1. DEPARTMENT Industrial Engineering
MASTER DEGREE IN Mechatronics Engineering
Concept for an operational low-cost
platform to develop, optimize and validate
approaches for decentralized control in
production processes
Supervisor: Graduant:
Paolo Bosetti Walter Gasparetto
DATE OF DISCUSSION: 21st
, December 2016

2. II
Table of contents
List of tables................................................................................................................................ IV
List of figures.............................................................................................................................. IV
List of appendices ....................................................................................................................... VI
List of abbreviations.................................................................................................................... VI
1. Introduction .......................................................................................................................... 1
2. State of the art: Industrial automation and cyber-physical systems .................................... 3
2.1 Industry models and industrial revolution................................................................. 3
2.2 Computer Integrated manufacturing ......................................................................... 4
2.3 The fourth industrial revolution ................................................................................ 5
2.4 Potential for growth................................................................................................. 12
2.1 CPS and IoT............................................................................................................ 16
2.2 Lean automation and Industrie 4.0.......................................................................... 18
2.3 Industrie 4.0 in the world ........................................................................................ 19
2.4 Best practices and practical examples of the implementation of Industrie 4.0 ....... 26
3. Decentralized controlled operational platform – theoretical concept................................. 28
3.1 Research question: How should a concept for a decentralized controlled operational
platform for low cost applications look like? ......................................................................... 28
3.2 Aim of the platform and state of the art .................................................................. 28
3.3 Reasons behind the choice to build a decentralized controlled operational platform:
Advantages and disadvantages of decentralization vs. centralization .................................... 30
3.4 Choice of the product and product variants management....................................... 32
3.5 Structure of the platform and process variants management .................................. 34
3.6 Key example and Octave simulation....................................................................... 39
3.7 Possible real case application of the model: Intelligent Workpiece Carrier ........... 43
4. Design of an Intelligent Workpiece Carrier ....................................................................... 45
4.1 Aim of a workpiece carrier and state of the art....................................................... 45
4.2 Description of the prototype.................................................................................... 47

3. III
4.3 Results of the prototype validation and simulation................................................. 63
5. Conclusion and outlook...................................................................................................... 65
6. Appendix ............................................................................................................................ 67
7. References ........................................................................................................................ 101

4. IV
List of tables
Table 1: The six key tasks according to the German Federal Government report “The new High-
Tech Strategy Innovations for Germany”, 2014............................................................ 6
Table 2: Overview about the different technologies in the field of application of Industrie 4.0
(Agiplan, Fraunhofer IML and ZENIT, 2015). ........................................................... 10
Table 3: Industrie 4.0 core technologies by areas (Agiplan et al., 2015).................................... 11
Table 4: Estimation of the potential benefits deriving from Industrie 4.0, based on Fraunhofer
IPA, 2016..................................................................................................................... 16
Table 5: Comparison between lean production and Industrie 4.0............................................... 18
Table 6: Industrie 4.0 in the world.............................................................................................. 25
Table 7: Practical examples of Industrie 4.0 technology ............................................................ 27
Table 8: Centralization vs. decentralization: pros and cons........................................................ 31
Table 9: Product variants. ........................................................................................................... 34
Table 10: Working stations/process steps and their description. ................................................ 36
Table 11: Different situations that can occur in a production line and may change the process
variants......................................................................................................................... 37
Table 12: Product variant chosen to investigate process variants............................................... 38
Table 13: Process variants model................................................................................................ 40
Table 14: Results of the recursive logic checker......................................................................... 42
Table 15: States of the two finite-state machines........................................................................ 53
Table 16: Data structures in charge of collecting data................................................................ 54
Table 17: States of the three finite-state machines...................................................................... 56
Table 18: NFC Forum abstraction levels .................................................................................... 57
Table 19: Example of conversion from string to code using Shunting yard and RPN evaluator.62
Table 20: Qualitative validation: Strengths and drawbacks of the decentralized controlled
operational platform..................................................................................................... 64
List of figures
Figure 1: The four stages of the Industrial Revolution (acatech, 2013)........................................ 3
Figure 2: CIM Automation Pyramid according to (moxietech.be., 2016) .................................... 4
Figure 3: Gross added value for the year 2013 in Germany (billions €) (Fraunhofer
IAO/BITKOM, 2014).................................................................................................. 12

5. V
Figure 4: Gross added value for the year 2025 (forecast) in Germany (billions €) (Fraunhofer
IAO/BITKOM, 2014).................................................................................................. 12
Figure 5: Expected economic effects through Industrie 4.0. (BMWi, 2015).............................. 12
Figure 6: Results of the survey "Industrie 4.0 - Volks- und Betriebswirtschaftliche Faktoren für
den Standort Deutschland" - Costs and revenue in the context of Industrie 4.0 (BMWi,
2015)............................................................................................................................ 13
Figure 7: Potential losses arising from a shift in the shares of value added (Roland Berger Strategy
Consultants GmbH and BDI, 2015)............................................................................. 14
Figure 8: Speed, scope and economic value at stake of 12 potentially economically disruptive
technologies (McKinsey Global Institute, 2013)......................................................... 15
Figure 9: The evolution of embedded systems into the Internet of Things, Data and Services
(acatech, 2011)............................................................................................................. 16
Figure 10: DFKI SmartFactory prototype. (SmartFactoryKL, 2015) ......................................... 29
Figure 11: Products of the decentralized controlled platform..................................................... 33
Figure 12: 2D representation of process variants........................................................................ 39
Figure 13: 2D representation of the process sequence................................................................ 39
Figure 14: 2D representation physical constraint........................................................................ 39
Figure 15: Octave simulation steps............................................................................................. 43
Figure 16: Intelligent agents and workpiece carrier together as a single unit. Reference from left:
youtube.com, http://www.bekuplast.com .................................................................... 45
Figure 17: Workpiece carrier with RFID to identify a product................................................... 45
Figure 18: Intelligent Workpiece carrier developed by the DFKI .............................................. 46
Figure 19: Intelligent Workpiece carrier developed by Fraunhofer IPA 2015. .......................... 46
Figure 20: RFID from left Passive Workpiece carrier. Intelligent and modular setup proposed by
Fraunhofer.IPA 2015................................................................................................... 46
Figure 21: Autonomous vehicle - Intelligent Workpiece carrier, developed by Frauhofer Italia47
Figure 22: Workshop case scenario: four stations with NFC communication and Base Station to
upload and download parameters to/from a workpiece carrier.................................... 48
Figure 23: Framework indicating all the steps that needs to be carried out to produce a specific
product using the decentralized controlled platform ................................................... 50
Figure 24: Setup composed by the IWC and the Station Simulator on the right, and the screen
connected to the Station Simulator on the left............................................................. 50
Figure 25: Front panel of the Station Simulator.......................................................................... 51
Figure 26: 3D model of the Station Simulator with Arduino UNO R3 on the bottom. .............. 51
Figure 27: 3D model of the IWC ................................................................................................ 51

6. VI
Figure 28: 3D model of the IWC with the plastic turnover box.................................................. 52
Figure 30: Normal information frame used to communicate between host and PN532 ............. 58
List of appendices
Appendix A: Octave simulation result........................................................................................ 67
Appendix B: Station Simulator simulation result ....................................................................... 69
Appendix C: Schematics for the Workpiece carrier.................................................................... 72
Appendix D: Schematics for the Station Simulator .................................................................... 73
Appendix E: Nfc_p2p.h .............................................................................................................. 74
Appendix F: Nfc_p2p.cpp........................................................................................................... 77
Appendix G: BooleanParser.h..................................................................................................... 88
Appendix H: BooleanParser.cpp................................................................................................. 89
Appendix I: Octave simulator..................................................................................................... 93
Appendix J: Questionnaire for the qualitative validation............................................................ 99
List of abbreviations
BMWi (Bundesministerium für Wirtschaft und Energie): Germany’s Federal Ministry for
Economics Affairs
CPPS: Cyber Physical Production Systems
CPS: Cyber Physical Systems
IoT: Internet of Things
IWC: Intelligent Workpiece Carrier
NFC: Near Field Communication
P2P: Peer to Peer
RFID: Radio Frequency Identification
SMEs: Small and Medium Enterprises

7. 1
1. Introduction
In the last years, Industrie 4.0 has been gaining importance and increasing attention in the
political and economic arenas. With the term Industrie 4.0, VDMA, Bitkom and ZVEI, who
introduced the term in 2011, wanted to highlight the strong connection to ICT as well as the
potential of the concept as the fourth industrial revolution. In this new era, all the elements of the
production process are not only highly electronic and IT equipped, but also intelligent and
connected as never before. Today, 90% of all computers are actually embedded computers. The
significance and spread of such intelligent environments is particularly evident in the automotive
industry, where today a regular car has got more than 200 cyber-physical systems embedded in
it. However, this technology is showing its potential also in other sectors like, for example,
housing and building technology and, in general, in the industrial production.
Since 2011, there has been an exponential growth in the use of the term “Industrie 4.0” as well
as in the practical implementation of its principles and concepts in the factories. Only in 2016,
more than 46.000 publications have been issued about Industrie 4.0. In Germany, where the
concept of Industrie 4.0 has first been developed, there are currently 10 competence centres for
Industrie 4.0, set up by the German government also to give advice to small and medium
enterprises (Wahlster, 2016).
Differently to other countries and economies, manufacturing production in Europe cannot rely
on low labour costs and large resources availability. European companies must therefore find
other ways to ensure that their products remain competitive on the market. High automation levels
enable companies to lower their production costs and hence to reduce product prices, all else
being equal. Although Industrie 4.0 represents a great opportunity for those companies, a wide
range of practical applications of Industrie 4.0, especially for SMEs, is still missing. Unlike large
multinational companies, SMEs do not dispose of sufficient internal R&D resources and know-
how in order to take advantage of the potential offered by Industrie 4.0.
The main goal of this master thesis is to develop a real Industrie 4.0 line in a laboratory in
order to give examples on how companies, and especially SMEs, can benefit from the
implementation of Industrie 4.0, bridging the gap between academic theory and practical
integration. The research question underlying the development of this concept is the following:
How should a concept for a decentralized controlled operational platform for low cost applications
look like?
The choice to develop a decentralized controlled operational platform has been driven by the
observation that the decentralization of the processes is one of the main factors of success for the

8. 2
4.0 revolution. The growing demand for customized products is forcing manufacturing to change
its organizational structure. According to Brettel et al. (2014), distributed (decentralized) systems
seem to be better at handling high complexity, compared to the rigid, centralized hierarchical
control of a pyramidal structure.
In the first part of my master thesis, I review the existing literature about Industrie 4.0 and
present the theoretical concept for the development of an Industrie 4.0 production line, based on
a decentralized controlled operational platform, while in the second part I describe the design and
construction of the decentralized controlled operational platform. Chapter 2 is focused on the
concept of Industrie 4.0, its definition as well as the history behind the fourth industrial revolution
and the technologies Industrie 4.0 is based on. Moreover, an overview of the different ways of
implementing the digitalization of processes around the world is provided. In chapter 3, I present
the research question, address the theoretical arguments for and against decentralization, and
explain the aim of the platform as well as the state of the art. Two particular paragraphs are
dedicated to the analysis of the process variants and to the Octave simulation for the identified
control model. In chapter 4, the design and construction of the decentralized controlled
operational platform are described. The platform is based on the implementation of an Intelligent
Workpiece Carrier, a solution that allows to implement the Industrie 4.0 concepts also in an SME.
This master thesis has been written during a period of internship at Fraunhofer Italia, which
supported the development and realization of the decentralized controlled operational platform.

9. 3
2. State of the art: Industrial automation and cyber-physical systems
2.1 Industry models and industrial revolution
The first industrial revolution took place at the end of the 18th
century with the introduction of
water- and steam-powered mechanical manufacturing facilities. The first mechanical loom,
invented in 1784, is an example of the mechanization of manufacturing that characterize this first
industrial revolution.
The second industrial revolution took place at the beginning of the 20th
century with the
introduction of the electrically-powered mass production, based on the division of labour.
Figure 1: The four stages of the Industrial Revolution (acatech, 2013).
Automation concepts were introduced in the 1970s with the third industrial revolution as
facilities started to use electronics and Information and Communication Technologies (ICT)
to achieve automation of manufacturing. The concept of Computer Integrated Manufacturing
(CIM) was developed in these years.

10. 4
2.2 Computer Integrated manufacturing
Computer Integrated Manufacturing (CIM) is the manufacturing approach of using computers
to control the whole production process. This model allows a main control to manage all the
processes in a centralized way. A typical CIM pyramidal structure is shown in Figure 2. During
the production process, products must go through four different levels (Field, Controller,
Management, Enterprise).
From a control point of view, this structure can be seen as a hierarchy within the company. At
the top level, supervisor and planning activities organize bottom level functions, where low-level
activities take place. With regard to time and rapidity, low-level activities are faster compared to
high-level activities, while less human-machine interactions take place at the bottom of the
pyramid.
At the bottom level, one deals with sensors and generic hardware, which are controlled by the
upper level (Programmable Logic Control (PLC) and Supervisory Control and Data Acquisition
(SCADA)).
The Manufacturing Execution System (MES) level tracks and documents the transformation
of raw materials into finished goods. MES acts as an intermediate between the Enterprise
Resource Planning (ERP) and the SCADA system.
Figure 2: CIM Automation Pyramid according to (moxietech.be., 2016)

11. 5
At the ERP level, all the business activities take place and, with the use of proper software,
are continuously monitored. Information is collected to help managers, also from different
facilities, to plan material and financial flows.
Although this model still finds large application in companies, the CIM approach has basically
failed due to the discrepancy between the rigid and hierarchical control architecture often deriving
from the integration of the different levels of the CIM pyramid and the instability and uncertainty
of the manufacturing environment (Monostori et al., 2015).
2.3 The fourth industrial revolution
The CIM approach has been improved through the introduction of the Lean approach (see
paragraph 1.1.8 Lean automation and Industrie 4.0). This model is characterized by a high level
of effectiveness achieved through the reduction of complexity and its ability to avoid non-value-
creating process steps. A further step forward was made with the development of the “Industrie
4.0” concept, also called “digitalization of processes”.
With the term 4.0, VDMA, Bitkom and ZVEI, who introduced the term in 2011, wanted to
underline the potential of the concept, i.e. the fourth industrial revolution and also the strong
connection to ICT. In this new era, all the elements of the production process are not only highly
electronic and IT equipped, but also intelligent and connected as ever before. Differently from
CIM, Industrie 4.0 focuses on a complementary integration of production technologies, ICT and
the human worker. This concept creates the instruments and enables the vision for the
development of the so-called “Smart Manufacturing”.
2.3.1 The fourth industrial revolution in Germany
The Industrie 4.0 concept was first presented at the “Hannover Messe 2011”, in Germany.
Since then, many definitions of Industrie 4.0 have been suggested, with slightly different
meanings from country to country (see paragraph 1.1.10 Industrie 4.0 in the world). In spring
2014, VDMA, Bitkom and ZVEI, three leading German associations of mechanical engineering,
ICT and electrical industry, released a definition for Industrie 4.0. According to VDMA, Bitkom
and ZVEI (2014), Industrie 4.0 represents a new level of organisation and control of the whole
value chain along the whole life cycle of the product (Acatech-Plattform Industrie 4.0, 2014)
In the last years, the concept of Industrie 4.0 has been increasingly gaining importance in the
German political and economic scenario and there has been an exponential growing in the number
of publication with the name Industrie 4.0 in the world showing up to 46.305 only in this year. In
2014, a report of the German Federal Government “The new High-Tech Strategy Innovations for
Germany” identifies Industrie 4.0 as one of the six priority key tasks relative to future prosperity

12. 6
and quality of life. According to this report, “The High-Tech Strategy stands for the aim of
moving Germany forward on its way to becoming a worldwide innovation leader”, at the same
time coping with the urgent challenges of our time and finding answers to the issues in sustainable
urban development, environmentally friendly energy, personalized medicine, digital society and
social innovation (German Federal Government, 2014).
Table 1 shows the itemized list of tasks that have been identified in the report “The new High-
Tech Strategy Innovations for Germany” (2014). The table below is aimed at highlighting the role
of Industrie 4.0 in a wider framework where different areas and subjects are highly
interconnected.
Digital economy and society
 Industrie 4.0;
 Smart services;
 Smart data;
 Cloud computing;
 Digital networking;
 Digital science;
 Digital education;
 Digital life environments;
Healthy living
 Fighting major disease
 Individualised medicine
 Prevention and nutrition
 Innovations in the care sector
 Strengthening drug research
 Innovations in medical technology
Sustainable economy and energy
 Energy research
 Green economy
 Bioeconomy
 Sustainable agricultural production
 Assuring the supply of raw materials
 Future of Building
 Sustainable consumption
 City of the future
Intelligent mobility
 Intelligent and capable transport
infrastructure
 Innovative mobility concepts and
networking
 Electromobility
 Aviation
 Vehicle technologies
 Maritime technologies
Innovative workplace
 Work in a digital world
 Innovative services for future
markets
 Competency building
Civil security
 Cyber security
 Civil security research
 IT security
 Secure identities
Table 1: The six key tasks according to the German Federal Government report “The new High-Tech Strategy
Innovations for Germany”, 2014.

13. 7
Industrie 4.0 includes a lot of different technologies, which have been identified and
categorized differently by a series of survey and studies.
Based on the wide field of publications focusing on this topic, a recent study commissioned
by Germany’s Federal Ministry for Economics Affairs (BMWi) and issued by Agiplan,
Fraunhofer IML and ZENIT (2015) gives a good overview about the different technologies in the
field of application of Industrie 4.0. These technologies are applied on different factory levels:
production, maintenance, assembly, planning & control and logistics. The categorization
suggested by this study allows us to deal with the complexity of the subject, identifying different
technologies and subdividing them into five functional areas along with their chances and risks
(see Table 2).

14. 8
Data collection and
processing
Assistance systems
Networks and
integration
Self-organization and
autonomy
Decentralization/ service
orientation
Key question
Which data are
sensed/produced and how
is the data used/processed
afterwards?
Which instruments are in
place to support
employees so that they can
concentrate on their core
competences?
Does the collaboration
network work in the
department, between
departments and partners?
Which data is exchanged?
What is controlled and
what is automatically
regulated?
Which services are offered
to other
partners/departments and
which are internally used?
Core
technologies
Sensors technic, RFID,
Barcode, Big Data, data
management, simulation,
data security
Visualization, augmented
reality, 3D print/scanning,
simulation (product,
production), mobile end
user device (tablet, control
panel), human-machine
interaction.
Vertical and horizontal
integration, flexible
connection between
plants/processes/products,
Internet of Things, Cloud
computing
Control loop, self-
organization, self-
configuration and
optimization, cyber
physical system, process
control.
Apps, Web-Service, XaaS,
new business model,
orchestration of services,
decentral control,
adaptability, mass
customization.

15. 9
Data collection and
processing
Assistance systems
Networks and
integration
Self-organization and
autonomy
Decentralization/ service
orientation
Chances
Reduction of
documentation efforts,
enhancement of data
quality, recognition of
correlations, simplification
of flaws and cause
analysis, improvement in
the surveillance of
processes and deadlines,
improvement of processes
and product´s quality
(maximum efficiency of a
machine, e.g. through high
process transparency or
manufacturer remote
control), improved
possibilities of forecasting,
optimization of
maintenance (remote and
local flaw analysis, KPI
controlling), predictive
maintenance, just in time
production.
Acceleration of the
integration process, quality
improvement,
enhancement of processes
and products,
simplification of variant
control, flaws
reduction/avoidance, work
security enhancement, cost
reduction, improvement of
ergonomics, augmented
operator, assisted fault
reparation, combination of
manual and automated
workplaces.
Simplification of intern
and network collaboration,
enhancement of
transparency in the
production chain,
improvement in
understanding
connections, generation of
a constant information
flow, strengthening of
customer loyalty,
innovation push and
product improvement
through client connection,
building of virtual
companies for market
reinforcement, device-
independent availability of
data, unification of
communication standards
(OPC-UA), synergy
between facilities,
overcoming of B2B limits,
overcoming of
supplier/producer/client
limits (fast product
delivery, fast supply,
lower effort for variant
management, small
batches, deterministic
supply).
Adaptability enhancement
of the factory, flexible
control of processes and
production, optimization
of the added value
activities, larger plant
availability, fast reaction
to unplanned events,
resource planning
simplification, decrease of
energy consumption,
support in quality
management, intelligent
workpiece carrier.
Additional added value
activities through new
business models (After-
Sales Service and
products), versatility and
flexibility through
decentral structures,
efficient and flexible use
of capacity, higher
production capacity, focus
on core competences,
lower investments cost
through purchase of
services, cost saving
through smaller
investments (modular,
scalable systems and Pay-
per-Use).

16. 10
Data collection and
processing
Assistance systems
Networks and
integration
Self-organization and
autonomy
Decentralization/ service
orientation
Risks
Data security as additional
challenge and risk, effort
for high amount of
information processing is
underestimated, shortage
of experts in data analysis
Low acceptance among
employees, dependence on
IT, lack of compatibility
due to not existing
standards
Raising competition and
cost pressure resulting
from transparency, loss of
know-how when changing
networking partners.
High investments, risks in
operational safety,
dependency on the
technology.
Unclear responsibilities,
decision making
difficulties, optimized
isles, loss of control, high
individual responsibility of
the employees.
Table 2: Overview about the different technologies in the field of application of Industrie 4.0 (Agiplan, Fraunhofer IML and ZENIT, 2015).

17. 11
The same study issued by Agiplan, Fraunhofer IML and ZENIT (2015) groups the core technologies into seven areas and then forms three categories
according to their Technology Readiness Levels (TRL).1
Table 3 represents a re-elaborated version of the categorization proposed by Agiplan, Fraunhofer
IML and ZENIT (2015), not considering the TRL.
Communication Sensors Embedded
Systems
Human Machine
Interface
Software/System
Technology
Standards and
norms
Actuators
 Real-time-enabled
bus-technology
 Real-time-enabled
wireless
communication
 Wired high-
performance
communication
 IT-security
 Self-organized
communication
networks
 Mobil
communication
channels
 Miniaturized
sensors
 Intelligent and re-
configurable
sensors
 Networked and
interconnected
sensors
 Sensor fusion
 Innovative
security sensors
 Intelligent
embedded
systems
 Energy
harvesting
 Miniaturized
embedded
systems
 Identification
device
 Voice control
 Gesture control
 Intuitive user
interface
 Sense
controlled
interface
 Remote
maintenance
 Context based
presentation of
information
 Semantics
visualization
 Virtual reality
 Multi-agent
systems
 Machine learning
 Big-Data Storage
and analysis
 Cloud
Computing
 Web
Service/Cloud
services
 Ontologies
 Simulated
environments
 Multicriteria
assessment of
situations
 Commun.
standards
 Semantical
standards
 Standards for
elements
 Identification
standards

 Intelligent
actuators
 Interconnected
actuators
 Secure actuators
Table 3: Industrie 4.0 core technologies by areas (Agiplan et al., 2015)
1
Technology Readiness Levels (TRL) are a method of estimating technology maturity. TRL are based on a scale from 1 to 9 with 9 being the most mature
technology.

18. 12
2.4 Potential for growth
The growing potential of Industrie 4.0 is supported by many independent studies. According
to a study issued by Fraunhofer IAO/BITKOM in 2014, a big economic potential is identified in
different economic fields: chemical, automotive, mechanical, electrical, agriculture and ICT.
In 2015, the BMWi (Bundesministerium für Wirtschaft und Energie) has issued a study, which
presents aggregated data about the expected economic effects through Industrie 4.0, estimated at
about 153.5 billion euros for the next five years (Figure 5). This study also presents the results of
a survey carried out with 53 industrial experts. Only 22 percent of all interviewed companies have
a high level of digitalization in their vertical and horizontal value chains. In the next five years,
this percentage is supposed to quadruple to ca. 84 percent. About two thirds of all interviewed
companies recognise this potential (BMWi, 2015).
Figure 3: Gross added value for the year 2013 in Germany (billions €) (Fraunhofer IAO/BITKOM, 2014)
Figure 4: Gross added value for the year 2025 (forecast) in Germany (billions €) (Fraunhofer IAO/BITKOM, 2014)
Figure 5: Expected economic effects through Industrie 4.0. (BMWi, 2015)

19. 13
According to this study, the main factors driving the implementation of Industrie 4.0 are
increasing productivity, turnover and production flexibility while reducing costs at the same time.
The greatest obstacle is the uncertainty about the economic efficiency of the required investments.
As depicted in Figure 6, German companies estimate the investments costs to be higher than
expected company growth on a mid-term basis, an aspect that may lead to some hesitation from
side of the SMEs. However, according to the survey carried out by the BMWi, the expected
income exceeds the costs after about six years, leading to think that increased investments in
Industrie 4.0 can be expected in the near future.
Figure 6: Results of the survey "Industrie 4.0 - Volks- und Betriebswirtschaftliche Faktoren für den Standort
Deutschland" - Costs and revenue in the context of Industrie 4.0 (BMWi, 2015)
According to two recent studies (Roland Berger Strategy Consultants GmbH, 2014 and Roland
Berger Strategy Consultants GmbH and BDI, 2015), if European manufacturing sector misses out
on the digital transformation, a sum of 605 billion euros could be at stake in the years ahead,
which would be equivalent to losing over 10 percent of the continent’s industrial value (Figure
7). In order to assume a leading role in the field of Industrie 4.0, Europe will have to invest 90
billion euros a year over the next 15 years to generate the necessary additional value added. This
would sum up to 1,350 billion euros over the next 15 years, an amount which is not so large at
European level, considered that it is far below numerous investment activities of European
politics, such as the bailout programs for indebted member states. Furthermore, six million of new
working places will be created in the period 2011-2020 as a consequence of the investments in
Industrie 4.0.

20. 14
Figure 7: Potential losses arising from a shift in the shares of value added (Roland Berger Strategy Consultants GmbH
and BDI, 2015)
The potential of a different single technologies in the framework of Industrie 4.0 in both
industries and in the value chain are analysed in a study of McKinsey Global Institute (2013) and
the results are promising. The results are shown in Figure 8.

21. 15
Figure 8: Speed, scope and economic value at stake of 12 potentially economically disruptive technologies (McKinsey
Global Institute, 2013)

22. 16
Table 4 shows different costs related to different areas in a company and the potential of
Industrie 4.0 in reducing these costs (Fraunhofer IPA, 2016).
Costs Effects Potential
Inventory costs
 Reduction of safety stocks
 Avoidance of bullwhip and
Burbidge effect
-30% to -40%
Production costs
 Improvement of OEE
(Overall Equipment
Effectiveness)
 Process control loops
 Improvement of vertical and
horizontal staff flexibility
-10% to -20%
Logistics costs
 Increase in the degree of the
automation (milk run,
picking, …)
-10% to -20%
Complexity costs  Extended span of control
 Reduced trouble shooting
-60% to -70%
Quality costs  Real time quality control
loops
-10% to -20%
Maintenance costs
 Optimization spare parts
inventory
 Status-oriented maintenance
(process data, measurement
data)
 Dynamic prioritization
-10% to -20%
Table 4: Estimation of the potential benefits deriving from Industrie 4.0, based on Fraunhofer IPA, 2016.
2.1 CPS and IoT
As mentioned before, through the different industrial revolutions, machines have become
smarter thanks to electronic and ICT. Particularly, embedded systems had a major impact on
different fields, from automotive to
medical equipment, from avionics to
consumer electronics. Timmermann
(2007) defines an embedded system as “a
subsystem of a larger system which is
capable of controlling the larger system
through measurement and control.”
Embedded systems consist of a
microcontroller that can be installed,
namely embedded, into a specific device,
enabling the implementation of a specific
logic/algorithms. An airbag is an example
Figure 9: The evolution of embedded systems into the Internet of
Things, Data and Services (acatech, 2011).

23. 17
of an Embedded system. A natural evolution of embedded systems is a Networked embedded
system, that is a bunch of single embedded units that compose a bigger system. A car is a good
example of Networked embedded system since it is made by completely different units that can
communicate with each other in order to achieve a common goal.
A Networked Embedded System can in turn evolve in a Cyber-Physical system (CPS).
According to Prof. Wahlster (2016), CPS are embedded systems that are not only connected with
each other but also to the internet. CPS are systems of collaborating computational entities, which
are intensively connected with the surrounding physical world, providing and using, at the same
time, data-accessing and data-providing services available on the internet. CPS include smart
machines, storage systems and production facilities capable of autonomously exchanging
information, triggering actions and controlling each other independently. This technology
facilitates fundamental improvements in various industrial processes, ranging from
manufacturing to engineering and material usage as well as in the supply chain and in the life
cycle management. In the area of manufacturing, CPS is a key technology to realize Smart
Manufacturing (acatech, 2013).
As supported by Monostori et al. (2014), a Cyber Physical Production System (CPPS) is a
CPS applied to the manufacturing system. Some examples of Cyber-Physical Production Systems
are smart machines, warehousing systems and production facilities that have been developed
digitally and feature end-to-end ICT-based integration, from inbound logistics to production,
outbound logistics and service.
Following the introduction of the IPv6 protocol in 2012, there are now sufficient addresses
available to connect smart objects to the internet. This means that, for the first time, it is possible
to network resources, information, objects and people to create the Internet of Things and
Services (for the industrial world, one should refer to the term Industrial Internet of Things). The
infrastructure is ready but is not fully implemented yet.
Although the Internet of Things can be seen as an evolution of CPS, the latest is more related
to physical interaction, while IoT is a network that embrace all the CPS and hence have a high
level of abstraction. IoT makes it possible to create networks that incorporate the entire
manufacturing process, thus converting factories into smart environments. In essence, Industrie
4.0 implies the technical integration of CPS into manufacturing and surrounding services or
process steps as well as the use of the Internet of Things and Services in industrial processes
(acatech, 2013).
According to Kang (2016) and Kolberg and Zühlke (2015), the fact that the development of
CPS and IoT is still at an early stage creates some confusion in the terminology used. Agreed-
upon definitions for the terms IoT and CPS are still missing. Sometimes the two terms are used

24. 18
interchangeably, sometimes there are different definitions across different researchers and
countries. For example, for the company Intel, IoT embedded products are defined as enablers for
the connection between embedded systems and the internet. In this thesis, I will refer to the above
mentioned definitions of IoT and CPS, which are based on acatech (2013).
2.2 Lean automation and Industrie 4.0
The term Lean Manufacturing refers to a specific approach based on the Toyota Production
System, developed by Ono at Toyota in order to reduce waste in a production system. The Lean
Manufacturing approach implies carrying out only those production process steps that add real
value to a product and thus reduces useless work and redundancies. The term lean was first
introduced in 1990 but the philosophy behind is mostly derived from the already existing TPS -
Toyota Production System (Womack et al., 1990).
The term Lean Automation refers to the application of the Lean Manufacturing principles in
automation processes. The term was first used in the mid-1990s but, successively, has not found
much application (Schling 1994, Franke 1993). As mentioned by Kolberg 2015, Industrie 4.0 is
a good opportunity to integrate lean solutions into the field of automation. He also presented some
examples of this integration, like the electronic Kanban or the robot-based solution of a Chaku
Chaku line. In fact, he affirms that Lean Automation does not exclude Industrie 4.0 or Lean
Manufacturing but, on the contrary, their similar approaches and principles can be integrated also
with the help of the ICT world. Staufen (2015) found that companies are convinced that the Lean
philosophy and Industrie 4.0 complete each other and that the former lays the basis for the 4.0
revolution. Nonetheless, Kolberg (2015) points out that despite the single technologies developed
in the context of Lean Automation, a wider framework that describes how to combine Lean
Manufacturing and CPPS is still missing.
Table 5 compares Lean Manufacturing principles with Industrie 4.0 application examples.
Lean Manufacturing
principles
Industrie 4.0 application examples
Identify value e.g. real-time gathering and processing of data  derive the specifications
of the product and of the process
Map the value stream e.g. real-time visualization of the value stream
Create flow e.g. avoidance of waiting time between gathering and processing of data.
No manual gathering of data
Establish Pull e.g. adaptable assembling lines with high variance
Seek perfection e.g. data on the value stream allow to recognize drifts
Table 5: Comparison between lean production and Industrie 4.0

25. 19
2.3 Industrie 4.0 in the world
After the term Industrie 4.0 has spread all around the world, different countries have started to
take action in order to develop their own programs. According to the country, Industrie 4.0 has
different names and there are different ways of actuating the digitalization of the processes.
Nevertheless, a common direction can be identified, as you can see from the overlapping in the
technologies used, shown in Table 6.
A short look into the world biggest economies can help us understand the magnitude of the
Industrie 4.0 phenomenon and the different approaches to its implementation. Kang et al. (2016)
had analysed and compared different Industrie 4.0 programs in the USA, Germany and South
Korea, highlighting the differences between the technologies considered by the different
countries. Table 6 represents an extension of their work.
The table is divided into six different fields:
Country: The world biggest economies are analysed in order to investigate whether they have
been already reached by the Industrie 4.0 terminology and approach and whether they are
developing some programs in this context. Only USA, Germany, Japan and South Korea have
been found to have similar detailed programs that focus on the promotion of the so-called
Industrie 4.0 core technologies. Other nations, like India and China, are aware of the Industrie 4.0
initiative, but for now, they have other priorities and are mainly concentrated on national growth
in general terms. Russia represents a particular case since the government is currently putting
efforts in single fields, like for example 3D printing, but it does not have a more general Industrie
4.0 program.
Name of the Industrie 4.0 (program): In general, different countries recognize and use the term
Industrie 4.0 but, at the same time, promote their specific programs under different names, for
example in South Korea “Manufacturing innovation 3.0”, in Japan “Industrial value chain
initiative”. As already mentioned, India’s and China’s programs embrace a wider field of
industrial growth and development and are respectively known under the names of “Make In
India” and “Made in China 2025”.
Coalitions: In the USA and in Germany, different coalitions composed by industry and
governmental organisms as well as by universities and other research institutions work together
to lead the 4.0 transition. In the USA, there is, for example, the NIST agency, in Germany the
DFKI. Moreover, other sub-coalitions are present and work in strict interconnection to develop
and implement specific programs in the Industrie 4.0 field.
Standardization program: According to Kolberg and Zühlke (2015) and Kang (2016), one of
the biggest issue related to Industrie 4.0 is that a software framework as well as agreed-upon

26. 20
standards in this field are still missing. In this regard, a lot of work is currently being done in
Germany by the Platform Industrie 4.0, with the project Reference Architecture Model Industrie
4.0 (RAMI 4.0), and in the USA by the Industrial Internet Consortium with the project Industrial
Internet Reference Architecture (IIRA). Both projects are aimed at developing some norms and
leading a standardization process the industry can benefit from. Currently, the USA and Germany
are working together to develop a common standard (Industrial Internet Consortium, 2016).
The purposes of the RAMI 4.0 project are:
 Structuring of all Industrie 4.0 aspects into manageable partial contents for focused
discussions;
 Identification and closing of gaps;
 Identification of overlaps and establishing preferred solutions;
 Identification of subsets of a norm or a standard for rapid implementation of partial
contents of Industrie 4.0;
 Identification and closing of technical gaps for the implementation of use cases;
 Identification of development opportunities for the future.
Despite these initial efforts, a lot needs to be done to produce practical results in the field of
standardization, as supported by Kolberg (2015), Kang (2016) and Wübbeke (2015).
Wübbeke (2015) reports that China considers Germany to be the biggest collaborator in
developing 4.0 technologies and standards. Standards that are currently being developed and will
be developed by Germany could be incorporated into the German-Chinese cooperation and
consequently be spread in China. The potential that standardization represents for Germany is
also recognized by Swiss Business Hub China (2015).
As mentioned by Nakayama (2015), Japan also recognizes the possibility to benefit from
setting standards even though, differently from the collaboration between Germany and the USA,
no international collaborations between Japan and other countries are in place yet.
South Korea and India are passive players in this field, while Russia does not recognize the
potential offered by standardization (Tekes, 2014).
Technologies involved: In this context, the differences between the leading manufacturing
nations, like the USA and Germany, and other nations clearly emerge. For example, Germany,
the USA, South Korea and Japan have developed a common set of technologies that can be
applied horizontally in almost every industry sector, whereas China and India are still focused on
the development of single area of interest, like “Aviation”, “Chemistry”,” Food”, etc.
Documents: For every country, a list of related programs and references is provided. For some
countries we can rely on official/institutional sources, while for other, for example China, only

27. 21
topic-related articles are available. Websites, like the one related to the “Make in India” initiative,
offer a look into the programs but detailed information is missing or translation is needed.

28. 22
Country Name of the Industrie
4.0 (program)
Coalitions Programs for
standardization
Technologies Documents
USA  Advance
Manufacturing
 Smart Manufacturing
 Smart
Manufacturing
Leadership
Coalition SLMC
 NIST
 AMP 2.0
 Industrial Internet
Consortium
 IIRA  CPS
 IoT/Wireless
 Big Data/Data Analytics
 Cloud Computing
 Sensor
 Smart Energy
 Reference Structure
 Robot System
 Suitable manufacturing
 System integration
 Additive manufacturing
 Interoperability
 Multi-scale dynamic modelling &
simulation
 Intelligent automation
 Cyber Security
 Kang et al. (2016)
 Industrial Internet consortium
(2016)
Germany/
(Europe)
 Industrie 4.0  Plattform Industrie
4.0
 Catapult High
Value
Manufacturing
 RAMI 4.0  CPS
 IoT
 IoS
 Big Data
 Cloud Computing
 Sensors
 Work Organization/Design
 Standardization, Reference
Architecture
 Complex System manufacturing
 Broadband-Infraband
 Security/Safety
 Resource Efficiency
 Training/Development
 Smart energy
 Adolphs (2015)
 Kang (2016)
 acatech-Plattform Industrie 4.0
(2014)
 BMWi (2015)
 agiplan, Fraunhofer IML and
ZENIT (2015)

29. 23
Country Name of the Industrie
4.0 (program)
Coalitions Programs for
standardization
Technologies Documents
 Regulatory Framework
    
South
Korea
 Manufacturing
Innovation 3.0
 Manufacturing 3.0
 Connected Smart
Factory
 MOTIE
 MSIP
None  CPS
 IoT
 3D Printing
 Big Data
 Cloud Computing
 Sensors
 Smart Energy
 Hologram
 Kang (2016)
Japan  Industrial value chain
initiative IVI
 Monozdukuri
 Industrial value
chain initiative IVI
None  IoT
 Big Data
 AI
 Mass Customization
 Standardization in Communication
 Sensors
 Standardization Security/Privacy
 B2B paradigm change
 Factories Connected. Intranet to
Internet
 3D printing
 Virtual Reality
 Robot revolution
 METI, MHLW and MEXT
(2015)
 Nakayama (2015)
 Pühringer (2016)
 Nirmala (2016)

30. 24
Country Name of the Industrie
4.0 (program)
Coalitions Programs for
standardization
Technologies Documents
China  Made in China 2025  Innovation Design
Alliance of China
(IDAC)
None, interested
in collaboration
with Germany
Itemized plan with key sectors.
 New Information Technology
 Numerical control tools
 Aerospace equipment
 High-tech ships
 Railway equipment
 Energy saving
 New materials
 Medical devices
 Agricultural machinery
 Power equipment
 Swiss Business Hub China (2015)
 Mizuhu Bank (2015)
 Bank of Tokyo (2015)
 Xihui Liu (2016)
 Domiguez, (2015)
 Wübbeke (2015)
 Staufen (2015)
 Fraunhofer IAO (2015)
India  Make in India  No big coalitions,
but rather
development of a
concept for smart
industrial cities
along five industrial
corridors each of
whom will have a
corporation
organism.
None No real technology but sectors of
development.
 Urban infrastructure
 Electronic and IoT
 Aviation
 Textile sector
 Biotechnology
 Automobiles
 Food processing
 Chemicals
 Defence manufacturing
 Space
 Wellness
 Mining
 Railways
 Tourism
 Pharmaceuticals
 Renewable energy
 Make in India
http://www.makeinindia.com/home
 Bilimoria, et al. (2016)

31. 25
Country Name of the Industrie
4.0 (program)
Coalitions Programs for
standardization
Technologies Documents
Russia  No Industrie 4.0 related
programs. Only R&D
requests from
Government or single
companies.
Implementing single
components related to
Advance
Manufacturing
 No networks or
coalitions, only
little efforts.
 Different
institutions and
initiatives working
independently:
INNPROM
(exhibition),
Skolkovo
Foundation,
Moscow Innovation
Development
Center
None  Robotic
 Advance manufacturing
 Logistic
 New Material
 Energy Efficiency
 IT solutions
 CAD/3D modelling
 3D printing
 IoT (low grow)
 Big Data (low grow)
 Tekes (2014).
Table 6: Industrie 4.0 in the world

32. 26
2.4 Best practices and practical examples of the implementation of Industrie 4.0
Table 7 shows some practical examples of the implementation of Industrie 4.0. The practical
application of Industrie 4.0 can cover a lot of different fields. In the production and logistics areas,
the use of CPPS is particularly widespread. This highlights the increasing importance of a modern
production system that is connected, decentralized and reconfigurable. More examples are
provided by VDMA (2016).
Field of application Technology Example
Development Virtual Reality, smart glasses Audi
Combination of real hardware and
Motion Capture system in order to add
virtual reality components to a real
prototype.
Production Line reconfiguration, decentral
control
Nike
Producing individualized shoes that
can be personalized through the
website directly by the customer
Big Data SIEMENS
Predictive maintenance on wind
turbine, reducing maintenance
downtime
3D printing Lufthansa Technik
Produce spare parts on demand by
additive manufacturing
IoT, CPPS, Big Data Intellimech
Predictive maintenance with local and
remote functionality. With remote
functionality, the producer of the
machine can assure rapid assistance
and continuously optimum set up.
Collect data for improving not only for
monitoring.
Human Machine Iteration,
individualized production
Adidas
Combination of human cognitive
capabilities with manufacturing
robotics to achieve optimized cutting
and sewing.

33. 27
Field of application Technology Example
Big Data, Security, Distributed
systems, smart sensor, IoT
Marathon Petroleum
Automatic gas level monitoring,
tracking employee’s location and
automatic warning of individuals about
nearby dangers.
Human Machine Collaboration Audi
The robot becomes an operator
assistant as it is able to select the right
component to be assembled and to
orient it the right way.
Logistic Virtual reality, big data, smart
data, IoT
Itizzimo
Assistance to workers by means of
information available through smart
glasses.
Decentral control, CPPS,
reconfiguration
Fraunhofer
Self-organizing transport system of
vehicles in a CPPS environment, in the
context of internal material flow
handling
IoT, CPPS, Smart Data Würth
Intelligent bin equipped with a camera
that can see when C-Parts need to be
re-order.
Decentral control, IoT, CPPS Claas
Harvesting machines connected in real
time and continuously sending
information for optimized harvesting
logistics.
Decetral control, IoT, CPPS InventAIR
Autonomous drone for automated
inventory logistics.
Table 7: Practical examples of Industrie 4.0 technology

34. 28
3. Decentralized controlled operational platform – theoretical
concept
3.1 Research question: How should a concept for a decentralized controlled operational
platform for low cost applications look like?
As mentioned in the previous chapter, Industrie 4.0 is a great opportunity, but practical
applications especially for SMEs are still missing. Unlike large multinational companies, which
dispose of sufficient internal R&D resources, SME needs an external support for research studies
as they do not have enough know-how and resources to conduct specific studies.
In the future, companies will be confronted with the introduction of CPPS under at least two
different perspectives. On the one hand, some companies will upgrade their production setup
towards CPPS. On the other hand, companies which will not implement such technologies, will
still need to understand how CPPS-based production concepts work in order to successfully
interact with companies that have upgraded to CPPS.
Based on the analysis of the needs of typical Italian SMEs in the production sector, Fraunhofer
Italia has developed an efficient approach to help companies benefit from current technology
developments in the short-term, while raising their efficiency. This project focuses on the setup
of a real 4.0 line in a laboratory in order to give examples on how companies can benefit from the
implementation of CPPS in scenarios or applications, bridging the gap between academic theory
and practical integration. The research question underlying the development of this concept is the
following: How should a concept for a decentralized controlled operational platform for low cost
applications look like?
3.2 Aim of the platform and state of the art
In this session, an overview of the projects already existing in the field of decentral controlled
operational platforms will be presented.
In Germany, the DFKI research centre, with the collaboration of 16 partners from the industry
field, has developed a line that shows a lot of useful concepts of Industrie 4.0. The project is
known as SmartFactoryKL and the product being manufactured in the production line is a simple
flashlight. Seven industry partners realized the modules, while the others created the infrastructure
and the communication. This production line concept was presented at the Hannover Messe in
2015.

35. 29
Figure 10: DFKI SmartFactory prototype. (SmartFactoryKL, 2015)
As the picture shows, the production line is made of several independent blocks that can be
easily put together thanks to electromechanical standards. Furthermore, they recognise each other
with RFID technology. All the important information is stored in the product itself and this happen
through an intelligent workpiece carrier that can communicate with the process flow.
The KITECH (Korea institute of industrial technology), as part of a coalition of different
associations and companies in the industrial field – the so-called CSF (Connected Smart Factory),
has also worked on the development of a decentral controlled operational platform (Cho, 2015).
After having analysed the example of DFKI mentioned above, KITECH is currently trying to
develop a similar solution. Following a new and innovative approach, the researchers are
developing a Virtual Reality model of a smart factory. The model will be then integrated in a
feedback loop where also a physical model is connected. This Virtual Reality model is able to
predict the productivity and the vulnerability of the production line, offering moreover a visual
feedback of what is happening in the production line (SF Lab UNIST, 2013; Cho, 2015).
To our knowledge, in Italy, a similar platform has not been developed yet. Nonetheless,
individual companies are making efforts towards digitalization. It is therefore important to build
a test bed that can be used by SMEs.
From the previous examples, we can see that the aim of these platforms is to translate the
concepts of Industrie 4.0 into a model that is as near as possible to the practical application in the
industry. Nevertheless, trying to put together as many concepts of Industrie 4.0 as possible may
be counterproductive. Hence, I decided to concentrate my attention and to include in the model
only the following four concepts:

36. 30
 Decentralized control of production processes;
 Self-optimization of process flow and product quality;
 Flexibility in production process;
 Personalization and batch size one production;
These concepts have already been explained in Chapter 2. In the next sessions, I will explain
how I developed a production line that covers each of these concepts and what are the advantages
and disadvantages related to the develop of the platform.
3.3 Reasons behind the choice to build a decentralized controlled operational platform:
Advantages and disadvantages of decentralization vs. centralization
In Chapter 2, the risks of decentralization and self-optimization have already been mentioned.
In this session, I will look deeper in this issue, with particular regard to the platform developed at
Fraunhofer Italia in the context of my master thesis.
One of the main factors of success for the 4.0 revolution is the decentralization of the
processes. The growing demand for customized products is forcing manufacturing to change its
organizational structure. According to Brettel et al. (2014), distributed (decentralized) systems
seem to be better at handling high complexity, compared to the rigid, centralized hierarchical
control of a pyramidal structure. Nonetheless, some disadvantages of decentralization can be
identified. For example, Saharidis (2006) identifies one possible disadvantage of decentralized
systems, showing that a decentralized control is not cost effective. Monostori et al. (2015) looks
into modern control theory literature to identify pros and cons of centralized vs. decentralized
systems. Based on the findings of Monostori et al. (2015) and other studies on this topic, Table 8
shows pro and cons of centralization and decentralization.
Centralized Decentralized
Pro Cons Pro Cons
 Successfully
implemented
(Monostori et al.,
2015)
 Difficulty to
handle growing
complexity
(Monostori et al.,
2015)
 Not easy to
reconfigure
(Monostori et al.,
2015)
 Rigid control
integration
(Monostori et al.,
2015)
 Openness, easy to
build and change
(Monostori et al.,
2015)
 Reliability, fault
tolerance (Monostori
et al., 2015)
 Performance,
distributed execution
of tasks (Monostori
et al., 2015)
 Scalability,
incremental design,
potential of
addressing large
problems and tasks
 Communication
overhead, cost and time
of sharing information
(Monostori et al., 2015;
Saharidis, 2006)
 Decentralized
information, global vs
local data (Monostori et
al., 2015)
 Security, confidentially
are difficult to be
guaranteed (Monostori
et al., 2015)
 Decision Myopia, local
optima (Monostori et
al., 2015)

37. 31
(Monostori et al.,
2015)
 Flexibility, easy to
be redesigned
(Monostori et al.,
2015)
 Cost, potential
reduction of costs
(Monostori et al.,
2015)
 Distribution,
consequence of
spatially separated
units (Monostori et
al., 2015)
 Chaotic behaviour,
bottlenecks and
butterfly effect
(Monostori et al.,
2015)
 More complex to
analyze compared to
centralized systems
(Monostori et al., 2015)
 Need for efficient
coordination (Brettel et
al., 2014)
 Lacking sharing of
information and data
between companies
(Brettel et al., 2014)
 Lacking
synchronization
(Brettel et al., 2014)
Table 8: Centralization vs. decentralization: pros and cons
Despite some disadvantages, a lot of studies and evidence suggest to move towards the
implementation of distributed systems. Along with Mass Customization, the product must be
produced in small batch size, a higher degree of flexibility is required and the production should
be able to follow the market requests and opportunities in a dynamic way (Spath et al. 2013, Stich
et al. 2013). Rapid Manufacturing (e.g. 3D printing) is a technology that would have the potential
to reduce the batch size, but today it cannot compete with the conventional manufacturing
methods (Brettel et al., 2014). According to a survey conducted by the Laboratory for Machine
Tools and Production Engineering in Aachen, the incorporation of flexibility into mass
production, and, in particular, in the early stages of product design, where 80% of costs of the
product are allocated, is seen as one of the main issues for managers in the manufacturing industry
(Schuh, 2010).
Although a decentralized structure cannot be applied in every field, for example in steel and
chemical industries (Murray, 1983), evidence from the aeronautics and automotive industries
shows that such systems are highly flexible and able to deal with highest complexity, managing
more than 20.000 components and 80 companies in the supply chain (Brettel et al., 2014).
Brettel et al. (2014) conducted face-to-face interviews with R&D managers and experts in the
field of Supply Chain Management in order to discuss the potential challenges of Industrie 4.0
implementation. The results show that experts agree on the fact that decentralization has a great
potential for growth in the field of mass customization, providing a way to tackle the problem of
high complexity. Nonetheless, experts point out that a lot of research must still be carried out. In

38. 32
Germany, a program called “Autonomics” and supported by the Federal Ministry of Economics
and Technology, has been developed with this specific purpose.
Research in this field can also contribute to developments in the area of self-optimization. In
general, self-optimization describes a process through which a system organizes and optimizes
itself, starting from an initial status of disorder and achieving some form of overall coordination
out of the local interactions between smaller components. According to the literature in this field
(see, for example, Zavadlav et al., 1996, Rekiek, 2002, Angelidis, 2012) the development and
implementation of self-optimization is still at an early stage and represents an open issue. For this
reason, the production line developed at Fraunhofer Italia in the context of my master thesis is
not based on self-optimization concepts, leaving this topic to future research. Rather, the platform
for the decentralized control is based on random choice principles. Nevertheless, this master thesis
lays the foundations for self-optimization since, although initially a random choice is
implemented, future developments of this platform will include a statistical analysis of the data
and a consequent optimized solution.
3.4 Choice of the product and product variants management
As a first step, I looked for a product suitable for the development of a production line that
includes the concepts identified in paragraph 3.2, namely:
 Decentralized control of production processes;
 Self-optimization of process flow and product quality;
 Flexibility in production process;
 Personalization and Batch Size 1 production.
The implementation of flexibility and personalization assumes that the product needs to be
produced in Batch Size 1, i.e. produced just-in-time to complete one-off customer orders. I started
from the design of the product in order to find a set of feasible processes that the product must
undergo and to create a high level of personalization of the product itself.
The chosen product is a vehicle, designed in a way that you can choose, for example, the colour
of the wheels or a personalized engraved word on one side of the vehicle. The product can either
be a car or, in order to add complexity to the production line and thus to test its flexibility, a
motorcycle. The possibility to produce two different products adds a degree of freedom but,
thanks to its flexibility, the line is able to manage the production of the vehicle independently of

39. 33
the number of wheels (two for the motorcycle, four for the car). As shown in Figure 11, the two-
wheeled vehicle is a Piaggio Vespa and the four-wheeled vehicle is a Fiat 500.
The Vespa is made of two pieces of material that defines the outer shape, a seat that holds
together the two external parts, two wheels and two force-fitted wooden dowels that fix the wheels
to the external parts.
The 500 is made of two sides with etched windows and mudguards, six internal components
with no windows and no mudguards but with holes to insert the wooden dowels, and four wheels.
The wheels are thought to rotate about their axes and to have a rubber O-ring inserted in the outer
etched profile. The two vehicles are designed in this way to be easily produced with a laser cutter
and common components.
For the two products there are a set of possible variants, for example different materials,
colours and other personalization features. Table 9 shows the number of different choices that can
be made by a potential customer.
Vehicle Vespa 500
Central shape material
 Wood
 Plastic
 Paper
n.a.
Outer shape material
 Wood
 Plastic
 Paper
 Wood
 Plastic
 Paper
Outer shape colour
 Yellow
 Red
 Blue
 Yellow
 Red
 Blue
Seat
 Wood
 Plastic
 Paper
n.a.
Body union typology
 Screw
 Glue
 Screw
 Glue
Figure 11: Products of the decentralized controlled platform.

40. 34
500 Wheels material n.a.
 Wood
 Plastic
 Paper
Vespa wheels material
 Wood
 Plastic
 Paper
n.a.
Wheels laser engraving
 Type 1
 Type 2
 Type 1
 Type 2
Body laser engraving
 Text
 Image
 Details
 Text
 Image
 Details
Body mechanical engraving
 Text
 Image
 Details
 Text
 Image
 Details
Final transparent paint
 Yes
 No
 Yes
 No
Table 9: Product variants.
Every component of the product can be chosen among different variants, not only different
materials, but also different colours, type of engraving and text to be engraved.
One of the concept of Industrie 4.0 this production line covers is the personalization. From the
observation of Table 9 we can infer that with an increase in the degree of freedom of the customer
more complex production machineries are required in order to be able to manage different
combinations.
3.5 Structure of the platform and process variants management
Having identified the product, I developed the assembling procedure and, consequently, the
working stations. Table 10 provides a list of all working stations with a description of the
production steps they carry out. Every working station corresponds to a production process step.
Working
station
Description
Loading
The first step is to load the pieces that compose the structure of the vehicle.
The pieces are available in different colours, materials and are stored in stack.
Gluing
The loaded parts are glued together. Depending on the type of material
chosen, different glues are used. The glue used for two pieces of wood is

41. 35
different from the one used for two pieces of plastic or two pieces of acrylic.
According to the glue used, also the drying times are different.
Pressing
When glue is used, a hydraulic press keeps the two pieces together so that the
glue can distribute uniformly.
Screwing
As an alternative to glue, screws can be used, but first holes must be drilled in
order to avoid cracking the wood when inserting the screw.
Drilling
Holes are drilled in the chassis in order to fix the assembling parts together
using screws.
Dowels
positioning
The dowels are placed in position in the chassis and then sent to the pressing
unit so that they can be fitted in place. In order to add complexity and
therefore be able to identify possible problems and find possible solutions,
only one hydraulic press is used for many different processes to create a
situation where bottlenecks can occur.
Wheels
inserting
The wheels are placed in position together with journals and stoppers. The
journal allows the rotation of the wheels and makes sure that the stopper does
not interfere with the movement of the wheel. The stopper is the last
component to be assembled and ensures that the wheel remains at its place.
Successively, the vehicle is sent to the pressing station, where the hydraulic
press fixes the wheels in place. It is worth noting that the wheels come from a
different production line process, where the O-Ring are assembled in the
outer profile of the wheels. Therefore, at this point, two different production
processes are merged.
Laser
engraving
The laser can be used to engrave a texture, a text or an image on the body of
the vehicle and/or on the external side of the wheels.
Mechanical
engraving
The CNC mill is used to engrave a texture, a text or an image on the body of
the vehicle and/or on the external side of the wheels.
UV painting
A smooth finishing is used to protect the vehicle from dirty and scratches. In
this station, a unit sprays the vehicle with the transparent paint.
UV curing
After the transparent colour has been sprayed, UV dry the paint on the
surface.
Automatic
Quality
Control
The station checks whether the quality of the components is good, measuring
the tolerances with cameras and reporting automatically if the quality is not
acceptable.
Unload The unloading unit takes the product out of the production line.

42. 36
Human
Quality
Control
A separate station supports the automatic production in case a working
station is out of service, or makes changes to the vehicle.
Table 10: Working stations/process steps and their description.
The malfunctioning or the overloading of one of these working stations may lead to a plant
downtime. Therefore, it is important to integrate some process variants in the production line, that
is some different directions that a product can take in order to be completely assembled. In this
section, I will explain how I identified the different process variants for my production line.
Table 11 shows different situations that can occur in the production line. With the exemption of
case L1 “Sequential Production Line”, all of these situations lead to some changes in the
production process that have to be addressed through process variants. The big square represents
the working stations and the small ones the product that must be processed.
Typology of process variant Description
L1: Sequential Production Line The product undergoes every process in a row.
L2: Under Maintenance shift The product finds that the working station 1 is
under maintenance and, consequently, it
switches to a redundant working station.
L3: Manual – Automatic shift The production is balanced between and
automatic and manual working station.

43. 37
L4: Parallel lines Two parallel lines produce the same product
independently. When a station is out of
service, the production is shifted to the
functioning line, reducing the costs associated
to a non-functioning production plant.
L5: Merging of Production Processes In a production line, a main product is
assembled with a secondary product coming
from a different production line. In this case,
there is a merging of two different workpiece
carrier.
L6: Traffic jam When the product encounters a traffic jam
(station 1), it jumps that station and undergoes
the process of station 2. This solves
bottlenecks during the production process.
L7: Priority pass When the priority of a product is higher than
that of others, the product can overtake the
others.
Table 11: Different situations that can occur in a production line and may change the process variants.
In order to analyse in depth how to manage a set of process variants, I focused on a single
product variant (see Table 12)
Vehicle 500
Central shape material n.a.
Outer shape material  Wood
Outer shape colour  Red

44. 38
Seat n.a.
Body union typology  Glue
500 Wheels material  Plastic
Vespa wheels material n.a.
Wheels laser engraving  Type 1
Body laser engraving  Text
Body mechanical engraving  Image
Final transparent paint  Yes
Table 12: Product variant chosen to investigate process variants.
The number of process variants is determined by the product configuration, the modularization
and the technology used.
In my production line, after having undergone a process, the product can decide among a list
of working stations to visit next. This distinguishes my production line from a classical
manufacturing line, where the production process is not flexible and only an option is possible at
every production step. The possibility of choice implies that the product does not have to follow
a specific sequence but, given a certain degree of constraint, the product can randomly undergo a
set of processes, choosing different process variants. Doing this presupposes the implementation
of a technology that is able to manipulate and process the product. For example, the CNC can
engrave the vehicle at every point of the assembling process only if the engraving station is able
to manipulate the product at every production stage. As mentioned, there are some constraints,
that is in some situations only one choice can be made. For example, in order to insert the dowels,
the holes are supposed to be drilled first.
I used graph theory as a first tentative to graphically represent the process variants. P-Graph
is a good solution to be used in workflow modelling to represent the flow/input/output of materials
through an operating unit (Jozsef, 2007). However, in my case and at this stage, I decided not to
use a P-Graph because of the high complexity given by the number of choices that can be made.
As we will see in the next paragraph (3.6), I decided to focus on making things easy to understand,
concentrating on only four working stations/process steps, which will be represented in the next
paragraph by the letters A, B, C, D.

45. 39
3.6 Key example and Octave simulation
In order to clearly represent the process variants, I use an easy 2D
example built with Lego bricks (see Figure 12). A, B, C, D, or
respectively 1,2,3,4, represent four process steps in my production
line (as we will later in this paragraph, I will use numbers in place of
letters when I will report on the Octave simulation results).
The sequence A, B, C, D would represent the sequence of
production steps in a traditional non-flexible production line. In my
production line, I add the possibility to choose between different
sequences, or respectively process variants (for example, A, C, B, D or A, B, D, C). There is no
fixed sequence for the product to be assembled but there are some constraints so that only a
defined set of sequences are possible.
A is the first step of the production process as well as the prerequisite for all other processes,
B, C, and D. After the first step, the product can choose one of the other three processes without
any constraint. The production process could, for example, occur in the following way: After A,
the product chooses B. Once B is carried out, the product chooses C. Once C is carried out, D is
the only option (see Figure 13).
If we try with another sequence, we can identify some physical
constraints in the production process. If the product chooses C/D as the
second step, then B must be carried out before D/C (otherwise B cannot
be carried out/placed, see Figure 14).
In order to control the process variants mechanism, I developed a
model that can also be applied to larger production lines. The minimal
data structure is composed by two fields that set the rules: “prerequisite”
and “successor” (see Table 13).
Figure 12: 2D representation of
process variants.
Figure 13: 2D representation of the process sequence.
Figure 14: 2D
representation physical
constraint.

46. 40
The prerequisite refers to the necessary condition for a specific process step. The successor
refers to all possible successive process steps. Process A does not have prerequisites, B has only
A as prerequisite. Process C has the following prerequisite: process A is executed AND one of
the following is true: process B is executed OR process D is not executed. The same logic applies
to the prerequisite for process D.
In order to validate this logic, I developed an Octave simulator that simulates a hypothetical
production run, based on the working stations/process steps identified for the construction of the
vehicle (see Table 10, paragraph 3.5). In this case, A represents the drilling working station, B
the gluing working station, C the spraying working station and D the engraving working station.
The selection of the working stations corresponding to the letters A to B has been randomly made.
The simulated process has been provided with tree main sections.
1. Data structure: In this section, the data structure that represents a working station/process step
is provided. An example of data structure is provided below (D, engraving working station). For
the complete program, please refer to the attachment.
process(p.D).name = 'ENGRAVING';
process(p.D).ID = 'D';
process(p.D).prerequ = 'ismember(p.A,executed) &&
(not(ismember(p.C, executed))|| ismember(p.B,executed))';
process(p.D).succ = [p.B,p.C];
process(p.D).first = 0;
process(p.D).parameters = [' SPINDLE_SPEED','
DRILLING_DIAMETER','HEAD_SPEED',' WRITING'];
DRILLING_SPEED'];
This data structure has been developed in such a way that it is possible to add or modify product
and process variants without having to modify the source code of the program. If I would have
programmed the “prerequisite” condition with a set of else/if conditions, the microcontroller
would have been able to produce only one specific product with a unique set of process variants.
Once the product or process variant changes, the program must be modified, re-compiled and
A B C D
Prerequisite: Prerequisite: Prerequisite: Prerequisite:
/ A A & (B | !D) A & (B | !C)
Successor: Successor: Successor: Successor:
B C D C D B D B C
Table 13: Process variants model.

47. 41
uploaded in the microcontroller. Instead, this data structure does have a unique prerequisite
condition with an argument that can be changed by uploading a new set of rules, according to the
product and process variant I want to implement. The argument includes the following six fields:
 Name of the station: the name identifies the working station
 Identification number of the station: the ID of the working station is used to manage the
station in the program logic.
 Prerequisite field: according to the logic previously presented (see Table 13), the
prerequisites are set and memorized as a function with Boolean values, wrapped as a text
(reported below).
process(p.C).prerequ = 'ismember(p.A,executed) &&
(not(ismember(p.D, executed))|| ismember(p.B,executed))';
The function “ismember” looks for the selected process in the list of the executed processes.
 Successor field: The successor represents all possible successive process steps.
 First process: If value is 1, the process step can go first in the production process, if value
is 0, the process step cannot go first in the production process. In each production process,
there is at least one process step that can go first. There may be also the production
processes when several processes step can go first. In this case, this field indicates which
one can go.
 Parameters field: In this master thesis, a hypothetical scenario has been developed, in
which four working stations are designed to process a product. In this field, all parameters
that a working station needs are stored. Before the process step is started, the working
station retrieves the data from the parameters field. Once the process step is ended a set of
parameters is transferred back and stored. In this way, we can analyse the parameters that
the working station has been able to collect during the process. As this is only a hypothesis
and not a real case scenario, this transfer of data is implemented only to show that the
working station is able to receive and send data (the parameters transferred and received
are randomly chosen).
2. Recursive logic checker: A research tree algorithm has been implemented to find every
feasible combination of the process variants which are allowed by the logic. Any error in the logic
is communicated with a warning message. Once the recursive function “check_plausibility” has
reached the exit condition, the algorithm returns the number process variants (in this case four)
and the depth of the research tree. In this example, zero is the depth associated to the first iteration,
whereas three is the last function call before the exit condition from the recursion. Table 14 shows

48. 42
the results of the recursive logic checker. As mentioned before, the numbers 1, 2, 3, 4 correspond
to the letters A, B, C, D. Numbers are used in the simulation in place of letters for simplicity.
3. Process simulation: In this section, an algorithm simulates the production process, showing
all the decisions that are made. A set of dummy functions are used to simulate the identification
of the next working station, the uploading of the parameters and the process that is taking place.
The first process is randomly chosen from the set of processes enabled to go first, and then the
simulation is carried out. The variable next memorizes the next process available, and it is
determined by the function next_available. This function takes the variable last (last
process executed) and can choose the next working station looking in the field successor,
stored in the data structure presented before. The function next_available takes the first
potential successor looking in this list and it accesses its prerequisite data structure (looking in
the field prerequisite). If the prerequisites are satisfied, the process step is added to a list of
possible successors: list_of_next_valid. The core part of the program deals with the
evaluation of the prerequisite. As seen before (see section 1. Data structure), an if-condition takes
the string 'ismember(p.A,executed) && (not(ismember(p.D, executed))||
ismember(p.B,executed))' as argument. In order to be executed by the program, this string
has first to be transformed into code by the eval function of Octave.
A new successor is then selected from the list successor always among the last
alternatives, and, at the end, a random choice among the list_of_next_valid is performed.
The whole decision process is reported in the attachment.
Figure 15 shows the steps implemented in the Octave simulator.
Combination
number
Process variants Depth of the research
1 1 2 3 4 3
2 1 2 4 3 3
3 1 3 2 4 3
4 1 4 2 3 3
Table 14: Results of the recursive logic checker.

49. 43
Figure 15: Octave simulation steps
3.7 Possible real case application of the model: Intelligent Workpiece Carrier
As mentioned before, practical applications of Industrie 4.0 concepts are still missing,
especially for SMEs, which do not dispose of sufficient internal R&D resources and know-how.
In Italy a lot of companies are SMEs and the implementation of Industrie 4.0 seems particularly
difficult also compared to other European countries. With the decentralized controlled platform,
I developed in my master thesis, I want to demonstrate that also a SME with low budget can
benefit from digitalization. The model described in the previous paragraphs could for example be
implemented in a metalworker workshop, a small company with no automation level and without
internet connection between the machines used.
First of all, the decentralized controlled platform could help avoiding bottlenecks. Using the
algorithm presented before in this chapter, if a machine is busy or out of service, an employee of
the company will have the possibility to choose among an automatically-compiled list of other

50. 44
possible working stations. This technology has therefore the potential to maximize the usage of a
machine, while reducing the transition time between two working stations.
Despite the fact that the machines cannot be connected to the internet, it can nevertheless take
advantage of another aspect of the Industrie 4.0, Big Data. Managing big amounts of complex
data usually implies that data have to be collected during production by real persons. Some
examples are the measurement of the time needed for every process to be completed, the
measurement of the time between two processes, the number of waste components, the frequency
of bottlenecks etc. If the product itself can collect data automatically, there would be no need for
additional persons to measure them in the workshop. In this way, instead of having few data
collected once in a while, a more frequent data collection would be possible, improving the
decision making process and making it faster. Moreover, if a machine is not connected to the
internet or intranet, the operator has to manually set the production parameters, as it is not feasible
for a central system to remotely control the process.
In order to solve these problems, the decentralized controlled platform will be designed in such
a way that the product can collect data, communicate the parameters of the process to the working
stations without an internet connection, and suggest alternative solutions when a station is busy
or out of service. An Intelligent Workpiece Carrier represents a solution that provides such
functionalities. As we will see in Chapter 4, an intelligent workpiece carrier is a device attached
to the product, working as an intermediary unit between different working stations with different
technologies.

51. 45
4. Design of an Intelligent Workpiece Carrier
4.1 Aim of a workpiece carrier and state of the art
A workpiece carrier is an object that contains a finished or a semi-finished product. In a
workshop floor it contains components and organizes as well as protects them in downstream
processing and storage. It is designed as a loading unit and it makes transportation logistic simple
and efficient. Workpiece carriers are largely used in the industry, for different product sizes and
applications, and are usually made of plastic.
Figure 16: Intelligent agents and workpiece carrier together as a single unit. Reference from left: youtube.com,
http://www.bekuplast.com
Some recent practical studies are analysing ways and methods to improve the quality and
efficiency of production, trying to figure out whether workpiece carriers can be further developed
and enhanced.
In the context of decentralized systems (see paragraphs 3.3), there is potential for developing
some intelligent agents that are able to cooperate with their counterparts and with the machines,
on every single production level. Such intelligent agents will also be the guardian of the product
that is being produced. In this way, product and agent would be considered as a single unit and
we could say that the product is provided with intelligence. Putting together intelligent agents and
the traditional workpiece carriers the final outcome is an intelligent workpiece carrier, that is, in
turn, a fundamental part of a cyber-physical system.
The company CONTRINEX developed and
implemented a first practical application that goes in the
direction of an intelligent workpiece carrier. The machine
identifies products in the production chain by means of a
radio frequency identification (RFID) (Figure 17). The
solution has been implemented in the automotive field, for
a company that wanted to wash different batches of
different products, with a personalized washing cycle.
Figure 17: Workpiece carrier with RFID to
identify a product. Source:
http://www.contrinex.com/

52. 46
Another example of intelligent workpiece carrier is
provided by the project SmartFactoryKL (DFKI),
previously discussed (see paragraph 3.2). The IWC
(Intelligent Workpiece Carrier) has been developed to
memorize all the parameters needed for the production and,
for example, the personalized word to be engraved in the
product case (Figure 18).
Fraunhofer IPA is working on an intelligent workpiece carrier with
the project “smartWT”, within the cluster for smart solution microTEC
Südwest (Figure 19). The vision is to build a single unit that can
contribute to process monitoring thanks to a set of integrated sensors.
The sensors are constantly gathering logistics and process data,
improving quality and traceability. The data gathered are uploaded via
wireless to the cloud. The user has access to current data at all times
and can intervene to improve the quality of production.
The Fraunhofer IPA project differs from the previous solutions in the self-awareness of the
product itself. While the RFID tag
technology used in the
CONTRINEX project provides
traceability, the more complex
device developed by Fraunhofer
IPA relies on a higher degree of
machine intelligence that can be
exploited to make better decisions
in the process.
Figure 20 shows the differences between the concept developed by Fraunhofer IPA and the
other examples previously discussed. The first workpiece carrier (WT) is equipped with a RFID
tag and provides a unidirectional communication. It has a product (represented here by a gear)
that is fixed and only identification capability is used. The second workpiece carrier (smartWT)
provides a bidirectional communication, relies on a higher degree of intelligence thanks to its
logic. Through modular sensor blocks and actuators, this workpiece carrier also supports
interactivity.
Figure 18: Intelligent Workpiece carrier
developed by the DFKI
(SmartManufacturingKL, 2015)
Figure 19: Intelligent
Workpiece carrier developed
by Fraunhofer IPA 2015.
Figure 20: RFID from left Passive Workpiece carrier. Intelligent and
modular setup proposed by Fraunhofer.IPA 2015.

53. 47
As a last example, I would like to mention a project of Fraunhofer Italy. As a first test for the
concept of decentralized control platform, a simple
factory of the future has been developed and a
prototype has been built (Figure 21). The factory
includes some working stations as well as an
autonomous vehicle that transport the product to the
different working steps it needs to undergo in order to
be produced. The vehicle is equipped with some
actuators that manipulate the product, some sensors
and a communication unit. The latter uses only Near
Field Communication technology to communicate
with the working stations. This vehicle is actually an
intelligent workpiece carrier, based on the same
principles of the project developed by Fraunhofer IPA.
4.2 Description of the prototype
In the previous paragraph, I described some technologies, which are particularly thought and
developed for the implementation in large companies with high levels of automation. In this
section, I will present an implementation of the Industrie 4.0 concept that is also interesting for
SMEs, and in our specific case, for Italian SMEs. Particularly, I will show the functionality that
an intelligent workpiece carrier can provide to a workshop, how this workpiece carrier has been
built as well as the features it provides.
4.2.1 Workshop case scenario and hardware used
In order to present the development and features of the intelligent workpiece carrier, I will use
the example of the metalworker workshop mentioned in the previous chapter (see paragraph 3.7).
We assume that a metalworker workshop wants to build a product that needs four different process
steps: drilling, gluing, spraying and engraving, also identified by the ID numbers 1, 2, 3, 4, or the
letters A, B, C, D.
The working stations have been purchased at different points in time and for that reason the
technology used by each of them is different. In order to implement the decentralized controlled
platform, the working stations has first to be upgraded with the integration of few electronic
components, a microcontroller, a software, so that they can communicate with their environment
through a Near Field Communication (NFC) pad.
Figure 21: Autonomous vehicle - Intelligent
Workpiece carrier, developed by Frauhofer Italia

54. 48
Furthermore, the workshop must be provided with a Base Station, a unit that can upload the
parameters and download the data collected by the workpiece carrier. A Base Station does not
add extra costs to the workshop since it is very cheap and it does require any particular
competence or knowledge of employees.
At this point, an Intelligent Workpiece Carrier (IWC) can be implemented. An IWC is able to
communicate with all the stations through the NFC pad. Furthermore, it assumes the role of
mediator between the different stations and the Internet. The working stations communicate with
the workpiece carrier using JSON, a unified lightweight text similar to XML, which is also
commonly used for client-server communication in web applications. JSON encodes common
computer data types in a readable form and enables a universal communication between different
machines as well as between machines and humans.
Thanks to the NFC technology, the communication between the workpiece carrier and the
working stations is bidirectional instead of unidirectional, as it is the case in the RFID technology.
A constant Wi-Fi connection is not necessary, as the only component that needs to be connected
to the internet is the Base Station. Adding a Wi-Fi device to the IWC is possible but the additional
costs and the increased complexity it would create cannot be justified by its advantages (e.g.
continuous real-time data exchange between workpiece carrier and the persons who are in charge
of controlling the production).
Figure 22: Workshop case scenario: four stations with NFC communication and Base
Station to upload and download parameters to/from a workpiece carrier

55. 49
This IWC is able to collect a lot of useful data: for example, it can measure the time a working
station needs to process the product, the time the product needs to move between two stations, the
number of times that a station is found to be busy. Moreover, the IWC stores the sequence of the
production steps as well as the parameters that the stations have measured themselves, so that the
quality of the product can be successively assessed and feedback can be given to the persons in
charge of the production planning.
In case the next station is temporarily busy, the IWC has also the responsibility to find an
alternative station. In order to achieve this goal, the logic of the Octave simulator is used (see
paragraph 3.6). The logic rules and the parameters related to a specific product variant are
uploaded into the workpiece carrier by the Base Station using the NFC pad. To this purpose, a
data structure is created as seen in paragraph 3.6. The Base Station has also the task to download
all the data collected by the IWC during the process, using the NFC pad.
Figure 23 represents a wider framework indicating all the steps that needs to be carried out to
produce a specific product using the decentralized controlled platform: from the choice of product
variant to the analysis of the data collected by the IWC and the successive development of
possible improvements. The steps implemented for my demonstration are highlighted in bold.
First of all, the customer can choose among a series of product variant: once the customer has
chosen one variant he can make an order for that specific product. At this point, the generation of
the logic and of the set of parameters for the working stations is performed and secondly, the logic
is tested and simulated by the Octave simulator. In the central column, the data are uploaded in
the IWC by the Base Station. The IWC starts visiting the different working stations. If a station
is found to be busy, an alternative will be generated. When it has visited all the stations, the IWC
heads to the Base Station to upload all the parameters that have been collected during production
and can be used successively to analyse the production process and implement some
improvements.

56. 50
Figure 23: Framework indicating all the steps that needs to be carried out to produce a specific product using the
decentralized controlled platform
In order to simulate such scenario, a IWC has been built and a Station Simulator is used to
simulate all the four working stations and the base station (see Figure 24).
Figure 24: Setup composed by the IWC and the Station Simulator on the right, and the
screen connected to the Station Simulator on the left.