Details
| Original language | English |
|---|---|
| Title of host publication | Springer Series in Advanced Manufacturing |
| Place of Publication | Cham |
| Publisher | Springer Nature |
| Pages | 13-30 |
| Number of pages | 18 |
| ISBN (electronic) | 978-3-030-77539-1 |
| ISBN (print) | 978-3-030-77538-4 |
| Publication status | Published - 2022 |
Publication series
| Name | Springer Series in Advanced Manufacturing |
|---|---|
| ISSN (Print) | 1860-5168 |
| ISSN (electronic) | 2196-1735 |
Abstract
Production planning and control can be supported by Digital Factory methods to optimize production processes and workflows. A central component of the Digital Factory is simulation, which is used to represent real objects and processes in a virtual environment. This virtual environment is suitable for performing analyses and planning processes so that understanding about the real system can be gained. Accordingly, planning processes such as factory planning, investment planning, capacity planning, bottleneck analyses, inventory planning and internal material transport can benefit from simulation by gaining more valid and far-reaching insights. However, simulations must be designed for specific use cases in order to be able to process them. Therefore, the corresponding parameters for the use of the simulation must be determined. The approach presented here focuses on several use cases to create a framework that is not only valid for a single use case, but allows for an arbitrary application which is as comprehensive as possible. This leads to a Digital Twin, which, in turn, can handle several use cases and is not focused on one use case like the simulation. The following chapter deals with the mentioned applications, primarily focusing on the requirements of the use cases for the simulation framework by identifying and specifying the required parameters. Accordingly, a comprehensive list of parameters and their exact properties is presented to support production planning and control. With this understanding, an efficient determination of these parameters can be carried out in the further course of this book, from which the generation of a Digital Twin is enabled.
Keywords
- Digital factory, Digital twin, Process optimization, Production, Simulation
ASJC Scopus subject areas
- Engineering(all)
- Industrial and Manufacturing Engineering
Cite this
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Springer Series in Advanced Manufacturing. Cham: Springer Nature, 2022. p. 13-30 (Springer Series in Advanced Manufacturing).
Research output: Chapter in book/report/conference proceeding › Contribution to book/anthology › Research › peer review
}
TY - CHAP
T1 - Requirements for the Optimization of Processes Using a Digital Twin of Production Systems
AU - Stobrawa, Sebastian
AU - Denkena, Berend
AU - Dittrich, Marc André
AU - von Soden, Moritz
PY - 2022
Y1 - 2022
N2 - Production planning and control can be supported by Digital Factory methods to optimize production processes and workflows. A central component of the Digital Factory is simulation, which is used to represent real objects and processes in a virtual environment. This virtual environment is suitable for performing analyses and planning processes so that understanding about the real system can be gained. Accordingly, planning processes such as factory planning, investment planning, capacity planning, bottleneck analyses, inventory planning and internal material transport can benefit from simulation by gaining more valid and far-reaching insights. However, simulations must be designed for specific use cases in order to be able to process them. Therefore, the corresponding parameters for the use of the simulation must be determined. The approach presented here focuses on several use cases to create a framework that is not only valid for a single use case, but allows for an arbitrary application which is as comprehensive as possible. This leads to a Digital Twin, which, in turn, can handle several use cases and is not focused on one use case like the simulation. The following chapter deals with the mentioned applications, primarily focusing on the requirements of the use cases for the simulation framework by identifying and specifying the required parameters. Accordingly, a comprehensive list of parameters and their exact properties is presented to support production planning and control. With this understanding, an efficient determination of these parameters can be carried out in the further course of this book, from which the generation of a Digital Twin is enabled.
AB - Production planning and control can be supported by Digital Factory methods to optimize production processes and workflows. A central component of the Digital Factory is simulation, which is used to represent real objects and processes in a virtual environment. This virtual environment is suitable for performing analyses and planning processes so that understanding about the real system can be gained. Accordingly, planning processes such as factory planning, investment planning, capacity planning, bottleneck analyses, inventory planning and internal material transport can benefit from simulation by gaining more valid and far-reaching insights. However, simulations must be designed for specific use cases in order to be able to process them. Therefore, the corresponding parameters for the use of the simulation must be determined. The approach presented here focuses on several use cases to create a framework that is not only valid for a single use case, but allows for an arbitrary application which is as comprehensive as possible. This leads to a Digital Twin, which, in turn, can handle several use cases and is not focused on one use case like the simulation. The following chapter deals with the mentioned applications, primarily focusing on the requirements of the use cases for the simulation framework by identifying and specifying the required parameters. Accordingly, a comprehensive list of parameters and their exact properties is presented to support production planning and control. With this understanding, an efficient determination of these parameters can be carried out in the further course of this book, from which the generation of a Digital Twin is enabled.
KW - Digital factory
KW - Digital twin
KW - Process optimization
KW - Production
KW - Simulation
UR - http://www.scopus.com/inward/record.url?scp=85151512344&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-77539-1_2
DO - 10.1007/978-3-030-77539-1_2
M3 - Contribution to book/anthology
AN - SCOPUS:85151512344
SN - 978-3-030-77538-4
T3 - Springer Series in Advanced Manufacturing
SP - 13
EP - 30
BT - Springer Series in Advanced Manufacturing
PB - Springer Nature
CY - Cham
ER -