technology strategy & goals it integration...28%use partial images and 8% use full digital...

Strategy & Goals

IT Integration

Technology

STUdy

INdUSTRy 4.0 BAROMeTeRIterative Assessment of Industry 4.0 Activities in German Industry.

The Study Iterative Assessment of Industry 4.0 Activi-ties in German Industry and its summary – have been published by:

MHP Management- und IT-Beratung GmbH in cooperation with the Ludwig Maximilian University, Munich.

All rights reserved. Reproduction, microfi lming, storage and processing on electronic media are only permitted with the consent of the publishers. The content of this publi-cation is intended to provide information to our custom-ers and business partners. It refl ects the authors’ state of knowledge at the time of publication. To resolve the rel-evant issues, please use the sources specifi ed in the pub-lication or contact the persons detailed above. Any views expressed here merely refl ect those of the relevant authors. Charts may contain rounding differences.

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Author

Esther Nagel

Ludwig Maximilian University, Munich –

Research Assistant and Doctoral Candidate

Publisher

Tilo Klüh

MHP – Associated PartnerHead of Operations

Performance & Strategy (OPS)

Project Manager

Robert Göbel

MHP – Senior Management ConsultantOperations Performance

& Strategy

Author

Caspar Koltze


& Strategy

Author

Philipp Noll

MHP – Management Consultant

Operations Performance & Strategy

Sponsor

Daniel Halbig

MHP – Manager IoT & Industrie 4.0 LeadOperations Performance

& Strategy

Author

Prof. Johann Kranz, PhD


Chair for Internet Business and Internet Services

Author

Matthias Grawe



Author

Thomas Stošic



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060603

00Table of Contents and Lists of Illustrations and Abbreviations

Table of Contents and Lists of Illustrations and Abbreviations 4

Summary 6

Industry 4.0 within the Value Network 121.1. The Origins of Industry 4.0 131.2. Opportunities and Potential Benefits of

Industry 4.0 131.3. Challenges and Risks of Industry 4.0 141.4. Potential Solutions and Best Practices 14

Procedure and Methodology 162.1. Motivation 162.2. Target Group of Respondents 182.3. Survey Design 182.4. Methods of Evaluation 182.5. Description of Respondents 19

Results of the Study 223.1. Technology 223.2. IT Integration 283.3. Strategy and Goals 32

Recommended actions 424.1. Technology 424.2. IT Integration 434.3. Strategy and Goals 43

Conclusion 46

Glossary and Bibliography 486.1 Glossary 486.2 Bibliography 50

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Illustrations

Fig. 1: Industry 4.0 Services 15Fig. 2: MHP Industry 4.0 Framework 17Fig. 3: Clustering of Potential Responses on

Likert Scales 18Fig. 4: Distribution of Business Sizes and Industries 20Fig. 5: Distribution of Functional Areas and Hierarchy

Levels 21Fig. 6: Distribution of Responses: Supply Chain

Transparency 23Fig. 7: Distribution of Responses: Traceability to

Manufacturer 23Fig. 8: Distribution of Responses: Digital Twin 24Fig. 9: Use of sensors by industries 24Fig. 10: Distribution of Responses:

Digital Working Models 25Fig. 11: Data Access from Mobile Terminals to Central

Systems by Industries 25Fig. 12: Automation & autonomous systems 26Fig. 13: Use of an Enterprise Service Bus by Industries 26Fig. 14: Distribution of Responses:

Digital Production Technologies 27Fig. 15: Distribution of Responses: Data Analytics 28Fig. 16: Recording of Operational and Machine Data 28Fig. 17: Distribution of Responses: IT Standards 29Fig. 18: Distribution of Responses: IT Architecture 29

Fig. 19: Distribution of Responses: Platform & Connectivity 30

Fig. 20: Integration of New Applications and Functions 31Fig. 21: Distribution of Responses: Big Data 31Fig. 22: Distribution of Responses: IT Security 32Fig. 23: Distribution of Responses: Scalability 33Fig. 24: Strategic focus on Industry 4.0 33Fig. 25: Distribution of Responses:

Entrepreneurial Orientation 34Fig. 26: Distribution of Responses:

Industry 4.0 Project Governance 34Fig. 27: Distribution of Responses:

Cross-Departmental Collaboration 35Fig. 28: Distribution of Responses:

Business Expertise of the IT Department 36Fig. 29: Distribution of Responses:

IT Expertise of Business Departments 36Fig. 30: Distribution of Responses: Market Dynamics 37Fig. 31: Rating of Market Dynamics 37Fig. 32: Distribution of Responses: Market Intelligence 38Fig. 33: Distribution of Responses:

Technology Intelligence 38Fig. 34: Distribution of Responses: IT Agility 39Fig. 35: Response Speed of IT Department to

the Needs of Business Departments 39Fig. 36: Distribution of Responses: IT Governance 40

Abbreviations

BMWi Bundesministerium für Wirtschaft und Energie (German Ministry for Economic Affairs and Energy)

BMBF Bundesministerium für Bildung und Forschung (German Ministry of Educa-tion and Research)

Fraunhofer IAO Fraunhofer-Institut für Arbeitswirtschaft und Organisation (Fraunhofer Institute of Labour Economics and Organisation)

OEM Original Equipment ManufacturerSOA Service-Oriented ArchitectureRFID Radio Frequency IdentificationNFC Near Field CommunicationAPI Application Programming InterfaceHRC Human-Robot CollaborationESB Enterprise Service Bus

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Details of the study

N = 222

Business size

N = 222

BarometerSummary

Industry 4.0 Barometer

automotive OEM

automotive suppliers

automotive and mechanical engineering

electrical industry

manufacturing and metal industry

traffi c and transport

other industries

Industry membership

24%

29%

13%

8%

8%

2%16%

small businesses (workforce under 1,000)

medium-sized business-es (workforce between 1,000 and 9,999)

28%

32%40%large businesses (workforce above 10,000)

35% of respondents are upper or senior management.

58% of respondents work in business departments.

50% of respondents work for businesses that have existed for over 50 years.

44% of respondents have had more than 10 years of professional experience.

Key Findings

59% do not use autonomously organised machines or robots.

71% of respondents say that where their company uses fully or partly automated decisions resulting from artifi cial intelligence or machine learning, such use is poor.

50% of all respondents are already using sensors in their systems for the transmission of environmental parameters and status data.

28% use partial images and 8% use full digital images of their products, containing process and status data.

The main objectives are to reduce costs and at the same time increase the quality and speed of the processes.

High levels of dependency between different IT systems are continuing to impede a modular approach to the system architecture.

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IT Integration

Strategy & Goals

11% 43% 45%

55%37%8%

7% 51% 42%

62%34%4%

Barometer value Disagree Neutral Agree

Cost reduction and increase in process quality and efficiency

Development of new market and customer segments

Provision of new services for one’s own products

Development of new business models

Supply chain transparency

Digital twin

Digital working models

Digital production technologies

Big data & data analytics

Automation & autonomous systems

Not in use

Use is planned

Practical testing

Partial use Full use

GermanyIndustry 4.0 Barometer

To be successful, it is essential to have clearly defined goals under Industry 4.0 as well as a strategy to achieve those goals. At the moment, businesses are focusing more on pursuing evolutionary than revolutionary change – a phenomenon which is related to a low view of market dynamics. This does, however, pose the danger of underestimating the disruptive impact of Industry 4.0.

Even where Industry 4.0 technologies are more mature, they are still used rather hesitantly in practice at the moment. Most of German industry is currently at an experimental stage. In particular, technologies are often implemented which aim to expand human capabili-ties and achieve higher levels of efficiency, quality and flexibility. In the Technology categories, which are based on a large number of high-quality data, the level of distribution is lower. This shows itself, in particular, in the creation of complex data analyses. The level of implementation is lowest in Automation & Autonomous Systems. This is mainly because this area has by far the highest level of innova-tion compared with the other technologies that were researched.

Strategic focus on Industry 4.0

Aggregated presentation of technology dissemination in German industry

0 10 20 30 40 50 60 70 80 90 100

Technology

Average level of agreement that business departments have good IT expertise.

Average level of agreement that IT departments have good business expertise.

DisagreeDisagree AgreeAgree

Business department

IT department

54%45%

39% 66%

Totally disagree

*Very poor

Neutral

*Indifferent

Totally agree

*Excellent

Germany

Aggregated overview of the rated performance of IT departments and systems

IT and business departments must work together more closely if they want to expand their expertise in each other’s disciplines. IT departments consistently rate their business expertise as higher than acknowledged by the rel-evant business department, and vice versa. This is an obstacle to improvements in the individual areas and also to innovation in general. However, the discrepancy is hardly surprising, considering that cross-departmental exchange tends to be rated more negatively than positively.

Many respondents note that Industry 4.0 is leading to rising complexities in their IT architecture and to inconsistencies regarding clear roadmaps, software platforms and IT strategies. Businesses see themselves as relatively well placed in the use of standards and IT security. Room for improvement can be seen in the scalability of the IT architecture and in data analytics. The negative assessment of data analytics is primarily due to lack of expertise among the workforce, insufficient data availability and a poor technical infrastructure. The lack of scalability shows, for instance, that cloud infrastructures are still not used very widely.

Use of standards

IT Security

Digital architecture

Platform & connectivity

Scalability

Data Analytics*

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StrategyRecommendations derived

TechnologyRecommendations derived

IT IntegrationRecommendations derived

Foster closer collaboration between IT and business departments Strongly encourage cross-departmental exchange of knowledge and

interdisciplinary teams Expand exchange with external carriers of expertise Centrally collect details of all Industry 4.0 initiatives and projects and, on that

basis, develop an agile general goal for Industry 4.0 Develop implementation management with clearly defi ned cycles

Defi ne more fl exible and open standards and aim instead to obtain 70% solutions that will boost speed Specifi cally analyse the performance and fl exibility of IT solutions and technologies through feasibility studies,

driven by business requirements Defi ne IT architecture roadmap both for the integration of legacy systems and for the fl exible integration of

the latest technologies Conduct feasibility studies with business departments on artifi cial intelligence

Intensify collaboration with strategic partners along the supply chain Focus on added value of individual technologies and less on using showcases to satisfy innovation policy claims Foster the gaining of experience through PoCs and pilot projects Conduct application-specifi c assessments of data material that is already available and of data material which still

needs to be acquired, and develop digital twins of products

Strategy – conclusionThe effective integration of Industry 4.0 throughout an entire business is always based on a comprehensive strategy. It must include the general goal, roadmap and implementation management.

Technology – conclusion

It is essential to go beyond the threshold of practical testing and feasibility studies and to move increasingly towards the company-wide operationalisation of Industry 4.0 solutions. On average, this has so far only happened for around 36% of respondents in the various Technology categories.

IT Integration – conclusion

The flexibility and innovative strength of a business should be secured through a pragmatic procedure that involves defining flexible architectural guidelines and their verification through piloting on specific business projects.

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01Industry 4.0 within the Value Network

Industry 4.0 has gained substantially in significance over the last few years and has become a strategic focus through-out the industry. Businesses have understood that digital transformation to achieve Industry 4.0 targets must be based on the intelligent networking of the real and virtual worlds. The idea is to increase the efficiency of business processes and organisation, improve the quality of prod-ucts and services and facilitate the creation of innovative business models and products. This makes it possible to develop additional customer and market segments.

Set against this background, businesses across all indus-tries are aiming to ensure greater competitiveness for their existing products and services and also for the relevant val-ue chains. To turn this into profit, businesses are prepared to make high investments in Industry 4.0 technologies over the next few years [GSM+15].

At the same time, businesses tend to be put off by the considerable financial outlay and complexities of imple-menting these ideal technological scenarios. Moreover, it is often the case that the required investment cannot be quantified, the solutions would only be profitable in the long term or there is too little expertise within the com-pany for the successful realisation of Industry 4.0 solu-tions. There are not enough examples where businesses have already successfully implemented complete Industry 4.0 principles. Most businesses are therefore still unsure as to what their general goal for Industry 4.0 should be, and how they might achieve it.

Based on this starting point, we can ask the following question: What does Industry 4.0 mean for businesses, and what would be a possible route towards a defined general goal?

To define this route, it would be necessary for a business to

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analyse its own starting point, and also the parameters that would need to be influenced. This raises the question of an evaluation method to determine the current state and to evaluate the results. By looking at all the necessary param-eters – e.g. product, process, technology, IT and organisa-tion – it becomes possible to conduct ongoing monitoring of progress towards the general goal.

The purpose of this study is to answer the questions asked above. The results should make it possible, on the one hand, to describe specific stages of digital transformation – a transformation that is often described in rather superfi-cial terms – and, on the other hand, to take account of the multidimensional character of Industry 4.0.

1.1. The Origins of Industry 4.0Industrial processes have been in a continuous state of flux since they started in the 18th century. The speed of transformation has been accelerating all the time, starting with the introduction of mechanical production plants, which was then followed by electrified mass production and eventually led to the use of electronics and IT in pro-cess automation. The rapid development of technology along all sections of the value chain has been so disrup-tive that existing business models and procedures have often been superseded, and the industrial environment has been revolutionised [KLW11].

In April 2011, the fourth industrial revolution was defined as a major strategic direction for German industry, enabling it to secure its position as a manufacturing coun-try. To ensure the ongoing development of Industry 4.0, the German Ministries for Economic Affairs and Energy

(BMWi) and for Education and Research (BMBF) have set up an Industry 4.0 Platform together with businesses, industry associations, trade unions and academic institu-tions [HH14].

As a critical force to sustain and expand the competitive edge of German industry, Industry 4.0 means far more than the digitisation of production. It means the intelli-gent networking of the entire value chain, thus develop-ing it into a value network and creating complete trans-parency for everyone who is involved in this network. Another fundamental element is the recording of life-cycle data of products and services, making it possible to change processes in the value chain in accordance with Industry 4.0. This should lead to the complete transpar-ency of processes that add value, while also permitting more flexible control of the business and enabling the best possible planning of activities.

But whereas the digitisation of value-generating manufac-turing processes opens up multiple opportunities, it also produces risks and great challenges for established com-panies and business models. New solutions are required to master such challenges as effectively and profitably as possible and to open up the available potential.

1.2. Opportunities and Potential Benefits of Industry 4.0Industry 4.0 is raising enormous expectations: The close integration of the physical and virtual worlds along the val-ue chain can potentially reach a hitherto unparalleled level of efficiency. Businesses are realigning their product and

service portfolios and often changing from being product-centred to service-focused companies.

Thanks to intelligent real-time networking, value chains are turning into value networks. Such a network receives infor-mation from all the participants in the relevant product life-cycle. This leads to the end-to-end mapping of processes, including the customer’s view, the provision of the product or service and indeed all the processes that form part of it. Existing manual processes and any necessary manual communication are thus streamlined or even replaced by new technologies. This makes it possible to run digital and autonomous processes.

One general goal for manufacturing companies under Industry 4.0 is that of a Smart Factory. This is an intelligent-ly networked factory in which intelligent products exercise control over themselves at each stage of the production process, based on innovative digitisation technologies and acting within a decentralised, autonomous production sys-tem. A Smart Factory has continuous access to information from the developing departments and is therefore in the best position to manufacture the product independently in the best possible way. Smart raw materials and products have digital memories where they can save their properties, production progress and purpose. This information makes it possible for them to interact in real time, both among themselves and also with other smart units, e.g. machines. Production plants and systems are also able to commu-nicate with each other and to carry out production tasks autonomously [Sch13]. Furthermore, intelligent network-ing helps to overcome major challenges, such as predictive maintenance, a higher level of flexibility and the reduction of production errors. Relevant information on parts and processes is saved and made available in a central location. As a result, the integration of all product-related details produces a digital twin of the product, the production sys-

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tem, the logistics and the supplier’s ecosystem. All the infor-mation on the developed, manufactured and dispatched products can be transmitted and, if necessary, analysed.

Any successful realisation of Industry 4.0 always requires high investments. Forecasts have shown that the investment share of Industry 4.0 in Germany will exceed 50% of all planned industrial equipment investments over the next five years [GSM+15]. These figures suggest that businesses have understood the potential of Industry 4.0 and that they are willing to invest in the vision described here.

The following potential has been specified for Germany as a major manufacturing country:

15 to 25% rise in productivity Up to EUR 30 billion annual increase in sales Investment boosts of up to EUR 250 billion over the next

10 years [Obe16]

According to forecasts in a study by the industry association BITKOM and the Fraunhofer Institute of Labour Economics and Organisation (Fraunhofer IAO), there will be a continu-ous increase in gross value until 2025, particularly in the automotive industry with its extreme dependence on sup-pliers [BSM+14]. The share of the automotive sector in the gross value of the manufacturing industry has been rising steadily since 2005. In 2025 it reached 19.6%, making it the most important manufacturing sector [SB 17].

1.3. Challenges and Risks of Industry 4.0Specific, comprehensive and structured strategies to devel-op the potential of Industry 4.0 are still rather rare. The

daily project work at MHP has shown that many businesses are still planning both present and future activities linked to Industry 4.0 in an unstructured manner. An integrated Industry 4.0 strategy does not exist, because the relevant mission statement is ignored, so that there are no synergies between the various Industry 4.0 initiatives. There are sev-eral reasons for this: the potential benefits of Industry 4.0 solutions are hard to quantify, and the individual divisions each conduct their own optimisation in isolation. Further-more there is a lack of company-wide information about existing Industry 4.0 initiatives, and any potential solutions along the entire value chain are extremely complex.

Businesses lack orientation and are failing to look at the relevant issues in a structured manner and in a way that is cross-functional and cross-divisional. Also, many businesses are finding it difficult to expand any specific islands of inno-vation to cover the whole company. Although technical feasibility studies do exist, they are not implemented. This is because there is no validated digitisation potential, or because an introduction is prevented by a lack of accep-tance within the organisation, or because any company-wide introduction is hindered by the heterogeneous nature of the IT landscape.

Of the problems we have listed, we can derive the follow-ing challenges for any successful realisation of Industry 4.0:

Evaluation and analysis of the current level of digitisation of plants and products

Definition of a comprehensive general goal for Industry 4.0

Determination of Industry 4.0 potential for different departments within the business

Organisational and cultural changes IT requirements in the implementation of Industry 4.0

solutions

Heterogeneous system environments and the diversity of legacy systems in businesses that have grown historically

1.4. Potential Solutions and Best PracticesUnlike former process-oriented optimisations, process adjustments are now mainly driven by technologies. As a result, businesses are facing the challenge of either inte-grating new technologies into existing processes or design-ing processes entirely from scratch.

This puts the main focus on the strategy of the business and on the need to either adjust that strategy or realign it completely. Revolutions are of course always accompanied by major transformations, and the fourth industrial revo-lution is no exception. The digitisation of a product and service portfolio permits new business models, yet it also changes the customer’s expectations and forces corporate structures to adjust or reorganise themselves. To realise such a project successfully, a business needs an agile Indus-try 4.0 strategy that covers all business departments and provides the content and the specifications to produce a comprehensive roadmap. The resulting strategy must not be understood as a rigid general goal, but rather as adapt-able guidelines which must be continually questioned and adjusted to suit new developments. The basis for defin-ing an Industry 4.0 strategy should primarily be an analysis of the current Industry 4.0 activities within the business. This analysis should be clearly structured, showing which foundations have already been laid and which still need to be addressed. In this process, it is helpful to identify the technologies that are already being used and to define synergies, best practices, and standards for their full-scale implementation. Special attention should be paid to a busi-

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Service to assess and implement an Industry 4.0 strategy

MHP Industry 4.0 FrameworkIndustry 4.0 Readiness Check

Industry 4.0 Roadmap

Industry 4.0 Future Scenario

Technology Integration/Technology Catalogue

Industry 4.0 Assessment & Strategy

Digitisation & Change Offensive

STRATEGY

TECHNOLOGY

ness case scenario that provides transparency concerning potential savings, complexities of implementation, poten-tial benefits and project risks. Of course, the integration of new technologies and the digital transformation across the entire value chain lead to major challenges for IT depart-ments. For example, new data sources need to be inte-grated into existing IT systems, and existing IT architectures need to be adjusted or developed from scratch. It is there-fore important that IT integration has an important place in the defined digitisation strategy. Another crucial element

for the successful implementation of Industry 4.0 solutions is attention to the organisation and its transformation. To gain vital leverage for a successful transformation, it is important to create acceptance, to make the system intui-tive to use and to generate enthusiasm for the solution. To sum up, it is important to approach the realisation of Indus-try 4.0 in a structured manner, covering the entire busi-ness, as insular solutions can only create optimal situations in isolated areas and would impose unnecessary limits on the potential of Industry 4.0. At the same time, however,

due to substantial technology and business dynamics, the resulting solutions must continually be put to the test in order to maintain the necessary flexibility and agility. Based on the best practices that have been described, MHP has developed a portfolio of six Industry 4.0 services (Fig. 1). All the necessary transformational processes can be mapped, ranging from the planning to the realisation of the strategy and solutions.

Fig. 1: Industry 4.0 Services

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02Procedure and Method-ology

Based on the aspect of motivation, we are now going to describe the structure of the study with regard to content and methods, explaining the target group, the design of the survey and the evaluation methods.

2.1. MotivationSo far there has been no clear idea of the exact posi-tion of German businesses on the digitisation of their processes, plants and products. This is true for each busi-ness’s own development of digitisation readiness and also for any comparison with customers, partners and com-petitors. The purpose of this study is therefore to spec-ify the status quo of businesses and to compare it with benchmark data, based on categories that are continu-ally being updated to take account of ongoing develop-ments. Moreover, we have used a broad statistical basis that increases the validity of the results and the reliability of the conclusions.

MHP and Prof. Johann Kranz from the Ludwig Maximil-ian University, Munich, both recognised this need, which then led to the joint design of the Industry 4.0 Barometer. The latest Industry 4.0 activities have been recorded with a substantial amount of technological detail, together with the corresponding information about deficiencies and recommendations for action. If a regular assessment is conducted, using the Barometer, it is possible to create a benchmark that allows a comparison with competitors, and also enabling a company to map its business’s own progress in each category. This leads to a detailed view of the areas where action is required to prevent the business from lagging behind its competitors.

This study is based on the MHP Industry 4.0 Framework with regard to its methods and technical approach. Busi-

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nesses are thus enabled to structure the complex area of Industry 4.0 and make efficient use of their potential. Development proceeded gradually, starting with the col-lection, analysis and thematic clustering of each busi-ness’s different requirements for Industry 4.0 solutions. Three main clusters were identified in this way: Technol-ogy, Strategy and IT Integration.

The MHP Industry 4.0 Framework (Fig. 2) forms a matrix with a horizontal and a vertical dimension. The horizon-

tal perspective covers five stages: Develop, Source, Make, Deliver and Provide, including all the central processes of a typical value chain. The vertical dimension, on the other hand, maps the main clusters identified through an analy-sis of corporate requirements. Each main cluster, in turn, can be divided into subclusters, leading to a considerable level of detail. The resulting matrix then permits a strate-gic approach to achieving the general goal for Industry 4.0. It involves a systematic and comprehensive view of all aspects of industrial networking and digitisation.

An example from the Technology cluster shows that the Framework requires no more than three simple selection stages: Stage one puts the focus on the Technology clus-ter and two processes: Source and Make. Next, the level of detail can be further increased by selecting sensors as a subcluster in the area of technology. The last stage would then involve selecting specific technologies, such as RFID, and gathering highly specific details.

The Industry 4.0 Framework is the central element in the analysis of Industry 4.0 activities within a business. Its pur-pose is to fairly quickly determine the focus of the busi-ness and any current projects and to show the potential use of new approaches and technologies.

In addition to the Industry 4.0 Framework, MHP also applies a so-called lndustry 4.0 Assessment to map the matrix structure of the framework. This enables the Framework to deliver a specific assessment of a busi-ness’s Industry 4.0 readiness in three categories: Technol-ogy, Strategy and IT Integration. Readiness is determined through face-to-face interviews with carriers of expertise from each of the various businesses. The interviews are guided by a catalogue of over 400 questions, which then allow a consolidated assessment.

The catalogue of questions in the lndustry 4.0 Assessment also serves as a basis for the questions that were asked in the Industry 4.0 Barometer.

Fig. 2: MHP Industry 4.0 Framework

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2.2. Target Group of RespondentsFor the purpose of this study, we targeted people with expert knowledge and with organisational and decision-making competencies in matters of Industry 4.0, i.e. deci-sion makers and key figures for the strategic and technical direction of business development. Suitably restricted selec-tion criteria were applied to this survey, which had a specific target group, to ensure that precisely this group of people was reached. Respondents included individuals from differ-ent industries, departments and business sizes.

Potential respondents were told about the context and aim of the study through personal emails and were then invited to complete an online survey. Where necessary, people were sent reminders during the survey period (July to November 2017).

2.3. Survey DesignTo ensure detailed evaluation, the respondents start by answering questions about themselves and their business-es. The survey of the Industry 4.0 Barometer has the same structure as the MHP Industry 4.0 Framework, consisting of three parts: Technology, IT Integration and Strategy and Goals.

The individual parts of the survey were sent to all respon-dents, without changes. In each question the response for-mat follows a Likert scale, with five or seven levels. The Technology section has a five-level Likert scale, allowing the responses: Not in use, Planned, Practical tests in prog-ress, Some use and Full use.

The IT Integration section has a seven-level Likert scale, ranging from Totally disagree to Totally agree. The midway point between the two extremes is Neutral1. The only sec-tion with an alternative scale is Data Analytics, with five possible responses, ranging from Very poor via Adequate to Excellent.

The Strategy and Goals sections has questions with two seven-level Likert scales. The two blocks of questions Appli-cation Governance and Governance of IT Infrastructure offer the options: Fully controlled by IT, Jointly controlled by IT and business departments and Fully controlled by business departments. The remaining nine questions have the above-mentioned seven-level Likert scale, ranging from Totally disagree to Totally agree.

1 The option “Neutral” describes an indifferent attitude to the question and does not mean that the respondent wishes to withhold their view or fails to understand the question.

2.4. Methods of Evaluation

To allow a more manageable evaluation of the responses, the potential responses are clustered (Fig. 3). In each of the scales used to map a level of agreement, the potential responses are grouped together as follows: Within each set of responses, the middle value is given special emphasis. In addition to the distribution of responses, the weighted arithmetic mean value is given as a percentage, described in the study as the barometer value. To derive numerical values, the five/seven-level Likert scales are converted to metric scales, ranging from 0 to 4 and 0 to 6 respectively. 0 stands for the responses Totally disagree, Very poor and

Fig. 3: Clustering of Potential Responses on Likert Scales

Very poor Adequate Excellent

Poor Adequate Good

Not in use PlannedPractical tests in progress

Some use Full use

Not in useUse planned or practical

tests in progressSome use Full use

Totally disagree Neutral Totally agree

Disagree Neutral Agree

Fully controlled by IT Jointly controlledFully controlled by

business departments

Fully controlled by IT Jointly controlled by IT and business department Fully controlled by business departments

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Fully controlled by business departments. The values 4 and 6 stand for Totally agree, Excellent and Fully controlled by IT. After multiplying the metric scale values by the relative frequencies gained from the responses in each instance, the weighted arithmetic mean value is divided by either 4 or 6 to relate it to the relevant scale and thus to obtain a percentage. This value is the barometer value.

The yellow vertical bar in the various diagrams in this study is the barometer value. It shows, for instance, that when the surveyed businesses are asked about the tracking of product parts and end products within the factory, they say that, as a mean value, they have reached the stage of either implementation planning or practical testing. Seeing that the Industry 4.0 Barometer is intended to be repeated at regular intervals, this mean value can therefore serve as a benchmark, so that a benchmark comparison of several surveys should then permit the identification of Industry 4.0 trends. It also allows businesses to compare themselves with their own peer group. To gain deeper insights, the evaluation also makes a distinction between industries (automotive and reference industries), business sizes (small, medium or large businesses) and department (IT department or business department). Furthermore, the Automotive section is divided into Automotive OEM and Automotive Suppliers. Absolute anonymity is ensured throughout the entire evaluation.

2.5. Description of RespondentsIn all, the evaluation is based on the responses of 222 persons from different industries, business sizes, hierarchy levels and functional areas in Germany.

Age and gender

84% of respondents are male, 16% female. 38% of respondents were aged between 20 and 40 at the time of the survey. 58% of respondents were aged between 41 and 60. Only 4% of respondents were in the 61-70 age group.

Industry membership and business size

53% of respondents come from the automotive industry (Automotive) (Fig. 4, next page). Automotive OEM and Tier 1 suppliers are the biggest groups of respondents, with 24% and 23% respectively. The Automotive group also includes Tier 2 Suppliers (6%). Moreover, there are respondents from Mechanical Engineering (10%), Auto-motive Engineering (3%) and Electrical Engineering (8%). Production (4%) and Metal Production and Processing (4%) are relatively small groups of respondents. The remaining respondents (18%) are from a range of indus-tries: Construction, Chemical, Retail and Wholesale Trade, Energy & Water, Food, Hard and Software, Medical Engi-neering, Traffic and Transport and other industries. They are grouped together as Others (19%).

40% of respondents work for businesses with workforces above 10,000 (large businesses) (Fig. 4, next page). Staff from medium-sized businesses (workforces of 1,000 to 9,999) constitute 32% of all respondents. 28% come from small businesses (with workforces of up to 999).2

Hierarchy Levels and Functional Areas of Respondents

35% of respondents come from the upper management levels of their companies, with no more than one hierar-chy level between themselves and the board of manage-

2 Please note at this point that this classification of size does not coincide with the EU definition, but has been chosen by the authors, as it best reflects reality in the relevant industries.

ment or executive board. For 41% of respondents this distance is two or three hierarchy levels, and for 22% it is four or five hierarchy levels. 2% of respondents do not fit any of the clusters (Fig. 5, next page).

Respondents’ functional areas are also recorded. The big-gest group come from business departments (58% of all respondents). 42% are from IT departments. The remain-ing groups are clustered as business departments in the evaluation. The remaining 58% comprise the following: Research & Development: 14%, Production: 10%, Sales & Marketing: 8%, Management: 7%, Procurement: 5%, and Others, a group covering Finance & Accounting (2%), HR Development (2%) as well as respondents not classi-fied under any functional area (10%).

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Business size

small businesses (workforce under 1,000)

medium-sized businesses (workforce between 1,000 and 9,999)

automotive OEM

automotive Supplierautomotive and mechanical engineering

electrical engineering

manufacturing and metal industry

traffic and transport

other industries

Industry membership

28%

32%

24%

29%13%

8%

8%

2%

16%40%large businesses (workforce above 10,000)

Fig. 4: Distribution of Business Sizes and Industries

N = 222N = 222

20

Hierarchy levels between respondents and the board of management or executive board

no level or one level

two to three levels

four or more levels

other

IT

research and development

production

marketing

management

procurement

other 35%

41%

22%

2%42%

14%10%

8%

7%

5%

14%

N = 222

Fig. 5: Distribution of Functional Areas and Hierarchy Levels

Functional areas

N = 222

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31% 32% 15%

41% 18%

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31% 32% 15%

41% 18%

03Results of the Study

The results of the study can be divided into three the-matic areas: Technology, IT Integration and Strategy and Goals. These areas are outlined and explained below.

3.1. TechnologyThe Technology complex focuses on the use of Industry 4.0 technologies. An overview is presented, showing to what extent processes, systems, plants and products have already been digitised.

Supply Chain Transparency

System states are mapped in real time, and meaningful parameters are created, ensuring a high level of transpar-ency along the value chain. This presupposes that plants and products are sufficiently well equipped to produce data that map the processes which have been conducted.

When it comes to the traceability of components to the manufacturer, the automotive industry is highly devel-oped when compared with the reference industries (Fig. 7). However, among all the respondents, the proportion specifying “full use” for traceability – 17% in all – seems to be a relatively small against the background of very accurate JIT and JIS processes in the automotive industry (Fig. 6).

This may be due to a wide variety of different break-downs of supplied parts and of components made by the company itself. For parts that are delivered as entire components or which are classified as relevant to safety, traceability is less complex than for parts such as joining elements and fasteners. It can therefore be expected that traceability in the supply industry will be lower than for automotive manufacturers.

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Supply Chain Transparency

Information on components can be traced back to the manufacturer and plotted along the timescale.

We can locate all product parts and end products across the entire value chain.

We can locate all product parts and end products within our manufac-turing plants.

Information on components can be traced back to the manufacturer and plotted along the timescale.

All our products have sensors already, or it is easy to add sensors, so that the products can record and transmit environmental parameters and status data.

automotive

Reference industries

N = 215 Barometer value Not in use Use planned or practical tests in progress Some use Full use

Barometer value Not in use Use planned or practical tests in progress Some use Full use

The greatest room for improvement therefore concerns the tracking of parts and products along the entire value chain. This is an area where 45% of respondents give the answer Not in use. Combining these findings with the responses on traceability, we can formulate the following hypotheses: Traceability of parts is indispensable for their reasonable tracking and assignment to the relevant prod-ucts. Also, the tracking of parts and products is currently likely to prove far more difficult and inaccurate in the field than within a production site. This may be because, even as early as the production stage, there is a certain fuzziness in the assignment of parts to end products, and this effect increases with the number of manufactured products.

The generally better score for the automotive industry may well be due to stricter production deadlines and a defined supply policy for each part. Also, the amount of booking processes necessary for balancing purposes, caused by storage and retrieval operations, is much wid-er than it is for the mainly smaller firms in the reference industries. This means that many of the production data which have to be recorded for legal reasons can be used profitably for the purpose of data-driven process design.

Fig. 6: Distribution of Responses: Supply Chain Transparency

Fig. 7: Distribution of Responses: Traceability to Manufacturer

N = 122

N = 93

13%

22% 31% 32% 15%

28% 41% 18%

45%

35% 31% 27%

36%

17% 29% 37% 17%

32% 19% 12%

32% 16% 7%

7%

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Our entire value chain is matched by a digital image which contains process and status data and allows simulations.

Each product is matched by a digital image which contains detailed process and status data associated with that product.

Our production plants are matched by a digital image which contains process and status data and allows simulations.

Each of our products has a “digital product memory” (RFID, NFC, Em-bedded System), which automatically sends the relevant data.

Our plants and systems in production, warehousing and logistics have sensors which record and transmit environmental parameters and status data.

Digital Twin



Digital Twin

Digital twins, which provide digital images of all the rel-evant details of a physical product, the production plants and mappable processes, are seen as strategic elements in Industry 4.0. To investigate the progress of value chain digitisation of each business, we specifically asked about the development of a digital twin for processes along the entire value chain.

We found that the degree of implementation is rather low for such digital mappings, both in products and in production plants (Fig. 8). This shows that a digital twin policy continues to be a mere vision, and that full realisa-tion still requires work on some fundamental conditions.

Automatic product memories are even less widespread. It can therefore be concluded that, in most cases, the col-lected product data are transmitted directly to back-end systems. When comparing industries, the automotive sec-tor turns out to be slightly ahead in the use of RFID, NFC and other technologies.

The degree of implementation is lowest for digital images applied to the entire value chain (Fig. 8). In view of the earlier values, this result is hardly surprising, as a compre-hensive image of the supply chain would need the highest level of complexity and also require the greatest amount of information. In particular, access to information outside the business, e.g. from suppliers, poses additional techni-cal and legal challenges. On the other hand, responses show that the degree of implementation is already very high for the use of sensors in production, warehousing and logistics. However, a large number of respondents, 46%, say that sensors are only partly in place – a fact which indicates that approaches tend to be less holistic, and more insular, or that sensors are only used for pilot

Fig. 9: Use of sensors by industries

Fig. 8: Distribution of Responses: Digital Twin

Each of our products has a “digital product memory” (RFID, NFC, Embedded System) which automatically sends the relevant data.

16%

25% 31% 37% 8%

30% 53% 2%

40%

28%

30%

36%

20% 30% 46% 4%

37% 24% 3%

35% 28% 6%

41% 23% 8%

39% 18% 2%

automotive


N = 122

N = 93

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Digital working modelsWe use digital technologies to increase the flexibility of our workforce.

We use working models (including exoskeletons) that integrate human-robot collaboration.

Our mobile terminals access central control and planning systems, e.g. Manufacturing Execution System or Enterprise Resource Planning).

We use mobile terminals (e.g. tablet PCs, smartphones, smart watches and data goggles) in production, warehousing and logistics to support our workforce.

N = 215



projects. Only 4% say that sensors are fully in use. Com-pared with the reference industries, the automotive sector actually produce even poorer results in terms of full use (Fig. 9). Moreover, this also shows that plants are having problems implementing any company-wide connectivity for the recording of status data.

Digital working models

Industry 4.0 increasingly involves the integration of tech-nologies that seek to extend human capabilities and thus to achieve greater efficiency, quality and flexibility. The use of mobile terminals is relatively widespread across all categories of technology (Fig. 10). This may be because industrial solutions are so readily available now, or it may be due to falling prices. Operability expenses and the resulting obstacles to user acceptance vary from one device to another. Access to central control and planning systems from mobile terminals is less widespread.

It is noticeable that technologies are often used in order to increase the flexibility of the workforce. This aspect is very likely to gain in significance, particularly for the future, as a way to provide job security. After all, if the workforce are more flexible, each person can be deployed more widely, despite increasing automation and artificial intelligence. Human-robot collaboration (HRC) is still quite rare, although it has major potential in terms of machines learning from humans. Practical experience has shown that HRC solutions do not currently offer any added value in areas of automotive manufacturing that are largely automated, such as body construction and painting. The only places where there is already the likelihood of potential savings are the ones that involve a large amount of manual work. Fig. 11 shows a comparison between the automotive and reference industries concerning data access to existing systems from mobile terminals. The only

element that is more prominent in the automotive sector is the provision of access to central control systems from mobile terminals, and this is due to a more powerful IT architecture at automotive OEMs. Respondents from the

automotive industry more often specify “full use”. This may be the result of area-wide initiatives on the use of mobile terminals.

Fig. 10: Distribution of Responses: Digital Working Models

Fig. 11: Data Access from Mobile Terminals to Central Systems by Industries

Our mobile terminals access central control and planning systems, e.g. Manufacturing Execution System or Enterprise Resource Planning).

15%

43%

17%

13% 28% 48% 12%

34% 41%

16%

18% 37% 40% 4%

33% 41% 11%

8%

36% 18% 3%

33% 45% 7%

automotive


N = 120

N = 92

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Our plants, devices and systems communicate with each other autono-mously via the internet (machine-2-machine).

Our plants, devices and systems communicate with each other via an enterprise service bus.

We use machines and robots that organise themselves autonomously.

We have business processes in production, warehousing and logistics that can autonomously respond to changes, control themselves or improve themselves.


Our plants, devices and systems communicate with each other via an enterprise service bus.




Under Industry 4.0, plants and systems which communicate autonomously assume a central role in the networking and optimisation of business processes.

The lowest degree of implementation across all categories of technology can be found in Automation & Autono-mous Systems (Fig. 12). A key reason is that the models we focused on here have the highest level of innovation. An autonomous system and process landscape can be realised either through central or non-central communication. The most typical example of a centrally controlled solution is that of an Enterprise Service Bus (ESB).

Not surprisingly, therefore, respondents report that the Enterprise Service Bus is the most widespread solution when communicating between plants, devices and systems. ESB solutions serve as a layer of communication connect-ing new technologies and existing IT systems to the Inter-net of Things. The use of an Enterprise Service Bus is more widespread in the automotive industry than in the reference industries. This is due to the greater complexity of existing IT infrastructures at automotive OEMs, and the resulting higher demand for connectivity (Fig. 13).

Autonomous plants and processes may also be based on non-central machine-to-machine communication. How-ever, where this is the case, the degree of implementation was rated as lower than for an Enterprise Service Bus. This is because there is a higher level of complexity in the reali-sation of machine-to-machine communication, requiring a case-specific benefit analysis and smart systems in the pro-duction environment. Because additional technical work is required, businesses currently prefer an Enterprise Service Bus to direct machine-to-machine communication as a way of already making profitable use of existing data material.

Fig. 12: Automation & autonomous systems

Fig. 13: Use of an Enterprise Service Bus by Industries

54%

38%

59%

41%

31%

46% 31% 20% 4%

30% 34% 4%

38% 15% 6%

29% 10% 2%

31% 28% 4%

29% 16% 1%

automotive N = 82

Reference industries N = 111

N = 193

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We use modular production technologies to increase the agility and flexibility of our production.

We integrate additive manufacturing methods in our production (e.g. 3D printing of spare parts).

Our plants and machinery can integrate with other plants and systems and work with them.

Our plants and machineries can be remote-controlled via software.


But there are also economic considerations why the com-plete implementation of machine-to-machine communica-tion may not make financial sense.

Modular production in Industry 4.0 is based on indepen-dently responding processes in conjunction with machines and robots that organise themselves autonomously. It is worth noting in this context that processes display a higher level of autonomy than machines. But it is doubtful whether the answers in this context really relate to autonomous deci-sions or whether perhaps the respondents also regard, for instance, driverless transport systems as autonomous. Such systems usually move along a specifically programmed path, thus acting in an automated manner, but not autonomously.


To increase flexibility and to speed up responses in a dynamic production environment, it is extremely impor-tant to use digital production technologies. This is the term applied to technologies that allow new production processes based on digital input, such as sensor data and 3D models.

The highest degree of implementation is achieved in the remote control of plants (Fig. 14). As the level of auto-mation is currently very high, especially in the automo-tive industry, this type of control is to some extent stan-dardised, and the high value is therefore plausible.

What is noticeable is that additive manufacturing tends to be rare. One major reason why this is not implemented very frequently may be the costs, which are still very high. It means that such methods are limited to isolated cases, e.g. highly customised interior vehicle elements or spare parts where the demand is low or sporadic. This circumstance can therefore be explained by the fact that businesses in the survey predominantly engage in mass production.

The mutual integration of plants and machines and the use of modular production technologies are further conditions for a modular production system. Considering that the degree of implementation is low for autonomous systems, the values for integrated plants and autonomous produc-tion technologies are relatively high.

Data Analytics

Data Analytics covers methods and technologies that allow the storage and analysis of large unstructured data volumes in order to optimise processes, e.g. through pre-dictive maintenance solutions. This may involve the use of both statistical correlation analyses and machine learning methods, such as neuronal networks.

Both isolated plant data and data from the entire value chain are being partly collected and analysed, in each case by 37% of respondents (Fig. 15), even though the higher complexity and the increased need for information might have suggested a lower value in data analysis for the entire supply chain. This is surprising, as it suggests that the rele-vant parts of the supply chain are already supplying a larger number of data.

What is noticeable here is the very low use of central data platforms, with 36% of respondents reporting no use and only 21% some use. This permits the conclusion that plant

Fig. 14: Distribution of Responses: Digital Production Technologies

28%

44%

33%

13% 19% 55% 13%

30% 30% 7%

33% 20% 3%

30% 35% 7%

N = 194

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IT department N = 83

Business department N = 114



data along the value chain are only stored in silos and are not available centrally. This considerably limits flexibility as well as any possibility of potential application scenarios.

Moreover, it is noticeable that the central collection and availability of operational and machine data are perceived very differently by IT staff and by those in business depart-ments. Although, on average, respondents from IT depart-ments report such data use more frequently, more than twice as many from business departments (11%) report full use (Fig. 16).

3.2. IT IntegrationUnder IT Integration, respondents of the study were asked about the performance of their companies’ own IT systems and departments.

IT Standards

At the beginning of the IT Standards section, respondents were asked how they view the use of data and communi-cation standards (Fig. 17). 44% see the IT infrastructure of their own companies and that of their partners along the value chain as compliant with the relevant applicable indus-try standards. A far lower percentage – 7% – express the opposite view. This result may partly be due to the grow-ing standardisation of public cloud infrastructures through open-source projects such as OpenStack and their increas-ing use in industry.

Nevertheless, 29% of respondents believe that coordination of data and communication standards takes place either inadequately or not at all. Only 23% say that there is coor-dination with partners on this topic. This shows that there needs to be better communication along the supply chain

Fig. 15: Distribution of Responses: Data Analytics

Fig. 16: Recording of Operational and Machine Data

36%

25%

20% 36%

19% 37%

17% 29% 51% 4%

11%26%40%23%

39% 5%

37% 8%

30% 37% 9%

37% 21% 6%We run a central data platform that integrates all the participants of our own value chain and makes data available for all.

Central data along the value chain are continually collected and analysed.

All operational and machine data from our plants and machines are centrally collected and are available for analysis at any time.

Our plants and machines send their operational and machine data to signal any need for maintenance, which they thus trigger independently (i.e. condition monitoring).

Data Analytics

All operational and machine data from our plants and machines are centrally collected and are available for analysis at any time.

N = 197

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on shared definitions of IT standards. As many as 27% of respondents say that non-proprietary standards are used within their own corporate boundaries, while 18% report that such standards are not in use. When compared with standards across corporate boundaries, this is a good posi-tion to be in, where communication between a company’s own plants, systems, devices and products is concerned. About half of all respondents express a neutral position on all three questions. This shows that permeating the IT land-scape with data and communication standards needs to be stepped up, as it is considered fundamental to any effective development of consistent Industry 4.0 applications.

IT Architecture

The IT Architecture is defined by the fundamental struc-ture of IT systems and their interaction. The results of the survey on the performance and flexibility of IT architecture are shown in Fig. 18. When it comes to communication between their own plants and their customers, 29% of respondents believe their own IT infrastructures to be effi-cient. Only 12% disagree. Here, too, the relatively low value suggests considerable room for improvement, as the data volumes which are transmitted today are likely to rise drasti-cally over the next few years. Service orientation is seen as even poorer. Only 18% use a service-oriented architecture (SOA), while 20% have none at all.

Even greater room for improvement can be seen with regard to modular architectural structures and the reduction of sys-tem dependencies. 23% of all respondents believe that the conditions are poor when it comes to the integration of new modules and the combination of existing modules, while only 14% see the architecture as positive in this respect. More than a quarter of all respondents – 26% – believe that system dependencies have not been reduced to a minimum. On the other hand, only 12% believe that their systems are

Fig. 17: Distribution of Responses: IT Standards

Fig. 18: Distribution of Responses: IT Architecture

Our IT infrastructure and our partners’ IT infrastructure along the value chain follow industry standards.

Any dependencies between the systems of our IT architecture have been reduced to a minimum.

Our IT architecture has been designed under a modular principle, so that modules can be quickly integrated and combined via defined interfaces.

We use a service-oriented IT architecture (SOA) throughout.

We have a powerful communication architecture at and between our plants and also with our customers.

To warrant interoperability between our systems, we use open, non-proprietary standards for communication between our plants, devices, systems and products wherever possible

We coordinate with our partners along the value chain on the use of uniform communication standards and data formats for Industry 4.0 projects.

IT Standards

IT Architecture

N = 190

N = 174

7% 49% 44%

18% 55% 27%

23%48%29%

26%

23%

20%

12% 59% 29%

62% 18%

63% 14%

61% 12%


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independent of one another. Both system dependencies and a modular structure are essential for the flexible integra-tion of new solutions, using the latest technologies. About 60% of all respondents have no clear view on all four state-ments – a fact which may well indicate room for improve-ment in matters of performance and flexibility.


This section focuses on the use of software platforms and the networking of systems and applications within the IT architecture.

Large companies and OEMs, in particular, often have IT structures that have grown heterogeneously and depend on legacy systems, major parts of which are several decades old. This is a critical deficit (Fig. 19). For example, only 20% of respondents say they have any specific plans to either integrate or replace their legacy systems. Moreover, soft-ware platforms which integrate partners along the supply chain are used very rarely, in only 18% of all cases across all industries. 34% report that their companies are not using them at all. Furthermore, nearly half – 48% – express a neu-tral position on this point.

There is a general consensus across all industries that the IT architecture will become more complex as a result of Indus-try 4.0. 40% of respondents agree that this is the case, while 45% are undecided, and only 14% say they anticipate no rise in complexity. As regards the redesign or reorganisation of the IT architecture for Industry 4.0, only 28% of respon-dents believe that their companies have a clear roadmap, while 32% feel that this is not the case. A similar proportion of respondents – 31% – say that their companies lack any plan for the integration or replacement of legacy systems to master the challenges of Industry 4.0. These answers sug-gest that, due to the current complexities of IT architectures

across departmental boundaries, there is no uniform under-standing of existing systems. Fig. 20 shows that respon-dents in the automotive sector believe there would be major problems with the integration of apps into existing systems and applications. This may be because IT architectures in the automotive sector are often older than in the reference industries, having played a pioneering role many years ago in the realisation of manual processes in systems. Interest-ingly, 48% of respondents in the automotive industry see the integration of critical applications as negative, while only 25% share this view in the reference industries. Only 9% of respondents from the automotive sector see such integration as positive, compared with more than twice as many (19%) in the reference industries. This shows that Industry 4.0 and the resulting challenges are indeed being registered, but a detailed approach to mastering those chal-

lenges rarely exists. It also suggests that some groundwork is still required in many instances, to avoid the emergence of insular solutions. Making use of the full potential of Industry 4.0 also means achieving data continuity across departmen-tal and corporate boundaries.

Big Data

In the Big Data section, respondents were asked to assess various capabilities and techniques required for the analy-sis of data at their companies. These include the storage, management and availability of data.

The vast majority (71%) of respondents say that the use of partially or fully automated decision-making through artificial intelligence or machine learning is poor (Fig. 21).

Fig. 19: Distribution of Responses: Platform & Connectivity

N = 187

31%

32%

34%

38% 49% 13%

48% 18%

14% 45% 40%

40% 28%

49% 20%


Our company has a plan for the future integration or replacement of legacy systems, to meet the requirements of Industry 4.0.

Our company has a clearly defined roadmap, specifying the future shape of our IT architecture with Industry 4.0.

Our IT architecture is made more complex by Industry 4.0, e.g. with point-to-point connectivity between applications.

We use a software platform to integrate our partners along the supply chain into our (IT and production) systems.

New applications and functions can be integrated quickly into critical applications, depending on the requirements of the end users.


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automotive N = 106

Reference industries N = 81

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Barometer value Poor Adequate Good


The reasons for this negative assessment may be inad-equate staff skills, as confirmed by 46% of respondents, or an inadequate technical infrastructure, given by 41%. Only 20% see the existing expertise as positive, and only 23% take a positive view of the technical infrastructure. This clearly shows rather overdue work that needs to be done, both with regard to the infrastructure and also the skills that are required of the workforce. The extreme def-icit in this area can be explained by obstacles in matters of data availability and also barriers due to legal require-ments. Unless they are overcome, it makes no sense for a business to develop skills and infrastructures, as they do not have any usable data to analyse within the analysis environment.

Furthermore, there seem to be problems with the roll-out of new technologies, such as artificial intelligence. Although initiatives already exist in most companies, the technology is apparently not being used yet, according to the answers in the Technology section. This is an area where the businesses surveyed are far behind the tech companies in Silicon Valley, where machine learning and artificial intelligence have long found their way into the businesses’ service portfolios.

Data processing and management routines, on the other hand, are rated as good by 47% of respondents and as poor by only 15%. Similarly, the automatic production of reports, analyses and messages based on the latest data is seen as positive by 41% and negative by only 27%. As such capabilities have long been major priorities on the IT agendas of businesses, it is nevertheless surprising that over 50% gave neutral or negative answers here. Although some first steps towards Industry 4.0-capable data analysis methods have been taken, many businesses are still considerably behind with the rollout of new IT and data analytics solutions.

Fig. 20: Integration of New Applications and Functions

N = 184

48%

25% 57%

71%

41%

46%

27%

15% 38% 47%

32% 41%

33% 20%

35% 23%

18% 11%

19%

42% 9%

New applications and functions can be integrated quickly into critical applications, depending on the requirements of the end users.

Big Data

Partially and fully automatic decisions made by artificial intelligence or through machine learning

Technical infrastructure for advanced data analysis (e.g. In Memory, a distributed file system, GPU server, Hadoop, Spark)

Staff skills in advanced data analysis methods (e.g. artificial intelligence, data mining, machine learning)

Automated creation of reports, analyses and messages based on up-to-date corporate data

Preparation and management of data (availability, consistency and whether it is up-to-date)

Please assess the capabilities of your business compared with your competitors in the following areas:

Fig. 21: Distribution of Responses: Big Data

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IT Security

The IT Security section is about experiences and policies concerning the use of encryption technologies to ensure the secure communication of machine data. Only 21% of surveyed businesses have end-to-end encryption for communication between actors, sensors and machines. 32% of all respondents say they do not have this type of encryption (Fig. 22). This indicates a lack of experience in the use of machine data and poses the danger of com-munication structures being attacked. Another reason may be that end-to-end encryption requires significantly more processing power when dealing with data, while there is an exponential rise in the data volume that needs to be transmitted. 38% of respondents believe that their own companies have a standard access management policy for operational and machine data, and 39% say they have policies on the use of such data, while only 17% disagree with the first statement, and 18% with the second one. 35% of respondents believe applications are being devel-oped which take into account current policies, and 32% believe that such a development involves comprehensive risk assessments. However, 23% of respondents disagree with both statements.

About 45% of all respondents express a neutral position on all statements. This shows that there is substantial room for improvement concerning existing policies, access man-agement and the encryption of machine communication. The said points will need to be addressed, so that data can be handled safely within businesses.

Scalability

The last section of the IT Integration category focuses on the scalability of the IT architecture that is used in each case, involving the use of Cloud solutions and Applica-

tion Programming Interfaces (APIs). The scalability of the IT infrastructure is perceived as particularly inadequate by 34% of respondents, while only 13% express the oppo-site view. 21% confirm that there is an opportunity to connect with business partners via APIs. On the other hand, half of all respondents are neutral on this question. 29% state that they cannot connect business partners to their own systems (Fig. 23). As a result, about 80% of respondents give neutral or negative replies concerning partner connectivity, so that there is considerable poten-tial for the connectivity of external systems and partners to a company’s own infrastructure. The generally poor

result concerning scalability may be partly due to business size and therefore the company’s more complex IT archi-tecture, which makes scaling more difficult.

3.3. Strategy and GoalsThe Strategy and Goals section focuses on strategic ori-entation in the realisation and implementation of Indus-try 4.0. It highlights fundamental requirements, the organisational structure and also internal and external communication.

Fig. 22: Distribution of Responses: IT Security

23%

23%

18%

17%

32% 47% 21%

45% 38%

42% 39%

42% 35%

45% 32%

IT Security

Prior to launching Industry 4.0 applications developed by our own com-pany or by third parties, we conduct comprehensive risk assessments.

In our development of Industry 4.0 applications, we always take account of the established security policies (e.g. end-to-end encryption, ISO 27001).

Our company has defined policies on the security and use of machine data

Access to operational and machine data is clearly specified in a standard identity and access management policy.

Communication between actors, sensors and machines is always subject to end-to-end encryption.

N = 165


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0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100

Scalability



Strategic focus on Industry 4.0

At this point of Strategy and Goals, respondents were asked about the goals of their businesses in relation to Industry 4.0. According to the respondents, each busi-ness mainly uses Industry 4.0 as a way of reducing costs, increasing the effectiveness of business processes and offering value-added services (Fig. 24). About 45% of respondents agree that they are using Industry 4.0 to solve customers’ problems, to open up new market and customer segments and to develop new business models. Respondents from IT departments put more of a focus on the development of new business models and on offering value-added services. In summary, however, these results do not yet show any strategic direction of Industry 4.0 under the classification presented here.

Fig. 24: Strategic focus on Industry 4.0

We can connect business partners via application programming interfaces (APIs).

... to reduce costs and at the same time to increase the quality, speed and efficiency of our business processes.

... to increase the effectiveness of our business processes.

... to open up new market and customer segments.

... to solve our customers’ problems.

... to offer new services for our products (e.g. predictive maintenance).

... to develop new business models.

We can scale our IT infrastructure up and down quickly (e.g. through the use of Cloud solutions).

N = 167

N = 166

Fig. 23: Distribution of Responses: Scalability

29%

34%

4%

2%

7%

5%

8%

11% 43% 45%

37% 55%

47% 48%

51% 42%

36% 61%

34% 62%

53% 13%

50% 21%

Strategic focus on Industry 4.0 Where Industry 4.0 is concerned, the main concern of our business is ...

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Barometer value Fully controlled by IT Jointly controlled Fully controlled by business departments

Entrepreneurial Orientation

Entrepreneurial Orientation refers to research and devel-opment, the corporate culture and the launch of products and services.

55% of respondents put the emphasis on research and development, innovation and a pioneering role in technology (Fig. 25). 38%, i.e. more than a third of all respondents, see their own corporate management as an essential driver for a highly dynamic and entrepreneurial culture within their companies. At the same time, they see their management as having a similarly good intuition for new business ideas and innovations, and believe that their management is prepared to take risks. Neverthe-less, about half of all respondents took a neutral position on this point, so that there is still room for improvement in all categories, except for the focus on research and development.

Digitisation can only be successful if employees on all lev-els of the hierarchy are involved in innovations and new technologies and, in particular, if they receive continuous professional development in these areas. The corporate management is regarded as a driver of cultural change within one’s own company.

Industry 4.0 Project Governance

Industry 4.0 Project Governance refers to the manage-ment and control of Industry 4.0 projects. The question was asked whether individual tasks come under the responsibility of the IT department or the business depart-ments or whether they are subject to joint control.

A clear majority of respondents, i.e. about 70%, say that the initiation and the management of Industry 4.0

Fig. 25: Distribution of Responses: Entrepreneurial Orientation

Fig. 26: Distribution of Responses: Industry 4.0 Project Governance

16%

13%

14%

15%

25% 55% 20%

30% 55%

55% 31%

50% 37%

46% 38%

Entrepreneurial Orientation

Industry 4.0 Project Governance Please specify how decision-making competencies are distributed

within your company for the following IT activities.

Our corporate management is an essential driver for a highly dynamic and entrepreneurial culture in our company.

Our corporate management has a good intuition for new business ideas (e.g. innovative products, new business models, profitable market niches).

Our corporate management encourages innovation and is prepared to accept the risks involved.

Our company puts a major emphasis on research and development, in-novation and playing a pioneering role in technology.

Our company is regularly the first one in our industry to launch new products, services and technologies.

Initiation of new Industry 4.0 projects

Management of important Industry 4.0 projects

Planning of applications for Industry 4.0 projects

Realisation and implementation of new applications for Industry 4.0 projects

N = 171

N = 161

4% 69% 27%

30%63%6%

5% 71% 24%

14%75%11%


34

0 10 20 30 40 50 60 70 80 90 100


projects and the planning and realisation of the relevant applications is jointly controlled by IT and the relevant business department (Fig. 26). In all areas cases of sole control by business departments are more frequent than constellations where the full responsibility is borne by the IT department. The highest level of balance in the distri-bution of responsibility can be found in the realisation and implementation of new applications for Industry 4.0 projects.

According to 30% of respondents, the management of important Industry 4.0 initiatives is controlled by business departments, while 6% of respondents say that such ini-tiatives are controlled by IT. Also, when it comes to the initiation of new Industry 4.0 projects, 27% of respon-dents see business departments as responsible, and 24% see those departments as responsible for the planning of applications for Industry 4.0 projects.

The figures show clearly that the business departments are still in the lead. An IT department, on the other hand, still tends to have more the role of operating the IT infra-structure and of acting as a service provider. If IT depart-ments don’t want to become obsolete, they must develop into genuine partners of the business departments and must become drivers of innovation.

Cross-Departmental Collaboration

Innovative technologies under Industry 4.0 can only be successfully implemented if borderlines between silos are dissolved and technology expertise is transferred to business departments. This must be based on a regular exchange between IT and the business departments.

26% of respondents say that there are no regular meet-ings between IT and business departments where knowl-

edge of the business environment is shared among them (Fig. 27). Neither is there any cross-departmental agenda. 27% of respondents say they are prepared to exchange information among those involved in decision-making, whereas 18% said they were not. Respondents from IT departments are more inclined to say that there is a joint agenda and an exchange between persons involved in decision-making.

Similarly to the previous thematic section, at least half of all respondents assume a new position on all four ques-tions. This shows that there is still a long way to go for corporate managements when it comes to promoting an

exchange of knowledge across different departments. After all, interdisciplinary collaboration can only be realised if it is resolutely supported by the management

Business Expertise of the IT Department

The section Business Expertise of the IT Department com-prises three areas: how well IT departments understand daily business routines of one’s own company, the oppor-tunities and risks of Industry 4.0, and the strategy of one’s business in the realisation of Industry 4.0.

25 to 43% of respondents believe that the IT depart-

Fig. 27: Distribution of Responses: Cross-Departmental Collaboration

There are regular meetings where the IT department and other business departments share their knowledge of the business environment.

At our company information is readily shared by everyone who is involved in a decision.

The IT department and other business departments of our company have a joint agenda.

The IT department and other business departments have a common understanding of the role of IT at our company.

N = 167

Cross-Departmental Collaboration

26%

18%

28%

22% 59% 19%

51% 21%

56% 27%

51% 23%

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ments of their companies have a good understanding in those areas (Fig. 28). 11 to 16% disagree. The majority of respondents are neutral on this point. With all three questions it is noticeable that respondents from IT depart-ments rate their own knowledge of business processes, opportunities, risks and strategies better than respon-dents from business departments. Again, these figures show that there are different views of the capabilities of an IT department. Lack of business expertise at IT depart-ments can be a hindrance to innovation, and it therefore makes sense that IT departments should assume proactive roles within business processes.

IIT Expertise of Business Departments

The efficient use of the performance potential of an IT landscape requires business departments to have an excellent understanding of IT systems and processes.

The majority of respondents rate the expertise of busi-ness departments in IT systems as neutral. 14% rate the expertise as good (Fig. 29). 27% believe that business department staff do not have a good understanding of the opportunities and risks of tools for the development of new applications. Software development expertise is rated by 40% as good, while 5% see it as requiring improvement. It is becoming more and more important that business departments, too, should expand their IT expertise, so that they can enter into high-quality collabo-ration on interdisciplinary teams with the IT department. This is the only way to make full use of the potential of Industry 4.0. Similarly to the previous set of questions, respondents from business departments tend to rate their own knowledge as much better than respondents from IT departments.

Fig. 28: Distribution of Responses: Business Expertise of the IT Department

Fig. 29: Distribution of Responses: IT Expertise of Business Departments

Business Expertise of the IT Department

IT Expertise of Business Departments

The staff of our IT department have a good understanding of our company’s strategy in relation to Industry 4.0.

Our business department staff have a good understanding of software development.

Our business department staff have a good understanding of the opportunities and risks of tools for the development of new applications.

Our business department staff have a good understanding of IT systems.

The staff of our IT department have a good understanding of the opportunities and risks of Industry 4.0.

The staff of our IT department have a good understanding of the daily business routines of our company.

N = 167

N = 84

15%

16% 53% 31%

43%46%11%

60% 25%

40%

27%

12% 74% 14%

64% 8%

55% 5%


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Fig. 30: Distribution of Responses: Market Dynamics

0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100



Our competitors behave very differently to one another.

Market Dynamics

The section Market Dynamics covers changes in the mar-ket caused by market débuts and exits as well as eco-nomic aspects, such as developments in profits, returns and prices.

According to the respondents, their own industry does not have a very dynamic market, i.e. not many businesses are leaving or joining the industry (Fig. 30). This result comes as a surprise, considering, for example, that increasing numbers of companies from other industries are crowd-ing into the automotive industry, for example, hoping to generate additional sales. Only 15% see the behaviour of competitors and customers as highly changeable or differ-ent to one another, and only 5% of respondents are sur-prised at their customers’ behaviour (Fig. 31). This clearly shows that businesses already have a good understanding of their customers’ behaviour and are in a position to map customers’ specific needs. Industry 4.0 innovations make it possible to be even more precise in one’s forecasts.

Fig. 31: Rating of Market Dynamics

Market Dynamics

Many companies enter and/or leave our industry.

Our competitors behave very differently to one another.

The behaviour of our customers often surprises us.

The basic technical knowledge in our corporate environment keeps changing.

N = 173

54%

26%

38%

25% 51% 27%

56% 5%

59% 15%

38% 8%

30%

24% 57% 18%

61% 9%



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Fig. 33: Distribution of Responses: Technology Intelligence

0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100


Market Intelligence

To adapt to disruptive technologies and innovations, there needs to be regular exchange both within and outside a business. However, less than one third of all respondents (29%) say they have any intensive exchange between IT and external partners (Fig. 32). 56% of respondents from IT say that their own departments quickly understand their customers’ needs. However, this is only endorsed by 24% of respondents from business departments.

Furthermore, only 36% agree with the statement that when internal solutions are created, work within the busi-ness proceeds along collaborative lines. Conflicting percep-tions of business departments and IT departments confirm that there is a lack of interaction with external and internal partners. Working collaboratively is an important require-ment for a business wanting to leverage its digitisation potential on a company-wide scale. Agile and interdisci-plinary working methods are indispensable for any change in corporate culture in the age of Industry 4.0.

A similar discrepancy (IT: 47%, business departments: 29%) can be observed in the level of agreement as to whether businesses work towards joint solutions. Only 34% of respondents from IT agree that they are involved in new projects. Similarly, only 22% of respondents from other departments agree to the project-specific integra-tion of the IT department.

Technology Intelligence

To become knowledgeable about the most innova-tive technologies, a business needs to look beyond its own borders and find optimisation opportunities and innovations.

Fig. 32: Distribution of Responses: Market Intelligence

Market Intelligence

Technology Intelligence

Our IT department and other business departments of our company always work towards joint solutions.

Our IT department often exchanges ideas with external partners (e.g. suppliers, customers, consultants, universities and competitors) on new and alternative technologies in the Industry 4.0 environment.

Our IT department quickly understands how we can use IT to improve processes and products.

Our IT department deals a lot with the use of new digital technologies in the Industry 4.0 environment.

Our IT department proactively looks out for specific innovative technolo-gies and companies in the Industry 4.0 environment, including those outside our own industry (e.g. purchases/investments, technology partnerships)

Our IT department often exchanges ideas with external partners (e.g. suppliers, customers, consultants, universities and competitors) on the development of Industry 4.0.

Our IT department quickly understands how we can use IT to improve our customer care or our service to customers.

Our IT department is involved from the very beginning in business department projects which run under Industry 4.0 and concern the development of new market and customer segments.

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N = 154

13% 51% 36%

29%56%15%

11% 52% 37%

27%54%19%

14% 62% 25%

29%59%12%

23% 49% 28%

26%51%23%


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0 10 20 30 40 50 60 70 80 90 100



Our IT department responds quickly to new needs and priorities of business departments.

29% of respondents believe that their IT departments have a good understanding of process and product improvements (Fig. 33). IT departments receive similarly good ratings for the following areas: use of new digi-tal technologies (28%), targeted quest for innovations (26%) and an exchange with external partners on new and alternative technologies (25%). When differentiat-ing the results for IT and business departments, we can see that IT departments perceive themselves as having far more technological expertise than is acknowledged by their colleagues in business departments. To avoid miss-ing technological trends and innovations, it is essential for businesses – especially in view of ever shorter cycles of innovation – to engage in intensive exchanges with external partners, such as universities, suppliers, custom-ers and indeed competitors. This ensures that they are continually informed about innovative technologies and their areas of application under Industry 4.0, especially outside their own corporate boundaries as well.

IT Agility

An IT department needs to be flexible and respond quick-ly whenever there are dynamic changes.

About half of all respondents take a neutral position on all three questions about IT agility (Fig. 34). The quality with the best rating, at 44%, is the ability of IT departments to improvise. In each instance, 18% of respondents believe that their IT department lacks flexibility and that it is too slow in responding to the needs of business departments. This also reflects the lack of trust among business depart-ments towards IT departments. To deal better with the needs of business departments, this is another area where borders must be broken down, so that interdisciplinary collaboration can be optimised.

When we compare the responses of IT departments with those of business departments, we can see a significant discrepancy. 69% of respondents from IT departments believe their units to be capable of improvisation. How-ever, this view is only shared by 28% of respondents from business departments. Also, 46% of respondents from IT departments are confident that they can respond quickly to the changing needs and priorities of the business depart-ments (Fig. 35). Moreover, 36% believe they are flexible.

However, only 23% of respondents from business depart-ments acknowledge speed and 20% flexibility, which is noticeably less. In order to be future-oriented, it is essential for a business to have an agile IT structure. To implement new technologies under Industry 4.0 as quickly as possible within a given company, without incurring major expenses, and to connect those technologies to existing systems, it is vital to ensure that the company’s IT department is modu-lar, flexible and highly responsive.

Fig. 34: Distribution of Responses: IT Agility

Fig. 35: Response Speed of IT Department to the Needs of Business Departments

IT Agility

Our IT department is flexible.

Our IT department can improvise.

Our IT department responds quickly to new needs and priorities of business departments.

N = 173

18%

9% 47% 44%

32%50%18%

56% 26%

23%

46%45%

54%

9%

24%



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IT Governance

Cross-departmental DevOps teams stimulate collaboration between IT and business departments. This also includes the joint management of systems by IT and business department staff.

The barometer values show that respondents see decision-making powers on IT governance as being clearly located in the IT department (Fig. 36).

Four areas can be distinguished for the governance of an IT infrastructure: operation and maintenance of IT, procure-ment of hard and software, provision and management

of a communication and network infrastructure, and user support for an infrastructure that is specific to Industry 4.0. When it comes to the areas of operations, maintenance and hard/software procurement, respondents from IT depart-ments believe that they are the ones who bear the full responsibility. This view, however, is only partially endorsed by respondents from business departments.

Again, this shows the traditional role of the IT department as operators. To ensure the sustained relevance of IT depart-ments for companies with Industry 4.0, it is necessary for such departments to rise above their traditional tasks.

Fig. 36: Distribution of Responses: IT Governance

Barometer value Fully controlled by IT Jointly controlled Fully controlled by business departments

IT Governance Please specify how decision-making competencies are distributed within

your company for the following IT activities.

Operation and maintenance of IT for Industry 4.0.

Procurement of hard and software for Industry 4.0.

Provision and management of a communication and network infrastructure for Industry 4.0.

User support for an infrastructure that is specific to Industry 4.0.

49% 41% 10%

12%47%41%

55% 39% 6%

15%46%40%

N = 164

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04Recommended actions

We will conclude the study with an evaluation of its results as well as recommended actions in technology and IT integration as well as strategy and goals.

4.1. TechnologyWhere supply chain transparency is concerned, the study shows that many businesses are already tracking parts and products at their plants. The ability to trace parts and products back to their manufacturers can also be realised, but involves manual work. According to the respondents, tracking along the entire value chain is only partly fea-sible, and then without automation, as the relevant prod-ucts are inadequately equipped with tracking systems. Results show that greater supply chain transparency usu-ally only exists at a test stage or that it is confined to certain isolated process stages.

To increase the transparency of the value chain, we recom-mend intensifying collaboration with important partners along the supply chain in creating a joint, comprehen-sive source of information. There also needs to be a case-specific analysis showing the points where, in addition, it makes economic sense to place sensors. This applies to products, machines and plants.

The use of digital images for plants and products is some-thing where many of the surveyed businesses are pressing ahead, but it is currently only happening in part. A digital image of the entire value chain is still quite rare. Neither do many companies use digital product memories with automatic data transmission. The results show that digital twins are still at a very early stage of realisation.

We would recommend applying a very clear structure of each manufactured product when approaching digital

42

imaging. It is important that this structure should be gen-erally applicable to the entire value chain. Next, a decision is required which process and status data from machines and plants should be used in supplementing the digital image. This decision should be based on economic effi-ciency criteria.

In many businesses mobile terminals are in use, helping the workforce to be flexible. Access to central control and planning systems is only partially available as support for staff. Man-robot collaboration is still not applied very widely. To achieve a noticeable improvement of those val-ues, we would recommend a technology-focused analysis that precisely defines at what points the workforce can be supported by specific technologies. The focus should be on creating clear added value for the workforce and less on showcasing to satisfy the demands of some innovation policy.

Autonomous systems, self-modifying processes and machine-to-machine communication are also used very rarely. When it comes to creating greater flexibility and modularity of the production environment, most respon-dents believe that their companies are still at the initial stage. The same applies to additive manufacturing pro-cesses. In these relatively innovative disciplines it is espe-cially important to collect experience through feasibility studies or pilot projects, as those technologies still have several years to go before they can be used cost-effec-tively on a wide scale. This is because further-reaching process adjustments are required before businesses can leverage the benefits of such technologies. So far, in most businesses, neither plant data nor data of the entire value chain are processed in productive environments, using progressive data analysis methods. Also, central data plat-forms for the collection, recording, analysis and process-ing of data are still only used rather rarely.

If a business wants to use progressive data analysis meth-ods, it first needs to check what data are actually required for the relevant application scenarios. This means classi-fying the data that are already available and determin-ing what further data need to be acquired. In addition, a technology base needs to be created that provides scal-able data storage and analysis methods.

4.2. IT IntegrationWhen a business seeks to digitise its operations, its IT department plays a central role with its expertise and efficiency. One major factor for the successful applica-tion of Industry 4.0 technologies is the use of standards in internal and external communication. There are not many instances where open standards are used in order to facilitate coordination with partners. In all, however, a relatively positive rating was given for the important communication architecture between plants and towards customers, albeit at a generally low level. This is an area where we would recommend interpreting standards less rigidly than in the past. In particular, a business should maintain its flexibility and continually adapt existing stan-dards, so that it can respond to the rapid speed of change in the technology landscape. Standards should be under-stood as future-focused guidelines which are backed up by processes requiring routine adjustments. At the same time, a business should not aim for 100% standards, so that it can reach the necessary speed in defining and adapting them.

Many respondents confirm that Industry 4.0 is leading to rising complexities in their IT architecture and to incon-sistencies concerning clear roadmaps, software platforms and IT strategies. There are not many instances where businesses are using SOA or modular principles, or where

they endeavour to reduce system dependencies. Neither are there many attempts to integrate partners via APIs or to ensure the scalability of the IT infrastructure.

These results show that a lot still needs to be done to improve the IT skills needed for the extensive use of the Industry 4.0 potential. Above all, this includes a high lev-el of modularity to allow the fast integration of a wide range of IT technologies. Set against this background, any decision about the IT infrastructure should place the high-est priority on flexibility. We would therefore recommend conducting feasibility studies that specifically check a vari-ety of IT solutions, technologies and suppliers for their efficiency and flexibility. Such feasibility studies should not focus exclusively on technological aspects, but also on specific areas of application in the relevant business departments. The next step, based on the knowledge gained, should define a roadmap that includes both the integration and replacement of legacy systems as well as the flexible integration of new IT technologies.

In the areas of data management, automatic reporting and data processing, the respondents report a high level of maturity in this study. At the same time, however, the necessary IT security is rated as inadequate for machine communication in terms of efficient end-to-end encryp-tion. Access and identity management, by contrast, are rated as positive.

4.3. Strategy and GoalsThe adaptation of technologies and an ongoing IT inte-gration process can only be successful if they follow a clear strategic direction. This makes it important to have a joint approach in the planning, initiation and manage-ment of Industry 4.0 projects.

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The results of the study show clearly that, when it comes to traditional areas of IT (e.g. hardware/software support, operation and maintenance), full control is largely held by the IT department. IT agility and an understanding of business processes are generally given positive ratings. At the same time, respondents rate the business expertise of the IT department as inadequate concerning the potential opportunities and risks of Industry 4.0. Where business departments are concerned, inadequate knowledge of software and application development are hindering col-laboration with IT departments.

The core statements listed here show that even closer col-laboration needs to be fostered between IT and business departments. This includes, in particular, a cross-depart-mental exchange of knowledge and also interdisciplin-ary teams and working methods. This is the only way to boost the role of IT and to improve its internal and exter-nal perception.

Major differences can also be observed in the percep-tion of expertise, when we look at the responses from business departments and IT departments separately. IT departments consistently rate their business expertise as higher than is acknowledged by business departments, and vice versa. This is an obstacle to improvements in each of the different areas and also to innovation in general. To pave the way for improvements, departments will need to develop a more realistic assessment of their abilities.

When it comes to dealing with new market potential and customers’ needs, the adaptability of IT departments is rated as positive. However, market dynamics in general are rated as low. Only very few respondents expressed surprise at the behaviour of competitors and customers. Such behaviour is generally regarded as highly homoge-neous and plannable. The development of new market

segments for the generation of new sources of income is seen as inadequate.

It is essential for a business to ensure the systematic obser-vation and analysis of its adjacent markets and thus of competitors and customers. Technology is racing ahead. The resulting effects on the prevailing market structure (e.g. volume, potential, growth and shares) impact all market players. It follows that new developments must be observed, and this means including them as part of a business’s current plans.

A similar picture emerges for the technical expertise of a business. This is an area where process and product improvements come out strongly. The identification and realisation of cross-departmental innovations, however, are rated as inadequate, and neither are they currently at the focus of managements. Throughout all industries, respondents agree that it is important to work out solu-tions with one’s partners quickly, although here, too, there is room for improvement.

An exchange with external experts is becoming more and more important, particularly as development cycles are getting ever shorter in highly dynamic markets. A strong focus on one’s core business is not enough for a company to be able to align its innovation management not just within, but also beyond, its own corporate boundaries. To obtain essential market analyses, businesses must foster external exchange, so that they can continue to operate in the face of increasingly complex market structures.

Managements are given positive ratings in the area of forward-looking corporate policies. The only area that should be stepped up even further is the introduction of technological innovations and innovative services. One striking discrepancy can be found in the business opera-

tions of the various departments. For example, there are major differences in the way businesses prioritise and handle any knowledge transfer between an IT depart-ment and the business departments (such as coordination meetings, joint agendas, and exchange of information). As regards the benefits created by Industry 4.0 solutions, the current focus is on the improvement of business pro-cesses, customer solutions and business models. Open-ing up new market and customer segments is regarded as secondary in importance. This is particularly danger-ous for companies that have grown historically as some developments have been so disruptive that we can see an increasing trend for certain market or customer segments to reduce in size or even to disappear altogether.

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05Conclusion

The introduction of new Industry 4.0 technologies is seen by many businesses as a critical success factor. However, the introduction of such technologies is currently still pro-ceeding rather hesitantly. For example, there are currently only isolated instances where businesses use sensors or where they aggregate data to achieve greater flexibil-ity for their processes and workforce in their endeavour to master the challenges of specific tasks. The results show that the technological potential is understood, but the process of complete cross-company integration of advanced technologies is still at a very early stage.

To achieve the transparent mapping of the entire value chain and to make proactive use of its potential, technol-ogies need to be employed and integrated into processes and products under a comprehensive digitisation strategy. Across industries, the focus must be placed on more net-working and autonomy. This is the only way to sustain and increase the competitive edge of a business. Whether a business is ready for Industry 4.0 is substantially deter-mined by the role of its IT department, the integration of IT into existing processes and its ability to master complex challenges in collaboration with business departments.

Most solution proposals for the scalability of the IT archi-tecture and for the integration of partners and platforms have not yet reached maturity and are not being driven forward with sufficient intensity by IT departments. This may be dangerous for many businesses, as an adaptable, high-performance IT architecture is a basic requirement for all core business areas and value-added processes in the age of digitisation. The focus should therefore be on analysing the needs of departments and improving the performance of IT systems, so that new technologies can be profitably integrated. The resulting flexibility would ideally reduce adaptation periods substantially, optimise processes and make the entire value chain more efficient.

The key to the successful identification of potential and strategic competitive advantages lies in cross-departmen-tal Industry 4.0 initiatives and in a close exchange both within the company and with external partners. The first step would be to record the business areas where feasibility studies or pilot projects on Industry 4.0 have already been conducted and to ascertain to what extent new technologies have already been successfully imple-mented. What can currently be observed is that the fea-

sibility of certain technologies has already been tested in numerous pilot studies, yet there is still no comprehensive implementation throughout all corporate units.

To define a general goal for Industry 4.0 that is also agile, it is essential to record all Industry 4.0 initiatives and projects, so that the general goal specifies detailed cross-departmental requirements and content for a com-prehensive roadmap towards digitisation. The general goal, however, must not be understood as a rigid vision but rather as an agile guideline that should be continu-ally questioned and adjusted to suit the most recent developments. To allow the subsequent implementation of specific technologies, there needs to be a structured implementation management that clearly defines certain implementation cycles (e.g. proof of concept (POC) – pilot – rollout), depending on the relevant application scenario. This would make it possible to succeed in the integration of technology and to go beyond today’s insular solutions. It would mean consistently and successfully implementing Industry 4.0 throughout the entire enterprise and ensur-ing that it assumes a value position.

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06Glossary and Bibliography

6.1 Glossary Additive Manufacturing Methods

Additive manufacturing is a manufacturing process where a component is developed in layers, based on digital 3D design data. An object, for example, is not milled from a solid block, but is composed in an additive process, layer by layer, using materials available in the form of fine powder.

Application / Application Governance

A broad, generic term in business IT for problem solutions involving a software system. The term is used for IT applica-tions that deal with specific operational problems or prob-lems in business departments. Application governance is the organisation and assignment of resources to individual applications. It involves the use of rules and procedures to define basic processes and priorities.

Application Programming Interfaces (APIs)

An application programming interface (API) provides the programmer with access to the functions of the hardware, the operating system, a framework or a standard library.

Artificial Intelligence

Artificial Intelligence (AI) means the investigation of intel-ligent problem-solving behaviour and the creation of intel-ligent computer systems. It deals with methods which enable a computer to solve problems that would otherwise require intelligence if they were to be solved by humans.

Big Data

Big Data are data stocks which cannot be stored in conven-

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tional – i.e. relational – databases on account of their vol-ume, variety, volatility and velocity and which may not allow SQL reporting (i.e. using a structured query language). As soon as a business or administrative body wants to carry out any specific evaluation of large volumes of data, social media, emails, heterogeneous document collections, etc., it needs to use NOSQL technologies. (NOSQL means Not Only SQL.)

DevOps

DevOps, i.e. development operations, stands for a process improvement policy which has the purpose of developing a culture of agile collaboration. The idea is to automate operational and development functions in order to achieve quality assurance and greater efficiency.

Digital Twin

A digital twin is a virtual model or image of a material or non-material object. It is based on real data that are deliv-ered by installed sensors and which represent, for instance, the position or working conditions of a machine. This asso-ciation of virtual and real worlds makes it possible to anal-yse data and to monitor systems.

Enterprise Resource Planning System

An enterprise resource planning system, or ERP system for short, provides support for all the business processes within a business, across all functional areas. To do so, it contains modules which are connected with one another via joint data stock and made available by a relational database. The modules are Procurement & Materials Management, Production, Sales, Research & Development, Asset Man-agement, Human Resources, Finance & Accounting, Finan-cial Controlling, etc. By consolidating data throughout a

business, it is possible to support planning across all corpo-rate levels, comprising the group level as well as different plants, divisions, departments and indeed warehouses.

Enterprise Service Bus

An Enterprise Service Bus (ESB) is a software architecture that delivers basic services for more complex architectures, thus supporting, in particular, the integration of distributed services.

Internet of Things

The Internet of Things (IoT) is a term that generally describes an increasingly intensive association between physically definable things and a virtual internet-like struc-ture. As things, e.g. machines, record a variety of data and can communicate through the internet, such data are, for instance, saved to Cloud applications. IoT tools are systems or programs that analyse Cloud data. Such systems are necessary due to the enormous data volumes (Big Data) involved, and also the substantial computing power that is therefore required.

Legacy System

A legacy system is an established corporate application that has grown historically and was often created as a cus-tomisation or proprietary development. It has a high level of complexity and a large number of interfaces.

Machine Learning

Machine learning involves applying and researching pro-cesses which enable computer systems to absorb and expand knowledge independently, so that they can solve a given problem better than before.

NFC

Near Field Communication (NFC) is a contactless technol-ogy to exchange messages across short distances. NFC was developed by NXP Semiconductors and Sony in 2002.

Non-Proprietary Standards

A proprietary object is an item that is owned by a person or entity, and the word “non-proprietary” is often applied to free or open-source software that is not owned by any-one. Protocols and file formats are described as proprietary if it is impossible or difficult for a third person to imple-ment them. Such files cannot therefore be opened without the proprietor’s consent and without first acquiring certain rights (e.g. MS Word). Non-proprietary standards, on the other hand, use open formats such as OpenDocument and are understood as freely accessible formats which can eas-ily be used by third parties.

OEM

An Original Equipment Manufacturer (OEM) is a buyer of hardware components, manufactured by a different hard-ware manufacturer (a supplier). The OEM then installs those components into its own products and sells them under its own name.

POC

Proof of concept (POC) is an important milestone in proj-ect development. It creates the foundation for further work, as it endorses the general concept of the project, providing the basis for decisions on the remaining part of the project. Carrying out a POC also allows the identifica-tion of risks and makes it possible to appraise the realisa-tion of the envisaged result.

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Predictive Maintenance

Based on historical system data, predictive maintenance aims to ensure the predictability of potential system fail-ure. By regularly monitoring and documenting the pro-gression of wear and tear within a system, it is possible to forecast developments under a wear-and-tear profile and therefore to recognise and avoid malfunctions at an early stage.

Process

A process is the totality of operations affecting one anoth-er within a system. Processes make it possible to trans-form materials, energy or information into new forms and indeed to store or transport the same.

RFID

Radio Frequency Identification (RFID) involves automatic and contactless recognition and identification via elec-tromagnetic waves. Along with magnetic cards and bar-codes, RFID is among the most widespread identification techniques. The aim of an RFID system is to identify any type of objects within logistical process chains and to associate certain details with those objects with a view to accelerating and improving the logistical processes.

Service-Oriented Architecture (SOA)

A service-oriented architecture (SOA) eliminates the direct and fixed dependencies of elements within a software system to such an extent that they are defined and imple-mented as independent services. Each service involves the provision of different capabilities, while also having cer-tain requirements with regard to its use. This concept con-veniently permits the combination of individual services,

which can be combined into new systems in a service-oriented manner.

Value Chain/Network

A value chain is the graphic representation of connected corporate activities in an operational goods production process. There are five primary activities that describe the actual value-adding process: internal logistics, produc-tion, external logistics, marketing & sales and service. Each corporate activity is an approach to permit differ-entiation and contributes to the business’s relative cost valuation compared with its competitors.

6.2 Bibliography[BSM+14] BAUER, W., SCHLUND, S., MARRENBACH, D. and GANSCHAR, 0.: Industrie 4.0-Volkswirtschaftli-che Potenziale für Deutschland. Bitkom, Berlin-Mitte, Fraunhofer IAO, Stuttgart, 2014

[BT13] BEYERER, J. and TACKE, M.: visIT – Industrie 4.0. Fraunhofer-lnstitut für Optronik, Systemtechnik und Bildauswertung IOSB, Karlsruhe, 2013

[FM16] FASEL, D. and MEIER, A.: Big Data: Grundlagen, Systeme und Nutzungspotenziale. Springer Verlag, Berlin, 2016

[Gab18] VARIOUS AUTHORS: Gabler Wirtschaftslexikon, at: https://wirtschaftslexikon.gabler.de/, last accessed on 28 March 2018

[GSM+15] GLEICH, R., SCHWARZ, M.; MUNCK, J. C. and DEY- LE, N.: Industrie 4.0 - zwischen Evolution und Revo-lution. In: HORVÁTH, P. and MICHEL, U.: Controlling im

digitalen Zeitalter - Herausforderungen und Best-Practice-Lösungen. Schäffer-Poeschel Verlag, Stuttgart, 2015

[Has18] HASSELBRING, W.: DevOps: Softwarearchitektur an der Schnittstelle zwischen Entwicklung und Betrieb - at http://eprints.uni-kiel.de/29215/1/2015-07-10Architek-turen.pdf, last accessed on 28 March 2018

[HH14] HERKOMMER, O. and HIEBLE, K.: Ist Industrie 4.0 die nächste Revolution in der Fertigung? Industrie Man-agement 1/2014. GITO Verlag, Berlin

[KLW11] KAGERMANN, H., LUKAS, W.-D. and WAHL-STER, W.: Industrie 4.0: Mit dem Internet der Dinge auf dem Weg zur industriellen Revolution. VDI, Hanover, 2011

[LR10] LANGER, J. and ROLAND, M.: Anwendungen und Technik von Near Field Communication. Springer Verlag, Berlin, 2010

[MHP14] MHP Management- und IT-Beratung: Studie Industrie 4.0 – Eine Standortbestimmung der Automobil- und Fertigungsindustrie. MHP, Ludwigsburg, 2014

[MHP14] MHP Management- und IT-Beratung: Der Ein-fluss der Digitalisierung auf die Workforce in der Auto-mobilindustrie. MHP, Ludwigsburg, 2018

[Obe16] OBERMAIER, R.: Industrie 4.0 als unternehm-erische Gestaltungsaufgabe: Strategische und operative Handlungsfelder für Industriebetriebe. In: OBERMAIER, R. (ed.): Industrie 4.0 als unternehmerische Gestaltungsauf-gabe - Betriebswirtschaftliche, technische und rechtliche Herausforderungen. Springer Fachmedien, Wiesbaden, 2016

[SB17] Statistisches Bundesamt (German Federal Office of

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Thank you

on behalf of MHP Management- und IT-Beratung GmbH: To all respondents who supported the study by provid-ing their views and assessments.

To the Ludwig Maximilian University, Munich for our successful and continu-ally productive collaboration. Our spe-cial thanks go to Prof. Johann Kranz, PhD, and Ms. Esther Nagel, Chair for Internet Business and Internet Services.

Statistics): Automobilindustrie trägt 4,5% zur Bruttow-ertschöpfung in Deutschland bei. Statistisches Bundesamt (Destatis), Wiesbaden, 2017

[Sch13] Scheer, A.-W.: Industrierevolution 4.0 ist mit weitreichenden organisatorischen Konsequenzen verbun-den! Eine Bestandsaufnahme von Prof. Dr. Dr. h.c. mult. August-Wilhelm Scheer. In: Scheer, A.-W. (ed.): Industrie 4.0. Wie sehen Produktionsprozesse im Jahr 2020 aus? 2013

[Win 17] WINKELHAKE, U.: Die digitale Transformation der Automobilindustrie. Springer Verlag, Berlin, 2017

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07About Us

52

MHPMHP Management- und IT-Beratung GmbH is a leading consultancy firm in the automotive sector.

Its special approach to consultancy involves a combination of management and IT consultancy. Operating within four areas – Management Consultancy, System Integration, Managed Ser-vices and Digital Services & Solutions – we are digitisation experts who optimise and digitise our customers’ processes along the entire value chain.

As industry experts, especially in mobility and manufacturing, we therefore offer our customers comprehensive IT expertise as well as in-depth management and process expertise. We also transfer strategic innovations to other industries.

Contacts

Sponsor

Daniel Halbig

MHP – Manager IoT & Industrie 4.0 LeadOperations Performance

& [email protected]

+49 151 4066 7521

Publisher

Tilo Klüh

MHP – Associated PartnerHead of Operations

Performance & Strategy (OPS)

[email protected]+49 151 4066 7520

Project Manager

Robert Göbel


& [email protected]

+49 151 2030 2763

Sponsor

Prof. Johann Kranz, PhD


Chair for Internet Business and Internet Services

[email protected]+49 89 2180 1875

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Your personal notes on the Industry 4.0 Barometer:

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Your personal notes on the Industry 4.0 Barometer:

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MHP : dRIVeN By exCeLLeNCe

www.mhp.com

https://www.mhp.com/de/das-unternehmen/studien/

13 MHP Offices in Germany, Switzerland, United Kingdom, United States, China and Romania.

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