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iQM > Internet of Production > Flashcards

Internet of Production Flashcards

1
Q

Name the three big parts of the product life cycle

A

Production Cycle
User Cycle
Development Cycle

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2
Q

What is a Data Warehouse

A

A Data Warehouse is an analytically-optimized central database that aggregates and condenses data from multiple, typically heterogeneous sources.

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3
Q

What is A Data Lake

A

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.

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4
Q

The Database should be chosen based on the Use Case

Name different databases and give for each database an example

A
  • 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)
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5
Q

Automation Pyramid classifies IT-Systems according to Organizational Level

Name the different Levels

A

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

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6
Q

Overview & Function of relevant Application Software in Production

A
  • Product Lifecycle Management (PLM)
  • Computer-Aided Design (CAD)
  • Finite Element Method (FEM)
  • Enterprise Resource Planning (ERP)
  • Manufacturing Execution System (MES)
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7
Q

What does the middleware+ do?

A

Middleware reduces the Complexity of Communication

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8
Q

Descriptive Analytics

A

What happened?

Historical analysis to establish statistical benchmarks, to refine existing rules, and to identify new rules.

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9
Q

Diagnostic Analytics

A

Why did it happen?

Where rules are lacking, identifying root causes, key factors, and unseen patterns.

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10
Q

Predictive Analytics

A

What will happen?

Analysis of patterns to establish trends, quantify probabilities, and reduce uncertainties.

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11
Q

Prescriptive Analytics

A

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.

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12
Q

Main Take-Aways from Today’s Lecture

A

▪ 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

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13
Q

Advantages of Data Warehouse

A
  • 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
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14
Q

Disadvantages of Data Warehouse

A
  • Data redundancy
  • Data not completely up to date
  • High administration effort
  • High costs
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15
Q

Advantages of – Data Lake

A
  • 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
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16
Q

Disadvantages of – Data Lake

A
  • 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
17
Q

Relational Databases: Pro’s & Con’s

A

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

18
Q

– Object-Oriented Databases: Pro’s & Con’s

A

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
19
Q

Object-Relational Database: Pro’s & Con’s

A

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
20
Q

Document Database: Pro’s & Con’s

A

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
21
Q

– Key-Value Database: Pro’s & Con’s

A

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
22
Q

Distributed Database: Pro’s & Con’s

A

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
23
Q

Product Lifecycle Management (PLM)

A

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.

24
Q

Computer-Aided Design (CAD)

A

Computer-Aided Design (CAD) describes the support of constructive tasks by means of
EDP for the production of a product.

25
Q

Finite Element Method (FEM)

A

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.

26
Q

Enterprise Resource Planning (ERP)

A

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.

27
Q

Manufacturing Execution System (MES)

A

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.

iQM (8 decks)

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