, decision trees, etc.)

• Overview of terms and definitions
• Basic algorithms and models
• Identification and evaluation of application areas in the context of the product development process of smart products
• Limitations of AI
• Generation and modification of texts
• Generation and modification of images and videos
• Audio generation and modification
• Prompting strategies (e.g. retrieval augmented prompting)
• Use and interaction with AI
• Local vs. Hosted AI Models
• Quality assurance of AI models
• Ethical considerations and implications when using AI models
• Limitations of different models and strategies

• Digital trends and waves of change
• Difference between digital and traditional companies or their development paths
• Design levels, framework conditions and challenges of digital change
• Frameworks for the assessment of digital maturity
• Smart products
• Development
• Ecosystem IoT and data
• Use cases of smart products and solutions
• Importance of change management
• Individual and social aspects of change
• Resistance, conflict and crisis
• Basic approaches to change management
• Types of change
• Models of change (e.g. Lewin, GE-CAP, etc.)
• (Project) management of change: Generic phase
• Models of change (e.g. Lewin, GE-CAP, etc.)
• (Project) management of change: Generic phase model and integration in projects
• Techniques and tools of change (e.g: Target circle, change stretch, WIIFM, empathy map, etc.)

• Fundamentals of the digital economy and trends
• Digital business models
• Importance of data
• Approaches to developing strategies, business models and business process models
• Business plans
• Risk reduction/implementation

• Introduction to data protection and its implementation
• Definitions: personal data, data registers, publicly accessible data, pseudonymization
• Informational self-determination, laws and regulations on data protection
• Rights of those concerned
• Organizational measures for the protection of personal data
• Criminal/civil law aspects: Trade secrets, refusal to testify
• Introduction to ethics as the science of good and right decisions and actions
• Relevant factors of ethical judgement
• Methods of ethical argumentation
• Concept of responsibility
• Business and technical ethics as sub-disciplines of ethics
• Ethical challenges in digitalized and global information and knowledge societies
• Effects of technological change on work life balance and family

• Introduction to digital twins, their importance and areas of application
• Communication of the theoretical principles and methods of modeling
• Overview of simulation techniques and their typical applications
• Getting to know various simulation software and practical exercises
• Creating and analyzing models for smart, communicating products
• Integration of digital twins into IoT systems and their advantages
• Introduction to advanced topics such as condition monitoring and predictive maintenance
• Discussion about the role of digital twins in future technology development.
• Planning and implementing your own digital twin project to apply what you have learned

• Fundamentals of Human–AI Interaction
• Human cognition, mental models, decision-making behavior
• HCI principles for AI systems
• UX Design for AI
• Interaction patterns (conversational UI, agents, multimodal interfaces)
• Prompt design, system messages, feedback loops
• Trust and user acceptance
• Human-in-the-Loop & Human-on-the-Loop
• Alignment of human–machine roles
• Task distribution, limits of automation
• Error prevention & recovery design
• Empathetic & Social Aspects of AI Interaction
• Social responses, trust
• Persuasive and adaptive systems
• Methods & Tools
• Usability testing for AI systems
• Prototyping tools
• Evaluation methods (task success, trust metrics, cognitive load)

• Basics of Descriptive Statistics
• Measurement System Analysis
• Sampling
• Statistical Process Control
• Process Control Charts
• Process Capability Analysis
• Components of Variants Analysis (COV)
• Repetition Basics of Concluding Statistics
• Failure Cause Determination via Hypothesis Testing (T-test, Chi-Sq, ANOVA) * Multiple Regression Analysis

• Evaluation tools with visual orientation, e.g. Bl tools such as MS PowerBl, Tableau, QlikView
• Display libraries, e.g. matplotlib. pyplot, gglot2
• Rules of visual communication, e.g. Hichert SUCCESSSS

• New technologies in the field of AI Engineering
• Trends in the field of programming languages for AI
• New design concepts in the field of AI
• New questions in the field of research in AI
• New questions in the field of AI practice

• New technologies in the field of Big Data Processing
• Trends in programming languages in data analysis
• New concepts of data processing (e.g. Data Lake)
• New questions in the field of data science research
• New questions in data science practice

• Current best practice approaches and concepts in application areas (e.g. Smart Home, Smart City, Smart Production, Connected Vehicles, etc.)
• Current best practice approaches with regard to development processes and tools
• Current research and development activities or research and development results

• IoT architecture (e.g. reference models)
• Requirements for IOT systems
• IoT data transmission protocols
• Use of IoT in an industrial context (examples)
• Basics of sensor technology
• Basics of embedded systems
• Procedure for implementing IOT systems
• Prototypical implementation of IOT systems
• Selection of sensors
• Collection, visualization and evaluation of data
• Implementation challenges

• Presentation of different user-oriented analysis platforms (e.g. KNIME, RapidMiner, Grafana)
• Presentation of different cloud solutions for data analysis (e.g. Google Cloud, AWS, Azure)
• Application of the platforms presented using the example of analysis data sets
• Discussion of the different approaches

• personnel deployment planning and resource management
• contract management and finance management (cost and activity accounting)
• quality management and quality assurance (reviews, audits, etc.)
• staff information and communication; IT strategy and IT portfolio management;
• risk management; IT governance and compliance
• ITIL, CoBIT
• service level agreements and outsourcing
• balanced score card methodology
• IT management tools (ADOit, etc.)

• Basic terms: Business process, workflow, BPMS, WFMS, RPA, etc.
• Selection criteria for workflow engines for process automation
• Architecture and integration of workflows for process automation
• Overview of interprocess communication
• Transactional properties of processes, simulation and code generation
• Basics of Microsoft Dynamics 365: Modules and navigation, basic entities and standard workflows
• Organizational and technical implementation with configuration and declarative programming

• Current developments in the area of business application systems with
special emphasis on ERP-systems. Models, examples, best practice cases

• Basics of project management
• Project management methods and tools
• Roles in projects; communication and documentation
• Problem, conflict, risk and crisis management
• Theories/models and procedures for teambuilding
• Tools for supporting teambuilding

• Understanding Manufacturing Processes and Assembly Techniques
• Material Selection and Application
• Integration of Material Science in Design
• Sustainability in Manufacturing
• Innovative Manufacturing Technologies
• Cost Analysis and Efficiency
• Quality Control and Testing

• International management in a country-specific context
• R&D strategy and systems at country level
• Technology and innovation approaches

• Advancing the methods and approaches of scientific work
• Research design for scientific questions
• Qualitative and quantitative research methods
• Use of methods and tools
• State of the art literature research and use of citation tools e.g. Citavi
• Preparation of the exposé for the Master thesis
• Notes on the formal design of the Master thesis

• Working in a team and team organization
• Deducing a problem
• Choosing a suitable methodology and corresponding tools
• Planning, implementing and managing projects
• Integrative applications of acquired skills and knowledge
• Preparing and communicating results
• Self-reflection

• Supervising the students during the preparation of the Master thesis
• Presentation and discussion of the question/hypothesis, structure of the Master thesis, scientific methodology and formal design of the Master thesis
• Review of the analysis part and critical reflection for evaluation
• Structure of the thesis, appropriate division, clear result structure and conclusion
• Short overview of the Master's examination and its requirements

• Support of the students during the preparation of the Master thesis
• Presentation and discussion of the question/ hypothesis, structure of the Master thesis, scientific methodology and formal design of the Master thesis

• System solutions and architecture of embedded systems and characteristics
• Embedded hardware (processors, memory, I/O, busses)
• Embedded software (operating system, middleware, application, drivers)
• Real-time operation (classification, implementation)
• Multi-processor operation
• Getting to know simple platforms (e.g. Raspberry Pi, Arduino, FPGA) and the development environment
• Implementing simple use cases concerning the processing of sensors and the control of actuators
• Implementing different possibilities of data transmission
• Realization of a more complex final project

• Types of product development processes for different products, markets and companies
• Traditional PE vs. Agile PE vs. Mixed - which concept under which conditions
• Design Thinking as a central development concept
• Methods of product development:
• Ideating and creative methods for solving problems like: Ishikawa, root-course, risk management, functional analysis, SWOT, FMEA,…..
• Requirements engineering
• Market and patent analysis
• Concept
• Systematical finding of technical solutions using AI
• Evaluation of ideas and concepts
• Design
• Simulation of functional aspects (stress/strain, displacements, movement,….)
• Prototyping
• Testing
• Special aspects of smart products

• Definition/classification of sensors
• Structure and function of a measuring chain
• Fields of application and functional principles of sensors
• Basics of microsensors
• Sensor properties (static and dynamic behavior, reliability, etc.)
• Measurement errors and sources of error
• Calibration
• Signal transmission/processing
• Basic terms of relevant measurement technology
• Application of sensors for different areas (e.g. temperature, weight, pressure, acceleration, position, etc.)
• Analysis and evaluation of functional principles and sensor properties
• Structure and properties of data transmission and data processing in a measurement chain
• Evaluation, interpretation and storage of measurements

• Laser engravers (Lasercut)
• Additive manufacturing methods (e.g. 3D printing)
• Digital prototypes (e.g. Click Dummies for Apps)
• Scan technologies
• Basics of CAx
• Virtual prototyping
• Design thinking
• Creativity methods
• Creating a Prototype

• Definition/delimitation of product design
• Classification in the product development process
• Procedure, approaches/principles and tools for product design with focus on generative AI
• Evaluation criteria and the evaluation of developed or existing concepts

• Product & process life cycle
• Risk determination strategies
• Planning of safe systems (concepts & strategies)
• Planning of secure systems (threat analysis, countermeasures)
• Structure & concepts of reliable control systems

• Special features of smart products
• New product management approaches
• Market research
• Product strategy
• Purchase decision criteria or acceptance (drivers, obstacles)
• Special features of introduction and marketing
• Data-based decisions
• Life cycle management