ERP Systems & Business Process Management

* Distributed databases
* distributed database management system (DDBMS)
* transaction management in DDBMS
* synchronization and recovery in DDBMS
* database management in ERP-systems.

The following content is discussed in the course:

- 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

* Concept of business intelligence and specific aspects such as datawarehouse, OLAP.
* ETL, ELT - process
* Methods and techniques of data mining
* Techniques and up-to-date tools in the field of data warehousing & data mining
* Case studies or projects in the subject area with practical application of tools
* Tools / Vendors in the field of business intelligence

Basics and modeling:
* Introduction to digital twins, their importance and areas of application
* Communication of the theoretical principles and methods of modeling

Simulation and software:
* Overview of simulation techniques and their typical applications
* Getting to know various simulation software and practical exercises

Smart products and solutions:
* Creating and analyzing models for smart, communicating products
* Integration of digital twins into IoT systems and their advantages

In-depth concepts and applications:
* Introduction to advanced topics such as condition monitoring and predictive maintenance
* Discussion about the role of digital twins in future technology development.

Practical project:
* 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

* 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

- 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

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

The contents of this course are not set, but will be adapted to the current prevailing trends. Content examples may include:

- 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

* Overview of a company from a process perspective (e-to-e-e processes)
* Insights into the functional areas of a company (production, logistics, purchasing, sales, finance, controlling)
* Transfer of organizational structures into an ERP system using SAP as an example
* Components of the IT infrastructure (function of computer systems, networks)
* Development of ERP system architectures and deployment models (on-premise, cloud, hybrid, SaaS)

• Elements of IT governance, IT strategy
• Role of IT as a cost center / service center / profit center
• Organizational forms of the IT department
• Operation and further development of software in IT (plan, build, run)
• IT budget, KPIs for IT management, IT performance billing
• IT procurement (make-or-buy)
• IT outsourcing and contract management

* Operational requirements in information management operational and planning tasks
* Overview of the structure and functional scope of typical ERP systems (company codes, business areas, processes)
* Integration of the individual modules of an ERP system
* Overview of ERP system SAP ERP, SAP S4/ Hana, etc.
* Specialization in the SAP modules SD, MM, PP.

* Benefits of requirements engineering
* Basic terms of requirements engineering
* Types of requirements
* Requirements engineering and system development
* Requirements engineering process
* System analysis in requirements engineering
* Business processes and requirements
* Techniques for determining requirements
* Natural language documentation of requirements
* Model-based documentation of requirements
* Deriving test cases from requirements
* Evaluating requirements
* Quality criteria for requirements
* Checking requirements
* Managing requirements
* Tool support

Basic Concepts:
* Business process, process management, workflow, BPMS, WFMS, RPA, low-code/no-code, process mining, hyperautomation

Classification and Differentiation
* WFMS, BPMS, RPA, AI platforms, low-code tools
* Interfaces and integration with ERP/CRM systems

Development and Trends
* From WFMS → BPMS → RPA → hyperautomation
* Digital transformation and the impact of AI

Architecture of digital process systems
* Process engine, orchestrator, bots, APIs, connectors
* Cloud architectures, security, governance

Process spectrum and degree of automation
* Repetitive – variable – knowledge-based
* Selection criteria and potential assessment

From business-oriented to digital process models
* BPMN 2.0, model transformation, executable models
* Connecting data, logic, and interfaces

* Modules for presenting information to decision makers
* Preparation of information for corporate planning and monitoring
* Introduction to SAP modules FI and CO

Introduction
* 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

Implementation
* Procedure for implementing IOT
* Prototypical implementation of IOT
* Selection of sensors
* Collection, visualization and evaluation of data
* Implementation challenges

* 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

* Get to know another ERP system (functional logic, user interface, data model, reporting)
* CRM systems (customer loyalty, sales, marketing automation)
* SRM systems (supplier integration, purchasing, procurement)

The following content is discussed in the course:

- 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

* ERP life cycle
* Overview of ERP systems
* Criteria for selecting ERP systems
* Procedures for implementing ERP systems
* Procedure for testing ERP systems
* Licensing models
* Overview of third-party product range (in addition to SAP)
* Basic functionality of the third-party vendor
* Selected transactions in the third-party vendor's software
* Advantages/disadvantages between SAP and the third-party vendor

- 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.)

* Goals, principles, and layers of IT architectures
* Architectural styles: monolithic, SOA, microservices, event-driven, cloud-native
* Role of IT architecture in the ERP and process management context
* Definition, goals, and benefits of EAM
* EAM frameworks: TOGAF, Zachman, ArchiMate
* Governance, architecture repositories, and EAM tools (e.g., LeanIX, Alfabet)
* Strategic alignment of IT landscapes and roadmapping
* System integration types: data, functional, and process integration
* Interface types: batch, EDI, web services, APIs, messaging
* Middleware, API management, ESB, and iPaaS (Integration Platform as a Service)
* Challenges: data consistency, security, performance
* Architecture and components of SAP BTP
* BTP - Integration Suite, Extension Suite, Workflow Management
* Example use cases (e.g., SAP integration) S/4HANA and non-SAP systems
* Low code and pro-code approaches at BTP

* Cultural/country-specific characteristics of corporate organizations
* Cultural influences on process improvement projects
* Country-specific differences in the selection and implementation of ERP systems

* Working through a specific ERP systems/process management project with a real client in a team
* Conducting IT/process analysis
* Developing system specifications/requirements/functional specifications or process improvement solutions
* Creating all relevant project (process) documents and product documentation.

The graduate, the student:
* Knows scientific methods
* Can formulate research questions and create a disposition to a subject
* Can work on a subject with scientific methods
* Can research literature independently

Independently study and work out a specialist topic from the field of economics and information technology using scientific methods.

* Accompanying the students during the preparation of the Master thesis.
* In the colloquium, the question/hypothesis and structure of the Master thesis are presented and discussed.
* In addition, the scientific methodology of the Master thesis is discussed and questioned and advice is given on the formal design of the Master thesis.

* Identifying customer and business requirements
* Modeling processes
* Applying process modeling tools
* Measuring process performance
* Analyzing processes
* Improving process performance

* The terms quality, quality assurance and quality management
* Types and dimensions of quality
* Conflict and the magic triangle of value-added systems
* Reasons for quality management systems and their benefits
* Historical development of quality management
* Quality costs
* Overview of methods, techniques, tools for implementing and anchoring QM

* Leveraging Process Management
* Process Management Framework
* Selecting Processes for Strategic Improvement
* Visualizing Process Performance
* Performing Process Audits
* Establishing Process Governance
* Process Interface Management
* Implementing Process Management

Facilitation:
* Facilitation writing exercises, flip chart design, pinboard layout, pictogram design, standard visualizations
* Stance, voice, speech, presentation style, and handling disruptions

Presentation:
* Presentation techniques
* Structuring presentations
* Presenting to a specific audience

Communication:
*S olution-focused communication
* Causes of conflict
* Methods for dealing with conflict
* Negotiation techniques
* Classic negotiation models
* Structuring model for conducting negotiations

Recap basics of project management with focus on Lean Six Sigma process improvement projects
* Set up a project (goals, scope, timeline, etc.)
* Planning of deliverables, tasks, time, and resources
* Set up and manage project team and communication with stakeholders
* Controlling deliverables, risks, quality, time and budget

Agile Project Management methods:
* Understanding challenges in traditional project management
* Overview of Scrum Framework and agile Values and principles
* Simulation: Scrum Roles, Events, Artefacts
* Release management in agile Projects
* Hybrid project Management approaches
* Outlook additional agile Methods

AI and Project Management:
* Use of AI in project initiation (e.g., forecasting, business case analysis), planning (e.g., scheduling, resource optimization), execution, and monitoring.
* Automating documentation, reporting, and risk prediction.
* Leadership and Ethics in the Age of AI
* Managing change, bias, and accountability when implementing AI tools.
* Fostering digital literacy and responsible innovation within project teams.

* 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 model and integration in projects
* Techniques and tools of change (e.g. target circle, change stretch, WIIFM, Empathy Map, etc.)