Internet of Production Flashcards
Name the three big parts of the product life cycle
Production Cycle
User Cycle
Development Cycle
What is a Data Warehouse
A Data Warehouse is an analytically-optimized central database that aggregates and condenses data from multiple, typically heterogeneous sources.
What is A Data Lake
A Data Lake is a large data store without previous ETL (extract, transform, load) that contains data in its original format from multiple, typically heterogeneous sources.
The Database should be chosen based on the Use Case
Name different databases and give for each database an example
- Relational Database (e.g. customer data base)
- Object-Oriented Database (e.g. eBay)
- Object-Relational Database (e.g. depth image)
- Document Database (e.g. Wikipedia)
- Key-Value Database (e.g. dictionary)
- Distributed Database (e.g. google search engine)
Automation Pyramid classifies IT-Systems according to Organizational Level
Name the different Levels
Level 5: Company Level
Enterprise Resource Planning (ERP)
Level 4: Operations Command Level
Manufacturing Executive Systems (MES)
Level 3: Process Control Level
Human-Machine-Interface (HMI) /Control and Data Acquisition (SCADA)
Level 2: Control Level
Programmable logic controller (PLC)
Level 1: Field Level Input Signals (Sensors) /Output Signals (Actuators)
Level 0: Process Level
Manufacturing /Production Process
Overview & Function of relevant Application Software in Production
- Product Lifecycle Management (PLM)
- Computer-Aided Design (CAD)
- Finite Element Method (FEM)
- Enterprise Resource Planning (ERP)
- Manufacturing Execution System (MES)
What does the middleware+ do?
Middleware reduces the Complexity of Communication
Descriptive Analytics
What happened?
Historical analysis to establish statistical benchmarks, to refine existing rules, and to identify new rules.
Diagnostic Analytics
Why did it happen?
Where rules are lacking, identifying root causes, key factors, and unseen patterns.
Predictive Analytics
What will happen?
Analysis of patterns to establish trends, quantify probabilities, and reduce uncertainties.
Prescriptive Analytics
What needs to happen for it to happen?
Methods which specify an optimal process to ensure business resources are being focused on KPIs and measurable results.
Main Take-Aways from Today’s Lecture
▪ Internet of Production represents a structure to use and benefit from the
increasing amount of data in production
▪ The raw data and application software are the basis for further analysis and
use of data for decision support
− Raw data has to be stored in the right database
− As different types of application software are more and more fulfilling the
same functions, the software architecture has to be designed individually
▪ Middleware+ connects the different software systems
▪ Smart data can be used for different purposes; depending on the purpose,
different machine learning algorithms might be chosen
▪ Smart Expert Systems enable a decision support respectively autonomous
decisions
Advantages of Data Warehouse
- Flexible, easy-to-use, multidimensional data analysis
- High data quality through integration, cleansing and aggregation of data from heterogeneous data sources
- Performance
- Independent of operational systems
Disadvantages of Data Warehouse
- Data redundancy
- Data not completely up to date
- High administration effort
- High costs
Advantages of – Data Lake
- Ability to derive value from unlimited types of data
- Ability to store all types of structured and unstructured
- Ability to store raw data
- Elimination of data silos
- Unlimited ways to query the data
Disadvantages of – Data Lake
- Does not work with traditional data storage and analytical technologies
- Takes enormous quantities of storage
- Analyzing the data needs a lot of processing power
- Danger of inaccessible data swamps
Relational Databases: Pro’s & Con’s
Pro:
- Ability to scale the database to the size of a very large organization
- Ability to access, update and share information among many user stations
- Ability to program a query to search all data tables for the exact information you need
- Incremental data storage gives a historical perspective of the data
Cons:
Expensive to set up and maintain the database system
- Some relational databases have limits on field lengths
- Relational databases are made for organizing data by common characteristics
– Object-Oriented Databases: Pro’s & Con’s
Pro:
- Capable of handling a large variety of data types
- Allows new data types to be built from existing types
- Capable of handling a large variety of data types
- Significant performance improvements over relational
DBMSs
Cons:
- Lack of universal data model
- Lack of experience
- Lack of standards
- Complexity
- Lack of support for security
Object-Relational Database: Pro’s & Con’s
Pro:
- Increased productivity
- Reuse and sharing
- Expandability
- Object relational databases allow the use of inheritance
Cons:
- Complexity and associated increased costs
- May be very difficult to extend index structures and query optimizers
Document Database: Pro’s & Con’s
Pro:
- Able to handle large volumes of structured, semi-structured, and unstructured data
- Easy storing of unstructured data
- Independence of documents
- Flexible and easy to use
Con’s
- Security issues
- Data consistency
- Lack of standardization
- Scalability
– Key-Value Database: Pro’s & Con’s
Pro:
- Scalability
- Fast and efficient data processing
- High flexibility
- High availability
Con’s
- Limited query possibility, because access to a record is done only by a key
- Lack of standardization
- Not intuitive, not easy to use
Distributed Database: Pro’s & Con’s
Pro:
- Easier expansion
- Protection of valuable data
- Systems can be modified, added and removed from the distributed database without affecting other modules
- Single-site failure does not affect performance of system
Con’s
- Complexity
- Costs
- Difficult to maintain integrity
- Lack of standards
Product Lifecycle Management (PLM)
A PLM system organizes and integrates the different phases of a product’s lifecycle. PLM is a collection of
software tools and working methods integrated together to address either single stages of the lifecycle,
connect different tasks or manage the whole process.
Computer-Aided Design (CAD)
Computer-Aided Design (CAD) describes the support of constructive tasks by means of
EDP for the production of a product.
Finite Element Method (FEM)
The Finite Element Method (FEM) is a numerical method for solid state calculation. With FEM deformations and various
other properties can be calculated and simulated. The method is used for targeted structural analysis and optimization.
Enterprise Resource Planning (ERP)
An Enterprise Resource Planning (ERP) system supports all business processes running in a company. It contains
modules for the areas of procurement, production, sales, asset management, human resources, finance and accounting,
etc., which are linked together via a common database.
Manufacturing Execution System (MES)
A Manufacturing Execution System (MES) provides information that help manufacturing decision makers
understand how current conditions on the plant floor can be optimized to improve production output.
