IN
DEGREE PROJECT INDUSTRIAL MANAGEMENT, SECOND CYCLE, 30 CREDITS
,
STOCKHOLM SWEDEN 2020
Capturing Value When
Implementing APM 4.0
Within the Swedish Automotive Industry
ARAN ANWAR
ARAVINTHAN KANANATHAN
Capturing Value When Implementing APM
4.0
Within the Swedish Automotive Industry
by
Aran Anwar
Aravinthan Kananathan
Master of Science Thesis TRITA-ITM-EX 2020:176 KTH Industrial Engineering and Management
Industrial Management SE-100 44 STOCKHOLM
Värde Fångande genom Implementering
av APM 4.0
Inom den Svenska Fordons Industrin
Aran Anwar
Aravinthan Kananathan
Examensarbete TRITA-ITM-EX 2020:176 KTH Industriell teknik och management
Industriell ekonomi och organisation SE-100 44 STOCKHOLM
Master of Science Thesis TRITA-ITM-EX 2020:176
Capturing Value When Implementing APM 4.0
Aran Anwar Aravinthan Kananathan 2020-06-14 Examiner Lars Uppvall Supervisor Pernilla Ulfvengren Commissioner IBM Contact person Yixin Fang Abstract
The automotive industry is in a period of significant change with different emerging technologies trying to impact the industry. It is in a time where the fourth industrial revolution, Industry 4.0, opens up opportunities for OEMs to improve their products and services with the help of transitioning towards Asset Performance Management (APM 4.0). This master thesis has identified various OEMs values with the help of the framework Value Mapping Tool to help the companies capitalize on these opportunities. The values were divided into the following sub-values; Value Captured, Value Missed, and Value Opportunities. OEMs have to adapt their existing business models with the help of business model innovation to improve the identification of values further and stay competitive or gain competitive advantages. Literature regarding the current state of the automotive industry and transitioning towards APM 4.0 has been combined with findings from six semi-structured interviews. The findings are based on interviews with employees with different positions from various automotive companies in Sweden. Furthermore, the findings and the literature have been compared to three benchmarking studies of similar research in Germany, China, and the USA to gain an overall view of the problem.
The missed values are characterized by Complexity, Ambiguity, and Knowledge. Moreover, the values captured show that new business models are needed due to the market rapidly transforming but companies lacking knowledge on how to capture value. The complexity concerns the new complex technologies arriving as well as the high level of uncertainties rising with the introduction of APM 4.0. The ambiguities indicate the problems the automotive companies have with searching for information since they do not know what to expect. The knowledge refers to knowledge gained during the process of implementing APM 4.0 in order to identify valuable unexplored data. Nonetheless, the study also led to finding potential value opportunities despite the lack of knowledge and a high level of uncertainty. There are unexplored business models which can improve the manufacturing processes for the automotive companies. Project benchmarking has shown positive signs but still has not reached its full potential due to low amounts of tests. The study concludes that many clear obstacles hinder a successful implementation of APM 4.0 within the Swedish automotive industry. In order for the automotive companies to optimize the implementation, they have to capture value relevant to their business model.
Keywords: Swedish Manufacturer, Automotive Industry, OEM, automotive, Industry 4.0, Artificial Intelligence, Asset Performance Management, Implementation, APM 4.0.
Examensarbete TRITA-ITM-EX 2020:176
Värde Fångande genom Implementering av APM 4.0 Aran Anwar Aravinthan Kananathan 2020-06-14 Examinator Lars Uppvall Handledare Pernilla Ulfvengren Uppdragsgivare IBM Kontaktperson Yixin Fang Sammanfattning
Fordonsindustrin befinner sig i en period av tydliga ändringar då ny teknologi försöker göra sitt avtryck på industrin. Det är i en tid då den fjärde industriella revolutionen, Industry 4.0, öppnar upp möjligheter för OEM:s att förbättra deras produkter och tjänster med hjälp av skiftet mot ”Asset Performance Management (APM 4.0)”. Denna masters arbete har identifierat olika värden i OEM:s med hjälp av ramverket Value Mapping Tool för att stödja företag att ta vara på dessa möjligheter. Värdena blev uppdelade i följande delvärden; Värde Fångat, Värde Missat och Värde Möjligheter. OEM:s måste anpassa deras nuvarande affärsmodell med hjälp av affärsmodellsinnovation för att kunna förbättra identifieringen av värden ännu mer och för att förbli konkurrenskraftig eller tjäna konkurrensfördelar. Litteraturen angående det rådande läget av fordonsindustrin och skiftet mot APM 4.0 har kombinerats med resultatet från de sex semistrukturerade intervjuerna. Resultatet är baserade på intervjuer med anställda inom olika positioner från varierande fordonsföretag i Sverige. Vidare, har resultatet och litteraturen jämförts med tre riktmärknings studier av liknande forskning i Tyskland, Kina och USA för att kunna få en överblick av problemet.
De missade värdena karaktäriseras av Komplexitet, Oklarhet och Kunskap. Vidare, visar de fångade värden att nya affärsmodeller krävs på grund av den snabbt ändrade marknaden i samband med den nya teknologin som kommer, men företag saknar kunskap av att veta hur de ska fånga denna värde. Komplexitet berör de nya komplexa teknologierna som kommer samt den höga nivå av osäkerhet som uppstår i samband med introduktionen av APM 4.0. Oklarhet syftar på de problem som fordonsföretag har med att hitta information eftersom de inte vet vad de ska förvänta sig. Kunskap hänvisar till kunskap erhållen under implementeringsprocessen av APM 4.0 för att identifiera värdefull outforskad data. Likväl, hittade studien också potentiella värde möjligheter trots bristen på kunskap och den höga nivån av osäkerhet. Det finns oupptäckta affärsmodeller som kan förbättra tillverkningsprocesserna för fordonsföretag. Riktmärkning projekten har visat positiva tecken men har fortfarande inte uppnått sin fulla potential på grund av det låga antalet tester. Studien drar slutsatsen att det finns många tydliga hinder för en lyckad implementering av APM 4.0 inom den svenska fordonsindustrin. För att dessa fordonsföretag ska optimera implementeringen måste de fånga värde som är relevant till deras affärsmodeller. Nyckelord: Fordons Industrin, Industri 4.0, Implementering, Artificiell Intelligens, APM 4.0, Förvaltning av Tillgångar, OEM
Table of Content
Table of Content 1 1. Introduction 6 1.2 Purpose 8 1.3 Research Question 8 1.4 Delimitations 8 2. Theoretical Framework 9 2.1 Artificial Intelligence 9 2.1.1 Four Crucial Technologies 9 2.1.2 Challenges of Implementing the Technology 10 2.2 Asset Performance Management 11 2.2.1 Industry 4.0 regarding Asset Performance Management 13 2.2.2 Implementation of APM 4.0 14 2.3 Value Mapping Tool 152.4 Business Model 18
2.4.1 Business models within the automotive industry 19 2.4.2 Business model Innovation 20 2.4.3 When new Business Models are required 20 2.4.4 Business Model Innovation Challenges 21 2.4.5 Business Model Innovation in the Automotive Industry 22 2.5 Connecting APM - APM 4.0 & Value Mapping Tool - Business Model 24
3. Methodology 26 3.1 Research Design 26 3.1.1 Research Setting 27 3.2 Research Process 27 3.3 Data Gathering 28 3.3.1 Interviews 29
3.3.2 Secondary Data Collection 30
3.3.3 Benchmarking 30 3.3.4 Coding 31 3.4 Data Analysis 31 3.5 Research Quality 31 3.5.1 Validity 32 3.5.2 Reliability 33
3.5.3 Generalisability 34
3.6 Ethics 35
4. Results and Analysis 36
4.1 Benchmarking 36
4.1.1 Germany's Industry 4.0 plan 36 4.1.2 Made in China 2025 38 4.1.3 Industrial Internet (US) 39 4.2 APM 4.0 - Benefits 40 4.3 APM 4.0 -Technology 41 4.4 APM 4.0 - Challenges 42
4.5 APM 4.0 -Strategy 44
4.6 APM 4.0 - Financial 46 4.7 Analyzing through application of Value Mapping Tool 47
4.7.1 Value Captured 47 4.7.2 Value Missed 48 4.7.2.1 Complexity 48 4.7.2.2 Ambiguity 49 4.7.2.3 Knowledge 50 4.7.3 Value Opportunities 51 4.7.3.1 Personalized Structure 52 4.7.3.2 Filling the Gap 53 5. Discussion and Conclusion 54
5.1 Main Findings 54
5.2 Sustainability Aspect 57
5.3 Recommendations 57
5.4 Limitations and Future Research 58
6. References 60
List of Figures
Figure 1. Operational Architecture 13
Figure 2. Different opportunities for Value Innovation 15
Figure 3. Value Mapping Tool 17
Figure 4. Simplified Value Mapping Tool 18
Figure 5. The five strategic circumstances who demand new business models 20 Figure 6. Relation between the more classic business model of Daimler and Car2go 24 Figure 7. Roadmap for capturing value from the transition to APM 4.0 25
Figure 8. Automotive Value Chain 27
List of Tables
Acknowledgment
First and foremost, we would like to show our gratitude to our supervisor, Pernilla Ulfvengren, who has supported and guided us throughout our research while also providing essential feedback throughout the process. We would also like to show our appreciation to Yixin Fang, European lead for Industry 4.0 as well as our supervisor at IBM, for helping us gain a contact network within our research subject. While also giving essential feedback regarding the research area. Furthermore, we would also like to thank the 6 individuals for putting time aside to assist us in carrying out our study by sharing their knowledge and experience within the field.
1. Introduction
This chapter will cover the background of the project as well as state the problem formulation,
purpose and research question the research will be aiming to answer. Ending with the
delimitations for the project.
The next industrial revolution called Industry 4.0 is near, paving the way for this revolution is the industry's urge to improve and become more efficient. However, Industry 4.0 is not emerging out of thin air. Its roots stem from the third industrial revolution and advancing computerized technology. The purpose of Industry 4.0 is to create an independent network solely for smart machines to evolve on their own, resulting in an overall impact on industries becoming more efficient and handling waste management better. Simply put, the aim of Industry 4.0 is to exchange information across different digital systems. (Marr, 2019)
Sweden is a country trying to refine the automotive manufacturing industry by implementing Industry 4.0 (Business-sweden.se, 2019). As Sweden seems to have already taken initiatives towards implementing Industry 4.0, it felt intriguing to study the phenomena of Industry 4.0 in Sweden and what possibilities the Swedish automotive industry can embrace. In a study carried out by Jan Olhager and Erik Selldin (2004), the studied companies stated that efficiency is valued highly since efficiency leads to the utilization of the company's resources and cost reduction. Furthermore, the study stated that most of the involved companies knew a lot about supply chain tools, but due to the current market, the use of these tools is very restricted. The companies valued forecasting highly, something the technology of Industry 4.0 enables. The implementation of Industry 4.0 would provide the companies with the possibility to achieve the goals they are thriving after. (Olhager & Selldin, 2004).
Asset performance management (APM) is defined as "a market of software tools and applications designed to improve the reliability and availability of physical assets (such as plants, systems of equipment, and infrastructure) essential to the operation of an enterprise)." (Bailey, 2020). Its objective is to assist the company with tools that enhance the reliability and availability of physical assets while simultaneously reducing the risk and operating costs (Arc Advisory Group, 2020).
Just like other industries, the automotive industry is experiencing pressure towards achieving higher efficiency and improving sustainability. These changes have already been felt by the industry and induced technical, economic, and social challenges. Automotive companies need to reinvent their manufacturing process in order to remain competitive on the market. By reinventing the manufacturing process, the automotive companies can achieve higher efficiency
in production as well as cutting down waste products from their process. Restructuring the manufacturing process with newly emerging and evolving technology poses challenges for the actors within the industry. At the same time, manufacturing companies are exploring the idea of implementing Asset Performance Management 4.0 (APM 4.0), which is a production area within the transformation to Industry 4.0, in order to achieve higher efficiency and sustainability. (Rever Team, 2019). However, several obstacles stand in the way for a smooth implementation. Although, the problem with the technology is not the lack of knowledge at the companies but the companies not knowing how to implement new production and manufacturing technologies as a whole system and how to overcome the various challenges that are present. These challenges include technical and economic ones. (Bose, 2017). For example, cybersecurity, to ensure the safe handling of the data collected for analyzing. Economic challenges such as being able to afford updating equipment in the manufacturing process to equipment with adequate technology. (Manohar, 2016). However, automotive companies know there is value in implementing APM 4.0 but have still not figured out how to approach capturing that value, which is why a framework is needed to guide the automotive companies towards a successful implementation. (West, 2020). Therefore, a framework such as Value Mapping Tool can be used to create a sustainable business model. In order to minimize current obstacles that hinder the automotive companies from seeing where to create value and where it is missed within the manufacturing process when implementing APM 4.0. (Bocken et al., 2013).
Our approach to this research is to study pilot projects carried out in different countries who are trying to achieve a more efficient manufacturing process by implementing the technology of Industry 4.0. However, the weakness of this approach is that the same strategy does not apply to all companies. Therefore, we have an idea of how different companies have to modify the implementation accordingly and only implement what they deem necessary for their manufacturing process instead of following a general guideline. By doing so, companies will enhance their efficiency in the manufacturing process. Following a general guideline might cause an unnecessary expense of equipment that does not benefit the specific company, and trying to improve the process blindly may be costly without giving any results.
The main idea for this research is to locate the significant challenges automotive companies face when trying to implement APM 4.0—trying to achieve higher efficiency. Furthermore, understanding the layout of the manufacturing process, which would enable easier management of the equipment in the production line. Hence, research questions regarding manufacturing processes and the dimensions of the transformation need answers in order to grasp where Swedish manufacturers stand in the transformation.
1.2 Purpose
The purpose of this study is to develop a tool to support value capturing in automotive companies in Sweden when implementing APM 4.0. In order to develop this tool the study will be identifying global challenges and success factors that impact the business models needed to capture value.
1.3 Research Question
To ensure the purpose of the study is achieved, the following research questions will have to be answered:
- What challenges exist globally when implementing APM 4.0?
- How can value mapping be adopted to develop automotive companies’ business models when implementing APM 4.0?
1.4 Delimitations
As the topic of Industry 4.0 is broad consisting of several aspects we have chosen to focus on Asset Performance Management, within the Industry 4.0 transformation, for this study due to limited time and instead focus on reaching an adequate depth within the aspects we have chosen to research. Therefore, the following limitations have been applied to the study; concepts such as Big Data, Cloud Computing, Cyber Security, as well as human-machine interactions will be considered in the study.
2. Theoretical Framework
The literature review for this study will consist of relevant existing research in order to guide the study. The information found throughout the literature review was gathered by conducting a systematic literature review.
2.1 Artificial Intelligence
Industrial Artificial Intelligence, combined with the technology emerging in recent years, technology such as the Internet of Things, cloud computing, and big data analysis. These technologies pave the way for the creation of Industry 4.0, which enables the manufacturing processes to become more efficient and sustainable. However, Industrial AI is just getting established and is, therefore, in a primary stage of its development. When it comes to industrial AI for Industry 4.0, different crucial elements need to be present to utilize the technology at an industrial scale. Among these elements are analytics technology, big data technology, and cloud computing. Industry 4.0 is based on the analysis of collected data from the manufacturing process; analytical technology is not enough. Big data technology is also essential for data gathering as well as cloud computing, providing a platform for Industry 4.0. While these elements are crucial, Domain know-how provides the system with the capability to adapt the data appropriately, collect correct data from the systems, and comprehend the problems from a systems perspective to a physical perspective. At the end of the process, the evidence is critical to validate the models and ensure the establishment of a system able to learn continuously. (Lee et al., 2018; Li et al., 2017; Zheng et al., 2018). Furthermore, Artificial Intelligence can detect asset failure before it takes place, leading it to be applied to operational performance within APM 4.0 (Aveva, 2020; Miklovic, 2018a). APM 4.0 is dependent on predictive analytics utilizing Artificial Intelligence to simulate different types of scenarios (Miklovic, 2018b). A process for AI has to be introduced for APM 4.0 in order to result in satisfying answers (Smolaks, 2019).
2.1.1 Four Crucial Technologies
When trying to implement and develop Industry 4.0 with the help of industrial AI, four technologies are crucial for a successful implementation. These four technologies are tied to the elements previously mentioned and are data, analytical, platform, and operations technology. (Lee et al., 2018).
Data technology is the technology that identifies what equipment and mechanisms are capable of acquiring useful data. Data technology is used to communicate the data, from the machines and the processes to the Cloud. It also translates the data from the physical to cyberspace. (Lee et al., 2018).
The analytic technology is used to convert the data collected into useful information. It helps triangulate correlations and unknown patterns in data collected from the automotive manufacturing systems. When combining other technologies with analytic technology, the company obtains the possibility to improve its manufacturing efficiency. (Lee et al., 2018). There are different types of platform technology; however, the most relevant for Industry 4.0 is Cloud Computing technology, as such technology considers aspects such as storage and servitization. The benefits with a cloud computing platform are obtaining abilities such as knowledge integration, quick service deployment, and effective visualization. (Lee et al., 2018).
Operation technology is essential for transforming the information collected from the data into decisions and actions. An Industry 4.0 system using this technology enables machines to make decisions and communicate solely based on the data results without human assistance. An example of such decisions is what adjustments should be made for optimal results. Combining the four technologies allows a successful implementation of a system such as Industry 4.0 in theory. (Lee et al., 2018).
2.1.2 Challenges of Implementing the Technology
The industry is very curious and driven to implement Industrial Artificial Intelligence to achieve an Industry 4.0 factory. However, several challenges exist hindering the implementation, even partially. Three of the main challenges are; machine to machine interaction, data quality, and cybersecurity. (Lee et al., 2018).
The issue that exists with the machine to machine interaction is the variation that occurs in the inputs between different types of machines. Making it crucial to ensure that the variation from one machine does not cause issues in the following parts of the process. (Lee et al., 2018). Ensuring that the data obtained by the system is of high quality is essential, collecting flawed and biased data can cause inaccurate results later on in the process. However, when combining manufacturing processes with connected technology, cybersecurity becomes an issue. The company puts its system in a vulnerable position, and the level of risk that comes along with a connected smart manufacturing system is still not understood. (Lee et al., 2018).
When it comes to the foundation of this implementation, as stated earlier, various technologies are needed. It presents the challenge of finding a successful collaboration b etween technologies and establishing a sufficient model for the implementation. A system that can handle both the software and machinery while still being a manageable system. These systems contain programs used to plan and manage the equipment. For accurate planning, access to performance statistics is granted, statistics such as equipment efficiency or information regarding the stock of products. For such a complex autonomous system to work, a well-established structure will need to be put in place, which may apply to the automotive industry. However, there is already an architectural structure called the automation pyramid. Although, with time a new structure will need to be established to fulfill the emerging systems, replacing the old standardized structure with a more decentralized and networked system. Another challenge with the implementation of Industry 4.0 is establishing a system that can work in real-time, a system that is capable of adapting and communicating the information transparently. Despite the goal being an automatized system, there will still be a part remaining, consisting of human-machine interaction. However, to make accurate decisions, the information provided to the employees from the machines needs to be in real-time. (Jeschke et al., 2017).
2.2 Asset Performance Management
The current situation is that many companies keep using a strategy that operates with reactive maintenance plans instead of strategies that are much more proactive. It is estimated that unplanned downtime costs a business ten times the cost of planned maintenance due to production losses, such as unexpected stoppages. (Bailey, 2020). Looking into the global automotive industry, one minute of downtime leads to costs approximating $22,000 (Manufacturing.net, 2006). APM can help reduce aspects such as unplanned downtime, failure of assets, maintenance costs and environment, health, and safety risks (EH&S risks). These factors contribute to the value of APM and to be able to minimize them as much as possible. (Bailey, 2020).
The process of APM revolves around four key components (Bailey, 2020):
1. Create an overall view of the firm's asset health by examining its data (both historical data as well as machine sensor data).
2. Predict potential signals of failures before they take place with the help of advanced analytics and "digital twin" models. If a piece of equipment, for example, displays an apparent deviation from how it usually operates, it could be a signal of possible downtime.
3. As earlier mentioned, minimizing EH:S risks is a major goal for the implementation of APM. This can be done by protecting asset integrity, compliance, and controlling potential risk conditions with the help of methods like risk-based inspection. Risk-based inspection is a process that examines the equipment in the company and sets up where work should be done. The risk-based inspection also utilizes the technology of real-time data in order to implement possible asset strategies.
4. Implement maintenance strategy development tools that make the company comprehend and manage potential reliabilities, asset risks, and operating costs. These tools could be included in a framework in which the company can locate and eliminate unnecessary maintenance, focusing on improving the equipment and risks that can have the most significant negative impact on the company.
Most companies are hoping to have a tool within APM that supports their asset health and improving their current maintenance strategies (West, 2020). Transitioning towards the new era of APM 4.0 requires a five-step process (Kasper & Miklovic, 2017):
1. Provide the firm optimal implementations - Utilize frameworks to connect people, processes, and technology. This integration results in the company's maintenance division synchronizing with other parts of the business, which generates possibilities to create value. Optimal implementations create space for flexibility and innovation to take place and adapt at a quick pace to the firm's needs and requirements.
2. Comprehend and operate Big Data within Maintenance and Operations - Make sure that the company has the knowledge needed for understanding and exploiting assembled data as it collected in different forms (e.g., historic, pictures/video, vibrations). Applying predictive analytics when combining all these different forms of data paves the way towards a transition towards APM 4.0. Analyzing a combination of structured and unstructured data.
3. Operate with Operational Architecture (OA) - OA is vital for the manufacturing industry to thrive and have a smooth implementation of APM 4.0. It distinguishes tactics from the strategy, which gives the company the ability to comprehend current APM capacities and potential ones, as illustrated in Figure 1. At this step, the company can go from as-is towards to-be.
4. Develop a business model - A company's business model directs the company from the states as-is towards to-be. In this path, the view of APM 4.0 transforms from cost-centric to value-centric.
5. Create an IIot Platform managing data - The final step of implementing APM 4.0 is the necessity of an IIot Platform. Depending on the type of problem and industry, different procedures are required when creating the platform. Although, general necessities are factors like; Big Data analytics, Cloud Computing, and development of applying the
technology. Successful integration of these factors results in technologies the Digital Twin.
Figure 1. Operational Architecture (Kasper & Miklovic, 2017)
2.2.1 Industry 4.0 regarding Asset Performance Management
One aspect within Industry 4.0 that is essential for its growth is Asset Performance Management 4.0 (APM 4.0). APM 4.0 is Asset Performance Management, which utilizes the technology of Industry 4.0 together with Internet of Things. In order for such utilization to occur, different industry initiatives need to be taken into consideration and implemented. The essential initiatives for the implementation of APM 4.0 are the following (Kasper & Miklovic, 2017);
Prescriptive Analytics: Big Data collection is the foundation for prescriptive analytics. It is based on the Big Data analytics framework, which contains the following aspects; Descriptive Analytics to know what happened, Diagnostic to know why it happened, Predictive to know what will happen, and then finally Prescriptive Analytics to know what action to take. There are two different aspects of prescriptive analytics. The first one enables the opportunity to prevent or at least postpone errors. The second aspect provides the ability to make changes within the operation to change how the equipment performs. Having the lateral aspect go hand in hand with the evolution of Industry 4.0 while the first aspect brings the technology to allow smart maintenance of the manufacturing process. (Kasper & Miklovic, 2017)
Machine Learning: Enables data analysis to triangulate patterns and uses those patterns to alter future actions and improve the overall processes. Machine learning provides an insight into the process and how to maintain it while also being able to learn from it. Companies can utilize
machine learning in two different ways; the first is to combine machine learning analytics with Big Data. Combining them gives new solutions to new issues, such as finding unknown correlations between different assets or production schedules. The second way is to use machine learning combined with regular data collection to apply old solutions to new issues. For example, it can predict future failures based on data providing insight into the temperature or flow of the process. (Kasper & Miklovic, 2017)
Smart Connected Assets: Cyber-physical systems, Cloud computing, and Industrial Internet of Things platforms are all essential parts of Industry 4.0. The combination of these provides smart connected assets with the purpose of maintenance. These assets provide real-time data collection enabling predictive analysis. Paving the way for the potential of having autonomous processes. (Kasper & Miklovic, 2017)
2.2.2 Implementation of APM 4.0
An example of how emerging technology combined with APM 4.0 could create possibilities for the automobile industry is when comparing the health of two different pumps. Combining the technologies enables quick visualization of the pumps. Due to APM 4.0, together with a digital twin (a digital version of the asset), the employee who is supposed to evaluate these pumps can see the two different pumps digitally. With the already existing technology, piping and instrumentation diagram(P&ID), an employee can identify the problems with the respective pumps and take actions accordingly to maximize efficiency. (Figueiredo, 2019).
Based on this example, the crucial technology needed in order to make APM 4.0 a reality is the adoption of digital twins together with Extended Reality technologies such as P & ID. These classify as visualization tools that help with the simulation and modeling of the process. In order for APM 4.0 to make a serious impact within the industry, gathering real-time data, and utilizing it immediately will be essential. The combination of this enables the collaboration between several parts of the industry, like customers, suppliers, and operations, to make decisions together and at a more efficient rate. (Figueiredo, 2019).
As mentioned in the example above, Digital Twins is a crucial technology that holds a fundamental role in the success of APM 4.0. It enables both the overview of the manufacturing process but also analyzes and implies any future decisions that can be made based on the data.
To achieve a prosperous Digital Twin establishment, a company will have to have adequate technology and high-quality data. (Miklovic, 2018c).
2.3 Value Mapping Tool
The research and literature study located the need to differentiate the types of values in the
emerging technology. Figure 2 illustrates the critical segments for value innovation
opportunities for companies and their respective stakeholders elaborated in this study.
Figure 2. Different opportunities for Value Innovation (Bocken et al, 2013).
The focal point of the network is the value proposition. The Value Proposition is how the stakeholders gain value through payment or other forms of value exchange. When presenting the value proposition, a single stakeholder and network can damage value. The category Value Destroyed can occur in different forms, not only through other actors but also through environmental issues such as pollution. The aspect Missed Value includes situations where single stakeholders are unable to thrive on current resources and skills. Stakeholders are not functioning on a desired industrial level and perhaps proving to be unsuccessful in gaining the desired advantages from its network. The reasons behind this possible failure can be poorly
designed value creation or capture systems. Leading to difficulties capturing value and incapacity to convince other stakeholders to invest in the transformation. Lastly, we have the factor, opportunities for new value creation, which helps the business venture into other markets and present new services and products. Successfully doing so provides new value to stakeholders. Other than the customers, this process may improve the well-being of employees or contribute positively to the surroundings by capturing value. (Chesbrough, 2010; Demil & Lecocq, 2010; Bocken et al., 2013).
The initial framework used in this study is the Value Mapping Tool (Bocken et al., 2013). The focus of this framework is to aid companies in producing value propositions to improve sustainable business modelling. Since the study has followed a qualitative approach, the tool provides the approach of value analysis. The goal of the Value Mapping Tool is to encourage the idea of creations and discussions. Furthermore, the model provides a repetitive procedure for reviewing the value creation opportunities from a stakeholder point of view. Hence, the purpose of the Value Mapping Tool is to accomplish the following three factors (Bocken et al., 2013):
● Comprehend every view, positive and negative, of the value proposition of the value network (i.e., which includes producing, delivering and attaining value associated with a product or service)
● Locate conflicting values (i.e., processes or technologies that can be an advantage for one stakeholder but be a disadvantage for another)
● Locate potential opportunities to transform and reposition the interests of the automotive companies to enhance the general result for the stakeholders of the value network - certainly for the society and environment
Based on the knowledge of value innovation, a Value Mapping Tool was created, as illustrated in Figure 3.
Figure 3. Value Mapping Tool (Bocken et al, 2013).
A simplified version of the Value Mapping Tool was developed due to the study's time restriction, and the use of the tool towards the study's aim effectively, see Figure 4. Another reason for using a simplified Value Mapping Tool was due to the researchers concluding that many of the main values within the original Value Mapping Tool resulted in similar types of value maps and outcomes. Data gained from articles, books, and discussions between the two researchers, set the base for the circular shape of the tool. The new design considerations of the tool involve:
● A systematic value determination has been simplified, demonstrating four values. The various types of values are illustrated in Figure 2. Locating these four values individually improves a more consistent evaluation of the present business model while simultaneously helping to locate areas in need of modification or enhancement.
● Stakeholder division in order to establish a view of the values from a multiple stakeholder perspective. The existing business modeling operations and mechanisms target the customer value proposition. The expected mechanism aims to increase the number of stakeholders or value consumers, counting both the environment and society. Every division serves as a stakeholder group.
● A network centered viewpoint instead of a firm centered one in order to strengthen the development of value within a network. A network centered viewpoint involves all the participants within the design, production, and distribution of a service or product.
Figure 4. Simplified Value Mapping Tool
Firms can examine oppositions and challenging value propositions when utilizing the tool. Nonetheless, the Value Mapping Tool's design is to establish and convert a company's business model to convey sustainability instead of being utilized as an opponent's analysis. Consequently, opponents are mostly excluded. Hence, the Value Mapping Tool aids APM's goal, which is to improve the assets of a company by identifying hinders within the companies while simultaneously preparing the companies for upcoming hinders. This tool also improves companies' business models by converting them into being proactive instead of being reactive.
2.4 Business Model
Business models describe how firms operate by explaining questions such as who the consumer is, how the firm earns money, and how the firm conveys value to consumers for a reasonable expense (Magretta, 2002). Four components constitute a business model that generates and produces a value (Johnson et al., 2008; Fielt, 2013):
● Customer Value Proposition ● Profit Formula
● Key Resources ● Key Processes
The first component, Customer Value Proposition, is generated when a company helps consumers by solving issues for certain occasions that require it. Profit Formula characterizes how the company will benefit when providing value to respective consumers. The aspects
included in the Profit Formula are revenue model, cost structure, margin model, resource velocity (how well they need to manage resources). The third component is Key Resources. Key Resources include employees, technology, products, facilities, equipment, channels, and brand. The main focus lies on the resources which generate a competitive advantage. Finally, the fourth component, Key Processes, consists of organizational and operational processes. Key Processes include planning, sales, services, and manufacturing. Furthermore, the company possesses rules, metrics, and norms, which assist the firm continuously and increasingly to deliver value. (Johnson et al., 2008; Fielt, 2013).
Executives aim to obtain sustainable competitive advantages by concentrating on selecting the correct types of business model (Dasilva & Trkman, 2014). Three criteria have to be fulfilled in order for a business model to be considered as good. (Casadesus-Masanell & Ricart, 2011):
1. The business model has to be in alignment with the company's objectives.
2. The business model has to be self-increasing, which results in managers' actions complementing each other to produce internal coherency.
3. A qualified business model has to be powerful enough to manage potential threats. Furthermore, when a business model is selected, it must be well performed with executives, steadily progressing and enhancing their respective organization's productive capacities. Hence, if a new opportunity or hindrance appears, the executives have to productively and in a well-timed manner, adjust their business model. (Dasilva & Trkman, 2014).
2.4.1 Business models within the automotive industry
The automotive industry has, on a universal level, a distinct and well-established business model. The term Value Captured concerns a product or service. For example, every steel vehicle body utilizing at least petrol or diesel engines that are sold to customers. Value Creations for the automotive industry is obtained by in-house capacities (e.g., the creation of automobile bodies and assembly of automobiles) together with well managed international supply chains. (Clarke, 1996; Marchand, 1991; Nieuwenhuis & Wells, 2007; Raff, 1991). An aspect that has had a subtle transformation in the automotive business model is the vastly improved manufacturing flexibility—mainly originating from automation and digitization to manufacturing. The flexibility has facilitated better productivity in various aspects, increasing work productivity and investments, higher diversities of products, and simultaneously a more reliable “platform” economies of scale. With this kind of improvement, the automotive business model has increased its quality regarding products and performances while still not suffering any significant expenses. (Nieuwenhuis & Wells, 2015).
2.4.2 Business model Innovation
Björkdahl and Holmen (2013) define business model innovation as “the implementation of a business model that is new to the firm.” Business model was previously stated as a term surrounding how companies generate value. This leads to business model innovation implicating a new meaning on how a company generates and seizes value to its consumers. Well-established companies usually produce new products or services which disrupt opponents while not adjusting their business models thoroughly simultaneously. (Johnson et al., 2008). There are occasions when companies develop new growth opportunities by completely transforming their existing business model. The main idea of business model innovation is not to explore new products or services. On the contrary, it is about improving or upgrading current products or services (e.g., by transforming the way it is delivered to the consumer and how the firm gains from the consumer contribution). (Björkdahl, 2009). Johnson et al. (2008) further encourage this idea by stating that new business models are necessary when firms are fundamentally transforming segments within their current business model.
2.4.3 When new Business Models are required
There are five strategic circumstances, illustrated in Figure 5, who demand new business models (Johnson et al., 2008).
In order for the new business model to be worth the investment and launch, it has to be new for both the company and refine its current models. (Johnson et al., 2008). Apple introducing the iPhone to the market is a clear example of when opponents within the same industry had to transform, readjust, and innovate their respective existing business models to survive and be competitive. iPhone radically transformed the industry by relocating the focus from hardware and technology to software and superficies. Competitors such as Samsung and Nokia had business models focusing on selling gadgets and text messages. However, Apple transformed this by contributing to an environment surrounded by applications and mobile services. (Hacklin et al., 2018). For a company to be successful, they need an attractive customer value proposition. The company needs the four components; Customer Value Proposition, Profit Formula, Key Resources, and Key Processes. The four components' productive cooperation while simultaneously managing upcoming unpredicted situations the new value proposition can have on the business model. Moreover, it determines that the business model disrupts opponents (Johnson et al., 2008).
By capitalizing on new emerging technology and services to improve the reliability and availability of a company’s assets, new business models can be utilized by APM in order to achieve this. This will innovate existing automotive business models and bring new technology as well as simplify current complicated solutions, which will further improve their manufacturing processes to run as a unit.
2.4.4 Business Model Innovation Challenges
To be able to form and enable new innovative business models in established companies who are innovative is demanding. These types of business models can compete with the current ones. Therefore, new types of organizational culture are needed in order to obtain various customers beyond the current ones. Hence, well-established companies have to utilize multiple business models simultaneously to operate innovative business models while preserving the current models. (Osterwalder & Pigneur, 2010).
Challenges with business model innovation can be categorized into several topics, one of which is to manage this type of innovation within well-established companies (Björkdahl & Holmen, 2013). Björkdahl and Holmen (2013) identified that many companies do not have an employee managing the company's business model innovation and also do not have any specific procedures for the model. There were other topics such as locking-in on current business models by executives, a limited amount of resources, testing with new business models, and gaining profits from business model innovation. A company can conduct business model innovation in various ways (Hacklin et al., 2018; Markides, 2013).
Facing these challenges can be done using various methods; a company can have several business models simultaneously, some even conflicting with each other. However, they can also transform the current business model and adjust it to the upcoming requirements. Researchers emphasize the dilemma of managing multiple business models simultaneously and how this is the primary reason for strategic deficiency in many cases. Two critical components regarding the evolution and construction of business models are learning and testing. Hence, difficulties may arise when constructing parallel business models in advance, since the two components might be excluded. (Hacklin et al., 2018).
When managing a new business model simultaneously with the current one, some of the business model components are entirely disconnected from the primary model. An example of this transformation is establishing a different customer value proposition by identifying new customer components through new distribution channels to display this transformation. An advantage of changing the primary business model while the business surrounding is transforming is that business models have to transform timely to remain competitive. Influential companies who are not transforming their existing business model in line with the business surrounding can deteriorate in the future. (Hacklin et al., 2018).
A vital decision for well-established companies is to decide to neglect innovative business models or to incorporate it within the organization (Osterwalder & Pigneur, 2010; Markides 2013). Osterwalder and Pigneur (2010) highlight the clashes between the business models, the resemblances of the strategies, and the hazardous effects depending on the decision. Hazard effects in this situation indicate how the new model can influence the already established model's aspects, such as brand image, revenues, and legal liabilities. Apart from the possibility of implementing or keeping the new model as a standalone, the implementation will have an upcoming influence on the company's potential success (Markides 2013). Markides (2013) states that structural options gain focus from business model research. Organizational factors like culture, values, vision, incentives, and employees will decide the success of running multiple business models at the same time.
2.4.5 Business Model Innovation in the Automotive Industry
Automotive companies are investing in new types of service offerings via the assembly of information from digital gadgets (Björkdahl et al., 2018). Digitization within this industry expands the urge for cooperation among the automotive companies. Some examples lay on the business aspect of cooperation, focusing on combining the primary qualifications of each automotive company. The cooperation between Volvo Cars and Uber is one such example. Cooperation also leads to companies being introduced to new business areas in which they are
insufficient regarding technical knowledge, such as the cooperation Volvo Buses did with ABB and Siemens concerning the improvement of electrical buses.
Many cases show that automotive companies are shifting towards end-users within their respective business networks (Björkdahl et al., 2018). Scania is an example as they seek business models where they can sell transport services. Transforming the business model at a company like Scania requires time since the company’s model has been similar for the past 100 years. Autonomous automobiles are anticipated to make automotive companies reevaluate how they generate value for their consumers (Björkdahl et al., 2018). Daimler’s Car2go is a clear example of business model innovation within the automotive industry (Osterwalder & Pigneur, 2010). The innovation model complements the primary business model of production, selling, and funding the automobiles at Daimler. The concept of Car2go is a so-called on-demand mobility solution having intelligent automobile fleets all over the city for its inhabitants. This intelligent automobile fleet is accessible all through the cities, with reservations being made before or at the location.
Figure 6 illustrates the relation between the main factors of Car2go and the main factors of the more classic business models of Daimler. Some factors which differ are the way Daimler administers new value propositions via new channels to identify new consumer components. (Osterwalder & Pigneur, 2010).
Figure 6. Relation between the more classic business model of Daimler and Car2go (Osterwalder & Pigneur, 2010).
2.5 Connecting APM - APM 4.0 & Value Mapping Tool - Business
Model
This section will clarify and summarize how the approach of APM to APM 4.0 is executed with
the help of Value Mapping Tool, and Business Models as well as illustrate how these factors
integrate to optimize the successful implementation of Industry 4.0 for automotive company's. The starting point lies in the definition of APM, as stated earlier, the goal of APM is optimizing the reliability and availability of a company's assets while minimizing its risks and operating costs. Since many companies nowadays are reactive rather than proactive, a transition towards APM 4.0 is needed to enhance their maintenance strategies. Next comes the transition towards APM 4.0, which includes the five steps needed for the transition mentioned in section 2.3. It is in
these steps that the framework Value Mapping Tool and utilization of business models come in. The steps highlight the importance of frameworks and have led to this research choosing Value Mapping Tool as the framework most suitable for the study's context. This is due to its ability to connect people, processes, and technologies by identifying conflicting values (e.g., technologies or services) and potential opportunities, while giving the people an overall view of the transition. Value Mapping Tool also seeks to enhance sustainable business modeling by producing value propositions, which takes us to the connection between Value Mapping Tool and business models. To achieve value rather than just being an expense for the company, one or several well developed and adapted business models has to be implemented with the help of business model innovation, which seeks to generate value by implementing new business models.
3. Methodology
This chapter presents the methodology used in this study. It contains the research design as well
as the reasoning behind using multiple case studies Research processes such as data collection
and analysis will also be examined in this section. This chapter will discuss topics such as
research quality, validity, reliability and generalisability. Additionally, ethical factors surrounding this study will be examined.
3.1 Research Design
This study takes a qualitative approach to data collection, where a combination of literature reviews and empirical data extracted from interviews have been conducted (Fig. 9). Following are three factors that determine which strategy should be used when conducting a research study (Yin, 1994, pp.4-9):
1. What type of design the research question possesses 2. The demand of controlling behavioral events
3. The focus on coeval events
Often, case studies focus on answering questions such as how and why. This form of research design is an appropriate alternative when there is a lack of requirements surrounding controlled behavioural events, while focusing on contemporary events , (Yin, 1994, pp.4-9). The research design selected is in accordance with the purpose of the study; to analyze the challenges and possible solutions that exist when implementing Industry 4.0 and Asset Performance Management, within the swedish automotive industry. Case studies manage large amounts of data, leading to a well rounded picture of the issue at hand. This results in research questions being answered. (Eisenhardt, 1989; Yin, 1994, pp.4-9; Blomkvist & Hallin, 2015, pp.63-66). The focus of this research study is to accomplish a wider contextualisation, making the choice of an exploratory multiple case study relevant in order to discover the existence of a complex phenomena (Eisenhardt, 1989). A multiple case study enhances the generalisability of the research which is viewed as being sufficient for research studies (Yin, 1994, pp.4-9; Eisenhardt & Graebner, 2007). However, these types of studies lead to complex and enormous amounts of data handling, which needs to be cautiously examined (Yin, 1994, pp.4-9; Eisenhardt & Graebner, 2007). The information collected was obtained through empirics and literature review (taken from secondary sources). The primary sources consisted of qualitative gathering of empirics from interviews and benchmarking, see Section 3.3. Data Gathering for more information. (Saunders et al., 2009). The research study is performed with an abductive
approach, which is a combination of the two approaches - inductive and deductive. The inductive aspect of the research approach is utilized to enhance general knowledge surrounding the main subject while constricting the scope and problem formulation. The deductive aspect of the approach is used to analyze the empirics. Studies by Saunders, Lewis and Thornhill (2009) and Blomkvist and Hallin (2015) agree that combining these two approaches is beneficial for a research study.
3.1.1 Research Setting
The study is performed in Sweden. Currently, the Swedish automotive industry is in the process of implementing Industry 4.0. As there are many different divisions creating value within the automotive industry, this study has chosen to focus on the manufacturing processes.To be more specific, this study will look at factors in Original Equipment Manufacturer (OEM). The model by Lind et al. (2012) has been used to illustrate where in the value chain this research focuses, see Figure 8.
Figure 8. Automotive Value Chain (Lind et al., 2012)
In this model shown above, there are six different segments presented which characterizes the value chain required for producing and distributing an automobile for end-customers. These segments are: (1) raw material suppliers, (2) refine raw materials suppliers, (3) component suppliers, (4) system suppliers, (5) OEM and lastly (6) automobile dealers. As depicted by the arrow in figure above, this study will examine OEM:s within this automotive value chain.
3.2 Research Process
The research question, the problem statement and the research topic were drawn from an iterative process as they developed over time while gathering knowledge about the area. When establishing these both a divergent and convergent thinking process were used (Blomkvist & Hallin, 2015, pp.31-32). The research study was performed using a nonlinear procedure and a method referred to as prototyping. Here the structure of the research is formed successively. (Blomkvist & Hallin, 2015).
Figure 9. How the Research Process occurred
3.3 Data Gathering
Both primary and secondary sources were utilised to collect the data in this research. Using various forms of data (multiple method design) helped increase generalisability. All the collected data has been critically evaluated. This will be further explained in Section 3.5 Research Quality. Several types of sources with independent data have been observed, which includes a literature review, 6 semi-structured interviews as well as benchmarking 3 projects implementing Industry 4.0 technology in different countries. The usage of several sources provided the possibility to triangulate the gathered data, which enhanced the validity of the discoveries (Saunders et al., 2009, chap. 5; Blomkvist & Hallin, 2015, chap. 2).
3.3.1 Interviews
Interviews were performed to discover what challenges the automotive companies face, when implementing Industry 4.0. They were also conducted to form a section of the data collection. The method of interviewing was selected in tandem with the purpose of the study- to discover the challenges of implementing Industry 4.0 based on the perception of workers placed in different divisions within automotive industries. Furthermore, interviews were conducted to find out what variables can be enhanced in order to strengthen the implementation process. Various interview techniques and methods were reviewed before creating an interview template and performing the interviews. In this study, semi-structured interviews were selected since they often answer specific questions while simultaneously leaving space for an open dialogue, follow-up questions as well as improvisation (Gill et al., 2008; Rowley, 2012; Creswell, 2009). Thus, conceived as the most suitable alternative for this study. Due to the coronavirus (COVID-19), all the interviews were conducted digitally. Interviews were also conducted in the interviewees preferential language, which was either English or Swedish. Interviews were executed with employees in various automotive companies, see Table 1.
The purpose of the interviews was to understand the interviewees perception on challenges faced within automotive companies regarding the production line. The interviews were also a way to get the employees input on possible solutions. Furthermore, they allowed us to compare the employees' views against each other and with the theoretical framework in Chapter 2. The interviews conducted pursued a template that concluded with a positivistic approach, permitting the interviewees to respond towards a “situation at hand, to the emerging worldview of the respondent, and to new ideas on the topic” (Merriam, 1998, p.74). This approach was used in order to achieve a high level of accuracy for the research. An interview template for every interview is featured in Appendix A.
All the six interviews were recorded, transcribed and coded in line with regards to the ethical aspects (see section 4.6 Ethics for more information) (Blomkvist & Hallin, 2015, pp.37-38). The transcription followed the guidelines of “The SAGE Handbook of Qualitative Data Analysis” which highlights that a very detailed description of the interviews is not necessary. Therefore, inaudible noise, mumbles and small pauses are not a part of the transcriptions. Only, transcription practices related to the subject of the interview were included (Flick, 2013). The language used in the transcription was English while the recordings varied between English and Swedish, depending on which language the interviewee felt most comfortable in. To maintain anonymity of the interviewees, code names were given, see Table.1 (Urban, 2019). The interviewees were promising candidates for this study as they (1) worked in different automotive
companies, (2) had various work experiences and (3) had different positions within the relevant area for the research, which enhanced the validity in the study.
3.3.2 Secondary Data Collection
The literature review in this study was performed in order to obtain knowledge, ideas as well as possible solutions regarding the scope of the research (Yin, 1994, p. 9). The abductive approach of literature review resulted in the usage of confirmation and apprehension of the empirics which promoted the literature and allowed it to either endorse or contradict the findings. The literature study also allowed feasible deductions as well as entailments to be supplied. Since the literature was critically reviewed, essential knowledge and theories were assembled within areas of research such as Real Time Data, Cloud Computing, Cyber Security, AI, Industry 4.0 and Asset Performance Management.
The literature in this study interprets current research, relevant theories and illustrates possible inadequacies and limitations (Blomkvist & Hallin, 2015, pp. 43-45). The assembled literature in the literature review consisted solely of journal articles which were peer-reviewed and critically examined in order to guarantee its reliability. Secondary sources were also utilized in the research study regarding Industry 4.0 and different concepts within this field such as Cloud Computing, Big Data, Cyber Security and Asset Performance Management, in order to understand their role in the current Swedish Automotive Industry. The literature used in this part was assembled through articles, published or internal documents supplied by the regime, government agencies and municipal governments. The main references used were from reviewed articles in order to find pertinent data with the help of a backward snowballing method (Jalali & Wohlin, 2012).
3.3.3 Benchmarking
In the literature review it was identified that multiple countries around the world had started attempting the implementation of Asset Performance Management 4.0. Therefore, a further investigation was conducted in order to grasp the ideas and execution strategies that had already been attempted. The investigation was done in order to understand how and why certain aspects of the implementation had been successful while others had not. The purpose of the benchmark was to locate how the implementation had previously been attempted, enabling the possibility to identify what obstacles these projects faced as well as how they could be overcome. A comparison of similar projects have been carried out in order to be able to benchmark how these projects tried to achieve the same goal while facing different obstacles and handling them accordingly. The first benchmarked project is Germany's Industry 4.0 plan. The second one is
Made in China 2025 and lastly Industrial Internet which is carried out in the United States of America.
3.3.4 Coding
A coding process was conducted to examine the transcribed interviews and to identify certain information relevant to the study (Flick, 2013). The procedure of coding was executed in six steps as this accomplishes validation of the results from the coding (Creswell, 2009). Firstly, all the conducted interviews were thoroughly scanned where vital keywords were identified.. Secondly, an iterative procedure was operated to scan through every interview multiple times so that new keywords corresponded better with the identified keywords (Urban, 2019). Furthermore, the results were categorized in 5 various topics: (1) Benefits, (2) Technology, (3) Challenges, (4) Solutions and (5) Financial. Every single topic related to its corresponding theme was coded and highlighted with various colours (Creswell, 2009). Lastly, every interview was scanned through repeatedly to assure that all the significant data was included. Nonetheless, there are some uncertainties with this performed procedure. There is always the human error factor which leads to the possibility for some essential information being excluded or unseen. Some of the keywords could also be treated as universal which could cause them to overlap with each other. Another factor is the potential risk of the interviewees being too scared to talk negatively about their respective company.
3.4 Data Analysis
Initially, data was gathered and sorted to get a deeper understanding of the research area, followed by analyzing and discussing the results. A framework using the five topics; (1) Benefits, (2) Technology, (3) Challenges, (4) Solution, and (5) Financial was used to categorize the results from the interviews. To get a more explicit structure of where value is missing and where it can be captured. The Value Mapping Tool was later applied to findings from the interviews as well as the empirics, allowing connection to be identified. The benchmarks in the empirics were only analyzed using the Value Mapping Tool. As these projects had already been implemented, the purpose of the benchmark was to locate what challenges and success factors were present. The purpose of the research was to identify the elements for creating value and capturing it. The Value Mapping Tool was used as both a framework and the reference point for the analysis and discussion.
3.5 Research Quality
A vital factor of a research study is to obtain a high level of research quality, which is to restrict the possibility that potential findings are inaccurate. To obtain a high level of research quality,
both reliability and validity must be established. Moreover, there must occur a pattern of consistency regarding the way data is collected. For example, data has to be collected systematically with the help of correct methods and the execution of critically reviewing the obtained data. (Saunders et al., 2009, chap.5; Blomkvist & Hallin, 2015, pp.52-53; Yin, 1994, pp.32-38).
3.5.1 Validity
The validity of this research study was established through several various sources and decisive criticism. In order for the validity to improve, it was of importance to decrease deceptive findings, which was executed by continuously discussing the accuracy of findings (Saunders et al., 2009, pp.156-158; Blomkvist & Hallin, 2015, pp. 52-53). Furthermore, during the collection of relevant data, it was vital to interpret for what intention the literature was written and to compare various types of sources claiming the same theory. The method of triangulation of various types of sources was used to improve further validity, which enabled an understanding of the research study. Triangulation also refers to various sources proving the same point. Which in our case was conducted by discussing the same subjects in all the interviews, resulting in the areas of knowledge, ambiguity, and complexity.
Another factor that improved the validity of the research study is external reviewers who provide neutral responses regarding the research. (Blomkvist & Hallin, 2015, pp.52-53; Creswell, 2009, pp.190-192; Eisenhardt, 1989; Yin, 1994, pp.32-36). These reviewers, in this study, are employees within the Swedish automotive industry, with both short and long experience within the area of Industry 4.0. Providing the study with different perspectives from experienced employees to inexperienced employees. The more experienced interviewees contributed with a more general view of Industry 4.0 and APM 4.0 within the automotive industry, meaning that they could describe a now and then perspective of the subject. This gave the researchers an understanding of how the situation of the problem has changed over the years. The inexperienced interviewees contributed to the new ideas of Industry 4.0 within the Swedish automotive industry, only providing the perspective of how it is now. This gave the researchers the ability to compare the experiences to one another. Since every interviewee works within their respective division of Industry 4.0 and APM 4.0 in their automotive company, they all are relevant to the study due to having theoretical and practical experience within the researched areas. Table 1 displays the employees' titles and how many years they have been in the automotive industry. The same questions were asked to all of the employees to discover their views (see Appendix A). However, as stated in section 4.3.2, the semi-structured interview model opened up for an open discussion, which led to the discussions varying from interview to interview depending on the interviewee's answers. The interview questions were asked to narrow down from a general
