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Courses

• Train-the-Trainers for MODAPTO Learning
• 14. Digital Transformation and Innovation for Enabling Reconfigurable Production Systems
• 13. Design Reconfiguration
• 12. Service Catalog
• 11. Orchestration of Smart Services
• 10. Notification Center
• 9. Virtual Commissioning and Testing
• 8. Predictive Maintenance
• 7. Self-Awareness
• 6. Local Analytics
• 5. Global Optimization
• 4. Local Optimization
• 3. Production Design, Configuration, and Resource Balancing
• 2. Simulation of logistics operations
• 1. Monitoring

Lessons

• Notes for Trainers: Module 13: Design Reconfiguration
• Notes for Trainers: Module 12: Service Catalog
• Notes for Trainers: Module 11: Orchestration of Smart Services
• Notes for Trainers: Module 10: Notification Center
• Notes for Trainers: Module 9: Virtual Commissioning and Testing
• Notes for Trainers: Module 8: Predictive Maintenance
• Notes for Trainers: Module 7: Self-Awareness
• Notes for Trainers: Module 6: Local Analytics
• Notes for Trainers: Module 5: Global Optimization
• Notes for Trainers: Module 4: Local Optimization
• Notes for Trainers: Module 3: Production Design, Configuration, and Resource Balancing
• Notes for Trainers: Module 2: Simulation of logistics operations
• Notes for Trainers: Module 1: Monitoring
• 6.1.2 Integration of FMUs as simulation model in standardized digital twins based on AAS standard
• 14.1.6. References – Extra Reading Material
• 14.1.5. Business Models of MODAPTO
• 14.1.4. Assessment Questionnaire for Reconfigurable Manufacturing Maturity of SMEs
• 14.1.3. Innovations in Manufacturing Business Models
• 14.1.2. Modapto under the lens of Digital Transformation
• 14.1.1. Manufacturing Digital Transformation
• 8.4.4 Example of algorithm use in MODAPTO
• 8.4.3 Example of algorithm use in MODAPTO
• 8.4.2 Example of algorithm use in MODAPTO
• 8.4.1 Example of algorithm use in MODAPTO
• 8.3.4 Maintenance Strategy Modeling
• 8.3.3 Maintenance Strategy Modeling
• 8.3.2 Maintenance Strategy Modeling
• 8.3.1 Maintenance Strategy Modeling
• 8.2.2 Modeling Dysfunctional Behavior
• 8.2.1 Modeling Dysfunctional Behavior
• 8.1.2 Mathematical Maintenance Optimization Process
• 8.1.1 Introduction to Maintenance Optimization
• 10.1.6 Task Notification Details Page
• 10.1.5 System Notification Details Page
• 10.1.4 Delete Notification
• 10.1.3 Notifications List
• 10.1.2 New Notifications
• 10.1.1. Notification Mapping Page
• 5.6.3 Optimization vs Re-optimization: Scheduling Horizon
• 5.6.2 Hybrid Approaches
• 5.6.1 Comparing Exact and Metaheuristic Methods
• 5.5.3 Population-Based Strategies on Production Scheduling
• 5.5.2 Search Strategies on Production Scheduling
• 5.5.1 Metaheuristics Overview
• 5.4.3: Limitations of Exact Methods
• 5.4.2: Integer and Linear Programming
• 5.4.1: Exact Methods Overview
• 5.3.2. Machine Properties
• 5.3.1. Types of Machines in Production Systems
• 7.5.3 Prognostics Evaluation Metrics
• 7.5.2 Prognostics Main Techniques for RUL
• 7.5.1 Prognostics Forecasting System Health
• 7.4.3 Fault Diagnosis Evaluating Models
• 7.4.2 Fault Diagnosis Review of Main Techniques
• 7.4.1 Fault Diagnosis Basics
• 7.3.3 Fault Detection Evaluation Metrics
• 7.3.2 Fault Detection Review of Main Techniques
• 7.3.1 Fault Detection Basics
• 7.2.2 Industrial Survey on Prognostics Understanding Context Challenges and Implementation Perspectives
• 7.2.1 Prognostics and Health Management The Big Picture
• 7.1.1 Introduction Maintanance Strategy evolution
• 9.1.2 Introduction to Virtual Commissioning
• 1.1.3. Using the system dashboard to monitor production cell performance and view its current capacity.
• 3.2.2. Comparing Setups for Optimal Performance
• 3.2.1. Simulation tools for evaluating configurations
• 3.1.2. Methods for reconfiguring production setups
• 3.1.1. Designing production steps, cells, and modules
• 6.1.1 Introduction standard FMI
• 6.1.1 Introduction to Virtual Commissioning
• 9.2.2 Metrics to evaluate robot designs and production line efficiency
• 9.2.1 Tools for integrating sustainability KPIs in virtual commissioning
• 9.1.1 Virtual environments for testing robot setups and production scenarios
• 13.6.2 MessageBus communication protocols and data exchange formats
• 13.6.1 Integration with MODAPTO Digital Twin Manager architecture
• 13.5.1 Mapping MODAPTO production processes to BPMN workflow models
• 13.4.1 BPMN notation elements and workflow modeling principles
• 13.3.2 Implementing AAS serialization formats and validation
• 13.3.1 Creating AAS submodels and properties for MODAPTO components
• 13.2.2 AAS metamodel components and data structures
• 13.2.1 Asset Administration Shell fundamentals and architecture
• 13.1.2 Module interface definition and compatibility assessment
• 13.1.1 Module classification and categorization methods
• 12.2.2 Managing service lifecycle and version control through registration process
• 12.2.1 Registering Smart Service Manifest in the service Catalogue
• 12.1.2 Defining service metadata and configuration parameters
• 12.1.1 Understanding Smart Service Manifest structure and components
• 1.5.2. Configuration and optimization techniques
• 1.5.1. Production cell design principles
• 1.4.3. Notification setup for delivery delay risks
• 1.4.2. Identifying delays, bottlenecks
• 1.4.1. Tracking inbound and outbound logistics
• 1.1.2 Using the System Dashboard to Monitor Production Cell Performance and Identify Bottlenecks
• 1.3.4. Tools for Real-time Issue Reporting
• 1.3.3. Reporting Process Drift and Production Issues
• 1.3.2. Importance of reporting time spent
• 1.3.1. How operators log the production cell
• 1.2.2 Analyzing data to understand causes of discrepancies
• 1.2.1 Methods for comparing identical production cells
• 11.3.2 Implementing orchestration logic and error handling
• 11.3.1 Designing service workflows and dependencies
• 11.2.1 Designing connector architecture and API specifications
• 11.2.2 Implementing and testing custom connectors
• 11.1.2 Managing service permissions and access controls
• 11.1.1 Creating and configuring a smart service profile
• 4.4.3. Visualization of Optimization Results
• 4.4.2. Workflow Configuration and Management
• 4.4.1. Interface Design for Optimization Tools
• 4.3.4. Input and Output Data Structure
• 4.3.3. Integration of Optimization Service with Digital Twins 
• 4.3.2. Optimization Service setup
• 4.3.1. Optimization Service description
• 4.2.2. Robot Picking Sequence Optimization for Pick-and-Place Tasks
• 4.2.1. Robot Movement Optimization of Individual Tasks
• 4.1.3. Input and Output data structures
• 4.1.2. Solution Approaches to Industrial Applications
• 4.1.1. Industrial Application of TSP and BTSP
• 2.2.2. Selecting the best sequence
• 2.2.1. Evaluating various sequences
• 2.1.2 Simulation of logistics processes
• 2.1.1 Introduction to material handling
• 5.2.1 Types of Constraints in Production Scheduling
• 5.1.3 KPIs of Scheduling Optimization
• 5.1.2 Key Objectives of Scheduling Optimization
• 5.1.1 Overview of Production Scheduling
• 1.1.1 Accessing and Viewing the Current Capacity of Production Cells

Quizzes

• QUIZ FOR MODULE 14
• Quiz: Modapto Notification System
• Quiz: Selecting Between Exact and Metaheuristic Methods
• Quiz: Metaheuristic Methods for Production Scheduling Optimization
• Quiz: Exact Methods for Production Scheduling Optimization
• Quiz: Machine Environments and Properties
• Quiz on Simulating and Assessing Different Setups
• Quiz on Fundamentals of Production Process Design
• 6.1 Quiz
• 6.1 Quiz
• Quiz: Introduction to Virtual Commissioning
• Quiz: Sustainability Assessment in Virtual Environments
• Quiz: Setting Up the Optimization Engine
• Quiz: Task-Specific Optimization
• Quiz: Design Reconfiguration Knowledge Assessment
• The following quiz tests knowledge of the Service Catalogue topics covered in sections 12.1 and 12.2.
• Introduction to Optimization
• Quiz for Topic 1.5
• Quiz for Topic 1.4
• Quiz for Topic 1.2
• Quiz on Operator Interaction and Reporting
• Quiz on Orchestration of Smart Services
• Quiz - 2.2. Comparing Different Sequences
• Quiz - 2.1. Simulating and Assessing Logistics Systems
• Quiz on Understanding Production Scheduling Constraints 
• Quiz on Introduction to Production Scheduling 
• Quiz on Real-Time Monitoring of Production Cells