STOCKHOLM SWEDEN 2020,
Analysis of the software
ecosystem in the Automotive industry
REEM SALEH
JONATHAN EDSMAN
KTH ROYAL INSTITUTE OF TECHNOLOGY
Analysis of the software ecosystem in the Automotive industry
Jonathan Edsman Reem Saleh
Master of Science Thesis TRITA-ITM-EX 2020:423 KTH Industrial Engineering and Management
Production Engineering
SE-100 44 STOCKHOLM
Analys av mjukvaruekosystemet i bilindustrin
Jonathan Edsman Reem Saleh
Master of Science Thesis TRITA-ITM-EX 2020:423 KTH Industriell teknik och management
Industriell Produktion
SE-100 44 STOCKHOLM
Abstract
Due to Industry 4.0 (I4.0) the software ecosystem is constantly changing, and new possibilities and challenges are arising. The purpose of this Master Thesis is to map the software ecosystem in the automotive industry in order to understand the position of Atlas Copco and what gaps an industrial tool supplier can close. In addition, find out what opportunities and challenges that exist in the ecosystem today.
Since Industry 4.0 and the software ecosystem are relatively new topics and alters continuously, the study was carried out in an abductive approach and a research design model was created according to that. By combining literature study with empirical study such as interviews, a better understanding was gained which laid the foundation of accumulating a deeper knowledge about the research field and made it feasible to answer the research questions.
The current structure in different markets of the software ecosystem are shaped according to an old industry standard for modelling system structures. Differences and similarities between the markets can be found, which explains difficulties in providing software globally. By analysing and combining the current structures for each market, including the answers gained from the conducted interviews, a model was created. A new model for the definition of a software ecosystem in the automotive industry was the result. Additionally, a new way of structuring and visualizing the generic structure of the software ecosystem for Atlas Copco in the Automotive industry was presented as well. The latter model could be used as a tool, that enables a better understanding of the software ecosystem as well as a way to present new solutions to customers. In the ecosystem Atlas Copco takes the role as a premium industrial tool supplier and a provider of software used to configure and monitor quality and errors in the assembly processes. Hence, they broaden their market to become a software supplier. However, thanks to I4.0 technologies, there are potential areas in the production where it is possible for Atlas Copco to extend their establishment further to get more market shares of the software market as well as to provide more value to their customers. Although, the recommendation for Atlas Copco is to shape a reference architecture and focus on software close to the operations of industrial equipment.
Keywords: Software ecosystem, Software, System, Automotive Industry, Industry 4.0 Master of Science Thesis TRITA-ITM-EX 2020:423
Analysis of the software ecosystem in the Automotive industry
Jonathan Edsman Reem Saleh
Approved
2020-06-11
Examiner
Lars Wingård
Supervisor
Gunilla Sivard
Commissioner
Atlas Copco Industrial Technique
Contact person
Mattias Andersson
Sammanfattning
Mjukvaruekosystemet är något som ständigt är i förändring där nya möjligheter och utmaningar uppstår i ekosystemet i anknytning till Industri 4.0 (I4.0). Syftet med denna masteruppsats är att kartlägga mjukvaruekosystemet i fordonstillverkningsindustrin för ta reda på vilken position Atlas Copco har och vilka luckor en industriverktygsleverantör kan sluta samt undersöka utmaningar och möjligheter som existerar idag.
En forskningsdesignmodell skapades och eftersom projektet omfattar ämnet Industri 4.0 samt mjukvaruekosystemet som är ett relativt nytt ämne som är under kontinuerlig förändring, så utfördes studien baserat på abduktiv strategi. Genom att kombinera litteraturstudier med empiriska studier som intervjuer erhölls en bättre förståelse som lade grunden för att vidare samla på djupare kunskap om forskningsområdet vilket gjorde det möjligt att svara på forskningsfrågorna.
Den nuvarande strukturen på olika marknader i mjukvaruekosystemet formas enligt en gammal branschstandard för ett modelleringssystemstrukturer. Skillnader och likheter mellan marknaderna har presenterats som förklarar svårigheterna med att tillhandahålla programvara globalt. I samband med att analysera och kombinera de nuvarande strukturerna för varje marknad, inklusive svaren från de genomförda intervjuerna, har modeller tagits fram. I resultatet presenteras en ny modell för att definiera ett mjukvaruekosystem inom bilindustrin. Dessutom presenterades också ett nytt sätt att strukturera och visualisera den generiska strukturen i mjukvaruekosystemet för Atlas Copco inom fordonsindustrin. Modellen kan användas som ett verktyg för att lättare förstå mjukvaruekosystemet, och till att presentera nya lösningar för kunderna. I ekosystemet tar Atlas Copco rollen som en premiumleverantör av industriella verktyg, inklusive att tillhandahålla programvara som används för att konfigurera, övervaka kvalitet och fel i monteringsprocesserna.
Detta breddar Atlas Copcos marknad till att även bli en mjukvaruleverantör. Tack vare I4.0 teknik finns det potentiella produktionsområden där det är möjligt för Atlas Copco att etablera sig ännu mer, för att få fler marknadsandelar på mjukvarumarknaden samt ge mer värde till sina kunder.
Rekommendationen är att Atlas Copco skapar en referensarkitektur samt fokusera på mjukvara nära kopplat till operationsnivån och industriverktyg.
Nyckelord: Mjukvaruekosystem, Mjukvara, system, Automationsindustri, Industri 4.0 Examensarbete TRITA-ITM-EX 2020:423
Analys av mjukvaruekosystemet i bilindustrin
Jonathan Edsman Reem Saleh
Godkänt
2020-06-11
Examinator
Lars Wingård
Handledare
Gunilla Sivard
Uppdragsgivare
Atlas Copco Industrial Technique
Kontaktperson
Mattias Andersson
Acknowledgements
This Master Thesis is the final part of the Master Program Production Engineering and Management at KTH and was carried during the spring semester of 2020.
We would like to thank our supervisor Mattias Andersson, Global Product Manager MVI and Jonas Andersson, Business Manager Controllers & Software MVI, for the opportunity to write our Master Thesis at Atlas Copco Industrial Technique and for the support we got during this project.
A special thanks to Joakim Zenk, Product Specialist Support, who gave us an introduction and guided us through the different types of tightening tools and software offered by Atlas Copco.
Also, a special thanks to Hanna Lindh for all the workshops and for supporting us with interview techniques and other knowledge which contributed a lot to this thesis. Further, we would also like to thank Atlas Copco’s customers centers for helping us finding people to interview as well as all the interviewees who were open and took their time to answer our questions.
Finally, we would also like to acknowledge our supervisor at KTH, Gunilla Sivard, for all the support throughout this journey and for the interesting discussions held which aided the completion of the Master Thesis project.
Thank you!
Reem Saleh & Jonathan Edsman
Stockholm, June 2020
Nomenclature/Abbreviations
Atlas Copco Atlas Copco Industrial Technique
SCM Supply Chain Management
CRM Customer Relationship Management
MVI Motor Vehicle Industry
I4.0 Industry 4.0
CPS Cyber Physical System
CPPS Cyber Physical Production System
IoT Internet of Things
IoS Internet of Service
ICT Information & Communication Technology
SMS Smart Manufacturing Systems
ERP Enterprise Resource Planning
MES Manufacturing Execution System
MoM Manufacturing Operation Management
SCADA Supervisory Control And Data Acquisition
DCS Distributed Control System
PLC Programmable Logic Controller
I/O Inputs & Outputs
HMI Human Machine Interface
SOA Service Oriented Architecture
R&D Research and Development
SDO Standards Development Organization
IT Information Technology
CMMS Computerized Maintenance Management System
WMS Warehouse Management System
QMS Quality Management System
ACDC Atlas Copco Data Communication
RAMI 4.0 Reference Architecture Model for Industry 4.0
DW Data Warehouse
CAx Computer Aided Technology (D-Design, M-Manufacturing)
DDS Data Driven Services
QA Quality Assurance
Table of Contents
1 INTRODUCTION ... 1
1.1 Background ... 1
1.2 Problem Definition ... 3
1.3 Purpose ... 4
1.4 Research questions ... 4
1.5 Delimitations ... 4
1.6 Expected Result ... 5
1.7 Focal case study company ... 5
2 METHODOLOGY ... 7
2.1 Research design ... 7
2.2 Data gathering ... 8
2.3 Creating models ... 11
2.4 Validation & depth ... 12
3 ATLAS COPCO INDUSTRIAL TECHNIQUE ... 13
3.1 Tools and software ... 13
3.2 Smart connected assembly ... 16
4 LITERATURE REVIEW... 19
4.1 Frameworks... 19
4.2 Theoretical Background ... 25
4.3 Ecosystem ... 40
5 CURRENT STATE ANALYSIS ... 45
5.1 Market pyramid models ... 45
5.2 Swedish Market ... 45
5.3 German Market ... 52
5.4 Asian Market ... 56
5.5 USA Market ... 61
6 RESULT ... 67
6.1 Software Ecosystem ... 67
6.2 Similarities & differences in markets ... 70
6.3 Generic structure ... 71
7 DISCUSSION AND CONCLUSIONS ... 83
7.1 Discussion ... 83
7.2 Conclusions ... 87
8 RECOMMENDATIONS AND FUTURE WORK ... 89
8.1 Recommendations ... 89
8.2 Future work & research ... 90
9 QUALITY OF RESEARCH ... 93
10 REFERENCES ... 95
APPENDIX A: PRODUCT LIFECYCLE STANDARDS ... I
APPENDIX B: PRODUCTION SYSTEM LIFECYCLE STANDARDS ... IV
APPENDIX C: MANUFACTURING PYRAMID STANDARDS ... VII
APPENDIX D: FIELD BUSSES AND PROTOCOLS ... X
APPENDIX E: SUMMERY OF GENERAL INTERVIEW QUESTIONS ... XII
APPENDIX F: GENERIC STRUCTURE (FULL SCALE MODEL) ... XIII
APPENDIX G: GENERIC STRUCTURE WITHOUT CONNECTIONS (FULL SCALE
MODEL) ... XIV
List of Figures and Tables
Figure 1. Visualisation of the research design. ... 7
Figure 2. The pyramid skeleton model. ... 11
Figure 3. Small part of the portfolio including associated controller and display with SQS3 (Atlas Copco Industrial Technique, 2020a). ... 15
Figure 4. Digital manufacturing drives big data, (Atlas Copco Industrial Technique, 2016). .... 17
Figure 5. Automation pyramid based on ISA95/IEC 62264. ... 20
Figure 6. The breakdown of the automation hierarchy into distributed services as a cyber physical based automation (Lu et al., 2016). ... 21
Figure 7. A service-oriented SMS (Lu, Y. et al., 2016). ... 21
Figure 8. RAMI 4.0 – the reference architecture model for Industry 4.0, including its crucial aspects and important entities for common picture of the future smart factory (Lydon, 2019). .. 22
Figure 9. CRM+SCM+ERP+PLM = the cornerstones of industry IT (Evans, M. 2001). ... 33
Figure 10. Manufacturing ecosystem according to Olaf & Hanser (2019). Ellipses represents external parts, production line represents the core or current platform and arrows represents processes Olaf & Hanser (2019). ... 41
Figure 11. Information exchange on shop floor (Mikler, 2019). ... 41
Figure 12. The skeleton model with defined levels. ... 45
Figure 13. The current IT and system structure of SCANIA. Based on the engine and transmission assembly department of SCANIA Södertälje (Interview 1, Scania, 2020; Interview 2, Atlas Copco, 2020; Alarcon, H., 2014). ... 47
Figure 14. Challenges of Scania (Interview 1, Scania, 2020). ... 49
Figure 15. Reference architecture for the Service Enablement Platform (Interview 8 & 9, Sandvik Coromant,2020). ... 51
Figure 16. Example of main responsibilities. ... 52
Figure 17. Describes the reason why Synatec became a part of AC. ... 53
Figure 18. Illustration of value proposition. Synatec SW value prop aut pyramid (Fischer, 2020). ... 54
Figure 19. The next generation of error proofing Synatec SW value prop aut pyramid (Fischer, 2020). ... 55
Figure 20. The current structure of the ecosystem in the German market. ... 55
Figure 21. The current structure of the ecosystem in the Asian market (Interview 3 & 4, Atlas Copco 2020). ... 58
Figure 22. Challenges in the Asian market (Interview 3 & 4, Atlas Copco, 2020). ... 60
Figure 23. The current structure of the ecosystem in the USA market (Interview 5, 2020). ... 61
Figure 24. Common things which customers wonder about when acquiring systems. ... 62
Figure 25. Describing the software challenges at the customer side... 62
Figure 26. Type of questions that should be asked to better understand how to support customer´s need. ... 63
Figure 27. Tells what the customers are interested to look at when it comes to the saved data? 64 Figure 28. The changing process, explaining what to help the customer with. ... 65
Figure 29. An illustration of the software ecosystem with a production perspective. ... 68
Figure 30. An exemplification of the software ecosystem of a manufacturing/assembly of a car in the automotive industry production with perspective of Atlas Copco (based on the software ecosystem model). ... 69
Figure 31. Distributed services as a cyber physical based automation (Lu, Y. et al., 2016) ... 71
Figure 32. A model of the structure of the software ecosystem in the Automotive Industry from
the focus point of Atlas Copco. A full-scale model of the structure (which is zoomable) can be
found in Appendix F. ... 72
Figure 33. Same model as in Figure 32, except without connections to make it simpler to
understand. A full-scale model of this (which is zoomable) can be found in Appendix G. ... 73
Figure 34. The generic software and system structure of the Automotive ... 76
Figure 35. The reference to the ISA95 pyramid. ... 78
Figure 36. Current and potential future targets for integrating Atlas Copco software. Deeper blue = More strength, Lighter blue = Lesser strength, White = no market shares and the targets as crosshairs. ... 80
Figure 37. Potential future software market areas, including suggestion of where Atlas Copco Industrial Technique should focus to be a market leader in. ... 89
Figure 39. RAMI 4.0 – the reference architecture model for Industry 4.0 (Lydon, 2019). ... 90
Table 1. Interviews conducted in this study. ... 9
Table 2. Short summary of most common hardware used in automotive assembly processes (Atlas Copco Industrial Technique, 2020a). ... 13
Table 3. Short summary of software available for the automotive industry (Atlas Copco Industrial Technique, 2020a). ... 15
Table 4.Sample of technologies of software, systems and applications in the ecosystem. ... 28
Table 5. Summary of standards, protocols and interfaces for communication (Lu, et al., 2016; Mikler, 2019; Löwen & Törnsten, 2019; Drury, 2009; Gilchrist, 2016; DIN/DKE, 2018). ... 34
Table 6. Describes the different types of products that Atlas Copco provides. ... 53
Table 7. Short summary of (common software used in the different market) software available for the automotive industry (Atlas Copco, 2020)... 70
Table 8. Value Proposition, Creation & Capture for Atlas Copco. ... 81
Table 9. Future potential customers to interview. ... 91
Table 10. Guidelines how to improve the interactive map. ... 92
1 INTRODUCTION
This chapter introduces the study and outlines the background to the problem. The problem is defined, and the purpose of the study is explained. Research questions are then stated according to the problem definition and the purpose. Delimitations are set and the expected result is described.
1.1 Background
The fourth industrial revolution is occurring in the manufacturing industries on a global level. It has been on the industries road map during the recent years and more companies are taking the step towards the transformation. Industry 4.0 introduces concepts such as Cyber-Physical Systems (CPS), Internet of Things (IoT), Internet of Service (IoS), Smart Manufactories and Servitisation among others, leading to production systems which are vertically and horizontally integrated (Atlas Copco Industrial Technique, 2019; Wuest, 2016). Cyber-Physical systems integrate computing elements with physical components and processes. The computing elements coordinate and communicate with sensors which monitor Cyber-Physical indicators and actuators. The sensor technology is used to connect different types of distributed intelligence systems, to gain a much deeper knowledge of the environment and enable process accuracy (Boulila, 2019).
Distributed system is the collection of autonomous computing elements which appears to the user as one single system. The characteristic of a distributed system is a collection of computing elements which can either be hardware devices or software. The second element refers to the user who only uses one single system which additionally means that all the hardware devices or software systems need in one way or another to collaborate with each other. How such collaboration can be established lies in developing that distributed system (Tanenbaum, 2016).
These concepts together, push production systems towards becoming more intelligent and connected, resulting in a “smarter” manufacturing. The Smart Manufacturing Systems (SMS) enables a fast and extensive digital information which are used to maximize the competitive capabilities of companies such as cost, delivery, flexibility and quality (Lu et al., 2016).
1.1.1 Industry 4.0
The definition Industry 4.0 refers to the fourth industrial revolution which embraces a set of technological advances that are having a high impact on the current industrial landscapes. The first industrial revolution improved productivity by utilizing steam water, the second one allowed mass production due to the use of electricity and the third one characterized production automation using electronics and IT. The term itself “Industry 4.0” appeared for the first time in an article published by a German government that resulted in an initiative regarding high-tech strategy for 2020.
In the last few years, the industrial landscape has changed drastically as a result of innovations and
disruptive developments specifically in the field of digital technology and manufacturing. Industry
4.0 involves fast and disruptive changes which embraces digital manufacturing, network
communication, computer and automation technologies etc. These will influence both products
and processes and drive efficiency as well as productivity improvements for the companies
adapting to such technologies.
Additionally, Industry 4.0 will lead to deep changes in the industry and manufacturing sectors, which will have a strong impact throughout the whole value chain as well as offer opportunities with regards to business models, production technology and how to organize the work. Since the new manufacturing paradigm suggests new ways of communicating along the entire supply chain, business models are being very influenced by Industry 4.0. With it comes the opportunity of improving the value creation process for the aim of improving self-organization capability as well as achieving real time integration and communication (Pereira & Romeo, 2017).
Industry 4.0 (I4.0) brings a few different concepts to the industry such as the “Smart Factory”
where everything in the production will be equipped with sensors and autonomous systems etc. in a connected landscape (Lasi et al., 2018). Other concepts introduced are the Cyber-Physical- System (CPS) which merge the physical manufacturing systems with the digital manufacturing systems, creating one entity which no longer can be divided (Lasi et al., 2018). Fundamentally CPS can be described as an embedded system which exchanges data in a network and enables smart production (Pereira & Romeo, 2017).
Smart factory is one of the key aspects when it comes to addressing the fourth industrial revolution, which have resulted in development of consisted integration, digitalisation as well as the use of flexible and adaptive processes. In a smart factory environment embraces the intercommunication between manufacturing research such as sensors, actuators, conveyors, machines and robots (Pereira & Romeo, 2017).
To meet customer demands today, drivers such as technology, sustainability, optimization have established the encouragement to transform the manufacturing industry and let it become more adapted, fully connected and familiar with its own power quality. The transformation is characterized by the goal of reducing costs, increasing the competitive advantages and creating more value add-ons (IEC 2015).
The IT transformation can be described as factory of the future (FoF) (IEC 2015). The goal of the FoF is to interconnect every step of the manufacturing process. To keep up with the advanced manufacturing technology, investments in both digital technologies combined with high skilled talents needs to be done to maximize the outcome of the offered benefits (IEC 2015).
Implementing concepts of FoF requires technologies supporting the integration of a manufacturing systems to be able to achieve information exchange and optimization through factories, production networks and ecosystems.
1.1.2 Atlas Copco´s role in Industry 4.0
One of the sectors leading this transformation is the automotive sector. According to a study done
by McKinsey (2018), the automotive industry will go through a disruption the upcoming years due
to the impact of I4.0, which will affect both the market and the technology. This disruption will
force automotive companies to have a digital and more connected production as well as reassessing
their business strategies. Data collected from the manufacturing floor cannot be ignored as it is
valuable for companies and is useful in the production chain as well as the lifecycle of the product
(Gilchrist, 2016). Data can be analyzed in real-time to prevent stops in production and historical
data can be used for quality assurance and to optimize processes (Gilchrist, 2016). By realizing
the importance of data, more data will be collected, and more will come to use in the manufacturing
industry. Additionally, the use of equipment in the manufacturing industry, will increase the
software systems and put higher requirements on the communication to other software used within the production (Atlas Copco Industrial Technique, 2016; Lu et al., 2016).
Atlas Copco Industrial Technique (Atlas Copco) have for a long time been the global leader of tightening techniques and assembly tools. They offer smart industrial equipment to the automotive industry amongst others.
Due to the transformation in Automotive industry, Atlas Copco’s values and possibilities might change as well as their role in the ecosystem. According to a recent study by Löwen and Törnsten (2019) the automotive industry transformation will lead to new possible structures of the ecosystem. The study discusses using IoT platforms as a backbone in the ecosystem to directly connect the IT-systems and software in production with the business IT-systems on the top level of companies. This scenario allows different software providers to connect to the same platform but would require them to use common standards and communication protocols. Another possibility discussed in the study is the scenario where production equipment suppliers such as Atlas Copco would offer industry total solutions, such as hardware, software and service. Thus, providing customers with both hardware equipment and software applications as a complete service or a scalable system, to assure quality, control equipment, collect data from processes as well as offer services to improve the production or prevent failures in a sustainable way. Hence, expanding their role as a supplier and move up levels in the hierarchy, increasing market shares.
From a business perspective of a production equipment supplier, the later scenario is the better, as the study suggests, however from a technical point view it might not be the best nor feasible at all.
Software companies or vendors of software have today moved towards a more open strategy in their business with regards to other software companies which have resulted in that they find themselves in an ecosystem constructed of several software companies, developers and partners.
Together they can offer customers a complete solution, that meets their requirements. Actors such as developers, partners and software companies are at the core of the ecosystem where the challenge is to understand the ecosystem and how the end result is influenced by their actions and relationships which will create value (Berk et al., 2018).
1.2 Problem Definition
Today there is a lack of technical knowledge of the software ecosystem, i.e. the system of software
and the relationships between them, and what type of challenges or possibilities that exist in
addition to the technical development, standards, state of art etc. A previous study within this topic
which only focused on the Swedish automotive industry (Löwen & Törnsten, 2019), presents the
perspective from a business point of view. However, a clear picture of how the current software
ecosystem looks like in a production environment, what is the data flow, as well as the future
structure and communication in the system. These are requested in order to understand the bigger
picture and to achieve a holistic view over the entire ecosystem. To achieve a broader knowledge
on what can be done to reach I4.0 in the automotive industry and what services an industrial
equipment supplier should provide to help reach I4.0 at the same time benefit from it, is the reason
why this study has surfaced.
1.3 Purpose
The purpose of this study is to find a common picture of how an ecosystem in the automotive industry looks like. Additionally, it is of interest from an industrial equipment supplier point of view to identify the existing gaps in the landscape of systems and software of the production and suggest how to close these gaps to broaden their software business. The aim is to also find the technical challenges and possibilities in an industrial equipment supplier’s software, used in the automotive manufacturing industry.
1.4 Research questions
In order to answer the purpose of the study and to generate relevant results three research questions were stated as seen below (RQ1-RQ3). The research questions will help the reader to understand the content of the report and simultaneously give the report a better structure in the process of providing the solution(s) to the problem. The questions will be answered from a technical point of view.
RQ1. What are the key elements to include in order to describe a software ecosystem in the automotive industry and how would it be modelled?
RQ2. What are the differences and similarities between different global markets in the automotive industry, with respect to the software ecosystem architecture?
RQ3. A. What role does Atlas Copco have in the software ecosystem of the automotive industry?
B. What values can Atlas Copco provide to their customers?
1.5 Delimitations
In order to reach the target and to achieve the purpose of this study, only the software ecosystem associated to production within the automotive industry (software related to final assembly and design) have been considered. The reason for this is that it is the industrial equipment suppliers’
main customer area. Design involves the systems and processes used when designing joints for
assemblies, from prototyping to final design. Furthermore, the investigation of system and
software ecosystem were only conducted in the larger markets of the industrial equipment supplier
in the automotive industry since they include the major stakeholders and systems regarding the
ecosystem. Finally, the mapping of the industrial equipment supplier’s own systems, only focused
on the latest software and hardware to reduce the number of different versions or updates of the
entities.
1.6 Expected Result
The expected result was to create a model of the software ecosystems which can additionally be used as a tool and guide for an industrial equipment supplier when developing a new software and delivering solutions to their customers. The software ecosystem model would also illustrate the key attributes which should be included in order for a software ecosystem to function.
Additionally, the illustration would describe how the data flows between the systems and the communication required between these.
1.7 Focal case study company
This study has investigated the ecosystem in several real-life industrial contexts thus, it is a
multiple case study. However, it has been conducted from the focal perspective from one case
study company, Atlas Copco Industrial Technique (shortened as: Atlas Copco). Atlas Copco is an
expert and a provider of assembly techniques with the focus on fastening. They provide industrial
power tools, joining techniques, quality assurance products and systems, software and services
through their global organization. They offer both standalone and system-integrated solutions to
their customers, which for the most part can be found in the Automotive, Aerospace, Electronics,
Home Appliances and industrial assembly industries. Atlas Copco products can be found in the
almost of any advance operation handled by humans or robots. Atlas Copco often works close with
their customers to develop customized solutions and are often involved in development projects,
which makes them a very important part of the future factory and Industry 4.0 (Atlas Copco
Industrial Technique, 2017).
2 METHODOLOGY
The following chapter explains the methodology of the study and presents the different methods used throughout the project. It also gives a description of the model used in the result and how the result was validated.
2.1 Research design
To answer the research questions, a research design model was created. This study has used an abductive approach since the topic of industry 4.0 and the software ecosystem is in general a relatively new topic and alters continuously. According to Blomkvist & Hallin (2015) an abductive approach is appropriate for these kinds of studies, since it is a sort of an exploratory nature and important to not narrow down the scope in the process of the study too early. By combining theory and literature with empirical studies, such as interviews, a better understanding was gained which enabled the possibility of answering the questions. In the beginning of the study, more theoretical methods were used, however once more knowledge of the topic was gained, the empirical studies could be conducted as well. The method process have passed back and forth, in an iterative way, between theoretical studies, empirical studies and reasoning, while the project went on, see Figure 1. Eisenhardt (1989) also mentions that an abductive approach allows for changes to be made throughout the study, which can be an advantage in an exploratory research where significant insights from new discoveries can be found constantly.
Figure 1. Visualisation of the research design.
Theory
Discussions
& reasoning Empiricism
Pre-phase
Analysis/Result
2.2 Data gathering
The data gathering has been an iterative process and is mainly divided in three parts: a pre-study and a literature review as well as interviews. The pre-study conducted in the beginning of the study became fused together with the literature review.
2.2.1 Pre-study & Literature review
A pre-study of Atlas Copco Industrial Technique (Atlas Copco) was conducted in the beginning of this study to gain a better understanding of Atlas Copco products, their vision and the future challenges. It included reading internal papers and documents, interviewing/discussing with employees and workshops. The workshops involved testing and using Atlas Copco tools and software in a test environment amongst other. After the pre-study a literature study followed which was essential for this study and was especially important in the starting phase. The focus on the literature mainly lie on the following areas: Digitalization, Motor Vehicle Industry, Industry 4.0, Information Technology, Internet of Things, Software Ecosystem. The purpose of the literature study is to acquire a deeper knowledge about the research field to be explored as well as gaining a deeper knowledge about today’s scenario to understand the opportunities and challenges of the future. Both frameworks and theoretical background were included in the literature.
The majority of the literature was retrieved from KTH library database, KTHB Primo, including the most well-known databases to the academic world, which was linked to the KTH database.
However, other databases were used as well such as Google, Google Scholar and Atlas Copco’s databases. The sources of the information were well investigated, including authors and publisher/institutes etc. and the information was used carefully with a critical point of view. Most of the literature were academic articles and journals, reports, theses as well as e-books, but other information was also collected from internal documents including PowerPoints and PDFs. The information about Atlas Copco can be found in Chapter 3 and the rest of the literature in Chapter 4 including theoretical frameworks.
2.2.2 Interviews
One of the main activities in the data collection was the conducted empirical qualitative interviews.
The method was chosen to enrich the study with the interviewees perspective as well as to consider their experiences and knowledge, with respect to the software ecosystem. In addition, these interviews also set the foundation of establishing analysis of the results. The type of interviews which was conducted were semi-structured interviews consisting of some prepared key questions, to enable the interviewees to freely add more to the topic which helps to define the areas to be explored additionally (Edwards & Holland, 2013).
All interviews had Atlas Copco as the starting point since it was the focal company of this study
(see Chapter 1.7 Focal case study company). The interviews include case studies of customers and
partners to Atlas Copco, but other players in the automotive industry software ecosystem were also
interviewed. Interviews with internal Atlas Copco employees were also conducted. When theory
is lacking or constantly changing in the specified research area, case studies can be a suitable
method (Blomkvist & Hallin, 2015). Thus, case studies and interviews were chosen as an
appropriate method. Several markets were investigated, hence, in a way, a multiple case study was
conducted. This gave the study a wider perspective and included several different viewpoints.
Interviews were conducted with stakeholders and employees of Atlas Copco in the: European market (containing of the Swedish and German market in this case due some limitations, see Chapter 9 Quality of Research), US-market and the Asian market. These three markets represent the major actors of the automotive industry.
Questions asked was dependent on what role the respondent had and in what market the interviewee were stationed, as well as if the person was an internal or external stakeholder. Some prepared general questions were asked but depending on the interviewees background and how the interview prolonged the questions were to some extend reformulated. The interviews were also recorded to avoid missing information and keep the answers as objective as possible. The general prepared questions were constructed to be open, thus allowing the respondent to answer freely, then more concrete follow up questions could be asked to get more details. The prepared general questions asked in the interviews can be found in Appendix E.
Table 1 below is a list of all stakeholders and persons interviewed, including additional information regarding them. Most of the people asked were recommended by the Atlas Copco supervisor and the rest were recommend by interviewees as the project continued. Due to time and other constraints, not all recommended persons were interviewed, see Chapter 9 Quality of Research.
Before conducting the interviews, an intensive course in interview techniques was taken, held by Atlas Copco.
Table 1. Interviews conducted in this study.
Interview no.
Company Position Date Market/Comments
1 Scania Group Manager IS/IT, Automation Engine Assembly
4/3 &
11/3-20
European market/Swedish market
2 Atlas Copco
Competence Development Manager SCE Operations
28/4-20 European market/Swedish market, previous
experience from Scania, Dynamite, quality 3 Atlas
Copco
Business Manager Software Asia
31/3-20 Asian market
4 Atlas Copco
Regional Product Manager Software Asia
25/3-20 Asian market
5 Atlas Copco
Product Specialist 7/4-20 US market
6 Atlas Copco
Business Manager Software 14/5-20 European market/
German market 7 Atlas
Copco
Product Manager Software 16/4-20 European market/Synatec
8 Sandvik Coromant
CPS Analytics Team Leader 26/3-20 European market,
Experience of software
ecosystems
9 Sandvik Coromant
Senior Manager CPS Analytics, Industry 4.0
26/3-20 European market, Experience of software ecosystems
10 ABB
Robotics Sweden
Production techniques
Manager 21/4-20 European market
11 Atlas Copco
IS/IT Tierp Production 1/4-20 Swedish market
12 Atlas Copco
Project engineer Assembly Production Technique, Tierp Production
30/2-20 Swedish market, Automation
13 Atlas
Copco Production Technician Assembly Production Technique, Tierp Production
30/2-20 Swedish market,
Automation, Final testing
14 Atlas Copco
Manager System Solutions Department, R&D
23/4 &
29/4-20
Global market
15 Atlas Copco
Product Manager Tools 21/2-20 Global market
16 Atlas Copco
Global Product Manager, Controllers & Software
15/4-20 Global market
17 Atlas Copco
ERP, BI &RPA Manager, Business Systems
17/3-20 Global market
18 Atlas
Copco Global Product Manager,
Tools 25/2-20 Global market
19 Atlas Copco
VP Global Software Solution Business
17/4-20 Global market/Synatec
20 Atlas Copco
Product Specialist 27/1 &
3/2-20
Swedish Support /workshops 21 Atlas
Copco
Global Product Manager, Software
Several occasions
Swedish & Global
market/Supervisor
2.3 Creating models
The qualitative data gained from the literature study and the interviews were analysed to create reference models, maps of the current states and a map of the software ecosystem in the automotive industry. The viewpoint of the models is from a tool supplier’s perspective. For the current state analysis, a pyramid i.e. the Automation pyramid (see Chapter 4.1.1 ISA95 Pyramid) were used to model and map the general system landscape in each market and the explicit customers in a specific market. The pyramid was chosen since it is a standard model in the industry, and is simple to understand (Åkerman, 2018). The purpose of the model is to give a better understanding of the major software used in the industry and its structure. In Figure 2., the skeleton model for all markets is shown, it includes different levels in the pyramid and important technologies are usually found in the pyramid for every market. On the right side of the pyramid are a handful of common software/hardware vendors for each level presented. On the left side of the pyramid are important common technologies outside of the pyramid listed, including few vendors for each technology attached. More about the model and the market specific models can be found in Chapter 5 Current state analysis.
When creating the models for the software ecosystem and a generic structure, see Chapter 6 Results, a combination of the hierarchal pyramid, Block Diagram (BD) and Data Flow Diagram (DFD) have been used. The purpose of this was to show the general dataflow and relationships between the software, hardware and other entities stated in and outside the pyramid for, as well as the function of each entity. It includes systems which are hard to place or does not belong in the pyramid. This was done in order to get a more complete picture of the ecosystem. More about this can be found in the result chapter. All models were created in the applications: PowerPoint and Visio.
Figure 2. The pyramid skeleton model.
2.4 Validation & depth
Both deductive and inductive reasoning have been used to form conclusions and models in this study. Logical conclusions were always made when able to, otherwise sometimes the most likely conclusion was made. As mentioned before, a multiple case study gives a broader view and the result is more applicable overall, thus increasing the weight and reliability of the study. Yet, a single case study would probably give more depth since more time could be put into the specific case. Although the purpose of this thesis was to investigate this matter in a larger perspective.
Since the interviews were recorded, the data generated from them were uninfluenced thus avoiding
bias data from our perspective. However, not from the perspective of the respondent. Hence, to
validate the data collected, triangulation was used meaning that several sources confirmed the
information to ensure a valid analysis (Eisenhardt, 1989). This validation was used as much as
possible, however this is discussed further in Chapter 9 Quality of Research.
3 ATLAS COPCO INDUSTRIAL TECHNIQUE
This chapter contains more detailed information about the case study company as well as the company’s roadmap and future model of a smart factory.
3.1 Tools and software
Atlas Copco Industrial Technique’s (Atlas Copco’s) product portfolio are extensive. In Table 2 &
Table 3 some of Atlas Copco’s major and recent products, software and services are described, which are common in the automotive industry and can be linked to the thesis topic.
Table 2. Short summary of most common hardware used in automotive assembly processes (Atlas Copco Industrial Technique, 2020a).
3.1.1 Power Tools
The major part of Atlas Copco product portfolio are their premium handheld power tools, used to fasten nuts and screws in assembly processes, fast and ergonomically. The industrial power tools can be both air and electrical driven. For the electrical driven tools, Tensor, both cable and battery driven tools exists where the battery driven tools are becoming more popular. The tools can be found in many different variants or families including different attachments, sockets and bits.
Hardware Function Variants
Assembly tools Fasten nuts and screws for safety and quality critical fastening applications including
communicating tightening results.
Handheld (Tensor):
Cable- and battery driven in various designs and shapes.
Fixtured (QST):
Cable driven in different shapes.
Controller Control and monitor assembly tools and the process.
Handheld tools:
PowerFocus 600, PowerFocus 6000 Fixtured tools:
PowerMACS FlexController Quality tools Check and control nuts and screws
for safety and quality critical fastening applications including communicating results.
STWrench, STBench, STa6000
Industrial PC/Tablets Robust PC and monitor all in 1 or rugged tablet to display software.
HLTQ, STpad
Beside from being used to fasten bolts and screws in various assembly processes tools can measure parameters such as torque and angle etc. However, only electrical tools can be used to measure parameters and send data digitally (Interview 14, Atlas Copco, 2020).
3.1.2 Controllers
Controllers, including software, are used to monitor and control connected electrical assembly tools. Controllers and its system can be used for programming operations, controlling assembly processes, assuring quality, and collecting or monitoring tightening results as well as guiding the operator through the operations. Several controllers exist but Power Focus 6000 (PF6) is the most common and recent one. PF6 has a touch screen which can be used to configure it, however through a web HMI, it can be configured anywhere from a PCs web browser. PF6 can be connected with up to 6 tools at the same time which makes it flexible in a changing assembly line (Atlas Copco Industrial Technique, 2020a). In addition to PF6 there is a PF6-FlexSystem which has the same capabilities as PF6 but the design is slimmed to be mounted on robots for fastening operations. In addition, there is also Power Focus 600 which is used for specific tools for more quality critical applications and PowerMacs 4000 which is used for fixtured tools, such as QST, in assembly lines.
3.1.3 Quality tools & Industrial PC
Atlas Copco also provides customers with manual tightening and quality assurance tools. These products are used to do advance tightening, verify performed tightening as well to calibrate tools.
Example of such equipment can be the STWrench which is a manual tool that can be performed and verify critical fastenings. Furthermore, Atlas Copco also offers industrial PC and tablets used to display software, tightening results and traces.
3.1.4 Software
Besides embedded software that is used in all of the electrical hardware, Atlas Copco offers application software. ToolsTalk2 is a software which can be used to configure controllers and power tools in a more intuitive and easier way (Interview 5, Atlas Copco, 2020). ToolsTalk2 can be installed in any PC and can configure several stations in one instant (Löwen and Törnsten, 2019).
ToolsNet8 is another application software which are used for collecting and reporting fastening
results. ToolsNet8 can also to some extent do analyses and trigger alerts for incorrect tightening
or poor trends in tightening results (Interview 21, Atlas Copco, 2020).
Figure 3. Small part of the portfolio including associated controller and display with SQS3 (Atlas Copco Industrial Technique, 2020a).
3.1.5 Error-proofing software
Another software called SQS3, Scalable Quality Systems, is an operator guidance, as seen in Figure 3, SQS3 is used to reduce the quality issues that occurs in the production plant and helps/guides the operator to do their job. That means that less amount of deviation can occur at the station itself where the operator stands as well as before the product leaves the plant and gets delivered to the customer (Interview 5, Atlas Copco, 2020). QA Supervisor is a web-based server which manages the scheduling of the tests requested as well as shows the status overview in tightening data. It is an always connected based platform which multiple users can access by logging into it from any device. (Atlas Copco, 2020a)
Table 3. Short summary of software available for the automotive industry (Atlas Copco Industrial Technique, 2020a).
Software Function
ToolsTalk2 (TT2) Controller configuration management ToolsNet8 (TN8) Tightening analysis system
SQS3 Error proofing
QA (Torque) Supervisor Error proofing
3.1.6 Data services
• DDS – Data Driven Services is a service which Atlas Copco’s Service/Support division offers. The service allows customers to choose to share certain data with Atlas Copco from the ToolsNet8 database (ACDC), via a cloud service (Microsoft Azure), to perform heavy data analysis on the tightening data (Löwen and Törnsten, 2019; Interview 21, Atlas Copco, 2020).
• ACDC – Atlas Copco Data Communication which is both a database and a data collecting software. ACDC receives and collects communicated tightening data from the controllers and stores it in SQL database, where it can be archived later on. ToolsNet8 retrieves data from ACDC (Atlas Copco Industrial Technique, 2020b).
3.1.7 Other fastening techniques and Competitors
Atlas Copco Industrial Technique also provides other fastening techniques or solutions such as, gluing, pressing, riveting and friction drilling (Atlas Copco, 2020). Thus, making them cover a wide spectrum of methods for joining and assembling products. Some of Atlas Copco’s competitors when it comes to industrial tightening (Interview 21, Atlas Copco 2020):
• Apex Tool Group
• Ingersoll-Rand
• Stanley Black & Decker
• Uryu
• Bosch 3.1.8 Standards
There are certain standards which Atlas Copco products support, in several systems. The tools support OP, Open Protocol which their system can use in order to control the tools and receive feedback on the production result. Atlas Copco also support fieldbus standard which can communicate with the PLC system. Other standards which they support is ethernet and IP, Profibus and ProfiNet. (Interview 5, Atlas Copco, 2020).
3.2 Smart connected assembly
The IT-systems in a manufacturing site have usually been based on a hierarchical structure for control and decision making with a defined set of logic at each hierarchical level. In the landscape of smart tools and devices, the systems with distributed intelligence, the control and decision making may be located anywhere in an ecosystem. The architecture of the future will be distributed and networked (Atlas Copco Industrial Technique, 2016).
The analytical capability is based on algorithms which can identify information in big data that
can turn to intelligence. The development is expected to build on existing assets and adding
systems rather than replacing them in the future. Even when the aim is to establish a greenfield
plant, a lot of existing assets are reused to avoid risks, obtain cost effectiveness as well as benefit
from corporate investments and standards (Atlas Copco Industrial Technique, 2016).
Atlas Copco´s new generation of systems is designed for the aim to achieve openness and collaboration in a network of distributed logic. It is different from the older designs where the architecture consists of a clear hierarchical layer of logic (Atlas Copco Industrial Technique, 2016).
The digital manufacturing is here and drives the big data through automation technology, handheld driven tools and fixtured solution, Figure 4.
The solutions at Atlas Copco is designed based on principles and assumptions with the examples that follows:
• The communication infrastructure in the factory is based on Wi-Fi. 5G, Bluetooth etc.
• Cyber Security is required at infrastructure and application layer
• The data storage in the factory has a cloud-based solution based on cloud service.
• Real time control should be performed locally at local computers and by the use of edge computing in the future.
• Services and applications such as big data analytics are designed to run in the cloud
• The data on the assembly process is accessible to third party applications using open standards, example Atlas Copco Open Protocol (Atlas Copco Industrial Technique, 2016).
Figure 4. Digital manufacturing drives big data, (Atlas Copco Industrial Technique, 2016).
The purpose of the system architecture is to allow an easy integration of Atlas Copco´s clients´
manufacturing systems landscapes. The manufacturing IT landscape and strategies varies across
industries and the different companies. The modularisation of the solution that Atlas Copco provide is compliant with the ISA95.
Manufacturers investing in Industry 4.0 to invest in a greenfield plant and stepwise upgrade the existing assembly line. The modular design of Atlas Copco is suited to support such scenarios and allows manufacturers to build on existing assets and with the ability to add Industry 4.0 application (Atlas Copco Industrial Technique, 2016).
• The products and services at Atlas Copco are developed for Industry 4.0
• The tools are equipped with sensors which can send data wirelessly
• The provided controllers control the process and the safety of an assembly process
• All the data and result from the assembly process are captured and stored in a database.
Additionally, the tool performance data is captured and stored.
4 LITERATURE REVIEW
This chapter contains previous knowledge and former researched linked to the topic. It includes information, theoretical background, frameworks from literature regarding IT/software architecture, reference models and ecosystems etc.
4.1 Frameworks
There are three frameworks discussed when talking about system structure and software architecture, these are the ISA95 Pyramid, the NIST-model and RAMI 4.0.
4.1.1 ISA95 Pyramid
The hierarchal structure in production sites is based on ISA95 or ANSI/ISA95 which is an international standard for structuring and integration of enterprise and control system (ISA, 2000).
It contains different models and terminology to determine what and how information will be exchanged between the different system of an organization (finance, logistics, production, maintenance and quality etc.) (ISA, 2000).
A common model used to visualize the structure of the ISA95 standard is the Manufacturing Pyramid, also called the Automation Pyramid, see Figure 5 below, (Lu, Y. et al., 2016). It contains different levels or layers from top to bottom. The top layer, level 4, involves systems which handle information on an enterprise-wide level, which has a direct impact on business plans and finical directives etc. These systems are often called ERP-systems (see Chapter 4.2.2 Industry software), hence this layer is often called the ERP-level. The second top layer, level 3, includes systems which handle information that are more related to the manufacturing operations and are often called MES/MoM-level (see Chapter 4.2.2 Industry software). The middle layer, level 2, are systems which are used to monitor and control the manufacturing processes and handle collected data as well. These are usually SCADA or HMI-systems (see Chapter 4.2.2 Industry software) and therefore the layer is called SCADA-level. The layer beneath the SCADA-level, level 1, is the PLC-layer which contains system and hardware used to control and manipulate equipment directly such as robots, conveyers or tools. These systems are usually PLC-system (see Chapter 4.2.2 Industry software) however more and more are switching to PC-based control systems. The bottom layer, level 0, is the field level or the production process which are the devices, sensors and tools controlled by the PLC/PC-based systems in the level 1 (Åkerman, 2018). The bottom level also includes software and embedded software closely associated with the hardware on the workshop floor ISA95 has also been defined as an international ISO/IEC standard in IEC 62264.
Traditionally the different layers have been very strict regarding communication within the
pyramid being step by step. This meant that for example systems on the ERP-level would only
communicate down to system on the MES-level as well as up to possible system beyond the ERP-
level such as Business Intelligence and Cloud-services. Then system on MES-level would only
communicate with system on the ERP-level and the SCADA-level etc. (Åkerman, 2018). It has
been a constant effort and a challenge to integrate or merge the communication between the higher
levels’ office network, which connects the information systems, and the lower levels’ field level
network, also called the industrial communication network for automation. In addition, the volume
of data has also increased the higher up in the pyramid one goes, due to the traditional decision making in the higher levels which has required more data (Mikler, 2019). This has also resulted in the timeframe being longer the higher up in the pyramid. Meanwhile the communicating speed decreases, the higher up in the pyramid, since it has been more important with fast communication between the floor equipment and surrounding system than on an ERP-level (Mikler, 2019). Today the ISA95-pyramid is still referred to in the automotive industry (Lindström & Nedersjö, 2017;
Löwen & Törnsten, 2019).
Figure 5. Automation pyramid based on ISA95/IEC 62264.
4.1.2 NIST-model
Standards are the building blocks used to achieve a robust end-result in different technological solutions. To enable smart manufacturing systems, standard development in the future is necessary. Multiple areas of where the standards can be extended or developed are identified and along with new initiatives focused on SMS will embrace the development of technology and standards.
Level 4
Business planing and logistics
ERP
Level 3
Manufacturing Operations Management
MES
Level 2
Monitoring & control
SCADA
Level 1
Automation & manipulating
PLC
Level 0
Production process
Devices
Figure 6. The breakdown of the automation hierarchy into distributed services as a cyber physical based automation (Lu et al., 2016).
Realization of SMS capabilities requires replacement of the classical hierarchical model based on ISA95. Figure 6 illustrates the new paradigm which is called Cyber Physical Systems and is based on the distributed manufacturing services. The shift is made possible by introducing the smart devices which are accessible as services on a network, embedded intelligence at each level, predicative analytics and cloud technology which enables control as well as engineering functions at every hierarchical level. By using new approaches given these capabilities, automation throughout the whole hierarchical levels is possible to be achieved (Lu et al., 2016).
This new type of service-oriented paradigm impacts the smart manufacturing ecosystem in the way that it brings the transformation to a fully connected and integrated platform as shown in Figure 7 below. According to Lu et al. (2016), the manufacturing functions along with the three dimensions and the manufacturing pyramid can be hosted as services and be virtualized. What is remained on the shop floor level are time-critical as well as safety-critical manufacturing functions.
Figure 7. A service-oriented SMS (Lu, Y. et al., 2016).
When it comes to the current manufacturing standards these are far from being enough for the
service-oriented smart manufacturing ecosystem. The areas which need support include
cybersecurity, factory networking, supply chain integration as well as data transfer from factory to enterprise level (Lu et al., 2016).
4.1.3 RAMI 4.0
Since the manufacturing floor has become more and more digital, increasing the communication within and between the levels of the pyramid of factory, an urgent realization of having digital data models have occurred. The “old” production model (ISA95 pyramid) was based on and limited to hardware, very hierarchically driven and with isolating products (Lydon, 2019). Within information technology, a reference architecture is an ideal model for a class of architectures to be modelled after (DIN/DKE, 2018). Reference architecture models’ purpose is to create a common ground in terms of vocabulary and requirements within in the specific domain and to have blueprint or template to follow. As stated by model theory, models must always have a purpose and a relationship to the original to form an abstract representation the original, with specified properties.
According to DIN/DKE in the German Standardization Roadmap - Industrie 4.0 (2018, p. 34) [quote]:
Digital models and/or prospective models in Industry 4.0 take the form of:
▪ An information model with its sub-models
▪ A property model for describing the properties of objects
▪ A system model used to describe the significance of objects
▪ A description of the connections between objects and sub-models
▪ A behavioral model used to describe the processes and variables
▪ A structural model with elements that actually exist
▪ A basic system for describing a system from a functional perspective
Figure 8. RAMI 4.0 – the reference architecture model for Industry 4.0, including its crucial aspects and important entities for common picture of the future smart factory (Lydon, 2019).
