Augmented reality tools for industrial applications: What are potential key performance indicators and who benefits?

Highlights

• An overview on Augmented Reality (AR) tools for industrial applications is given.

• Potential key performance indicators (KPIs) are identified.

• A usability test of an AR tool in the workplace was used for hypothesis testing.

• Users had to evaluate the KPIs before and after the training situation.

• Novice users constitute a potential target group reporting reduced time and errors.

Abstract

Augmented reality (AR) tools are poised to have great potential for organizations when it comes to complex processes in the field of industrial applications – like construction or maintenance in the automotive industry. The human-centred technology displays context-specific 3-D information in a real environment related to a specific targeted object. Immersive experiences are expected to boost task efficiency, the quality of training and maintenance purposes. However, ready-for-market AR tools are still rarely used and benefits seldom demonstrated. This paper focuses on the key performance indicators (KPIs) that are able to benchmark the impact of using ready-for-market AR tools on automotive maintenance performance. After a comprehensive literature review on the benefits of AR for several industrial applications in design, education and training, KPIs were extracted and evaluated by experts from the automotive industry. They were used in an empirical study – based on the technology acceptance model (TAM) – with users evaluating the KPIs before and after training situations. In addition, ‘Perceived Usefulness’ and ‘Intention to Use’ were investigated. Significant enhancements of all KPIs were observed and novice users were identified as a potential target group.

Introduction

Embedded in a global market, the automotive industry is continuously challenged to be competitive in reducing costs, offering a diverse portfolio of customized products and guaranteeing a high standard of quality (Anastassova & Burkhardt, 2009; Ili, Albers, & Miller, 2010). Innovations and the transformation of business models are triggered by sustainability, for example in the form of e-mobility (Donada & Lepoutre, 2016) or consumer electronics and digitalization (Yoo, Henfridsson, & Lyytinen, 2010). But, simultaneously, lean working processes relying on flexible manufacturing environments and new service information technologies are also required to fulfil customers’ demand for individual products and shorten product life cycles (Wang, Ong, & Nee, 2016). Technological support such as electronic devices and virtual reality have been introduced in the industry to “increase the working performances, in order to speed up the accomplishing time and decrease the costs of an operation” (Re, 2013, p. 1).

Since the complexity of assembly processes in identifying possible components or troubleshooting at the same time is becoming a stronger issue for workers, interactive information aid systems might compensate or even boost task efficiency (Funk, Kosch, Greenwald, & Schmidt, 2015). Among these technologies, augmented reality (AR) is one promising aid tool that can support users with regard to “a wide range of problems ( …), e.g., planning, design, ergonomics assessment, operation guidance and training” (Wang et al., 2016, p. 1). More precisely, it provides people with 3-D relevant information for their work. Furthermore, this information is displayed in a real environment related to a specific workpiece (Azuma, 1997). Khuong et al. (2014) emphasize the promising role of AR for automotive assembly, referring to the Boeing prototype system supporting the assembly (but also maintenance and repair) of wire bundles (Azuma, 1997). AR tools can provide a guide to the user through unfamiliar or complex use cases (Borsci, Lawson, & Broome, 2015; Re, 2013).

Although AR may have a beneficial effect on overall performance efficiency in several industrial applications, only recently has a review on AR-based assembly systems been completed (Wang et al., 2016). Some evaluation criteria have been established based on the complexity of the assembly task and the time needed (Funk et al., 2015). However, Grubert et al. (2010) point to the users having been under-explored. As reasons they give factors such as “current immature AR hardware and user interfaces, high safety demands from industrial partners, difficulties in implementing prototypical AR systems into productive work processes and thus high costs of conducting studies outside the laboratory” (Grubert et al., 2010, p. 229). Initial approaches have investigated and measured stress when using or not using AR support in laboratory settings (Tümler et al., 2008).

According to the current Gartner Hype Cycle 2017 – which assesses the maturity and adoption time of emerging technologies – AR is positioned in the “Trough of Disillusionment” (Fenn & LeHong, 2011; Gartner Inc., 2017). In this phase, the first hype is considered to be inflated and upcoming shortcomings or challenges become apparent. In 2005 Gartner predicted that it would take 5–10 years for a regular and effective utilization of AR to become realistic. In 2017 the years to mainstream adoption of AR are still left unchanged with 5–10 years. Given that companies are still conducting ‘proofs of concept’ (PoCs) in particular for industrial AR applications, this might imply that there is uncertainty about the benefits of AR, e.g. in terms of time savings or error rates, but also psychological and physiological reactions of users, e.g. stress or head and eye discomfort (Grubert et al., 2010). Nee, Ong, Chryssolouris, and Mourtzis (2012, p. 674) come to the conclusion that in industry “[t]he limited understanding of human factor issues is likely to hinder widespread adaptation of AR systems beyond laboratory prototypes”. Users accept and adopt new technologies like AR when they have benefits that are superior to current existing technology in terms of relevant key performance indicators (KPIs) such as performance improvements or reduction of usage effort (Bhattacherjee, Limayem, & Cheung, 2012; Davis, Bagozzi, & Warshaw, 1989; Rogers, 2003).

This paper takes up these considerations using the ‘Bosch Common Augmented Reality Platform’ (Bosch CAP) as an example for a ready for market AR tool. Bosch CAP visualizes the construction parts of a vehicle by displaying the interior of a filmed vehicle on a tablet. The user acceptance of Bosch CAP in real operating conditions in automotive maintenance is evaluated. KPIs of AR usage are identified by experts and compared by self-assessment in training situations using AR technology with traditional technology. In addition, a research model based on the technology acceptance model (TAM) is developed and tested. The results are investigated against the background of inhomogeneous user groups (Grubert et al., 2010) in terms of demographic factors and usage behaviour.

The main goal of the paper is to investigate the following research questions:

• What are the KPIs for AR in automotive maintenance?

• How do these KPIs differ in the evaluation by the users when comparing their use of AR with traditionally existing technology?

• How are the KPIs related to central constructs of the TAM model (‘Perceived Usefulness’, ‘Intention to Use’)?

• What are the benefits of AR for inhomogeneous user groups, in particular novice or inexperienced users?

The paper is structured as follows: the next section provides a definition and a literature review with a focus on AR-use in industrial environments. The studies presented give an initial overview of the benefits of AR and potential KPIs that can be compared before and after usage. In the third section the conceptual and theoretical background for the TAM and the research model are described. In Chapter 4, the study design and user experience questionnaires are introduced before Chapter 5 presents the results of the empirical study testing an AR application of Bosch (Bosch CAP) with users in automotive maintenance. Finally, in the last section theoretical and practical implications, limitations and avenues for further research are discussed. The paper closes with concluding remarks.