A factory operating system for extending existing factories to Industry 4 0

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Computers in Industry 115 (2020) 103128
Contents lists available at ScienceDirect
Computers in Industry
jou rn al hom ep age: www.elsev ier .com/ locate /compind
 factory operating system for extending existing factories to Industry
.0
ander Lass ∗, Norbert Gronau
hair for Business Informatics, esp. Processes and Systems, University of Potsdam, August-Bebel-Str. 89, 14482 Potsdam, Germany
 r t i c l e i n f o
rticle history:
eceived 7 March 2018
eceived in revised form
4 September 2019
ccepted 16 September 2019
vailable online 20 December 2019
a b s t r a c t
Cyber-physical systems (CPS) have shaped the discussion about Industry 4.0 (I4.0) for some time. To
ensure the competitiveness of manufacturing enterprises the vision for the future figures out cyber-
physical production systems (CPPS) as a core component of a modern factory. Adaptability and coping
with complexity are (among others) potentials of this new generation of production management. The
successful transformation of this theoretical construct into practical implementation can only take place
with regard to the conditions characterizing the context of a factory. The subject of this contribution is a
eywords:
actory operating system
PPS
PS
ecentralized production control
concept that takes up the brownfield character and describes a solution for extending existing (legacy)
systems with CPS capabilities.
© 2019 Elsevier B.V. All rights reserved.
ndustry 4.0
etrofit
. Introduction
Affected by industrial trends and the progressing technological
evelopment [1,2], manufacturing enterprises are confronted with
 growing complexity within the factory. Quick market changes
nd a strong individualization of products create the necessity for
igh agility of the production management and the factory struc-
ure [3]. Hence, the implementation of adaptability [4] is essential
or achieving this agility in an efficient and dependable way. CPS,
hich are characterized through local information processing and
utonomy coupled with interconnectedness [5], are part of the
olution for coping with the growing challenges which manufac-
uring industries are facing [6].
The term CPS, which in the German-speaking world is shaped
hrough the research agenda CPS [7], is an integral element of the
iscussion around future industrial trends: CPS. These are software-
ntensive and embedded systems in integrated applications that
ealize the use of data and services anywhere in the world. CPS
re essential key building blocks and create the technological base
or self-controlling methods in production [8,9]. Made possible
y the usage of CPS decentralization allows to adequately meet
he multi-dimensional and growing complexity. Decentralization
sing intelligent and autonomous entities - in contrast to conven-
∗ Corresponding author.
E-mail address: slass@wi.uni-potsdam.de (S. Lass).
ttps://doi.org/10.1016/j.compind.2019.103128
166-3615/© 2019 Elsevier B.V. All rights reserved.
tional control structures in central monolithic form - is a suitable
approach to reduce complexity in manufacturing scenarios [10].
Equipping the production system - a complex socio-technical sys-
tem [11] - with decentralized and autonomous production units
[12] enables, among other things, a high degree of reconfigura-
bility [13]. Using several, initially independent CPS, they combine
existing plant components and entire plants into a cyber-physical
production system (CPPS). The high adaptability to changing sur-
rounding conditions through inbuilt flexibility and autonomy is
highlighted as a potential of the emerging cyber-physical produc-
tion systems [7]. CPPS realize a high level of ability to adapt [14].
A high degree of networking of their elements characterizes these
kind of production systems.
1.1. Research questions
It can be assumed that when implementing the general CPPS
idea in a factory, existing machines and systems cannot easily be
replaced by new ones. One possible solution is to enable existing
plants to act as part of a CPPS. The resulting research object is a
concept that takes up the brownfield character and allows the CPS
extension of existing systems. Research questions are:
• How can closed legacy systems be enabled to act as an element
of a CPPS?
• How can an appropriate integration be designed to use the advan-
tages of the CPPS concept effectively and sustainably?
https://doi.org/10.1016/j.compind.2019.103128
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https://doi.org/10.1016/j.compind.2019.103128
2 ters in
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 S. Lass and N. Gronau / Compu
How can the autonomy and local information processing as well
as the necessary networking and communication of the elements
be implemented in a practical way?
To this end, the development of an appropriate solution is one of
he tasks of this work. It not only answers the formulated research
uestions but also develops a concept proposal for the realization
f CPPS elements using existing systems. A corresponding architec-
ural concept is the essential artifact of knowledge.
.2. Methodology and expected results
Based on the formulated objectives, this research uses a pro-
ess model from the Design Science portfolio. The generation of the
esired artifact therefore follows the process of the Design Science
esearch Methodology (DSRM) according to Peffers et al. [15]. In
articular, these are the phases of problem analysis, requirements
nalysis, conception and prototypical implementation as well as
alidation.
The problem analysis elaborates the general conditions of the
iscourse area, the factory. Similar to a requirements analysis,
t provides further insights into practice-relevant demands and
hows the expansion necessity of classical automation technology.
he conceptional part correlates functions for data transmission
nd abstraction levels using a layer modell with device classes. The
ssignment of PLC and CPS shows the necessity of functional expan-
ion of the classic automation instrument and provides information
bout the functions to be added. The subsequent concretion of the
olution idea includes both a hardware and a software concept. The
esult is a concept of a CPS component (I4.0 box) including architec-
ure model (factory operating system) and hardware proposal. The
ubsequent prototypical implementation transforms these theo-
etical artifacts into real device. In addition to showing practical
easibility, it serves the basis for the initial concept evaluation and
alidation.
Due to the limitation of the possibilities for experiments on the
riginal factory systems, a function-related validation is carried out
sing a case study approach, which can be classified methodically
s a combination of case study and simulation study. With regard to
he validation of constructs as a field of application of case studies
16], these represent a suitable instrument for the intended valida-
ion. That means an investigation of a single case or a small number
f cases that produces findings which are applicable to a larger
umber of cases [17]. In concrete, six cases are part of the case
tudy, structured in two implementations: a classic one centrally
ontrolled by one PLC and a decentralized version using the I4.0
ox. A layout reconfiguration of each case provides insights regard-
ng the achievable flexibility respectively effort for its realization.
oth implementations each contain three scenarios. The simulation
nvironment of the Research and Application Center Industry 4.0
otsdam forms the technical platform for experimental testing and
omparisonof both cases. A simulation-based implementation of
he case study avoids the high effort of an implementation in the
eal factory. In addition to the proof of concept, the results are quan-
itative statements on the effectiveness of the decentralized control
aradigm for this application. Under the premise of the same under-
ying complexity model, a generalization is possible by scaling and
an be transferred to other systems of the factory.
.3. State of the art
A comprehensive overview regarding cyber-physical systems
nd distributed systems in manufacturing is given by Monostori
t al. [18]. This literature analysis states that although many the-
retical concepts (such as multiagent systems, SelfX, etc.) can
e implemented in practice using CPS as a basic technology and
 Industry 115 (2020) 103128
research activities already take place in this respect, considerable
further R&D&I activities are necessary. The realisation of CPPS in
real factories or CPS-based production systems can be found there
as a three-stage model to support companies in developing their
own I4.0 vision and support their strategy finding process. How-
ever, the brownfield situation is not explicitly addressed.
For integration capability and interoperability, a theoretical
framework for a implementation of CPPS arrange the different
actors of the system into conceptual areas with regard to func-
tionalities and properties. [19]. This conceptual framework is
supplemented by individual solution modules. In particular, a
Manufacturing Service Bus (MSB) in the form of a software infras-
tructure as an object oriented API, which serves as a bridge between
the applications running on each component.
In addition to an iterative deployment to transform produc-
tion existing facilities to industry 4.0 setups, Bader et al. propose
a self-descriptive integration approach based on virtual models
(called Virtual Representations) of the shop floor objects [20]. These
objects equipped in this way encapsulate the relevant information
at the resources itself and act as element of distributed, loose cou-
pling of systems using internet-based communication. Although
it is a more concrete concept, the local implementation of the
required capabilities at the objects itself stays open. Statements
on the implementation of the CPS capabilities formulated as a pre-
requisite - especially for legacy systems - are missing.
An overview of retrofit-related research projects is provided by
Ehrlich et al. [21]. In particular, the application-specific extension
of monitoring and diagnostic capabilities in existing systems by
migration from classic fieldbus systems to ethernet-based systems,
possibly in combination with wireless communication technolo-
gies (Zigbee, etc.), is the subject of activities, as is the integration
of mobile devices (e.g. to support fault detection and manage-
ment). The common feature is the focus on a specific application
of retrofitting, i.e. to find a specific problem solution as quickly as
possible with little effort. A common scheme or procedure is not
recognizable in order to obtain a structured procedure or plan for
a general concept of retrofitting industrial production lines.
Also considering the use case of predictive maintenance, Civer-
chia et al. describe a pervasive monitoring of industrial machinery
through battery-powered IoT sensing devices, which fulfill IoT fea-
ture of globally reachable nodes via IPv6 address in the industrial
environment [22]. Furthermore, the implementation of local appli-
cation logic is part of this approach, which enables pre-evaluation of
the measured variables and the representation of all available infor-
mation as network resources according to the RESTful paradigm.
However, the focus is on the appropriate design for battery opera-
tion. Used test field is an electricity power plant and contains data
acquisition of temperature und vibrations.
Horn and Krueger identify missing standard interfaces between
production objects (machinery, plants, etc.) as restrictive element
for a effective integration in CPPS, especially machinery without
ethernet-based communication [23]. They propose a concept of
a connector layer using PLCs and micro-controllers as connector
technologies and, as a further essential insight, they pointing out,
that the fulfillment of cycle timing requirements for industrial pro-
cesses is possible through the appropriate use of non-PLC-based
components.
A further existing approach structuring the implementation of
CPS is the unified 5-level architecture concept with Smart Connec-
tion, Data-to-Information Conversion, Cyber Level, Cognition and
Configuration as functional, data-related aggregation levels [24].
Though, many of the existing only provide structuring frame-
works or guidelines for manufacturing industry, but does not deal
with brownfield implementations and leave the technical design
open. The contribution of this research is a concept that takes up the
special needs of grown, heterogeneous infrastructures in the fac-
ters in
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S. Lass and N. Gronau / Compu
ory. With the prototypical implementation, a solution is available
o successfully integrate existing, non-CPS plants into CPPS.
. Underlying conditions
A successful transformation towards CPPS requires the con-
ideration of the conditions shaping the application context of a
actory. The information technology (IT) of a factory consists mainly
f automation systems. In comparision with the office IT used
y information systems like Manuvacturing Execution Systems
MES) or Enterprise Resource Planning (ERP), the IT on factory level
see automation pyramid, e.g. [25]) has to comply with additional
nderlying conditions (see [26,27]). Among others these include (a)
eal time capabilities of the control circuits concerning information
rocessing and information transmission, as well as long duty and
nnovation cycles (> 7 years), causing (b) the implementation in
rownfield scenarios.
(a) The task of implementing control circuits for physically
cting components requires real time capabilities of the execut-
ng computer systems. Real time means that a system has all of
he following properties: First, the guarantee that the system will
eact within a certain time span (timeliness). Second, it allows
he concurrent execution of several tasks (concurrency). Third,
ystems behavior is schedulable and deterministic (predictabil-
ty), especially regarding timing requirements. Fourth, in defined
urrounding conditions, the system will operate properly (depend-
bility). Due to the strong connection with external technical
rocesses the information processing and communication needs to
appen synchronously and timely. This means the dimension time
as explicit relevance in contrast to its role in general information
rocessing [28]. There are deadlines or periodic time constraints
or the execution of tasks, so that there is either no value for the
xecution of the task after the deadline (hard real time) or only a
educed value (soft real time) [29].
(b) The typical implementation of CPS is to be seen as a brown-
eld scenario. The construction of new production facilities from
cratch is rather exception than standard. The typical situation
s the reorganization of existing plants and processes. Due to
tructures which have developed over a long period of time, this
eorganization can require a lot of effort [30]. If necessary, existing
ystems have to be included as required by investment protec-
ion or a lack of willingness to invest. The application of CPS while
imultaneously redeveloping a plant completely is not a realistic
ption. An essential research question is the appropriate and suit-
ble integration of CPS in existing information systems [31]. (c)
ue to their focus on device automation, the programming options
rovided by a standard PLC make it difficult to implement CPS
apabilities. As potentialelements of a CPPS, manufacturing units
eed sufficient communication capabilities, to realize complexity
educing autonomy and cooperation and to implement the advan-
ages of decentralization. The term manufacturing unit subsumes all
ossible entities of manufacturing and production planning. Man-
facturing units are elements of the production system. In this
ontext, they are primarily machines and tools, workpieces and
orkpiece carriers, as well as logistics equipment like automotive
ransportation systems, haulways and buffer elements. Referring
o autonomy, decentralization, and CPS these manufacturing units
ecord information, process information, and make decisions as
ell as execute them by themselves with their own resources
32]. Therefore, these subsystems require environment perception,
xtended storage and communication capabilities to allow exten-
ive information exchange, and autonomous task execution [33].
hese requirements of local information processing and decision
xecution imply the implementation of appropriate algorithms.
 task quickly reaches its limits when it comes to implementa-
 Industry 115 (2020) 103128 3
tion with the classic programming methods of automation systems
according to IEC-61131 [34]. Trying to realize scenarios regarding
quick and possibly automatic reconfiguration of plants (change-
ability) as well as the local provision of aggregated functions
and system states (complexity control) by the means of classi-
cal programmable logic controller (PLC) systems no appropriate
and satisfactory solution was found. The implementation of the
required algorithms, based on common step chain programming,
adversely affects real time capabilities (cycle time) of the system or
resulted in hard-to-maintain software. Hence, the necessity of an
adequate local implementation of complex algorithms is another
underlying condition next to the real time capabilities and the
brownfield scenario.
The combination of these three premises (a), (b), and (c) leads
to the conlcusion, that the implementation of CPS capabilities
requires the adequate extension of existing manufacturing units.
These extensions have to respect real time capabilities as well as
the possible implementation of complex algorithms. This means
in particular the avoidance of restrictions of classical automation
programming paradigms.
3. CPS empowerment for existing manufacturing units
With respect to the brownfield situation, it is necessary to
empower existing components to be able to act as a part of a
CPPS. In particular, the integration of closed legacy systems is a
typical use case. In order to meet this challenge adequately, the
presented concept includes a device which allows the retrofitting
of the demanded properties and equips a production unit with
CPS-capabilities. The device allows the implementation of different
middleware concepts like the theoretical approach of the reference
architecture model Industry 4.0 (RAMI 4.0) with its conceptional
term administration shell [35].
3.1. Operating principle
In accordance with the principle of CPS, with the extensive per-
ception of their environment by sensors and their interaction by
actuators (cf. [36]), there are two basic tasks of enhancing existing
systems: the acquisition of environmental information as a pas-
sive stage and the influence on the physical environment as an
active stage. The passive stage implements the collection of data
by existing sensors or by using additional devices. It solely accesses
the system read-only. In analogy to the differentiation in read and
write operations, it means that there are no critical intervention in
the system and the consideration of side effects and the regulation
of write operations are not necessary.
In contrast, the active stage is an intervention in the system
behavior for example by means of control loops or actuators.
Depending on the existing features of the actuator’s controllers
(e.g. electrical connection, basic logic, status feedback if neces-
sary), the suitable synchronization of the external signals with the
existing control system (e.g PLC and its program) is an important
task.
The combination of the pyramid of wisdom (DIKW hierarchy –
data, information, knowledge and wisdom; cf. [37]) and the layer
architecture of reference models for network protocols (cf. [38])
delivers the types of access as follows. The levels of interconnec-
tion are signal, data and information as well as aggregation. The
signal level refers to the physical interconnection. It covers mainly
electric signal specifications. Elements of transmission are typi-
cally bits or analogue values. Based on these elements the data
level defines basic types of data, e.g. numeric values. In connec-
tion with protocols and data models the information level provides
an enhancement with semantic aspects or complex data structures.
4 S. Lass and N. Gronau / Computers in Industry 115 (2020) 103128
Fig. 1. Comparison of PLC and CPS usin
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ing certain time intervals. The time logging is triggered by timed
events, fitting the timed monitoring into the event driven paradigm.
Fig. 2. Operating principle of the I4.0 box.
he aggregation level allows a preprocessing as well as a aggregation
f data or information. It provides high level functions for commu-
ication and operation. With reference to this layered model, three
evice classes are useful:
A connector realizes a connection on physical and data level.
A gateway establish links on information level.
An information node serves the aggregation level.
Figure 1 reflects the three classes of control devices and assigns
LC and CPS regarding levels, objectives, and elements of trans-
ission. The elements of transmission represents the objects of
ommunication. A PLC at best covers the two lower levels. Inter-
ctions regarding levels L3 and L4 are only possible by using the
PS concept. CPS additionally offers abilities to semantically inter-
ret and aggregate data as well reduce complexity by providing
igher function calls. Though, CPS empowerment includes the task
f implementing information nodes.
A real world implementation of such an information node is a
evice, which presents a CPS and complements an existing plant.
ased on the term ”Industry 4.0”, this device is called I4.0 box.
igure 2 summarizes its operating principle. The device receives
ccess to the built-in sensors and actuators of manufacturing
bjects. If necessary, it supplements additional acquisition mech-
nisms. The CPS based device connects sensors via discrete wiring
r existing fieldbus networks of the control system (e.g. PLC). Addi-
ionally it uses independent sensors installed in a suitable position.
or access of actuators the box uses a field bus network in combi-
ation with additional input modules of the PLC or in special cases
ia direct wiring to the underlying control units (e.g. motor control
evice).
g layer model and device classes.
3.2. Factory operating system
With regard to operating systems and their function - abstrac-
tion from the underlying hardware and the management of
hardware resources [39] - the software components of the boxes
are subsumed with the term factory operating system (FabOS). The
FabOS is based on the architecture illustrated in Figure 3. Inte-
gral elements are the runtime environment, Connection Service,
Monitoring Service as well as KPI Service and Control Cen-
ter.
The runtime environment splits into two functional areas. The
Low-Level-Runtime (LLR) allows the implementation of real time
critical functions. The realization of that happens through PLC or
real time capable micro-controllers, that are programmed using
typical development environments of automation (e.g. CODESYS).
Practical tests have shown that an implementation following this
style of programming is stretched to its limits for complex algo-
rithms: The design of a freely configurable transportation system
using a central PLC as control unit, proved itselfto be difficult to
manage with sequential control programming paradigms. There-
fore, the concept includes another implementation possibility. The
High-Level-Runtime (HLR) enable the usage of high-level program-
ming languages and programming paradigms, which delivers quick
and simplified extensions for complex information processing.
Depending on the manufacturing unit and its intended applica-
tion, the operation uses from the LLR, the HLR implementation or
a combination of both.
The Connection Service allows for the communication of the
components. Similar to the driver concept of operating systems it
abstracts from the technical details and gives access to functions
of the FabOS. It allows the implementation of a gateway function
between the internal communication of the system components
and the protocols of external components or devices. This can hap-
pen using standards (like OPC-UA in the case of coupling LLR and
HLR) or using the way explicitly defined by the component sup-
plier. Instances of the Connection Service can be designated as
central service or be implemented decentrally in the relevant com-
ponents.
The perception of the environment is an essential property of a
CPS [5]. The local module Monitoring Service is an important part to
meet this requirement. In particular, each manufacturing unit logs
and saves occurring events using this component. This includes
communication processes and changes to the environment data
as well as amounts and properties of the internal model and, in
addition, user interactions. Also, the course of values is logged dur-
In addition to the local storage of events, the paradigm inversion of
control (IoC) is implemented by listeners, offering another possi-
S. Lass and N. Gronau / Computers in Industry 115 (2020) 103128 5
d com
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Fig. 3. Architecture an
ility for communicating and obtaining information. The use case
ddressed is primarily the provision of real time data and the avoid-
nce of inefficient polling. In that case, the term real time refers
o the currentness. This means the data reflects the value actually
resent at the time of observation (as-is situation).
The Process Service coordinates temporal procedures and is
esponsible for the time synchronization of the manufacturing
nits. Dependencies concerning necessary resources during exe-
ution are modeled by the Resource Service. In particular, these are
equired material and tools, staff, assemblies or semi-finished prod-
cts. A Virtual Device can represent a model of a certain element,
hich acts as virtual (simulated) subsystem or as virtual represen-
ation (digital twin) of a real device.
. Proof of concept
The prototypical implementation of boxes and their application
ithin the test platform of the Research and Application Center
ndustry 4.0 Potsdam is part of the validation. First step of the con-
ept validation is a prototypical implementation of the box itself.
t includes hardware as well as the application of factory operating
ystem as the software part. Additionally, another requirements
volve from the objective of a simple applicability: little technical
omplexity and a cost-efficient implementation. High complexity
f the components would lead to an undesired increase of the total
omplexity. And because of the extensive networking of the sys-
em’s elements as an essential part of the I4.0 concept and the
ssociated large number of nodes to be used this gains importance.
hus, apart from the validation of the concept, the simplicity of the
argeted component to be realized is also a relevant characteristic.
Because of that, the box prototypes are based on the the Rasp-
erry Pi. Additional component is an I/O board, which is adapted to
actory specific application. This board not only provides industry-
ypical connectors (according to level 1 and 2) but also allows
nhancement of further interfaces by means of an internal back-
one. Demonstrating the concept, boxes of this prototypes are part
ponents of the FabOS.
of use cases within the test platform of the Research and Application
Center Industry 4.0.
One of these use cases is a modular transport system.
Retrofitting this existing plant by using the box prototypes a case
study represents the second part of validation. The objective is to
evaluate the discussed theoretical approach in terms of its prac-
tical feasibility and degree of goal achievement. Resulting task is
implementing a decentralized control system that integrates the
existing components. In this respect, the feature of the system con-
sidered is its flexibility. That means, the change agent is external to
the system [40]. For operationalization, this is done by determin-
ing the effort for externally caused changes to the transport system,
which require a reconfiguration. The time required to reconfigure
the system or plant is the measured value used. In other words, it is
a matter of increasing the flexibility of existing plants, or more pre-
cisely, reducing the effort for system changes through the described
approach.
4.1. Case study setup
Therefore, the installation is based on a conventional roller con-
veyor as a representative for pre-existing systems. It consists of
different types of transport modules, which are combined to con-
veyors within the production layout. Each module has a motor
control unit, whose activities are determined by simple 24 Volt sig-
nals. The modules used are shown in Figure 4. The following basic
modules are available: switches (3 ports and 1 position), straight
modules (2 ports and 2 positions as well as 2 ports and 4 positions),
and curved segments (2 ports with 2 positions) as well as one dis-
patcher (6 ports and 1 position). The following models describes the
relevant properties of a module and serve to quantify the of com-
plexity in sum. All modules are each characterized by the installed
sensor and actuator signals, number of ports for in/out-operation
and the internal positions. Ports are the transfer points between the
modules that act as input or output. Positions represent the loca-
tions of the entities to be transported within the module. They are
essentially determined by the sensors of a module. A total of 110
6 S. Lass and N. Gronau / Computers in Industry 115 (2020) 103128
and de
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Fig. 4. Setup of modules 
ncoming and outgoing signals as well as 65 variables describing
 configuration or state (ports and positions) must be taken into
ccount during programming. Additionally, the configured routes
rom the relevant start and end points add complexity. There are
wo variants of implementation:
A) a decentralized control of the plant by intelligent elements in
line with the CPS concept using the I4.0 boxes
B) a central control with discrete wiring of all components using
the classic PLC and sequence programming paradigm
Implementation (A) also addresses the reduction of complexity
hrough decentralized control and the possibility of quick reconfig-
ration of the transportation system [41,42]. The whole system is
ivided into segments which individually integrate a small amount
f modules using the I4.0 boxes. All signals are directly connected
o the interface module of the box. On LLR level the basic func-
ions are implemented (like motor on until light barrier triggers).
he HLR abstracts from these elementary procedures and provides
igher-level function calls (like transit something from position A
o position B), that are accessible by external entities, too. Further-
ore, the HLR - possibly in cooperation with other CPS - solves
omplex tasks (like routing or resolving conflicts). The solution
lgorithms for these are implemented using high-level program-
ing languages (in this case Python).
Implementation (B) represents todays typical approach of con-
rol. Using a standard PLC as the central control authority the
ransport system ensures the material flow. This typeof implemen-
ation connects all sensors and actuators to one device via discrete
iring. The program creation corresponds to the PLC-typical step
hains and is based on instruction lists and partly on function block
iagrams. The comparison is drawn with the help of three scenar-
os each for (A) and (B): partial changes to the layout (failure of a
odule, addition of an element) and major changes to the layout
reconfiguration of more than a half of the modules). Figure 5 shows
he principal setup.
.2. Case study results
The efficient realization of flexibility is the general task. For
easuring flexibility the case study uses time efforts required for
scriptive characteristics.
reconfiguration. Figure 6 shows the efforts needed for the scenar-
ios of implementation (A) and (B). These are structured in the three
phases hardware setup (primarily wiring), coding and deployment
with final test of the whole plant.
Module failure scenario (A.1 and B.1): In comparison, the
decentralized variant shows less total effort (87%). This is mainly
due to the fact that the program does not need to be adapted.
The use of the CPS capability for independent adaptation from
within itself (adaptability) makes reprogramming obsolete, which
also has a positive effect on the necessary scope of tests during
(re)commissioning. The adjustment in the case of failure with a
short test of the plant of (A) is contrasted with a much higher
effort for adapting the source code of (B). In particular, using
CPS-based devices and their appropriate networking reduces the
effort for coding. In contrast to this the hardware setup only
requires minimal additional effort for variant (B). However, this
time advantage does not include the possibly increased effort of
initial program creation to realize this conversion capability. Since
this can only be done once - in contrast to the recurring time
requirement of the central PLC of variant (B) - or, in the case of
the implementation considered here, already provides most of the
functions per design (via the HLR), this is unproblematic. in con-
trast to the recurring time requirement of the central PLC of variant
(B).
Scenario adding a module (A.1 and B.1): The decentralized vari-
ant requires less time in all three phases (80%). In this scenario, too,
it can be seen that reprogramming also entails longer test phases,
i.e. commissioning requires more resources. Also in the decentral-
ized case there is less setup effort. This in turn confirms the thesis
that advantages arise from the local cabling of the decentralized
variant.
Scenario layout change (A.3 and B.3): Addresses the case that the
system cannot make the necessary adjustments on its own and the
change must be made externally. The scenario confirms the find-
ings of the previous scenarios. The required program modification
of the CPS-based variant takes less time than the creation of the
central PLC variant (25%). This is also due to the possibility of using
a high-level language contained in the software architecture and
its better problem adequacy compared to program creation using
an automation language.
S. Lass and N. Gronau / Computers in Industry 115 (2020) 103128 7
Fig. 5. Experimental setup and applied segmentation of implementation (A).
of imp
5
b
o
v
c
p
n
u
t
p
a
p
s
c
t
a
e
T
s
a
s
o
a
l
o
l
d
b
t
f
t
w
e
p
Fig. 6. Comparision 
. Conclusion
The presented concept addresses above all the typical industrial
rownfield situation. In addition to the complexity-reducing effect
f the decentralized control concept compared to the centralized
ersion, the case study described above shows that this type of
ontrol is also possible using existing non-I4.0 systems. The appro-
riate extension of existing production units to I4.0 information
odes enables the potentials of a decentralized organization to be
sed for existing systems. The first validation refers in particular
o the effort involved in system changes. Under the premise of the
rogressive increase in the complexity of information processing
nd reconfiguration effort depending on the amount of data to be
rocessed, the results scale progressively. This means that as the
ize of the system increases, the positive effects identified in the
ase study are expected to increase.
The results indicate that, through appropriate segmentation of
he control task, a reduction in the reconfiguration effort can be
chieved if the required capabilities - in this case, through the
xtension of existing systems - can be installed with less effort.
he presented concept works in this direction and covers both the
oftware and hardware level. The I4.0 box extends existing systems
nd creates the prerequisites for integration. The factory operating
ystem offers a flexible software architecture for easy application
f the box and allows the combination of classic PLC programming
nd implementation of complex algorithms.
The case study aims above all at a proof of concept. Possible
imitations result (a) from the concentration on the optimization
f a target variable - the efficient realization of flexibility, (b) the
imited size of the simulation facility used, and (c) from the vali-
ation on a model. Though, the simulation-based case study is to
e understood as a step that shows the basic potentials of decen-
ralized control in existing plants and provides a starting point for
urther test activities in real factory systems. This is consequently
he next step within the validation.
The results of this research paves the way for further testing
ithin real systems and strengthens the argumentation of the ben-
fits of Industry 4.0. At the same time, the I4.0 box prototype
rovides an approach for cost-effective implementation of CPPS in
lementation efforts.
existing factories. In addition to increasing the reconfiguration effi-
ciency of existing plants, other applications for retrofitting include
cross-machine process monitoring in heterogeneous system land-
scapes and the design of real-time control loops, i. e. the evaluation
of data with information generation takes place at the time of its
creation. Both cases are the subject of further research work.
References
Jovane, F., Westkaemper, E., Williams, D., 2008. The ManuFuture Road: Towards
Competitive and Sustainable High-Adding-Value Manufacturing. Springer
Berlin Heidelberg.
Westkaemper, E., 2013. Structural change through megatrends. In: Westkaemper,
E., Spath, D., Constantinescu, C., Lentes, J. (Eds.), Digital Production, in german.
Springer.
Kagermann, H., Wahlster, W., Helbig, J., 2013. Securing the future of German manu-
facturing industry. Recommendations for implementing the strategic initiative.
In: Final report of the Industrie 4.0 Working Group, acatech - National Academy
of Science and Engineering.
Andresen, K., Gronau, N., 2005. Adaptability concepts for enterprise resource plan-
ning systems-a component framework. AMCIS 2005 Proceedings, 150.
Lee, E.A., 2008. Cyber physical systems: Design challenges. In: in: Object Oriented
Real-Time Distributed Computing (ISORC), 2008 11th IEEE International Sym-
posium on, IEEE, pp. 363–369.
BMBF, Future picture Industry 4.0, Federal Ministry of Education and Research -
Department IT-Systeme, in german, 2014.
Geisberger, E., Broy, M., 2015. acatech study: Integrated research agenda Cyber-
Physical Systems. acatech - National Academy of Science and Engineering.
Gronau, N., Theuer, H., 2016. Determination of the optimal degree of autonomy in a
cyber-physical production system. Procedia CIRP 57, 110–115.
acatech, acatech POSITION: Cyber-Physical Systems: Driving force for innovation in
mobility, health, energy and production, acatech - National Academy of Science
and Engineering, Springer Berlin Heidelberg, 2011.
Barbosa, J., Leit ao, P., Adam, E., Trentesaux, D., 2015. Dynamic self-organization in
holonic multi-agent manufacturing systems: The adacor evolution. Computers
in industry 66, 99–111.
Neumann, M., Constantinescu, C., Westkaemper, E., 2012. A method for multi-scalemodeling of production systems. In: Enabling Manufacturing Competitiveness
and Economic Sustainability. Springer, pp. 471–475.
Windt, K., Jeken, O., 2009. Allocation flexibility - a new flexibility type as an enabler
for autonomous control in production logistics. In: 42nd CIRP conference on
manufacturing systems. Citeseer.
ElMaraghy, H.A., 2005. Flexible and reconfigurable manufacturing systems
paradigms. International journal of flexible manufacturing systems 17 (4),
261–276.
Francalanza, E., Borg, J., Constantinescu, C., 2017. A knowledge-based tool for design-
ing cyber physical production systems. Computers in Industry 84, 39–58.
8 ters in
P
Y
G
M
R
B
E
C
H
L
H
G
D
H
IEEE 14th International Conference on, IEEE, pp. 1293–1299.
 S. Lass and N. Gronau / Compu
effers, K., Tuunanen, T., Rothenberger, M.A., Chatterjee, S., 2007. A design science
research methodology for information systems research. Journal of Manage-
ment Information Systems 24 (3), 45–78.
in, R.K., 2009. Case study research: Design and methods, 4th edition. Sage publica-
tions, Inc.
erring, J., 2015. Case study research: Principles and practices, 2nd Edition. Cam-
bridge university press.
onostori, L., Kádár, B., Bauernhansl, T., Kondoh, S., Kumara, S., Reinhart, G., Sauer,
O., Schuh, G., Sihn, W., Ueda, K., 2016. Cyber-physical systems in manufacturing.
CIRP Annals 65 (2), 621–641.
ojas, R.A., Rauch, E., Vidoni, R., Matt, D.T., 2017. Enabling connectivity of cyber-
physical production systems: A conceptual framework. Procedia Manufacturing
11, 822–829.
ader, S.R., Wolff, C., Voessing, M., Schmidt, J.-P., 2018. Towards enabling cyber-
physical systems in brownfield environments. In: in: Exploring Service Science:
9th International Conference, IESS 2018, Karlsruhe, Germany, September 19-21,
2018, Proceedings, Vol. 331, Springer, p. 165.
hrlich, M., Wisniewski, L., Jasperneite, J., 2015. Usage of retrofitting for migration of
industrial production lines to industry 4. 0. Jahreskolloquium Kommunikation
in der Automation (KommA).
iverchia, F., Bocchino, S., Salvadori, C., Rossi, E., Maggiani, L., Petracca, M., 2017.
Industrial internet of things monitoring solution for advanced predictive main-
tenance applications. Journal of Industrial Information Integration 7, 4–12.
orn, C., Krueger, J., 2016. Feasibility of connecting machinery and robots to indus-
trial control services in the cloud. in: 2016 IEEE 21st International Conference
on Emerging Technologies and Factory Automation (ETFA), IEEE, 1–4.
ee, J., Bagheri, B., Kao, H.-A., 2015. A cyber-physical systems architecture for indus-
try 4.0-based manufacturing systems. Manufacturing Letters 3, 18–23.
ollender, M., 2010. Collaborative Process Automation Systems. ISA.
umzej, R., 2010. Real-time Systems’ Quality of Service: Introducing Quality of Ser-
vice Considerations in the Life Cycle of Real-time Systems. Springer London.
amm, W., Olderog, E., 2003. Formal Techniques in Real-Time and Fault-Tolerant
Systems: 7th International Symposium. In: FTRTFT 2002, Co-sponsored by IFIP
WG 2.2, Oldenburg, Germany, September 9-12, 2002, Proceedings, Lecture Notes
in Computer Science, Springer Berlin Heidelberg.
alang, W.A., 1989. Focus of international research in the field of real-time systems.
In: Henn, R., Stieger, K. (Eds.), PEARL 89 – Workshop on real-time systems, Vol.
231 of informatics technical reports, in german. Springer Berlin Heidelberg, pp.
1–12.
 Industry 115 (2020) 103128
Stankovic, J.A., Ramamritham, K., 1990. What is predictability for real-time systems?
Real-Time Systems 2 (4), 247–254.
Kuehn, W., 2006. Digital Factory, in german. Hanser München.
Gronau, N., 2014. The influence of cyber-physical systems on the design of produc-
tion systems. In: Kersten, W., Koller, H., Loedding, H. (Eds.), Industry 4. 0 - How
intelligent networking and cognitive systems change our work, Vol. Publica-
tion series of the Hochschulgruppe für Arbeits- und Betriebsorganisation e. V.
(University Group for Work and Business Organisation) (HAB), in german. GITO
Berlin, pp. 279–295.
Theuer, H., 2012. Extension of value stream design for the simulation of autonomous
production systems. In: in: Enabling Manufacturing Competitiveness and Eco-
nomic Sustainability - Proceedings of the 4th International Conference on
Changeable, Agile, Reconfigurable and Virtual production (CARV2011), Mon-
treal, Canada, 2-5 October 2011, Springer-Verlag Berlin Heidelberg, pp. 586–591.
Freitag, M., Herzog, O., Scholz-Reiter, B., 2004. Self-control of logistical processes -
a paradigm shift and its limits, in german. Industry Management 20 (1), 23–27.
IEC, IEC 61131-3: Programmable controllers - Part 3: Programming languages, ed.
3.0 Edition, International Electrotechnical Commission (IEC), 2013.
Adolphs, P., Epple, U., 2015. Status Report Reference Architecture Industry 4.0
(RAMI4. 0), Verein Deutscher Ingenieure e. V. (VDI) und German Electrical and
Electronic Manufacturers Association (ZVEI), in german.
Lee, E.A., Seshia, S.A., 2014. Introduction to Embedded Systems - A Cyber Physical
Systems Approach - Edition 1.5, LeeSeshia.org. Berkley.
Ackoff, R.L., 1989. From data to wisdom. Jorunal of Applied Systems Analysis 16, 3–9.
ISO, ISO/IEC/IEEE 42010:2011: Systems and software engineering - Architecture
description, International Organization for Standardization (ISO), Genf, 2011.
Tanenbaum, A.S., 2009. Modern operating systems, 3rd edition. Pearson Education,
Inc.
Ross, A.M., Rhodes, D.H., Hastings, D.E., 2008. Defining changeability: Reconciling
flexibility, adaptability, scalability, modifiability, and robustness for maintaining
system lifecycle value. Systems Engineering 11 (3), 246–262.
Gronau, N., Grum, M., Bender, B., 2016. Determining the optimal level of autonomy in
cyber-physical production systems. In: in: Industrial Informatics (INDIN), 2016
Theuer, H., Gronau, N., Lass, S., 2013. The impact of autonomy on lean manufacturing
systems. In: Azevedo, A. (Ed.), Advances in Sustainable and Competitive Man-
ufacturing Systems, 23rd International Conference on Flexible Automation and
Intelligent Manufacturing. Springer, pp. 1413–1423.
	A factory operating system for extending existing factories to Industry 4.0
	1 Introduction
	1.1 Research questions
	1.2 Methodology and expected results
	1.3 State of the art
	2 Underlying conditions
	3 CPS empowerment for existing manufacturing units
	3.1 Operating principle
	3.2 Factory operating system
	4 Proof of concept
	4.1 Case study setup
	4.2 Case study results
	5 Conclusion
	References