Using the Industrie 4.0 Maturity Index in Industry - acatech COOPERATION Current challenges, case studies and trends
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acatech COOPERATION
Using the Industrie 4.0
Maturity Index
in Industry
Current challenges, case studies and trends
Günther Schuh, Reiner Anderl,
Roman Dumitrescu, Antonio Krüger,
Michael ten Hompel (Eds.)
INDUSTRIE 4.0
MATURITY CENTER
acatech COOPERATION
Using the Industrie 4.0
Maturity Index
in Industry
Current challenges, case studies and trends
Günther Schuh, Reiner Anderl,
Roman Dumitrescu, Antonio Krüger,
Michael ten Hompel (Eds.)
INDUSTRIE 4.0
MATURITY CENTER
Contents
Project 5
Executive Summary 6
1 Introduction 8
2 Experiences with using the acatech Industrie 4.0
Maturity Index in industry 9
2.1 What progress have companies made so far? 10
2.2 The digital diseases afflicting companies 12
2.3 Areas requiring urgent action by companies 14
2.3.1 Individual parts of products as information carriers 16
2.3.2 Replacing automation pyramids with edge and cloud computing 16
2.3.3 Practical example of agile work organisation: 72-hour prototyping 16
2.3.4 Empowering staff to shape the transformation 17
3 Case studies 18
3.1 Using the acatech Industrie 4.0 Maturity Index in companies 18
3.2 ZF Friedrichshafen AG: Industrie 4.0 rollout across 230 sites 18
3.3 HARTING Stiftung & Co. KG: using the Industrie 4.0 Maturity Index
as the starting point for an integrated strategy 21
3.4 Kuraray Co Ltd.: increasing overall equipment effectiveness 23
3.5 Increasing productivity in a chocolate factory 26
4 Outlook: the next steps in the digital transformation 29
4.1 Self-organising resources, agile infrastructures 29
4.2 Artificial intelligence for industrial applications 30
4.3 Changes in the interactions between humans,
organisations and technology 31
4.4 Management and Leadership “4.0” in the digitalised workplace 33
References 36
Project
Project — Thomas Kämper, HARTING KGaa
— Jonas Kaufmann, Industrie 4.0 Maturity Center at RWTH
Aachen Campus
Project management — Jörn Steffen Menzefricke, Heinz Nixdorf Institute, Paderborn
University
— Laura Mey, Industrie 4.0 Maturity Center at RWTH Aachen
— Prof. Dr.-Ing. Günther Schuh, RWTH Aachen University/ Campus
acatech — Matthias Müssigbrodt, FIR e. V. at RWTH Aachen
— Markus Obermeier, HARTING IT Services GmbH & Co. KG
— Felix Optehostert, Industrie 4.0 Maturity Center at RWTH
Project group Aachen Campus
— Dr. Daniel Porta, German Research Center for Artificial Intel-
— Prof. Dr.-Ing. Reiner Anderl, Department of Computer Inte- ligence, DFKI
grated Design (DiK), Technical University of Darmstadt — Jannik Reinhold, Heinz Nixdorf Institute, Paderborn University
— Prof. Dr.-Ing. Roman Dumitrescu, Heinz Nixdorf Institute, — Dr. Sebastian Schmitz, Industrie 4.0 Maturity Center at RWTH
Paderborn University/Fraunhofer Institute for Mechatronic Aachen Campus
Systems Design IEM — Roman Senderek, FIR e. V. at RWTH Aachen
— Prof. Dr. Antonio Krüger, German Research Center for Artificial — Yübo Wang, Computer Integrated Design (DiK), Technical
Intelligence, DFKI/acatech University of Darmstadt
— Prof. Dr.-Ing. Günther Schuh, RWTH Aachen University/ — Lucas Wenger, FIR e. V. at RWTH Aachen
acatech — Dr. Violett Zeller, FIR e. V. at RWTH Aachen
— Prof. Dr. Michael ten Hompel, Fraunhofer Institute for Material
Flow and Logistics IML, TU Dortmund University/acatech
Project coordination
Experts — Christian Hocken, Industrie 4.0 Maturity Center at RWTH
Aachen Campus
— Mark Gallant, PTC Inc. — Joachim Sedlmeir, acatech Office
— Markus Hannen, PTC Inc. — Dr. Johannes Winter, acatech Office
— Dr. Florian Harzenetter, PTC Inc.
— Howard Heppelmann, PTC Inc. We would like to thank PTC Inc., the Industrie 4.0 Maturity Center,
— Prof. Dr.-Ing. Boris Otto, Fraunhofer Institute for Software and and all professional partners for supporting this companion pub-
Systems Engineering ISST, TU Dortmund University lication to the acatech Industrie 4.0 Maturity Index.
— Prof. Dr. Volker Stich, FIR e.V. at RWTH Aachen
— Kevin Wrenn, PTC Inc.
— Rene Zölfl, PTC Inc.
Consortium partners/project team
— Nazanin Budeus, Fraunhofer Institute for Material Flow and
Logistics IML
— Stefan Gabriel, Fraunhofer Institute for Mechatronic Systems
Design IEM INDUSTRIE 4.0
— Marcel Hagemann, Industrie 4.0 Maturity Center at RWTH MATURITY CENTER
Aachen Campus
— Dr. Tobias Harland, Industrie 4.0 Maturity Center at RWTH
Aachen Campus
5
Executive Summary interdepartmental cooperation and inadequate employee involve-
ment in change processes.
The lessons learned from three years of using the acatech Indus- At present, companies should therefore focus on the systematic
trie 4.0 Maturity Index in industry make it plain that the fourth implementation of common data platforms throughout the busi-
industrial revolution cannot be accomplished simply through the ness, for example using cloud-based Industrial Internet of Things
implementation of individual, isolated prototypes. German manu- (IIoT) platforms that enable the aggregation of data from differ-
facturing industry has now recognised this fact and is focusing on ent sources such as business application systems, machine con-
the development of systematic, rigorously structured transforma- trollers and sensors. These platforms also provide a framework for
tion programmes aimed at delivering clear value added. developing individual visualisations and applications. The lessons
learned so far from the application of the Industrie 4.0 Maturity
Index in industry show that there are a number of serious general
The acatech Industrie 4.0 Maturity challenges in all four structural areas. These include end-to-end
Index in use data delivery across the entire value chain in the “resources” area,
the use of edge and cloud computing to break down the hier-
archies of the automation pyramid in the “information systems”
Since the publication of the acatech STUDY three years ago, the area, and the use of agile methods and promotion of employee
Industrie 4.0 Maturity Index has proven its worth as a tool of- involvement in the “organisation” and “culture” areas.
fering practical guidance on how to achieve a structured digital
transformation. The widespread use of the Index and extensive The use of the Index by the major global automotive supplier
interest in the themes it addresses indicate that companies are ZF Friedrichshafen AG illustrates the value of consolidating the
increasingly intent on adopting an integrated approach to Indus- results of individual sites at group level in order to synchronise
trie 4.0. The Index has been used for everything from drawing up digitalisation activities. HARTING KGaa was already involved
systematic roadmaps for the digital transformation of individual in the 2017 acatech STUDY and has since achieved the third
manufacturing sites to the synchronisation of different sites and maturity stage by producing guidelines, creating a digitalisation
the formulation of a global digitalisation strategy. The Index team and integrating its machinery in order to improve data
can also be used to measure and manage progress towards the delivery for its employees. The increase in overall equipment ef-
digital transformation and to carry out technical due diligence for fectiveness achieved through the transformation programme at
corporate acquisitions. These examples illustrate the significant specialty chemicals manufacturer Kuraray demonstrates that the
value added that can be generated by using the Index. Index can have a positive impact in a variety of different indus-
tries. At Kuraray, for example, the adoption of an IIoT platform
80% of the companies that took part in a survey by the Indus- has contributed to the implementation of Industrie 4.0. The case
trie 4.0 Maturity Center said that they have attained “connectiv- study of a chocolate factory illustrates how the Index’s main use
ity”, the second of the Index’s six maturity stages. In other words, in the food industry is to increase efficiency and output.
they are currently working on the measures needed to achieve
“visibility”, which is the first stage of Industrie 4.0. Just 4% of
companies said that they have already achieved this next stage. Areas requiring future action
These are the Industrie 4.0 pioneers or champions among the
companies that took part in the survey. While it has delivered successful results over the past three years,
the use of the Industrie 4.0 Maturity Index also raises a number
of medium-term strategic issues. In the future, manufacturing
Areas currently requiring action industry will be increasingly concerned with the “predictive capac-
ity” and “adaptability” maturity stages. Here, the focus will be
The widespread “digital diseases” afflicting many companies can on industrial applications of artificial intelligence and the socio-
be cured or mitigated by adopting the structured approach out- technical interactions between humans, technology and organisa-
lined in the acatech Industrie 4.0 Maturity Index. These diseases tions. New HR management concepts are also becoming more
include the lack of common standards for machine controllers, and more important in the context of the digital transformation.
fragile information system integration, a reluctance to engage in
6
Executive Summary
The ultimate goal is to become a learning, agile company capa- This companion publication presents examples of companies
ble of adapting continuously and dynamically to a disruptive that are already successfully using the Maturity Index in prac-
environment. In particular, unexpected developments such as tice. As well as reviewing the status quo, it identifies current
the coronavirus crisis show companies the importance of a high trends, areas requiring urgent action and future challenges.
degree of adaptability and resilience. This publication complements the 2020 update of the acatech
Industrie 4.0 Maturity Index that revisits the methodological
Having already proven its practical value in industry, the Indus- basis by incorporating a number of editorial changes into the
trie 4.0 Maturity Index provides a structured, integrated frame- STUDY of 2017 together with updates to some of the illustrations
work for tackling these future challenges, too. (see Figure 1).
Methodology Industrie 4.0 Maturity Index Application
2017 2020 Industrie 3.0 Industrie 4.0 2020
How can an autonomous response be achieved?
“Self-optimising” Resources Information
Information
Digital capability processing systems
2.2 2.1
What will happen?
“Being prepared” Structured Integration of
Value
1.9 1.9
communication IT systems
Why is it happening? Organic 2.3
internal organisation
2.8 Willingness
to change
“Understanding”
acatech STUDY acatech COOPERATION
acatech STUDY Dynamic collaboration 2.6 2.8
What is happening? Using the Industrie 4.0 in value networks Social collaboration
Maturity Index Organisational
Industrie 4.0
Industrie 4.0 Maturity Index
“Seeing” in Industry
structure Culture
Maturity Index
Managing the Digital Transformation – current challenges, case studies and trends
Managing the Digital Transformation of Companies
of Companies
Computerization Connectivity Visibility Transparency Predictive capacity Adaptability
Update 2020
Günther Schuh, Reiner Anderl,
Roman Dumitrescu, Antonio Krüger,
Günther Schuh, Reiner Anderl, Günther Schuh, Reiner Anderl, Michael ten Hompel (Eds.)
14,58 mm
Jürgen Gausemeier, Michael ten Hompel, Roman Dumitrescu, Antonio Krüger, 16 % 80 % 4% 0% 0% 0%
Wolfgang Wahlster (Eds.) Michael ten Hompel (Eds.)
Predictive
Computerization Connectivity Visibility Transparency capacity Adaptability
INDUSTRIE 4.0
MATURITY CENTER
n=70
INDUSTRIE 4.0
MATURITY CENTER
1 2 3 4 5 6
acatech STUDY update Case study publication
• Editorial changes • Status quo of studied companies
• Updated content • Presentation of case studies
• Revised illustrations • Description of next steps/trends
Figure 1: Study update and companion publication with case studies
7
1 Introduction which aims to achieve real-time integration of entire value chains.
As well as providing a framework for individual projects, the acat-
ech Industrie 4.0 Maturity Index sets out the fundamental techni-
“What’s so new about Industrie 4.0? We have been using informa- cal requirements and the organisational and cultural changes
tion technology in our factories for decades.” Only a few years ago, required to support them. It provides a guide that companies
this was still typical of the responses given by German companies can use to determine their own status quo and start their journey
when asked about Industrie 4.0. “Industrie 4.0” was already a towards becoming a learning, agile organisation.
buzzword when acatech published the first edition of its Indus-
trie 4.0 Maturity Index in 2017. However, people weren’t really sure The Industrie 4.0 transformation is inextricably linked to the de-
what the systematic rollout of Industrie 4.0 in a manufacturing velopment of the digital business models known as smart services.
company would actually involve or what the benefits might be. However, the Industrie 4.0 Maturity Index focuses on optimis-
With its Industrie 4.0 Maturity Index, acatech created a tool that ing companies’ value chains through digital integration – it is
provides the answers to these questions. The Index was clearly aimed at improving the digital maturity stage of the company’s
focused on the step-by-step evolution of manufacturing companies internal processes. Once they have achieved digital connectivity
into “Industrie 4.0 companies”. After all, most companies want to in their core industrial processes, businesses will be in a position
continue using and upgrading their existing, established facilities, to develop digital services and collaborate with partners to build
since they are simply not in a position to build new, state-of-the- ecosystems offering joint smart services.
art factories. This is consistent with the fundamental Industrie 4.0
concept of establishing and systematically learning from integrated, Today, we are witnessing how the two trends of Industrie 4.0
automated information flows throughout a company’s value chain. and digital business models can combine to generate synergies.
Internal business processes such as development, production and
Many companies were already working on isolated prototypes service can systematically learn from data generated throughout
such as the implementation of predictive maintenance prototypes these internal processes and while the company’s products are
or proof of concept projects for autonomous forklift trucks. While being used by their customers. Many companies have already
these projects undoubtedly raised awareness of the opportuni- made significant strides towards the vision of becoming a learn-
ties presented by the technology, their piecemeal nature failed ing, agile business by combining digitally connected processes
to properly implement the systematic approach of Industrie 4.0, with digital business models.
8Experiences with using the acatech Industrie 4.0 Maturity Index in industry
2 Experiences with makes it possible to build up a detailed picture of which areas
have already attained a high digital maturity stage and where
using the acatech there is potential to do more. The results can be used to systemati-
cally identify the necessary measures and prioritise them using
Industrie 4.0 Maturity the maturity stage model. This approach helps to stop companies
from initiating too many measures all at once, as well as ensuring
Index in industry that none of the key elements are overlooked.
Since its publication in 2017, the acatech Industrie 4.0 Maturity Synchronisation of digitalisation activities through-
Index has been used by several companies to guide their digital out the company’s facilities
transformation process. The fact that the STUDY has been down-
loaded ten thousand times from the websites of acatech and By using this methodology across all of their facilities, compa-
its project partners indicates just how widely it has been used. nies can compare and synchronise their digital processes. The
Feedback from manufacturing companies themselves and from individual status quo analyses help to identify company-wide
the STUDY authors who have participated in their transforma- priorities that can be addressed synergistically through centrally
tion projects has also provided deep insights into the use of the delivered solutions. This prevents each site from coming up with
acatech Industrie 4.0 Maturity Index in industry. The following different solutions for the same problem.
scenarios describe its main applications.
Global rollout and progress monitoring of the
Digitalisation roadmap for individual manufactur- digitalisation strategy
ing facilities
Similarly to the previous scenario, companies also use the
Initially, companies use the acatech Industrie 4.0 Maturity Index methodology to roll out their centrally formulated digitalisation
to establish their digital status quo. At this point, it is essential strategy across their different sites. As with the introduction of
for a site’s core processes to be investigated separately, since this new production systems, this is accomplished through a central
Return
on business
simplify repetitive streamline make data-based grasp complex prepare for leave the control
Objectives manual tasks business and IT decisions interactions upcoming situations to the system
simulate possible let systems adapt
build a real-time
introduce IT on shop- connect and integrate run data analytics future scenarios itself where possible
Building blocks floor and elsewhere business processes
digital shadow in a
and understand effects to enable in self-configured
paper-less factory
decision support processes
Level Computerization Connectivity Visibility Transparency Predictive capacity Adaptability
1 2 3 4 5 6
Figure 2: Maturity stages of the acatech Industrie 4.0 Maturity Index (source: Industrie 4.0 Maturity Center)
9digitalisation organisation that employs internal experts to intro- digital data and documents and these are also widely accessible
duce the relevant standards and technical solutions at site level. throughout the organisation (maturity stage 2 “connectivity”).
The experts determine the site’s digital status quo and draw up However, they have yet to achieve near real time aggregation
a digitalisation roadmap in conjunction with the local colleagues. and visualisation of data from different source systems (maturity
The roadmap is subsequently used to monitor progress. stage 3 “visibility”).
Technical due diligence for factories in the context Resources Information
Information
Digital capability 2.2 2.2 processing systems
of mergers and acquisitions
The methodology can also be used in a technical due diligence
context. When carrying out due diligence for potential acquisi-
Structured 2 2 Integration of
tions, the Maturity Index can help to identify technical risks or communication IT systems
previously unrecognised potential.
2.3 2.7
Organic Willingness
internal organisation to change
The methodology’s two core principles are key to all of the above
scenarios. The first of these principles is the prioritisation of pro-
jects on the basis of maturity stages (see Figure 2) in order to
Dynamic collaboration 2.7 2.9
focus on the technological solutions that are most relevant to the in value networks Social collaboration
company’s current situation. The second involves focusing on the Organisational
structural areas of resources, information systems, organisational structure Culture
structure and culture. These core principles help to ensure the
effectiveness of the technologies that are deployed. Figure 3: Average scores for the eight principles of the acatech
Industrie 4.0 Maturity Index; n = 70 (source: Industrie 4.0 Ma-
turity Center)
2.1 What progress have companies
made so far?
In terms of the companies’ overall maturity stage, i.e. their aver-
age score for all eight principles, the majority are currently at
The Industrie 4.0 Maturity Center in Aachen has carried out over maturity stage 2 “connectivity” (see Figure 4). 80% of the studied
seventy maturity stage studies (see Figure 3) of companies that companies have already taken the first steps towards connecting
are already using the Maturity Index. On average, the companies their machines, systems and people. However, there is still a long
in question have already taken measures to enable the use of near way to go – none of the companies have yet attained maturity
real time data in the structural areas of culture and organisational stage 4 “transparency” or higher, while a small proportion of
structure (see Figure 3, maturity stage 3 “visibility”). What this companies (16%) are still starting out on their journey and have
means in practice is that the organisation regularly consults data yet to move beyond maturity stage 1 “computerisation”. To date,
and implements measures based on this data in its (day-to-day) just 4% of the studied companies have attained an average ma-
operations. For example, shop floor meetings might be held at the turity stage score of 3 (“visibility”). Since Industrie 4.0 is defined
start of each shift to discuss the indicators for the previous shift. as beginning at the “visibility” stage, only a small proportion of
companies can be said to have successfully accomplished digitali-
In the structural area of culture, the attainment of maturity stage sation (maturity stages 1 and 2) and commenced the large-scale
3 might mean that the business practises a philosophy of continu- implementation of Industrie 4.0.
ous improvement. The attainment of “visibility” in the areas of
culture and organisational structure can be attributed to the The companies can be divided into three overall categories
fact that many companies have implemented lean management based on their current progress towards implementing Indus-
programmes in recent years. trie 4.0: the “laggards” who are still at the “computerisation”
stage, the “beginners” who are at the “connectivity” stage, and
There has been somewhat less progress in the areas of resources the “leaders” whose average maturity score puts them at the
and information systems. Companies now mostly work with “visibility” stage.
10Experiences with using the acatech Industrie 4.0 Maturity Index in industry
Computerization Connectivity Visibility Transparency Predictive capacity Adaptability
16 % 80 % 4% 0% 0% 0%
n=70
Figure 4: Companies by average overall maturity stage; n=70 (source: Industrie 4.0 Maturity Center)
Figure 5 shows the distribution of the maturity stages in all four the key success criteria of the Maturity Index has not been met.
structural areas (resources, information systems, organisational In the leaders group, on the other hand, the average scores are
structure and culture) for the three categories of laggards, begin- very similar to each other. Figure 5 shows a much smaller spread
ners and leaders. The maturity stage of the structural areas was in the maturity stage scores for this group (2.6 to 3.7). The leaders
plotted for each of the companies in these groups. Most of the have recognised the importance of this success factor and tried
companies in the laggards group have a maturity stage score of to attain a similar maturity stage across all the structural areas.
between 1.1 and 2 for the structural areas (blue box). However,
it should be stressed that this group shows a wide spread of Figure 6 looks at the next level down, showing the average scores
structural area maturity scores, ranging from 1 to 3.3 (lines either for the individual structural areas. The companies have already
side of the blue box). A similarly wide distribution is evident in achieved a relatively high maturity stage in the areas of culture
the beginners group. This significant variation in the maturity and organisational structure. In the structural area of “culture”,
stage scores indicates that the four structural areas of resources, 31% of companies have reached the “visibility” stage or higher
information systems, organisational structure and culture are not (“transparency”, “predictive capacity” or “adaptability”). In the
well coordinated. This failure to achieve consistent maturity stage area of “organisational structure”, 86% of companies have at
scores across the individual structural areas means that one of least reached the “connectivity” stage. The studied companies
4
3.5
3
Maturity stage
2.5
2
1.5
1
Laggards Beginners Leaders
Figure 5: Distribution of overall maturity stages for the different company categories; n = 70 (source: Industrie 4.0 Maturity Center )
110% 20 % 40 % 60 % 80 % 100 %
Resources
Information systems
Organisational
structure
Culture
Computerization Connectivity Visibility and higher
Figure 6: Distribution of companies’ maturity stages for the four structural areas; n = 70 (source: Industrie 4.0 Maturity Center)
are thus making good progress in the structural areas of culture connected to form a single system. The fact that many compa-
and organisational structure. However, both the statistics and the nies’ structures have been built up over long periods of time is
experience of acatech Industrie 4.0 Maturity Index users indicate readily apparent in this area – over the years, these companies
that they still have a long way to go on the technological front. have failed to ensure common machine control standards when
Just under half (49%) of companies using the Maturity Index expanding or successively upgrading their plant. As a result, they
are still at the “computerisation” stage in the structural area of now have an assortment of different protocols and data models.
“resources”. This means that they have yet to achieve widespread Connecting the controllers to newer systems is a job that can
“connectivity” of their machines and equipment. The picture is only be performed manually and often requires a retrofit. Both
similar in the structural area of “information systems”. 45% of these operations call for qualified experts in control technology
companies have still not achieved extensive horizontal and verti- and OT-IT (Operation Technology and Information Technology)
cal integration of their in-house systems. integration – two areas where experts are currently in short supply.
As well as machine controllers, auto-ID technologies (Radio Fre-
2.2 The digital diseases afflicting quency Identification – RFID and QR codes) are also an important
companies source of data. These technologies enable real-time data-based
transparency throughout the entire order management process
without the need for manual data collection. Besides capturing
Many of the companies using the acatech Industrie 4.0 Matu- a variety of different indicators, auto-ID technologies also make
rity Index are affected by similar problems, the most obvious of it possible to record various operations and trigger additional
which is that they tend to focus more on implementing isolated processes. However, many companies are deterred from introduc-
Industrie 4.0 prototypes and technology studies than on coherent, ing RFID systems throughout the business by the investment costs.
company-wide transformation programmes. This means that the This is because they are not looking at the bigger picture – their
necessary structural changes in the organisation and IT architec- calculations only consider the direct labour cost savings (e.g. for
ture are being neglected in favour of comparatively minor projects. manual recording), and do not take account of the planning and
Analysis of the Maturity Index’s individual structural areas reveals management benefits of real-time data or the enhanced data
that the same “digital diseases” keep cropping up time and again. quality.
Structural area “resources” Structural area “information systems”
The structural area of “resources” addresses machine connectivity, The vision of Industrie 4.0 relies on fully integrated and au-
i.e. the extent to which different types of machinery are digitally tomated information flows throughout a company and even
12Experiences with using the acatech Industrie 4.0 Maturity Index in industry
Interview with Mark Colwell and Mark Jaxion of have to be business-case-driven, but instead of chasing high-
Danish Vestas Wind Systems A/S flown business cases, we focus on using the aforementioned
capabilities and infrastructure to enable sustainable value for
Vestas Wind Systems A/S, based in Aarhus, Denmark, is the business. So we focus among other things on eliminating
the world’s largest manufacturer of wind turbines in terms of time to market, which doesn’t necessarily end in a reduction
turnover and installed capacity (as of 2018). With more than 40 in cost, but does significantly strengthen our competitive
years in the wind industry, Vestas has installed more than 113 advantage.
gigawatts of wind power capacity. Vestas turbines have been
installed in 81 countries around the world, operating on every What do you consider the greatest obstacles to successful
kind of site, from high altitude to extreme weather conditions. digitalization and how do you overcome them?
Mark Colwell: Use cases cannot be brought into the organi-
Mark Colwell is Chief Engineer of product lifecycle manage- zation top down. It is important to talk to the people who
ment (PLM) at Vestas. will be most affected, i.e. on the shopfloor and in the field.
The people have to be willing to trust the system and the
Mark Jaxion is Senior Specialist and Director of Vestas’ IoT decisions or suggestions it makes. Therefore, they have to
and Industry 4.0 Strategy. be integrated into the search and development of use cases.
Mark Jaxion: One word: Master Data. In all our efforts to
What does digitalization mean for your business? learn from data it comes down to legacy data. For instance,
Mark Colwell: Our business is based on a global and highly augmented reality is not possible for wind turbines we
distributed infrastructure, which is designed, manufactured, erected in the 80s. We don’t have the CAD data. Taking the
erected, and maintained by us. We have had our wind tur- physical world into the digital world is much more difficult
bines and plants connected and streaming near real time than vice versa. That is the major challenge and requires a
data from around the globe for decades. We have learned lot of basic work and effort.
a lot about data and information management from this
experience and for the last few years we have been focused What would you like to share with others who are on their
on connecting our design systems and operations systems way to Industrie 4.0?
together. Utilizing data and creating information is a huge Mark Colwell: You should look at your ambition: what do you
opportunity for better decision making in the complex envi- want to achieve in the short and medium term? This should
ronment we work in. be evaluated against the foundation of what is already there:
what is possible and where do you actually need to improve?
How would you describe the business value you experience A long-term vision and roadmap taking the companies’ stra-
with Industrie 4.0? tegic goals into account is essential.
Mark Jaxion: Our management is willing to invest in capabili- Mark Jaxion: Implementing Industrie 4.0 is not just an ap-
ties and basic infrastructure such as the digital connectivity plication of technology, it means changing the skill set within
of our systems and machines. Even if they do not necessarily management, on the shopfloor and in the field. You should
return in the short term, these investments give us capabilities focus on capabilities as enablers and not just on technologies.
that will be important in the future. Of course, you always They are just a tool.
between the company and the outside world. Human intervention and processing by their employees. The Excel sheet used for
is no longer necessary at any point from the generation of the raw detailed day-to-day production order planning is a typical
data by the sensors to its aggregation, analysis and presentation. example – every day, this sheet is exported from the Enterprise
Two main problems are apparent in this area: Resource Planning (ERP) system and updated by the shift
supervisor. Feedback about the order is input manually by
1. While integration of different systems is occurring, it is very the shift supervisor or in some cases directly by the machine
fragile. Many companies have a variety of home-grown so- operators. While the operators do often have access to a sys-
lutions, databases and tables that support data collection tem with rudimentary Manufacturing Execution System (MES)
13functionality, it does not usually allow for the bi-directional
Structural area “culture”
exchange of data with the ERP system or the machine control-
lers. In other words, despite the use of IT systems, a lot of One weakness shared by many companies in the area of “culture”
data and information is still recorded and shared manually. is that there is no or not enough active employee involvement in
As well as higher personnel costs, this means that real-time the change projects that affect them. For example, new IT systems
data is not available. are almost always chosen exclusively by the IT department. This
can lead to resistance in the departments that will be using them
2. The quality of the data, particularly the master data, is not and low acceptance of the systems in question. More modern
good enough. For example, although operating and test in- approaches recognise that a system’s requirements should primar-
structions are available digitally as PDFs or worksheets, they ily be defined by its users who should therefore be involved in
are not standardised, have not been assigned to the relevant choosing and implementing it.
master data and are scattered across the company’s different
file servers. The upshot is that although there are no technical Raising awareness of the importance of data quality is another
obstacles to the factory introducing new systems such as a challenge. Employees often don’t realise the consequences of
modern MES, a huge amount of work is required to cleanse errors in the creation of master data, or the benefits of carefully
the master data. documenting faults and their solutions.
Structural area “organisational structure”
2.3 Areas requiring urgent action by
Many manufacturing companies still have hierarchical organisa-
companies
tional structures based on functional areas, with correspondingly
structured target systems for management. As a result, there is lit-
tle incentive for different parts of the business to cooperate with The use of the Industrie 4.0 Maturity Index in industry has clearly
each other. However, interdepartmental collaboration is essential highlighted the challenges for companies and the areas where
for many digital transformation projects. While certain data may urgent action is required. In the next section, these are described
be of no direct value to the department where it was generated, in detail for the four structural areas of resources, information
it could well have a valuable use in another department. For systems, organisational structure and culture, providing a basis
instance, the data from the design department’s highly detailed for the subsequent formulation of roadmaps for the different
CAD models can be of use during the rest of the product’s life stages of the digital transformation.
cycle and for maintenance purposes.
as-developed as-produced as-installed as-maintained
generic product elements inbuilt part and batch installed part and batch replaced part and batch
numbers are known numbers are known numbers are known
Figure 7: Components as information carriers throughout the product life cycle (source: Industrie 4.0 Maturity Center/TU Darmstadt)
14Experiences with using the acatech Industrie 4.0 Maturity Index in industry
Months
Days Level 4 ERP
Business planning
and logistics
Level 3
Hours MES
Manufacturing Operations
Management
Minutes Level 2 SCADA/HMI
Monitoring & supervising
Seconds Level 1 PLC
Sensing & manipulating
µs/ms Level 0 Sensors & Signals
Production process
Figure 8: Automation pyramid based on the ISA 95 model (source: Åkerman 2018, p. 2)
Benefits
Secure Data Transfer Analytics
Data Aggregation !
Store/Forward (no lost data) HMI, SCADA, Historian, MES
High Data Fidelity (on premise or remote)
Edge Analytics
MQTT, REST,
OPC-UA CoAP, AMQP
Edge Boundary etc.
Encrypted Data
Edge Server Embedded in Hardware for IoT Gateway IoT Gateway IoT Gateway
local data collection and analytics Device Device Device
PLC, RTU etc. PLC, RTU etc. PLC, RTU etc.
Sensor, Motor, Drive …
Figure 9: Combination of edge and cloud computing for data aggregation and analytics (source: PTC)
152.3.1 Individual parts of products as information to responsiveness, autonomy and data protection, including the
carriers protection of production know-how.4 Edge computing involves the
use of smaller, decentralised, on-premises data centres capable
End-to-end data delivery throughout the entire value chain must of the local aggregation and analysis of data from very differ-
include the manufactured product. This means that the product’s ent sources (see Figure 9). While complex applications can still
individual parts – made up of modules and individual compo- mostly be carried out in the cloud, pre-processing takes place in
nents – are just as important. Accordingly, we recommend the the edge environment. Various combinations of cloud and edge
adoption of the “components as information carriers” approach, computing are becoming more and more common in industry,
in which information about components is collected and com- with businesses and cloud providers increasingly pooling their
bined throughout the entire product life cycle, from development know-how and forming partnerships in order to implement these
and production right through to the usage phase. architectures. One example launched last year is the partnership
between Volkswagen, Amazon and Siemens, which aims to de-
Applications include the creation of bills of materials (see liver significant material flow and logistics optimisation by con-
Figure 7). Throughout the entire product life of a machine or necting equipment, factories and – in the future – also suppliers
piece of equipment, a product data model can be used to store in the Volkswagen Industrial Cloud. The GAIA-X concept is also
information about the properties of its parts. The product data based on central technical requirements for the architecture of a
model, which describes the design properties and values, can federated, open data infrastructure, for example on decentralised
be augmented with the actual physical properties and values of or distributed data processing via multi-edge, multi-cloud or edge-
each manufactured component. This approach makes it possible to-cloud processing. This data infrastructure aims to guarantee
to combine information from different production data sources the trustworthiness and sovereignty of users and their data.5
in order to generate part-specific information that can be used
to add value across every life cycle stage. The components be- 2.3.3 Practical example of agile work organisation:
come information carriers, since they collect information about 72-hour prototyping
themselves throughout their entire life cycle.1 The company uses
solutions such as Product Lifecycle Management (PLM) systems Until recently, most agile organisations were found in the IT,
in order to manage this key challenge of collecting the relevant services and marketing industries. However, a family business
data, processing the resulting information and combining it to that uses modern management techniques has shown how it is
add value. possible to create an agile organisation in manufacturing indus-
try, too. This agile organisation was introduced in parallel to the
2.3.2 Replacing automation pyramids with edge functional, hierarchical manufacturing organisation. It all began
and cloud computing with a hackathon, where staff at BEULCO GmbH & Co. KG6,7 (here-
after Beulco) were given 72 hours to develop a smart prototype
Many companies use the automation pyramid as a model for their for a product outside of their usual product spectrum. But this
IT architectures (see Figure 8). However, the progressive introduc- was just the start of the company’s transformation into an agile
tion of digitalisation technologies in the shape of cloud and edge organisation. To drive the transformation process, twenty “CoPs”
computing is now breaking down hierarchical IT system architec- (Communities of Practice) were formed from the company’s 180
tures.2 The networked physical platforms and software-defined employees. These were the in-house points of contact for transfor-
platforms described in the Smart Service Welt3 layer model are mation projects and were also responsible for their orchestration
particularly key in this regard. The large software companies are (moderation, coordination and documentation). In addition to
migrating from the public cloud to an edge computing approach two CoPs per project, there are also “theme owners” who take
to production optimisation and management. Capacity is being on responsibility for the development of a project or project idea.
moved from data centres (cloud computing) to local networks Employees join whichever project they are interested in, develop-
(edge computing) in order to better address requirements relating ing and implementing it together with the theme owner. Rather
1 | See Anderl 2015, pp. 753–765.
2 | See Verein Deutscher Ingenieure e. V. 2013.
3 | See Arbeitskreis Smart Service Welt/acatech 2015.
4 | See Willner 2019.
5 | See BMWi 2019a.
6 | BEULCO produces water supply systems and solutions, especially for the domestic connection and mobile water supply markets.
7 | BEULCO was the winner of the 2019 Digital Champions Award in the “SME Transformation” category.
16Experiences with using the acatech Industrie 4.0 Maturity Index in industry
than the theme owner being solely responsible for the project, Importantly, shop floor workers who take on this role must also
responsibility is shared between the whole project team. be empowered to shape the digital transformation on an equal
footing with other members of the company.
Employees are given time off and a room to work on their projects.
There is also a pinboard where any employee can post new ideas This bottom-up approach where the transformation process is
for transformation projects. If the idea is deemed to be worth handed over to the workforce calls for a mindshift on behalf of
pursuing following discussion with one of the CoPs, the employee management, since it involves relinquishing control of project
becomes the project’s theme owner. planning and management. In other words, the hierarchical
structures typically found in manufacturing companies must be
When it comes to initiating projects, the company’s management broken down. Thus, as well as providing management with initial
adheres to the agile methodology. This means that they cannot and ongoing in-house training in modern leadership methods, it
start a project themselves – a bottom-up approach is employed should be recognised that the transformation process is also a
where projects must be initiated by the workforce. matter of their individual personal development.
2.3.4 Empowering staff to shape the transforma- Just as management must learn to hand over responsibility, the
tion workforce must grow accustomed to having more responsibil-
ity. Agile organisations rely on engaged employees who take on
When building an agile organisation, the key to a successful more individual responsibility across every level of the company
digital transformation is not just to involve staff but to empower hierarchy. Staff take the lead on everything from proposing ideas
them to shape the transformation processes (see 2.3.3). For exam- to initiating and carrying out projects. Importantly, no-one is
ple, it is important to identify and train a diverse mix of change compelled to participate or take on more responsibility – doing
catalysts across all levels of the company hierarchy (e.g. CoPs, so is always entirely voluntary. This ensures that the employees
see 2.3.3). These are the people who will orchestrate the digital who come on board are motivated to make change happen and
transformation and ensure that genuine change is achieved. passionate about the subject in question.
173 Case studies Corporate level Plant level
1
3.1 Using the acatech Industrie 4.0 Strategic goals of
the transformation
Maturity Index in companies
2
Digital status quo &
Published in 2017, the first edition of the acatech STUDY Indus- individual roadmap
trie 4.0 Maturity Index aimed to develop a standard tool for Handlungsstränge Konnektivität Sichtbarkeit Transparenz Prognosefähigkeit
3
Rolle der
1 Werks IT
18 ...
1 12 SAP in
2 19 ...
guiding manufacturing companies through their digital trans-
Vernetzung 2 20 32 33 34 Massenbestell.
3 EDI-Anbindung 20 ...
Administrationsprozesse 5 6 21
Dig. Verknüpf.
4 21 ...
3 4 19 25 Warenannahme
13 26 5 ... 22 ...
28 6 ... 23 ...
14
7 ... 24 ...
Integriertes 30
7 8 9 10
Produktionssystem 15 16 17 18 29 8 ... 25 ...
27 31 ...
9 26 ...
10 ... 27 ...
22 11 ... 28 ...
Innovationsproduktivität 11 23 12 ... 29 ...
formation. The goal was to provide companies with immediate Consolidation & global
13 ... 30 ...
24
14 ... 31 ...
Maßnahmen und Abhängigkeiten 15 ... 32 ...
16 ... 33 ...
# Organisation # Ressourcen # IT-Systeme # Kultur
17 ... 34 ...
Abhängigkeit
standard tools
support in developing a strategy that could then be directly ex-
ecuted. Rather than simply launching a few initial pilot projects,
Handlungsstränge Konnektivität Sichtbarkeit Transparenz Prognosefähigkeit
Rolle der
1 Werks IT
18 ...
1 12 SAP in
Handlungsstränge Konnektivität Sichtbarkeit Transparenz Prognosefähigkeit 2 19 ...
Vernetzung 2 20
32 33 34
Rolle der
Massenbestell.
1 3 EDI-Anbindung
18 ... 20 ...
Administrationsprozesse 1 512 6 21 Werks IT
4
SAP in Dig. Verknüpf.
Handlungsstränge Konnektivität Sichtbarkeit Transparenz Prognosefähigkeit 2 4 19 ... 21 ...
Vernetzung 3 4 19 25 32 33 34 Warenannahme
Massenbestell.
2 20 Rolle der
Administrationsprozesse 13 26 518 ...... 20 ...
1 Werks IT3 EDI-Anbindung 22 ...
1 512 6 21
SAP in Dig. Verknüpf.
Handlungsstränge Konnektivität Sichtbarkeit Transparenz Prognosefähigkeit 619 ......
4 Warenannahme
2 Massenbestell. 21 ... 23 ...
Vernetzung 3 4 19 25 32 2833 34
2 20 14 Rolle der
Integriertes
Administrationsprozesse 13 26 30 1 Werks IT3 EDI-Anbindung518 ...... 720 ...... 22 ... 24 ...
1 7 512 8 6 9 10 21
SAP in Dig. Verknüpf.
Handlungsstränge Produktionssystem
Konnektivität Sichtbarkeit
3 4 Transparenz 19
15 16 17 18 28
Prognosefähigkeit
25
29 619 ...... 821 ......
4 Warenannahme
2 Massenbestell. 23 ... 25 ...
Vernetzung 2 20 14 32 33 34
Rolle der 31
Integriertes
Administrationsprozesse 13
27
26 518 ...... 720 ...... 922 ......
30 1 Werks IT3 EDI-Anbindung 24 ... 26 ...
1 7 512 8 6 9 10 21
SAP in Dig. Verknüpf.
Produktionssystem 3 4 19
15 16 17 18 28
25
29 2 Massenbestell. 619 ...... 821 ...... 10
4 Warenannahme 23 ...... 25 ... 27 ...
Vernetzung 2 20 14 32 33 34
31
27 3 EDI-Anbindung
5 ... 720 ...... 922 ...... 11 24 ...... 26 ...
this would allow them to implement a structured and efficient
Integriertes
Administrationsprozesse 13 26 22 30 28 ...
7 5 8 6 9 10 21
Dig. Verknüpf.
Produktionssystem 3 4
15 16 17 18 28 29 6 ...
4 Warenannahme 821 ...... 1023 ...... 1225 ...... 27 ... 29 ...
Innovationsproduktivität 14
19 11 25 23
31
Integriertes 13
27
26 22 30 5 ... 7 ... 922 ...... 1124 ...... 1326 ...... 28 ... 30 ...
7 8 9 10 24
Produktionssystem 15 16 17 18 28 29 6 ... 8 ... 1023 ...... 1225 ...... 1427 ...... 29 ... 31 ...
Innovationsproduktivität 14
11 23
31
27 7 ... 9 ... 24 ...... 1326 ...... 1528 ...... 30 ... 32 ...
Integriertes Maßnahmen und 22 30 11
7 8 9 Abhängigkeiten
10 24
Produktionssystem 15 16 17 18 29 8 ... 10 ... 1225 ...... 1427 ...... 1629 ...... 31 ... 33 ...
Innovationsproduktivität
# Organisation 11
# Ressourcen # IT-Systeme 23 # Kultur 31
27 9 ... 26 ...... 1528 ...... 1730 ...... 32 ... 34 ...
Maßnahmen und Abhängigkeiten 22 11 ... 13
Abhängigkeit 24
10 ... 12 ... 27 ......
14 29 ......
16 31 ... 33 ...
Innovationsproduktivität
# Organisation 11
# Ressourcen # IT-Systeme 23 # Kultur
Maßnahmen und Abhängigkeiten 22 11 ... 13 ... 28 ......
15 30 ......
17 32 ... 34 ...
Abhängigkeit 24
12 ... 14 ... 29 ......
16 31 ... 33 ...
Innovationsproduktivität
# Organisation 11
# Ressourcen # IT-Systeme 23 # Kultur
Maßnahmen und Abhängigkeiten 13 ... 15 ... 30 ......
17 32 ... 34 ...
Abhängigkeit
Deployment and controlling
24
14 ... 16 ... 31 ... 33 ...
# Organisation # Ressourcen # IT-Systeme # Kultur
15 ... 17 ... 32 ... 34 ...
Maßnahmen und Abhängigkeiten
transformation process. Three years on from its publication, the
Abhängigkeit
16 ... 33 ...
# Organisation # Ressourcen # IT-Systeme # Kultur
17 ... 34 ...
Abhängigkeit
STUDY must now be judged against this goal.
Over the past three years, the Index has been used by several
companies across a range of different industries – in other words,
its use has not been confined to discrete manufacturing. For Figure 10: Digital transformation strategy at ZF (source: Indus-
instance, the Index supported the digital transformation of a Ger- trie 4.0 Maturity Center and ZF Friedrichshafen AG)
man chocolate manufacturer, where it helped to identify potential
productivity gains in the production of high-quality chocolate
products. While in this case, the Industrie 4.0 Maturity Index was 3.2 ZF Friedrichshafen AG:
only employed within the company itself, synergies are created Industrie 4.0 rollout across 230
when it is applied across an entire value chain.
sites
In the following sections, we present case studies of four dif-
ferent companies. As a company with over 230 sites worldwide, The implementation of Industrie 4.0, digitalisation of the manu-
the main challenge for ZF Friedrichshafen AG was to ensure facturing value chain and the use of artificial intelligence can
that the Index was used in a standardised manner (see 3.2). create opportunities for cost and efficiency optimisation, en-
The HARTING Technology Group was one of the first compa- hanced profitability and the development and implementation
nies to use the Industrie 4.0 Maturity Index in practice, as of innovative new business models. At the same time, however, it
already described in the 2017 acatech STUDY. Based on the also entails radical and dynamic changes to the business environ-
findings of the status quo analysis carried out at the time and ment, structural changes in value networks and high investment
on the subsequently formulated roadmap, HARTING created costs. These are the challenges facing ZF Friedrichshafen AG (ZF),
an interdisciplinary Industrie 4.0 task force that was charged a leading global technology company based in Friedrichshafen,
with implementing the transformation and has successfully Germany. ZF has several divisions, each with various business
carried out a number of initial projects (see 3.3). The produc- units that develop different products and pursue different stra-
tion chain of Kuraray Europe GmbH (see 3.4) comprises three tegic objectives. Since the company has over 230 sites, precise
separate facilities. This case study provides an impressive il- planning of the digital transformation in the different parts of the
lustration of the synergies that can be achieved by analysing business is essential. In a global company like ZF, even though
the production chain and formulating a joint roadmap. Finally, the sites that make up the business units can have a similar
the case study of a chocolate factory (see 3.5) focuses on how structure, they have often evolved differently over time, which can
the Index can be used to increase efficiency and output in the make it difficult to compare them at a global level. A four-stage
food industry. digital strategy was employed in order to ensure that both the
overall group-level strategy and the individual site-level issues
were addressed.
18Case studies
The first step was to define the strategic goals at the group level. the reasons for choosing the acatech Index was the objectiv-
The status quo of each individual site was then determined and ity provided by the questionnaire and standardised procedure.
the results used to produce a site roadmap. Finally, all the results The Index also incorporates organisational and cultural aspects,
from the individual sites were consolidated into a global roadmap. enabling seamless integration with lean management activities.
Implementation was supported by centrally provided technolo- Moreover, ZF wanted a tool that its own employees would be
gies (see Figure 10). able to use objectively, so that all sites could be included in the
digital transformation after an initial pilot phase.
ZF established an Industrie 4.0 system house at group level in
order to develop and supply group-wide standards and solutions During the initial pilot phase, ZF used the acatech Industrie 4.0
e.g. for strategic production management systems, device connec- Maturity Index to analyse processes such as production, logistics,
tivity and IT security in production. In order to realise the digital quality and planning at 14 facilities in 10 countries spread across
transformation, it was necessary to strike a balance between a Europe, Asia and North America. Supported by staff from the
central strategy for leveraging synergies and individual changes Industrie 4.0 Maturity Center, each facility carried out a maturity
at the business unit and site levels. The Industrie 4.0 system stage study to establish its status quo, focusing on the site’s core
house is the central organisation tasked with performing this processes (see Figure 11). The results of the status quo analyses
function. It facilitates best practice sharing, is responsible for the revealed that although the details vary from one site to another,
deployment of task forces and creates a common framework that they have a number of key issues in common. For instance, almost
provides a standard structure for transformation activities both all the sites were stronger in the areas of culture and dynamic
within and across individual sites. cooperation throughout the value network than in the structural
areas of information systems and resources. There were few differ-
While ZF had already started to develop the Systemhouse Indus- ences between the individual sites’ internal processes (planning
trie 4.0 at group level, the company lacked a standardised tool & management, manufacturing & assembly, logistics & warehous-
for objectively measuring the status quo in the individual plants ing, engineering & maintenance, quality). This indicates that the
and feeding the results into a digitalisation strategy. The existing implementation of Industrie 4.0 has been relatively consistent
ZF Production System provided a model for the introduction of across the different parts of the business, with the result that the
such a tool across the entire organisation. In the ZF Production different sites have achieved a similar degree of progress for the
System, employees are locally responsible for lean management, individual processes.
but organisational structure and standardisation are managed
centrally. The acatech Industrie 4.0 Maturity Index was chosen The next step was to draw up individual roadmaps for each site
as the standardised digital transformation tool and has now based on the results of the local status quo analysis. As illus-
been incorporated into the Industrie 4.0 system house. Among trated in Figure 12, these roadmaps systematically set out the
measures required to achieve a higher maturity stage in the dif-
Resources Information
Information ferent areas of the business. As well as ensuring a uniform rate of
Digital capability processing systems
2.2 2.1 progress across the different principles, this systematic approach
minimises the risks associated with the implementation of the
digital transformation as a whole. A group-wide roadmap was
drawn up incorporating the areas of planning & management,
Structured Integration of
1.9 1.9
communication IT systems manufacturing & assembly, logistics & warehousing, engineering
& maintenance, quality and global levels. Up to five measures
Organic 2.3 2.8 Willingness
internal organisation to change
were identified for each area, and detailed profiles were produced
for all of the measures proposed in the roadmap, including de-
tailed descriptions of the costs and benefits, profit contribution,
Dynamic collaboration 2.6
structural area and maturity stage in the Industrie 4.0 Maturity
2.8
in value networks Social collaboration Index, and the affected processes.
Organisational
structure Culture In order to create synergies throughout the company, the changes
Figure 11: Example of status quo scores for a ZF site (source: ZF must go beyond the level of the individual site. Consequently,
Friedrichshafen AG and Industrie 4.0 Maturity Center) when identical or similar measures are identified, the site
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