# Evaluation of Potential Benefits of Augmented Reality for Industrial Services

Stephan Bollinger, Volker Stich, Lennard Holst, Florian Defèr, Florian Schuldt

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Stephan Bollinger, Volker Stich, Lennard Holst, Florian Defèr, Florian Schuldt. Evaluation of Potential Benefits of Augmented Reality for Industrial Services. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2022, Gyeongju, South Korea. pp.135-144, 10.1007/978-3-031-16407-1_17. hal-05386040

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# Evaluation of Potential Benefits of Augmented Reality for Industrial Services

Stephan Bollinger<fnref n="1" />, Volker Stich<fnref n="2" />, Lennard Holst<fnref n="2" />, Florian Defer<fnref n="2" />, and Florian Schuldt<fnref n="2" />✉

Florian.Schuldt@fir.rwth-aachen.de

**Abstract.** Augmented reality seems to offer great potential benefits in the field of industrial services. However, the question of the exact benefits, both monetary and qualitative, is difficult to evaluate, as is the case with IT investments in general. Within the framework of the DM4AR research project, an evaluation model was therefore developed. Based on group discussions and interviews on potential AR use cases, a list of monetary and qualitative benefits was compiled to form the basis for selecting suitable evaluation modules in the existing literature. These include an impact chain analysis in the form of a strategy map, a monetary evaluation as a calculation of the return on investment, based on the assumptions of the use case as well as existing studies, and a qualitative evaluation in the form of a utility analysis. The outcome is an evaluation model in the form of a multi-perspective approach that considers the impact of AR in the four perspectives of the balanced scorecard (financial, customer, internal business processes, learning and growth). The results of the qualitative and monetary evaluation can be summarized in a 2D matrix to support decision-making.

**Keywords:** Augmented reality · Potential benefits · Evaluation · Return on investment

## 1 Introduction

The topic of Augmented Reality (AR) is becoming increasingly relevant in industrial practice [1]. This is primarily due to technological advances in the areas of graphics processors, displays and RAM [2]. AR glasses and handheld devices such as tablets or smartphones can superimpose computer-generated objects on the user's natural perspective and thus provide context-related information [3].

In contrast to virtual reality (VR), the focus is on the real environment in which work can be performed on real objects [4]. AR technology therefore can offer great potential in the area of industrial services, in which activities are carried out for the inspection, maintenance, repair and optimization of machines and systems [5].

<footnote n="1">¹ YNCORIS GmbH & Co., KG, Industriestr. 300, 50354 Hürth, Germany</footnote>

<footnote n="2">² Research Institute for Industrial Management (FIR) at RWTH Aachen, Campus-Boulevard 55, 52074 Aachen, Germany</footnote>
However, as with IT investments in general, the question of ex ante evaluation of the benefits is difficult, since qualitative effects are often expected as well and a one-dimensional evaluation (only financial indicators) is therefore not necessarily expedient, although monetary values are ultimately the deciding factor in an investment decision [6]. In the literature, there are various criteria for evaluating AR, but often no reproducible methodology [7].

In the research project “Data Management for Augmented Reality” (DM4AR), group discussions and interviews with relevant stakeholders were used to analyze various AR use cases, their potential benefits, and impacts on the company. Based on this, a multi-perspective approach [8] was chosen for a balanced evaluation and suitable evaluation modules for monetary and qualitative benefits were selected from existing literature. The evaluation should already be possible at an early stage in the implementation process, when only an elaborated use case concept is available [9]. To validate the model, a first application was conducted by the project team at YNCORIS. The superior goal of this work is therefore to evaluate the benefit of the usage of AR in industrial service not only monetarily but holistically. This represents a gain in knowledge to the extent that only the monetary evaluation has been in the foreground up to now.

## 2 State of the Art

There are already some approaches that allow different forms of evaluation and consider the content focus of AR and industrial service in different ways. The utility analysis or scoring model, as a classical evaluation approach of the multi-criteria decision analysis, enables a qualitative evaluation, where the uni-dimensional methods of the economic investment calculation are not sufficient [10]. The four perspectives of the Balanced Scorecard (BSC) by Kaplan and Norton can be used to assess the impact on the entire company and also consider the potential of information systems as part of the learning and growth perspective [11].

Schuh et al. developed a procedure to evaluate Industry 4.0 use cases at an early stage by using a two-part approach, monetary in the form of the payoff method and non-monetary in the form of the aforementioned utility analysis. The results are compiled in a portfolio matrix [9]. In their multi-criteria evaluation of industrial assistance systems, Zigart and Schlund provide an overview of possible evaluation methods. For a balanced evaluation, five perspectives are considered, based on the BSC. However, a precise methodology for the application and consolidation of the results is not provided [12]. Müller et al. developed a multi-perspective approach [8] primarily for evaluating controlling application systems, but it is also applicable to information systems in general [13]. Different evaluation methods are assigned here to the various potential benefits to enable an adequate evaluation of these benefits.

Concrete return on investment calculations for the use of AR are made by Forrester Research, Inc. in their Total Economic Impact approach [14, 15], as well as by the AR for Enterprise Alliance (AREA) [16]. Both use data from fictitious enterprise use cases for this purpose.
# 3 Model for Evaluating Potential Benefits of Augmented Reality in Industrial Service

## 3.1 Identification and Classification of Potential Benefits of AR

In the considerations of possible use cases in the DM4AR research project and an additional literature review, various potential benefits of AR technology were identified. Potential benefits, as all possible positive effects of an investment on the company's objectives [13], can be differentiated according to their quantifiability and their monetary evaluability [17]. Table 1 categorizes the potential of AR technology in terms of its monetary value. The potentials that can be monetarily valued are primarily those that can lead directly to cost savings but also to increases in revenue.

<table><caption>**Table 1.** Categorization of potential benefits of AR</caption><thead><tr><td>Monetizable benefits</td><td>Non-monetizable benefits</td></tr></thead><tbody><tr><td><ul><li>Increase service productivity</li><li>Increase expert productivity</li><li>Reduce process lead time</li><li>Operational processes more efficient</li><li>Support processes more efficient</li><li>Avoid waiting/driving times</li><li>Improve service quality</li><li>Avoid rework & follow-up visits</li><li>Improve error identification</li><li>Reduce training time</li><li>Avoid travel/trip costs</li><li>Reduce material costs</li><li>Increase turnover</li></ul></td><td><ul><li>Improve data currency & quality</li><li>Improve knowledge transfer</li><li>Increase flexibility</li><li>Improve employee satisfaction</li><li>Increase transparency</li><li>Ensure compliance</li><li>Improve safety/working conditions</li><li>Promote sustainability</li><li>Improve service quality</li><li>Reduce customer complaints</li><li>Shorten response time</li><li>Reduce customer downtime</li><li>Differentiate service offerings</li><li>Improve corporate brand awareness</li><li>Improve customer satisfaction</li></ul></td></tr></tbody></table>

## 3.2 Methodology – Multi-perspective Approach

The basic structure of the methodology for evaluating the potential benefits is based on the multi-perspective approach [8]. The advantage here is that suitable evaluation methods can be selected depending on the type of potential benefit. The identified potential benefits can be grouped into several overarching categories. For a balanced view of the impact on the overall company or business unit, these categories should cover all four perspectives of the BSC (see Fig. 1). Different evaluation methods can then be assigned to the various categories. For the evaluation of AR usage in industrial service, three different evaluation methods were selected: The strategy map of the BSC as an impact chain analysis, a calculation of the return on investment for monetary evaluation, and the qualitative evaluation in the form of a utility analysis. The monetary evaluation
includes the financial perspective and those improvements in processes and quality that
can be monetized, such as reduced process times and increased first-time fixed rates
(FTFR). All other factors are only evaluated qualitatively. To combine the monetary and
qualitative assessment into a decision-making framework, a two-dimensional matrix
can be used to plot the utility score against the ROI. Different areas can recommend
implementation, refrain, or further discussion. This specific multi-perspective evaluation
model is explicitly designed for the AR application use case (see Fig. 1).

<figure><img src="image_3.png" /><figcaption>Fig. 1. Overview of the evaluation model based on a multi-perspective approach</figcaption></figure>

3.3 Cause-Effect Relations of Potential Benefits – Strategy Map

The strategy map as an impact chain analysis is less of an evaluation procedure, but it
does reveal the dependencies of the various potential benefits and thus ensures trans-
parency and a better understanding. Another advantage is that all potential benefits can
be considered in this analysis. In this way, it also provides the basis for considerations in
the later evaluation procedures. As mentioned in the introduction, this specific strategy
map is based on group discussions and interviews with relevant stakeholders of different
AR use cases.

Starting from the potentials offered by a new information system, it is possible to
infer the consequential effects for the other perspectives all the way to the financial result
for the company through logical linkage (see Fig. 2). The benefits of an AR information
system initially have a positive impact on the employees by empowering them for new
tasks and increasing their flexibility, which leads to increased employee satisfaction.
From a process perspective, this results in increased service productivity by shortening
the training time for new employees, reducing process cycle times, eliminating trips and
travel, and saving materials. This also promotes sustainability. Security and compliance
can be ensured through workflows and checklists that provide contextual security guid-
ance, whereas automated documentation leads to greater transparency. In combination
with better empowerment of employees, this also increases the quality of work. There
are also direct positive effects for the customer. Faster response times and more efficient
processes reduce plant downtime. At the same time, the increased quality leads to fewer
complaints. Through AR, differentiated service offers can be made to differentiate from the competition. All in all, this leads to increased customer satisfaction, can also raise the company's brand awareness and result in sales increases. Together with cost savings on the process side, this leads to a positive ROI.

<figure><img src="image_4.png" /><figcaption>Fig. 2. Strategy Map for AR in maintenance</figcaption></figure>

## 3.4 Monetary Evaluation – ROI

The monetary evaluation should lead to transparency in the question of whether the use of augmented reality in industrial service also leads to a measurable return on investment (ROI). In a 2018 survey, 62% of respondents answered that this is the case [18]. Direct added value for the company can result from efficiency gains, more flexible employee deployment, time or cost savings in both training and processes, and improved quality [1]. ROI can generally be calculated using the following formula [14]:

$$ROI = \frac{\text{net benefits}}{\text{costs}} = \frac{(\text{benefits} - \text{costs})}{\text{costs}} \quad (1)$$

The ROI thus compares the benefits with the costs. For the calculation, the cash flows associated with the use of AR must be estimated and their present value (PV) determined
by discounting them to the valuation date using a discount rate i and a period t. A typical assessment period for IT investments of 3 years [16] was selected.

$$PV = \text{cash flow} \times (1+i)^{-t} \quad (2)$$

Benefits and costs can be divided into different categories, which are later summed up. The costs can be assumed on the basis of the total cost of ownership approach [19] and the benefit categories result from possible cost savings and sales increases (see Fig. 3). Each of these categories can be calculated as an annual metric by estimation. To account for the uncertainty of the assumptions, the results are adjusted by a risk factor.

<table><caption>**Fig. 3.** Benefit and cost categories with related calculation formula</caption><thead><tr><th></th><th>Measurand</th><th>Calculation formula</th></tr></thead><tbody><tr><td rowspan="6">Potential benefits</td><td>Training efficiency</td><td>Σ saved training hours × new hire labor costs per hour</td></tr><tr><td>Service efficiency</td><td>Σ saved service hours × technician labor costs per hour</td></tr><tr><td>Expert productivity</td><td>Σ expert hours saved × expert labor costs per hour</td></tr><tr><td>Travel/trip costs savings</td><td>avg. travel or ride costs per trip × number of avoided trips</td></tr><tr><td>Material cost savings</td><td>Avg. material savings per user × number of AR users</td></tr><tr><td>Sales increase</td><td>annual sales of industrial services × AR increase × profit margin</td></tr><tr><td rowspan="4">Costs</td><td>Hardware</td><td>Σ AR devices required × purchase price</td></tr><tr><td>Software</td><td>licensing costs × number of AR users</td></tr><tr><td>Planning, implementation and administration</td><td>Σ expended hours × labor costs per hour</td></tr><tr><td>AR training</td><td>Σ training hours × labor costs per hour</td></tr><tr><td colspan="3">Estimation based on existing studies and own assumptions<br>incl. risk adjustment</td></tr><tr><td colspan="3">ROI calculation with discount rate i over a period of three years</td></tr></tbody></table>

## Benefits

Newly hired technicians in industrial service initially accompany experienced employees for the first few months before working independently. AR can shorten this training time and thus increase new hire productivity. Less qualified employees can be deployed earlier in the field if they are guided by AR assistance and can call in an experienced employee via remote support in the event of a problem. Service efficiencies result from service technician efficiencies with AR support, shortened process cycle times, and reduced rework due to increased first-time fix rates. The productivity of experts or operations managers can be increased, as they spend less time on documentation, staff training and knowledge transfer, and on-site visits.

The use of AR can make trips to the site obsolete through remote support and increased FTFR. The greater the distance, the greater will be the savings. For regional companies, only the transportation costs are saved, but for globally active service providers, high travel costs including flight, hotel and expenses can be avoided.
Material costs can also be saved, e.g. for paper, printer ink or even personal protective equipment (PPE). Attributing an increase in sales to a particular technology is difficult, as it is not possible to establish exactly which factors are responsible for the customer's decision to buy [16]. For the sake of completeness, however, this potential benefit was also included in the evaluation.

## Costs

When selecting AR hardware, companies have several options. The AR visualization can be displayed through AR glasses, but also on a tablet or smartphone. However, the hardware should be determined by the use case, e.g. the need for hands-free work favors glasses. One advantage of handhelds, however, is that they are already in widespread use in many companies and thus incur only minor additional hardware costs [20].

The use of an AR software solution requires licensing costs per user, which must be taken into account too. The time and cost, as well as expertise and employee acceptance, required to successfully implement an AR solution should not be underestimated. While the remote function is implemented quickly, a significantly greater effort must be made for work instructions and 3D visualizations, which have to be iteratively improved until the application delivers the added value for the user. [15] Lastly, personnel costs during employee training for AR technology must also be considered.

## Exemplary ROI Calculation

To validate the model, an exemplary calculation of the ROI for a possible application scenario at YNCORIS was carried out (see Fig. 4). Existing studies on improvements from AR [14–16, 18, 21, 22] can be used as a reference to better estimate the potential benefits. In the further course of the project, the evaluation can be carried out again with more precise information, e.g., through own tests on process efficiencies.

<figure><img src="image_5.png" /><figcaption>Fig. 4. Exemplary ROI calculation of a possible AR implementation at YNCORIS</figcaption></figure>

## 3.5 Qualitative Evaluation – Utility Analysis

The purely monetary evaluation does not consider many of the identified potential benefits. The utility analysis is a suitable instrument for evaluating the benefits of AR technology in qualitative terms as well. It was originally developed to compare different alternatives [10]. In this case, this purpose is slightly modified by evaluating the changes to the status quo. However, another use case can be the comparison between different application options in terms of teams or business segments.

This module evaluates the benefit categories of all perspectives except financial. Quality and process improvements were also partially evaluated in monetary terms, but
this is not possible for other potential benefits in these categories, such as improved transparency, which is why these are also evaluated qualitatively.

The rating is based on a cardinal scale of 1–5, with the mean value of 3 corresponding to no change compared to the status quo (see Table 2).

<table><caption>**Table 2.** Cardinal scale for qualitative evaluation</caption><thead><tr><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th></tr></thead><tbody><tr><td>Strong deterioration/neg. Impact</td><td>Slight deterioration/neg. Impact</td><td>No change/impact</td><td>Slight improvement/pos. Impact</td><td>Strong improvement/pos. Impact</td></tr></tbody></table>

In the customer perspective, it can be evaluated whether there are direct effects (pos./neg.) on the customer and how customer satisfaction and brand awareness change. In the internal process perspective, safety and sustainability are assessed in addition to process and quality changes. And in the learning and growth perspective, changes in data timeliness and quality, knowledge transfer and flexibility, and the impact on employee satisfaction are evaluated. Preferences of the decision makers, which should be selected in an interdisciplinary and use case dependent manner, can be taken into account by weighting the different criteria, e.g. by pairwise comparison [9]. The qualitative score is calculated by multiplying the criteria values by the respective weighting and then summing them up [17]. When evaluating the criteria, the challenges and risks of AR use should also be considered. These include network connectivity, IT security, privacy rights of employees, user-friendliness and user acceptance, ‘motion sickness’ and a possible increased distance between service technician and customer [15, 23].

## 3.6 Consolidation of Results and Decision Support – 2D-Matrix

Finally, the ROI and the score of the utility analysis (total score, or the partial scores of the different perspectives) can be compared in a two-dimensional coordinate system of profitability and qualitative utility (see Fig. 1). The further up on the right the application scenario can be plotted, the more beneficial the implementation of the project. The area at the bottom left, on the other hand, represents an unfavorable implementation. If the point lies in between, further discussions and investigations may be necessary. Each company must determine the exact ranges according to its own preferences in order to perform an appropriate assessment in each case. An example of such an assessment is included in Fig. 1.

# 4 Conclusion

The model presented enables an initial evaluation of AR use in industrial service, both economically and qualitatively. In doing so, it can be used to evaluate AR technology in general based on a possible use case, or to compare different application scenarios.
The model is being tested with different use cases as part of the DM4AR project. The evaluation is based on own assumptions and data from previous studies, which can be replaced at a later stage with improved information from own user tests and time and motion studies, for example before the approval of piloting or the approval of roll-out. In addition, the evaluation of concrete hardware and software solutions and their impact on users should also be investigated in a next step. Additionally, subject evaluations need to be conducted regarding the usability and task load of the use of AR applications.

## References

1. Adelmann, R.: Augmented Reality in der industriellen Praxis. In: Orsolits, H., Lackner, M. (eds.) Virtual Reality und Augmented Reality in der Digitalen Produktion, pp. 7–32. Springer Fachmedien Wiesbaden, Wiesbaden (2020)

2. Kind, S., Ferdinand, J.-P., Jetzke, T., Richter, S., Weide, S.: Virtual and augmented reality. Status quo, Herausforderungen und zukünftige Entwicklungen (2019). https://www.tab-beim-bundestag.de/de/pdf/publikationen/berichte/TAB-Arbeitsbericht-ab180.pdf. Accessed 4 Nov 2021

3. Syberfeldt, A., Danielsson, O., Gustavsson, P.: Augmented reality smart glass-es in the smart factory: product evaluation guidelines and review of available products. IEEE Access 5, 9118–9130 (2017). https://doi.org/10.1109/ACCESS.2017.2703952

4. Shinde, G.R.: Internet of things integrated augmented reality. In: SpringerBriefs in Applied Sciences and Technology Ser. Springer Singapore Pte. Limited, Singapore (2021)

5. DIN EN 13306: DIN EN 13306:2018–02, Instandhaltung – Begriffe der Instandhaltung; Dreisprachige Fassung EN_13306:2017. Beuth Verlag GmbH, Berlin ICS 01.040.03, 03.080.10(13306:2018–02) (2018)

6. Kesten, R., Müller, A., Schröder, H.: IT-Controlling. Messung und Steuerung des Wertbeitrags der IT, 1st edn. Vahlen, München (2007)

7. Zigart, T., Schlund, S.: Evaluation of augmented reality technologies in manufacturing – a literature review. In: Nunes, I.L. (ed.) Advances in Human Fac-tors and Systems Interaction. Proceedings of the AHFE 2020 Virtual Confer-ence on Human Factors and Systems Inter-action, USA, 16–20 July 2020, vol. 1207. Springer eBook Collection, vol. 1207, 1st edn., pp. 75–82. Springer, Cham (2020)

8. Müller, A., Lang, J., Hess, T.: Wirtschaftlichkeit von Controlling-Anwendungssystemen: Konzeption und Erprobung eines Multiperspektiven-Ansatzes. In: Hess, T. (ed.) Anwen-dungssysteme im Controlling: Was treibt die Entwicklung? ZfCM-Sonderheft, vol. 2, pp. 58–66. Gabler Verlag, Wiesbaden, s.l. (2003)

9. Schuh, G., et al.: Industrie 4.0: Implement it! Ein Leitfaden zur erfolgreichen Implementierung von Industrie 4.0-Lösungen, 1st edn. Werkzeugmaschinenlabor der Rheinisch-Westfälischen Technischen Hochschule Aachen, Aachen (2018)

10. Zangemeister, C.: Nutzwertanalyse in der Systemtechnik. Eine Methodik zur multidimen-sionalen Bewertung und Auswahl von Projektalternativen. Teilw. zugl.: Berlin, Univ., Diss., 1970, 5th edn. Books on Demand, Norderstedt (2014)

11. Kaplan, R.S., Norton, D.P.: The Balanced Scorecard. Translating Strategy into Action. HBS Press, Harvard (1996)

12. Zigart, T., Schlund, S.: Multikriterielle Evaluation von industriellen Assistenzsystemen am Beispiel von Augmented Reality-Anwendungen in der Produktion. In: Gesellschaft für Arbeitswissenschaft e.V. (ed.) GfA-Frühjahrskongress 2020 // Digitale Arbeit, digitaler Wandel, digitaler Mensch? Digitaler Wandel, digitale Arbeit, digitaler Mensch? // 66. Kongress der
Gesellschaft für Arbeitswissenschaft, TU Berlin, Fachgebiet Mensch-Maschine-Systeme/HU
Berlin, Professur Ingenieurpsychologie, Berlin, 16–18 März 2020, pp. 1–5. GfA-Press,
Dortmund (2020)

13. Becker, A.: Nutzenpotenziale und Herausforderungen Service-orientierter Architekturen. SpringerLink Bücher. Gabler, Wiesbaden (2011)

14. Forrester Research, Inc.: The Total Economic Impact™ Of PTC Vuforia. Cost Savings And Business Benefits Enabled By Industrial Augmented Reality (2019). https://www.ptc.com/-/media/Files/PDFs/Augmented-Reality/The-Total-Economic-Impact-of-PTC-Vuforia_2019.pdf. Accessed 24 Nov 2021

15. Forrester Research, Inc.: The Total Economic Impact Of Mixed Reality Using Microsoft HoloLens 2 (2021). https://download.microsoft.com/download/e/1/3/e1364937-5f62-4a0c-bb9e-664c270ad4fe/Forrester-Total-Economic-Impact-Mixed-Reality-Microsoft-HoloLens-2_Cover.pdf. Accessed 14 Feb 2022

16. AR for Enterprise Alliance (AREA): Case Study – ROI Analysis for AR Use in Maintenance & Repair Operations (2017). http://thearea.org/wp-content/uploads/2018/05/AREA-Enterprise-AR-ROI-Case-Study.pdf. Accessed 24 Jan 2022

17. Pietsch, T.: Bewertung von Informations- und Kommunikationssystemen. Ein Vergleich betriebswirtschaftlicher Verfahren, 2nd edn. E. Schmidt, Berlin (2003)

18. Mainelli, T.: How augmented reality drives real-world gains in services, training, sales and marketing, and manufacturing (2018). https://www.ptc.com/-/media/Files/PDFs/IoT/IDC-AR-Use-Cases-Report.pdf. Accessed 18 Oct 2021

19. Mieritz, L., Kirwin, B.: Defining gartner total cost of ownership (2005). https://barsand.files.wordpress.com/2015/03/gartner_tco.pdf. Accessed 11 Apr 2022

20. Llamas, R.T., Dialani, M.: Augmented reality is ready and open for business (2020). https://www.ptc.com/-/media/Files/PDFs/Augmented-Reality/IDC-AR-Is-Ready-and-Open-for-Business.pdf. Accessed 24 Jan 2022

21. Lyden, S.: Fix it right the first time: first-time fix rate calculation (2015). https://fsd.servicemax.com/2015/04/13/first-time-fix-rate-field-service-metrics-that-matter/. Accessed 15 Jan 2022

22. Porter, M.E., Heppelmann, J.E.: Why every organization needs an augmented reality strategy. Harv. Bus. Rev. 95 (2017), 46–57 (2017)

23. Gutsche, K., Eigenstetter, M.: Dienstleistungsproduktivität in der smarten Produktion. Mensch und Automation in der industriellen Instandhaltung. wt (2019). https://doi.org/10.37544/1436-4980-2019-07-08-4