Role of simulation in industrial engineering: focus on manufacturing systems

Abstract

Simulation has recently grown its importance thanks to the Industry 4.0, based on CPS (Cyber-Physical Systems). Especially, simulation is becoming more central for improved decision-making.
The article provides a literature analysis of peer-reviewed surveys about simulation applications in industrial engineering in manufacturing. In particular, a three-axis framework, called 3D-SAM, is proposed to classify the applications and to critically analyse them. The framework can be used as a first input for the Cognition Level of CPS (5C architecture for CPS development) in order to develop integrated simulation models within a Decision Support System (DSS).

Full text

IFAC PapersOnLine 51-11 (2018) 496–501
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2405-8963 © 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
Peer review under responsibility of International Federation of Automatic Control.
10.1016/j.ifacol.2018.08.367
©
2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
10.1016/j.ifacol.2018.08.367 2405-8963
Role of simulation in industrial engineering:
focus on manufacturing systems
A. Polenghi*, L. Fumagalli*, I. Roda*
* Department of Management, Economics and Industrial Engineering, Politecnico
di Milano, Piazza Leonardo da Vinci 32, 20133 Milan (MI), Italy
Corresponding author e-mail: adalberto.polenghi@polimi.it
Abstract: Simulation has recently grown its importance thanks to the Industry 4.0, based on CPS (Cyber-
Physical Systems). Especially, simulation is becoming more central for improved decision-making.
The article provides a literature analysis of peer-reviewed surveys about simulation applications in
industrial engineering in manufacturing. In particular, a three-axis framework, called 3D-SAM, is
proposed to classify the applications and to critically analyse them. The framework can be used as a first
input for the Cognition Level of CPS (5C architecture for CPS development) in order to develop
integrated simulation models within a Decision Support System (DSS).
Keywords: simulation, review, simulation application, manufacturing, DSS.
1. INTRODUCTION
In the Digitalization era, simulation covers a primary role in
every field (finance, management, and manufacturing) and at
all levels (strategic, tactical, and operational). In particular,
the industrial engineering field, to which manufacturing
belongs to, is a very flourished land for simulation
development, taking advantage of the recent deployment of
Cyber-Physical Systems (CPS) (Negri, Fumagalli and
Macchi, 2017).
The importance of simulation in industrial engineering was
already highlighted by the first pioneers in simulation
research studies, since the 80s: it was considered among the
first top three methodologies used by industrial engineers,
managers and operations researchers (Banks, 1998).
Moreover, simulation is ranked as the second most used
methodologies for the OM (Operations Management) field of
studies (Shafer and Smunt, 2004), only after optimisation.
This view of simulation as central in company and research is
demonstrated by many research papers and empirical studies,
whose number has been continuously increased since the first
conceptualisation of possible uses of simulation in IE, in the
mid of 80s. To figure out the amount of works done, some
numbers are here provided: from 2002 to 2013, the searching
process with the keywords “simulation” plus
“manufacturing” finds out about 3000 published papers,
whereas the overall research process with other keywords,
such as “industrial engineering” or “operations”, results in
around 12000 published papers in the same periods
(Negahban and Smith, 2014). This results are impressive and
are confirmed by another survey made in the same year
(Mourtzis, Doukas and Bernidaki, 2014), for which the final
detailed graph is reported.
Figure 1. Numbers of publications related to simulation
(Mourtzis, Doukas and Bernidaki, 2014).
Many definitions of simulation consider computer
involvement as a consistent part of the definition itself:
“Simulation modelling and analysis is the process of creating
and experimenting with a computerised mathematical model
of a physical system” (Chung, 2004). However, this
definition is narrowed because it limits the application of
simulation with a computerised system: the success of
simulation is strictly related to computers advent (Smith,
2003), but the concept of simulation exists independently.
A simple but meaningful definition is provided by (Maria,
1997): “Simulation is a way of evaluating a proposed system
for various parameters within a specific period of time”. A
complete definition, and maybe the most notable, is:
“Simulation is the imitation of the operation of the real-world
process or system over time” (Banks, 1998). The importance
of these two statements is confirmed by the continuous
recalling of them into recently published papers, and so they
are taken as a reference to analyse simulation-related articles
in this work.
Proceedings,16th IFAC Symposium on
Information Control Problems in Manufacturing
Bergamo, Italy, June 11-13, 2018
Copyright © 2018 IFAC 503
Role of simulation in industrial engineering:
focus on manufacturing systems
A. Polenghi*, L. Fumagalli*, I. Roda*
* Department of Management, Economics and Industrial Engineering, Politecnico
di Milano, Piazza Leonardo da Vinci 32, 20133 Milan (MI), Italy
Corresponding author e-mail: adalberto.polenghi@polimi.it
Abstract: Simulation has recently grown its importance thanks to the Industry 4.0, based on CPS (Cyber-
Physical Systems). Especially, simulation is becoming more central for improved decision-making.
The article provides a literature analysis of peer-reviewed surveys about simulation applications in
industrial engineering in manufacturing. In particular, a three-axis framework, called 3D-SAM, is
proposed to classify the applications and to critically analyse them. The framework can be used as a first
input for the Cognition Level of CPS (5C architecture for CPS development) in order to develop
integrated simulation models within a Decision Support System (DSS).
Keywords: simulation, review, simulation application, manufacturing, DSS.
1. INTRODUCTION
In the Digitalization era, simulation covers a primary role in
every field (finance, management, and manufacturing) and at
all levels (strategic, tactical, and operational). In particular,
the industrial engineering field, to which manufacturing
belongs to, is a very flourished land for simulation
development, taking advantage of the recent deployment of
Cyber-Physical Systems (CPS) (Negri, Fumagalli and
Macchi, 2017).
The importance of simulation in industrial engineering was
already highlighted by the first pioneers in simulation
research studies, since the 80s: it was considered among the
first top three methodologies used by industrial engineers,
managers and operations researchers (Banks, 1998).
Moreover, simulation is ranked as the second most used
methodologies for the OM (Operations Management) field of
studies (Shafer and Smunt, 2004), only after optimisation.
This view of simulation as central in company and research is
demonstrated by many research papers and empirical studies,
whose number has been continuously increased since the first
conceptualisation of possible uses of simulation in IE, in the
mid of 80s. To figure out the amount of works done, some
numbers are here provided: from 2002 to 2013, the searching
process with the keywords “simulation” plus
“manufacturing” finds out about 3000 published papers,
whereas the overall research process with other keywords,
such as “industrial engineering” or “operations”, results in
around 12000 published papers in the same periods
(Negahban and Smith, 2014). This results are impressive and
are confirmed by another survey made in the same year
(Mourtzis, Doukas and Bernidaki, 2014), for which the final
detailed graph is reported.
Figure 1. Numbers of publications related to simulation
(Mourtzis, Doukas and Bernidaki, 2014).
Many definitions of simulation consider computer
involvement as a consistent part of the definition itself:
“Simulation modelling and analysis is the process of creating
and experimenting with a computerised mathematical model
of a physical system” (Chung, 2004). However, this
definition is narrowed because it limits the application of
simulation with a computerised system: the success of
simulation is strictly related to computers advent (Smith,
2003), but the concept of simulation exists independently.
A simple but meaningful definition is provided by (Maria,
1997): “Simulation is a way of evaluating a proposed system
for various parameters within a specific period of time”. A
complete definition, and maybe the most notable, is:
“Simulation is the imitation of the operation of the real-world
process or system over time” (Banks, 1998). The importance
of these two statements is confirmed by the continuous
recalling of them into recently published papers, and so they
are taken as a reference to analyse simulation-related articles
in this work.
Proceedings,16th IFAC Symposium on
Information Control Problems in Manufacturing
Bergamo, Italy, June 11-13, 2018
Copyright © 2018 IFAC 503
Role of simulation in industrial engineering:
focus on manufacturing systems
A. Polenghi*, L. Fumagalli*, I. Roda*
* Department of Management, Economics and Industrial Engineering, Politecnico
di Milano, Piazza Leonardo da Vinci 32, 20133 Milan (MI), Italy
Corresponding author e-mail: adalberto.polenghi@polimi.it
Abstract: Simulation has recently grown its importance thanks to the Industry 4.0, based on CPS (Cyber-
Physical Systems). Especially, simulation is becoming more central for improved decision-making.
The article provides a literature analysis of peer-reviewed surveys about simulation applications in
industrial engineering in manufacturing. In particular, a three-axis framework, called 3D-SAM, is
proposed to classify the applications and to critically analyse them. The framework can be used as a first
input for the Cognition Level of CPS (5C architecture for CPS development) in order to develop
integrated simulation models within a Decision Support System (DSS).
Keywords: simulation, review, simulation application, manufacturing, DSS.
1. INTRODUCTION
In the Digitalization era, simulation covers a primary role in
every field (finance, management, and manufacturing) and at
all levels (strategic, tactical, and operational). In particular,
the industrial engineering field, to which manufacturing
belongs to, is a very flourished land for simulation
development, taking advantage of the recent deployment of
Cyber-Physical Systems (CPS) (Negri, Fumagalli and
Macchi, 2017).
The importance of simulation in industrial engineering was
already highlighted by the first pioneers in simulation
research studies, since the 80s: it was considered among the
first top three methodologies used by industrial engineers,
managers and operations researchers (Banks, 1998).
Moreover, simulation is ranked as the second most used
methodologies for the OM (Operations Management) field of
studies (Shafer and Smunt, 2004), only after optimisation.
This view of simulation as central in company and research is
demonstrated by many research papers and empirical studies,
whose number has been continuously increased since the first
conceptualisation of possible uses of simulation in IE, in the
mid of 80s. To figure out the amount of works done, some
numbers are here provided: from 2002 to 2013, the searching
process with the keywords “simulation” plus
“manufacturing” finds out about 3000 published papers,
whereas the overall research process with other keywords,
such as “industrial engineering” or “operations”, results in
around 12000 published papers in the same periods
(Negahban and Smith, 2014). This results are impressive and
are confirmed by another survey made in the same year
(Mourtzis, Doukas and Bernidaki, 2014), for which the final
detailed graph is reported.
Figure 1. Numbers of publications related to simulation
(Mourtzis, Doukas and Bernidaki, 2014).
Many definitions of simulation consider computer
involvement as a consistent part of the definition itself:
“Simulation modelling and analysis is the process of creating
and experimenting with a computerised mathematical model
of a physical system” (Chung, 2004). However, this
definition is narrowed because it limits the application of
simulation with a computerised system: the success of
simulation is strictly related to computers advent (Smith,
2003), but the concept of simulation exists independently.
A simple but meaningful definition is provided by (Maria,
1997): “Simulation is a way of evaluating a proposed system
for various parameters within a specific period of time”. A
complete definition, and maybe the most notable, is:
“Simulation is the imitation of the operation of the real-world
process or system over time” (Banks, 1998). The importance
of these two statements is confirmed by the continuous
recalling of them into recently published papers, and so they
are taken as a reference to analyse simulation-related articles
in this work.
Proceedings,16th IFAC Symposium on
Information Control Problems in Manufacturing
Bergamo, Italy, June 11-13, 2018
Copyright © 2018 IFAC 503
Role of simulation in industrial engineering:
focus on manufacturing systems
A. Polenghi*, L. Fumagalli*, I. Roda*
* Department of Management, Economics and Industrial Engineering, Politecnico
di Milano, Piazza Leonardo da Vinci 32, 20133 Milan (MI), Italy
Corresponding author e-mail: adalberto.polenghi@polimi.it
Abstract: Simulation has recently grown its importance thanks to the Industry 4.0, based on CPS (Cyber-
Physical Systems). Especially, simulation is becoming more central for improved decision-making.
The article provides a literature analysis of peer-reviewed surveys about simulation applications in
industrial engineering in manufacturing. In particular, a three-axis framework, called 3D-SAM, is
proposed to classify the applications and to critically analyse them. The framework can be used as a first
input for the Cognition Level of CPS (5C architecture for CPS development) in order to develop
integrated simulation models within a Decision Support System (DSS).
Keywords: simulation, review, simulation application, manufacturing, DSS.
1. INTRODUCTION
In the Digitalization era, simulation covers a primary role in
every field (finance, management, and manufacturing) and at
all levels (strategic, tactical, and operational). In particular,
the industrial engineering field, to which manufacturing
belongs to, is a very flourished land for simulation
development, taking advantage of the recent deployment of
Cyber-Physical Systems (CPS) (Negri, Fumagalli and
Macchi, 2017).
The importance of simulation in industrial engineering was
already highlighted by the first pioneers in simulation
research studies, since the 80s: it was considered among the
first top three methodologies used by industrial engineers,
managers and operations researchers (Banks, 1998).
Moreover, simulation is ranked as the second most used
methodologies for the OM (Operations Management) field of
studies (Shafer and Smunt, 2004), only after optimisation.
This view of simulation as central in company and research is
demonstrated by many research papers and empirical studies,
whose number has been continuously increased since the first
conceptualisation of possible uses of simulation in IE, in the
mid of 80s. To figure out the amount of works done, some
numbers are here provided: from 2002 to 2013, the searching
process with the keywords “simulation” plus
“manufacturing” finds out about 3000 published papers,
whereas the overall research process with other keywords,
such as “industrial engineering” or “operations”, results in
around 12000 published papers in the same periods
(Negahban and Smith, 2014). This results are impressive and
are confirmed by another survey made in the same year
(Mourtzis, Doukas and Bernidaki, 2014), for which the final
detailed graph is reported.
Figure 1. Numbers of publications related to simulation
(Mourtzis, Doukas and Bernidaki, 2014).
Many definitions of simulation consider computer
involvement as a consistent part of the definition itself:
“Simulation modelling and analysis is the process of creating
and experimenting with a computerised mathematical model
of a physical system” (Chung, 2004). However, this
definition is narrowed because it limits the application of
simulation with a computerised system: the success of
simulation is strictly related to computers advent (Smith,
2003), but the concept of simulation exists independently.
A simple but meaningful definition is provided by (Maria,
1997): “Simulation is a way of evaluating a proposed system
for various parameters within a specific period of time”. A
complete definition, and maybe the most notable, is:
“Simulation is the imitation of the operation of the real-world
process or system over time” (Banks, 1998). The importance
of these two statements is confirmed by the continuous
recalling of them into recently published papers, and so they
are taken as a reference to analyse simulation-related articles
in this work.
Proceedings,16th IFAC Symposium on
Information Control Problems in Manufacturing
Bergamo, Italy, June 11-13, 2018
Copyright © 2018 IFAC 503
Role of simulation in industrial engineering:
focus on manufacturing systems
A. Polenghi*, L. Fumagalli*, I. Roda*
* Department of Management, Economics and Industrial Engineering, Politecnico
di Milano, Piazza Leonardo da Vinci 32, 20133 Milan (MI), Italy
Corresponding author e-mail: adalberto.polenghi@polimi.it
Abstract: Simulation has recently grown its importance thanks to the Industry 4.0, based on CPS (Cyber-
Physical Systems). Especially, simulation is becoming more central for improved decision-making.
The article provides a literature analysis of peer-reviewed surveys about simulation applications in
industrial engineering in manufacturing. In particular, a three-axis framework, called 3D-SAM, is
proposed to classify the applications and to critically analyse them. The framework can be used as a first
input for the Cognition Level of CPS (5C architecture for CPS development) in order to develop
integrated simulation models within a Decision Support System (DSS).
Keywords: simulation, review, simulation application, manufacturing, DSS.
1. INTRODUCTION
In the Digitalization era, simulation covers a primary role in
every field (finance, management, and manufacturing) and at
all levels (strategic, tactical, and operational). In particular,
the industrial engineering field, to which manufacturing
belongs to, is a very flourished land for simulation
development, taking advantage of the recent deployment of
Cyber-Physical Systems (CPS) (Negri, Fumagalli and
Macchi, 2017).
The importance of simulation in industrial engineering was
already highlighted by the first pioneers in simulation
research studies, since the 80s: it was considered among the
first top three methodologies used by industrial engineers,
managers and operations researchers (Banks, 1998).
Moreover, simulation is ranked as the second most used
methodologies for the OM (Operations Management) field of
studies (Shafer and Smunt, 2004), only after optimisation.
This view of simulation as central in company and research is
demonstrated by many research papers and empirical studies,
whose number has been continuously increased since the first
conceptualisation of possible uses of simulation in IE, in the
mid of 80s. To figure out the amount of works done, some
numbers are here provided: from 2002 to 2013, the searching
process with the keywords “simulation” plus
“manufacturing” finds out about 3000 published papers,
whereas the overall research process with other keywords,
such as “industrial engineering” or “operations”, results in
around 12000 published papers in the same periods
(Negahban and Smith, 2014). This results are impressive and
are confirmed by another survey made in the same year
(Mourtzis, Doukas and Bernidaki, 2014), for which the final
detailed graph is reported.
Figure 1. Numbers of publications related to simulation
(Mourtzis, Doukas and Bernidaki, 2014).
Many definitions of simulation consider computer
involvement as a consistent part of the definition itself:
“Simulation modelling and analysis is the process of creating
and experimenting with a computerised mathematical model
of a physical system” (Chung, 2004). However, this
definition is narrowed because it limits the application of
simulation with a computerised system: the success of
simulation is strictly related to computers advent (Smith,
2003), but the concept of simulation exists independently.
A simple but meaningful definition is provided by (Maria,
1997): “Simulation is a way of evaluating a proposed system
for various parameters within a specific period of time”. A
complete definition, and maybe the most notable, is:
“Simulation is the imitation of the operation of the real-world
process or system over time” (Banks, 1998). The importance
of these two statements is confirmed by the continuous
recalling of them into recently published papers, and so they
are taken as a reference to analyse simulation-related articles
in this work.
Proceedings,16th IFAC Symposium on
Information Control Problems in Manufacturing
Bergamo, Italy, June 11-13, 2018
Copyright © 2018 IFAC 503

A. Polenghi et al. / IFAC PapersOnLine 51-11 (2018) 496–501 497
Role of simulation in industrial engineering:
focus on manufacturing systems
A. Polenghi*, L. Fumagalli*, I. Roda*
* Department of Management, Economics and Industrial Engineering, Politecnico
di Milano, Piazza Leonardo da Vinci 32, 20133 Milan (MI), Italy
Corresponding author e-mail: adalberto.polenghi@polimi.it
Abstract: Simulation has recently grown its importance thanks to the Industry 4.0, based on CPS (Cyber-
Physical Systems). Especially, simulation is becoming more central for improved decision-making.
The article provides a literature analysis of peer-reviewed surveys about simulation applications in
industrial engineering in manufacturing. In particular, a three-axis framework, called 3D-SAM, is
proposed to classify the applications and to critically analyse them. The framework can be used as a first
input for the Cognition Level of CPS (5C architecture for CPS development) in order to develop
integrated simulation models within a Decision Support System (DSS).
Keywords: simulation, review, simulation application, manufacturing, DSS.
1. INTRODUCTION
In the Digitalization era, simulation covers a primary role in
every field (finance, management, and manufacturing) and at
all levels (strategic, tactical, and operational). In particular,
the industrial engineering field, to which manufacturing
belongs to, is a very flourished land for simulation
development, taking advantage of the recent deployment of
Cyber-Physical Systems (CPS) (Negri, Fumagalli and
Macchi, 2017).
The importance of simulation in industrial engineering was
already highlighted by the first pioneers in simulation
research studies, since the 80s: it was considered among the
first top three methodologies used by industrial engineers,
managers and operations researchers (Banks, 1998).
Moreover, simulation is ranked as the second most used
methodologies for the OM (Operations Management) field of
studies (Shafer and Smunt, 2004), only after optimisation.
This view of simulation as central in company and research is
demonstrated by many research papers and empirical studies,
whose number has been continuously increased since the first
conceptualisation of possible uses of simulation in IE, in the
mid of 80s. To figure out the amount of works done, some
numbers are here provided: from 2002 to 2013, the searching
process with the keywords “simulation” plus
“manufacturing” finds out about 3000 published papers,
whereas the overall research process with other keywords,
such as “industrial engineering” or “operations”, results in
around 12000 published papers in the same periods
(Negahban and Smith, 2014). This results are impressive and
are confirmed by another survey made in the same year
(Mourtzis, Doukas and Bernidaki, 2014), for which the final
detailed graph is reported.
Figure 1. Numbers of publications related to simulation
(Mourtzis, Doukas and Bernidaki, 2014).
Many definitions of simulation consider computer
involvement as a consistent part of the definition itself:
“Simulation modelling and analysis is the process of creating
and experimenting with a computerised mathematical model
of a physical system” (Chung, 2004). However, this
definition is narrowed because it limits the application of
simulation with a computerised system: the success of
simulation is strictly related to computers advent (Smith,
2003), but the concept of simulation exists independently.
A simple but meaningful definition is provided by (Maria,
1997): “Simulation is a way of evaluating a proposed system
for various parameters within a specific period of time”. A
complete definition, and maybe the most notable, is:
“Simulation is the imitation of the operation of the real-world
process or system over time” (Banks, 1998). The importance
of these two statements is confirmed by the continuous
recalling of them into recently published papers, and so they
are taken as a reference to analyse simulation-related articles
in this work.
Proceedings,16th IFAC Symposium on
Information Control Problems in Manufacturing
Bergamo, Italy, June 11-13, 2018
Copyright © 2018 IFAC 503
Role of simulation in industrial engineering:
focus on manufacturing systems
A. Polenghi*, L. Fumagalli*, I. Roda*
* Department of Management, Economics and Industrial Engineering, Politecnico
di Milano, Piazza Leonardo da Vinci 32, 20133 Milan (MI), Italy
Corresponding author e-mail: adalberto.polenghi@polimi.it
Abstract: Simulation has recently grown its importance thanks to the Industry 4.0, based on CPS (Cyber-
Physical Systems). Especially, simulation is becoming more central for improved decision-making.
The article provides a literature analysis of peer-reviewed surveys about simulation applications in
industrial engineering in manufacturing. In particular, a three-axis framework, called 3D-SAM, is
proposed to classify the applications and to critically analyse them. The framework can be used as a first
input for the Cognition Level of CPS (5C architecture for CPS development) in order to develop
integrated simulation models within a Decision Support System (DSS).
Keywords: simulation, review, simulation application, manufacturing, DSS.
1. INTRODUCTION
In the Digitalization era, simulation covers a primary role in
every field (finance, management, and manufacturing) and at
all levels (strategic, tactical, and operational). In particular,
the industrial engineering field, to which manufacturing
belongs to, is a very flourished land for simulation
development, taking advantage of the recent deployment of
Cyber-Physical Systems (CPS) (Negri, Fumagalli and
Macchi, 2017).
The importance of simulation in industrial engineering was
already highlighted by the first pioneers in simulation
research studies, since the 80s: it was considered among the
first top three methodologies used by industrial engineers,
managers and operations researchers (Banks, 1998).
Moreover, simulation is ranked as the second most used
methodologies for the OM (Operations Management) field of
studies (Shafer and Smunt, 2004), only after optimisation.
This view of simulation as central in company and research is
demonstrated by many research papers and empirical studies,
whose number has been continuously increased since the first
conceptualisation of possible uses of simulation in IE, in the
mid of 80s. To figure out the amount of works done, some
numbers are here provided: from 2002 to 2013, the searching
process with the keywords “simulation” plus
“manufacturing” finds out about 3000 published papers,
whereas the overall research process with other keywords,
such as “industrial engineering” or “operations”, results in
around 12000 published papers in the same periods
(Negahban and Smith, 2014). This results are impressive and
are confirmed by another survey made in the same year
(Mourtzis, Doukas and Bernidaki, 2014), for which the final
detailed graph is reported.
Figure 1. Numbers of publications related to simulation
(Mourtzis, Doukas and Bernidaki, 2014).
Many definitions of simulation consider computer
involvement as a consistent part of the definition itself:
“Simulation modelling and analysis is the process of creating
and experimenting with a computerised mathematical model
of a physical system” (Chung, 2004). However, this
definition is narrowed because it limits the application of
simulation with a computerised system: the success of
simulation is strictly related to computers advent (Smith,
2003), but the concept of simulation exists independently.
A simple but meaningful definition is provided by (Maria,
1997): “Simulation is a way of evaluating a proposed system
for various parameters within a specific period of time”. A
complete definition, and maybe the most notable, is:
“Simulation is the imitation of the operation of the real-world
process or system over time” (Banks, 1998). The importance
of these two statements is confirmed by the continuous
recalling of them into recently published papers, and so they
are taken as a reference to analyse simulation-related articles
in this work.
Proceedings,16th IFAC Symposium on
Information Control Problems in Manufacturing
Bergamo, Italy, June 11-13, 2018
Copyright © 2018 IFAC 503
Role of simulation in industrial engineering:
focus on manufacturing systems
A. Polenghi*, L. Fumagalli*, I. Roda*
* Department of Management, Economics and Industrial Engineering, Politecnico
di Milano, Piazza Leonardo da Vinci 32, 20133 Milan (MI), Italy
Corresponding author e-mail: adalberto.polenghi@polimi.it
Abstract: Simulation has recently grown its importance thanks to the Industry 4.0, based on CPS (Cyber-
Physical Systems). Especially, simulation is becoming more central for improved decision-making.
The article provides a literature analysis of peer-reviewed surveys about simulation applications in
industrial engineering in manufacturing. In particular, a three-axis framework, called 3D-SAM, is
proposed to classify the applications and to critically analyse them. The framework can be used as a first
input for the Cognition Level of CPS (5C architecture for CPS development) in order to develop
integrated simulation models within a Decision Support System (DSS).
Keywords: simulation, review, simulation application, manufacturing, DSS.
1. INTRODUCTION
In the Digitalization era, simulation covers a primary role in
every field (finance, management, and manufacturing) and at
all levels (strategic, tactical, and operational). In particular,
the industrial engineering field, to which manufacturing
belongs to, is a very flourished land for simulation
development, taking advantage of the recent deployment of
Cyber-Physical Systems (CPS) (Negri, Fumagalli and
Macchi, 2017).
The importance of simulation in industrial engineering was
already highlighted by the first pioneers in simulation
research studies, since the 80s: it was considered among the
first top three methodologies used by industrial engineers,
managers and operations researchers (Banks, 1998).
Moreover, simulation is ranked as the second most used
methodologies for the OM (Operations Management) field of
studies (Shafer and Smunt, 2004), only after optimisation.
This view of simulation as central in company and research is
demonstrated by many research papers and empirical studies,
whose number has been continuously increased since the first
conceptualisation of possible uses of simulation in IE, in the
mid of 80s. To figure out the amount of works done, some
numbers are here provided: from 2002 to 2013, the searching
process with the keywords “simulation” plus
“manufacturing” finds out about 3000 published papers,
whereas the overall research process with other keywords,
such as “industrial engineering” or “operations”, results in
around 12000 published papers in the same periods
(Negahban and Smith, 2014). This results are impressive and
are confirmed by another survey made in the same year
(Mourtzis, Doukas and Bernidaki, 2014), for which the final
detailed graph is reported.
Figure 1. Numbers of publications related to simulation
(Mourtzis, Doukas and Bernidaki, 2014).
Many definitions of simulation consider computer
involvement as a consistent part of the definition itself:
“Simulation modelling and analysis is the process of creating
and experimenting with a computerised mathematical model
of a physical system” (Chung, 2004). However, this
definition is narrowed because it limits the application of
simulation with a computerised system: the success of
simulation is strictly related to computers advent (Smith,
2003), but the concept of simulation exists independently.
A simple but meaningful definition is provided by (Maria,
1997): “Simulation is a way of evaluating a proposed system
for various parameters within a specific period of time”. A
complete definition, and maybe the most notable, is:
“Simulation is the imitation of the operation of the real-world
process or system over time” (Banks, 1998). The importance
of these two statements is confirmed by the continuous
recalling of them into recently published papers, and so they
are taken as a reference to analyse simulation-related articles
in this work.
Proceedings,16th IFAC Symposium on
Information Control Problems in Manufacturing
Bergamo, Italy, June 11-13, 2018
Copyright © 2018 IFAC 503
Role of simulation in industrial engineering:
focus on manufacturing systems
A. Polenghi*, L. Fumagalli*, I. Roda*
* Department of Management, Economics and Industrial Engineering, Politecnico
di Milano, Piazza Leonardo da Vinci 32, 20133 Milan (MI), Italy
Corresponding author e-mail: adalberto.polenghi@polimi.it
Abstract: Simulation has recently grown its importance thanks to the Industry 4.0, based on CPS (Cyber-
Physical Systems). Especially, simulation is becoming more central for improved decision-making.
The article provides a literature analysis of peer-reviewed surveys about simulation applications in
industrial engineering in manufacturing. In particular, a three-axis framework, called 3D-SAM, is
proposed to classify the applications and to critically analyse them. The framework can be used as a first
input for the Cognition Level of CPS (5C architecture for CPS development) in order to develop
integrated simulation models within a Decision Support System (DSS).
Keywords: simulation, review, simulation application, manufacturing, DSS.
1. INTRODUCTION
In the Digitalization era, simulation covers a primary role in
every field (finance, management, and manufacturing) and at
all levels (strategic, tactical, and operational). In particular,
the industrial engineering field, to which manufacturing
belongs to, is a very flourished land for simulation
development, taking advantage of the recent deployment of
Cyber-Physical Systems (CPS) (Negri, Fumagalli and
Macchi, 2017).
The importance of simulation in industrial engineering was
already highlighted by the first pioneers in simulation
research studies, since the 80s: it was considered among the
first top three methodologies used by industrial engineers,
managers and operations researchers (Banks, 1998).
Moreover, simulation is ranked as the second most used
methodologies for the OM (Operations Management) field of
studies (Shafer and Smunt, 2004), only after optimisation.
This view of simulation as central in company and research is
demonstrated by many research papers and empirical studies,
whose number has been continuously increased since the first
conceptualisation of possible uses of simulation in IE, in the
mid of 80s. To figure out the amount of works done, some
numbers are here provided: from 2002 to 2013, the searching
process with the keywords “simulation” plus
“manufacturing” finds out about 3000 published papers,
whereas the overall research process with other keywords,
such as “industrial engineering” or “operations”, results in
around 12000 published papers in the same periods
(Negahban and Smith, 2014). This results are impressive and
are confirmed by another survey made in the same year
(Mourtzis, Doukas and Bernidaki, 2014), for which the final
detailed graph is reported.
Figure 1. Numbers of publications related to simulation
(Mourtzis, Doukas and Bernidaki, 2014).
Many definitions of simulation consider computer
involvement as a consistent part of the definition itself:
“Simulation modelling and analysis is the process of creating
and experimenting with a computerised mathematical model
of a physical system” (Chung, 2004). However, this
definition is narrowed because it limits the application of
simulation with a computerised system: the success of
simulation is strictly related to computers advent (Smith,
2003), but the concept of simulation exists independently.
A simple but meaningful definition is provided by (Maria,
1997): “Simulation is a way of evaluating a proposed system
for various parameters within a specific period of time”. A
complete definition, and maybe the most notable, is:
“Simulation is the imitation of the operation of the real-world
process or system over time” (Banks, 1998). The importance
of these two statements is confirmed by the continuous
recalling of them into recently published papers, and so they
are taken as a reference to analyse simulation-related articles
in this work.
Proceedings,16th IFAC Symposium on
Information Control Problems in Manufacturing
Bergamo, Italy, June 11-13, 2018
Copyright © 2018 IFAC 503
Role of simulation in industrial engineering:
focus on manufacturing systems
A. Polenghi*, L. Fumagalli*, I. Roda*
* Department of Management, Economics and Industrial Engineering, Politecnico
di Milano, Piazza Leonardo da Vinci 32, 20133 Milan (MI), Italy
Corresponding author e-mail: adalberto.polenghi@polimi.it
Abstract: Simulation has recently grown its importance thanks to the Industry 4.0, based on CPS (Cyber-
Physical Systems). Especially, simulation is becoming more central for improved decision-making.
The article provides a literature analysis of peer-reviewed surveys about simulation applications in
industrial engineering in manufacturing. In particular, a three-axis framework, called 3D-SAM, is
proposed to classify the applications and to critically analyse them. The framework can be used as a first
input for the Cognition Level of CPS (5C architecture for CPS development) in order to develop
integrated simulation models within a Decision Support System (DSS).
Keywords: simulation, review, simulation application, manufacturing, DSS.
1. INTRODUCTION
In the Digitalization era, simulation covers a primary role in
every field (finance, management, and manufacturing) and at
all levels (strategic, tactical, and operational). In particular,
the industrial engineering field, to which manufacturing
belongs to, is a very flourished land for simulation
development, taking advantage of the recent deployment of
Cyber-Physical Systems (CPS) (Negri, Fumagalli and
Macchi, 2017).
The importance of simulation in industrial engineering was
already highlighted by the first pioneers in simulation
research studies, since the 80s: it was considered among the
first top three methodologies used by industrial engineers,
managers and operations researchers (Banks, 1998).
Moreover, simulation is ranked as the second most used
methodologies for the OM (Operations Management) field of
studies (Shafer and Smunt, 2004), only after optimisation.
This view of simulation as central in company and research is
demonstrated by many research papers and empirical studies,
whose number has been continuously increased since the first
conceptualisation of possible uses of simulation in IE, in the
mid of 80s. To figure out the amount of works done, some
numbers are here provided: from 2002 to 2013, the searching
process with the keywords “simulation” plus
“manufacturing” finds out about 3000 published papers,
whereas the overall research process with other keywords,
such as “industrial engineering” or “operations”, results in
around 12000 published papers in the same periods
(Negahban and Smith, 2014). This results are impressive and
are confirmed by another survey made in the same year
(Mourtzis, Doukas and Bernidaki, 2014), for which the final
detailed graph is reported.
Figure 1. Numbers of publications related to simulation
(Mourtzis, Doukas and Bernidaki, 2014).
Many definitions of simulation consider computer
involvement as a consistent part of the definition itself:
“Simulation modelling and analysis is the process of creating
and experimenting with a computerised mathematical model
of a physical system” (Chung, 2004). However, this
definition is narrowed because it limits the application of
simulation with a computerised system: the success of
simulation is strictly related to computers advent (Smith,
2003), but the concept of simulation exists independently.
A simple but meaningful definition is provided by (Maria,
1997): “Simulation is a way of evaluating a proposed system
for various parameters within a specific period of time”. A
complete definition, and maybe the most notable, is:
“Simulation is the imitation of the operation of the real-world
process or system over time” (Banks, 1998). The importance
of these two statements is confirmed by the continuous
recalling of them into recently published papers, and so they
are taken as a reference to analyse simulation-related articles
in this work.
Proceedings,16th IFAC Symposium on
Information Control Problems in Manufacturing
Bergamo, Italy, June 11-13, 2018
Copyright © 2018 IFAC 503
The importance of simulation in IE is evident, but which are
the main applications within this field? The next sections try
to answer this question, making a preliminary review of the
most cited surveys in the field of IE: section 2 deals with the
literature analysis of published survey/reviews, which shows
up two important trends in the use of simulation; section 3
presents an elaboration of the literature review, concerning a
distinction between applications used in different lifecycle
phases of the industrial systems and a classification of the
applications according to a three dimensional framework
called 3D-SAM (3 Dimensional – Simulation Applications
Model); section 4 states some conclusions about integrated
simulation model for DSS (Decision Support System) in
CPS; finally future works and improvements are described in
section 5.
2. LITERATURE ANALYSIS
The second half of the 20th century is characterized by
simulation, either in research studies either in industrial
fields. The usefulness of this methodology is well-
recognised, since it helps in most of the lifecycle phases of
product/process/asset, thus providing important support in the
decision-making process. Industrial engineers may decide for
the selection of a cellular configuration for the production
system instead of a transfer line layout, and asset managers
could optimise the maintenance policies of a system based on
simulated scenarios.
However, simulation use has evolved during these last years.
The present work is confined to the analysis of “mega-trends”
(high-level trends in the evolution of simulation, in the
following, called simply “trend/s”) in simulation applications
in the manufacturing field.
To tackle this goal, aiming at not increase variability in
definitions and applications, but to use well-established
concepts and analysis to create a big picture of simulation in
IE, the literature review is performed as follow:
• Databases: Scopus and WoS (Web of Science)
(Google Scholar is neglected due to the focus on
peer-reviewed literature only, thus avoiding looking
for grey literature here extensively present);
• “Search by keywords” approach: Simulation AND
(review OR literature review OR survey);
• Limited to English peer-reviewed papers concerning
application in manufacturing field (screened through
abstract reading).
Therefore, the analysis of the literature has led to the
individuation of two main trends in the use of simulation in
IE. The first trend is determined by the shift from the view of
manufacturing design simulation only to a wider perspective
of simulation as useful during the lifecycle, for the operations
management (Smith, 2003); the second trend is the increase
in the number of application fields of simulation in IE
(Negahban and Smith, 2014).
In the next, these two trends are analysed in the details,
highlighting for each the most significant research studies.
2.1 Design to Operations trend
The first simulation application in IE regards mainly the
improvement in the design phase (BOL – Beginning-Of-Life)
of the system: the possibility to simulate the system before its
installation has favoured all the decisions mainly regarding
the layout, in terms of plant configuration, and the material
movement, in terms of materials handling system. The
subsequent evolution was the enlargement of the scope of
simulation to the operational phase of the system, as
production planning and maintenance policies (Shafer and
Smunt, 2004). This increase in the use of simulation in the
MOL (Middle-Of-Life) of the system has been pushed by the
increase in the computing power and, mainly, by the increase
in the availability of real-time shop-floor data (or close-to-
real-time). This evolution could be highlighted in the next
table, looking at the change in the percentage of simulation-
related papers for manufacturing system design and
manufacturing system operation (data adapted by (Negahban
and Smith, 2014)).
Table 1. Percentages of simulation papers in function of
lifecycle phases BOL and MOL.
Simulation papers
< 2002 2003 < 2013
Design (BOL) 54,45 % 26,32 %
Operations (MOL) 45,55 % 73,68 %
In spite of its importance, the EOL (End-Of-Life) of systems
is not yet well analysed with a simulation perspective:
decommissioning is seen as lifecycle phase involving costs
(or savings) and, for these reasons, only for complex and
safety-related systems simulation is used for addressing it.
For instance, the decommissioning of nuclear plants has
always taken advantages from simulation application (Kim et
al., 2006).
In this look, future research studies may be focused on the
EOL lifecycle phase of industrial systems: this will improve
the performance in designing and managing systems.
2.2 Applications increase trend
The increase in the use of simulation for OM purposes has
brought together the increase in the interests towards some
simulation-related topics or applications rather than others,
but this is a continuously fluctuating trend (Pannirselvam et
al., 1999).
Nevertheless, it is possible to state that the applications are
continuously evolving and what has certain name and scope
before could have different name and scope after. It is
therefore difficult to relate all the works done to the same
nomenclature or classification: for this reason, a summary of
the main simulation applications found in the reviewed
papers is hereby reported (Table 2).
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Table 2. Simulation applications per author/s.
Simulation application Reference survey/s Description
System design and facility
design/layout
(Meredith et al., 1989)
(Pannirselvam et al., 1999)
(Smith, 2003)
(Mourtzis, Doukas and
Bernidaki, 2014)
(Negahban and Smith, 2014)
This application concerns the design of the
system and the layout of the facility to respect
some constraints imposed by the process.
Material handling system design
(Smith, 2003)
(Mourtzis, Doukas and
Bernidaki, 2014) adapted
(Negahban and Smith, 2014)
This application includes all the studies to
optimise material movement.
Operations planning and scheduling
(Meredith et al., 1989)
(Pannirselvam et al., 1999)
(Smith, 2003)
(Shafer and Smunt, 2004)
(Negahban and Smith, 2014)
This application involves the optimisation and
schedule of all the operations needed by the
transformation process of the raw material in the
short- and mid-term.
Real-time control (Smith, 2003)
(Negahban and Smith, 2014)
This application deals with controlling of the
system regarding process parameters
Operating policies (Smith, 2003)
(Negahban and Smith, 2014)
This application analyses the policies in use to
run the system.
Performance analysis (Smith, 2003) This application concerns with performance.
Supply chain design
(Terzi and Cavalieri, 2004)
(Mourtzis, Doukas and
Bernidaki, 2014)
This application involves the design of the
supply chain regarding management strategies.
Supply chain management
(Terzi and Cavalieri, 2004)
(Shafer and Smunt, 2004)
(Mourtzis, Doukas and
Bernidaki, 2014)
This application is the evolution of the previous
one, used in the MOL lifecycle phase.
Process design
(Meredith et al., 1989)
(Pannirselvam et al., 1999)
(Shafer and Smunt, 2004)
(Mourtzis, Doukas and
Bernidaki, 2014)
This application deals with the design of the
process of the system, which involves objectives
and constraints.
Inventory management
(Shafer and Smunt, 2004)
This application considers stocks management.
Maintenance management
(Jahangirian et al., 2010)
(Negahban and Smith, 2014)
adapted
This application is devoted to the management of
maintenance and its strategies to improve
production system availability.
Resource allocation
(Shafer and Smunt, 2004)
adapted
(Jahangirian et al., 2010)
adapted
This application involves the study regarding the
most suitable allocation for each task.
Purchasing
(Shafer and Smunt, 2004)
adapted
(Jahangirian et al., 2010)
adapted
This application deals with the purchasing
process.
Product design (Mourtzis, Doukas and
Bernidaki, 2014)
This application includes the simulation needed
for the product design.
Ergonomics (Mourtzis, Doukas and
Bernidaki, 2014)
This application could be considered in a larger
and wider goal of health management.
Knowledge management
(Jahangirian et al., 2010)
(Mourtzis, Doukas and
Bernidaki, 2014)
The application regards knowledge
dissemination through the organisation.
*”adapted” means that the name of the simulation application is not the same, but it could be easily connected to the already mentioned application (e.g.
in (Mourtzis, 2014) “material handling system design” is called “material flow simulation”, but the descriptions coincide).
In the proposed summary, not all the applications present in
the cited papers are itemized: as a general rule, we consider a
simulation application as something that responds to the
following sentence: “The simulation is used for the
application in … [activity]”. For example, the virtual reality
and CAD are not considered in the above table because
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Table 2. Simulation applications per author/s.
Simulation application Reference survey/s Description
System design and facility
design/layout
(Meredith et al., 1989)
(Pannirselvam et al., 1999)
(Smith, 2003)
(Mourtzis, Doukas and
Bernidaki, 2014)
(Negahban and Smith, 2014)
This application concerns the design of the
system and the layout of the facility to respect
some constraints imposed by the process.
Material handling system design
(Smith, 2003)
(Mourtzis, Doukas and
Bernidaki, 2014) adapted
(Negahban and Smith, 2014)
This application includes all the studies to
optimise material movement.
Operations planning and scheduling
(Meredith et al., 1989)
(Pannirselvam et al., 1999)
(Smith, 2003)
(Shafer and Smunt, 2004)
(Negahban and Smith, 2014)
This application involves the optimisation and
schedule of all the operations needed by the
transformation process of the raw material in the
short- and mid-term.
Real-time control
(Smith, 2003)
(Negahban and Smith, 2014)
This application deals with controlling of the
system regarding process parameters
Operating policies
(Smith, 2003)
(Negahban and Smith, 2014)
This application analyses the policies in use to
run the system.
Performance analysis
(Smith, 2003)
This application concerns with performance.
Supply chain design
(Terzi and Cavalieri, 2004)
(Mourtzis, Doukas and
Bernidaki, 2014)
This application involves the design of the
supply chain regarding management strategies.
Supply chain management
(Terzi and Cavalieri, 2004)
(Shafer and Smunt, 2004)
(Mourtzis, Doukas and
Bernidaki, 2014)
This application is the evolution of the previous
one, used in the MOL lifecycle phase.
Process design
(Meredith et al., 1989)
(Pannirselvam et al., 1999)
(Shafer and Smunt, 2004)
(Mourtzis, Doukas and
Bernidaki, 2014)
This application deals with the design of the
process of the system, which involves objectives
and constraints.
Inventory management
(Shafer and Smunt, 2004)
This application considers stocks management.
Maintenance management
(Jahangirian et al., 2010)
(Negahban and Smith, 2014)
adapted
This application is devoted to the management of
maintenance and its strategies to improve
production system availability.
Resource allocation
(Shafer and Smunt, 2004)
adapted
(Jahangirian et al., 2010)
adapted
This application involves the study regarding the
most suitable allocation for each task.
Purchasing
(Shafer and Smunt, 2004)
adapted
(Jahangirian et al., 2010)
adapted
This application deals with the purchasing
process.
Product design
(Mourtzis, Doukas and
Bernidaki, 2014)
This application includes the simulation needed
for the product design.
Ergonomics
(Mourtzis, Doukas and
Bernidaki, 2014)
This application could be considered in a larger
and wider goal of health management.
Knowledge management
(Jahangirian et al., 2010)
(Mourtzis, Doukas and
Bernidaki, 2014)
The application regards knowledge
dissemination through the organisation.
*”adapted” means that the name of the simulation application is not the same, but it could be easily connected to the already mentioned application (e.g.
in (Mourtzis, 2014) “material handling system design” is called “material flow simulation”, but the descriptions coincide).
In the proposed summary, not all the applications present in
the cited papers are itemized: as a general rule, we consider a
simulation application as something that responds to the
following sentence: “The simulation is used for the
application in … [activity]”. For example, the virtual reality
and CAD are not considered in the above table because
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virtual reality is not a proper activity, but it is something that
empowers, for instance, maintenance management, and CAD
is not an activity, but it is a tool which allowed improved
design activity. In our opinion, some reviews make some
confusion in this regarding, for example (Mourtzis, Doukas
and Bernidaki, 2014) lists CAM and Process Simulation
together, as if they cover the same role in simulation.
As a final remark, it is important to recall how operation
phase in system is a fertile field for simulation application
and how its use is increasing in time. It should be noticed
that, among the possible numbers of applications, scheduling
covers a primary role, as it is one of the main targets of
simulation, arising to more than 30% of published papers
(Jahangirian et al., 2010).
3. ANALYSIS OF SIMULATION APPLICATIONS
The simulation applications presented in Table 2 are the most
used in IE according to the reviewed surveys. During the
analysis of these works, it becomes evident that some authors
consider some applications, while other authors consider
others. The aim of this preliminary work is trying to make
order to the previously listed simulation applications in a
double way: firstly, defining more accurately which of them
could be applied in the BOL phase of the system and which
could be instead used in MOL; then the applications are
classified according to a 3 axes model, called 3D-SAM (3
Dimensions – Simulation Applications Model).
3.1 Simulation applications review
Before addressing the two goals highlighted above, it seemed
to be useful a review of the simulation applications, trying to
look for the general classification. This newborn
classification is hereby presented in Table 3, in which some
adjustment has been made, for example uncoupling
“Operations planning” and “Scheduling”, due to the
importance of this last one, as anticipated in section 3.2.
Table 3. Simulation applications review.
Application
System design
Facility design/layout
Material handling system
design
Operations planning
Scheduling
Real-time control
Operating policies
Supply chain design
Supply chain management
Process design
Inventory management
Maintenance management
Purchasing
Product design
Ergonomics
Knowledge management
The next section proposes the correlation of such simulation
applications to the lifecycle phases of the system.
3.2. Lifecycle association
The association to the BOL and MOL phase is hereby
presented (Table 4). This association between simulation
applications and lifecycle phases is carried out by looking at
the descriptions in the reviewed surveys. None of them
clearly stated the use of simulation in EOL phase, thus no
evidence to include this dimension: it should be highlighted
that simulation is barely used in EOL phase in IE, except for
high-risk application, as in nuclear plants decommissioning.
Table 4. Lifecycle of simulation applications.
Application Lifecycle
System design
BOL
Facility design/layout BOL
Material handling system
design BOL
Operations planning
MOL
Scheduling MOL
Real-time control MOL
Operating policies
MOL
Supply chain design BOL
Supply chain management MOL
Process design BOL
Inventory management MOL
Maintenance management
MOL
Purchasing MOL
Product design BOL
Ergonomics BOL
Knowledge management BOL/MOL
It is notable that some of the simulation applications
associated with the MOL lifecycle phase could be used in
EOL, too. This enlargement of scope can highly influence
both the design phase and the operational phase of the
system.
3.3 3D-SAM
This paper proposes a classification of simulation
applications and, for this aim, a framework was developed,
named 3D-SAM, according to which the simulation
applications listed in Table 3 will be classified.
The presented frame work consists of three axes defined as
follows:
• Internal dimension - design: this dimension group
together all of the simulation applications whose
goal is to design an outcome (e.g. process design,
whose aim is to design the manufacturing process to
realise the product);
• Internal dimension - management: this dimension
collects all the simulation applications involving the
management of an entity (e.g. inventory
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management, whose goal is the management of the
warehouse);
• External dimension: this dimension gathers
simulations applications whose goals involve actors
outside the company (e.g. supply chain design &
management).
The scope of this classification is to understand the main
process areas in the company in which existing simulation
applications are acting. This framing is thought as a reference
to support the development of integrated simulation models,
composed of two or more integrated simulation applications,
as better explained in section 3.4.
3.3.1 Internal dimension - design
The internal dimension - design describes the application of
simulation techniques with the aim of designing an outcome,
whose features and requirements are to meet organisation
objectives.
Among the simulation applications enlisted in Table 3, the
ones that fit such definitions are:
• System design
• Facility design/layout
• Material handling system design
• Process design
• Product design
• Ergonomics
3.3.2 Internal dimension - management
The internal dimension - management collects all the
simulation applications whose goal is to enhance the
management performance of an entity to reach the
organisation objectives.
Referring to Table 3, the simulation applications that fall into
this dimension are:
• Operations planning
• Scheduling
• Real-time control
• Operating policies
• Inventory management
• Maintenance management
• Knowledge management
3.3.3 External dimension
The external dimension gathers all of those simulation
applications whose goals involve actors outside the company.
This dimension is fulfilled, looking at Table 3, by:
• Supply chain design
• Supply chain management
• Purchasing
3.4 3D-SAM as a pioneer for improved decision-making
The proposed framework 3D-SAM was proposed and built
up with the goal of being a first tentative guideline to pave
the way for the development of integrated simulation models
that support the decision-making process within an
organisation, especially in the manufacturing field.
In particular, the 3D-SAM aims at giving a starting reference
to be used for the Cognition Level of the CPS
implementation (Lee, Bagheri and Kao, 2015) providing
useful hints about the main three categories of organisation
processes (internal design, internal management, and
external) and needed simulation applications. In line with the
definition of the CPS Cognition Level, considering the three
dimensions of internal design processes, internal management
processes, and external processes, highlights and unveil what
simulation application may be useful to support an integrated
simulation model for informed decision-making.
The understanding of what type of simulation is needed to
correctly address a decision is vital to building a solid
Decision Support System (DSS) that can rely on reliable,
useful and quantitative data (Lee et al., 2015).
The creation of an integrated DSS underlies a new bigger
challenge manufacturing companies are now facing: the
integration of an Asset Management (AM) system within
their organisation to be more value-prone (El-Akruti, Dwight
and Zhang, 2013), within DSS plays a central role to support
asset-related decision-making.
Such a decision-making structure will foster the creation of a
“simulation economy” of the organisation: each decision, at
the strategic, tactical and operational level, will leverage on
reliable data from shop-floor and will be assessed through the
power of simulation in testing and validating new design and
management solutions.
4. CONCLUSIONS
The analysis performed in this work is a preliminary
elaboration of the reviews regarding simulation in IE in
manufacturing made in the last years. The goal is to create a
summary of simulation applications and make order in such a
crowded field in terms of when some simulation applications
could be used (lifecycle phases), and where they act in the
company regarding impacted processes in terms of internal
design, internal management, and external dimensions,
thanks to the proposed 3D-SAM framework.
The main outcome of this preliminary analysis, which should
be further assessed, are nevertheless interesting:
• A lack of simulation applications in EOL lifecycle
phase is showed up;
• The first input to Cognition Level of the 5C
architecture to build a CPS that may pave the way
for the creation of a robust integrated simulation
model for improved DSS;
• The long-term perspective of a “simulation
economy” that would enhance every decision-
making within the company.
Secondary outcomes of this work regard the integration of an
AM system in production companies. The creation of a DSS
is pushing towards this direction and, looking at one of the
basic factors driving AM implementation, that is lifecycle
orientation (Roda and Garetti, 2015), also the proposed cross-
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management, whose goal is the management of the
warehouse);
• External dimension: this dimension gathers
simulations applications whose goals involve actors
outside the company (e.g. supply chain design &
management).
The scope of this classification is to understand the main
process areas in the company in which existing simulation
applications are acting. This framing is thought as a reference
to support the development of integrated simulation models,
composed of two or more integrated simulation applications,
as better explained in section 3.4.
3.3.1 Internal dimension - design
The internal dimension - design describes the application of
simulation techniques with the aim of designing an outcome,
whose features and requirements are to meet organisation
objectives.
Among the simulation applications enlisted in Table 3, the
ones that fit such definitions are:
• System design
• Facility design/layout
• Material handling system design
• Process design
• Product design
• Ergonomics
3.3.2 Internal dimension - management
The internal dimension - management collects all the
simulation applications whose goal is to enhance the
management performance of an entity to reach the
organisation objectives.
Referring to Table 3, the simulation applications that fall into
this dimension are:
• Operations planning
• Scheduling
• Real-time control
• Operating policies
• Inventory management
• Maintenance management
• Knowledge management
3.3.3 External dimension
The external dimension gathers all of those simulation
applications whose goals involve actors outside the company.
This dimension is fulfilled, looking at Table 3, by:
• Supply chain design
• Supply chain management
• Purchasing
3.4 3D-SAM as a pioneer for improved decision-making
The proposed framework 3D-SAM was proposed and built
up with the goal of being a first tentative guideline to pave
the way for the development of integrated simulation models
that support the decision-making process within an
organisation, especially in the manufacturing field.
In particular, the 3D-SAM aims at giving a starting reference
to be used for the Cognition Level of the CPS
implementation (Lee, Bagheri and Kao, 2015) providing
useful hints about the main three categories of organisation
processes (internal design, internal management, and
external) and needed simulation applications. In line with the
definition of the CPS Cognition Level, considering the three
dimensions of internal design processes, internal management
processes, and external processes, highlights and unveil what
simulation application may be useful to support an integrated
simulation model for informed decision-making.
The understanding of what type of simulation is needed to
correctly address a decision is vital to building a solid
Decision Support System (DSS) that can rely on reliable,
useful and quantitative data (Lee et al., 2015).
The creation of an integrated DSS underlies a new bigger
challenge manufacturing companies are now facing: the
integration of an Asset Management (AM) system within
their organisation to be more value-prone (El-Akruti, Dwight
and Zhang, 2013), within DSS plays a central role to support
asset-related decision-making.
Such a decision-making structure will foster the creation of a
“simulation economy” of the organisation: each decision, at
the strategic, tactical and operational level, will leverage on
reliable data from shop-floor and will be assessed through the
power of simulation in testing and validating new design and
management solutions.
4. CONCLUSIONS
The analysis performed in this work is a preliminary
elaboration of the reviews regarding simulation in IE in
manufacturing made in the last years. The goal is to create a
summary of simulation applications and make order in such a
crowded field in terms of when some simulation applications
could be used (lifecycle phases), and where they act in the
company regarding impacted processes in terms of internal
design, internal management, and external dimensions,
thanks to the proposed 3D-SAM framework.
The main outcome of this preliminary analysis, which should
be further assessed, are nevertheless interesting:
• A lack of simulation applications in EOL lifecycle
phase is showed up;
• The first input to Cognition Level of the 5C
architecture to build a CPS that may pave the way
for the creation of a robust integrated simulation
model for improved DSS;
• The long-term perspective of a “simulation
economy” that would enhance every decision-
making within the company.
Secondary outcomes of this work regard the integration of an
AM system in production companies. The creation of a DSS
is pushing towards this direction and, looking at one of the
basic factors driving AM implementation, that is lifecycle
orientation (Roda and Garetti, 2015), also the proposed cross-
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relationships between simulation applications and lifecycle
phases enhances it.
5. FUTURE WORKS
Future works will focus on the assessment of this preliminary
analysis in a double way:
• firstly, by enlarging the number of peer-reviewed
papers actually included in this research by means of
a more extensive literature analysis;
• secondly, by investigating more in details, through
case studies from production companies, how
different simulation applications, impacting on
different organisation processes with different
objectives, may be combine to develop an integrated
DSS.
6. REFERENCES
Banks, J. (1998) Handbook of simulation: principles,
methodology, advances, applications, and practice. John
Wiley & Sons.
Chung, C. A. (2004) ‘Simulation modelling handbook’, A
practical approach.
El-Akruti, K., Dwight, R. and Zhang, T. (2013) ‘The strategic
role of Engineering Asset Management’, International
Journal of Production Economics. Elsevier, 146(1), pp. 227–
239. doi: 10.1016/j.ijpe.2013.07.002.
Jahangirian, M. et al. (2010) ‘Simulation in manufacturing
and business: A review’, European Journal of Operational
Research. Elsevier B.V., 203(1), pp. 1–13. doi:
10.1016/j.ejor.2009.06.004.
Kim, S. K. et al. (2006) ‘Development of a digital mock-up
system for selecting a decommissioning scenario’, Annals of
Nuclear Energy, 33(14–15), pp. 1227–1235. doi:
10.1016/j.anucene.2006.07.009.
Lee, J. et al. (2015) ‘Industrial Big Data Analytics and
Cyber-physical Systems for Future Maintenance & Service
Innovation’, Procedia CIRP. Elsevier B.V., 38, pp. 3–7. doi:
10.1016/j.procir.2015.08.026.
Lee, J., Bagheri, B. and Kao, H. A. (2015) ‘A Cyber-Physical
Systems architecture for Industry 4.0-based manufacturing
systems’, Manufacturing Letters. Society of Manufacturing
Engineers (SME), 3, pp. 18–23. doi:
10.1016/j.mfglet.2014.12.001.
Maria, A. (1997) ‘Introduction to modeling and simulation’,
Proceedings of the 29th conference on Winter simulation -
WSC ’97, pp. 7–13. doi: 10.1145/268437.268440.
Meredith, J. R. et al. (1989) ‘Alternative research paradigms
in operations’, Journal of Operations Management. Elsevier,
8(4), pp. 297–326.
Mourtzis, D., Doukas, M. and Bernidaki, D. (2014)
‘Simulation in manufacturing: Review and challenges’,
Procedia CIRP. Elsevier B.V., 25(C), pp. 213–229. doi:
10.1016/j.procir.2014.10.032.
Negahban, A. and Smith, J. S. (2014) ‘Simulation for
manufacturing system design and operation: Literature
review and analysis’, Journal of Manufacturing Systems. The
Society of Manufacturing Engineers, 33(2), pp. 241–261. doi:
10.1016/j.jmsy.2013.12.007.
Negri, E., Fumagalli, L. and Macchi, M. (2017) ‘A Review of
the Roles of Digital Twin in CPS-based Production Systems’,
Procedia Manufacturing. The Author(s), 11(June), pp. 939–
948. doi: 10.1016/j.promfg.2017.07.198.
Pannirselvam, G. P. et al. (1999) ‘Operations management
research: an update for the 1990s’, Journal of Operations
Management. Elsevier, 18(1), pp. 95–112.
Roda, I. and Garetti, M. (2015) ‘Application of a
Performance-driven Total Cost of Ownership (TCO)
Evaluation Model for Physical Asset Management’, in
Amadi-Echendu, J., Hoohlo, C., and Mathew, J. (eds) 9th
WCEAM Research Papers. Cham: Springer International
Publishing, pp. 11–23.
Shafer, S. M. and Smunt, T. L. (2004) ‘Empirical simulation
studies in operations management: Context, trends, and
research opportunities’, Journal of Operations Management,
22(4 SPEC. ISS.), pp. 345–354. doi:
10.1016/j.jom.2004.05.002.
Smith, J. S. (2003) ‘Survey on the use of simulation for
manufacturing system design and operation’, Journal of
Manufacturing Systems, 22(2), pp. 157–171. doi:
10.1016/S0278-6125(03)90013-6.
Terzi, S. and Cavalieri, S. (2004) ‘Simulation in the supply
chain context: A survey’, Computers in Industry, 53(1), pp.
3–16. doi: 10.1016/S0166-3615(03)00104-0
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