Managing a transformation towards industry 4.0: A study within the bus manufacturing industry

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Mattias Eriksson & Oskar Ekebring

Managing a transformation towards industry 4.0

A study within the bus manufacturing industry

Industrial Management Degree Project

30hp

Term: Spring 2020 Supervisor: Alexandre Sukhov

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Acknowledgment

First and foremost, we wish to express our sincere appreciation to our supervisor, Alexandre Sukhov for his extraordinary cooperation, invaluable guidance and professional supervision.

We wish to pay our gratitude towards our second supervisor, Bo Rundh, and our dear fellow students in the opposition group for all your valuable critique and inspiration.

A special thank you towards our supervisors at the focal company for your endless support, giving us the opportunity to realize our master thesis. Thank you for providing the necessary resources and assistance, even during these unusual Covid- 19 circumstances.

In addition, we wish to express a grateful thank you to each and every one of our interviewees for all of your valuable insights. Thank you to all whom supported us with the master thesis.

Lastly, we would thank our family and friends for your support during the process.

Acknowledging our own efforts, the workload was distributed equally throughout the thesis. Oskar focused more on calculations, while Mattias focused on the theoretical framework. We led five interviews each.

Thank you.

Karlstad, June 2020

_____________________ _____________________

Mattias Eriksson Oskar Ekebring

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Abstract

Prior research suggests that industry 4.0 could bring numerous benefits to firms manufacturing operations. Despite industry 4.0’s overwhelming popularity, there is considerable confusion surrounding how to approach a transformation, and additionally how it will affect organizations. Bus manufacturing has seen minor advancements in initiating change towards industry 4.0 due to characteristics with low volumes, high product varieties, high complexity and a large variety of customer adaptations. This thesis intends to give clarity to the opportunities and challenges that industry 4.0 technologies entail for bus manufacturing operations.

Furthermore, contribute with a roadmap for how to manage a transformation towards industry 4.0 directed for bus manufacturing. The thesis was conducted studying a case using the process of systematic combining. Qualitative methods were applied to address the research questions, collecting data from ten interviews with operational- and manufacturing managers.

Empirical findings indicate that the characteristics of bus manufacturing influence the industry 4.0 technologies that are applicable and suited for implementation. The findings suggest opportunities in terms of increased production efficiency, improved flexibility and reduced takt times with data management and real-time capabilities from adapting data management, cloud technology and sensors. Other opportunities such as virtualization with digital twin and virtual training were also suggested to enable factory simulations and virtual representations of manufacturing stations. Prominent challenges identified were lack of knowledge regarding industry 4.0 technologies, data analytics, how to motivate investments, system integration and using robotics in the bus manufacturing operations.

Furthermore, the proposed roadmap for how bus manufacturing can manage a transformation towards industry 4.0 consists of eight steps that include important factors identified from the study such as vision, determination, barriers, responsibility, consistency and working in small steps.

This study’s theoretical contribution is providing knowledge about industry 4.0 in the context of bus manufacturing operations. The findings suggest that knowledge is a primary factor for managing a transformation towards industry 4.0, where educating and engaging personnel could facilitate the change work. Further, the results of this study lend support to the argument that approaching industry 4.0 is dependent on the organization and industry. The study also emphasizes that future research should focus on establishing a uniform definition of industry 4.0, and further examine what the costs for a transformation towards industry 4.0 could necessitate for bus manufacturing operations.

Keywords: Industry 4.0, bus manufacturing operations, change management, knowledge-based view, roadmap

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Sammanfattning

Tidigare forskning tyder på att industri 4.0 kan ge många fördelar för företag som bedriver tillverkningsverksamhet. Trots industri 4.0’s överväldigande popularitet finns det betydande förvirring kring hur man närmar sig en transformation, samt hur det kommer att påverka organisationer. Busstillverkningsindustrin har sett en obetydlig utveckling i förändringsarbetet mot industri 4.0 på grund av dess egenskaper med låga volymer, stora variationer, höga komplexitet samt många kundanpassningar. Denna uppsats har för avsikt att klargöra de möjligheter samt utmaningar som teknologier inom industri 4.0 innebär för verksamheten inom busstillverkning, då industri 4.0 anses medföra stora förändringar inom produktionen. Studien bidrar vidare till en färdplan för hur man kan hantera en omvandling mot industri 4.0 inriktad för busstillverkning. Studien genomfördes genom att studera ett fall med tillvägagångssätt enligt “systematic combining”.

Kvalitativa metoder tillämpades för att kunna behandla forskningsfrågorna med datainsamling från tio intervjuer med operativa- och produktionschefer.

Empirin indikerar att kännetecknen för busstillverkning påverkar vilka teknologier inom industri 4.0 som är tillämpbara och lämpliga för implementering. Analys av empirisk data tyder på möjligheter i form av ökad produktionseffektivitet, flexibilitet samt reducerad takttid genom datahantering och real-tidsfunktioner från användning av artificiell intelligens, molnteknik samt sensorer. Ytterligare möjligheter antyddes inom virtualisering med digitala tvillingar samt virtuell träning för att simulera fabriken och produktionsstationer i virtuella miljöer.

Framträdande utmaningar som antyddes var brist på kunskap om teknologierna inom industri 4.0, att analysera stora mängder data, att kunna motivera investeringar, systemintegration samt tillämpningen av robotik i produktionen. Den föreslagna färdplanen för hur busstillverkning kan hantera en omvandling mot industri 4.0 består av åtta steg som inkluderar viktiga faktorer som identifierades från studien såsom vision, beslutsamhet, hinder, ansvarighet, kontinuitet samt att arbeta i små steg.

Det teoretiska bidraget från studien är kunskap om industri 4.0 i kontexten för busstillverkning. Resultatet tyder på att kunskap är en primär faktor för att hantera en förändring mot industri 4.0, där informera och engagera personal kan underlätta förändringsarbetet. Resultaten av denna studie ger stöd till argumentet att hur man närmar sig industri 4.0 är beroende av organisationen samt inom vilken industri som verksamheten bedrivs inom. Studien understryker även att framtida forskning bör fokusera på att skapa en enhetlig definition av industri 4.0, samt att vidare undersöka de kostnader som bussproduktion kan kräva vid en förändring mot industri 4.0.

Nyckelord: Industri 4.0, busstillverkning, förändringsarbete, kunskapsbaserat synsätt, färdplan

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Abbreviations

AGV - Automated Guided Vehicle

AI - Artificial Intelligence

AR - Augmented Reality

CIM - Computer Integrated Manufacturing

CPS - Cyber-Physical Systems

CTO - Chief Technology Officer

FMS - Flexible Manufacturing System

IIoT - Industrial Internet of Things

Industry 4.0 - Referred as the fourth industrial revolution

IoT - Internet of Things

RMS - Reconfigurable Manufacturing System

RQ - Research Question

VR - Virtual Reality

3D - Third Dimension, “3” Dimension.

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List of Figures

Figure 1. Illustrates the connection between IoT, IIoT, Industry 4.0 and CPS in a Venn diagram, (Sisinni et al. 2018)………... 16 Figure 2. The principle of industry 4.0, by Liu & Xu (2017)………. 18 Figure 3. Presents the theoretical framework, inspired by Kotter (2014)…... 23 Figure 4. Illustrates the systematic combining research process, inspired by Huhtala et al. (2014)………... 26 Figure 5. Displays the main themes, generated nodes and sub-categories from the thematic analysis………..…… 32 Figure 6. Describes costs of a case comparing VR to traditional training in production……….. 48 Figure 7. Illustrates a roadmap for managing a transformation towards industry 4.0 for bus manufacturing operations………... 53

List of Tables

Table 1. Presents the ingoing steps for bus manufacturing operations……. 12 Table 2. The disposition of the paper……….... 13 Table 3. A selection of industry 4.0 definitions from academia……..……. 15 Table 4. Kotter’s eight-steps change management model, (Kotter 2014)…. 21 Table 5. Describes the eight-step change management theoretical framework, inspired by Kotter (2014)………... 24 Table 6. Displays the conducted interviews with the interviewees position and perspective, along with the length of the interview and its respective date……… 28 Table 7. Describes the six phases used in thematic analysis (Braun & Clarke 2006)……….. 30 Table 8. Presents the complete list of technologies and distinctions mentioned during the interviews……….. 36 Table 9. Cost groups related to traditional training………... 46 Table 10. Cost groups related to VR training………. 47

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1. Introduction

The introduction section of this paper gives a brief presentation to the background of the research area, including the research problem, the purpose of the thesis, research questions and a disposition of the paper.

Background

The introduction of industry 4.0 induce major change in the automotive manufacturing industry. Industry 4.0 is the transformation of the manufacturing industry by utilizing advanced information analytics and networked machines (Lee et al. 2015). Tay et al. (2018) further describe industry 4.0 as an overall change by digitalization and automation within organizations. Industry 4.0 is signified by the combination of internet technologies and future-oriented technologies in the field of “smart” objects. However, reviewing the current literature there is no uniform definition of industry 4.0, which limits theory building and research comparability (Culot et al. 2020). Regardless, Lasi et al. (2014) state that industry 4.0 is a new paradigm shift in industrial production due to the advanced digitalization within factories. A paradigm shift is according to Merriam-Webster (n.d.) defined as an important change that happens when the usual way of thinking about or doing something is replaced by a new and different way. In the context of industry 4.0, it implies that emerging technologies integrating with each other induce change for manufacturing processes.

Despite industry 4.0’s popularity, research does not give clarity on how the industry 4.0 transformation happens nor how to manage it. The bus industry has due to its characteristics and distinct differences compared to other automotive industries such as cars and trucks, not undergone much change for approaching industry 4.0.

Although, there are numerous research-based recommendations providing knowledge for approaching industry 4.0 (e.g. Prinsloo et al. 2019; Ghobakhloo 2018; Wang et al. 2016; Ganzarain & Errasti 2016). However, prior research is not generally accepted within the bus industry because of the characteristics of bus manufacturing such as having long takt times, high complexity, large variations, high customer adaptations and low volumes. Therefore, due to insufficient empirical studies examining a transformation towards industry 4.0 applicable to the bus industry, there are knowledge barriers and inexperience within the industry. Qin et al. (2016) further strengthen this as their study identified a research gap between current manufacturing systems and industry 4.0 for a system with customized flexibility. Furthermore, this incites missing out on potential benefits that research has shown derives from industry 4.0, e.g. increased productivity, greater quality, enhanced efficiency, flexibility, optimized energy consumption and improved use of production-related resources (Prinsloo et al. 2019; Sisinni et al. 2018 Chen et al.

2017; Qin et al. 2016). Focusing on the bus industry is meaningful to facilitate change by examining and providing access to the knowledge required for managing a transformation towards industry 4.0. According to Ghobakhloo (2018),

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10 manufacturers need to ready themselves to embrace a potential fourth industrial revolution to remain competitive. Also, Synnes & Welo (2016) states that one aspect of sustaining competitiveness within a rapidly changing industry is dependent on a company’s ability to absorb new technologies. Furthermore, to provide flexibility within the work environment-production system to maximize capacity utilization (Synnes & Welo 2016).

Industry 4.0 entails change (Ghobakhloo 2018) and to achieve competitive advantage the change requires knowledge (Kotter 2014). Thus, acquiring knowledge is essential for managing a transformation towards industry 4.0. Both explicit knowledge about the phenomenon but also knowledge for managing the change. Research has found that involving and utilizing the knowledge from workers is important in change management (Kotter 2014). A knowledge-based view of the firm offers comprehension for how knowledge is connected to individuals working within an organization (Grant 1996). Hence, applying change management and adopting a knowledge-based view of the firm constructs a framework for managing a transformation towards industry 4.0 for bus manufacturers. Moreover, guidelines are useful to define and manage a transformation. Hence a roadmap is suggested. A roadmap is a strategic plan that defines a goal or desired outcome and includes the major steps or milestones needed to reach it. According to Leitão et al. (2013) roadmaps are considered a very useful tool for deploying a manufacturing company strategy into operations. Therefore, providing a roadmap will serve as guidance for bus manufacturers and contribute with knowledge for managing a transformation towards industry 4.0.

Problematization

In practice, a transformation towards industry 4.0 induces change to the automotive manufacturing operations as advanced emerging technologies utilized together lead to a complex manufacturing environment. This requires profound knowledge to fully take advantage of the opportunities that originate from industry 4.0. Improved flexibility, cost reductions, improved productivity, improved quality and delivery time reduction are examples of suggested opportunities from industry 4.0 (Moeuf et al. 2018; Mittelstädt et al. 2015). Prior paradigm shifts in industrial production have brought major transformation to the automotive industry, and research shows that industry 4.0 will have a major impact on existing manufacturing processes (Tay et al. 2018).

Succeeding the many opportunities that research has identified for manufacturing operations with implementing industry 4.0, it is therefore important to consider a transformation towards industry 4.0 in order to maintain long-term competitiveness. However due to the substantial differences of bus manufacturing (e.g. low volumes, high customer adaptations, large variations and high complexity) compared to other automotive industries such as cars and trucks, previous research

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11 and empirical evidence of industry 4.0 lack applicability for bus manufacturers.

Therefore, focusing on the bus industry is meaningful to provide access to the knowledge required to undergo a transformation towards industry 4.0. Even though industry 4.0 will affect all sections of an organization (Tay et al. 2018; Ghobakhloo 2018), literature states that industry 4.0 will have major effects on manufacturing operations (Lu 2017). Due to the previous deficient research, technologies involved within industry 4.0 (e.g. Oztemel & Gursev 2020; Ghobakhloo 2018; Mittal et al.

2017; Kagermann et al. 2013) needs to be further investigated in the context of bus manufacturing operations (Pfeiffer 2017). There is a need for more knowledge within bus manufacturers in order to manage a transformation towards industry 4.0.

For this, both technology aspects and change management will have a major contributing factor.

Purpose

This study aims to build and provide explicit knowledge about industry 4.0 by examining opportunities and challenges of involved technologies and concepts for the bus manufacturing industry. Furthermore, adapting a knowledge-based view of the firm supports the understanding of how knowledge is connected throughout organizations. Thus, a change management model can be developed that accounts for the required knowledge when managing a transformation. Moreover, a composed roadmap with steps for approaching and managing a transformation towards industry 4.0 will facilitate for bus manufacturers to initiate their transformational journey.

Research questions

Two research questions have been formulated to give clarity to the purpose of this thesis and the answers it aims to provide.

RQ1: What opportunities and challenges does industry 4.0 technologies entail within bus manufacturing operations?

RQ2: How would a roadmap for managing a transformation towards industry 4.0 look like for bus manufacturing operations?

By addressing the research questions, this thesis contributes with knowledge of industry 4.0 by identifying opportunities and challenges with high potential technologies within bus manufacturing operations and how to manage a transformation towards industry 4.0. By using a recognized change management model and adapting it for industry 4.0 it will provide a framework for bus manufacturers to employ. This thesis will provide answers to two critical research questions which facilitates future work in the industry 4.0 paradigm within the bus industry.

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12 Empirical context

The study is conducted in partnership with a Swedish bus manufacturer. There are few actors in the bus manufacturing industry, and the selection of the focal company was justified by them having a complete bus manufacturing operation. Thus, enabling the research questions to be studied in the desired context. Compared to other automotive industries such as cars and trucks, bus manufacturing is characterized by low volumes, much manual labor, high customer adaptation and large variations. The focal company manufactures their buses in production sites around the globe, where some sites focus on certain production steps and others have a complete bus manufacturing operation. Bus manufacturing operations is suggested to consist of seven main production steps that are commonly carried out with a number of dedicated stations in a production line. The steps are presented in Table 1.

Table 1. Presents an overview of the production steps for bus manufacturing operations.

1. Pre-manufactured components

A complete bus consists of thousands of components manufactured in-house or outsourced.

2. Chassis Chassis frame with engine, axles, wheelbase and front floor zones.

Welded or bolted together.

3. Powertrain The powertrain is assembled with other driveline components and electrical systems.

4. Body structure Body structural elements are welded or bolted to chassis. Elements can to a different degree be pre-assembled with body components.

5. Body components Interior/exterior panels and equipment are mounted to the bus.

6. Exterior finish Painting of the exterior.

7. Test and verification Final test of bus functionalities and quality assurance.

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13 Disposition

The thesis is structured as presented in Table 2.

Table 2. Disposition of the thesis.

Chapter 1 Describes the background and problematization of the thesis along with its purpose.

Chapter 2 Introduces the reader to a theoretical background where definitions, key concepts and technologies of industry 4.0 from literature are described. Furthermore, knowledge-based theory and change management are presented which leads to the theoretical framework.

Chapter 3 Describes the methodology of the study and the research process using systematic combining. Further, it explains the data collection, data analysis. Also, delimitations and a discussion on trustworthiness are presented.

Chapter 4 This chapter presents an analysis of empirical data, using the methods described in chapter 3.

Chapter 5 Presents a virtualization case on costs for training using virtual reality vs. traditional training.

Chapter 6 A discussion on the collected data and relating it to theory.

Chapter 7 Presents the conclusion of this thesis and provides answers to the research questions.

Chapter 8 Gives limitations of the conducted research, along with potential future research.

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2. Theoretical background

The theoretical background provides a theoretical description for the foundation of this paper. Attention is directed to the industry 4.0 phenomenon with its involved concepts and technologies. Further, it presents a change management model associated to the knowledge-based view for managing transformation into industry 4.0. Lastly, the theoretical framework is presented.

Industry 4.0

Industry 4.0 is described by Drath & Horch (2014) as the fourth industrial revolution and is the concept of using cyber technologies with connected physical objects to enable service applications. Even though this definition may soar in the highest transcendental visions, this overview is commonly returned to due to the futuristic accuracy without digging deep in its fundamental characteristics (Culot et al. 2020). Furthermore, Culot et al. (2020) and Oztemel & Gursev (2020) suggest that the term industry 4.0 implies digitalization in society at large. Prior operational positions will be computerized due to the increased usage of computerization and robotics, which is debated to affect the unemployment rate. Emerging technologies focusing on manufacturing aims to achieve increased productivity and competitive position with e.g. autonomous solutions using robotics, increased flexibility and high customizability to meet various customer requirements (Culot et al. 2020;

Oztemel & Gursev 2020).

At the time of introduction, the concept of industry 4.0 was introduced in a framework of governmental support, though without clear managerial procedures for its implementation (Sommer 2015). Currently, industry 4.0 is a top priority for many organizations, research centers and universities. Yet, the majority of experts in academia believe that the industry 4.0 term itself is unclear (Ghobakhloo 2018).

According to Oztemel & Gursev (2020), academic research on industry 4.0 focuses on understanding and defining the concept, trying to develop systems and business models. The industry on the other hand, focuses its attention on the change of industrial machine suits and intelligent products as well as potential customers on this progress (Oztemel & Gursev 2020). Considering the novelty of the subject, manufacturing firms are facing difficulties when it comes to understanding the industry 4.0 phenomenon (Ghobakhloo 2018).

Researchers have different perceptions on the meaning of industry 4.0 (Tay et al.

2018) and it still does not exist a pronounced definition of the concept (Culot et al.

2020). The definition of industry 4.0 adapted throughout this thesis is based on commonly used definitions from literature presented in Table 3.

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Table 3. Industry 4.0 definitions from academia.

Definition Reference

A new phase in manufacturing through information and communication technology (ICT) driven innovation.

Culot et al.

2020, p. 4 Industry 4.0 is defined as an amalgamation of advanced technologies where the

internet is extensively used to support certain technologies such as embedded systems.

Tay et al.

2018, p.

1384 The concept of Industry 4.0 (where 4.0 represents the fourth industrial revolution) arises when the IoT paradigm is merged with the CPSs idea.

Sisinni et al.

2018, p.

4726 ...defining Industry 4.0 as a set of initiatives for improving processes, products and services allowing decentralized decisions based on real-time data acquisition.

Moeuf et al.

2018, p.

1118 Recent advances in manufacturing industry has paved way for a systematical deployment of Cyber-Physical Systems (CPS), within which information from all related perspectives is closely monitored and synchronized between the physical factory floor and the cyber computational space. Moreover, by utilizing advanced information analytics, networked machines will be able to perform more efficiently, collaboratively and resiliently. Such trend is transforming manufacturing industry to the next generation, namely Industry 4.0.

Lee et al.

2015, p. 18

Culot et al. (2020) suggest that industry 4.0 is not about a single breakthrough invention but comprises several “tech ingredients” that are still evolving into new enabling technologies by convergence and mutual combination. They further mention that the landscape of technological aspects is extremely vast and heterogenous (Culot et al. 2020). The technologies themself are not necessarily new, but it is rather the usage of emerging technologies in combination together that is being described as the fourth industrial revolution. The literature identifies difficulties in distinguishing concepts and technologies involved in industry 4.0 (Culot et al. 2020). Therefore, the next section will examine the different concepts and technologies related to industry 4.0.

Key concepts within industry 4.0

Internet of Things (IoT) is a commonly mentioned concept within industry 4.0. IoT is described by Oztemel & Gursev (2020) as “the inter-networking of physical devices, vehicles, buildings, and other items embedded with electronics, software, sensors, actuators, and network connectivity that enable these objects to collect and exchange data.” (Oztemel & Gursev 2020, p. 28). In addition, Sisinni et al. (2018) further elaborate by describing the close bond between IoT and IIoT (Industrial Internet of Things). IIoT is machine-to-machine and industrial communication technologies with automation applications (Sisinni et al. 2018). “Things” named within IoT includes consumer electronic devices while for IIoT, “things” refer to integrated operational technology of machines and control systems (Sisinni et al.

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16 2018). Thus, IIoT is closer related to industry 4.0 than IoT. Cyber-Physical System (CPS) is described as “the integration of computing and physical processes which are essential components.” (Oztemel & Gursev 2020, p. 15). According to Hermann et al. (2016), this integration enables computers to monitor and control physical processes, and within a feedback loop the physical process affects computations and vice versa. Moreover, Sisinni et al. (2018) state the relevance of communication technologies within the IIoT environment which summarizes in machine-oriented solutions with centralized network management. In their article they present the relationship between the different distinctions, as shown in Figure 1. below (Sisinni et al. 2018).

Figure 1. Illustrates the connection between IoT, IIoT, Industry 4.0 and CPS in a Venn diagram, (Sisinni et al. 2018).

From Figure 1. it is apparent that industry 4.0 is the overlap between IIoT and CPS.

This means connecting industrial assets, control systems and machines with the information systems and the business processes (Sisinni et al. 2018). The distinction that IoT and CPS are key components in industry 4.0 is supported by numerous researchers (e.g. Oztemel & Gursev 2020; Ghobakhloo 2018; Liao et al. 2017;

Hermann et al. 2016; Kagermann et al. 2013). As research indicates, industry 4.0 is the future of global manufacturing (Tay et al. 2018). A summary of recurring concepts and technologies that the literature mentions being part of the phenomenon of industry 4.0 is presented in Appendix A.1.

Enabling technologies for industry 4.0

The literature identifies three distinctive characteristics of enabling technology groups within industry 4.0. These groups are consistent with the properties of the most frequently mentioned technologies. The enabling technology groups are virtualization, real-time information sharing and autonomy (Culot et al. 2020).

● Virtualization

Virtualization relates to the virtual representation of the real world, which enables the replication of the entire value chain (factory, equipment, machinery and

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17 products). This is achieved by merging sensor data acquired from the physical world into virtual or simulation-based models (Ghobakhloo 2018). The virtual environment is referred to as a “digital twin” (Ghobakhloo 2018). The digital twin enables improvements to existing processes by optimizing the functionality of production lines in complete isolation without disrupting the physical processes (Gilchrist 2016). Virtualization technology enables manufacturers to have a complete digital footprint of their products, increasing understanding because the production of the product can be virtually evaluated (Ghobakhloo 2018).

Simulation modelling is a way of testing virtual processes or systems to estimate or find out the output of the modelled system. Other related technologies in this area are virtual reality (VR) and augmented reality (AR), mostly used for educational purposes. VR is a computer-simulated reality and AR is an interactive experience of a real-world environment (Oztemel & Gursev 2020). VR can, for example, enable operators to simulate experiences on the production floor that is similar to the real world. Virtualization is heavily dependent on real-time capability to handle information (Ghobakhloo 2018).

● Real-time information sharing

Real-time capabilities mean that information can be collected and analyzed in real- time, allowing for immediate decisions at every moment (Ghobakhloo 2018).

Integrating and analyzing data in real-time will optimize resources in the manufacturing process and achieve better performance (Lu 2017) because having immediate access to reports and live status reports enables greater control and more timely management. Cloud computing, sensors and big data analytics are important technologies to enable information in real-time. Cloud technology allows data collected from sensors to be stored and processed through the cloud environment making information accessible to be processed at any moment. According to Gilchrist (2016), an advantage with real-time analytics is that it creates a cognitive computing system that is capable of detecting or predicting flaws, failures, or anomalies in the system that a human operator could not detect.

● Autonomy

Autonomy implies that machines and people in manufacturing systems are able to decide for themselves and react in novel situations without external guidance (Culot et al. 2020). Artificial intelligence is a technology that enables autonomy (Culot et al. 2020). Nonetheless, autonomy also relates to organizational changes in terms of control mechanisms and structure (Hermann et al. 2016). Furthermore, automated guided vehicles (AGV) are mentioned in the context of autonomy as automated transporting solutions (Hermann et al. 2016). Additionally, robotics contributes to autonomy in production.

Culot et al. (2020) further mention a fourth enabling technology group that is process integration: “Process integration refers to the impact of interoperability

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18 solutions in unifying product and process data within and across organizational boundaries.” (Culot et al. 2020, p. 8). Process integration is an important enabler for communication between CPS and IoT for manufacturers. Furthermore, horizontal and vertical system integration play an important role for industry 4.0 manufacturing. Horizontal integration ensures that smart devices, machines and engineering processes smoothly operate together. Vertical integration allows production data to be used for making decisions by allowing communication between the horizontally integrated shop floor network and systems. In addition, Hermann et al. (2016) suggest a technical assistance technology group that involves technological devices to give workers support in their tasks. The tasks are often unpleasant, too exhausting or unsafe for the worker to do them without support (Hermann et al. 2016).

Liu & Xu (2017) combines the principles of industry 4.0 according to Figure 2.

below. The figure shows an overall factory view of the principles of industry 4.0 where several smart factories are connected via the internet and all the physical production elements comprise a “cyber twin” respectively. Cyber twin in the figure is equivalent to the concept of digital twin mentioned prior.

Figure 2. Displays the principle of industry 4.0, by Liu & Xu (2017)

Multiple factories are horizontally integrated, i.e., the physical assets and the digital twin are integrated to enable optimized decision-making in production across the network.

Pfeiffer (2017) concludes that there is no single industry 4.0. What innovations will be adapted in which industries and by which companies depends on the specific settings as defined by factors included but not limited to the degree of automation,

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19 product complexity, value chains, and production technology. Consequently, this means a great responsibility for industries but also for each individual organization.

In order for businesses to adapt to the concepts of the industry 4.0, they need to realize the company’s current position regarding their processes, procedures, philosophy, strategy, and current technologies in relation to the level of adaptation they wish to achieve. Knowledge is a recurring building block when it comes to managing change and there are many roadmaps which a company can follow on their journey toward digital transformation. However, not one roadmap fits all businesses or industries. Each company’s motivations, intent, goals, priorities, budgets, and problems differ (Gilchrist 2016). Results from Erol et al. (2016) study indicated a strong need for guidance in assisting companies with their industry 4.0 vision and roadmap. Adapting a knowledge-based view of the firm is a contemporary approach to strategic management and organizational design (Santoro & Bierly 2006), it acts as a foundation for the roadmap introduced in the following sections.

Knowledge-based view of the firm

In accordance with the importance of a firm's knowledge about industry 4.0, Grant (1996) describes the knowledge-based view of the firm as an extension of the resource-based view. According to Santoro & Bierly (2006), the resource-based view explains performance differences by identifying unique, valuable and inimitable resources and capabilities. However, in most cases, the resource that ultimately leads to a sustainable competitive advantage is the firm’s unique knowledge base (Santoro & Bierly 2006). The knowledge-based view involves the shifted focus from identifying numerous resources increasing competitiveness, both tangible and intangible, to increase focus on knowledge among the employees (Conner & Prahalad 1996; Foss 1996; Grant 1996; Hoskisson et al. 1999).

Principles for a knowledge-based view establish and form a basis for human capital involvement in structural and routine activities of the firm. The knowledge-based view of the firm is conceptualizing firms as heterogeneous, knowledge-bearing entities (Hoskisson et al. 1999), supported by Grant (1996), Foss (1996) and Conner

& Prahalad (1996).

In his well-acknowledged article, Grant (1996) describes two main types of knowledge: tacit and explicit. Tacit knowledge, described as “knowing how” is abstract knowledge received through individual experiences and personalized contexts in which could involve minor aspects of one's act in a certain situation (Grant 1996). Explicit knowledge, described as “knowing about” is concrete knowledge easier to transfer where information is stored and accessed and therefore easier to obtain (Grant 1996). According to Santoro & Bierly (2006), tacit knowledge is more difficult for individuals to identify, understand and transfer into the organization. In addition, Grant (1996) discusses the importance of involving people and emphasizes on careful coordination of individual specialists who

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20 possess a large variety of knowledge types. In the knowledge-based firm, rules and directives exist to facilitate knowledge integration; their source is specialist expertise which is distributed throughout the organization. While analysis of delayering has concentrated upon cost reduction and increasing the speed of decision making, the knowledge-based view suggests to the extent that 'higher-level decisions' are dependent upon immobile 'lower-level' knowledge. Then, according to Grant (1996) hierarchy impoverishes the quality of higher-level decisions.

Kengatharan (2019) presents findings for which knowledge, being the intellectual capital of an organization, has a positive correlation on productivity and the performance of a firm. Moreover, Abreu (2018) states the direct relation of knowledge and competitive intelligence, which emphasizes on the importance of knowledge among employees. Abreu (2018) further states that having productive processes aligned with the acquisition of new technologies are not enough for an organization to be successful. Adapting the knowledge-based view, with the need for flexibility and innovation in today's fast-developing society, it yields carefully thoughtful ideas when it comes to managing the transformational change towards approaching industry 4.0. Thus, change management will have a major contributing factor, and it will therefore be presented next.

Change management

Fettig et al. (2018) state that when a company starts dealing with industry 4.0, it moves out of its comfort zone, encouraging innovating thinking that leads to new business capabilities. They argue that managing a transformation becomes most significant (Fettig et al. 2018). However, this is also where traditional management methodologies come to its boundaries. Therefore, moving forward Fettig et al.

(2018) mention the need for a defined approach to managing a transformation towards industry 4.0. According to Bucy et al. (2016) at McKinsey & Company, research shows that 70 % of complex, large-scale change programs do not reach their stated goals. Pitfalls that are common include a lack of employee engagement, inadequate management support, poor or non-existent cross-functional collaboration, and a lack of accountability (Bucy et al. 2016). Results from Agostini

& Filippini (2019) seem to point to the fact that firms need to be shaped at all levels to reach high levels of implementation of industry 4.0 technologies. According to Gupta (2011), there is a need for a model to bring change to the corporate culture, making it ready for innovation. The need entails a systematically developed framework for innovation, ready for mass adaptation in the information-saturated knowledge age (Gupta 2011). There are several models for leading change. In this study, a commonly used model for managing change will be adapted: Kotter’s eight-step model (Kotter 1996). The steps in this model entail a systematic way to deal with change in today’s business world where change is necessary (Kotter 2014). Kotter’s model is in line with the principles of industrial transformation because of its adaptability to the organizational structure and ability to incorporate

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21 different staff responses to change (Small et al. 2016). Moreover, Appelbaum et al.

(2012) conclude that Kotter’s eight-steps remain an excellent starting point for managers implementing change in their organizations. They suggest that applying the model is likely to improve the chances of success (Appelbaum et al. 2012).

Kotter’s change management model became an instantaneous success at the time of introduction, and it remains an eminent key reference model in the field of change management (Appelbaum et al. 2012). The model has since been updated (Kotter 2014). Kotter argued that the eight-step process, as it was, needed to be revised in order to work well for continuous change in organizations due to the complexity of faced challenges (Kotter 2012). One of the most important differences between the old and new process was to emphasize the importance of involving as many people as possible, not just a core group. “You need more eyes to see, more brains to think, and more legs to act in order to accelerate” (Kotter 2014, p. 23). In reminisce, this draws from Grant’s knowledge-based view where the knowledge of each individual in the firm is important since the firm is conceptualized as an institution for integrating knowledge (Grant 1996). Kotter’s revised eight-step change model for transforming an organization is presented in Table 4. below. The steps are coexisting and always working (Kotter 2012).

Table 4. Presents the eight steps in Kotter’s change management model (Kotter 2014).

Step Description

1. Establishing a sense of urgency 2. Forming a powerful guiding coalition 3. Form a strategic vision and initiatives 4. Enlist a volunteer army

5. Enable action by removing barriers 6. Generating short-term wins 7. Sustain acceleration

8. Institute change

Using a model facilitates structure and assurance. A description of the eight steps in the context of industry 4.0 follows in the theoretical framework.

Theoretical framework

Based on the literary definitions in Table 3. and descriptions from the theoretical background, a compiled definition of industry 4.0 is derived from the authors. This study proposes a working definition of industry 4.0:

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22 Industry 4.0 is defined as an amalgamation of advanced technologies where the internet is extensively used to support certain technologies such as embedded systems. It is a general concept for improving manufacturing processes, products and services. Technologies involved are e.g. smart sensors, digital twin, big data analytics, cloud technology, artificial intelligence and robotics. Industry 4.0 arises when the Internet of Things paradigm is merged with the Cyber- Physical System idea and by utilizing advanced information analytics, networked machines will be able to perform more efficiently, collaboratively and resiliently. Allowing decentralized decisions based on real-time data acquisition.

The literature describes the paradigm shift of industry 4.0 inducing major change for manufacturing operations that undergoes a transformation (Ghobakhloo 2018;

Lu 2017). Lack of knowledge can result in failure when managing a transformation towards industry 4.0. To undergo change, change management provides a structure for meeting change and obtaining favorable circumstances. Considering the uncertainties of approaching industry 4.0, this puts a lot of demand on each company for managing a transformation and obtaining the required knowledge. The theoretical framework is formulated based on Kotter’s eight-step model and adapted to industry 4.0 by establishing critical questions for managing a transformation. Since a transformation towards industry 4.0 requires change, change management becomes appropriate as it contributes to a structured approach for dealing with and managing major change. This model will serve as the theoretical framework for constructing the roadmap, directed for bus manufacturers. The framework is presented in Figure 3.

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Figure 3. Presents the theoretical framework, inspired by Kotter (2014).

Detailed information for each step of the framework in Figure 3. is described in Table 5.

Acknowledging the questions in the theoretical framework supports the knowledge- based view of the firm. To establish why industry 4.0 is urgent, this requires knowledge from employees to see which opportunities are applicable for the organization. Furthermore, knowledge is required for selecting the responsible persons for initiating and managing a transformation. It can also be drawn to the knowledge-based view to involve people to buy into the whole flow of action.

Involving workers contributes to wider knowledge and experiences, and also having more people assisting the transformation. This can also counteract people opposing change if they instead are involved in the change management work.

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Table 5. Describes the eight-step change management framework, inspired by Kotter (2014).

1. Establishing a sense of urgency

Why is industry 4.0 urgent?

Create and maintain a strong sense of urgency among as many people as possible about industry 4.0. People will not change if they cannot see the need to do so.

2. Forming a powerful guiding coalition Who is responsible for managing the

transformation?

This step entails building the core of the network structure.

Assembling a group with enough power to lead the change effort in a transformation towards industry 4.0. This core group needs to have the drive, the intellectual and emotional commitment, the connections, the skills, and the information to make things happen. Also be able to work effectively together as a team.

3. Form a strategic vision and initiatives

What’s our industry 4.0 vision and strategies?

Clarify a vision for how a future change towards industry 4.0 will be different from the past. Establish how to make that future a reality through strategic initiatives linked directly to achieving that vision.

4. Enlist a volunteer army How do we effectively communicate to the organization?

Communicate the industry 4.0 vision and strategic initiatives to the organization in ways that lead large numbers of people to buy into the whole flow of action. Done well, this process results in many individuals wanting to help.

5. Enable action by removing barriers What barriers do we have for approaching industry 4.0?

Identify and remove obstacles around managing a transformation towards industry 4.0. Such as inefficient processes and hierarchies, which slow or stop change in order to provide the opportunities to generate real impact.

6. Generating short-term wins

What are short-term goals we can accomplish?

Create an ongoing flow of strategically relevant wins, both big and small. Ensure that the wins are visible to the entire organization and that they are celebrated, even the small wins.

These wins, and their celebration can carry great psychological power and play a crucial role in building and sustaining credibility, leading to respect and more cooperation within the organization.

7. Sustain acceleration How can we be more consistent in our transformational work?

Continue the change towards industry 4.0 even though the general human’s motivation tends to diminish after a few wins.

It is built on the recognition that many wins come from sub- initiatives which by themselves may be neither substantial nor particularly useful in a strategic sense. Larger initiatives will lose steam and support unless related sub-initiatives are also completed successfully.

8. Institute change How can we institute change?

Institutionalize wins and integrate the successful industry 4.0 initiatives. In effect, this helps to implement the changes into the culture of the organization.

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3. Research method

The methodology chapter describes the research method used in this study. First, it explains the research process based on the study’s problematization. Next, it describes how data collection and analysis has proceeded. It ends with a discussion on research ethics, trustworthiness and delimitations.

The problem that this thesis aims to address is the current lack of knowledge within bus manufacturing operations related to the transformation towards industry 4.0.

By examining which opportunities and challenges that industry 4.0 technologies entail within bus manufacturing operations this thesis aspires to provide explicit knowledge of how the bus manufacturing operations can manage the change into industry 4.0. The research approach is based on the purpose of the thesis. To investigate the research questions in this exploratory research a qualitative method was used to study a case, as it applies to explore a topic in great depth (Bryman &

Bell 2011). Moreover, the systematic combining (Dubois & Gadde 2002, 2014, 2017) approach was used for abductive reasoning as it contributes to handling the interrelated elements in the research work that occurs because of the intertwined activities in the research process (Dubois & Gadde 2002). Adopting systematic combining, multiple sources may contribute with information revealing unknown aspects to the researcher (Dubois & Gadde 2002), for example, discover new dimensions of the relationship between industry 4.0 and the bus manufacturing operations. The study focuses on one bus manufacturer to generate an in-depth understanding of the bus industry’s complex real-life context. According to Easton (1995), studying one case causes a greater depth since researching a greater number of cases with the same amount of resources means more breadth but less depth.

Research process

The process of systematic combining is for theoretical framework, empirical fieldwork and case analysis to evolve simultaneously. Systematic combining is thus a non-linear, path-dependent process with the objective of matching theory and reality (Dubois & Gadde 2002). The continuous movement of going back and forth, matching and redirecting, is more likely to lead to deep structure than in linear research (Dubois & Gadde 2014). The systematic combining research process, inspired by Huhtala et al. (2014), can in retrospect be identified as in Figure 4 below. It follows the framework by Dubois & Gadde (2002) and presents how focus shifted due to our understanding of the phenomenon. It started with a suggested research focus to raise awareness of industry 4.0 within the bus industry. A thorough literature review about industry 4.0 transformation was conducted to obtain a greater understanding of the phenomenon. Later, open-ended interviews were held with four participants in the bus industry at the focal company with the purpose of gaining knowledge about the organization, learning about their products and their current situation.

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Figure 4. Illustrates the systematic combining research process, inspired by Huhtala et al.

(2014).

Returning again to the theoretical realm to understand the literature describing industry 4.0 transformation, the research focus was directed towards the industry 4.0 technologies and organization theory (e.g. Schwer & Hitz 2018; Daft 2008;

Taylor 2004). This was due to revelations that the bus industry with its differences in characteristics compared to other automotive industries had not yet identified their needs. Reasons for this was the lack of knowledge about the overall industry 4.0 phenomenon within the bus industry. Consequently, we reoriented the focus to involve manufacturing operations dominated by low volume with the belief of producing more generalizable findings. The research process continued with five semi-structured interviews with bus manufacturing managers. Matching again to the theoretical realm and literature, it became apparent that another perspective from the researched theories was needed to engage an industry 4.0 transformation since it proved difficult to involve other industries dominated by low volume, i.e.

aircraft manufacturing, due to major differences between industries. Therefore, the research focus was reoriented once again. Now, focus shifted back to the bus industry, specifically to bus manufacturing operations. Also, by exploring various organizational views the roadmap was commenced from knowledge-based theory (Grant 1996) and change management (Kotter 2014) to approach industry 4.0. This approach for managing transformation was found suited due to its conceptualizing view of firms as heterogeneous, knowledge-bearing entities and prior success in change management (Appelbaum et al. 2012).

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27 Data collection

Qualitative methods were used in order to collect data. Corbin & Strauss (2015) underlies the use of qualitative methods in circumstances where relatively little is known about the phenomenon. Moreover, if the phenomenon is not fully understood or is poorly understood and further exploration is necessary to increase understanding. Furthermore, Gray (2017) stresses that in qualitative research the adoption of strategies and data collection methods tends to be highly flexible, which suits the continuous movement of the approach according to systematic combining.

A strength of qualitative data is its capacity of providing detailed information to explain complex issues (Gray 2017). Qualitative data has the ability to develop rich descriptions of meaning, behaviors and emotions. This is something that is difficult with quantitative methodologies due to its coarse-grained outcroppings of variables and events, which tends to only skim the surfaces of processes (Langley 1999).

Unstructured and semi-structured interviews were considered suited for data collection. According to Gray (2017), if the objective of the research is exploratory then interviews may be the best approach since it allows the researcher to explore for more detailed responses where respondents are asked to clarify their responses. The study is based on data collection from a bus chassis production facility in Sweden, a coach production facility in Northern America and a complete bus production facility in Eastern Europe.

Sampling

Interview candidates were selected using purposive sampling, as it is a suggested method for qualitative research since it seeks to obtain insight into a specific context (Gray 2017). Using purposive sampling to identify and select the information-rich candidates allowed for the most proper utilization of available resources (Etikan et al. 2016; Patton 2014). Meaning that the operational managers and manufacturing managers were carefully selected due to their position and knowledge about managing transformational change and their technological expertise about bus manufacturing operations. In addition, it further meant selecting candidates based on geographical position, so a face-to-face interview would be possible. Marshall et al. (2013) argue that research has shown that data saturation occurs at twelve interviews. Therefore, twelve interviews were considered satisfactory to collect sufficient data that could be analyzed within the given timeframe. Unfortunately due to the circumstances of Covid-19, the number of interviews were reduced to ten. However, this was still considered satisfactory due to the adequate data collected throughout the hour-long interviews. The collection of data was carefully considered since no amount of analysis can make up for improperly collected data (Etikan et al. 2016). Table 6. presents information about the interviews.

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Table 6. Displays the conducted interviews with the interviewees position and perspective, along with the length of the interview and its respective date.

Interview Position Perspective Length

(h:min:sec)

Date Interview Type

1 Manufacturing

Engineering Director

Manufacturing manager

0:59:56 2020-02- 24

Face-to- face 2 Industry Vice President Manufacturing

manager

0:55:43 2020-02- 25

Face-to- face 3 Global IT Delivery

Manager

0:52:18 2020-02- 26

Face-to- face 4 Vice President Value

Chain

0:42:23 2020-02- 26

Face-to- face 5 Industrialization and

Engineering Director

0:59:29 2020-03- 12

Skype

6 Senior Vice President Europe & South America

Manufacturing

Operational manager

1:05:58 2020-03- 20

Face-to- face

7 Senior Vice President Bus Chassis

Operational manager

1:00:21 2020-03- 18

Face-to- face 8 Senior Vice President

Global Bus Technology

Operational manager

0:59:15 2020-03- 19

Face-to- face

9 Preparation and

Development Director

Operational manager

0:55:31 2020-03- 23

Skype

10 Digitalization Support Director

0:25:18 2020-03- 27

Skype

Interview procedure

Interviews were held with interviewees from two business functional areas:

operational management and manufacturing management. Operational management are responsible for organizational and daily operation topics whereas manufacturing management are technical/process managers responsible for areas within the bus manufacturing operations. The two perspectives of interviewees were selected since they were considered to possess relevant information for the study concerning technical knowledge and transformational work. The intention of interviewing operational management was to obtain insight into to their experience and competence about managing change. Furthermore, the intention of interviewing manufacturing management was to obtain insight and knowledge about bus manufacturing operations and its current technologies. Observations from visiting production facilities in Sweden and Eastern Europe contributed to the

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29 understanding of the bus manufacturing process. It also generated new questions on which interviews could be based.

An interview guide was constructed with questions based on unstructured interviews with personnel at the focal company and the theoretical background.

Depending on which business area the interviewee belonged, their aspects of the questions in the interview guide were considered. Details about the interview guide are presented in Appendices B.1. and B.2. To the greatest extent possible, interviews were held face-to-face since according to Bryman & Bell (2011) this allows for the collection of non-verbal data such as facial expressions. Furthermore, in order to obtain rich answers, interviews were held in the language that the interviewee felt most comfortable in; English or Swedish. The interview set-up was as recommended by Arksey & Knight (1999) having two interviewers present and their roles explained to the interviewees. One interviewer led the interview while the other took notes in silence. Advantages of this set-up were that the silent interviewer could notice things of interest not identified by the interviewer asking the questions, whose attention was often held by group dynamics. Further, having two views of what happened helped to clarify key themes and areas for enquiry and analysis (Arksey & Knight 1999).

Data analysis

The thematic analysis approach described by Braun & Clarke (2006) was chosen as a method of analysis due to the ability of freely identifying categories and themes within the empirical data. Moreover, thematic analysis is flexible due to its relative independence of theory. Thematic analysis can however lead to inconsistency and a lack of coherence due to its flexibility which was carefully considered during the process by creating a clear analyzing method. The analysis involved identifying patterns within the empirical and theoretical findings and to identify and create themes and categories in different levels to uphold the exploratory stance (Braun &

Clarke 2006). The thematic analysis followed the six phases described by Braun and Clarke (2006) presented in Table 7.

Managing a transformation towards industry 4.0: A study within the bus manufacturing industry

Mattias Eriksson & Oskar Ekebring