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Procedia CIRP 00 (2017) 000–000www.elsevier.com/locate/procedia
2212-8271 © 2017 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018.
28th CIRP Design Conference, May 2018, Nantes, France
A new methodology to analyze the functional and physical architecture of
existing products for an assembly oriented product family identification
Paul Stief *, Jean-Yves Dantan, Alain Etienne, Ali Siadat
École Nationale Supérieure d’Arts et Métiers, Arts et Métiers ParisTech, LCFC EA 4495, 4 Rue Augustin Fresnel, Metz 57078, France
* Corresponding author. Tel.: +33 3 87 37 54 30; E-mail address: paul.stief@ensam.eu
Abstract
In today’s business environment, the trend towards more product variety and customization is unbroken. Due to this development, the need of agile and reconfigurable production systems emerged to cope with various products and product families. To design and optimize production systems as well as to choose the optimal product matches, product analysis methods are needed. Indeed, most of the known methods aim to analyze a product or one product family on the physical level. Different product families, however, may differ largely in terms of the number and nature of components. This fact impedes an efficient comparison and choice of appropriate product family combinations for the production system. A new methodology is proposed to analyze existing products in view of their functional and physical architecture. The aim is to cluster these products in new assembly oriented product families for the optimization of existing assembly lines and the creation of future reconfigurable assembly systems. Based on Datum Flow Chain, the physical structure of the products is analyzed. Functional subassemblies are identified, and a functional analysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the similarity between product families by providing design support to both, production system planners and product designers. An illustrative example of a nail-clipper is used to explain the proposed methodology. An industrial case study on two product families of steering columns of thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach.
© 2017 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018. Keywords: Assembly; Design method; Family identification
1. Introduction
Due to the fast development in the domain of communication and an ongoing trend of digitization and digitalization, manufacturing enterprises are facing important challenges in today’s market environments: a continuing tendency towards reduction of product development times and shortened product lifecycles. In addition, there is an increasing demand of customization, being at the same time in a global competition with competitors all over the world. This trend, which is inducing the development from macro to micro markets, results in diminished lot sizes due to augmenting product varieties (high-volume to low-volume production) [1]. To cope with this augmenting variety as well as to be able to identify possible optimization potentials in the existing production system, it is important to have a precise knowledge
of the product range and characteristics manufactured and/or assembled in this system. In this context, the main challenge in modelling and analysis is now not only to cope with single products, a limited product range or existing product families, but also to be able to analyze and to compare products to define new product families. It can be observed that classical existing product families are regrouped in function of clients or features. However, assembly oriented product families are hardly to find.
On the product family level, products differ mainly in two main characteristics: (i) the number of components and (ii) the type of components (e.g. mechanical, electrical, electronical).
Classical methodologies considering mainly single products or solitary, already existing product families analyze the product structure on a physical level (components level) which causes difficulties regarding an efficient definition and comparison of different product families. Addressing this
Procedia CIRP 93 (2020) 371–376
2212-8271 © 2020 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 53rd CIRP Conference on Manufacturing Systems 10.1016/j.procir.2020.04.019
© 2020 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 53rd CIRP Conference on Manufacturing Systems
53rd CIRP Conference on Manufacturing Systems
ScienceDirect
Procedia CIRP 00 (2019) 000–000
www.elsevier.com/locate/procedia
2212-8271 © 2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 53rd CIRP Conference on Manufacturing Systems
53rd CIRP Conference on Manufacturing Systems
Assessment of Smart Manufacturing Solutions Based on Extended Value
Stream Mapping
Niels L. Martin
a,*, Antal Dér
a, Christoph Herrmann
a, Sebastian Thiede
aa Chair of Sustainable Manufacturing and Life Cycle Engineering, Institut of Machine Tools and Production Technology (IWF), Technische Universität
Braunschweig, Langer Kamp 19b, 38106 Braunschweig, Germany
* Corresponding author. Tel.: +49-531-7693; fax: +49-531-5842. E-mail address: n.martin@tu-braunschweig.de
Abstract
Manufacturing systems are getting more and more digitalized and connected. However, production planners of small and medium-sized enterprises (SME) struggle to decide, which “Smart Manufacturing Solution” is of value for a given manufacturing system. This paper suggests an extension to the established value stream mapping method (VSM) in order to assess smart manufacturing solutions. Therefore, existing extensions to the VSM are reviewed with a focus on information flows and the integration of necessary key performance indicators. After introducing and discussing the developed method, it is exemplarily applied in the manufacturing system of an SME.
© 2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 53rd CIRP Conference on Manufacturing Systems Keywords: Smart Manufacturing, Industry 4.0, Value Stream Mapping, Key Performance Indicator, Information Flows
1. Introduction
The future of manufacturing systems lies in Smart Manufacturing Systems. Those are typically digitalized with a high degree of connectivity and information flow between devices and enterprise units, e.g. production planning or maintenance. [1] The core of Smart Manufacturing Systems is the use of data in Cyber-physical Production Systems (CPPS) for gaining the ability of predictive engineering, i.e. anticipatory rather than reactive manufacturing [2]. This ability leads to a high competitive advantage for evolved enterprises.
In this paper, Smart Manufacturing Solutions (SMS) are defined as elements of a CPPS, i.e. they either
have a physical appearance on the shop floor, like machines, robots or a technical building service (Physical World),
are entities which treat or store data (Data Acquisition), use the stored data within a cyber-entity performing advanced data analysis and simulation approaches (Cyber World) or
Figure 1. Examples of two Cyber-physical Production Systems consisting of Smart Manufacturing Solutions embedded in a Smart Manufacturing System
Cyber World (1) Path planning simulation (2) Downtime analyses Data Acquisition (1) Surrounding images (2) Machine parameters,
states and variables
Feedback/Decision (1) Collision control (2) Maintenance decision support Physical World (1) Robot arm (2) Machine Cyber-physical Production System Manufacturing System Legend Smart Manufacturing Solutions (SMS)
Smart Manufacturing System
Available online at www.sciencedirect.com
ScienceDirect
Procedia CIRP 00 (2019) 000–000
www.elsevier.com/locate/procedia
2212-8271 © 2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 53rd CIRP Conference on Manufacturing Systems
53rd CIRP Conference on Manufacturing Systems
Assessment of Smart Manufacturing Solutions Based on Extended Value
Stream Mapping
Niels L. Martin
a,*, Antal Dér
a, Christoph Herrmann
a, Sebastian Thiede
aa Chair of Sustainable Manufacturing and Life Cycle Engineering, Institut of Machine Tools and Production Technology (IWF), Technische Universität
Braunschweig, Langer Kamp 19b, 38106 Braunschweig, Germany
* Corresponding author. Tel.: +49-531-7693; fax: +49-531-5842. E-mail address: n.martin@tu-braunschweig.de
Abstract
Manufacturing systems are getting more and more digitalized and connected. However, production planners of small and medium-sized enterprises (SME) struggle to decide, which “Smart Manufacturing Solution” is of value for a given manufacturing system. This paper suggests an extension to the established value stream mapping method (VSM) in order to assess smart manufacturing solutions. Therefore, existing extensions to the VSM are reviewed with a focus on information flows and the integration of necessary key performance indicators. After introducing and discussing the developed method, it is exemplarily applied in the manufacturing system of an SME.
© 2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 53rd CIRP Conference on Manufacturing Systems Keywords: Smart Manufacturing, Industry 4.0, Value Stream Mapping, Key Performance Indicator, Information Flows
1. Introduction
The future of manufacturing systems lies in Smart Manufacturing Systems. Those are typically digitalized with a high degree of connectivity and information flow between devices and enterprise units, e.g. production planning or maintenance. [1] The core of Smart Manufacturing Systems is the use of data in Cyber-physical Production Systems (CPPS) for gaining the ability of predictive engineering, i.e. anticipatory rather than reactive manufacturing [2]. This ability leads to a high competitive advantage for evolved enterprises.
In this paper, Smart Manufacturing Solutions (SMS) are defined as elements of a CPPS, i.e. they either
have a physical appearance on the shop floor, like machines, robots or a technical building service (Physical World),
are entities which treat or store data (Data Acquisition), use the stored data within a cyber-entity performing advanced data analysis and simulation approaches (Cyber World) or
Figure 1. Examples of two Cyber-physical Production Systems consisting of Smart Manufacturing Solutions embedded in a Smart Manufacturing System
Cyber World (1) Path planning simulation (2) Downtime analyses Data Acquisition (1) Surrounding images (2) Machine parameters,
states and variables
Feedback/Decision (1) Collision control (2) Maintenance decision support Physical World (1) Robot arm (2) Machine Cyber-physical Production System Manufacturing System Legend Smart Manufacturing Solutions (SMS)
are a decisional entity, which can either support different stakeholders or control processes (Feedback/Decision) [2], [3].
When those SMS are connected via various information flows to a loop, CPPSs arise. As an example of different SMS, the elements of two CPPS - a cobot (1) and a predictive maintenance application (PMA) (2) - are displayed in figure 1. Even though SMS have a high potential, production planners, especially at SMEs, struggle with the implementation of SMS. This is mainly due to the following obstacles:
Lack of knowledge about available SMS [4] and the potentials associated with their implementation [5] Capital constraints limit SME [6], while SMS often
require high investments and have unknown follow-up costs
Difficulties in forecasting positive and negative impacts resulting from the implementation of SMS
For this reasons, there is a need to develop a methodology, which supports production planners to address the following questions:
Q1 Which manufacturing process has a high potential for a
SMS implementation?
Q2 Which SMSs are appropriate to exploit this potential? Q3 How valuable is the impact of the appropriate SMS on
the manufacturing process?
In general, the manufacturing of goods is performed inside a process chain that is composed of a number of process steps. These process steps are linked with each other and therefore influence the performance of the whole process chain. Improvements with an isolated view on single process steps fail to address the inherent interdependencies within production systems and may only lead to suboptimal or even reduced performance of the process chain. Value Stream Mapping (VSM) is an established methodology for the identification of improvement potentials in manufacturing systems. [7] VSM allows to assess a process chain from a holistic perspective and addresses the interdependencies between the processes. Based on the VSM methodology, an approach is presented extending VSM towards the aforementioned questions. The paper is structured as following. Chapter 2 provides a literature review on the methodological background of VSM, its adaptations and approaches for assessing SMS. In chapter 3, the methodological framework for the assessment of SMS is explained. Chapter 4 presents the application of the methodology in an exemplary case study. A conclusion and an outlook regarding further research questions in chapter 5 complements the paper.
2. Assessment of Smart Manufacturing Solutions and Literature Review
One central element of CPPS is the presence of information flows among the SMS. Different approaches on different scales exist. Thiede et al. [8] consider information flows on an information entity level, simulating these flows in order to assess the impact of information flows on SMS in a quantitative way. Wagner et al. [9] are assessing information flows on a factory scale, considering a qualitative vertical and/or horizontal integration of production data.
Nevertheless, both approaches are not able to display the information flows in an appropriate way in order to identify SMS potential. The most appropriate way found in literature is the approach of Lewin et al.[10]. It states the requirements for modelling and analyzing SMS, is also the assessment basis for the literature review seen below and is further described in the next section.
As already introduced the approach of VSM emerged from the lean management philosophy [7]. VSM is an accepted pen and paper method in industrial practice, amongst others for its strength of easy as-is anaylsis and calculation of simple but relevant KPI. Its original purpose aims at eliminating waste along the value chain considering lead time, quality and flexibility. [7], [11] Over the years, VSM has been extended in multiple approaches, amongst others addressing the energy intensity of manufacturing, e.g. [12]–[14].
An evaluation of methods using VSM answering the above-mentioned core questions (Q1-Q3) for the assessment of SMS is shown in table 1. Besides the three core questions, the appropriate use of information flows is evaluated as well.
Meudt et al. [15] developed a VSM framework for the qualitative assessment of digitalization potential using swim lanes focusing on the utilization and the storage device of data. Uckelmann [16] describes design options regarding information flows considering SMS. Lewin et al. [10] combine the approaches of Uckelmann and Meudt et al. considering the requirements of SMS to identify potentials within the production process. The VSM extension used by Lewin et al. has a strong focus on the visualization and characterization of data and information flows. Hashemi and Roessler [17] suggest a Smart VSM considering a degree of automation, integration and digitalization before considering a deep analysis such as the examples mentioned above. Wagner et al. [18] extended the VSM with a design thinking approach to achieve a KPI-oriented decision support for SMS technology.
With respect to the above-mentioned approaches, the assessment of SMS is not sufficiently fulfilled in order to support SMEs appropriately in their decision-making. Most of the approaches focus rather on the potential identification (Q1) than on the specific SMS which can exploit the potential (Q2) and show the impact (Q3). Furthermore, none of the above-mentioned approaches integrates energy flows, which is however a further important aspect of SMS.
Authors (Q1) Potential identification (Q2) Exploiting the potential (Q3) Impact of SMS Informa-tion flows Meudt et al. Uckelmann et al. Lewin et al. Wagner et al. Haschemi and Roessler Answering of questions /
Illustration of information flows Good Poor Table 1. Overview of VSM extensions for the assessment of SMS
3. Methodological Framework for the Assessment of SMS Using an Extended Value Stream Mapping
The developed iterative approach builds up on the VSM methodology and extends it towards assessing the eligibility and improvement potential of SMS in process chains. In order to provide the necessary information base for this purpose, the methodology integrates the four functional dimensions – time, quality, flexibility and energy – and maps the necessary data and information flows. The approach comprises five successive, iterative methodological steps, which are explained in detail in the following subsections.
Extended Value Stream Analysis
The methodology starts with the as-is analysis of a selected manufacturing system defined by system boundaries. In order to acquire the required data for the identification of suitable processes, a traditional VSM is carried out that focuses on the functional dimensions of time, flexibility and quality. In addition to that, the VSM methodology is complemented with the extended energy value stream mapping methodology according to [13]. The corresponding KPIs are calculated and displayed in the information box. The information box also displays the process type (assembly, transport, production, storage) and the grade of automation of the respective process step as shown in figure 2.
Furthermore, the existing information flows (arrows) are mapped in a swim lane model according to the CPPS framework [3]. Mapping the information flows ranges from
Measurements over Data Acquisition and Cyber World to Feedback. Each CPPS swim lane is further broken down in
sub-categories, e.g. Cyber World in Data Mining and Simulation. Each element contains a proposed structure that can be altered according to case specific requirements. The authors propose a generic structure shown in figure 2.
A fully developed CPPS is characterized by a closed loop involving all CPPS elements. [3] Those elements are interconnected via information flows which are characterized according to the requirements of Lewin et al. [10]. Those information flows for SMS should represent the…
data transfer which is represented () as an arrow data sinks and sources as boxes (comp. fig. 2) storage medium in the Data Acquisition swim lane degree of processing in the loop’s completeness data processing point and the analysis mechanism .
in the Cyber World swim lane
the localization of data in the source of a data or information flow and
human machine interface which can be represented in the Feedback swim lane.
Qualitative Classification of SMS
This step is a prerequisite for finding suitable SMS for the identified processes and can be performed parallel to the previous step. Knowing that a case specific classification has a higher accuracy, the classification in this step is performed on a generic basis and should serve as an extensible library of SMS. Figure 4 illustrates in this regard the classification scheme that is exemplified for two SMS.
In particular, this step clarifies for each CPPS which functional dimension (time, quality, flexibility or energy) of a production system is influenced and how it is influenced from a qualitative perspective. The impact 𝑖𝑖𝑥𝑥𝑥𝑥 of a CPPS 𝑥𝑥 on a
functional dimension 𝑓𝑓 is rated on an ordinal scale, shown in figure 3. This is carried out on a scale between -2 (strong negative impact) and 2 (strong positive impact). The avg. impact of a given CPPS on all four dimensions can be calculated. CPPS with higher are more favourable than the ones with lower avg. impact.
Furthermore, this step also classifies the required SMS for the CPPS. The required SMS are expressed in textual form and are listed in requirement vectors 𝑟𝑟⃗⃗⃗ . They can be broken down 𝑥𝑥
on the CPPS elements – Physical World, Data Acquisition, Cyber World and Feedback (comp. fig. 1). Furthermore, the requirements can also express functional prerequisites from a manufacturing system perspective, e.g. ERP system, flow principle, Kanban principle, etc.
Figure 3. Classification scheme for smart manufacturing solutions Figure 2. Depiction of the methodology's extended value stream analysis step
Info box title a/p/m Process type
Machines
Time Quality Flexibility
Q ua lity ra te Cy cl e tim e EPEI Ener gy in te ns ity Measurements (Data frequency / volume)
Data A cq uis itio n Cy be r W orld Feed bac k Knowledge EMP Paper Smart product Local IT Data mining Simulation Visualization Decision Support Control Data sink open end source Centralized IT Characteri-zation Key performance indicators Functional dimensions
automated/ partly automated/ manual assembly, transport, production, storage
Methods/ models etc.
Further descriptions
can be used to clarify entities or data flows
Pur po se de sc ript io n o f C PP S Functionality
is shown within the loop and the purpose
Energy Parameters/ state variables Parameters/ state variables Parameters/ state
variables Parameters/ state Parameters/ state Parameters/ state variablesvariablesvariables
Impact on
CPPS Required SMS Time Flexibility Quality Energy Avg. Impact
1 Cobot 𝑟𝑟 𝑟𝑟𝑟𝑟𝑟𝑟 𝑟𝑟𝑟 𝑖𝑖𝑟 𝑖𝑖𝑖 𝑖𝑖𝑖𝑟𝑖𝑖 𝑟𝑖𝑖𝑟𝑖 𝑖𝑖 𝑖𝑖 𝑖𝑖 1 𝑖𝑖 2 PMA 𝑟𝑟 𝑟 𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖𝑟𝑟𝑟𝑖 𝑖 𝑖 𝑖𝑖𝑖𝑖 𝑖𝑖 𝑖𝑖 𝑖𝑖 𝑖𝑖 -2 -1 0 1 2
negative impact positive
Ordinal Scale:
are a decisional entity, which can either support different stakeholders or control processes (Feedback/Decision) [2], [3].
When those SMS are connected via various information flows to a loop, CPPSs arise. As an example of different SMS, the elements of two CPPS - a cobot (1) and a predictive maintenance application (PMA) (2) - are displayed in figure 1. Even though SMS have a high potential, production planners, especially at SMEs, struggle with the implementation of SMS. This is mainly due to the following obstacles:
Lack of knowledge about available SMS [4] and the potentials associated with their implementation [5] Capital constraints limit SME [6], while SMS often
require high investments and have unknown follow-up costs
Difficulties in forecasting positive and negative impacts resulting from the implementation of SMS
For this reasons, there is a need to develop a methodology, which supports production planners to address the following questions:
Q1 Which manufacturing process has a high potential for a
SMS implementation?
Q2 Which SMSs are appropriate to exploit this potential? Q3 How valuable is the impact of the appropriate SMS on
the manufacturing process?
In general, the manufacturing of goods is performed inside a process chain that is composed of a number of process steps. These process steps are linked with each other and therefore influence the performance of the whole process chain. Improvements with an isolated view on single process steps fail to address the inherent interdependencies within production systems and may only lead to suboptimal or even reduced performance of the process chain. Value Stream Mapping (VSM) is an established methodology for the identification of improvement potentials in manufacturing systems. [7] VSM allows to assess a process chain from a holistic perspective and addresses the interdependencies between the processes. Based on the VSM methodology, an approach is presented extending VSM towards the aforementioned questions. The paper is structured as following. Chapter 2 provides a literature review on the methodological background of VSM, its adaptations and approaches for assessing SMS. In chapter 3, the methodological framework for the assessment of SMS is explained. Chapter 4 presents the application of the methodology in an exemplary case study. A conclusion and an outlook regarding further research questions in chapter 5 complements the paper.
2. Assessment of Smart Manufacturing Solutions and Literature Review
One central element of CPPS is the presence of information flows among the SMS. Different approaches on different scales exist. Thiede et al. [8] consider information flows on an information entity level, simulating these flows in order to assess the impact of information flows on SMS in a quantitative way. Wagner et al. [9] are assessing information flows on a factory scale, considering a qualitative vertical and/or horizontal integration of production data.
Nevertheless, both approaches are not able to display the information flows in an appropriate way in order to identify SMS potential. The most appropriate way found in literature is the approach of Lewin et al.[10]. It states the requirements for modelling and analyzing SMS, is also the assessment basis for the literature review seen below and is further described in the next section.
As already introduced the approach of VSM emerged from the lean management philosophy [7]. VSM is an accepted pen and paper method in industrial practice, amongst others for its strength of easy as-is anaylsis and calculation of simple but relevant KPI. Its original purpose aims at eliminating waste along the value chain considering lead time, quality and flexibility. [7], [11] Over the years, VSM has been extended in multiple approaches, amongst others addressing the energy intensity of manufacturing, e.g. [12]–[14].
An evaluation of methods using VSM answering the above-mentioned core questions (Q1-Q3) for the assessment of SMS is shown in table 1. Besides the three core questions, the appropriate use of information flows is evaluated as well.
Meudt et al. [15] developed a VSM framework for the qualitative assessment of digitalization potential using swim lanes focusing on the utilization and the storage device of data. Uckelmann [16] describes design options regarding information flows considering SMS. Lewin et al. [10] combine the approaches of Uckelmann and Meudt et al. considering the requirements of SMS to identify potentials within the production process. The VSM extension used by Lewin et al. has a strong focus on the visualization and characterization of data and information flows. Hashemi and Roessler [17] suggest a Smart VSM considering a degree of automation, integration and digitalization before considering a deep analysis such as the examples mentioned above. Wagner et al. [18] extended the VSM with a design thinking approach to achieve a KPI-oriented decision support for SMS technology.
With respect to the above-mentioned approaches, the assessment of SMS is not sufficiently fulfilled in order to support SMEs appropriately in their decision-making. Most of the approaches focus rather on the potential identification (Q1) than on the specific SMS which can exploit the potential (Q2) and show the impact (Q3). Furthermore, none of the above-mentioned approaches integrates energy flows, which is however a further important aspect of SMS.
Authors (Q1) Potential identification (Q2) Exploiting the potential (Q3) Impact of SMS Informa-tion flows Meudt et al. Uckelmann et al. Lewin et al. Wagner et al. Haschemi and Roessler Answering of questions /
Illustration of information flows Good Poor Table 1. Overview of VSM extensions for the assessment of SMS
3. Methodological Framework for the Assessment of SMS Using an Extended Value Stream Mapping
The developed iterative approach builds up on the VSM methodology and extends it towards assessing the eligibility and improvement potential of SMS in process chains. In order to provide the necessary information base for this purpose, the methodology integrates the four functional dimensions – time, quality, flexibility and energy – and maps the necessary data and information flows. The approach comprises five successive, iterative methodological steps, which are explained in detail in the following subsections.
Extended Value Stream Analysis
The methodology starts with the as-is analysis of a selected manufacturing system defined by system boundaries. In order to acquire the required data for the identification of suitable processes, a traditional VSM is carried out that focuses on the functional dimensions of time, flexibility and quality. In addition to that, the VSM methodology is complemented with the extended energy value stream mapping methodology according to [13]. The corresponding KPIs are calculated and displayed in the information box. The information box also displays the process type (assembly, transport, production, storage) and the grade of automation of the respective process step as shown in figure 2.
Furthermore, the existing information flows (arrows) are mapped in a swim lane model according to the CPPS framework [3]. Mapping the information flows ranges from
Measurements over Data Acquisition and Cyber World to Feedback. Each CPPS swim lane is further broken down in
sub-categories, e.g. Cyber World in Data Mining and Simulation. Each element contains a proposed structure that can be altered according to case specific requirements. The authors propose a generic structure shown in figure 2.
A fully developed CPPS is characterized by a closed loop involving all CPPS elements. [3] Those elements are interconnected via information flows which are characterized according to the requirements of Lewin et al. [10]. Those information flows for SMS should represent the…
data transfer which is represented () as an arrow data sinks and sources as boxes (comp. fig. 2) storage medium in the Data Acquisition swim lane degree of processing in the loop’s completeness data processing point and the analysis mechanism .
in the Cyber World swim lane
the localization of data in the source of a data or information flow and
human machine interface which can be represented in the Feedback swim lane.
Qualitative Classification of SMS
This step is a prerequisite for finding suitable SMS for the identified processes and can be performed parallel to the previous step. Knowing that a case specific classification has a higher accuracy, the classification in this step is performed on a generic basis and should serve as an extensible library of SMS. Figure 4 illustrates in this regard the classification scheme that is exemplified for two SMS.
In particular, this step clarifies for each CPPS which functional dimension (time, quality, flexibility or energy) of a production system is influenced and how it is influenced from a qualitative perspective. The impact 𝑖𝑖𝑥𝑥𝑥𝑥 of a CPPS 𝑥𝑥 on a
functional dimension 𝑓𝑓 is rated on an ordinal scale, shown in figure 3. This is carried out on a scale between -2 (strong negative impact) and 2 (strong positive impact). The avg. impact of a given CPPS on all four dimensions can be calculated. CPPS with higher are more favourable than the ones with lower avg. impact.
Furthermore, this step also classifies the required SMS for the CPPS. The required SMS are expressed in textual form and are listed in requirement vectors 𝑟𝑟⃗⃗⃗ . They can be broken down 𝑥𝑥
on the CPPS elements – Physical World, Data Acquisition, Cyber World and Feedback (comp. fig. 1). Furthermore, the requirements can also express functional prerequisites from a manufacturing system perspective, e.g. ERP system, flow principle, Kanban principle, etc.
Figure 3. Classification scheme for smart manufacturing solutions Figure 2. Depiction of the methodology's extended value stream analysis step
Info box title a/p/m Process type
Machines
Time Quality Flexibility
Q ua lity ra te Cy cl e tim e EPEI Ener gy in te ns ity Measurements (Data frequency / volume)
Data A cq uis itio n Cy be r W orld Feed bac k Knowledge EMP Paper Smart product Local IT Data mining Simulation Visualization Decision Support Control Data sink open end source Centralized IT Characteri-zation Key performance indicators Functional dimensions
automated/ partly automated/ manual assembly, transport, production, storage
Methods/ models etc.
Further descriptions
can be used to clarify entities or data flows
Pur po se de sc ript io n o f C PP S Functionality
is shown within the loop and the purpose
Energy Parameters/ state variables Parameters/ state variables Parameters/ state
variables Parameters/ state Parameters/ state Parameters/ state variablesvariablesvariables
Impact on
CPPS Required SMS Time Flexibility Quality Energy Avg. Impact
1 Cobot 𝑟𝑟 𝑟𝑟𝑟𝑟𝑟𝑟 𝑟𝑟𝑟 𝑖𝑖𝑟 𝑖𝑖𝑖 𝑖𝑖𝑖𝑟𝑖𝑖 𝑟𝑖𝑖𝑟𝑖 𝑖𝑖 𝑖𝑖 𝑖𝑖 1 𝑖𝑖 2 PMA 𝑟𝑟 𝑟 𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖𝑟𝑟𝑟𝑖 𝑖 𝑖 𝑖𝑖𝑖𝑖 𝑖𝑖 𝑖𝑖 𝑖𝑖 𝑖𝑖 -2 -1 0 1 2
negative impact positive
Ordinal Scale:
Identification of Suitable Processes for SMS Implementation
While the previous methodological steps result in an extensive information base about the system under consideration, it is still unknown which processes are eligible for the integration of a SMS. In order to support the identification of suitable processes, a decision tree is proposed. The procedure is displayed in part A of figure 4. The decision tree starts with the definition of the objectives. It needs to be clarified which functional dimension is of interest and which KPIs will be considered. Secondly, the value stream map is screened for bottleneck/ hotspot processes. When a bottleneck process is identified, its information flows are considered in more detail. If there is an already existing closed information loop regarding the desired KPI, the effectiveness of the existing CPPS is checked. For this purpose, figure 4 provides a set of key questions. When there is no CPPS implemented on the desired KPI, it can be checked, whether another CPPS can be synergistically used that was initially designed for other KPI in the same process (vertical integration) or that is already implemented in another process (horizontal integration). If none of these alternatives is possible, part B of figure 4 depicts the implementation of a new CPPS for the process.
Matching SMS to Suitable Processes
After the extended Value Stream Analysis has been completed, hotspot processes have been identified and SMS classified, suitable SMS need to be chosen. Since SMS seldom have an impact only on one functional dimension, cross impact influences also need to be considered to avoid problem shifting. The matching takes place on the basis of the qualitative classification of SMS. The number of potential SMS is narrowed down by applying exclusion criteria on the list.
The matching logic is displayed in part B of figure 4 and explained on a demonstrative example which is later used in the application section. In this example, the functional dimension Time of the process needs to be increased, while the altering of the production layout is restricted. First, the list of SMS is filtered for solutions that have a positive effect on the functional dimension Time. Next, solutions that need a change in the production layout are excluded from the list of potential solutions. The remaining solutions are ranked in a descending
order according to their line total. If necessary, further dimensions and the grade of influence (e.g. strong positive) could be integrated as exclusion criteria. Applying exclusion criteria in a stepwise manner should result in a manageable number of potential SMS whose impact will be assessed in the following subsection.
Assessment of Costs/ Benefits
There are several ways of assessing the impact of SMS, e.g. empirical demonstrators, simulation approaches, benchmarks or the net present value method. In order to assess whether a SMS should be implemented, the authors suggest an easy applicable potential estimation. Therefore, the implementation costs need to be compared with the expected performance gain and respective cost reduction potential of the SMS. Since the cost reduction potential of a SMS is case-specific, the presented approach proposes to assess the economic feasibility of SMS based on the implementation costs, a defined amortization period and the resulting potential of cost reduction.
Figure 5 displays the heuristic. According to that, the ongoing costs of the reference case (without SMS) (cref) are put
into relation with a previously calculated break-even point and the implementation costs (cim) of an SMS. Increasing costs
through the SMS implementation can effect cref. The
break-even point is derived from the required ROI and the yearly production volume. The implementation costs are estimated based on the costs of the SMS described in requirement vector 𝑟𝑟𝑥𝑥
⃗⃗⃗ (compare fig. 3). The running costs are calculated via a VSM-based cost model. Based on the comparison, the required performance gain of the SMS is derived. Afterwards, the production planner needs to decide whether the cost reduction need is in a realistic range and then consider the SMS for implementation.
Figure 4. Identification and matching suitable SMS with suitable process steps Identification of suitable processes
for the implementation of SMS Matching SMS to suitable processes
Filter SMS according to their impact on functional dimension Example: time improvement Is a closed information loop
regarding this KPI existing? Are the CPPS components correct?
Check if further data could be assessed.
Check if data storage is reasonable.
Check if the data analysis in the cyber world is useful.
Check if there are better feedback possibilities.
Can a CPPS be integrated? Vertically check: Can other
SMS or CPPS of the same process (other KPI) be used. Horizontal check: Can SMS or CPPS of other processes be used.
Check if new CPPS can be
implemented.
Define objectives Which functional dimension is of
interest?
Which KPI(s) is (are) considered?
Check for hotspot/ bottleneck processes no T F Q E ∑ Cobot 1 1 1 -1 1 Predictive Maintenance Application 2 0 0 0 4 Tracking & Tracing 0 0 2 0 2
Automated Guided
Vehicle 1 2 0 -1 1 Live Energy
Monitoring 0 0 0 2 2
yes
Exclude SMS not matching the constraints Example: no new production
layout T F Q E ∑ Cobot 1 1 1 -1 2 Predictive Maintenance Application (PMA) 2 0 1 0 3 Automated Guided Vehicles (AGV) 1 2 0 -1 2 Rank remaining SMS T F Q E ∑ Rank Predictive Maintenance Application 2 0 1 0 3 1. Cobot 1 1 1 -1 2 2.
Part A
Part B
Figure 5. Economic estimation approach for the evaluation of SMS costs Implemen-tation costs (cim) time Amortization period α β Required performance gain of CPPS α β 4. Application
The methodology allows the production planner to set the system boundaries ranging from the whole process chain to the examination of single processes. If the system boundaries are chosen, the granularity of the processes and sub processes need to be defined. This also applies for the level of detail of the swim lanes. While a detailed analysis of sub processes requires high efforts, the granularity should be chosen depending on the aim of the analysis gaining a good effort benefit ratio.
To demonstrate the applicability of the developed approach an SME with a logistically decoupled manufacturing system is chosen. The SME produces multi-variant fittings for the supply network. The manufacturing systems under consideration is the assembly process which consists of four different process steps – preassembly, bolting, final assembly and packaging. The system boundaries are set accordingly. Due to the fact that the analyzed manufacturing system is not using any CPPS and due to the amount of four process steps, a medium detailed approach of the extended Value Stream Analysis is chosen.
Extended Value Stream Analysis
The analyzed data for the processes are listed in table 2. For the sake of clarity, figure 6 depicts only an extract of the extended VSM analysis illustrating only the bolting process in detail. The bolting process is a batch process in which an employee is pre-bolting two pieces and lays this assembled part in a bolting machine, which carries out the final bolting and checks for leaks using compressed air. In the meanwhile, the employee is pre-bolting the next pieces. The entire process time for one piece takes on average 69,7 sec. For all on average 200 pieces in one batch the cycle time is 232 sec. Figure 6 shows that only few data is acquired in a centralized IT infrastructure. This data is used to provide information for management considerations. This fact was not noted on the extended VSM, because the data was not used in the idea of a CPPS.
Identification of Suitable Processes
Following the methodology in figure 4, the greyed rows in table 2 mark the highest potential due to high cycle times and failure times of the required machine. Therefore, time is the functional dimension under consideration and the objective is to reduce either machine failure times (MTBF, MTTR) or cycle times. Selecting the hotspot process, the bolting process is in relation to the other processes one of the hotspot processes and allows the consideration of failure times and cycle times.
Regarding the functional dimension Time, no closed information loop is applied. As a last methodological step of the identification of suitable processes, the question arises if a CPPS can be implemented. Considering vertical processes, there are already data acquisitions present. Nevertheless, no complete CPPS loop exists in the whole manufacturing system. Therefore, for this process it needs to be checked if a new CPPS can be implemented. This is done within the next step.
Matching SMS to Suitable Processes
To find suitable SMS, part B of figure 4 shows a list of ranked SMS which match the objectives and constraints for this use case. It is shown that a “PMA” and a “Cobot” are part of the solution set. The PMA approach of a CPPS loop is illustrated with dotted lines and boxes in figure 6. Due to the fact that the PMA is ranked better, this CPPS will be assessed in the next step.
Assessment of Costs/ Benefits
For the assessment of the PMA in a quick potential estimation approach, described in chapter 3, some cost and time figures need to be collected. First the amortization period (ap) of the company under consideration is 5 years. The cost for the implementation (cim) of a PMA are composed of the costs for
the required sensors (2,500 €), an edge device for data storage and treatment (1,000 €), a data analytics model for predictive
Process steps Pre-assembly Bolting Final assembly Packaging
Process time (sec) 8 69,7 80,56 24
Process quantity (p.) 100 200 100 4
Cycle time (min) 13 232 134 2
MTBF - 200:42 h - 24:00 h
MTTR - 1:20 h - 3 min
Scrap rate (‰) 0,2 1 1 0
Rework rate (‰) 1 1 1 5
Changeover time (min) 6 2,5 3 6
Amount of varieties 132 168 108 108
EPEI (days) 27 21 16 23
Installed load - 16A/400V 10 bar; 0,09 kW *2 Table 2. Overview of VSM extensions for the assessment of SMS
Final assembly m Assembly 0,75 Machines 0 Packaging p Assembly 2 Machines 1 Warehouse A Warehouse A
Figure 6. Extended Value Stream to assess bolting process of the use case
Data A cq uis itio n Cyb er W orld Feed bac k Knowledge EMP Paper Smart product Local IT Data mining Simulation Visualization Decision Support Control Centralized IT Measurements (data frequency /volume)
Preassembly m Assembly 0,25 Machines 0 Bolting p Assembly 0,75 Machines 1
Time Quality Flexibility Energy 69,7 200 232201 80 1 1 2,5 168 21 -Timestamp (order start and end) Cy cle tim e (m in ) Pr oces s ti m e (se c) Pr oces s q ua nti ty (p arts) Scr ap ra te (‰) Re w ork ra te (‰) EP EI (d ay s) Ch an ge ov er ti m e (m in ) A m ou nt of v arieties En erg y in ten sity M TT R (m in ) M TBF (h ) Product quality issues Machine failure times Sensor data for state variables Analytical data approach for the reasons of failure
times Monitor displays critical machine elements and giving
maintenance directives Purpose: Extending the time between machine failures and reducing the troubleshooting
times
Legend
: Existing data flows : Designed data flows
Warehouse A
Identification of Suitable Processes for SMS Implementation
While the previous methodological steps result in an extensive information base about the system under consideration, it is still unknown which processes are eligible for the integration of a SMS. In order to support the identification of suitable processes, a decision tree is proposed. The procedure is displayed in part A of figure 4. The decision tree starts with the definition of the objectives. It needs to be clarified which functional dimension is of interest and which KPIs will be considered. Secondly, the value stream map is screened for bottleneck/ hotspot processes. When a bottleneck process is identified, its information flows are considered in more detail. If there is an already existing closed information loop regarding the desired KPI, the effectiveness of the existing CPPS is checked. For this purpose, figure 4 provides a set of key questions. When there is no CPPS implemented on the desired KPI, it can be checked, whether another CPPS can be synergistically used that was initially designed for other KPI in the same process (vertical integration) or that is already implemented in another process (horizontal integration). If none of these alternatives is possible, part B of figure 4 depicts the implementation of a new CPPS for the process.
Matching SMS to Suitable Processes
After the extended Value Stream Analysis has been completed, hotspot processes have been identified and SMS classified, suitable SMS need to be chosen. Since SMS seldom have an impact only on one functional dimension, cross impact influences also need to be considered to avoid problem shifting. The matching takes place on the basis of the qualitative classification of SMS. The number of potential SMS is narrowed down by applying exclusion criteria on the list.
The matching logic is displayed in part B of figure 4 and explained on a demonstrative example which is later used in the application section. In this example, the functional dimension Time of the process needs to be increased, while the altering of the production layout is restricted. First, the list of SMS is filtered for solutions that have a positive effect on the functional dimension Time. Next, solutions that need a change in the production layout are excluded from the list of potential solutions. The remaining solutions are ranked in a descending
order according to their line total. If necessary, further dimensions and the grade of influence (e.g. strong positive) could be integrated as exclusion criteria. Applying exclusion criteria in a stepwise manner should result in a manageable number of potential SMS whose impact will be assessed in the following subsection.
Assessment of Costs/ Benefits
There are several ways of assessing the impact of SMS, e.g. empirical demonstrators, simulation approaches, benchmarks or the net present value method. In order to assess whether a SMS should be implemented, the authors suggest an easy applicable potential estimation. Therefore, the implementation costs need to be compared with the expected performance gain and respective cost reduction potential of the SMS. Since the cost reduction potential of a SMS is case-specific, the presented approach proposes to assess the economic feasibility of SMS based on the implementation costs, a defined amortization period and the resulting potential of cost reduction.
Figure 5 displays the heuristic. According to that, the ongoing costs of the reference case (without SMS) (cref) are put
into relation with a previously calculated break-even point and the implementation costs (cim) of an SMS. Increasing costs
through the SMS implementation can effect cref. The
break-even point is derived from the required ROI and the yearly production volume. The implementation costs are estimated based on the costs of the SMS described in requirement vector 𝑟𝑟𝑥𝑥
⃗⃗⃗ (compare fig. 3). The running costs are calculated via a VSM-based cost model. Based on the comparison, the required performance gain of the SMS is derived. Afterwards, the production planner needs to decide whether the cost reduction need is in a realistic range and then consider the SMS for implementation.
Figure 4. Identification and matching suitable SMS with suitable process steps Identification of suitable processes
for the implementation of SMS Matching SMS to suitable processes
Filter SMS according to their impact on functional dimension Example: time improvement Is a closed information loop
regarding this KPI existing? Are the CPPS components correct?
Check if further data could be assessed.
Check if data storage is reasonable.
Check if the data analysis in the cyber world is useful.
Check if there are better feedback possibilities.
Can a CPPS be integrated? Vertically check: Can other
SMS or CPPS of the same process (other KPI) be used. Horizontal check: Can SMS or CPPS of other processes be used.
Check if new CPPS can be
implemented.
Define objectives Which functional dimension is of
interest?
Which KPI(s) is (are) considered?
Check for hotspot/ bottleneck processes no T F Q E ∑ Cobot 1 1 1 -1 1 Predictive Maintenance Application 2 0 0 0 4 Tracking & Tracing 0 0 2 0 2
Automated Guided
Vehicle 1 2 0 -1 1 Live Energy
Monitoring 0 0 0 2 2
yes
Exclude SMS not matching the constraints Example: no new production
layout T F Q E ∑ Cobot 1 1 1 -1 2 Predictive Maintenance Application (PMA) 2 0 1 0 3 Automated Guided Vehicles (AGV) 1 2 0 -1 2 Rank remaining SMS T F Q E ∑ Rank Predictive Maintenance Application 2 0 1 0 3 1. Cobot 1 1 1 -1 2 2.
Part A
Part B
Figure 5. Economic estimation approach for the evaluation of SMS costs Implemen-tation costs (cim) time Amortization period α β Required performance gain of CPPS α β 4. Application
The methodology allows the production planner to set the system boundaries ranging from the whole process chain to the examination of single processes. If the system boundaries are chosen, the granularity of the processes and sub processes need to be defined. This also applies for the level of detail of the swim lanes. While a detailed analysis of sub processes requires high efforts, the granularity should be chosen depending on the aim of the analysis gaining a good effort benefit ratio.
To demonstrate the applicability of the developed approach an SME with a logistically decoupled manufacturing system is chosen. The SME produces multi-variant fittings for the supply network. The manufacturing systems under consideration is the assembly process which consists of four different process steps – preassembly, bolting, final assembly and packaging. The system boundaries are set accordingly. Due to the fact that the analyzed manufacturing system is not using any CPPS and due to the amount of four process steps, a medium detailed approach of the extended Value Stream Analysis is chosen.
Extended Value Stream Analysis
The analyzed data for the processes are listed in table 2. For the sake of clarity, figure 6 depicts only an extract of the extended VSM analysis illustrating only the bolting process in detail. The bolting process is a batch process in which an employee is pre-bolting two pieces and lays this assembled part in a bolting machine, which carries out the final bolting and checks for leaks using compressed air. In the meanwhile, the employee is pre-bolting the next pieces. The entire process time for one piece takes on average 69,7 sec. For all on average 200 pieces in one batch the cycle time is 232 sec. Figure 6 shows that only few data is acquired in a centralized IT infrastructure. This data is used to provide information for management considerations. This fact was not noted on the extended VSM, because the data was not used in the idea of a CPPS.
Identification of Suitable Processes
Following the methodology in figure 4, the greyed rows in table 2 mark the highest potential due to high cycle times and failure times of the required machine. Therefore, time is the functional dimension under consideration and the objective is to reduce either machine failure times (MTBF, MTTR) or cycle times. Selecting the hotspot process, the bolting process is in relation to the other processes one of the hotspot processes and allows the consideration of failure times and cycle times.
Regarding the functional dimension Time, no closed information loop is applied. As a last methodological step of the identification of suitable processes, the question arises if a CPPS can be implemented. Considering vertical processes, there are already data acquisitions present. Nevertheless, no complete CPPS loop exists in the whole manufacturing system. Therefore, for this process it needs to be checked if a new CPPS can be implemented. This is done within the next step.
Matching SMS to Suitable Processes
To find suitable SMS, part B of figure 4 shows a list of ranked SMS which match the objectives and constraints for this use case. It is shown that a “PMA” and a “Cobot” are part of the solution set. The PMA approach of a CPPS loop is illustrated with dotted lines and boxes in figure 6. Due to the fact that the PMA is ranked better, this CPPS will be assessed in the next step.
Assessment of Costs/ Benefits
For the assessment of the PMA in a quick potential estimation approach, described in chapter 3, some cost and time figures need to be collected. First the amortization period (ap) of the company under consideration is 5 years. The cost for the implementation (cim) of a PMA are composed of the costs for
the required sensors (2,500 €), an edge device for data storage and treatment (1,000 €), a data analytics model for predictive
Process steps Pre-assembly Bolting Final assembly Packaging
Process time (sec) 8 69,7 80,56 24
Process quantity (p.) 100 200 100 4
Cycle time (min) 13 232 134 2
MTBF - 200:42 h - 24:00 h
MTTR - 1:20 h - 3 min
Scrap rate (‰) 0,2 1 1 0
Rework rate (‰) 1 1 1 5
Changeover time (min) 6 2,5 3 6
Amount of varieties 132 168 108 108
EPEI (days) 27 21 16 23
Installed load - 16A/400V 10 bar; 0,09 kW *2 Table 2. Overview of VSM extensions for the assessment of SMS
Final assembly m Assembly 0,75 Machines 0 Packaging p Assembly 2 Machines 1 Warehouse A Warehouse A
Figure 6. Extended Value Stream to assess bolting process of the use case
Data A cq uis itio n Cyb er W orld Feed bac k Knowledge EMP Paper Smart product Local IT Data mining Simulation Visualization Decision Support Control Centralized IT Measurements (data frequency /volume)
Preassembly m Assembly 0,25 Machines 0 Bolting p Assembly 0,75 Machines 1
Time Quality Flexibility Energy 69,7 200 232201 80 1 1 2,5 168 21 -Timestamp (order start and end) Cy cle tim e (m in ) Pr oces s ti m e (se c) Pr oces s q ua nti ty (p arts) Scr ap ra te (‰) Re w ork ra te (‰) EP EI (d ay s) Ch an ge ov er ti m e (m in ) A m ou nt of v arieties En erg y in ten sity M TT R (m in ) M TBF (h ) Product quality issues Machine failure times Sensor data for state variables Analytical data approach for the reasons of failure
times Monitor displays critical machine elements and giving
maintenance directives Purpose: Extending the time between machine failures and reducing the troubleshooting
times
Legend
: Existing data flows : Designed data flows
Warehouse A
maintenance (10,000 €) and a monitor (500 €) at the side of the machine. The reference costs per month (cref) for the production
system under consideration are composed of downtime costs (cd | 400 €/month) for the machine and maintenance costs (cm |
1000 €/month). Besides rising energy costs (ce | 20 €/month),
an increase of the servicing frequencies and therefore the servicing costs (cs | 200 €/month) can be expected when
implementing the SMS and have to be considered as increasing costs. The result of function (1) shows the required performance gain of 94 %, which the PMA needs to offer in order to amortize its implementation costs.
𝛼𝛼𝛽𝛽 cot(𝑐𝑐
𝑟𝑟𝑟𝑟𝑟𝑟∗𝑐𝑐𝑖𝑖𝑖𝑖 𝑎𝑎𝑎𝑎)
cot(𝑐𝑐𝑟𝑟𝑟𝑟𝑟𝑟) ≈ 94 % (1)
This reduction potential can be seen as unrealistic and therefore the implementation of the PMA makes no sense for this scenario. The approach should be iterated.
By using this method for the described use case, the lead questions of this paper can be answered as follows:
Q1: Which manufacturing process has a high potential for a SMS implementation? At the process step bolting for the
KPIs of the functional dimension time.
Q2: Which SMS are appropriate to exploit this potential? Out
of the available solution set, the SMS of a PMA and a Cobot would be able to exploit the potential.
Q3: How valuable is the impact of the appropriate SMS on the manufacturing process? The assessed PMA is not of value
for the manufacturing system.
5. Conclusion and Outlook
On the basis of different approaches, a VSM extension has been developed in order to support production planners to choose supportive SMS. The methodology is able to answer the question of where, which and how useful a SMS can be applied. For that, the assessment of the necessary data is described, guiding questions for the potential identification are provided, an approach for matching suitable SMS and different assessment approaches are shown as well as a quick estimation method described.
The method was applied on a SME and the outcome can be considered as typically. This is due to the reason that the maturity level of the considered SME is still low. Before considering SMS, other measures should be taken such as integrating an ERP system or a flow principle.
Further research should investigate how the impact of a SMS can be effortlessly forecasted also regarding the complex interdependencies between processes and the effect on the overall performance, e.g. with generic simulations or empirical demonstrators. This may lead to a holistic consideration of the production system and its multi-variant products.
Acknowledgements
This paper evolved of the research project “Synus” (Methods and Tools for the synergetic conception and evaluation of Industry 4.0-solutions) which is funded by the
European Regional Development Fund (EFRE | ZW 6-85012454) and managed by the Project Management Agency NBank.
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