Industrie 4 0 and international perspective - ABB

Prévia do material em texto

CHAPTER 3
Industrie 4.0 and international
perspective
Iiro Harjunkoskia, Heiko Koziolekb, and Dirk Schulzb
aABB Power Grids Research, Mannheim, Germany
bABB Corporate Research, Ladenburg, Germany
Contents
1. Introduction 59
2. RAMI 4.0 61
2.1 Motivation 61
2.2 Layers 62
2.3 Life cycle and value stream 63
2.4 Hierarchy levels 64
2.5 Example usage of RAMI 4.0 64
3. Asset administration shell 65
3.1 Motivation 65
3.2 AAS requirements 66
3.3 AAS design 67
4. Applications 70
4.1 Value-based services 70
4.2 Adaptable factories 72
4.3 Order-controlled production 75
4.4 Seamless dynamic engineering of plants 77
4.5 Summary 77
5. Roadmap/ongoing research 78
6. Conclusion 80
References 81
1. Introduction
At first glance Smart Manufacturing and Industrie 4.0 seem very similar and in fact they
share many of the final targets: to support the digitalization and increase the value creation
through new opportunities. Nevertheless, there are a few clear differentiations that are
discussed in the following. Oversimplified, one could claim that Smart Manufacturing
has been initiated with a top-down mindset with the main focus on process industries,
whereas Industrie 4.0 has mainly been implemented with the bottom-up philosophy and
initial target on manufacturing industries. This statement is not exactly true but can help
to identify a characteristic difference between the two activities.
59
Smart Manufacturing © 2020 Elsevier Inc.
https://doi.org/10.1016/B978-0-12-820027-8.00003-4 All rights reserved.
https://doi.org/10.1016/B978-0-12-820027-8.00003-4
60 Smart manufacturing
Industrie 4.0 started as a “future project” of the German government in 2011 and was
supported by establishing the “Plattform Industrie 4.0” as a joint network and organiza-
tion of companies, trade associations, labor unions, science organizations, and politicians.
The German government funds administration of the Plattform Industrie 4.0, which
includes six working groups as of 2019. The technical work in these working groups
is sponsored by the participants’ organizations, not by the government. As of 2019,
the steering committee of Plattform Industrie 4.0 comprised members of the German
Federal Ministry for Economic Affairs and Energy, the Federal Ministry of Education
and Research, Deutsche Telekom, SAP, Fraunhofer, Siemens, and Festo. The industrial
associations BITKOM (IT), VDMA (Mechanical Engineering), and ZVEI (electrical
industry) support Plattform Industrie 4.0 via their own working groups.
In its definition [1] “Smart Process Manufacturing” (SPM) describes the technology
and applied capability in which computationally enabled models are the integrating
points for data, expertise, decision, and discovery. It focuses on the means of casting data
and knowledge into useful forms that can be broadly applied. The knowledge and exper-
tise embodied in SPM need to become key next-generation operating assets and invest-
ments, so industry can achieve a globally competitive capability. Thus, the actual function
and use (applications) are at the forefront of the activities. The more detailed description
about smart process manufacturing also mentions optimization on process and supply
chain levels and today the main driver of the joint activities is the Clean Energy Smart
Manufacturing Innovation Institute (CESMII)a,b [2]. Simultaneously, systemic infra-
structural capabilities are needed to mobilize a knowledge- and model-enabled process
industry environment over the entire product and process life cycle. For this another col-
laboration scheme exists: Industrial Internet Consortium.c
On the other hand, the main definition of Industrie 4.0 is more bottom-up focused,
highlighting the technologies that can enable collaborative solutions. The term Industrie
4.0 stands for the fourth industrial revolution, the next stage in the organization and con-
trol of the entire value stream along the life cycle of a product [3]. The life cycle foresees
increasingly individualized customer wishes and ranges from the idea, the order, devel-
opment, production, and delivery to the end customer through to recycling and related
services. Fundamental here is the availability of all relevant information in real time
through the networking of all instances involved in value creation, as well as, the ability
to derive the best possible value stream from data at all times. This has also been one of the
very early foci. Connecting people, objects, and systems leads to the creation of dynamic,
self-organized, cross-organizational, real-time optimized value networks, which can be
optimized according to a range of criteria such as costs, availability, and consumption of
resources. Thus, not very different from Smart Manufacturing, Industrie 4.0 enables
a https://www.cesmii.org/.
b https://www.energy.gov/eere/amo/clean-energy-smart-manufacturing-innovation-institute-cesmii.
c https://www.iiconsortium.org/.
https://www.cesmii.org/
https://www.energy.gov/eere/amo/clean-energy-smart-manufacturing-innovation-institute-cesmii
https://www.iiconsortium.org/
61Industrie 4.0 and international perspective
manufacturers to intelligently connect machines and equipment across the production
line to build a seamless connected ecosystem where machines capture and exchange data
through machine-to-machine communications and to human operators. As a final
remark, Industrie 4.0 is not driven through a government managed implementation
of a prescribed roadmap, as it is not trivial to define an exact vision of Industrie 4.0
because of the different interests and views of the range of businesses. Instead, Industrie
4.0 is the result of incremental progress on implementing specific applications (including
analysis of the potential benefits and potential for value creation). Today, the most
remarkable results have been achieved in the platform development and selected case
studies to illustrate the capability of Industrie 4.0.
2. RAMI 4.0
2.1 Motivation
Due to the broad scope of Industrie 4.0, participants of the initiative felt the need to struc-
ture the discussion around use cases and standards. Inspired by the “Smart Grid Architec-
ture Model” (SGAM), which was conceived for similar purposes albeit a different
application domain, the Industrie 4.0 committees agreed to define the “Reference Archi-
tectureModel for Industrie 4.0” (RAMI 4.0). This model has been published as IEC-PAS-
63088, which is a public prestandard approved by the majority of the involved working
group, but not yet finalized. It aggregates different viewpoints on the topic of Industrie
4.0 and shall enable well-scoped discussions around certain use cases and standards.
Fig. 1 shows the three-dimensionally shaped model covering layers (IT- and business-
related aspects), production life-cycle and value stream, as well as plant hierarchy levels.
Fig. 1 Reference Architecture Model for Industrie 4.0 (RAMI 4.0) [4].
62 Smart manufacturing
The model is not intended to serve as a blueprint to construct an Industrie 4.0 system,
as the naming as “reference architecture” would imply. Rather, the creators of the model
meant it as an instrument to support the public discussion and technical work in industry
committees, as participants with different perspectives can define a constrained scope
from the model to have a focused discussion. A working group can agree to focus on
a single subcube in the overall model, working out use cases and standardization gaps
for this bounded context. For example, a working group could focus on the
“communication” layer during “production” for the hierarchy level “station,” and then
have a detailed discussion on protocols, quality-of-service, and security on this level,
which are rather different than for example for the “development stage” of “field
devices.” The following subsections will describe the content and rationale behind each
dimension of the RAMI 4.0 model.
2.2 Layers
Layers cover different aspectsof entities in the Industrie 4.0 world. While many discus-
sions around Industrie 4.0 pertain information- and communication-related aspects,
functional aspects and business aspects may undergo important changes too. Overall,
RAMI 4.0 defines the following layers:
• Business layer: This layer concerns Industrie 4.0 business models, assures integrity of
Industrie 4.0 functions in the value stream, and looks at legal and regulatory condi-
tions. While different companies will come up with new business models they may
want to exploit individually, the focus of this layer is rather about crosscompany
aspects and general rules. The business layer should not be confused with actual sys-
tems, such as an Enterprise Resource Planning (ERP) system. Business aspects concern
all hierarchy levels and functions. For example, shared pay-per-use concepts for field
devices would be discussed in this layer.
• Function layer: Integrating different assets in an Industrie 4.0 factory or plant may
require a formalization of their functionality. This may allow algorithms to dynami-
cally compose them for a more flexible production. Functionality can be simple (e.g.,
measuring a sensor value) or complex (e.g., production capabilities of a plant segment).
This layer summarizes the discussion around the functionality of individual assets, as
well as required platform and infrastructure functionality needed to enable the effec-
tive collaboration of different entities.
• Information layer: Each entity should provide information about itself in machine-
readable and possibly standardized formats. The information may be stored on the
device itself or on related IT infrastructure. At runtime the information may include
event processing, while at engineering time the information may include design and
construction data. Information integrity, information access via services, andmeans for
finding information are also discussed on this layer. Standards considered for this layer
63Industrie 4.0 and international perspective
are, for example, IEC Common Data Dictionary (IEC 61360), eCl@ss, Automa-
tionML (IEC 62714), ProSTEP iVIP, Electronic Device Description (EDD), Field
Device Tool (FDT), and Field Device Integration (FDI).
• Communication layer: Industrie 4.0 requires appropriate protocols to communi-
cate the information and functions of assets among each other. On this layer, existing
and new communication standards are discussed. Important aspects are appropriate
adherence to specific quality-of-service properties, as well as assurance of privacy
via encryption. Communication spans from low-level, hard real-time interaction
between devices in close proximity, over edge connectivity to cloud computing sys-
tems, up to cloud-to-cloud communication. Use cases and standards for wireless com-
munication are also in scope. A candidate implementation technology for the
communication layer is Open Platform Communications Unified Architecture
(OPC UA, IEC 62541).
• Integration layer: This layer is concerned with the challenge of bringing machines,
robots, products, and virtual entities into the IT world. This may span from using QR
codes to identify entities without active communication capabilities. RFID tags may
be used to provide IT systems a means of accessing a device. Additional sensors may be
used to extract information about assets or products and weave this information into
systems optimizing the production. Furthermore, human machine interfaces may be
used by human operators to add information via interpretation and integrate this into
the IT systems.
• Asset layer: Finally, the asset layer includes physical components, such as robots,
machine, pipes, motors, automated guided vehicles, but also human beings. Assets
may require physical changes in certain Industrie 4.0 scenarios, e.g., adding mobility
to certain machine or new robots mimicking human motions. Humans may be
equipped with augmented reality devices for a more sophisticated interaction with
the physical world. In Industrie 4.0 scenarios, which combine assets from different
vendors, new physical hardware interfaces between the assets may be required.
2.3 Life cycle and value stream
Industrie 4.0 concerns the whole life-cycle of products and production facilities. The
RAMI 4.0 expresses this dimension as x-axis, which is structured according to IEC
62890. This postulates a distinction of type and instance:
• Type: Early in the life-cycle of each product or machine, its type is precisely defined
during development. Later this type can be instantiated many times during produc-
tion, but all instances follow the template create at the type level. For example, a
hydraulic valve may be defined on the type level with all its properties, functionality,
and shape. The type description consists of several engineering documents, such as
specifications, CAD drawings, and testing data. The type definition may also undergo
maintenance and be refined in subsequent revisions.
64 Smart manufacturing
• Instance: Once the type definition of a product is completed, instances are first pro-
duced and then enter maintenance and usage. Instances are also considered for the dif-
ferent hierarchy levels of the model, from field device to “Connected World.” For a
hydraulic valve, series production may require different Industrie 4.0 aspects, such as a
unique identification of each instance and a recording of the production history for
each instance to allow for later production optimizations. During maintenance/usage
the hydraulic valve may communicate condition monitoring data to a cloud platform
to enable predictive maintenance.
2.4 Hierarchy levels
Finally, RAMI 4.0 considers different plant hierarchy levels to scope discussions. These
hierarchy levels are derived from IEC 62264/ISA-95 and IEC 61512 and include the
following levels:
• Product: for example, IoT-enabled products.
• Field device: for example, smart Industrie 4.0 sensors.
• Control device: for example, programmable logic controllers.
• Station: for example, the capper station in a bottle-filling machine.
• Work center: for example, a building or plant segment for a batch process.
• Enterprise: for example, a company in beverage production.
• Connected world: for example, a cloud platform for different supply chain
participants.
Product, field device, and “connected world” have been added in the RAMI 4.0 model
due to their significance in Industrie 4.0, while the other hierarchy levels have been
directly taken out of the respective standards.
2.5 Example usage of RAMI 4.0
Fig. 2 shows an example on how working groups on Industrie 4.0 can use RAMI 4.0.
This example decomposes RAMI’s communication layer. The layer is expanded into the
seven ISO/OSI layers (International Organization for Standards, Open Systems Inter-
connections model) for communication protocols. OSI layer 1–4 likely exhibit similar
protocols across all RAMI life-cycle stages and hierarchy level, such as Time-Sensitive
Networking (TSN), Long-Term Evolution (LTE), and 5G (the Long-Term Evolution
and 5th Generation of cellular communication). On OSI layer 5–7 there is a distinction
between the RAMI type and instance dimension. On the type level, the suggestion is to
communicate engineering artifacts via the HTTPS protocol, since they are mostly static
and may be communicated across the Internet. On the instance level, the proposal is to
use OPC UA Client/Server and Pub/Sub for the hierarchy levels up to Work Center,
and then use MQTT (Message Queuing Telemetry Transport) for communication on
the Enterprise and “Connected World” hierarchy level. In this case, RAMI 4.0 ensures
that all life-cycle stages and hierarchy levels are adequately considered.
7 Applica�on
6 Presenta�on
5 Session
4 Transport
3 Network
2 Data link
1 Physical
OSI layers
UDP, TCP
IP, IPsec
Ethernet, WiFi, GSM/G4
Wired/Wireless
TSN, 5G
Product
Field device
Control device
Sta�onWork center
Enterprise
Connected world
HTTP(s)
MQTT
OPC UA
Type 
Develop-
ment
Type
Main-
tenance
& Usage
Instance
Main-
tenance
& Usage
Instance
Produc�on
Communica�on Layer
Fig. 2 RAMI 4.0 in action: decomposing the communication layer and identifying standardized
communication protocols for different life-cycle stages and hierarchy levels [4].
65Industrie 4.0 and international perspective
While useful for committee work in Industrie 4.0, RAMI 4.0 has also drawn criti-
cism, since it does not provide guidelines on how to create Industrie 4.0 assets and prod-
ucts. Furthermore, most committees focus their discussions on the communication and
information layer, rendering the other layers less relevant.
3. Asset administration shell
3.1 Motivation
To improve the interoperability of industrial assets on all hierarchy levels of RAMI 4.0,
the Plattform Industrie 4.0 proposed so-called Asset Administration Shell (AAS), which
can be roughly described as partially standardized virtual representations of assets, which
can be exchanged using standardized communication protocols. Any conventional asset
could become an “Industrie 4.0-Component” if equipped with such an AAS (Fig. 3).
Because such components would rely on partially standardized semantics, they can better
communicate, in some cases even without the need for human programming. Thus, this
concept shall ease the implementation of the different Industrie 4.0 application scenarios,
even if they involve assets from different vendors.
The specific realization of such an AAS is still under discussion and may vary depend-
ing on the application context. For example, during plant operation of a chemical plant,
an AAS for a temperature transmitter could be realized by an OPC UA server that pro-
vides a standardized information model (e.g., based on NAMUR,d NE131,e and IEC
d User Association of Automation Technology in Process Industries, originally founded as
“Normenarbeitsgemeinschaft f€ur Mess- und Regeltechnik in der chemischen Industrie” or
“Association for Standardization of Measurement and Control Engineering in the Chemical Industry.”
e https://www.namur.net/en/recommendations-and-worksheets/current-nena.html.
https://www.namur.net/en/recommendations-and-worksheets/current-nena.html
Asset:
Machine
Asset Administration Shell
Asset:
Electrical Axis
Asset Administration Shell
Asset: 
Terminal Block
Asset Administration Shell
Asset: 
Standard Software
Asset Administration Shell
[…]
Asset
Not an Industrie 4.0 component Examples for Industrie 4.0 components
Asset gives access 
to Asset Administration Shell
Superordinated system 
gives access 
to Asset Administration Shell
Industrie 4.0 compliant 
communication
Unknown,
Anonymous,
Individually known,
Entity
Fig. 3 The concept of “Industrie 4.0 components.”
66 Smart manufacturing
Common Data Dictionaryf ). During engineering, an AAS for an automotive production
facility could be a web server exposing an AutomationML-file, which can be imported in
various engineering tools.
3.2 AAS requirements
Members of the Plattform Industrie 4.0 formulated several requirements for AAS, which
shall enable multiple Industrie 4.0 application scenarios. AAS shall allow any Industrie 4.0
component to connect to any other Industrie 4.0 component, given the appropriate
access rights and network configurations. To enable this each AAS shall be uniquely iden-
tifiable. The virtual representation of an asset shall be deployed either on the asset itself, in
case it provides the necessary computing capabilities, or on a surrounding IT system. In
the latter case, the integrity between the AAS and the asset must be assured. An AAS shall
enable service-oriented interaction of Industrie 4.0 components with appropriate man-
agement of quality-of-service, independent of proprietary field buses. It shall be possible
to nest different AAS, for example to have hierarchical AAS structures that model entire
machines assembled from individual Industrie 4.0 components. As an asset may need
information and functions from different technical domains, the virtual representation
f https://cdd.iec.ch/.
https://cdd.iec.ch/
67Industrie 4.0 and international perspective
shall be segmented into “submodels” mapping to these technical domains. AAS may ref-
erence other AAS, e.g., nested AAS or associated AAS. While full interoperability
between AAS is only achieved by standardized submodels and properties, AAS may also
contain proprietary, vendor-specific properties and functions, for example to access
market-differentiating features.
3.3 AAS design
In line with the RAMI 4.0 model, AAS may exist across the entire life-cycle of assets and
thus cover types during development and instances during production and operation. As
a type during development, the AAS may for example contain references to CAD
(computer-aided design) drawings, product specifications, and embedded software. Dur-
ing type usage, additional external information, such as marketing material, may be added
to the AAS, as well as updates to the product design. During production, instances of the
assets are derived from the asset type. In the same manner AAS instances are derived from
the AAS type. These AAS instances maymanage information about production, logistics,
qualifications, tests, etc. of a particular asset instance and expose live data to other systems.
After production, the AAS instance can cover usage data, emit condition monitoring data
to allow for better service, and finally capture information about decommissioning,
before being archived.
There are different identifiers to refer to AAS and their elements. Uniform Resource
Identifiers (URI) from RFC 3986 for example identify AAS, assets, submodels, and pos-
sibly property semantics. The recommendation is to use an URI structure composed of
organization, organizational subunit, submodel, version, revision, property, instance
number. Companies or developers may derive URIs themselves from registered
domains, an example URI is “http://www.zvei.de/SG2/aas/1/1/demo11232322.”
International Registration Data Identifier (IRDI) from ISO 29002-5 are usually specified
within a consortium or international standardization body and thus cannot be freely
defined by developers. The AAS specification recommends using IRDIs for submodels
and properties, which then are internationally precisely defined. For example, the IRDI
“0173-1#02-BAE430#005” from the eCl@ss Associationg identifies the upper range
limit of a temperature sensor, which is the highest value that a device can be adjusted
to measure within its specified accuracy limits. Finally, AAS may also contain custom
identifier defined by the developers of the AAS. These may be used to support
company-internal identification systems.
The high-level AAS structure is generic and not tied to an application domain. An AAS
refers to a particular asset and contains a number of submodels. Permission rules guard the
access to the AAS contents. Each submodel relates to a technical domain, has an iden-
tifier, and contains a number of “submodel elements.” These can be properties, file
g https://www.eclass.eu/.
http://www.zvei.de/SG2/aas/1/1/demo11232322
https://www.eclass.eu/
68 Smart manufacturing
references, binary large objects, relationships, operations, and events, and can be grouped
into collections. As such submodel elements may reference the concept dictionary of an
international standard (e.g., eCl@ss, IEC Common Data Dictionary) that defines their
semantics, they provide the targeted AAS interoperability, as developers may program
against these international standards without referring to a particular AAS in advance.
AAS may also contain self-defined, task-oriented views, which filter the AAS contents
(i.e., submodel elements, etc.) for certain users or scenarios.
The Plattform Industrie 4.0 aimed at specifying the AAS concepts independent of
specific technologies to assure broadapplicability in many different usage scenarios.
Yet, the Plattform foresees multiple technology mappings. XML (Extensible Markup
Language) and JSON (JavaScript Object Notation) serializations of the AAS support sce-
narios involving file transfer (e.g., procurement) and technical communication. Automa-
tionML is a specific XML file format aiming at file exchange between engineering tools
and may hold AAS elements. A “Resource Description Framework” (RDF)h represen-
tation of an AAS aids scenarios involving semantic searches. The OPC Unified Archi-
tecture (OPC UA)i provides servers and clients, which may be structured according
to the AAS concepts and support data access during production and operation, including
live values. The Plattform Industrie 4.0 has specified a serialization format for AAS based
on the Open Packing Conventions (OPC)j and defined the file extension “.aasx.” There
is a Windows application called “AASX Package Explorer,” which allows to browse and
manipulate the contents of “.aasx” AAS files.
Fig. 4 shows the example of an AAS for a Motion System. The AAS provides a virtual
representation of an asset. An asset may be composed of multiple inner assets, e.g., dif-
ferent motion devices with multiple attributes includes axes and power trains. Each AAS
is structured according to submodels, carrying properties and operations. Related AAS
reference each other, so that all elements of a Motion System can be accessed via AAS.
The AAS specification is work in progress. Part 1 covers life-cycle, structure, access
control, and XML mappings. Future specifications shall elaborate service access APIs,
additional technology mappings, as well as an infrastructure to host and interconnect
AAS (including registry, discovery, services, and endpoint handling).
While initiated by the German Plattform Industrie 4.0, the AAS specification is being
discussed in international committees, such as the French Alliance Industrie du Futur or
the Italian Piano Industria 4.0. The concepts will be brought to IEC/TC 65 working
groups on smart manufacturing to determine enhancements of existing IEC standards,
as well as possible creation of new standards.
h https://www.w3.org/RDF/.
i https://opcfoundation.org/about/opc-technologies/opc-ua/.
j https://www.ecma-international.org/publications/standards/Ecma-376.htm.
https://www.w3.org/RDF/
https://opcfoundation.org/about/opc-technologies/opc-ua/
https://www.ecma-international.org/publications/standards/Ecma-376.htm
Asset
AAS: Motion System
Submodel: Identification
Submodel: Motion Devices
Submodel: Topology
Asset
AAS: Controller
Submodel: Identificat ion
Submodel: Controller
Submodel: Topology
Asset
AAS: Motion Device 1
Submodel: Identification
Submodel: Motion Device
Submodel: Topology
Asset
AAS: Motion Device 2
Submodel: Identificat ion
Submodel: Motion Device
Submodel: Topology
Asset
AAS: Axis 1
Submodel: Identificat ion
Submodel: Axes
Submodel: Topology
Asset
AAS: Powertrain 1
Submodel: Identificat ion
Submodel: Powertrain
Submodel: Topology
Asset
AAS: Axis 2
Submodel: Identificat ion
Submodel: Axes
Submodel: Topology
Fig. 4 Example AAS of a Motion System.
70 Smart manufacturing
4. Applications
Since the very beginning, Industrie 4.0 has been using reference applications to illustrate
its scope and objectives. It also is maintaining a map of implemented Industrie 4.0 use-
cases [5], containing close to 200 examples from industry and academia at the time of
writing. In 2016, the platform developed [6] and refined [7] a set of 10 consolidated ref-
erence application scenarios to illustrate the potential of digitalization for manufacturing
industries. These scenarios integrate the life-cycle aspects of both the product and the
production facilities from design to operation. They envision how products and produc-
tion facilities adapt to changing market situations through software configuration, cov-
ering all value-chain partners from suppliers to consumers. They also address how
humans will work and interact with increasingly self-organizing production ecosystems.
The scenarios are summarized in Table 1. Focusing on the life-cycle of production facil-
ities, we describe four of these scenarios in more detail, using an application example
from Busch-Jaeger Elektro GmbH into which the authors have deep first-hand insight.
Further examples of Industrie 4.0 use-cases, factories and testbeds can be found in
Refs. [8, 9].
4.1 Value-based services
At the heart of digitalization, an essential idea is to unlock data as a new type of raw mate-
rial. By collecting data from production assets, processes, materials, and products, one can
gain deeper insights into the production process and optimize its operational aspects for
higher productivity. For instance, by relating the measured quality of products to
machine parameters and condition data, the parameters can be retuned to optimize
the product quality for a specific machine condition. By further relating machine con-
dition data to their effective maintenance records, maintenance schedules and needed
actions can be optimized and predicted.
With an increasing number of sensors, high-fidelity simulation models, and AI/
machine-learning technologies, future digital machines will increasingly be able to
self-monitor, maintain, and optimize based on data originating directly within the
machines. Such intelligent machines will provide VBS even without remote access
and 3rd-party service providers. However, for the installed base of existing, less digita-
lized machines and for entire fleets of standard machines in general, data must inherently
be collected, analyzed, and processed remotely from the machines in edge and cloud
installations. This holds even more when correlating data from very different types of
sources, in addition to the machines, e.g., process analytics, test labs with focus on prod-
uct and material quality, and computerized monitoring or maintenance management sys-
tems (CMMS) with focus on machine condition.
Making vast amount of data available in centralized locations is both an opportunity
and a challenge for manufacturers and service providers. On the one hand, this opens up a
Table 1 Industrie 4.0 reference applications.
Acronym Description
OCP Order-controller production: driving the planning and organization of
production resources based on individual customer orders instead of stock-keeping
targets
AF Adaptable factory: enabling plug and produce through modular, reconfigurable, and
self-describing machines that automatically integrate and collaborate with each other
SAL Self-organizing adaptive logistics: automatic organization of material transport
on any level of the enterprise, spanning from supply chain partners to intralogistics
between machines inside of a factory
VBS Value-based services: using data captured from production assets to analyze them
and improve product quality and productivity, e.g., by understanding material
quality or offering predictive maintenance
TAP Transparency and adaptability of delivered products: using, e.g., built-in
connectivity and software features of physical consumer products to offer after-sales
services like condition monitoring, updates and maintenance, and reconfiguration
support
OSP Operator support in production: enabling humans to efficiently and safely
collaborate with increasingly adaptive and complex production assets both in the
physical and cyber world, i.e., with reconfigurable robots, machines, and software
systems
SP2 Smart product development for smart production: reusing data from product
design to engineer the production process and set up the supply chain, but also to
make them available to product owners to facilitate the use of the physical product
IPD Innovative product development: dynamically setting up distributed,
interdisciplinary teams of experts to conceive and design novel products and the
related productionfacilities, recombining the exact expertise needed to address a
present market demand to get the right product with short time to market
SDP Seamless dynamic engineering of plants: moving from a waterfall-model to an
agile engineering process, which integrates the constant flow of dynamic changes
done bottom-up or top-down into a complete model of the past, present, and
planned future of a production facility
CRE Circular economy: achieving a closed circle for product materials by reusing,
repairing, or refurbishing preowned products or by at least upcycling the contained
materials
71Industrie 4.0 and international perspective
new data analytics market with new business models, and allows improving production
without added capital invest, protecting above all the existing investment into machines.
On the other hand, it requires data from a variety of distributed data sources to be easily
understood, e.g., by data scientists or subject-matter experts to devise and operate
the VBS.
The first wave of digitalization offerings in Industrie 4.0 or the IIC comprise ABB
Ability, GE Predix, SAP Asset Intelligence Network, Siemens Mindsphere, etc. These
72 Smart manufacturing
offerings can be regarded as the “minimally invasive” approach of Industrie 4.0 that
requires little or no change to (existing) products and plants by wrapping (existing) oper-
ational data and functions from (existing) products and systems and making them acces-
sible to (new) data-driven services.
While in process industry, the NAMUR Open Architecture (NOA) particularly
addresses the customer need to digitalize existing plants while actively prevent direct
influence on the mission-critical functions of the distributed control systems (DCS)—
NOA coins the term of a “data diode” for that matter—the challenge of safe and secure
process influence is not formulated in the VBS scenario.
Summarizing, we see the following subscenarios in VBS:
• Tuning the configuration of machines and production processes based on knowledge
of machine conditions, material or preproduct properties, and the product to be man-
ufactured to improve quality and resource efficiency.
• Monitoring the operational state and conditions of machines to detect or predict qual-
ity deteriorations and maintenance needs; this yields higher productivity by
• Increased availability of the machines or plannable maintenance outage.
• Controlled product quality resulting in enhanced product availability or at least
transparency of (reduced) product quality (used, e.g., in TAP).
• Including monitoring of individual specialized machines or entire fleets of standard
machines.
• Monitoring the quality of raw materials as part of product quality control and creating
audit trails for pharmaceuticals, food, and beverages.
• End-to-end tracking of raw materials and product parts in consumer products or
ingredients in food and beverages, providing added transparency to the consumer
(see also TAP).
In conclusion, VBS is the base case of Industrie 4.0: it can provide value for already exist-
ing assets by leveraging data that can be made available with little effort. It also scales with
the increasing level of digitalization of machines and processes, in particular when inte-
grating previously not available data. The challenges to address are data privacy with 3rd-
party service providers, making sure that data from heterogeneous sources are understood
by the software services, and maintaining process safety and availability with increasing
levels of autonomy based on data-driven decision making.
4.2 Adaptable factories
For a factory, adaptability means that it is able to adjust to changing demands that were
unforeseen at design time [10]. This goes beyond flexible production as known in auto-
motive industry, where all intended variations of production are known in advance, and
which still requires significant upfront investment into machine building and system
integration.
Fig. 5 ABB-tacteo KNX production workshop—example of an adaptable factory with order-controlled
lot-size one production.
73Industrie 4.0 and international perspective
With Industrie 4.0, the goal is to make adaptable factories technically and econom-
ically feasible to build. This is desired for two reasons: enable faster reaction to changing
market situations and yield higher return on the investment in product assets. Increasing
market demand will reward manufacturers who can scale up their production quickly,
and being able to scale down production volume with shrinking demand greatly reduces
operational risk; for new product or product variants, time to market is a key competitive
advantage.
An example is the production of the ABB-tacteo KNX sensor for room automation
manufactured by Busch-Jaeger Elektro GmbH shown in Fig. 5. This production work-
shop consists of modular machine stations that self-describe using OPC UA information
models and are orchestrated through ABB Ability Operation Management Zenon [11].
To increase production volume, existing machines can be duplicated, and the additional
production resources are then discovered and integrated into production orchestration.
To elaborate, such adaptable factories have five main enablers:
1. Strictmodularity of the physical production process so the system can be composed
of independent, encapsulated process functions (built into machines),k also allowing
the size of the system to scale with product demand by adding or removing modules.
2. Highly standardized interfaces of themodules somaterial, energy, and information
flows can be (re-)configured and integrated without a priori knowledge of a particular
module and with minimal risk that modules cannot collaborate where needed.
k In the remainder of this section, we use module and machine synonymously.
74 Smart manufacturing
This can be achieved by standardizing model properties according to eCl@ss or IEC
61987 and generally through standards from industrial interest groups reflected in
OPC UA companion specifications.
3. Network-accessible self-description capability of each module so the system can
discover, configure, and monitor the available process functions. This can be
achieved, e.g., through the Industrie 4.0 Administration Shell accessed as serialized
xml file or a live OPC UA model.
4. Flexible infrastructure, including power supply, network connectivity, andmaterial
transport facilities to integrate any composition of modules into a working system.
This can be achieved through standardized machine plugs containing power and
network interfaces and deterministic software-defined and IP-converged network
infrastructures (SDN) based on IEEE TSN (time-sensitive networks) or 5G (fifth-
generation cellular communication). And it can be implemented through AGVs
(autonomous guided vehicles) and flexible tray-based material transport systems such
as B&R’s ACOPOStrak.
5. Easy reconfigurability of the physical process within by a module, e.g., by up/
downloading design data for a potentially unique product into a software-defined
machine. This can be achieved by standardizing configuration interfaces to receive
complex product design data, e.g., in CAD (Computer-Aided Design) files, but it
also requires a physical process within the machine that can be freely reconfigured.
The first four enables address a system perspective: modularity, standardized interfaces,
and self-description capabilities of machines (1–3) serve to supply plug-and-produce-
enabled modules (devices, machines) as composable components. Domain-specific
examples are the Process Automation Device Information Model (PA-DIM) OPC
UA companion specification for process instruments and modular chemical plants based
on VDI/VDE/NAMUR 2658. Such standardized models are also a key prerequisite for
the seamless digital engineering of plants (SDP) application scenario. A flexible infrastruc-
ture (4) is then required to obtain an adaptable system(line, factory) where machines can
in fact be flexibly recomposed into a new production process.
Independent of this, the fifth enabler addresses a component perspective: reconfigur-
ability of a physical process (5) enables adaptable production already within a single
machine. A good example is the 3D printing of jet engine (spare) parts where the overall
production process consists of just a single industrial 3D printer and the adaption process
does not depend on a reconfigurable system but on the software configurability of one
machine. Another example is the near-infrared (NIR) laser by ALLTEC GmbH used in
the ABB-tacteo KNX production workshop. It can receive any set of shapes through 2D
CAD data to imprint on the sensor plate, only limited by certain core design consider-
ations for the product look and feel (Fig. 6).
In consequence, to build adaptable factories, one must solve two main challenges:
• Understanding how to apply complex process technologies in machines to a particular
material or preproduct, i.e., not only understanding the properties of an industrial
Fig. 6 FOBA near-infrared laser—example of a software-defined machine.
75Industrie 4.0 and international perspective
welding robot but also understanding how fast and precise a safety-critical part of an
aluminum car-body can/must be welded.
• Arranging machines and material flow according to process steps needed by the prod-
uct and according to the targeted throughput, i.e., having all process steps available in
sufficient quantity and ensuring material passes through in the right order.
These are vastly different from PnP in consumer situations where there is no complex
“product,” and in consequence no sequence of production steps, limited machine and
material properties, and no material flow to organize. Considering the typical example
of PC periphery devices, there is typically one keyboard, one mouse, and one printer,
none of which can be confused with each other, and the most complex settings might
be choosing color or duplex printing modes.
In conclusion, adaptable factories are becoming a reality as the introduced enablers
become available. The remaining key challenge is about software services on ISA-95
levels 2 and 3 (planning, orchestration, supervision) understanding complex machine
skills well enough to automatically reconfigure and orchestrate them.
4.3 Order-controlled production
Today, mass-produced commodity consumer products are typically made to stock. Based
on experience andmarket observations, companies plan production throughmaintaining
min/max limits on their stock using ERP systems. This occupies storage space and binds
operational capital, increases the time-to-market in unforeseen demand situations, and is
unsuited to manufacture customizable products with a large number of variants let alone
fully individualized products. Industries such as automotive, where billions of variants can
be configured for a single car, achieve order-controlled production in part by increasing
their delivery time to several months. Still, OCP in the automotive industry comes at
76 Smart manufacturing
high up-front costs to design and construct production facilities capable to cover a large
but still completely predetermined set of customization options.
With Industrie 4.0, order-controlled production is expected to become economically
feasible for any level of customization and any lot size. This requires a seamless integration
of the entire value-creation chain, from the consumer to the manufacturer (a business-to-
consumer (B2C) context), to material suppliers and distributors (a business-to-business
(B2B) context), and eventually to the manufacturing resources (a machine-to-machine
(M2M) context).
It also requires flexible planning, scheduling, and coordination functions that can han-
dle large amounts of small orders, dynamic changes to fast-track express orders, based on
an in-depth knowledge of the capabilities, states and conditions of all production
resources. In turn, this means the design and operational characteristics of machines,
the availability and quality of material, and the state and quality of the (intermediate)
product must be available digitally at any time.
A particular solution approach is the so-called smart product or smart workpiece where
the product guides itself through its production steps. This means any machine for any
product must be capable to verify the sequence of production steps, retrieve the specific
step configuration [and reconfigure accordingly in an adaptable factory (AF)], and mate-
rial transport must be able to reliably guide the product to the next machine or
manufacturing station. This can either be achieved by the product carrying directly its
design data and productionmemory, by storing this information temporarily on transport
container, or by retrieving the design data from a central storage. In any case, any instance
of a smart product must have a unique identifier to track it during manufacturing. Fig. 7
shows a tagged tray loaded with six also tagged preproducts for the ABB-tacteo KNX
sensor introduced in the AF scenario. The tags are used to guide the material through
the production process and pull the matching design data from operations management
to each machine.
In conclusion, order-controller production is not only a matter of ISA-95 Level 2/3
systems for planning, scheduling, and coordination but also a matter of adaptive,
Fig. 7 ABB-tacteo KNX smart products loaded into a tray for flexible material transport.
77Industrie 4.0 and international perspective
reconfigurable machines and a complete digitalization of all production assets down to
the individual tracking of any product instance. For small lot sizes, quality assurance is
an additional challenge.
4.4 Seamless dynamic engineering of plants
Today, plant or factory engineering typically follows a waterfall model with data discon-
tinuities between basic planning, detail engineering, and reconfiguration. System integra-
tors receive requirements specifications in various document formats andmust design and
implement the integration of machines, robots, and material transport facilities from sub-
vendors into a production system using a variety of vendor-specific tools. This impacts
engineering cost and time to market during the initial conception of a plant and it
requires added diligence during any reconfiguration to document the new “as-built” sit-
uation for auditing and as input to other applications such as quality assurance.
Industrie 4.0 aims at a seamless integration of engineering tasks along the life-cycle
and system hierarchy dimensions of the RAMI 4.0 to efficiently yield a gapless, high-
quality model representation of an entire production facility at any point in time.
Seamless dynamic engineering allows
• Auditing the change history of the plant configuration andmaking sense of history data
captured in a particular configuration of the plant for data analytics and machine
learning.
• Maintaining, adapting, extending, and scaling the plant at any point in time from a
software perspective (within the limits of the physical resources) with known good
results, both through top-down/front-end engineering and bottom-up/back-end
reconfiguration.
Seamless engineering is enabled by
• Available and accessible description models of machine capabilities provided by the
machine builders that can be used by system integrators to select suitable machines
and configure them.
• A standardized communication layer, i.e., formats for exchange of these model descrip-
tions between machine builders and system integrators, such as the Industrie 4.0
Administration Shell, AutomationML, OPC UA companion specifications, etc.
• A standardized information layer, i.e., standardized capability models for process tech-
nologies such as extruding (e.g., EUROMAP63/77), packaging (e.g., PackML),
welding, drilling, etc. or semanticproperty standards such as IEC 61987, eCl@ss,
etc. so the model content is semantically understood to guarantee interoperability.
4.5 Summary
The selected applications illustrate what digitalization means from a factory perspective:
fine-granular, semantically understood descriptions of all mechanical, electrical, and
78 Smart manufacturing
software interfaces as well as machine properties/capabilities, states and conditions must
be made network-accessible over said software interfaces. This is essential because there is
a vast number of description formats and machine standards and a system must be given
the opportunity to integrate any of them to engineer (SDP), adapt (AF), and optimize
(VBS) production processes for goods made to stock or made to order (OCP). Toward
a Plug and Produce factory, seamless dynamic engineering is needed to be able to plan,
orchestrate, and operate the resources of an AF. Toward Lot-Size-One Production, both
the ability to cost-efficiently adapt production assets (AF) and to orchestrate those assets
based on individual customer orders (OCP) are required. Using standardized means to
describe and exchange the asset descriptions like the Industrie 4.0 Administration Shell,
SDP is a key enabler for the manufacturing of individualized products. However, the
reconfigurable machines at the heart of such an AF can be a two-edged sword: a deployed
machine might manually be adapted during operation (bottom-up) to manufacture a sin-
gle unit of a product with properties that were not considered even during smart product
development (SP2, top-down). However, if the adaptations from this “manual override”
are not directly reflected into the metamodel of the production facility, the digital shadow
of the product will have gaps; transparently tracking product quality, or monitoring,
maintaining, or replacing the product at a later time (TAP) becomes impossible in an
automated fashion because the capability of the production asset and the digital model
diverge. The ability to adapt both the machines and the related digital models dynami-
cally during production is highly desirable in adaptive factories.
With the digitalization of production assets and the software-definition of machine
behavior progressing, the application scenarios of Industrie 4.0 are continuously becom-
ing reality. Remaining challenges lie in the coverage of existing device and machine stan-
dards, the availability of Industrie-4.0-ready components based on these standards, and
the self-organization of plants and factories toward autonomous production systems.
5. Roadmap/ongoing research
The research roadmap in Fig. 8 shows the main R&D topic areas identified by the
“Plattform Industrie 4.0.” It roughly follows the RAMI 4.0 dimensions, further adding
societal aspects and crosscutting topics like security.
Based on Ref. [6], we consider five topics to be of particular importance because they
address core technical gaps in digitalization, system autonomy, and human-technology
collaboration, including legal questions around privacy and liability:
• Semantics and models for Industrie 4.0: technical interoperability among machines
and software services.
• Negotiation and contract award in automated value networks: legal interoperability
among legal entities.
Fig. 8 Overview of the research roadmap [6].
79Industrie 4.0 and international perspective
• Systems engineering for adaptable systems: designing production systems to reconfi-
gure even for product variations unknown during planning and engineering.
• Safety and securityl: ensuring that environment, human health, and production assets
are still effectively protected in a “connected world” of deeply integrated, reconfigur-
able autonomous production systems without losing the benefits of such systems.
• Organization of work, assistance systems, and the human digital shadow: fostering
human acceptance, qualification, and data privacy.
The potential and needed research topics for artificial intelligence (AI) and machine
learning are addressed in a dedicated working paper [12].
With regard to technical standards, the GermanCommission for Electrical, Electronic
and Information Technologies (DKE) is giving over 20 consortia and standardization
bodies [13] and claims close to 700 norms [14] to be central for Industrie 4.0. It is apparent
that the continued development of this number of technical standards cannot be centrally
managed through any interest group. Rather, the “Plattform Industrie 4.0” has
l Here, we deviate from the Industrie 4.0 discussion paper, which suggested “Logistics 4.0” to be one of the
top five topics.0.
80 Smart manufacturing
functioned as a catalyst for the discussion and standardization of digitalization technolo-
gies, focusing on addressing key gaps in the existing standards with, e.g., the Reference
Architecture Model Industrie 4.0 (RAMI 4.0) and the Industrie 4.0 Administration Shell
introduced earlier.
With regard to societal aspects, in particular human acceptance of novel job profiles
and the disruption of the job market, the roadmap implies might be one of the first com-
pleted topics. At the same time, a study done at the University of Oxford concludes that
47% of jobs (in the United States) are likely to be replaced over the next decade(s) in the
wake of digitalization [15]. In the general public, however, awareness of the impact of
digitalization is still rudimentary. Novel job profiles and education curricula are mostly
missing—bedsides perhaps for data science and AI/machine learning. In this context,
societal aspects might in fact be the biggest (nontechnical) challenge that Industrie 4.0
and the digitalization of industry in general are facing.
6. Conclusion
In this chapter we have introduced the Industrie 4.0 concept and compared it to some of
the main features of Smart Manufacturing. As can be seen from the discussed architecture
model, asset admin shell, and the selected applications, themain focus has been to develop
tools and concepts that enable the creation of a very broad set of digitalization scenarios
and use cases. Special attention has been placed on the engineering, which is key in con-
necting the hardware and software layers in a systematic manner. Many presented use
cases are manufacturing industries driven, but the proposed methods and tools are mostly
fully applicable also to process industries.
Thus, the Industrie 4.0 and its focus on the feasibility of connecting the various layers
(sensor, control, planning, communication, etc.) can be easily adopted and utilized in
Smart Manufacturing scenarios. The standardization work has already taken place during
the last few years and perhaps the most significant result is the ongoing international stan-
dardization work based on RAMI 4.0: IEC PAS 63088. Most existing standards for
industry 4.0 have been linked to this work.m
As a conclusion, one can see Industrie 4.0 as a support on how to realize many Smart
Manufacturing visions in practice. On the other hand, SmartManufacturing activities can
also point the way for future application scenarios in Industrie 4.0, especially from the
area of process industries. Neither Smart Manufacturing nor Industrie 4.0 are complete
or even close to ready, but the results so far are very important steps in reaching tangible
results and benefits in the digitalization of our industries. Knowing both activities is vital
as they can complement each other in a collaborative way and together pave the way
toward more safe, sustainable, and cost-efficient industries.
m http://i40.semantic-interoperability.org/.
http://i40.semantic-interoperability.org/
81Industrie 4.0 and international perspective
References
[1] J.F. Davis, T.F. Edgar, Y. Dimitratos, J. Gipson, I. Grossmann, P. Hewitt, R. Jackson, K. Seavey,
P. Porter, R. Reklaitis, B. Strupp, Smart process manufacturing: An operations and technology road-
map, in: Smart process manufacturingengineering virtual organization steering committee, Los
Angeles, CA, Tech. Rep, 2009.
[2] J. Wetzel, Clean energy smart manufacturing innovation institute (CESMII) overview: accelerating the
smart manufacturing transformation, in: Next-Gen Manufacturing 2017—Topical Conference at the
2017 AIChE Annual Meeting, Minneapolis, US, 2017, pp. 150–157.
[3] E.V. Bitkom, E.V. Vdma, E.V. Zvei, Implementation Strategy Industrie 4.0: Report on the Results of
the Industrie 4.0 Platform, Bitkom e.V., Berlin; VDMA e.V., Frankfurt; ZVEI e.V., Frankfurt, 2016.
https://www.bitkom.org/sites/default/files/file/import/2016-01-Implementation-Strategy-
Industrie40.pdf.
[4] Plattform Industrie 4.0, RAMI4.0—A Reference Framework for Digitalization, https://www.
plattform-i40.de/PI40/Redaktion/EN/Downloads/Publikation/rami40-an-introduction.pdf?__
blob¼publicationFile&v¼3.
[5] https://www.plattform-i40.de/PI40/Navigation/EN/Services-Results/Industrie-4-0-Map/
industrie-4-0-map.html.
[6] R. Anderl, K. Bauer, B. Diegner, J. Diemer, A. Fay, J. Firtz, D. Goericke, J. Grotepass, C. Hilge,
J. Jasperneite, J. Kalhoff, Aspects of the Research Roadmap in Application Scenarios, BMWi,
Berlin, 2016.
[7] R. Anderl, C. Bauer, T. Bauernhansl, B. Diegner, J. Diemer, A. Fay, D. Goericke, J. Grotepass,
C. Hilger, J. Jasperneite, J. Kalhoff, Fortschreibung der Anwendungsszenarien der Plattform Industrie
4.0, BMWi, Berlin, 2016.
[8] T. Bauernhansl, M. Ten Hompel, B. Vogel-Heuser (Eds.), Industrie 4.0 in Produktion, Automati-
sierung und Logistik: Anwendung-Technologien-Migration, Springer Vieweg, Wiesbaden, 2014,
pp. 1–648.
[9] A. Ustundag, E. Cevikcan, Industry 4.0: Managing the Digital Transformation, Springer, Switzerland,
2017.
[10] P. Nyhuis, G. Reinhart, E. Abele, Wandlungsf€ahige Produktionssysteme: Heute die Industrie von
morgen gestalen, PZH, Hannover, 2008.
[11] D. Schulz, L. Simora, Individualization of production, ABB Rev. 4 (2018) 12–19.
[12] Federal Ministry for Economic Affairs and Energy (BMWi), Technologieszenario, K€unstliche Intelli-
genz in der Industrie 4.0, March, 2019.
[13] E.V. Din, German Standardization Roadmap Industries 4.0. VDE - Association for Electrical,
Electronic & Information Technologies, Germany, 2018, pp. 40–45.
[14] https://www.dke.de/de/arbeitsfelder/industry/die-deutsche-normungs-roadmap-industrie-4-0.
[15] C.B. Frey, M.A. Osborne, The future of employment: How susceptible are jobs to computerisation?
Technol. Forecast. Soc. Change 114 (2017) 254–280.
http://refhub.elsevier.com/B978-0-12-820027-8.00003-4/rf0010
http://refhub.elsevier.com/B978-0-12-820027-8.00003-4/rf0010
http://refhub.elsevier.com/B978-0-12-820027-8.00003-4/rf0010
http://refhub.elsevier.com/B978-0-12-820027-8.00003-4/rf0010
http://refhub.elsevier.com/B978-0-12-820027-8.00003-4/rf0015
http://refhub.elsevier.com/B978-0-12-820027-8.00003-4/rf0015
http://refhub.elsevier.com/B978-0-12-820027-8.00003-4/rf0015
https://www.bitkom.org/sites/default/files/file/import/2016-01-Implementation-Strategy-Industrie40.pdf
https://www.bitkom.org/sites/default/files/file/import/2016-01-Implementation-Strategy-Industrie40.pdf
https://www.plattform-i40.de/PI40/Redaktion/EN/Downloads/Publikation/rami40-an-introduction.pdf?__blob=publicationFile%26v=3
https://www.plattform-i40.de/PI40/Redaktion/EN/Downloads/Publikation/rami40-an-introduction.pdf?__blob=publicationFile%26v=3
https://www.plattform-i40.de/PI40/Redaktion/EN/Downloads/Publikation/rami40-an-introduction.pdf?__blob=publicationFile%26v=3
https://www.plattform-i40.de/PI40/Redaktion/EN/Downloads/Publikation/rami40-an-introduction.pdf?__blob=publicationFile%26v=3
https://www.plattform-i40.de/PI40/Redaktion/EN/Downloads/Publikation/rami40-an-introduction.pdf?__blob=publicationFile%26v=3
https://www.plattform-i40.de/PI40/Navigation/EN/Services-Results/Industrie-4-0-Map/industrie-4-0-map.html
https://www.plattform-i40.de/PI40/Navigation/EN/Services-Results/Industrie-4-0-Map/industrie-4-0-map.html
http://refhub.elsevier.com/B978-0-12-820027-8.00003-4/rf0035
http://refhub.elsevier.com/B978-0-12-820027-8.00003-4/rf0035
http://refhub.elsevier.com/B978-0-12-820027-8.00003-4/rf0035
http://refhub.elsevier.com/B978-0-12-820027-8.00003-4/rf0040
http://refhub.elsevier.com/B978-0-12-820027-8.00003-4/rf0040
http://refhub.elsevier.com/B978-0-12-820027-8.00003-4/rf0040
http://refhub.elsevier.com/B978-0-12-820027-8.00003-4/rf0045
http://refhub.elsevier.com/B978-0-12-820027-8.00003-4/rf0045
http://refhub.elsevier.com/B978-0-12-820027-8.00003-4/rf0045
http://refhub.elsevier.com/B978-0-12-820027-8.00003-4/rf0050
http://refhub.elsevier.com/B978-0-12-820027-8.00003-4/rf0050
http://refhub.elsevier.com/B978-0-12-820027-8.00003-4/rf9000
http://refhub.elsevier.com/B978-0-12-820027-8.00003-4/rf9000
http://refhub.elsevier.com/B978-0-12-820027-8.00003-4/rf9000
http://refhub.elsevier.com/B978-0-12-820027-8.00003-4/rf0055
http://refhub.elsevier.com/B978-0-12-820027-8.00003-4/rf0060
http://refhub.elsevier.com/B978-0-12-820027-8.00003-4/rf0060
http://refhub.elsevier.com/B978-0-12-820027-8.00003-4/rf0060
http://refhub.elsevier.com/B978-0-12-820027-8.00003-4/rf9005
http://refhub.elsevier.com/B978-0-12-820027-8.00003-4/rf9005
http://www.dke.de/Normen-Industrie40
http://refhub.elsevier.com/B978-0-12-820027-8.00003-4/rf0075
http://refhub.elsevier.com/B978-0-12-820027-8.00003-4/rf0075
	Industrie 4.0 and international perspective
	Introduction
	RAMI 4.0
	Motivation
	Layers
	Life cycle and value stream
	Hierarchy levels
	Example usage of RAMI 4.0
	Asset administration shell
	Motivation
	AAS requirements
	AAS design
	Applications
	Value-based services
	Adaptable factories
	Order-controlled production
	Seamless dynamic engineering of plants
	Summary
	Roadmap/ongoing research
	Conclusion
	References