DANIAL ETEMADY QESHMY JACOB MAKDISI1265672/FULLTEXT01.pdf · KTH Industriell teknik och management...

IN THE FIELD OF TECHNOLOGYDEGREE PROJECT MECHANICAL ENGINEERINGAND THE MAIN FIELD OF STUDYINDUSTRIAL MANAGEMENT,SECOND CYCLE, 30 CREDITS

, STOCKHOLM SWEDEN 2018

Human error management 4.0Augmented Reality Systems as a tool in the quality journey

DANIAL ETEMADY QESHMY

JACOB MAKDISI

KTH ROYAL INSTITUTE OF TECHNOLOGYSCHOOL OF INDUSTRIAL ENGINEERING AND MANAGEMENT

Human error management 4.0

Augmented Reality systems as a tool in the quality journey

by

Danial Etemady Qeshmy

Jacob Makdisi

Master of Science Thesis TRITA-ITM-EX 2018:188 KTH Industrial Engineering and Management

Industrial Management SE-100 44 STOCKHOLM

Hantering av mänskliga fel 4.0 Augmented Reality som ett verktyg i

kvalitetsresan


Jacob Makdisi

Examensarbete TRITA-ITM-EX 2018:188 KTH Industriell teknik och management

Industriell ekonomi och organisation SE-100 44 STOCKHOLM

Master of Science Thesis TRITA-ITM-EX 2018:188

Human Error Management 4.0

Augmented Reality Systems as a tool in the quality journey


Jacob Makdisi Approved

2018-06-05 Examiner

Luca Urciuoli Supervisor

Jannis Angelis Commissioner

Scania CV AB Contact person

Christer Wilhelmsson

Abstract

The manufacturing industry is shifting, entering a new era with smart and connected devices. The fourth industrial revolution (Industry 4.0) is promising increased growth and productivity by the Smart Factory and within the enabling technologies is Augmented Reality (AR). This is a technology that can be used to augment the reality with digital information. At the same time as the technology is introduced, errors in manufacturing are a problem which are affecting the productivity and the quality. The errors can be caused by humans and companies strive to eliminate the errors caused by humans.

This research aims to find the main causes of human errors in assembly lines and thereafter explores whether AR is an appropriate tool to be used in order to address those issues. Based on a literature review that identified and characterized a preliminary set of root causes for human errors in assembly lines, these causes were empirically studied in an exercise that covered an in-depth case study at a multinational automotive company. Data in form of interviews and deviation reports have been used to identify the causing factors and the result showed that the main causes of human errors are the amount of thinking, deciding and searching for information which affected the cognitive load of the operator and in result their performance. Several interviews with experts in AR allowed to verify if this technology would be feasible to solve or mitigate the found causes.

Besides that, in repetitive manual assembly operations, AR is better used showing the process in order to train new operators, at the same time as for experienced operators AR show information only when an error occurs and when there is a need of taking an active choice is more appropriate. Nevertheless, while theoretically able to managing human error when fully developed, the desired application makes the augmentation of visual objects redundant and increasingly complex for solving the identified causes of errors which questions the appropriateness of using AR systems. However, the empirical findings showed that for managing human errors, the main bottleneck of an AR system is the software and AI.

Key-words

human error management, artificial intelligence, human performance, vehicle assembly, augmented reality, smart factory, industry 4.0, human machine interaction, mental workload.

Examensarbete TRITA-ITM-EX 2018:188

Hantering av mänskliga fel 4.0

Augmented Reality som ett verktyg i kvalitetsresan


Jacob Makdisi Godkänt

2018-06-05

Examinator

Luca Urciuoli Handledare

Jannis Angelis Uppdragsgivare

Scania CV AB Kontaktperson

Christer Wilhelmsson

Sammanfattning

Den tillverkande industrin skiftar och går in i en ny era där smart och uppkopplad teknologi introduceras i de operativa delarna av tillverkningen. Denna fjärde industriella revolution (Industry 4.0) som den även kallas för med smarta fabriker, utlovar ökad produktivitet och tillväxt.

Bland de teknologier som representeras i detta nya landskap återfinns Augmented Reality (AR), vilket är en teknik som används för att förstärka verkligheten med digital information. I samband med att denna nya teknik introduceras, är avvikelser i produktion ett problem som påverkar företags produktivitet och kvalitet. Den mänskliga faktorn är en bidragande del till detta problem och företag strävar efter att eliminera felen orsakade av människor.

Denna studie syftar till att hitta orsakerna till att människor orsakar fel i produktion och därefter utforska om AR är ett lämpligt verktyg att använda för att råda bot på dessa orsaker och därmed eliminera felen. Genom en litteraturstudie har det identifierats ett antal faktorer som påverkar den mentala belastningen hos människor i produktionssammanhang. Dessa faktorer har därefter undersökts genom en fallstudie hos en multinationell tillverkare av kommersiella fordon. Datainsamling i form av intervjuer och avvikelsedata har använts för att identifiera de påverkande faktorerna och resultaten pekade på att behovet av att behöva tänka, leta efter information och fatta beslut påverkade den mentala belastningen mest. Intervjuer hölls med forskare och montörer för att definiera en lämplig AR funktion som sedan undersöktes genom flera intervjuer med forskare inom AR för att verifiera om AR är en lämplig teknik att använda för de identifierade orsakerna.

I termer av AR i en arbetsmiljö med repetitiva aktiviteter efterfrågas en funktion som visualiserar fel för montörer som är erfarna medan det för oerfarna montörer är bättre med visualisering av hela arbetsprocessen. Men, trots att systemet i teorin är lämpligt att använda för att hantera orsakerna till att felen uppstår så är den efterfrågade funktionen överflödig då visualisering kommer visas väldigt sällan samt att tekniken är väldigt komplex. Detta gör att det går att ifrågasätta hela funktionen av att använda AR system i det fall som studerades. Dessutom visade sig tekniken vara olämplig att använda i den miljö fallet utspelar sig i på grund av svårigheter med artificiell intelligens (AI).

Nyckelord

human error management, artificiell intelligens, human performance, fordons montering, augmented reality, smart factory, industry 4.0, människa-maskin interaktion, mental belastning

DEDICATION AND ACKNOWLEDGEMENTS

We want to thank friends and family for lending us support throughout the thesis - no onementioned, no one forgotten. We also want to thank our supervisor Christer Wilhelmssonand all the participants at the case company for their support throughout the study. A

special thanks to our supervisors at the Royal Institute of Technology: Jannis Angelis and EliasRibeiro Da Silva for helpful guidance and useful remarks.

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TABLE OF CONTENTS

Page

List of Tables xi

List of Figures xiii

1 Introduction 11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.1.2 Problematization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.1.3 Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.1.4 Research question . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.1.5 Delimitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.1.6 Expected Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2 Positioning of the Study 72.1 Positioning of the Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.1.1 Previous Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3 Literature Review 113.1 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123.2 Engineering Psychology and Human Errors . . . . . . . . . . . . . . . . . . . . . . . 12

3.2.1 Human Information Processing . . . . . . . . . . . . . . . . . . . . . . . . . . 123.2.2 Signal Detection, Sensitivity and Vigilance . . . . . . . . . . . . . . . . . . . 143.2.3 Rasmussens skill-rule-knowledge framework . . . . . . . . . . . . . . . . . . 153.2.4 Mental workload, Attention and Performance . . . . . . . . . . . . . . . . . . 17

3.3 Augmented Reality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203.3.1 What is Augmented Reality? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203.3.2 Augmented Reality systems - Conceptualization . . . . . . . . . . . . . . . . 21

3.4 Framework Connecting the Theories . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4 Method 294.1 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

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4.2 Method to fulfill the purpose of the study . . . . . . . . . . . . . . . . . . . . . . . . . 304.3 Scientific Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314.4 Research Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314.5 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

4.5.1 Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334.5.2 Documents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344.5.3 Interviews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344.5.4 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

4.6 Quality of the Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364.6.1 Validity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364.6.2 Reliability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374.6.3 Research Ethics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

5 Result 395.1 Result Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405.2 Pre-Study Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405.3 Human Errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

5.3.1 Exposed Positions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415.3.2 Observations at the main-line and RS3 . . . . . . . . . . . . . . . . . . . . . 425.3.3 Mental Workload at RS3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

5.4 Augmented Reality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495.4.1 Observations on the setting of RS3 position . . . . . . . . . . . . . . . . . . . 495.4.2 Desired Functionality from the Assemblers Point of view . . . . . . . . . . . 505.4.3 Researchers Point of View on Augmented Reality Systems . . . . . . . . . . 52

6 Analysis 596.1 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

6.1.1 Answering SRQ1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 606.1.2 Answering SRQ2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 626.1.3 Answering SRQ3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

7 Discussion 697.1 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 707.2 Sustainability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

8 Conclusions 758.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

8.1.1 Answering MRQ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 768.1.2 Managerial Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 778.1.3 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

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8.1.4 Study limitations and future work . . . . . . . . . . . . . . . . . . . . . . . . 78

A Appendix A 81A.0.1 Appendix - Interview Questions . . . . . . . . . . . . . . . . . . . . . . . . . . 83

B Bibliography 85

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LIST OF TABLES

TABLE Page

4.1 Key words in literature search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324.2 Interviews conducted . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

5.1 Type of Errors at RS3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

7.1 Settings where HMD Augmented Reality would be more appropriate . . . . . . . . . . 73

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LIST OF FIGURES

FIGURE Page

3.1 Information Processing (Wickens and Hollands, 2000) . . . . . . . . . . . . . . . . . . . 123.2 Rasmussens Performance Level and Errors . . . . . . . . . . . . . . . . . . . . . . . . . . 153.3 Relationship between Mental Workload and Performance (Wickens and Hollands, 2000) 193.4 Mixed Reality Continuum (Milgram and Kishno, 1994) . . . . . . . . . . . . . . . . . . . 213.5 Concept of Augmented Reality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213.6 Key Pieces of Augmented Reality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223.7 Schematics of Augmented Reality and Key Components . . . . . . . . . . . . . . . . . . 253.8 Mental Workload - Waterfall . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

4.1 Research Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314.2 Research Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334.3 Data Analysis Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

5.1 Deviations and Stoppage time on each MO . . . . . . . . . . . . . . . . . . . . . . . . . . 415.2 Human error on each position at MO4 during studied two months . . . . . . . . . . . . 415.3 Mental Workload Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445.4 Summary of the mental workload aspects at the RS3 Position . . . . . . . . . . . . . . 485.5 Summary of assemblers thoughts on Augmented Reality systems . . . . . . . . . . . . 525.6 Summary of researchers thoughts on Augmented Reality systems . . . . . . . . . . . . 56

6.1 Distribution between slips and lapses at RS3 . . . . . . . . . . . . . . . . . . . . . . . . . 616.2 Schematic Figure of The causes of errors . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

A.1 Interview Questions - Human Error . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83A.2 Interview Questions - AR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

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1INTRODUCTION

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CHAPTER 1. INTRODUCTION

1.1 Introduction

This study explores the possibility of using Augmented Reality systems in manufacturing as amean to manage human made errors. The study was conducted as a case study at a multinationalvehicle manufacturer to determine what the characteristics of the errors are, the causes of theerrors and later on, if Augmented Reality is an appropriate tool to manage those causes.

1.1.1 Background

Throughout the history, technology has been in the epicenter of what has driven different indus-trial revolutions, with increased productivity and growth as a result. These industrial revolutionshave had their underpinnings in the mechanization of the industry, to the electrification andrecently the automation of the industry (Boston Consulting Group, 2015; McKinsey, 2015). How-ever, due to the rapid development of different smart and digital technologies, the industry isnow entering the fourth industrial revolution, or Industry 4.0 as it has been known for (BostonConsulting Group, 2015). According to both consulting firms, McKinsey (2015) and Boston Con-sulting Group (2015), entering this new form of industrial revolution will bring benefits andvalue to companies embracing its potential by utilizing the digital technologies throughout thevalue chain. McKinsey (2015) mentions that among the different disruptions that is drivingthis industrial transformation is the different human- machine interaction, where augmentedreality systems being part of this. The Boston Consulting Group (2015) defines nine differenttechnological trends that are part of the fourth industrial revolution where Augmented Reality isone of those.

The concept of augmented reality is something that has been around for a long time, although ithas not had any breakthrough until recently when the enabling technology has been developed forit to be used (Gilchrist, 2016). In its simplest description, augmented reality is used to augmentthe reality with information to enhance the performance of the task being conducted in the SmartFactory (Syberfeldt et al., 2017). It is within assembly settings where augmented reality has thepotential improving the assembler performance. For instance, Accenture (2017) has in collabora-tion with Airbus developed a wearable augmented reality system with the aim of decreasing thecomplexity in the assembly and reducing the assembly time of seats by utilizing visualization.Another actor who has applied the concept of augmented reality in the operations is Boeing, whohas applied the technology for the installation of wiring with the aim of reducing the errors of thetechnicians and increase the productivity (Boeing, 2018). For Boeing, it is of highest importancethat no errors occur and augmented reality has according to them the potential of reducing it(Boeing, 2018). In terms of utilizing augmented reality in warehouse logistics, field tests hasaccording to Heutger and Kuckelhaus (2014) and McKinsey (2015) the potential of reducing theerrors caused by humans with as much as 40 percent in picking activities thanks to augmented

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1.1. INTRODUCTION

reality. Moreover, other research has stated that AR has the possibility of reducing errors causedby humans, as it can work as a guide system (Porter and Heppelmann, 2017; Abraham andAnnunziata, 2017; Regenbrecht et al., 2005; Boston Consulting Group, 2015; Scalabre, 2018).

Errors in manufacturing occurs and one factor causing errors is the human and are said tobe more difficult to manage than technical ones (Groover, 2007). Human errors have effect on theefficiency on manufacturing, and in a study it showed that 23 percent of the unplanned downtimeis caused by the workers in manufacturing, indicating that there is a need to manage it (Wright,2017). Scania CV AB (Scania) is a global manufacturer of trucks and engines and is a part ofthe Volkswagen Truck and Bus Group (Scania CV AB, 2017; Volkswagen AG, 2018). Scania is aworld leader within its field and has continuously aimed to improve their operating processesthrough their corporate culture "The Scania Way" (Scania CV AB, 2018).

Since Volkswagens acquisition of Scania CV, the aim of Truck and Bus Group has been de-veloped to becoming a global champion when it comes to commercial vehicles (Lundin, 2015).This includes synergy effects in the group where Scania will play an important role (ScaniaCorporate Relations, 2014). For a company like Scania CV, which is well-known for their highquality products and their efficient processes, further pressure is put on them to continuously looksolutions in order to manage human errors in their processes in order to live up to their reputation.

This study has been conducted at Scania CV during a period of 20 weeks.

1.1.2 Problematization

As new technologies are approaching and making their way out from laboratory settings, thereis a need for an understanding what value they can actually bring. Augmented Reality is onepromising technology within the new Smart Factory landscape that is promised to be able toreduce human errors. At the same time companies such as Scania CV are facing challenges withhuman errors as they have achieved a plateau in their quality journey where human errors arebeing more difficult to manage through traditional quality management and waste eliminationmethods. The assemblers on the production line are causing errors which have an effect onthe productivity as well as the quality. With the entrance of new technology, there is a need ofunderstanding if this new technology is appropriate to use in order to manage the errors causedby humans at a production line.

1.1.3 Purpose

The purpose of this study is to investigate what the causes of human errors are in manualassembly operations at a vehicle manufacturer. Further on, the purpose includes investigating ifan Augmented Reality system is an appropriate tool for managing those causes.

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CHAPTER 1. INTRODUCTION

1.1.4 Research question

In order to fulfill the purpose of this study, the following research question was defined:

MRQ: Is an Augmented Reality system an appropriate tool to manage human errors in manual

assembly?

For the main research question to be answered, three sub research questions were developed. Tounderstand what the characteristics of errors made by assemblers were, SRQ 1 was defined asfollowing:

SRQ 1: What are the main types of human errors at the production line?

Once the error types were known, we sought to find the reasons why they occurred, henceSRQ2 was defined as:

SRQ 2: What are the main causes of human errors at the production line?

Since the characteristics of Augmented Reality are well covered in the literature and are generic,the focus of the next sub research question was not on the characteristics of Augmented Reality.The focus was rather if Augmented Reality can deliver the solutions needed to manage the causesof human errors, thus SRQ3 was defined as:

SRQ 3: Is an AR system a feasible tool to manage those causes?

1.1.5 Delimitations

This study was carried out at the Gearbox Assembly plant at Scania CV in Sodertalje. Asthe research period was limited to 20 weeks, some delimitations were made in order to makethe problem researchable. The study limited itself on one single position on the productionline in order to manage the time-scope of the study. As the research was conducted withinthe field of Industrial Management and Economics, the study did not develop any software ortested an specific hardware to verify any conclusions or hypothesis. Moreover, as this study wasconducted within the field of Industrial Management and Economics, it did not conduct a deepinvestigation on the enabling technologies of an Augmented Reality system but rather consideredif an Augmented Reality system practically can provide the desired function generated by the indepth study of human errors.

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1.1.6 Expected Contribution

The expected contribution of this study is divided into two, one academic contribution and onecompany contribution. From the academic perspective, was expected to provide with empiricalfindings for smart factory research and the appropriateness of Augmented Reality systems inmanual assembly.

Moreover, this study was expected to contribute to the case company with an understanding ofthe root causes of the errors conducted on the line, how to target the root causes and conceptualmethods on how to manage them. Lastly, the case company is expected to be provided withguidance on the appropriateness of the usage of Augmented Reality systems in manual assembly.

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2POSITIONING OF THE STUDY

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CHAPTER 2. POSITIONING OF THE STUDY

2.1 Positioning of the Study

This literature review aims to describe and comment previous studies in the field of AugmentedReality and human errors and thus describe the positioning of the study in relation to previousresearch and the identified academic gap.

2.1.1 Previous Research

Hudoklin and Rozman (1992) studied the human errors in a man-machine system and its re-lation to stress and errors. Their conclusions were that human errors are the most probablecause of failures and that stress has a high impact on human performance. This is furtherelaborated by the work of Yeow et al. (2014) where they evaluated the impact of stress, fatigueand work environment on human errors in manufacturing industries. They concluded that thereis a relation between stress and human errors in the manufacturing industry. They said thatstress makes industrial workers divert their attention and lose their focus on the objective ofa task. A study made by Wilson and Russel (2003) describes that the mental workload of thehuman operator is critical to optimal performance. More studies that have been conducted invarious settings has also shown that the mental workload of an individual has a clear impacton attention and thus their performance (Yurko et al., 2010; Wilson, 2002 ;Bailey and Iqbal, 2008).

A study made by Tang et al. (2003) showed that in an laboratory assembly setting, 3D in-structions through head mounted displays reduced the test subjects perceived mental workloadas some mental processes in the assembly task was offloaded to the augmented reality systemsand thus reduced the error rate by 82 percent. In the same study, their conclusions were thataugmented reality systems can relieve mental workload in assembly tasks and that since "expert"assemblers are difficult to train, augmented reality systems are the next step in the process ofaugmenting human attention. The authors also stated that there is some work that needs tobe done, both technical, as well as fitting augmented reality systems to the actual problem it isintended to solve. The study made by Beitzel et al.(2016) tested an augmented reality system in alaboratory setting where the result showed that through the aid of an augmented reality system,the test subjects perceived a decrement in stress and mental workload and their conclusion wasthat augmented reality systems are in particular beneficial in time-sensitive operations.

In a study made by Syberfeldt et al. (2015), an augmented reality prototype was tested ina laboratory environment where they simulated assembling tasks. They concluded that theiraugmented reality system consisting of head-mounted displays and 3D visualized objects thatwere overlaid on the real world did positively assist their test subjects in assembling procedureswhen compared to no visual aid. In the same study, the authors also concluded that one of thereasons for augmented reality systems has not yet had a breakthrough, excluding the technologi-

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2.1. POSITIONING OF THE STUDY

cal immaturity, is the user acceptance. They argue that the current augmented reality systemsbeing tested in laboratory settings are not designed to do what the assemblers actually need andthat the end-users are still not convinced that augmented reality systems will make the usersmore efficient.

In the work made by Fite-Georgel et al. (2011), he describes that only a small portion of aug-mented reality systems has successfully moved to real-user tests, and he argues that one of thereasons for this is that there is a need of a deeper understanding of the industrial setting inorder to make augmented reality systems applicable in the industry. He further argues thatmore involvement in the development of augmented reality systems is needed, in particularfrom industry stakeholders which includes the end-users. His recommendation is that academicresearchers must strengthen their collaborations with industrial partners in order to improveaugmented reality prototypes in an iterative process of development that is based on the user’sfeedback and viewpoints.

The statements of Fite-Georgel et al. (2011) is in line with what Regenbrecht et al. (2005)says about augmented reality systems. They begin with describing that most prototypes havebeen developed and tested in laboratory settings with a focus on service, maintenance, design anddevelopment, and training. They argue that it has been shown to be difficult to bring research outfrom laboratory settings in regard to Augmented Reality. One of the reasons is that in laboratorysettings, the augmented reality projects and tests have taken place in pre-configured and speciallyprepared hardware and software environments, but when it comes to the real trial, most caseshave failed, and this could be because the researchers have worked in "silos", meaning not havingan end-user-centric design or application. They elaborate on this, saying that in many instancesthe end-users have not been integrated into the development of augmented reality systems andthat their viewpoints have been overseen. They conclude, that from researcher’s point of view,the best-augmented reality solutions might not be the ones with the highest level of originalityor novelty. The end users might find that there are simpler and more elegant solutions for theirspecific problems (Regenbrecht et al., 2005). Nee and Ong (2013) is on the same page, sayingthat in order to make augmented reality applicable in manufacturing, the desired functions mustconsider the users and have them in the center to bridge the issues of acceptance.

To conclude, although mental workload and its relationship to performance and errors is atopic that has been researched, not much study has been directed toward the industry and themental workload of assemblers in manual assembly and the causes of high/low mental workload.It is with great effort the presented studies have been found, and it is this gap in research thatincreases the originality of this study. As presented, augmented reality systems have a greatpotential for reducing the mental workload of operators and thus increase their performance.

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CHAPTER 2. POSITIONING OF THE STUDY

However, a common theme among the presented research is that all tests have been conducted ina laboratory setting where the operators have been new to the task to be conducted. Moreover,an additional theme in the research presented is that there is an inquiry for a deeper under-standing of the end-users viewpoints, the real environmental setting and the real world problemaugmented reality systems shall manage and solve. This study bridges this gap of understanding,by providing what the real world manual assembly needs in Augmented Reality systems andwhat functions is desired in order to manage common issues and causes of errors with the hopeof accelerating the development of Augmented Reality systems in manual assembly.

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3LITERATURE REVIEW

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CHAPTER 3. LITERATURE REVIEW

3.1 Literature Review

This section first presents literature relevant to understand why and how human errors occur.This include literature in the information processing of humans [3.2.1] and [3.2.2], followed bythe different cognitive performance levels described by Rasmussen [3.2.3]. Thereafter, mentalworkload and its relationship to attention and performance is presented [3.2.4]. This is thenfollowed by a generic conceptualization of Augmented Reality [3.3] and last, a chapter connectingthe presented theories [3.4].

3.2 Engineering Psychology and Human Errors

The field concerning the design of machines that accommodates the limits of the human users isreferred to as the field of Human Factors Engineering (Wickens and Hollands, 2000). The humanfactor has various definitions but Meister (1989) provides a simple but explanatory descriptionwhere he says that it is how people accomplish work tasks in the context of human and machineoperations and how the behavioral and non behavior dimensions affect the accomplishment. Themain goal of the academic field is to reduce errors, increase productivity, and enhance safetyand comfort when humans interact with machines. Engineering Psychology arises from theintersection between Human Factor Engineering and Psychology where much focus is on theinformation-processing capacities of the human brain which will be further elaborated in thefollowing chapters.

3.2.1 Human Information Processing

Wickens and Hollands (2000) presents a model for stages of the human information processingprocess (see figure 3.1). This model has been widely used and is similar to the model presented byGroover (2007). The authors describe that the information processing of humans is representedby a series of stages and they argue that there is no fixed starting point in the sequence of stages.

Figure 3.1: Information Processing (Wickens and Hollands, 2000)

Sensory: Through the human senses, information and events in their environment cangain access to the brain of humans. Properties of human visual and auditory receptors have a

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3.2. ENGINEERING PSYCHOLOGY AND HUMAN ERRORS

great impact on the quality of the information received by the brain (Wickens and Hollands, 2000).

Perception: For the human performance to be efficient, it is not sufficient with only the sensory.The information that is received by the brain must be interpreted and translated in order tocreate a perception. There are two key features in the perception stage. Firstly, it generates aperception automatically and rapidly which means that the perception stage needs minimalattention resources allocated to it. Secondly, it is driven by both the input (information throughthe sensory) and it is also driven by its long-term memory. The speed of this stage and theautomaticity is what distinguishes this stage from the other stages in the information processingmodel (Wickens and Hollands, 2000; Groover 2007).

Cognition and memory: Cognitive operations requires, in general, a greater time and moreattentional resources than the other stages. This is due to the fact that cognitive operations areinitiated by the working memory and other sets of activities such as rehearsal, reasoning and/orimage transformation. The working memory of humans is a sensitive temporary storage of acti-vated information. Working memory is a key feature when conscious activities are needed whichis dependent on the processing and transformation of information. The reason behind making theworking memory sensitive is the fact that it is vulnerable when other activities need attentionand mental presence allocated to them. In some cases, the well-rehearsed working memory cantransform to long-term memory which is much less vulnerable (Wickens and Hollands, 2000 ;Groover 2007 ).

Response selection and execution: When an individual has gained an understanding ofa situation which has been achieved through the earlier stages and augmented by cognitivetransformation, a selection of response is done by the human. The selection of an action isdifferent from executing a task since the execution requires physical motions to be done in orderto reach the goal of the selected action (Wickens and Hollands, 2000).

Feedback: In the feedback loop, the actions chosen by an individual are directly sensed bythe human. One of the implications of the feedback loop is that the flow of information can beinitiated at any point and the flow of feedback information is continuous (Wickens and Hollands,2000 ; Groover 2007).

Attention: The last property of the human information processing model is attention. Many men-tal operations do require the selective application of these limited attention resources and theyare not carried out automatically. To the left of the model, the attention is selectively allocated tochannels of sensory materials to be processed, which is also referred to as selective attention. Inthe case of visual information, the limited resource is called foveal vision which is/can be directed

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to different channels in the existing environment of a human. In the case of selective attention,the scanning process of the eyes are many times driven by the past experience were the humanknows where to look. But this experience can result in being the cause of errors since humanscan miss the details in the rest of the environment. If an individual has many operations goingon at the same time, the individual must have a strategy for dividing the attention resourcesamong two or more activities (Wickens and Hollands, 2000 ; Groover 2007).

3.2.2 Signal Detection, Sensitivity and Vigilance

The information processing of humans presented in the chapter above usually begins with thedetection of some environmental event. The associated problems of information processing ofhumans are those of detection, recognition, and diagnosis. Signal detection theory can be appliedin any situation where there are two discrete states of the world, signals and noise, that cannoteasily be distinguished (Wickens and Hollands, 2000).

Signals could be referred to as something that initiates the information processing of humans, itcould be an instruction, light or any situation. The ability to detect a signal that initiates theinformation processing of humans is highly dependent on the sensitivity. Sensitivity refers to theseparation of noise and signal distribution of the environment. The sensitivity measure is calledd’ which corresponds to the degree of ability to separate signals from noise. It is said that a majorcause to deviations from the intended goal of an action to occur in assembly settings is becausethe operator’s poor memory for the precise physical characteristics of the signal that initiates theinformation processing. When memory aids are provided to remind the operator of what and howthe signal looks, the d’ value approaches its optimal value (Wickens and Hollands, 2000).

In vigilance tasks, the operators are required to over a long period of time detect signals that areperiodic, unpredictable and infrequent. Two conclusions have derived from studies in the area ofvigilance: Operators show lower vigilance levels than desired, and the vigilance levels fall steeplyduring a shift (Wickens and Hollands, 2000; Groover 2007). As a subject’s target signal is reducede.g. the target signal is hard to detect, the sensitivity decreases and vice versa (Parasuraman etal., 2008) . Sensitivity usually decreases when there is uncertainty about time and location ofwhen the subjects target signal will appear (Parasuraman et al., 2008; Wickens and Hollands,2000) . When the even rate is increased (number of activities per unit time), the sensitivity leveldecreases of operators (Wickens and Hollands, 2000).

Wickens and Hollands (2000) and Parasuraman et al., (2007) explain that vigilance tasks andperformance is influenced by factors such as the display, task type, environmental stressors anddesign of a work-station. Further more, they have concluded that in settings where visual signalshave been applied, the sensitivity decrement has still taken place due to the need of remain

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focused on the visual signals during longer periods of time extract a till in fatigue (Wickensand Holland, 2000; Parasuraman et al., 2007) . Because of the fatigue, there are difficulties insustaining the attention and the sensitivity to detect signals decreases and that activities thatrequire more mental effort will lower the sensitivity of the individuals. They argue that there arein general four techniques to combat the loss of vigilance and hence increasing the sensitivity:1. Show target examples, 2. Increase target salience, 3. Vary event rate and 4. Train observerswhen novice (Wickens and Hollands, 2000).

3.2.3 Rasmussens skill-rule-knowledge framework

Rasmussen (1983) has developed a framework which is directed toward operators in industrialsettings. Rasmussen (1983) presents three type of performance levels and their associated errors.The performance levels are divided into three cognitive processing models that is used by anoperator when he or she performs a task.

The framework describes that an operator first perceives and interprets information in theprocessing system described earlier and that the information is processed cognitively in one ofthe following levels: Knowledge-based, Rule-Based or Skill-Based (Reason, 2009; Rasmussen,1983). Furthermore, the associated errors in the three performance levels can be divided intothree categories: Mistakes, Slips, and Lapses (see figure 3.2).

Figure 3.2: Rasmussens Performance Level and Errors

Knowledge-Based Performance: At this performance level, the operator has been pre-sented to an unfamiliar situation and an environment where previous know-how or rules arenot available and thus the control of the performance needs a higher conceptual level anda knowledge-based approach. The individual must assess the situation and the goal is explic-itly formulated which derives into an action plan (Rasmussen, 1983; Reason, 2009; Groover 2007).

The errors associated with this performance level occur due to failure in understanding the

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situation. What is typical for these types of errors is that the operator’s memory limits areoverloaded, and the operator fails to consider different alternatives. This form of error may alsobe a result of the operator failing to interpret complex information (Wickens and Hollands, 2000).In regard to knowledge-based errors, typical control measures are clear displays, diagnostic toolsand decision-making aids and organizational learning and training (Stewart and Grout, 2001)

Rule-Based Performance: The rule-based performance level refers to a sequence of routines ina familiar work situation which is typically controlled by an operator’s stored rule or procedurewhich the operator empirically has derived through the previous occasion or instructions. Furthermore, the rule based performance level is characterized by the use of checklists during the work,telling the operator what to do and in what order. At this performance level, the goal is notexplicitly formulated but is generated implicitly by being in a situation where the rules areinitiated (Rasmussen, 1983; Reason, 2009; Groover 2007).

The errors associated with this performance level differs from the knowledge-based errorsin the way that these occur even though the operator is more confident and sure about thesituation. Since the operator thinks he/she knows the situation, they invoke a rule or plan ofaction to deal with the situation. However, the operator fails to involve the correct rule or planof action (Wickens and Hollands, 2000; Reason, 2009; Groover 2007). The control measures inorder to tackle rule-based errors are similar to the control measures of knowledge-based errorsbut with regular drills and exercises (Stewart and Grout, 2001).

Skill-Based Performance: At this performance level, the human performance is linked tostored patterns of pre-programmed instructions. The skill-based level represents performanceduring acts or activities where the individual follows a statement of a created intention and theperformance take place without conscious control and smoothly follows an automated and highlyintegrated pattern of behavior (Rasmussen, 1983; Reason, 2009; Groover 2007).

The errors associated with the skill-based level are slips and lapses. These errors occur due tothe perception and interpretation of the situation (Wickens and Hollands, 2000; Reason, 2009). Avery common slip are what is referred to as capture errors (Wickens and Hollands, 2000). Thismean that the intended stream of behavior is "captured" by a similar, well-practiced behavior androutine. These capture errors are usually initiated and take place for three reasons: (1) the actionsequence involves a slight departure from the more frequent action, (2) some characteristics ofthe stimulus environment or the action sequence is related to the wrong (but more frequent)action; and (3) The action sequence is relatively automated and therefore not monitored by one’sattention.

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What differs slips from lapses is that slips occur when an operator is carrying out the rightintention incorrectly. A slip represents a deviation from the intended action, however, a lapsetakes it one step further where the operator does not execute any action at all . Lapses can betightly connected to the loss of memory, meaning that the operator has understood what to do,know how to do it, but forgot to do it. What is considered a major factor causing these forms oferrors (Lapses and Slips) is an interruption of the operator and loss of attention causing the oper-ator to forget what to do (Wickens and Hollands, 2000; Reason, 2009). Ishi et al. (2013) describesthat slips are caused by mistakes in judgment such as misidentifying or misunderstanding asituation and when an operator forgets to do an activity it is considered as a lapse.

Ishi et al. (2013) argue that in contrast to the measures that can be taken at the previousperformance levels (knowledge-based and rule-based), slips and lapses cannot be prevented bytraditional training or discipline for thorough conformance to manuals. They argue that theseerrors do not depend on the years of experience of an operator but depends more on their physicaland psychological conditions. Tafton et al. (2011) argues that skill-based errors are prevalent andhave fundamental cognitive underpinnings and says that these types of errors can’t be reducedby policy or training but can rather be solved with robust systems by applying cognitive theory tothe design of systems. Wickens and Hollands (2000) says that checklists and reminders whereprocedures with "place markers" which tick off each step in the process as well as warnings andalarms to detect errors is a way of tackling skill-based errors. Ishii et al. (2013) are in the sameline saying that double-check assistance with a computer is a way of reducing skill-based errors.

Stewart and Grout (2001) says that by using mental aids such as computer-based intelligentdecision support systems and memory aids that reduce the mental workload one can controlskill-based errors.They also argues that for skill-based errors, error detection systems are goodtools that help to reduce the mental workload of the individual as long as it is easy to trace backwhere the error occurred. This error detection can be carried out by the operator themselvesor an external system. Zhang et al. (2004) describes that memory aids, decreased multitasking,decision support, action tracking, information reduction and immediate feedback are among themethods to control skill-based errors.

3.2.4 Mental workload, Attention and Performance

There is no unilateral definition of mental workload, but the most self-explanatory definition ofthe mental workload is provided by Parasuraman et al. (2008) defining mental workload as "Therelationship between the function relating the mental resources demanded by a task and thoseresources available to be supplied by the human operator", p.145. The definition presented isin line with how Wickens and Hollands (2000) refers to mental workload, meaning that mentalworkload is the demands a specific task put on the cognitive capacity, similar to the relationship

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between physical demand and the amount of energy put into pulling, pushing and moving.

There are both objective and subjective measurement techniques in order to assess the mentalwork of an individual or group (Wickens and Hollands, 2000). Nasa Task Load Index (TLX) isa subjective method that was developed by the Human Performance Research Group at Nasa’sAmes Research Center (National Aeronautics and Space Administration (NASA), 2017). It hasbeen cited in many studies and is considered to have a high reliability and applicability (Wickensand Hollands, 2000). The Nasa TLX assesses the perceived workload of an operator among sevendimensions, mental workload being one of them. In this study, the mental workload is of primaryinterest hence the question to be answered has been derived only from that dimension and beencustomized to fit our case. The Nasa TLX describes that there are a number of factors thataffect the mental workload of an individual and among these are: how much thinking, deciding,calculating, looking and searching a task requires (Wickens and Hollands, 2000). The validity ofthese factors is further strengthened by a study made by Brolin et al. (2017) where they studiedthe cognitive aspects affecting human performance in manual assembly. Their study showed thatwhen assemblers needed to look for components, searching for the right information and decidingwhat to assemble, the mental workload of the assemblers increased and the task performancedecreased. Berlin and Adams (2017) also describes that the degree of searching for informationaffects the mental workload of an operator and that the process of always thinking if the operatorhas done a task correctly, in turn, affect the mental workload of the operator.

Wickens and Hollands (2000) describes that there is a supply and demand relationship (seefigure 3.3) associated with the mental workload and that there is a "red line" where the taskperformance of an operator decreases as the mental workload increases and passes the red line. Ifan activity or task requires a high degree of cognitive effort that surpasses the cognitive resourcesof the operator, a decrement in task performance will be noted. Kantowitz (2000) takes it onestep further stating that whenever the mental workload is too high or too low, the performance ofthe operator will decline.

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Figure 3.3: Relationship between Mental Workload and Performance (Wickens and Hollands,2000)

When discussing mental workload, attention cannot be neglected since these concepts arerelated (Kantowits, 2000; Wickens and Hollands, 2000). According to Kantowitz (2000) the twoconcepts overlaps each other in a Venn diagram, but that there is a disagreement about the sizeand form of this intersection. Further more, the concept of stress can not be neglected, as Wickensand Hollands (2000) explains, the mental workload that exceeds the cognitive resources of anoperator increases the stress level and hence affects their ability to sustain the desired attentionlevel. This, in turn, affects the operators’ ability to detect the signals that initiate the informationprocessing of humans by decreasing the sensitivity level which was discussed in earlier chapters.According to Endsley et al. (1999) an increased mental workload can affect the attention andthus the situational awareness of an operator as only a subset of the information received can beattended or also lead to a misperception of information provided.

Automation is often introduced to alleviate the mental workload of an operator or to augmentsystem performance to reduce human errors (Groover, 2007). Wickens et al. (2012) refers toautomation as "The performance by machines (typically computers) of functions that previouslycarried out, whether fully or partially, by humans", p. 378. The principal benefit that comes withautomation is that if it is carefully designed, the systems can reduce human workload, bothmental and physical. The workload reductions can occur in execution stages, e.g. an automatedscrewdriver, in decision choices and/or in the acquisition of information (Wickens et al., 2012).The potential for automation to reduce mental workload by providing cognitive support makesthe concept attractive in environments where an operator has a high time pressure and wherecognitive efforts need to be minimized in order for an operator to carry out many tasks (Parasura-man et al., 2009; Wickens et al., 2012; Brolin et al., 2017).

According to Norman (1990) automation is efficient to the user when the user receives feed-back if a task is carried out correctly. Receiving multimodal feedback as a result of automation

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has shown to be an efficient method in supporting assemblers by reducing the mental workload ofusers. There are in general three types of feedback that can support assemblers: haptic feedback,visual feedback and auditory feedback (Vitense et al., 2003; Ishii et al., 2013). Auditory feedbackrefers to the process where an operator is provided with audio support, haptic feedback is whenoperators can sense a vibration on their skin, and visual feedback refers to when operators visualsenses are exposed to some form of stimuli (Vitense et al., 2003; Ishii et al., 2013) . In their study,Vitense et al. (2003) found that in an assembly setting, haptic and visual feedback are moreefficient when used alone as well as in a combination with each other. When these feedback typeswere implemented, the assemblers perceived mental workload decreased and their performanceincreases.

To conclude, the mental workload of an operator is determined by how much thinking, deciding,calculating/counting, looking and searching they must do. If the mental workload of an operatorexceeds his or her capability, the task performance will decline as a consequence of the increasedlevel of stress and decreased the level of attention. As the workload, stress, and attention is notat its optimum level, it is more difficult for operators to detect signals and/or misperceive signalsin their environment that initiates the information processing. There are different techniques tocombat the high level of mental workload. Automation is often introduced to alleviate the mentalworkload by replacing redundant activities of an operator. The cognitive support the operator canget is in general haptic, auditory and visual feedback. The feedbacks main target is to reduce theaspects that affect the mental workload of an operator to an optimum level since too low or toohigh mental workload reduce task performance and increase the probability of human errors tooccur

3.3 Augmented Reality

This chapter will present Augmented Reality on a conceptual level in order to understand whatthe concept and aim of Augmented Reality is and the key parts to consider.

3.3.1 What is Augmented Reality?

As the name is stating, Augmented Reality is a medium that has the ability to enhance events inthe real work by adding digital content. Poelman and van Krevelen (2010) described AugmentedReality systems as a set of technologies that together enables humans to see and hear more thanthey otherwise would do. According to Wang et al. (2016) Augmented Reality can be consideredas a set of innovative and effective human-computer interaction techniques. Poelman and vanKrevelen (2010) and Palmarini et al. (2018) base their perceptions of Augmented Reality on thedefinition made by Milgram and Kishino (1994) and their mixed reality continuum as a way ofAugmenting the real world with virtual objects (3.4).

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3.3. AUGMENTED REALITY

Figure 3.4: Mixed Reality Continuum (Milgram and Kishno, 1994)

Wang et al. (2016) have conducted a comprehensive survey of Augmented Reality systems inthe domain of assembly processes and they concluded that the concept of Augmented Reality has apotential future within the assembly, but the technology has to be developed within different fields.In the study made by Ong et al. (2008) where Augmented Reality applications in a manufacturingsetting were studied, they found that Augmented Reality has made much progress in the recentyears but it is still on an exploratory stage. In their research they found that one of the basicfundamental issues that has to be addressed when designing an Augmented Reality system forassembly assistance, is to determine the factors when, where and what. According to Ong et al.(2008) when having the answers to these factors, the function of the visual AR system will bedetermined.

3.3.2 Augmented Reality systems - Conceptualization

Each and every time an Augmented Reality system is going to enhance the reality with digitalcontent for the user, the system undergoes a process consisting of several steps from acquisitionof the reality to the visualization and augmentation of it (Peddie, 2017; Heutger and Kuckelhaus,2014; Furth, 2011; Horejsi, 2015). However, for this thesis we will use the the process describedby Craig (2013) as it is less technical. This simplified process is divided into two steps:

Figure 3.5: Concept of Augmented Reality

1. Determining what is happening in the reality. This means that the system acquiresdata from the operating environment through various sensors. This data is then processed inorder to make use of it.

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2. Visualize the processed data. This means that the system is displaying the content thatis derived from the data that has been processed on a display. This augmentation can take itsshape in different forms depending on the purpose and functionality of the system.

In the research made by Peddie (2017) regarding visual augmented reality, he makes a dis-tinction between the different forms of visual augmentation.The author argues that there aregenerally two ways of augmenting the reality: (1) either the system provide general informationand/or (2) providing specific instructions with digital overlays. What distinguishes the two typesof augmentation is the degree of passivity or interaction the user has with the digital content.Informative Augmented Reality is considered more passive in the sense that the user do not haveany direct interaction with the digital content. Such form of augmentation takes its shape inform of information side-by-side with the reality, and is not integrated in to the real environment.On the other hand, the instructional Augmented Reality is the method of incorporating digitaloverlays that are laid upon the reality and the objects within it. This type of Augmented Reality isalso more interactive, where the user can manipulate the augmentation and move them around.According to Peddie (2017), one must remember that even though these two types of visualaugmentation exists, there is nothing that says that the system utilizes one over the other, it isequally common to have a mix between them.

However, in order to generate augmentations on the reality, there is a need of an AugmentedReality system. According to Craig (2013) and Wang et al. (2016) there is no universal AugmentedReality system that suits all the use cases, but rather this is something that is engineered tospecific cases. But what Craig (2013) emphasizes on is that for all systems, there must be aconjunction between hardware and software. The software will tell the system what to do, whilethe hardware is the piece that conducts the task. According to Poelman and van Krevelen (2010),the enabling technologies for an functional Augmented Reality system has remained the sameever since the first Augmented Reality system was developed back in the 60s. Conceptually, thekey pieces are (Poelman and van Krevelen, 2010; Azuma, 1997; Peddie, 2017; Craig, 2013):

Figure 3.6: Key Pieces of Augmented Reality

Hardware

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The hardware of Augmented Reality systems can roughly be divided into displays, sensors andprocessors.

Displays

Mainly for vision based augmented reality systems, the taxonomy can be divided into two maincategories of display types, namely wearable devices and non-wearable devices (Peddie, 2017).Normally, when people refer to Augmented Reality systems, they refer to wearable devices, asthese have been used in industry, military, medics, logistics and more (Cirulis and Ginters, 2013;Heutger and Kuckelhaus, 2014; Furth, 2011; Wang et al., 2016; Poelman and van Krevelen, 2010).Wearable systems are also referred to as Head-Mounted-Displays (HMD) and could be integratedinto helmets, contacts and headsets, were headsets typically refer to smart glasses. What theseHMDs does, is that they place the virtual environment, in conjunction with the real world, in thesight of the user (Heutger and Kuckelhaus, 2014; Peddie, 2017; Furth, 2011). The advantagesof using wearable devices are that they allow the human to use both his/her hands in workingconditions and provides the user with more flexibility in overall (Syberfeldt et al., 2017).

On the other hand, the non-wearable devices can be clustered as hand-held mobile devices,stationary devices, and head-up displays. The mobile devices refer to smartphones, tablets etc.According to Furht (2011) and Peddie (2017) it is the mobile devices that are driving the develop-ment of the concept of Augmented Reality forward and is a key contributor to why AugmentedReality will most probably be a commonality in the future. The stationary systems refer toAugmented Reality system that usually uses some set of projection or hologram. These systemsare more rigid and often requires a bit more space than other AR systems (Peddie, 2017; Furht,2011; Poelman and van Krevelen, 2010; Azuma, 1997; Wang, et al., 2016). Head-up displays havebeen used in different settings, and they are mostly known for being used in aircraft, both fighterjets, and commercial planes, and are also being introduced to commercial vehicles (Peddie, 2017)

Sensors

The sensors role in the Augmented Reality system is to provide information of the reality to thesystem, with the purpose of making the system aware of what is happening in the environment.There are several different sensor types that can be used, ranging from camera sensors to GPSsensors and these can work either in isolation or in conjunction with each other (Craig, 2013).Pri-marily the sensors are used for giving input to the tracking system, which is considered the heartof the system. The tracking system is what enables the system to determine the position (localiza-tion and orientation) of the user relative to the reality. For an accurate tracking, it is suggestedthat several sensors are used in conjunction with each other (Craig, 2013; Peddie, 2017; Poelmanand van Krevelen, 2010; Azuma, 1997; Furth, 2011). What Peddie (2017), Poelman and vanKravelen (2010) and Craig (2013) are stating, is that since the development of high-performance

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smart phones camera systems has achieved exponential development in terms of resolutions,size and price, these have pushed the optical tracking systems of Augmented Reality forward,and there is a promising future within this technology for vision based tracking and sensing.However, for this to work, the system must have computer vision software that can analyze thecaptured reality accurately, either by using fiducial markers (marker-based vision system) ornatural features of the environment (markerless vision system) (Craig, 2013; Poelman and vanKrevelen, 2010). According to Lima et al. (2017), the marker less vision system is what will pushthe acceptance level of Augmented Reality forward.

However, not only shall the sensors acquire information for the tracking system. Sensors are alsoused in order for the system to get an understanding of what the user is doing. For this purpose,there are both passive as well as active sensors. The use of physical buttons on the system is amore active way for the system to get an input from the user, while using the camera to detectgestures is a more indirect way of giving input to the system (Craig, 2013).

Processors

The role of the processor is to function as the brain of the system and conduct the computationfor the system along with other computer units (Craig, 2013). The computing can be carried outin different ways, and the different ways has their own benefits and drawbacks. Mainly, the twoways of processing the information is either onboard or external. For instance, in their study,Hashem et al., (2015) mentions that cloud computing will provide devices with computationalpower that will further enhance their advances in computing. Furth (2011) mean that since thetechnology has rapidly developed with mobile systems, a lot of computing power is today availablein a small package. However, what is crucial for the processor is that it is powerful enough to beable to provide augmentation in real time. This mean that the system must understand what ishappening and proceed without any lag/latency (Craig, 2013).

SoftwareThe software of the Augmented Reality system plays a vital part for the system, as this has therole of making use of the gathered information from the environment. The software must becapable of integrating with the sensors in order to turn their input into something valuable. Forinstance, if mainly the input is gathered through camera sensors, there is a need of using visionsystem AI algorithms that are powerful enough to make something valuable of the input (Craig,2013).

InteractionThe interaction with the Augmented Reality system is the way the user is giving inputs to thesystem. As the Augmented Reality system gives information and/or instructions to the user, it

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3.4. FRAMEWORK CONNECTING THE THEORIES

must somehow get acknowledge or feedback from the user in order to proceed to the next stepor to understand that it is done with the augmentation. What is considered important when itcomes to the interaction with the Augmented Reality system, is that it has to be smooth, and notbecome a disturbing factor for the user to fulfill his/her task (Peddie, 2017; Nilsson et al., 2009;Lik-Hang and Pan, 2017). According to Lik-Hang and Pan (2017), there are three common waysof interacting with the Augmented Reality system today, namely: touch, non-touch or throughhand-held devices. However, most of these types of interacting methods requires some sort ofhuman effort in order for the system to understand that the user want to proceed. Some of theinteracting methods might not even be appropriate for different situations. For instance, voiceinteraction is not appropriate in noisy environments. Now when we have provided an overviews

Figure 3.7: Schematics of Augmented Reality and Key Components

of the different dimensions of human errors and provided a conceptualization of AugmentedReality, next chapter [3.4] presents the derived framework of the study where human error andAugmented Reality is connected.

3.4 Framework Connecting the Theories

From the presented theories and previous research, we have developed the following frameworkand customized it to our case (see figure 3.8) in order to simplify and visualize the relationshipbetween mental workload, attention, and performance as well as automations stake in the modelwhich in our case is Augmented Reality.

According to the reviewed and presented literature five overlapping dimensions affect themental workload of an individual and these are: Thinking, Deciding, Counting, Looking andSearching (National Aeronautics and Space Administrations (NASA), 2017; Brolin et al., 2017;Berlin and Adams, 2017). Thinking refers to the amount an individual must evaluate his workand how much an individual must remember the steps that must be taken in a process. Decidingrefers to the times where an individual must take active choices. Counting refers to how muchthe individual must count or calculate in an activity. Searching refers to the process of searching

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Figure 3.8: Mental Workload - Waterfall

and extracting information and looking refers to the process of looking for necessary materialand components to perform a task. An optimum level of mental workload is desired since eithertoo low or too high mental workload negatively affects the attention of an assembler (Wickensand Hollands, 2000; Kantowits, 2000). Mental workload and attention overlap each other, butthere is no agreement about the size and the form of this overlap. So, in order to make it simpleto understand, we have concluded with the support of Endsley et al., (1999) and Wickens andHollands (2000) that lack of attention is a residual of too high or too low mental workload.Too high mental workload results in a sensitivity decrement where the assemblers ability todistinguish visual signals from noise that initiate the cognitive process of an assemblers whichlays the basis for the performance of an assembler.

One can through subjective measures understand which of the five dimensions is perceivedhigh at an assembly position and from there take active means to optimize the dimensions stand-ing out. Technologies can be adopted in the effort of reducing the mental workload and hencereducing the probability of loss of attention, decreased performance and ultimately the probabilityof human error to occur. Wickens et al., (2012) emphasizes on automation of different processeshas the potential of optimizing the dimensions of mental workload. Their definition of automationis "The performance by machines (typically computers) of function that previously was carriedout, whether fully or partially, by humans", p.378. This can be seen from two perspectives, eitherautomation of physical activities and/or mental activities. In this study, automation of mentalactivities is of relevance hence what technologies that can provide this, and its here AugmentedReality is connected. According to Norman (1990), automation is efficient when feedback is given

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to the user on whether or not a task is carried out correctly. Augmented reality is a way ofenhancing the reality with digital information, where visual augmented reality being one of those.As visual feedback has proven to be efficient in manufacturing processes and reduced mentalworkload of assemblers (Vitense et al., 2003; Ishii et al., 2013), a carefully designed AugmentedReality system is theoretically able to be the medium that optimizes the different dimensionsaffecting the mental workload of an assembler (Endsley et al., 1999; Wickens and Hollands, 2000).However, the prerequisites are that the augmented reality system must be able to 1. Determinewhat is happening in the reality and 2. Visualize the processed data (Craig, 2013).

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CHAPTER 4. METHOD

4.1 Method

This section presents the method used in this study. First the general method to fulfill the purposeof the study is presented in [4.2] followed by the scientific approach presented in [4.3]. Thereafterthe research design is presented in [4.4]. Continuing, the data collection method and the analysisprocess is presented in [4.5]. Last, the method to obtain a high quality study is presented in [4.6].

4.2 Method to fulfill the purpose of the study

The study has been structured with a purpose which derived into the main research question andthen followed by sub research questions. The study answered the sub-research question one byone which provided a basis for discussion and later a conclusion on the main research questionwhich fulfilled the purpose of the study.

In order to answer the first research question - "What are the main types of human errorsat the production line", the study has conducted a participative and non-participative observationon the assembly line on a total of 40 hours in order to extract what the assemblers naturalworking behaviors are. Moreover, quantitative data regarding process deviations from the casecompany’s database has been gathered to get an objective view of the characteristics of the humanerrors. The empirical findings were then compared to Rasmussen’s Performance level frameworkand conclusions were drawn.

The second research question was - "What are the main causes of deviations at the produc-tion line"? In order to answer this question, the study has conceptually asked "5 why" and usedliterature to derive the framework presented. The empirical findings from the previous obser-vation as well as semi-structured interviews with a total of five assemblers, three team leaders,and one process developer have been conducted in order to get a subjective understanding of whydeviations occur. The empirical findings were then analyzed with the developed framework inconsideration.

The third research question was - "Is an Augmented Reality system a feasible tool to man-age those causes?". In order to answer this question, the literature from section 3.2 and 3.3were combined to address the question. Moreover, a situational analysis was conducted basedon the previous research questions to derive what methods are required to solve the causes andconceptually what the Augmented Reality system would be required to do. This was based ontheory and what the desires are from the assemblers. Furthermore, interviews with four expertsin the field of production engineering, smart factory and computer science were held to furtherelaborate on an supporting function for the assemblers where additional meetings were held withthe researchers to deepen the understanding of the feasibility of an HMD Augmented Reality

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4.3. SCIENTIFIC APPROACH

system given the desired functionality.

Answering these sub-questions provided a basis for us to to discuss and to conclude an an-swer for the main research question which was - "Is an Augmented Reality system an appropriatetool to manage human errors at manual assembly?"

4.3 Scientific Approach

Collis and Hussey (2014) describes that two main paradigms guide how scientific research is con-ducted: Positivism and Interpretivism. Positivism emphasizes that the reality is independent ofus with the goal of, by observing and experimenting, discovering new theories. Theories are usedas a base when explaining a phenomenon or predicting their occurrence. The explanations consistof causal relationships between factors that are linked to a deductive theory. In contrast, Inter-pretivism emphasizes that the social reality is not objective but subjective because it is shaped byour perceptions. Interpretation seeks to through various methods describe and translate find-ings to understand the meaning of a phenomenon and not just how frequent a phenomenon occurs.

Due to the nature of the study, an interpretivistic approach has been chosen. Since the pur-pose is to investigate a single phenomenon and explain it in its natural setting and through acase study produce a subjective qualitative result, this study fits best with the characteristics ofthe Interpretive Paradigm.

4.4 Research Design

The research design of the study is illustrated in the figure below:

Figure 4.1: Research Design

The first steps of the study included a comprehensive pre-study and investigation to under-stand and identify the real problem of the expressed concerns of the case company. This wasdone in order to get an understanding and deeper knowledge of what the research area was. Thepreliminary investigations was also made to identify what Augmented Reality display is most

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suitable on the production line since the displays has a great influence on any Augmented Realitysystem that is applied in any setting. The preliminary investigations also included practical workon the assembly line alongside the rest of the assemblers as well as informal interviews withassemblers, team leaders and process developers. The result of the preliminary investigations wasformulated into a problem formulation, purpose and research questions. After the preliminaryinvestigations, a literature review was conducted to get a broader understanding of the researcharea. This was done via Royal Institute of Technology’s e-resources with the key words presentedin table 4.1, this enabled the study to further narrow down the scope as well as a development ofa theoretical framework that was used as a basis in the study.

Key words in literature search

Human error Process error Mistake proofing Assembly attentionMistake proofing Process deviation Lean Workplace design

Human performance Cognitive load Mental workload Manufacturing errorIndustry 4.0 Augmented Reality AR and mental workload AR use case

AR in manufacturing AR display AR key component AR assembly

Table 4.1: Key words in literature search

After the literature review, a human error framework was developed in order to connecttheories from the literature review as it was discovered that a customization of a framework wasneeded to fit the case study which is presented in [3.4]. The framework directed the empiricalcollection in the different phases of the study described in chapter [4.5]. As a starting point, datawas extracted from the case company’s database with the aim of categorizing process deviationson the assembly line. The next step was further collection of data in the form of semi-structuredinterviews with five assemblers, three team-leaders, one process developer and four researcherswith expertise within the field of computer science, smart factory and production engineering.Out of four researchers, three of them were found via Royal Institute of Technology’s e-resourceswhile one was found via contacts at the case company. All was contacted through mail to set upan meeting which all agreed upon and follow up meetings were held with those needed to verifyconclusions drawn from the previous meeting.

By analyzing the collected data and cross-checking the observations with the theoretical frame-work in a reiterative process, we were able to present results and analyze the empirical findingsin order to draw conclusions on the sub-research questions. This then provided a foundation forthe discussion chapter which derived into conclusions on the main research question aimed atfulfilling the purpose of the study.

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4.5 Data Collection

The study is based on a case study where qualitative data is collected through interviews,observations and quantitative data from databases at the case company as well as interviewswith academic experts in smart factory research. The research activities are summarized infigure 4.2 seen below:

Figure 4.2: Research Activities

The research activities illustrates the study’s three main research areas and the empiricaldata collection given the phase of the study.

4.5.1 Observations

Observation is a method that can take place in an artificial setting or in a natural setting. Theinterpretivist paradigm is closely linked with the observation of work in a natural setting (Collisand Hussey, 2014) and it has thus been chosen as the method for this study. There are in generaltwo types of observations, participative and non-participant observation. In this study, both typeshave been used. During the first week of the study, the participative observation was conductedwith the purpose of obtaining a detailed understanding of the practices, values, and motives ofthose that will later be observed in a non-participative method.

However, most of the observation has been conducted in a non-participative way where thesubjects of interest have been observed and their activities have been recorded without theobservers being involved. Permission of the subjects of interest has been granted before anyobservation has taken place which has made the subjects fully aware that they are being observedand may have affected their natural behavior when conducting an activity. The observers have

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only recorded the observed activities through notes which means that no audio or video recordingshave taken place.

4.5.2 Documents

A quantitative document collection has been conducted with the purpose of getting an objectiveunderstanding of what type of deviations mostly occur at the assembly line and which positionsare mostly exposed to deviations caused by human operators. The documents were collectedfrom a database at the case company where the assembly line managers fill in deviations thathave occurred at the assembly line for each shift. The quantitative data would later direct thequalitative data collection.

This approach is beneficial when aiming to answer the questions of who or whom and latersearching to answer the questions of how and why. The population and method must first bedefined (Collis and Hussey, 2014). The document collection of this study has followed the non-random selection due to the comfort and availability of data. The initial population of data wasmuch more than the researchers realistically and practically would be able to manage duringthe time. Furthermore, due to various degree of completeness of documents available, the non-random selection method has been beneficial to collect complete documents rather than half-filleddocuments and hence gain more validity. The disadvantage of this method is that it does notprovide sufficient data to draw conclusions with absolute certainty (Blomkvist and Hallin, 2015).

4.5.3 Interviews

Most of the collected data has been collected through interviews which according to Blomkvistand Hallin (2015) is the most common data collection methods when conducting a study insocial sciences. In line with the Interpretivist method the context of an interviewee has alwaysbeen assessed which is needed in order to understand the perspective of the interviewee (Collisand Hussey, 2014). Furthermore, the purpose of the interviews has been to explore data onunderstandings, opinions, attitudes and feelings in order to create an understanding of theinterview subjects’ perspectives and get to the root cause of why some particular human deviationsoccur. Moreover, interviews were held with four smart factory researchers and six assemblers atthe case company (see figure 4.3).

The interviews have been semi-structured in their nature where some questions were pre-pared in order to encourage the interviewee to talk about the main topic of interest and furtherquestions are generated and asked when needed during the interviews. The interview methodchosen was Face-to-Face meeting at the workplace with one exception, where the interviews withresearcher H were conducted though telephone. The strengths of this method is that comprehen-sive data could be collected and enable the study to gain data on sensitive and complex questions.The main disadvantage with the chosen interview method was that it is time-consuming which

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Interviews conductedInterviewee PositionAssembler A Assembler at Case CompanyAssembler B Assembler at Case CompanyAssembler C Assembler at Case CompanyAssembler D Assembler at Case CompanyAssembler E Assembler at Case CompanyAssembler F Assembler at Case CompanyResearcher G Smart Factory Researcher at Case CompanyResearcher H Professor in the Department of Production EngineeringResearcher I Associate Professor in the Department of Computational Technology and ScienceResearcher J Researcher in Cyber-Physical systems and information flow visualization

Table 4.2: Interviews conducted

result in the interviewees did not have time to elaborate their thoughts.

The interviewed assemblers was contacted via their team leaders a couple of days in advance inorder to not create any disturbances on the production line while being there. Three out of fourinterviewed researchers was found through publications found via Royal Institute of Technology’se-resources while one was found internally through our supervisor at the case company. Theresearchers was contacted via email and telephone to set up a meeting which all agreed upon.

The type of interview questions has been varied, with open questions, hypothetical questions,comparison questions and summary questions which are all useful for different needs but alsonot useful in different settings (Collis and Hussey, 2014). The interviews has been recorded withthe permission of the interviewees and the audio recordings were later deleted.

4.5.4 Data Analysis

Collis and Hussey (2014) describes that there are in general four elements when analyzingqualitative data: Comprehension, synthetization, theorization, and reconceptualization. The dataanalysis has a varying degree of emphasis on the elements. This study integrated all of theelements in the study but with emphasis on the reconceptualization of the data so that theoriescould be applied to other settings and populations as well as synthesizing the merging of differentthemes and concepts from the conducted research to form integrated patterns in order to give ageneral explanation of what is happening (Collis and Hussey, 2014). The qualitative data analysisfollowed three overlapping stages with an inductive approach presented by Collins and Hussey(2014) seen in the following figure:

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Figure 4.3: Data Analysis Process

In the first stage of the data analysis, data was reduced, where the study discarded irrelevantdata and evaluated data where relationships of interest existed. However, the elimination of datawas done after a comprehensive analysis of what is relevant and what is not in order to stay inline with the interpristic method and not lose any valuable data that could help explaining aspecific phenomenon. In the second stage, the study designed data displays aimed at answeringthe sub-research questions in a narrative way in accordance to how Blomkvist and Halin (2015)describes a narrative analysis. In the third stage, conclusions was drawn on the presented data.

4.6 Quality of the Study

As this study involved scientific work, the assessment of the quality of the study was of highimportance (Blomkvist and Hallin, 2015). The quality of the study can be assessed in relation tothe terms validity and reliability (Yin, 2009; Gibbert et al., 2008; Blomkvist and Hallin, 2015).Validity refers to whether the authors are measuring what they intended to measure, whilereliability refers to the accuracy and precision of the measurement and the absence of errors ifthe study was to be repeated. In other words, the difference between the two terms is that validityaim to make sure that the right thing is measured, while reliability entails the measurement ofthe right thing in the right way. In regard to validity, the term can be divided into three differentbranches: Internal validity, external validity and constructing validity (Yin, 2009; Gibbert et al.,2008). In this study, only construction and external validity are considered.

4.6.1 Validity

Construction of validity refers to investigating what the study claims to do (Yin, 2009; Gibbertet al., 2008). In other words, the extent to which a procedure leads to an accurate observationof the reality. Construction of validity is foremost considered in the data collection phase of astudy where two dimensions needs to be considered. The first dimension is to establish a chain ofevidence by which the reader can reconstruct the research process, from initial research questionsdown to final conclusions. The second dimension is to triangulate, meaning that the researcheruses different angles to look at the phenomenon studied which can be done by using differentdata collection strategies and different data sources (Gibbert et al., 2008).

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4.6. QUALITY OF THE STUDY

This study has a high degree of constructed validity since it manages both the dimensionsstated by (Gibbert et al., 2008). In regard to the first dimensions, the research process was clearlydescribed to the readers, from the initial research question to the final conclusions. In regardto the second dimension, the phenomenon has been studied by triangulating of data throughvarying data sources and through different data collection strategies.

External validity refers to the generalizability of the study (Gibbert, et al., 2008). The keypoint is that the theories that has been used in the study must be applicable on the samephenomenon but in a different setting from the studied. However, the difficulties with casestudies (neither single or multiple ones) is that they cannot allow for statistical generalizability(Blomkvist and Hallin, 2015; Gibbert et al., 2008). To obtain an external validity in a case studythere is a need of a discussion on how the results from the study is applicable to other cases.However, for this to be appropriate, the study requires a well defined description of the case(primarily in the findings section).

In this study, the case study has been carried out at one company. To obtain external valid-ity, the analytical generalizability is reached by providing case description in the findings of thestudy. Since the study examined a particular new phenomenon, few or no similar studies hasbeen conducted within the same field, which makes it difficult to make cross-case comparisonsand analysis.

4.6.2 Reliability

Reliability refers to the accuracy and precision of the measurement and the absence of errorsin the research if it would be repeated (Gibbert et al., 2008). According to Blomkvist and Hallin(2015) the reliability of a study refers to the degree of reproducibility and its ability then toyield the same result and conclusions where transparency and replication is the key to achievereliability in a study (Blomkvist and Hallin, 2015). Transparency can be achieved through carefuldocumentation of the research procedure and explaining how the case study was conducted andin regard to replication Gibbert et al. (2008) says that including the case material so it can beused by other researchers is a mean to achieve high reliability.

This study has in detail described the method used and process of the researchers, hence provid-ing a transparency to the readers. The data gathering methods and the data are presented withinterview questions included. If the same research is to be repeated, the transparency of thisstudy will make it possible for others to reproduce the study and achieve the same result giventhe conditions are the same. In terms of replication, this thesis has mainly used qualitative datae.g. semi-structured interviews, observations, informal interviews and databases to get insightand an understanding of the phenomenon studied. These forms of data collection can decrease the

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repeatability as the results can differ from time and setting. In order to maintain a high degreeof reliability, each interviewee were presented to what is being studied and the guidelines of theinterviews. In addition, interviewing any people on one single topic provides a good arithmeticreliability if the responses are similar to each other.

4.6.3 Research Ethics

According to Blomkvist and Hallin (2015) it is expected that researchers behave with high ethi-cal manners when conducting a research. Throughout the entire research process, the ethicalprinciples derived from the Swedish Research Council have been followed to have a high ethicalbehavior. The first principle says that people studied should be informed about the purpose ofthe study. This was evident throughout all of the interviews that were held, both at the casecompany and externally. The second principle is the consent requirement which means thatthe studied people must agree to be studied. During the conducted research all the interviewswere asked about their consent before the interview was held and they were free to cancel theinterview at anytime. The third principle is the confidentiality requirements which is abouttreating the collected material confidential. A non-disclosure agreement has been filled by theauthors and has been respected from the start. In addition, the interviewed persons has beenremained anonymous and no confidential data is presented in the final report. The fourth andlast principle is the good use requirement which states that the collected material should only beused for its purpose. This has been followed throughout the entire thesis.

Due to the restrictions of presenting performance data, the quantities on stoppage time andnumber of deviations have been covered. This includes the numbers on figures 5.1 and 5.2. Thisaction does not have any effect on the understanding of the thesis.

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CHAPTER 5. RESULT

5.1 Result Outline

This section presents the results from the different phases of the study. The preliminary investi-gations are first presented in chapter [5.2]. Thereafter, the results from the Human Error phaseillustrated earlier in figure 4.2 are presented in chapter [5.3]. Chapter [5.3] is divided into threebranches of results were chapter [5.3.1] presents the number of human errors and the type oferrors at the targeted assembly position. This is then followed by the observations made on themain-line and the targeted position in chapter [5.3.2]. Lastly, the result from the interviewsinvestigating the mental workload dimensions of the assembler are presented in chapter [5.3.3]where chapter [5.4.1] presents the observations on the targeted assembly position in an Aug-mented Reality context. This is then followed by the results from the desired functionality of anAugemented Reality system from the assemblers point of view in chapter [5.4.2] on when, where,

what and the how interaction must be in order to not disturb their work. Lastly, this chapter endswith researchers point of view on Augmented Reality systems in chapter [5.4.3] with the sameset of questions as in [5.4.2] but with the addition on bottlenecks of Augmented Reality systemsto deliver the desired functionalities.

5.2 Pre-Study Findings

The preliminary findings showed that the assemblers subjective opinion on the causes of errorsat the production line are that they believe stress and loss of attention are the causes of errors.Another preliminary finding was that their perceived stress was different among the differentworking positions, with those position where many activities had cognitive support stated thatthey did not feel stressed at all while the assemblers stationed on a position with low/noncognitive support stated that the stress level is high. Moreover, the preliminary findings showedthat under the working conditions of all the assemblers on the main and pre assembly line,their work requires both hands to be free and they are all standing in a tight space where theycontinuously shift their stance depending on what task to be done. Based on this, the AugmentedReality display type must be mobile and not occupy the hands of the assemblers hence the studycontinued with HMDs as the way of visualizing the augmented content.

5.3 Human Errors

This chapter is divided into three branches. [5.3.1] presents the errors on the production line andtargets a position and presents the types of errors that occur. [5.3.2] presents the observationsmade on the targeted position and [5.3.3] presents the results from the interviews made regardingthe different dimensions of mental workload on the targeted position.

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5.3.1 Exposed Positions

The database of the case company showed that out of five working areas (MO), MO4 is the mostexposed position in terms of human errors that have led to deviations on the production line.MO4 has under a two month period contributed with a total of x deviations and a total stoppagetime of x minutes, see figure (5.1). Performance data has been covered on figure due to restrictions.

Figure 5.1: Deviations and Stoppage time on each MO

After further investigation of MO4, it was shown that MO4 consist of ten working positionsranging from 4,18 to 4,27. Out of the ten positions, accumulating the errors conducted, theworking position 4,23 is the most contributing working position in terms of human errors at MO4(see figure 5.2). Performance data has been covered on figure due to restrictions.

Figure 5.2: Human error on each position at MO4 during studied two months

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Moreover, when conducting deeper investigation on the 4,23 working position, the investiga-tion found that the deviations logged at working position 4,23 as the causing station were in facterrors that were caused by the pre-assembly area and in specific a working position referred toas RS3, where x percent of the errors registered at 4,23 were caused by RS3 during the studiedtwo months. The most frequent errors caused by RS3 were the following:

Type of Errors at RS3

Category Type of errorComponent Incorrectly Assembled 1. Not sufficient grease on componentComponent Missing 2.Forgetting to add componentAssembled Wrong Component 3.Picked the wrong screw

Table 5.1: Type of Errors at RS3

5.3.2 Observations at the main-line and RS3

The observation took place on the production line where the focus was on the RS3 position. Thedifferent positions that were observed had varying degree of cognitive support for the assemblers,some of the support systems provided direct instructions on what to do while other providedinformation to have in mind when performing an activity. The cognitive support could for instancebe pick-to-lights or a display screen providing general information.

All of the assemblers on both the main-line and the pre-assembly areas followed a specialroutine when being introduced to a new position, and once they passed the criteria for a positionthey are considered to be "green" and knows what to do on their granted position. It was alsonoticed that all the assemblers, both on the main-line and the pre-assembly line worked in a au-tomatic pattern. Moreover, the observation showed that whenever a variant was to be assembled,the routines just slightly differed from the routines of the standard component.

A great majority of the positions on the main-line had a display screen informing the assemblerabout general information that is required by the assemblers to read, and the assemblers mustverify that they have read the information by placing their ID card against a receiver placed onthe display.

Continuing, looking at the RS3 position, it was observered that it did not have any sort ofcognitive support except for one pick-to-light configuration at one activity. However, the lightbulb was not placed in a logical and self-explanatory way and it required some effort from anewly introduced assembler to pick the right component. The tasks on the RS3 position were notcomplex nor difficult to learn during the participative/practice observation. When participatingand doing the activities required at the RS3 position, it was noticed that after a few turns we felt

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more comfortable when doing the activities and the more automatic we were working. As a result,more time was spent on talking and less attention was directed toward the activities needed to bedone. Nevertheless, this automatic pattern of behavior enabled us to stay within the cycle time(pre-set time limit for activities to be carried out). However, this was possibly the factor that ledus to misinterpret information from the sequence patch that tells what activity to perform. Whenreading the sequence patch, we misidentified the information given and conducted the same setof activities done the six previous takt times. Moreover, it was seen that the sequence patchesare involved in many activities on the RS3 position and provides the general information neededto chose the course of action. Despite its importance, the content of the sequence patches wereorganized in a way where effort is required in order to extract the right information.

In regard to the detected errors at the RS3 position, the assemblers, team leaders and processdevelopers were asked about their opinions on why they believe the three deviations presentedin table 2 occurs. In regard to error type (1) - not sufficient grease on component, assembler Asaid that they know that the component must be greased, but due to lack of attention and focus,they carry out the activity incorrectly. This was supported by assembler B, D,E and F. When theassemblers on the RS3 position were asked about their thought on why assemblers occasionallyconduct error type (2) - forgetting to add component, all the assemblers A,B, D, E and F werein the same line saying that they simply forget it, even though they know that the componentshould be added on every product (even if it is variation). Furthermore, when asked about errortype (3) - picking the wrong screw, assembler A, B, C and D described that the picking of thewrong screw usually happens when variants are to be assembled. They further described thatvariants comes much more rarely than the standard component and if the assemblers are notfocused enough, they sometimes misidentify the information given from the sequence patchesindicated on what screw to pick. However, although these errors occur on this position, they arenot leaving the production line as the processes control system detects the errors further downthe line. The drawback is that it creates stop time.

Finally, when asking assembler D, E and F on how errors are usually dealt with, they re-ferred to the philosophies of the company, saying that continuous training is the main methods tomanage human errors on this position but it rarely gives any effect. They say that even thoughthey are trained, the errors still occurs.

5.3.3 Mental Workload at RS3

The result of the interviews has been divided into five aspects of investigation: Thinking, De-ciding, Searching, Counting and Looking. These have according to the developed framework adirect effect on the mental workload of an individual and hence the attention of the individuals(see figure 5.3) and ultimately their performance. When the assemblers were asked about their

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subjective thoughts on the mentioned aspects, they were asked (when needed) to put the RS3position in comparison to the RS1 position which is a position with zero deviations and whereevery task has a high degree of cognitive support.

Figure 5.3: Mental Workload Factors

Thinking at RS3According to assembler A, the RS3 position requires a great deal of thinking even though oneeasily enters a "robotic mode". He says that what is unique about the RS3 position, is that you cando each and every activity without knowing that you have done it right or wrong and still send itdownstream to the mainline. If an assembler is aware that this can happen, assembler A says thatone start doubting oneself whether or not it is assembled correctly and this accumulates duringa rotation (the time on the position before they shift position). This is supported by assemblerD, E and F. Assembler D compares the RS3 position with other positions on the pre-assemblyline, saying that RS3 do not have any system that cognitively supports the assembler in termsof instruction or information and hence assuring that the assembler has done his or her workaccording to the standard. He further elaborates on this saying that all the responsibility lays atthe assembler to control, check and verify that a component is assembled correctly. During therotation, one can start doubting himself, and starts checking previously assembled componentsplaced on a trolley to see if he or she has done it correctly. Assembler E agrees that this positionrequires one to think a lot even though you are in a robotic mode and they have to stay sharp andconsidered what line of action to take based on the information given from the sequence patches.Assembler A says that in comparison to other positions, you have to actually remember whatactivities to do since there are no cognitive support helping the assemblers. Assembler E, F andD agrees on this by saying similar statements. Assembler F adds that the position before RS3 donot need any remembering of activities since the activities are few and much cognitive support incomparison to the RS3 position, and once thrown into the RS3 position, the number of activitiesto remember and to be conducted can become overwhelming. However, assembler E and D alsosays that even though there are many activities to have in mind, the difficulty is not to rememberhow to conduct a task, but it is mentally demanding to keep track on which activity you havedone since they can sometimes have memory loss.

Assembler A says that working on the position for a longer period of time can lead to mem-ory loss and one start to question themselves if they have actually done an activity. AssemblerF agrees on this, saying that the assemblers must sometimes re-evaluate their work which iswith time frustrating and adds that the need of thinking on your work is much greater than the

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need of muscle memory at the RS3 position. Assembler D compares the RS3 position with theRS1 position saying even though the complexity of components to be assembled is much less atthe RS3 position, he would rather be positioned at the RS1 position because you ultimately getmentally tired on the RS3 position of remembering the activities and making sure that everythingis right. He adds that one of the factors increasing the need of thinking at the RS3 position is thefact that two distinct and different operations has been merged into one which the assemblerneeds to have in his or hers mind. However, assembler C only partly agrees with the statementsmade by assembler A, D, E and F, saying the the low level of variants on the RS3 position deletesthe need of staying sharp, but he also questions himself when saying it is easier to assemble theright parts on the RS1 position compared to the RS3 position because you have quality assurancesystem on the the RS1 position. He further says that the need of thinking is varied on the RS3position, believing that as a new employee, the number of activities can be overwhelming and theneed of remembering the activities is much more on this position compared to other positions.Lastly, assembler C says that the control of sequence patches requires a mental presence forthem to be read right and that another example of the need of thinking is in the activity relatedto placing the gaskets and checking if they are fitted correctly which can easily be forgotten. TheRS3 position requires more mental presence since no one/nothing is controlling the position whichmakes the assembler responsible for the quality assurance. There is a process of continuouslyre-evaluating what one has done which causes them to feel stressed.

Decision at RS3Assembler A says that there is only one activity that has a guiding system that helps the as-semblers to decide upon what to do or what to pick, and this activity has never had any errors.Nevertheless, the activity with a cognitive supporting system has the components placed in away that is not obvious for anyone to understand. He then continuous saying that the one timethe assemblers must take an active choice is when he or she must pick the right screw to the RShouse (main component). The only information the assembler can get on what screw to pick comesfrom the sequence patches where the assemblers must extract information to know what to do.Assembler D agrees with assembler A, saying that there is a need of taking active choices for theassembles due to no technical cognitive support system, especially when it comes to picking theright screws. When extracting information from the sequence patch, the assembler must take anactive choice when picking the screws, and although the level of variants are low, assemblers inmany cases takes the wrong decision when an variant is to be assembled. Assembler D ends withsaying that one of the reasons an assembler occasionally picks the wrong screw is because theyhave entered a robotic mode and are not being attentive enough when deciding on what screwto pick. He adds that even though he is working in an robotic pattern, the knowledge that it iseasy to decide to pick the wrong screw is something that he always has in the back of his mind.Assembler E agrees with the presented statements, adding that deciding to pick the right part

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CHAPTER 5. RESULT

is not complex at all, but breaking out of the line of behavior is the difficult part and by takingactive decisions at least once every takt time one can get comfortable in the decision making ofpicking the right part and loosing the attention. Assembler A described that due to the knowledgethat one can easily make a trivial misjudgment when deciding what screw to pick he always triesto force his attention when conducting that activity to make sure he is doing the right thing. Healso says in the case where end-yoke has to be picked, he has to take active decision since theplacement of the pick-to-light system is not placed in a logical manner. Assembler F agrees andsays that the more different the variant is compared to the standard component, the more activedecisions that needs to be taken. Assembler C says that the sequence patches indicated that theif the RS house to be assembled is of UKR type, he needs to take active decisions but sometimeshe just works on muscle memory rather than stopping and taking a moment to take a decision.

Searching at RS3Assembler A explains that the RS3 position requires a great deal of effort in searching andextracting information in order to assemble and pick the right parts as well as verifying thatcomponents are matching by comparing the IDs of the sequence patches. He further elaboratesthis by saying that during one takt time, he must at least check the sequence patches threeto four times in order to determine that the correct component to assemble and verify thateverything is in order. Since there are no control system at the RS3 position, he sometimesdouble-checks the sequence patches in order to convince himself that he is doing the right thing.One issue that assembler A experiences from the search of information is that the sequencepatches contain a lot of information, were the needed information does not stand out from therest of the information given on the sequence patch, which makes it difficult to extract theright information he is searching for. There are also occasions where the sequence patches areplaced upside down, making the process of extracting the right information more complicated.Assembler D agrees on the statements made by assembler A, saying that he is convinced thatthe assemblers sometimes believe that they have read the sequence patches correctly, but thatthey did not process it correctly. He also agrees in the fact that the sequence patches are notdesigned in a way that makes it simple to extract the right information and thus left room formisinterpretations. Lastly, assembler D says that the need of searching and extracting the rightinformation is much higher on the RS3 position compared to other positions and he feels thatactivities connected to searching for information and matching sequence patches are time andenergy consuming activities that does not provide any real value. Assembler A adds that when itcomes to the UKR variant, the only source of information helping him in his decision whether heshould pick the UKR screws instead of the RS screws is the information provided on the sequencepatch. Assembler A also adds that he feels that he must concentrate and make sure that he hasread everything correctly. This is further elaborated by assembler E, saying that since there areno technical system that aids them in the searching and extracting information, he finds this

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5.3. HUMAN ERRORS

process unnecessarily complex. He describes that among the other information provided from thesequence patches, it can either say RS UKR or RS. Meaning that the the word RS is written onboth types of sequence patches. He explains that the misinterpretation is mostly because one has"lowered his guard", and simply misinterpreted the information. Assembler F agrees, saying thatthe need of searching for different ID-numbers is great on RS3 and that different componentshave sometimes different ID-numbers that do not match which is important to discover. Headds that the finding and distinguishing the ID-numbers from its environment is unnecessarilydifficult because the sequence patches are not structured in a way that makes it an easy processfor the assembler.

Assembler B says that on the RS3 position you have to actively search for information in order toperform the activities correctly and says that there are two cases were this is extra important:When picking the screws and when verifying that parts match. Assembler B line of thought aresimilar to the rest of the assemblers, saying that if you are tired or just make a quick glanceat the sequence patch it is easy to misinterpret or misidentify the information on the sequencepatch and thereby taking the wrong actions.

Looking at RS3All of the assemblers agrees that the need of looking for material and components are relativelylow since everything is placed in a fare distance and sorted to be picked easily with one exception.When it comes to end-yokes, they have a pick-to-light system that guides the assemblers on whatto pick, however, the way the end-yokes are placed on the shelf makes it difficult for them to pickthe right component and they must put some extra effort when looking for the right end-yoke topick.

Counting at RS3According to assembler A, B and E, the only counting required on this position is when they countthe number of gaskets. However, assembler C and D experiences that there is no need of countingwhen picking and placing the gaskets on the main component since one automatically knows howmany to pick and assemble and they pick a bunch of gaskets from the box and put as many asneeded on the empty spaces of the main component. Assembler A do not completely agree withassembler C and D, saying that depending on what variant he is going to assemble, he needs tokeep count on the number of gaskets picked and placed.

The findings was summarized in matrices in excel, print-screened and uploaded below to providea overview of the results from the interviews (see figure 5.4).

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Figure 5.4: Summary of the mental workload aspects at the RS3 Position

Having presented the results from the empirical data collection regarding phase one and twoof the study and presented the various views, we will proceed to the empirical data collectionfrom phase three of the study regarding Augmented Reality.

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5.4 Augmented Reality

This chapter presents the empirical data from phase three of the study, Augmented Reality. First,the observations on the targeted position is presented in [5.4.1] which is needed to detect thecontext the future desired function must work in. Thereafter, the results from the interviewswith assemblers on the targeted position is presented in [5.4.2] on when, where and whatAugmentation is needed and how the interaction with the system must be in order to not disturbtheir work. Further more, chapter [5.4.3] presents the result from the interviews with theresearchers where they were asked the same set of question. However, the addition in [5.4.3] isthat the researchers saying on the bottlenecks of an Augmented Reality system to deliver thedesired functionality are presented.

5.4.1 Observations on the setting of RS3 position

The RS3 position was observed during a non-participative observation in order to get an under-standing of the work conditions of the assembler. On the position, the assemblers were workingon different work areas which required physical movement. When the initial activities were done,the assemblers needed to move the main part to another table located behind him/her. On thisnew place, the assemblers placed the screws on the article that was intended to be sent downthe production line. When this was done, the assemblers moved to a different area where anadditional product was to be assembled. In other words, the assemblers moved between threedifferent areas when they were performing their tasks during a cycle time.

Further on, the assembly areas are very small and tight. There is not much free space infront of the assembler or around him/her where additional material or equipment can be placed.There is material that has to be used on the table tops as well as on the walls where articles arehanging. The articles that the assemblers have to work with and assemble on the bigger piece areof varying size. However, most of the articles included were not bigger than 5 centimeters, weresome parts even where as small as 1 centimeter in diameter. This includes the gaskets and screws.

The assemblers perform their task by using both hands and they are using gloves which isa safety requirement. When they are performing their tasks, their hands often cover the articlesbeing assembled on the bigger piece due to the small size on the parts that are going to befitted. The assemblers very rarely turned their heads in order to find the articles needed for theassembly. In most cases, their view is on the main part being assembled and not turning theirhead toward the articles to be grabbed. However, this does not mean that their heads are fixeddirectly above the product that is going to be assembled. During the observation, it was evidentthat the assemblers flack with their heads, moving it from side to side.

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Finally, the assemblers also work under time pressure, meaning that they have to performtheir tasks during a cycle time where they are going to conduct a number of activities to finishthe assembly of the component.

5.4.2 Desired Functionality from the Assemblers Point of view

To understand the desired functionalities that would fit the RS3 position the assemblers has beenasked when, where and what an augmentation is desired and how the interaction should be withan Augmented Reality system to not affect them in their work.

When should it be augmentedAssembler E mentions that he doesn’t want an augmentation all the time. The reason was as hesays that if something should be popping up right in front of his direct line of sight, for everytask he is performing during his work shift he would as he said, "break the system". He wasalso very sure that many assemblers would agree with this statement. He said that havingsomething digital in his line of sight for every task he is performing would be irritating and hesaid it would be mentally challenging. Instead, he argues that an augmentation should only bepresented when something is deviating from the standard and he want the augmentation only tobe presented when he has conducted an error for the tasks that are repetitive and do not haveany variance. In the case of variety, he says that in those situations, a visualization on what topick would be beneficial. Assembler A and D agree on this, saying that an augmentation is onlyappropriate when they have done something incorrectly. They further elaborated on the sayingsof Assembler A that visualization on activities where variety occurs would be beneficial. Whenanswering the question on what would support them in the picking of screws they all referred topick-to-light systems and they argued that the pick-to-lights systems only give cognitive inputwhen an activity is to be conducted or when something is wrong and they prefer this since it isnot in their direct line of sight.

Where should it be augmentedWhen asking the assemblers about where an augmentation preferably should be placed, assem-bler A, D and E all emphasize on the importance of having it logically placed where they don’thave to put any effort in order to find the information needed. Assembler A referred again to thescreen that is placed on the gearbox carrier and mentions that it is so badly placed, that one mustremove the attention from the work in order to look at it. Assembler D and E mentions that pickto lights are logically placed, since they are placed right on the products that are to be pickedand in the sight of the assembler. Assembler E further mentions that the pick to light gives theassembler direct feedback whether or not the component has been picked, without having to puteffort in understanding what was made wrong. Assembler D mentioned "you don’t have to do a180 degree turnaround to see what to do" when mentioning the location of pick to lights. When

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asking them regarding the box containing the screws, assembler A, D and E mentioned that theywould prefer to have the visualization on the box, just like a pick to light system. That wouldaccording to them help them to know what to pick in a easy way, without putting any effort indeciding on what to pick.

What should be augmentedAssembler A mentions that since most assemblers are considered experienced on the RS3 position,he feels that instruction on what to do would only become frustrating. As a reason, he points outthat the assemblers already know what to do and when to do so, and something pointing outan instruction would with time create resistance. Assembler D and E elaborates on this, sayingthat something telling him what to do would only create irritation and ultimately, he wouldneglect the aid, especially since the work is considered repetitive and they would experience thesame information numerous times during a shift. Assembler A, D, and E refers to the screenson the production line that are hanging on the carrier of the gearboxes and mentions that thesystems over there are neglected due to its bad function. They say that since it is presentinginformation all time, and in most cases, the same sort of information, the assemblers with timestart neglecting it, as it becomes irritating to think of it. Additionally, they say that the screenis not in the sight of the assemblers, which requires the user to orients his/her attention tothe screen to extract the information the screen is providing. What all three assemblers, A, D,and E emphasizes on is that the augmentation should be minimalistic and only provide theinformation needed in order for them to be able to fulfill their tasks. This could according toassembler A be a signal telling him that something is wrong . Assembler D and E especiallyemphasizes the importance of keeping the augmentation simple, but also that it should not beany fancy visualizations in the view. The presented information should only "wake them up"as they mentioned. As assembler A put it, only a highlighting of the component itself would besufficient for them, as symbols, arrows and other 3D figures would became annoying with time.

The desired interactionWhen the assemblers were presented with the different ways of interaction (like touch interac-tion) with an augmented reality system, all the interviewees were hostile to interacting with thesystem directly. Assembler A said that he had experiences from pick-to-voice systems and that inthis setting, audio interactions would not work due to the noise in the background. Additionally,physical interaction with any system would be overwhelming since there are already too manyactivities to be done and would just interrupt the assemblers work. He added that since the workis repetitive, with many tasks to be done, telling the system with physical means to proceed tothe next step would not be appreciated with interviewee D and E agreeing on this. According tothem, they require a system where interaction was conducted indirectly. They exemplified thiswith pick to light systems that did not require them to push on the light in order for the system

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to understand that a task was carried out. They mentioned that it is a matter of productivity aswell as keeping the assembler focused on the assembly process instead of having to control thesystem. According to them, each time the user will interact with the system, it will interrupt theuser, even though it is touch, voice or using different hand gestures.

All the interviewees were pushing on the fact that for an augmented reality system to beeffective for them, the interaction must be indirect. What they mention is that they want thesystem to understand that they have conducted a task and the system would proceed to next taskautomatically, without being dependent on the assembler giving it an input to proceed.

The findings from the interviews was summarized in Excel, print screened and uploaded below(see figure 5.5).

Figure 5.5: Summary of assemblers thoughts on Augmented Reality systems

5.4.3 Researchers Point of View on Augmented Reality Systems

Researcher I has mostly conducted tests with augmented reality where the focus has been towardnovice users, to investigate whether their learning curve increases during a shorter time wheninstructing the users. The results of his tests have shown that novice users learn quicker and ARsystems can accelerate the learning progress. The results from researchers I’s tests are furthersupported by the research made by researcher H, saying that in her research augmented reality

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systems has shown to be a powerful tool of increasing the competence of user through guidance,particularly during training (when the user is novice) and when the product type to be assembledhas a great variance. Researcher G and J are on the same line, saying that AR can be usefulduring periods when the user is under training.

In regard to augmented reality systems to provide assistance to people considered skilled and/orexperienced, researcher I is very careful when expressing himself. He says that skilled and/orexperienced people in assembly settings, most likely won’t increase their performance in theway AR systems are portrayed to work as guiding systems. He says that there is no greatermeaning of guiding or instructing experienced and/or skilled workers in the given setting in theirwork, especially when it comes to repetitive tasks. He says the augmentations will only becomedisturbing and irritating, hence lead to decrease in performance. Researcher H agrees on thisstatement, saying that in her research when they have tested augmented reality systems onexperienced users in assembly settings, frustration has raised and ultimately, damaged the workflow more than it contributed to it.

When to augmentResearcher G, H, I and J emphasizes that skilled people should get an augmentation when needed,saying that for assemblers in a production setting with repetitive tasks and little variance, thisaugmentation should only occur when a deviation has taken place. Researchers I, J and H saysthat for experienced assemblers to benefit from the augmentation as a mean of unloading themental workload, the augmentation should inform the users that they have done somethingwrong. Researcher I and J mentions that there is no meaning of displaying information when atask is being conducted correctly especially not if the tasks are repetitive with little to no variance.They say that since the assembler knows what they are doing, they do not need confirmation thatthey have done a task correctly, they only need a warning when the task is carried out incorrectly.Researchers J, H and I say that the assembler will, in the end, disregard the information as it willbecome disturbing instead of helping if presented all the time. Furthermore, all researchers saysthat there are situations were augmentations can be beneficial for experienced and/or skilled as-semblers to be visualized all the time. They say that when a user has to take decisions on whetheror not to place or pick a component in a container where there are multiple choices/variance it isbeneficial as the system can act as a decision support system. In tests by researcher G he said itshowed to be beneficial with augmented reality systems where the container is highlighted ormarked out in some way for the user to determine from where to pick the component.

Where to augmentResearcher J, G, I and H says that there are some benefits with HMDs as the information can bepresented in real time, in the sight of the user, hence making the user aware of what is happening

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in their assembly process. Researcher G and H says that visualization on what to pick is good, asit makes it easier for the assembler to know what to pick. But, researcher I and J questions ifthere is a need of information to be displayed on HMDs. They specifically say that the only timeit might be beneficial with information displayed on an HMD is when the visualization must takeplace on a specific object. In those cases, researcher I say it can be beneficial with an augmentedreality system that highlights from where to pick something. Researcher J further elaborates onthis and says that it can be beneficial to highlight on parts on the component where articles aremissing or if something is incorrectly assembled. But, they both say that only giving a warning onthe display when something is deviating from the standard has more appropriate ways of doingso than an augmented reality system consisting of HMDs. Researcher J say that the HMD:s havethe benefit of making it possible for the user to trace back to where the error has occurred byhaving something highlighted. He also mentions that for this to even work and be viable theaugmented reality system must be able to control the work and refers to it as context awarenesswhich he says requires sophisticated artificial intelligence, as well as capable hardware.

What to augmentResearcher I and J says that a well designed augmented reality system has the potential ofrelieving the cognitive load of the assemblers since they can rely on the augmented reality systemto understand what is happening and inform the user when an error has been made or whena decision has to be made. Researcher G, J, I, and H mention that if information should bepresented for a skilled and/or experienced worker, it should be minimalistic and only contain theinformation needed for them to understand that something is wrong or where to pick somethingfrom. Researcher H and I says that having fancy 3D overlays, might not be appropriate for skilledassemblers in repetitive tasks as this can cause frustration. Researcher J, H and I says thatthe information needed can be in form of a warning that gives immediate feedback to the userthat something is wrong or something highlighting components to be picked. In tests made byresearcher G, they programmed a HMD to guide the user with directing spirals to the box fromwhere the user should pick the product from. He has also conducted tests in logistics where theAR system consisting of HMD gave information on what product should be picked and fromwhere it should be picked. What characterized the test was that it included a lot of variance, aseach product should be picked from a specific place. He said that this showed to be beneficial forthe operators, as they only had the information needed instead of having to look for the rightinformation among other texts on a paper. Further on, researcher G refers to "pick-to-light"systems. He says that the minimalistic information from the "pick-to-light" system in form of alight should be enough for the assembler to understand the current situation of the assembly. Hesays that the pick to lights give information if the task has been carried out or not and also givesthe user decision support to understand from where to pick.

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InteractionIn terms of interaction, researcher I says that in order for an AR system to be valuable forassemblers that are working under time pressure and have several activities to conduct, theinteraction must be indirect. He says that the system must be able to understand by itself thatsomething has been conducted (either correctly or incorrectly) and proceed accordingly withoutdisturbing the user. He says that this is important as it contributes to unloading of the mentalworkload of the user. According to him, adding a system by which the user must control mightincrease the workload, as the user has to think on both assembly operation as well as controllingthe system. He also says that it is a matter of productivity, as each interaction with the systemwill consume time, especially if each task is going to be analyzed by the system. Researcher Halso mentions indirect interaction with the system as a prerequisite for skilled people in orderfor the system to be efficient. She refers the indirect interaction to having a system that is notuser dependent. She says that adding input from the users perspective to the system will onlymake the system inefficient hence disturb the users in their work. Researcher J and G furtherelaborates on the sayings of researcher I and H, saying that the assemblers should not care aboutgiving inputs to the system. Researcher G says that if the assembler is giving input to the systemfor each task, it will become disturbing and frustrating.

Summary and summarizing tableTo summarize what the researchers have said regarding skilled people and AR, researcher Jresults indicate that novice users benefit more from AR systems when being instructed by thesystem and makes them learn faster. Researcher I elaborates on this, saying that AR as guidingsystem is a powerful tool for novice users. Research G and L says the same as researcher I, thatAr systems are good for training. In terms of skilled people, researcher J says that they shouldnot have guidance and instructions for each task to be conducted as this will not improve theirperformance. Interviewee I says that skilled people will only be frustrated when being guided ineach task.

The following figure is a summary made in excel on the sayings of researchers in terms ofwhat should be augmented, when it should be augmented, where the augmentation should takeplace and how the assemblers want to interact with the system. This is done in accordance towhat Ong et al., (2008) considers as the fundamental issue when defining an augmented realityfunction, but also in order to get the users (in our case the assemblers) point of view on what theyconsider the AR system should do.

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Figure 5.6: Summary of researchers thoughts on Augmented Reality systems

Researchers thoughts on requirements on desired AR functionAll researchers J, H, and G says that in order for an AR system to function as derived, there isa need for the system to have sophisticated AI computer vision algorithms. Researcher J and Irefers to this as the system to be context aware. Researcher J says that this is when the systemis capable of understanding what is happening in the reality. Researcher I, J and H says thatcontext awareness is crucial in order for the system to be able to control the user and determinewhether or not the task has been carried out correctly. Further, they all say that this is neededfor the indirect interaction to function properly. Researcher J, H and G also says that this isimportant in order for the system to be able to visualize and point out specific objects in thereality. In terms of being able to acquire information from sequence patches, researcher G, Hand J says that the computer vision must be capable of determining text in the reality. Whatresearcher H further says in terms of computer vision, is that there is a need of having depthperception. She says this is important in order for the system to understand where the assemblerhands are in space. Further, she says this is needed for the system to be able to control if thehands have been somewhere in the room for the system to control if the user has for examplebeen over a container with components.

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In the research made by researcher H at an assembly plant, she tested to see if HMDs couldbe used in the assembly of hose clamps and control whether they have been assembled. Theconclusion she says was that with the proper and capable AI algorithms it is working, as longas the user kept their sight pointed directly on the part, had a free sight and kept their sightfixed on the part. She also said that due to this, there is an ability of the system to not be userdependent in terms of interaction. Researcher I and J further elaborates on this. Researcher Isays that it can be difficult to use vision systems in assembly processes for continuous controlof the work since the assembler’s hands often block the parts that should be controlled, withresearcher J saying the same thing. What both researcher I and J says is that if the camera isnot fixed over the part that is going to be controlled, it will be difficult for the vision system todetermine what is happening. Researcher J says that movement of the head will generate errorsin the image analysis, hence not making it reliable. He also says that it will affect the trackingof the user in relation to the visualization if errors occur in the image analysis. Researcher Gsays that computer vision has difficulties to be used today as they are not fast nor precise enoughto be used in assembly. What researcher H further says is that geometry and size of what is tobe controlled matters, where she says that big components are easier to be controlled by thecomputer vision than small ones. This is further elaborated by researcher J who says that forsmaller parts it is difficult for the computer vision to be able to control.

Researcher H and J said that since the AR systems that are dependent on computer visionrequires much adaption from the users in order for them to work, both said that it is appropriateto utilize external sensors that provides the AR system with input data which is not computervision dependent. Researcher H said that using sensors over boxes to determine if the userhas been there is more reliable and less complex than computer vision. This is elaborated byresearcher J who says that the input data from sensors is more like "yes or no" data, while thecomputer vision data will contain errors.

Researcher G and his team has conducted tests with HMDs in logistics. What they did wasthat they replaced the manual picking list with a digital one that was displayed on the HMD.However, in terms of the HMD, he says that the users experienced headaches and loss of orienta-tion when using the glasses. Researcher H elaborates on this as she says that she has experiencedresults, with headache as a result of using HMD:s during a longer period. She says that she isnot sure of the cause but mentions that it can be the combination of how much information ispresented and the optics of the HMD. She says that HMD development is going forward in arapid pace and that those issues will probably be engineered away. Researcher J says that usingthe HMD for a longer period of time can cause the user to become dizzy, with no real reason towhat causes it.

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Researcher H says that due to difficulties with using computer vision, it is difficult for HMDs toleave laboratory settings in order to function as desired. Researcher I and J further elaborates onthis and saying that for the desired function, it is difficult for the system to work independentlyin assembly. In terms of hardware, they say that the development might go faster than thedevelopment of the AI and vision systems.

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6ANALYSIS

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CHAPTER 6. ANALYSIS

6.1 Analysis

In this section the empirical data is analyzed with respect to the literature and theoreticalframework of this study. The sub-research questions is answered and conclusions upon thesub-research questions is drawn. The chapter begins with analyzing the empirical data related toSRQ1 and is then followed by the same process for SRQ2 and SRQ3

6.1.1 Answering SRQ1

What are the main types of human errors at the production line?

We have used the framework developed by Rasmussen (1983) as a basis to define what typeof human errors occur at the line. The framework consists of three performance levels withtheir respective characteristics and associated errors. It was evident that the assemblers are notapproved to work on the position unless they know what to do and how to do it. The interviewedsubjects explained that in their work, they do not follow any derived "if rules" or followed anychecklists and are all familiar to the positions. These findings disclaims the knowledge-basedand rule-based performance. Through the observations, participatory and non-participatory, wecould conclude that the assemblers follow automated and highly integrated patterns of behaviorssimilar to the characteristics of skill-based performance presented by Rasmussen. The automaticpattern of behavior was seen at all activities at the positions on the production line and hencethe possible human errors at this position are slips and lapses.

By extracting information from the database, with focus on the RS3 position we could seethat the most common deviations were: (1) No sufficient grease on component, (2) forgetting toadd component and, (3) picked the wrong screw to the component. The information from thedatabase indicates that at least in one of the most common deviations, (1) - no sufficient greaseon component, the right intention was created but carried out wrongly which is in line with thecharacteristics of what is considered as a slip. Categorizing (1) as a slip is further supportedby the assemblers where assembler A, B, C, D, and F saying that they sometimes lack focuswhen conducting this activity. However, the right intention is always created and the activity isconducted, but not sufficiently and wrongly.

In the case of (2) - forgetting to add component, it was not sufficient by just observing theinformation provided from the database to draw any conclusions if the deviation was a slip or alapse. Through the observation and the interviews, it was found that the assemblers subjectiveperception of this deviation is that all assemblers know what do do, how to do it, but simplyforgetting to do it which is characterized as a lapse.

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Moving on to the case of (3) - picking the wrong screws, the observation and interviews showedthat the conditions when this activity is to be performed cause the assemblers to do the wrongaction even though the intention of picking a screw is right. These types of deviations mostlyoccurred when a variant is to be assembled. According to assembler A, B, C and D the new actionsto be performed when a variant is to be assembled is very similar to the more frequent actions,performed previously which had a higher event rate. The new actions also just required a slightdeparture from the more frequent action, in this case picking the screws from the left box insteadof the right one as well as that the actions were cognitively and physically automated and wasnot monitored by the assemblers attention. All of this is in line with what Wickens and Hollands(2000) defines as capture behavior and further more as a slip.

Figure 6.1: Distribution between slips and lapses at RS3

In summary, the assemblers at the RS3 position as well as most of the production lineperform their activities according to the skill-based performance level of Rasmussens performanceframework. The human errors at this performance level can be divided into slips and lapses.At the chosen RS3 position, 72 percent of the human errors are categorized as slips while 28percent of the human errors are categorized as lapses which is demonstrated in figure 6.1. Oneinteresting finding is that according to Ishi et al. (2013) and Wickens and Hollands (2000) is thatthese kinds of errors are not ones that can be eliminated or reduced through traditional trainingwhich assembler D and F says is one of the practices of the case company on the targeted position.

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What are the main causes of human errors at the production line?

From the previous research question, we have derived that the assemblers perform their work atthe skill-based performance level and that the errors associated with this performance level areslips and lapses. According to theory, slips and lapses are related to loss of attention because ofthe cognitive load of an individual. A common theme among the interviews that were held withassemblers, team leaders, and process developers was that they mentioned the loss of attentionas a cause for the errors presented. From the presented theory, we have concluded that lossoff attention is not a cause, but rather a symptom of physical and/or mental workload and itultimately affects the performance of individuals see figure (3.3). From the interviews, threefactors affecting the mental workload stood out: thinking, deciding and searching, and these haddifferent weight in how they affect the mental workload of the assemblers (see figure 6.2).

Figure 6.2: Schematic Figure of The causes of errors

In regard to thinking; remembering activities and re-evaluating their work, the assemblersperceive the RS3 position to be particularly exposed. During the interviews, the assemblers wereasked to put the position in comparison to the RS1 position with almost the same number of activ-ities, but with cognitive support on almost all activities in form of pick-to-light systems. Further,the RS1 position showed few to no errors carried out by humans. The interviews revealed thatthe number of activities to have in mind on the RS3 position were high, and since the assemblersdo not have any cognitive support that guides the assemblers on what activity to do, with time,they can perceive a fatigue in attention. Further on, the interviews revealed that assemblers areresponsible for their own quality assurance, meaning there is no tool securing that they havedone an activity correctly. This caused them to on numerous occasions think for themselves if they

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have actually assembled according to the standard. Thinking in general, showed to have a higherweight in its effect on the mental workload during the discussions with the assemblers. Most ofthe assemblers found the aspect of being responsible for the quality assurance as a burden andoff all discussed topics, this was the most influential on the mental workload of the assemblers.

Moving on to the case of decisions. It has shown that the number of active decisions needed to betaken are few, but still more than the number active decisions that need to be taken comparedto positions where the error rate is less. The occasions an assembler have to take decisions iswhen deciding on what screw to pick and picking the end-yoke, given the information from thesequence patch. The interviews revealed that it is not the action of picking the screw that per seis demanding, it is rather the fact that you need to have high mental presence when taking thatdecision which further adds strain in the attention during a shift. As some assemblers expressed,as it is easy to decide the wrong component to pick due to the robotic behavior of the assemblers.They feel that they need to wake themselves up to break the chain of behavior whenever decisionon the screws are being made. This disturbs their natural behaviors when putting it in relation tohow many times decision needs to be made during a shift. Further, as the assemblers enters anautomatic work pattern, they sometimes misinterpret the information given from the sequencepatches in order to take the right decision, hence causing them to carry out wrong action. Asmentioned previously in the theory chapters, the aspects affecting the mental workload of anindividual are not independent, so the active decisions that need to be taken results in a cycle ofre-evaluation if they actually picked the right component. This in turn affects the thinking ofthe individuals and therefore we perceive that the active decision that needs to be made on theposition has an effect on the mental workload of the assemblers.

Searching for information has shown to be a mentally demanding aspect when interviewing theassemblers. The information needed to be extracted influences parts of their activities, and theinformation needed to be extracted is similar to its environment, as one of the interviews said, "itis like extracting a particular text string from a receipt when you have been grocery shopping".The searching is viewed as a mentally demanding activity since the information is not presentedor designed in a way that makes it simple for the assemblers and they have to focus on a line oftexts. Further more, searching and extracting of information is carried out in many occasions inorder to both know what activity to do, and also to make sure that the parts are matching. Lastly,the information itself did not cause any major problems, but having to think about it does as theoperators knew that they had to compare patches to make sure they are on the right path.

The theories and the developed framework shows that skill-based errors are a symptom ofdecreased performance which in turn is a residual of loss of attention. The loss of attention occursmainly due to a high mental load which could also be referred to as the cognitive load. According

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to the NASA TLX, the mental workload of individuals is determined by the how much thinking,deciding, counting, looking and searching an operator has to do when performing an activity ortask. In this study, we determined that the degree of thinking, decision making and searching forinformation was the major causes of high cognitive load that in the end affects the performanceof the operators, with more weight on thinking. In regard to thinking, it was shown that there-evaluation whether an operator has performed a task correctly or not has a high influence onthe mental workload as well as the many tasks that must be taken into consideration. In regardto searching and extracting the right information, it was shown that the continuous extraction ofinformation from sequence patches was perceived demanding, and the design of the sequencepatches also left room for an assembler to misidentify and misunderstand the information. Whenit comes to decision making, it has showed that the decisions that needs to be made on theposition has an tendency to disturb the natural working behavior of the assemblers as they knowthat it is fairly easy to take the wrong decision. This is connected to the lack of control systems ofthe investigated position which links back to the re-evaluation of ones performance which addsstrain on both the dimension of thinking and deciding.


Is an Augmented Reality system a feasible tool to manage those causes?

In the previous research question, it was determined that the causes of errors is the highmental workload of the assemblers. The aspect that affected the assemblers perceived mentalworkload the most was that they during their shifts on the RS3 position continuously controltheir work in order to ensure they have assembled it correctly according to the standard andon some occasions start questioning themselves whether or not they have conducted an activityaccording to the standard. Additionally, the extraction of information from the sequence patchesand that they have to take active decisions without any cognitive support system has its effect onthe mental workload of the assemblers. The combination of research question 1 and 2 revealsthat the skilled based errors have cognitive underpinnings in this case, which goes in line withwhat Trafton et al. (2011) have identified being the the causes of skilled based errors.

From the presented framework (figure 3.8), automation of cognitive processes can be utilized inorder to reduce the mental workload. For the automation to be supportive in this case, it has to beadapted to how one faces skill-based errors. Ishii et al. (2013) argues that skill based errors cannotbe prevented by traditional training as these do not depend on the years of experience. Wickensand Holland (2000) argue that warnings and alarm signals to detect errors are techniques to faceskill-based errors. Ishii et al. (2013), Stewart and Grout (2001) and Zhang et al. (2004) are on thesame position in this, saying that mental aids such as computer-based intelligent decision supportand error detection systems are good means to reduce the mental workload of an assembler, as

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long as it is easy to trace back where and when the error occurred. Zhang et al. (2004) adds thatpresentation of the most relevant information is also a method to reduce the mental workload ofassemblers during the skill-based performance.

Researchers and assemblers thoughtsAs this study is exploring the use of Augmented Reality systems, the theoretical methods offacing the analyzed causes of errors must be placed in an Augmented Reality context.

In terms of when an augmentation should be available, all the interviewed assemblers andresearchers says that an AR system should provide a visualization whenever an error occurs andimmediately after it warn the assembler. Variety is also a common theme in the answers, whereassembler A, D and E say that visualization in situations when there is variety on what to do isbeneficial, and this is supported by all the researchers.

Further on, in terms of where augmentation should be placed, all the assemblers are sayingthat placing it somewhere logical is preferable and refers to pick-to-lights, as this gives inputon where to pick components from. This does also give information to the user if they failed topick the component. Researcher G and H says that whenever a picking activity with varietytakes place, something should point out the place to pick from. Researcher I is on the same line,while researcher J further says that it should also visualize on the part missing or incorrectlyassembled in order for the assembler to know what was wrong.

In terms of interaction, it is evident that both the researchers and assemblers have the same viewon how the users should give input to the system. All agree that for the system to be efficient,the interaction must be indirect. In terms of what researcher I says, this is important in orderto reduce the workload of the user, as well as not having a negative effect on the productivity.What the indirect interaction is pointing at is that the interaction with the system should not beuser dependent, like researcher H and the assembler says. This mean that the system must becapable of understanding what is happening in the reality as researcher I says.

Desired functionalityCombining the sayings of the researchers and assemblers with the causes of errors, and put it incomparison into how skilled based errors should be managed, it cultivates into a desired functionof an AR system consisting of HMDs.

As all the assemblers and the researchers agree on that an augmentation should be avail-able whenever an error occurs, and that the assemblers have to re-evaluate their work in orderto make sure they have assembled it correctly, leads to a function where the system must be able

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to acquire information from its environment and process it with the aim of detecting if somethinghas been carried out incorrectly. In such cases, the system must give an immediate feedback tothe user, telling them that something is wrong. Researcher J says the system must be capable ofpointing out what is wrong. This goes in line with what previous studies says on making it easyfor the user to trace back the errors in order for the detection system to be valuable for the userin terms of reducing the mental workload.

The Augmented Reality system must also be able to in an evoking way, highlight a part tobe picked when there is a variation and multiple choices that can be made. This is in orderto help the assembler in what to pick in order break the "capture behavior" of the assemblers.Having a decision support system that highlights what to pick due to variation is somethingthat assemblers considers as important for an AR system, and which the researchers considersappropriate for skilled workers and the digital information should be placed on the box to bepicked from.

Moreover, the Augmented Reality system must be able to process information from lines oftext and present the relevant information for a particular component (if needed), as extractionof information has shown to be a factor affecting the mental workload of the assemblers and itshould be presented only when an activity needs the information.

Furthermore, as interaction is the way the system gets input from the user, theory says thatit should be as smooth as possible in order to be accepted by users. As the interaction with asystem has effects on mental workload according to researcher I, the indirect interaction that isrequested by the assemblers and suggested important by the researchers, will play an importantrole for the functionality.

Additionally, this desired function of an Augmented Reality system must be able to functionefficiently under the conditions where there are many head movements, small components beingassembled, where the hands of the assemblers occasionally cover the parts being assembled andworking under time pressure. Lastly, it is of great importance that the assembler shall not adapttheir working behavior to the technology, but rather the other way around.

These desired functionalities helps managing the causes of errors as these reduces the mentalworkload of the assemblers by replacing previously mental activities carried out by the assemblerto let the Augmented Reality system automate those activities instead. This function can reducethe need for the assembler to think, decide and search for information.

Looking deeper into this, from chapter 3.2.2 it was presented that no one can have a sus-

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tained attention over a longer period of time and even though a high attention level is desired, afatigue in attention is always a reality which was also referred to as a sensitivity decrement. Byhaving visual signals that distinguishes situational signals from the "noise" in the background,the sensitivity decrements can be reduced (Wickens and Hollands, 2000; Parasuraman, 1979).The visual signals are also categorized to what Wickens and Hollands (2000) refers to "targetsalience" which was described in chapter 3.2.2. This target salience enables assemblers to workunder the skill-based performance level without having any mental effort in their activities.

Since an decrement in attention is always expected, the control system, with decision sup-port when multiple choices are presented, and providence of the most relevant information onthe display can through minimalistic visualization spark the attention when needed instead ofletting the attention of an assembler only to decrease under a shift. This is done by separatingthe "noise" from the "signals" by giving digital information that otherwise would be difficult todistinguish in the real environment.

These desired functionalities can help the assembler to detect environmental or task relatedsignals and enables them to in a more distinct way chose a response and an execution as thefunctionalities of an Augmented Reality system provides a clear understanding of the task situ-ation and thus reduces the likelihood of a deviating component leave the assembly position inaccordance to the framework developed for this study (see figure 3.4).

When the desired functionalities of a HMD Augmented Reality system is stated, the ques-tion of feasibility to deliver the function must be analyzed, i.e. the capability to deliver the desiredfunctions under the case conditions. As mentioned in the section of what an Augmented Realitysystem conceptually does, Craig (2013) stated that an Augmented Reality system conceptually:

1. Determines what is happening in the reality.

2. Visualize the processed data.

From the interviews with the researchers within the field of Augmented Reality systems, itwas found that the enabling technologies i.e. hardware and software, are not developed to thelevel that they can provide the derived function that would support the assemblers.

In regard to the display, according to researcher G, the HMDs present issues in the usagethat makes the user feel headache and experience loss of orientation. Researcher H and J elabo-rates on the sayings, where researcher H says that this can be due to the combination of havingdigital information displayed with the use of optical devices like glasses. However, researcherH state that even though HMDs present ergonomic issues today, they are being developed fastand it should not be the major issue in the future. As the researchers have stated that there are

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ergonomically issues with the HMDs, it can be considered that it is not appropriate to use themtoday in assembly as the safety of the assemblers cannot be risked due to headaches or loss oforientation. Furthermore, as headaches wont make the system usable, the system wont be ableto manage the causes of errors as the assemblers probably wont use the HMDs.

All the researchers says that in order for the AR system to be capable of delivering the de-rived function, there are needs of having sophisticated AI computer vision algorithms, whereresearcher I and J says that it is a prerequisite to become context aware. Researcher H furthersays that depth perception is needed for the AI in order for the system to be able to understandwhere the assemblers hands are in the room. However, as the researchers has stated that inorder for the computer vision to function properly and making it possible for the system to becontext aware, the users must fix their view on the object and keep their sight free. In the settingof the case, it is evident that the users moves their heads while working as well as covering thepart that is going to be analyzed by the system, hence making it difficult for a system to suit ourcase. Further on, researcher H and J says that the geometry and size of the components that aregoing be analyzed matters, as bigger parts are easier to analyze than smaller ones. In the casesetting, the parts that are going to be analyzed are small, hence one can question whether thesystem will be capable of analyzing them. Researcher G says that computer vision is slow today,and relating this to the work conditions of the assemblers, it is evident that their work requiresspeed of the system, as they work under time pressure due to their cycle time, hence making iteven more difficult for the system to fit the purpose.

Connecting back to what an Augmented Reality system conceptually does, the result fromthe study presents that under the stated conditions of this study and for the desired function, anHMD Augmented Reality system cannot:

1. Determine what is happening in the reality without forcing the users to adapt theirwork behavior. As the assemblers move their heads and are not fixing their sight on the objects,as well as blocking the part from the sight with the hands, the system will generate errors as wellas well as not being capable of being context aware. This will make the system to not functionproperly.

2. Visualize the processed data. As the system wont function properly under the conditions,the system might not be capable of visualizing the information requested properly in time as wellas on the right place. Furthermore, as the HMDs present ergonomically issues and are used forthe visualization, the use of HMD as display is not feasible.

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CHAPTER 7. DISCUSSION

7.1 Discussion

The aim of this study has been to investigate what the causes of human errors are in a manualassembly setting and explore if these can be managed with technologies that are associated withthe new industrial setting of Industry 4.0 and in more specific if Augmented Reality systems arean appropriate tool to manage those causes of errors. In order to fulfill this aim, a case study at acommercial vehicle manufacturer has been conducted. The study involved document collection atthe case company as well as a literature study and semi-structured interviews with assemblersand researchers in the field of production engineering and computer science. The study wasstructured into three phases, the first phase was a pre-study, the second phase considered humanerrors and the third phase of the study was about Augmented Reality. The results and analysisfrom the second phase (Human Errors) is presented briefly bellow and then followed by theresults and analysis from the third phase of the study.

The results of phase two of the study showed that:

1. The assemblers on the production line perform their work according to Rasmussens skill-basedperformance where the characteristics of the errors are slips and lapses and have cognitiveunderpinnings.

2. Since the errors have cognitive underpinnings the customized Nasa TLX was used to geta subjective understanding of the dimensions affecting the mental workload, where the amountof thinking, deciding and searching for information affect the assemblers mental workload. Theinterviews with the assemblers revealed that the need of re-evaluate their own work (Thinking),taking active decisions when picking components (Decision) and the need of extracting informa-tion (Searching) had a significant effect on the mental workload of an assembler and is consideredas causative factors to human errors in manual assembly to occur.

With the aim of investigating if Augmented Reality systems is an appropriate tool to man-age the causes of errors, the result and analysis from phase three showed that:

3. Augmented Reality systems consisting of HMDs are the most suitable display type based on theworking environment of the assemblers. Given this, the result also showed that HMD AugmentedReality system is not a feasible option today to manage the causes of errors at the case company.The main bottleneck of this technique is the system’s AI computer vision capabilities and thatthe display causes the head-aches to the users.

The most unanticipated finding considers how a well-established commercial vehicle manu-facturer manages the human errors on the production line. Since the assemblers perform their

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task according to Rasmussens skill-based performance level, theory explicitly says that trainingis not the most suitable method to manage the errors. Nevertheless, the findings showed thatthe case company uses training as a method to manage human errors and where they otherwiseshould introduce proper incitements for managing human error. This is an important findingbecause it was found that the slips and lapses on one of many working positions provide witha high amount of deviations which affect the productivity of the assembly line. In this case, itcontributed with x minutes of stoppage time during a two-month period and ultimately increasesthe costs of the company. We believe these findings are generalizable to other manufacturing set-tings because training is an important part of current best practices in manufacturing industriesto manage human errors as many of the philosophies the major vehicle manufacturers in generalfollows, emphasizes on training of assemblers. Introducing the proper means to manage theseerrors would save companies both time and in the end money. These means should derive fromwhat performance level that the assemblers are performing in as well as from the root cause ofwhy the errors occurs.

Moreover, the results from the Augmented Reality perspective showed that at the time ofwriting, HMD Augmented Reality systems cannot deliver the desired function to manage thecauses of errors and bring value to the assemblers. For the system to be able to support theassemblers in the way that they desire, and how theory suggests one should face the causesof errors, the interviewed researchers agree that the underlying technologies is highly depen-dent on artificial intelligence and the development of it has not reached the desired stage yet.The only premise when an HMD Augmented Reality system would work today is when theuser adapts his entire work and behavior to the system which is not desirable. These insightsincrease the complexity of the measure to manage simple causes of errors which makes theconcept questionable if it is appropriate to even use this tool when mature enough, as there aresimpler means to manage these kinds of causes of errors which has been seen at the case company.

In the presented literature and previous case studies, HMD Augmented Reality systems hasbeen used with respect to novice people i.e. person new to or inexperienced in field or situation,where the characteristics of them resemble those performing on a knowledge-based performancelevel. These studies present HMD Augmented Reality systems as a tool to increase performanceand steepen the learning curve of people and tests has also revealed a decrement of the mentalworkload of a novice assembler. However, the characteristics of the function of the previoususe cases have been the visualization of 3D objects with instructions and guidance throughoutthe assembly process, pointing out what to do for each task. As discovered in this study, theassemblers already know what, how and when to do a task and they are doing their work ina repetitive manner under skill-based performance. This questions the need of assemblers tobe guided in the way HMD Augmented Reality is portrayed today. According to the theories

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presented the assemblers should rather be supported by simple systems that provide immediatefeedback whenever a task is deviating with one exception, where multiple choices can be made,and only under that circumstance guidance in form of visualization as Augmented Reality isportrayed today can be beneficial. Additionally, both the interviews researchers and assemblerssay the visualization is only beneficial in exactly the moment when something deviating fromthe standard. If we would consider this fact, the number of visualizations will be relatively fewwhen putting it in relation to previous use cases. This means that the HMD Augmented Realitysystem will assist the user more as a control system rather than being a system that activelyvisualizes information and/or providing instructions to the user which makes the core function(Visualization) of an Augmented Reality system redundant.

If mature enough, the AR functionalities may have the potential of reducing the mental workloadof the assemblers and hence reduce the errors but once again the question is if enhancing thereality with digital information, which is the main purpose an Augmented Reality system, is theright way. Considering the relatively few times an augmentation would appear as our results areindicating, why should the assemblers wear or be supported by an complex Augmented Realitysystem to get immediate feedback in relation to the simplicity of the causes of error? Why shouldmanufacturing companies equip the assemblers with Augmented Reality devices in order to getan augmentation whenever an error has occurred, especially when the number of deviations is afraction of the number of times the work is done correctly? These are questions that we cannotargue for, but we can provide with arguments why Augmented Reality systems should be avoided,and companies should manage the causes of errors with simpler and tested solutions. We arguethat the findings of this study provide insights that the most complex and original solution is notalways the most appropriate even though there is much of hype around them.

Connecting back to the background of this study, these findings have its implication on theIndustry 4.0 research and industry, as organizations have predicted an increased productivityand increased efficiency in the assembly line. McKinsey (2015) argues that the upcoming tech-nologies associated with Industry 4.0 will contribute with value to the manufacturing companiesand smart glasses has the potential of reducing the error rate by 40 percent in the logistics.Accenture (2017) has developed smart glasses for Airbus to increase the efficiency of the assemblyprocess of aircraft seats. These major consultancy firms have all portrayed Augmented Realityas the next big thing that solves specific issues but with little focus on solving the causes oferrors. By implementing an Augmented Reality system with the purpose of guiding assemblersthroughout the assembly process has shown to be beneficial for those performing their workunder the knowledge or rule-based performance level which is not perhaps equally as effective atother performance levels. The result of this study is at least pointing toward this; hence one mustremain critical in relation to the hyped technologies of Industry 4.0 and actually look at the real

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problem they are aimed to solve. The underlying meaning of the study says that one should notstare oneself blind on specific deviations, but rather understand why the deviation occurs and actfrom there with simple and tested methods and not aim to adopt hyped technologies just to showthat your organization is in the forefront. In our case, HMD Augmented Reality systems couldtheoretically be applied to manage those causes of errors when mature enough, but practicallyit is not the most appropriate way. An additional dimension of the inappropriateness of HMDAugmented Reality systems is that the mental workload of each position differs and so do thetasks, hence the functionality of the system must be customized for each position which increasesthe complexity. Meanwhile, a simple and tested tool such as pick-to-light systems has the similardesired outcome as the complex Augmented Reality systems.

Areas where HMD Augmented Reality could be beneficialHigh degree of customization on componentsComplex assemblyTraining

Table 7.1: Settings where HMD Augmented Reality would be more appropriate

As understood during the study, Augmented Reality systems are more appropriate wherewast amount of information and guidance is needed and it is under those circumstances the mainpurpose of Augmented Reality systems is not redundant. Under skill-based performance it showedthat it is not appropriate for the case and a contributing variable was the case company’s highlydeveloped poke-yoke processes. However, in circumstances where assemblers follow checklists orrules and or when assemblers are novice and must have a high mental presence for each andevery task, in other words rule and knowledge based performance, it is showed to be beneficialwith Augmented Reality systems. Those circumstances are presented in table 7.1. High degreeof customization means that the variant is much, and it is more difficult for an assembler tokeep track of what is going to be done, thus high degree of information is needed and AugmentedReality can provide this. In complex assembly where the assembly difficulty is increased, theassembler once again needs information and/or guidance when conducting a task. For these casesAugmented Reality systems can be a supportive tool. Lastly, in line with previous research andthe interviewed experts in the field of Augmented Reality, one of it’s biggest potential lays withintraining of new assemblers and or whenever new products are introduced on the assembly lineas it has proven to steepen the learning curve.

7.2 Sustainability

Sustainability concerns the long-term viability aspects of any company. The concept referred to asthe "Triple Bottom Line" consist of three aspects and these are: economical, environmental and

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social. According to Elkington (2004) the economical aspects is about the monetary efficiency ofcompany’s and the economical well-being. The environmental aspect is about the environmentalfootprint of a company which could include (but not restricted) to energy consumption, CO2emissions and overall impact on the climate. Lastly, the social aspects discussed the human healthand well-being of individuals. Due to the nature of this study, only economical sustainability andsocial sustainability is discussed as environmental sustainability is not applicable on this study.

Economical SustainabilityIt was discovered in the study and presented as an unanticipated finding that the current way tomanage human errors on the assembly line is training. As discovered, the assemblers performtheir work under skill-based performance were training is not the most suitable method tomanage them. This implies that vast monetary resources are placed on a method that is shownnot to be effective. As expressed outside of the study by assemblers discussed with, the traininghas become a moral support for the assembler rather than a way of managing errors. Presented inthe results, an error from one single position of many contributes with x minutes of stoppage timeduring a period of two months. This implies that the problem is bigger than that just x minutes,as other positions also contribute with stoppage time. Therefor people with insight can calculatethe cost of the stoppage time. As economical sustainability is about monetary efficiency, the timeand money spent on training are considered effective as it does not give the desired outcome, thesame errors occur in a loop. Focusing on the workplace design which is presented further belowin conclusion [8.1.2-8.1.3], is a mean to achieve economic sustainability by investing time andmoney on methods that would achieve the desired results.

Social SustainabilityIt was discovered in the study that the mental workload of assemblers is a causative factor forerrors to occur. Social sustainability is about the well-being of individuals, and in this case, theremust be incitements that reduce the mental workload of individuals as it directly affects theirwell-being. However, reducing the mental workload of individuals to absolute zero would alsoprovide with negative results as one would have no direct influence on their work and wouldmost likely lead to boredom. This highlights the importance of designing assembly positionsin conjunction with those who will be stationed on the position to find an optimum balance ofworkload for the assembler.

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8CONCLUSIONS

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CHAPTER 8. CONCLUSIONS

8.1 Conclusions

The purpose of this research was to investigate what the causes of human errors are in manualassembly operations at vehicle manufacturers. Further on, the purpose included investigating ifan Augmented Reality system is an appropriate tool for managing those causes. This study hasresearched this by identifying what the type or errors are at manual assembly and compared itto Rasmussens performance level framework and then used one dimension of the NASA TLX toassess the mental workload of assemblers at an position exposed to human errors. The desiredfunctionality was then derived and was opposed to the feasibility of a HMD Augmented Realitysystem to deliver the desired functionality which was presented in the analysis chapter. Thefindings then derived to a discussion which enabled a conclusion on the main research questionwhich is demonstrated below.

8.1.1 Answering MRQ

Is an Augmented Reality system an appropriate tool to manage human errors in manual assembly?

From SRQ3 we concluded that the HMD Augmented Reality systems has feasibility issuesi.e. the capability to deliver the desired function under the studied condition, in both hardwareand software. In terms of hardware and more specifically the display, HMDs which is the onlysuitable option given the workplace conditions, presents ergonomic issues such as loss of orienta-tion and headache, nevertheless, the findings presented that this is not the major feasibility issuewith HMD Augmented Reality systems as the development of these are going rapidly forward.The feasibility issues lie rather in the maturity of the software used in the devices. Today, theusers must adapt their working behavior, physical movements, and workplace design, for theHMD to be able to function properly and these adjustments that need to be made from the useris not desired at all in the studied setting. For an HMD Augmented Reality system to have asupportive role and deliver the desired functionalities it must be context-aware and this requiressophisticated algorithms in terms of AI which increased the complexity of the system and has atthe time of writing not been developed properly.

The core purpose of an Augmented Reality system is to augment the reality with digital informa-tion which either instructs or informs the user with supportive instruction and/or informationwhen conducting a task. However, as discovered in this study and mentioned in the discussion,the assemblers already know what, how and when to conduct a task and all the empirical datapointed toward that an augmentation is only beneficial at exactly the same moment a deviationhas happened and when multiple choices can be made hence a control and semi-decision supportsystem is desired. The discussion chapter took this into account and additional question arousedwhich questions the need of having a complex HMD Augmented Reality system for such trivial

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errors when mature enough? Considering the number of times an augmentation would appear onthe display in relation to the work being done, it would be relatively few. This means that theHMD Augmented Reality system will assist the user more as a control system rather than beinga system that actively visualizes information and/or providing instructions to the user whichmakes the core function (Visualization) of an Augmented Reality system redundant.

Considering this, we can conclude that an Augmented Reality system is not an appropriatetool to manage human errors in manual assembly. The underlying technologies and especiallythe software are not ready today to take a supportive role in manual assembly. Moreover, even ifthe Augmented Reality systems would reach the desired maturity level, the core function of anAugmented Reality system which is visualization of information and/or instructions would beredundant as the augmentation would appear in the display a fraction of the time an assembleruses the device. The system would be unnecessary complex as there are much simpler meanspresented by the researchers to manage human errors in manual assembly.

8.1.2 Managerial Implications

The managerial implications of the study is that organizations that aims to reduce the humanerrors at the production line must categorize the human errors correctly instead of referring toall errors as mistakes. This influences the steps taken to manage the human errors as differenterrors have different methods to face them. In the production line, where most of the humanerrors are referred to as skill-based errors, the best practice of training must shift toward methodsthat reduces the cognitive load on the assembly positions. This can be done through utilizing theframework of the study as a source when developing new assembly positions. Having the mentalworkload dimensions in mind in the development process of an assembly position, if carefullydesigned, one can in a proactive way increase the performance of assemblers.

Moreover, organizations that have came a long way in poke-yoke systems should continuewith previously proven and tested quality assurance methods instead of implementing the mostoriginal and novice solutions such as Augmented Reality systems. However, if Augmented Re-ality systems should be implemented, organizations should consider the amount of times theaugmentation will take place in order to not make the core functionality of an Augmented Realitysystem redundant.

8.1.3 Contribution

The academic contribution of this study is a new theoretical framework to derive the causes oferrors at a production line. Moreover, the academic contribution is empirical contribution on thedesired functionality of an HMD Augmented Reality system which would be supportive in man-ual assembly which was the research gap identified in the positioning of the study. The desired

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CHAPTER 8. CONCLUSIONS

functionalities are what the researchers should aim to develop for Augmented Reality systems tosuccessfully be taken out from laboratory settings. Lastly, another academic contribution is ofmethodological nature and is a new iterative way of investigating if Augmented Reality systemsis appropriate which is derived from the criticism toward the method used in this study which ispresented in [8.1.4].

The industry contribution is of methodological nature and is a new developed framework foridentifying the causes of human errors at an assembly position. The framework is also suitablewhen developing an assembly position where the mental workload dimensions should be con-sidered since it can in a pro-active way reduce the cognitive load of the assemblers and thusreduce the risks of human errors to occur. Additionally, a semi methodological and semi empiricalcontribution to the industry concerns that the best practices today for managing human errorsis not the most suitable method. Less weight should be on training and more effort should beon designing workplaces to be less mentally demanding whenever people are performing underskill-based performance. Lastly, the analytical and empirical industry contribution includesevidence that Augmented Reality systems is not an appropriate tool to manage human errorsgiven that the setting is the same.

8.1.4 Study limitations and future work

There are a few limitations of this study and methods used which are important to acknowledge.The document collection from the case company’s database has followed the non-random selectiondue to comfort and the availability of data. This was done because the documents from thedatabase of the case company had a population that was not manageable within the time limitof 20 weeks. The data reduction is seen as a limitation of the study as it is not presenting theentire truth and hence affect the validity of the distribution of errors among the working areas.Moreover, the documents were unstructured, and the categorization has been structured to fulfillour own needs and preferences with no academic support. Another limitation of the study isthat no HMD Augmented Reality system was physically been tested with the desired function.A physical test under a longer period would strengthen and verify the results of the study andhence increase the reliability. The developed framework to assess the mental workload of theassemblers is supported by theory, but not verified in any academic forum which makes theframework questionable. In addition to that, there are both subjective and objective ways ofmeasuring the mental workload of the assemblers. We have chosen the subjective approach whichleaves room for misinterpretation while the objective approach would have given raw numbers.

Moreover, the study narrowed down its scope to one specific working position at the pre-assemblywhich affects the generalizability of the study. The setting was chosen to see the boundaries of anAugmented Reality system thus the results on Augmented Reality was kind of given since the

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8.1. CONCLUSIONS

processes are already highly developed. Closely related to this, the biggest criticism we as authorshave toward the study is the logic behind SRQ3. In retrospect, a better SRQ3 would be "What isthe most appropriate AR application to manage the causes of errors". Answering this questionwould provide similar results and discussion topics, but it would not force the study to look atthe feasibility of the technical aspects to understand if Augmented Reality is an appropriatetool, which is the weakest part of the study. If doing the study again, we would be structuringthe study with the following design: SRQ3 - What is the most appropriate AR application? Andif the application showed to be beneficial and appropriate for the assembly position, the nextquestion would be: SRQ4 - Is it feasible in terms of enabling technologies? This would create abetter logic behind the questions as there is no need of investigating the enabling technologies ifthe applications showed to be not appropriate.

Nevertheless, one can see this drawback of the logic of research questions as a learning forfuture studies. The mistakes made in this study derived into a logical process of investigation ofAugmented Reality systems where the first step is to identify the appropriateness of the desiredfunction and if it showed to be appropriate, the next step would be to investigate if AugmentedReality technologies are feasible to deliver the desired solutions. The next step would be toconduct a similar study with the proposed iteration on another setting with much less developedpoke-yoke system as this study wanted to push the boundaries of AR and it showed not to beappropriate in a well developed poke-yoke production line.

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AAPPENDIX A

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APPENDIX A. APPENDIX A

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A.0.1 Appendix - Interview Questions

Figure A.1: Interview Questions - Human Error83

APPENDIX A. APPENDIX A

Figure A.2: Interview Questions - AR

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