Conference PaperPDF Available

Abstract and Figures

In today's business environment, the efficiency of warehouses can be critical for the efficiency of the overall supply chains they belong to. As a result, new technologies are being tested and adopted in industry to improve the performance of warehouse operations. An example technology that has recently gained interest by both academia and industry is augmented reality. In this paper, we investigate the opportunities arising from the usage of augmented reality in warehouses as well as the barriers for its industrial adoption. This is done via a series of practitioners interviews and via an experiment designed using Google Glass. Our results indicate that even though the technology is not mature enough at the moment, the potential benefits it can offer make it promising for the near future.
Content may be subject to copyright.
Augmented Reality in Warehouse
Operations: Opportunities and Barriers
Marie-H´el`ene Stoltz ,Vaggelis Giannikas ,
Duncan McFarlane ,James Strachan ∗∗,Jumyung Um ,
Rengarajan Srinivasan
Institute for Manufacturing, Department of Engineering, University
of Cambridge, Cambridge, CB3 0FS, United Kingdom
(e-mail: {mhays2, v.giannikas, dcm, ju220}
∗∗ James and James Fulfilment Ltd, Liliput Road, Brackmills,
Northampton, NN4 7DT , United Kingdom
In today’s business environment, the efficiency of warehouses can be critical for the efficiency
of the overall supply chains they belong to. As a result, new technologies are being tested
and adopted in industry to improve the performance of warehouse operations. An example
technology that has recently gained interest by both academia and industry is augmented
reality. In this paper, we investigate the opportunities arising from the usage of augmented
reality in warehouses as well as the barriers for its industrial adoption. This is done via a series
of practitioners interviews and via an experiment designed using Google Glass. Our results
indicate that even though the technology is not mature enough at the moment, the potential
benefits it can offer make it promising for the near future.
Keywords: augmented reality, warehousing, cyber-physical systems, wearables
1The role of warehousing in modern logistics becomes
more and more significant due to several factors such as
the rapid increase of e-commerce transactions and the need
for reduced inventory and faster response time (Lu et al.,
2016). As a result, the effective management of warehouse
operations can be critical for the overall performance of the
supply chain they belong to. In response to this, several
decision support models, that assist warehouse managers
in scheduling and controlling their warehouses, have been
developed for warehouse operations (Gu et al., 2007).
In the last two decades and with the emergence of com-
puter systems, the deployment of information and com-
munication technologies has often been suggested as an
effective solution to managing warehousing operations
1This is a machine-readable rendering of a working paper draft
that led to a publication. The publication should always be cited in
preference to this draft using the following reference:
Stoltz, M.H., Giannikas, V., Strachan, J., McFarlane, D. and
Um, J. (2017), “Augmented reality in warehouse operations:
Opportunities and challenges”, in 20th IFAC World Congress
2017, Toulouse, France.
This material is presented to ensure timely dissemination of schol-
arly and technical work. Copyright and all rights therein are retained
by authors or by other copyright holders. All persons copying this
information are expected to adhere to the terms and constraints
invoked by each author’s copyright. In most cases, these works
may not be reposted without the explicit permission of the copyright
(Davarzani and Norrman, 2015). Recently, a technology
that has caught the researchers’ and practitioners’ atten-
tion with regards to its potential to support manufacturing
and logistics processes is augmented reality, an emerging
new area for mobile cyber-physical applications (White
et al., 2010). Augmented reality involves the combination
of the physical and the digital world in real time through
a wearable device (Glockner et al., 2014; Ong and Nee,
2013; Cirulis and Ginters, 2013) and has been recognised
as an enabler for industrial cyber-physical systems (Leitao
et al., 2016; Khalid et al., 2014; Gorecky et al., 2012).
This research study was designed to investigate the oppor-
tunities, benefits, barriers and limitations that augmented
reality could bring in warehouse management. For this
reason, two activities were conducted. The first involved
in depth interviews with practitioners aiming at gaining
insight in terms of the current situation and the future of
the technology. The second involved a set of experiments
with an augmented reality application developed by the
authors, aimed at collecting user feedback and examining
the issue based on a hands-on experience. The experiments
were designed for the sorting process used in warehousing
and ran both in a lab setting and in a warehouse trial.
The remainder of this paper is organised as follows. In
Section 2, we review existing applications of augmented
reality in logistics in general and in warehousing in partic-
ular. Section 3 then describes our methodology and data
gathering activities in more detail. Section 4 provide some
results while Section 5 present the key findings of our
study. Finally, we conclude in Section 6.
In this section we provide a brief review of augmented
reality and its usage in logistics and we focus our attention
on its applications in warehouse management.
2.1 Augmented Reality in Logistics
Augmented Reality (AR) is a wide term to define a method
to add virtual elements, items or information in real time
in the physical world. It can be supported by different tech-
nologies (e.g. computers, TV, smartphones and tablets,
glasses, wearables). The term is not constrained to the
visual aspect, since it can also include audio or involve
other senses of the user. For this paper, we will focus only
on the visual aspect. The definition of an an augmented
reality system can therefore vary, however, three key char-
acteristics that a system should have can be concluded
from literature (Azuma, 1997; Van Krevelen and Poelman,
2010): The system should i) operate in real time, ii) mix
virtual elements with the reality, iii) be integrated in a 3D
In logistics in particular, even though the available liter-
ature is quite limited, augmented reality is seen as one of
the technologies that could bring the “next big wave of
change” in the industry (Glockner et al., 2014). It has
also been argued that it can improve the execution of
several logistics processes (Cirulis and Ginters, 2013). The
interested reader is referred to (Glockner et al., 2014) for
a practice-oriented review of use cases in logistics covering
warehousing operations, transportation optimization, last-
mile delivery, and enhanced value-added services.
2.2 Applications of Augmented Reality in Warehousing
We present our review of existing work in terms of the four
main warehouse operations (Gu et al., 2007; Davarzani and
Norrman, 2015): i) receiving, ii) storing, iii) order picking
,iv) shipping.
Order picking is by far the most studied area (see for
example (ISW, 2012)), perhaps due to the fact that it ac-
counts for more than 50% of warehousing costs (Giannikas
et al., 2016). Existing research has focused on how routing
of a human operator could be enhanced using AR (Reif
and G¨unthner, 2009; Schwerdtfeger et al., 2009), what is
the most effective way to indicate a storage location to a
picker (Schwerdtfeger and Klinker, 2008) and comparisons
between pick lists communicated by voice, via a head-
mounted display, using lights, or on printed paper (Tumler
et al., 2008; Weaver et al., 2010; Guo et al., 2014). From an
industrial perspective, companies like Knapp, 2Picavi, 3
Itelligence and SAP, 4DHL 5and Generix 6have started
developing solutions focusing on different hardware and
software elements of an AR solution. The goal is to enable
picking which is fast, error free and user friendly while
guiding a human operator. Nevertheless, some aspects are
1206814-04- 04_Recompense_Prix-Innovatio.pdf
missing from industrial solutions, especially concerning
barcode readers and real-time 3D projections. Most of the
existing solutions with wearable glasses manage only to
display the equivalent of printed pick list in front of the
eyes of the user.
Relevant studies and solutions for the other three key
operations, —i.e. receiving storing, shipping— are more
limited (Real and Marcelino, 2011) and they mainly aim
to present potential use cases for future developments
(Glockner et al., 2014). However, the application of aug-
mented reality in receiving, storing and shipping could
potentially lead to benefits similar those observed in order
picking, i.e. decreased error rates and faster execution of
operations. For this reason and in order to stimulate future
areas of work, in Table 1 we present a list of potential
uses of augmented reality across the four key warehouse
Table 1. Potential uses of augmented reality in
warehouse operations
Operation Potential uses
Receiving Indicate the unloading dock to incoming truck driver
Check received goods against delivery note
Show where to put the items/how to arrange them in
the waiting zone
Storing Inform an operator about a new allocated task
Display the storage location of incoming items
Display picture and details of the item to be stored
Indicate route to storage location
Indicate picker’s current status as well as next step of
the process
Check locations requiring replenishment while storing
Picking Inform an operator about a new task allocated to him
Display picture and details of the item to be picked
Display the storage location of the item to be picked
Display picking route
Highlight the physical location with the item required
Inform about errors and disruptions
Scan the item’s barcode to assign to picking cart or
to see more information
Highlight where to put each item on the picking cart
for sorting while picking
Give information to prevent congestion in aisles
Monitor picker’s condition and performance
Shipping Show what type of cardboard to use
Show the best way to place picked items in a package
Indicate the right location/pallet for the shipment
Show where to place each order on a pallet/in a truck
according to type of orders, destination, fragility
Indicate appropriate loading area
Check/Count products/orders to be loaded on a truck
2.3 Overview of existing work
The conclusions we can draw from our analysis of existing
work can be summarised in three key points:
Head mounted displays and smart glasses are of par-
ticular interest from an industrial point of view as
they offer a hands-free solution. At the same time,
they can give the opportunity to the user to communi-
cate effectively with the physical world while wearing
them which is crucial in industrial applications.
Even though, there are a number of proof of concept
and conceptual demonstrations of future applications,
a study of the benefits and limitations of this technol-
ogy is key before implementation.
Order picking has been the main area of focus al-
though existing solutions focus on routing and picking
only, without considering other value-added steps like
packing, labelling and sorting.
Motivated by the need to examine and understand the
potential benefits and challenges from the adoption of aug-
mented reality in warehouses before the implementation
of a real industrial system, two activities were designed.
The first, a set of interviews with practitioners from the
industry was used in order to gain the general insight
on the issue. The second, an experimental study using
an AR app was designed in order to examine the user’s
perspective in using AR in warehouse operations in a more
realistic setting. We describe each activity in more detail.
3.1 Methodology for Practitioner Interviews
A survey through semi-structured phone interviews was
conducted with solution providers as well as warehouse
managers and experts in the field of logistics and aug-
mented reality. These interviews identified ways aug-
mented reality could be implemented and used in ware-
houses, discussed its potential benefits, and raised the
main concerns expressed in the industry about the tech-
nology. Twelve phone interviews were conducted in total
with five warehouse managers, five solution providers and
two logistics experts from six countries in total (Austria,
China, France, Germany, Netherlands and United King-
The interviewees were asked to comment on both existing
solutions and solutions under development. They were also
asked to identify those technological features that are/
will be required for the adoption and usage of augmented
reality in warehousing and how well existing solutions
support them. Finally, they were asked to express their
views on the future of the technology and its general
usability in the future.
3.2 Methodology for Experimental Study
In order to collect more detailed user feedback, a set
of experiments were designed that required users to sort
packages using a head-mounted display.
Specifically, the warehouse process chosen was the sorting
process for packaged orders; this allowed a warehouse com-
pany to interact with physical items (packages) they have
tagged/label with a label of their choice (e.g. QR code)
after packing is completed. At the same time, it provided
an extension beyond the areas existing applications have
focused on (which normally end with the picking of items).
In this study, the user is required to sort several packaged
orders in two different sorting bins depending on the last-
mile logistics provider that will transport the order to the
Fig. 1. App view: Display app
Real world view
through the camera
Virtual cube
Sorting bin
to the user
Fig. 2. App view: Cube app
end-customer. Each bin was indicated by a number (1 or
2) and a marker.
The wearable technology chosen for the experiment was
the Google Glass, as a technology a warehouse company
would purchase and adopt without the support of third-
party companies. Another reason the Google Glass was
selected was that it offered a light-weight solution that
allowed a user to easily interact with the physical world
of the warehouse. For our experiments, two different apps
were developed and tested. The first one (“Display App”)
provides the same information with existing applications
that do not use augmented reality: it shows the reference of
the sorting bin on a screen by displaying a small sentence
and the number corresponding to the bin (See Figure 1).
This step is designed so that the user can experience the
benefit of the hardware itself without any impact from
the software. The second app (“Cube App”) adds an
augmented reality element: a virtual box appears on the
marker linked to the bin while the user is looking at it (See
Figure 2). In this second version, the operator is required
to look at the right marker before proceeding to the next
sorting step and a feedback is given if no bin has been
scanned by the Glass. It can be considered as a checking
step to automatically verify the process.
The apps were coded with Android Studio in Java lan-
guage. Two open source libraries were used: ZBarScan-
ner 7for the Barcode reader and TheArtToolkit 8for the
AR functionality. The final app is available on Github. 9
The experiments were conducted in two phases: Firstly, in
a laboratory environment with 19 operators. Secondly in
a real warehouse with five operators who are familiar with
existing non-AR solutions (see Figures 3 and 4). In each
set of experiments, each participant was required to sort
10 packaged orders in three trials:
(1) Using a scanning app on a smart phone that simulates
the current technology (a scanner). The scanner was
assumed to be attached on a fixed computer.
(2) Using the “Display App” on the Google Glass. This
was designed so that the user can understand the
benefits of the wearable technology itself without
impact from the software
(3) Using the the “Cube App” on the Google Glass to
integrate an augmented reality element in the process
by projecting a virtual cube.
Operators were asked to compare the apps using four
criteria: preference, ease, speed and error rate impact.
Fig. 3. Sorting bins
Fig. 4. Operator with Google Glass
4.1 Practitioner Interview Results
During the practitioner interviews, seven features were
identified by the interviewees as being very important
for the future adoption and success of augmented reality
applications. Those features were unprompted responses
(i.e. without giving the interviewees a list of features to
comment on their importance). Table 2 summarises these
and indicates how frequently each one was mentioned.
The great majority of interviewees, generally saw good
potential for AR in warehouses, especially due to the fact
that there are a big number of possibilities and areas
that it could be used in. Moreover, the fact that the
technology can offer benefits in processes that cannot be
automated is of particular importance to practitioners.
Even though the technology has already been implemented
in certain cases, the battery is still a major technical issue.
In terms of R&D, further research is required regarding the
Table 2. Required features from practitioners
Feature Description No.
User interface Does not require special knowledge to
use, user friendly and easy to use, with
minimum interactions required
Ergonomics The device is comfortable to wear (bal-
ance, weight etc) and does not obstruct
the view of the user
Scanning Should be done quickly and in high ac-
curacy using a high precision auto-focus
camera, barcode or QR code reader
Screen Big enough to read information, wide-
field view, at a natural field of view
Battery No additional device needed to be worn
for the battery to last
Robustness The device can be used in industrial
environment, (i.e. support dust, can be
dropped without breaking etc.)
Programming Easy to program, using a well-
established programming language
barcode and QRcode readers, the latency and quality of
the attached camera, the field of view, the miniaturisation
of existing powerful processors that do not overheat and
the robustness of the device. It is believed that with the
topic being considered by big companies like Microsoft,
Google or Amazon, the research will speed up in the next
3-5 years. Also, the price of an AR solution needs to be
reduced in order to allow early adoption and use. Finally,
an important issue, is a change in the existing mind-set
of both warehouse managers and operators who need to
accept the new technology.
4.2 Results from Experimental Study
The following results per criterion were reported:
Preference: The preference was first for the Cube
App, then the Display App and finally the scanner.
Nevertheless, some of the reasons were that partici-
pants simply enjoyed using new technology. This can
be an ephemeral effect.
Ease of use: All participants agreed that the Glass
was easier to use than the phone. They mostly agreed
on the fact that the Cube App is a convenient way
to display the information and it is better than
just giving the bin number. It also requires less
concentration which, however, can make the overall
task tedious. The importance of clear and simple
instructions was also noticed.
Speed: The Google Glass apps seemed faster than
the phone one. This is mainly due to the fact that
by using a Glass, the participants could save time
walking between the scanner table and the bins.
Error rate: Participants felt that using the Cube
App would reduce the error rate compared to the
other two apps as it automatically checks if the right
bin is in the view of the operator.
With regards to technological features required for a suc-
cessful AR solution, participants were asked to identify the
three most important ones according to their experience
in the experiments. These are summarised in Table 3.
Table 3. Features from experimental study
Group Top three features
A fast and accurate barcode reader
A long-life battery
A user-friendly interface
Voice-control or other without need to click
Receiving audio instructions besides visual ones
A good battery and a fast processor
A number of technical limitations were observed during the
experiments. Some of these limitations are device-specific
but they can provide recommendations for other devices
designed for warehousing applications:
(1) The distance to read a barcode or QR Code depends
on the size of the printed code but also on its quality.
As a result, big codes were attached to items.
(2) The view is not central since the screen is at the top
right field of view. This proved to be a difficulty for
several users since it is not optimal for the comfort.
(3) The camera and processor are not designed for inten-
sive use. As a result, the device was running slower,
lagging or overheating after a few minutes of usage.
(4) The Glass is not designed for industrial use, thus
making the device not robust enough for warehouses.
(5) Even in situations when the number of interactions
(i.e. clicks) per item was the lowest possible (only one
click), it could cause confusion to some users. Voice-
control could be examined as an alternative.
Finally, some quantitative results were collected with re-
gards to the speed of each solution. Even though not
directly relevant to the objectives of this study, these
figures are presented here to demonstrate the relative
differences between solutions. Table 4 demonstrates the
timing of each solution in terms of i) total time required
to sort all items, ii) time spent to sort each item (waiting
excluding) iii) time the user took between sorting items.
The results indicate that the Display App offer a faster
solution compared to the traditional scanner, although
the Cube App was much slower. Indeed, the Cube App
required more time for the processor to open the camera
and show the virtual cube, mainly because it needed a few
seconds to wait before doing the next item.
Table 4. Timing per solution (in seconds)
Measure Phone App Display App Cube App
Total 161 137 181
Per item 9 4 10
Between items 6 7 7
In this section, we summarise our findings from the inter-
views and the experimental study.
5.1 Potential Benefits
The benefits from using augmented reality in warehousing
via the usage of wearable technologies like glasses depend
significantly on the hardware and results can vary a lot
between devices with different specifications. The benefits
proposed here do not take into account the technological
barriers (which are discussed in the next section). Table 5
presents potential benefits of the technology identified
via this study. For each benefit, the table also presents
some reasons mentioned by the practitioners, lab testers
or operators to justify their opinion.
Table 5. Expected benefits from using aug-
mented reality in warehouse operations
Type of
error rate
No need to remember the action to complete
(portability of information)
Device shows a picture of the product sought in the
field of view
Limited decision making required (device can show
next steps)
Automatic double checking can be easily done,
e.g. with an automated recognition of a product,
storage location etc.
If the operator is disturbed, his next steps are not
More flexi-
Device offers a hands-free solution when an item/
package has a big size
The information can be displayed anywhere in the
warehouse and at any time
If an operator is disturbed, there is no need to walk
to a station to check
With a device proposing central field of view op-
tion, it becomes an “eye-free” solution
Opportunity to share a video or a photo of a defect
or issue with a manager not on site
Less concentration is required as instructions are
easily shown to operator
Even if AR solutions are not necessary faster, by
decreasing the error rate, re-work is not necessary
Avoid unnecessary travelling to access fixed com-
puters, carry a scanner etc.
For certain operations, such as routing, it helps
anticipate the moves and result in faster movements
(especially for seasonal workers).
Adaptability The solution does not need a specific environment
as the user carries it with him
It can be suitable for people with disabilities espe-
cially with regards to the usage of hands
Safety Being hands-free, it can be safer for a human
Device can provide feedback and information for
safety purposes or warn from an immediate danger
New tech-
It brings enthusiasm to many operators especially
young ones. This allows acceptance from end user
From a marketing point of view, it shows that the
company adapts itself to latest innovations
5.2 Challenges and Limitations
The main barriers identified that could apply to multi-
ple organisations, technologies and business scenarios are
summarised in Table 6.
In this study, we investigated the usage of augmented
reality in warehouse operations aiming to identify the
key potential benefits and barriers for its adoption. Even
Table 6. Barriers for using augmented reality
in warehouse operations
Type of
Commercial scanners and smartphone cameras
provide a faster and more reliable solution for scan-
ning barcodes and QR codes.
The battery is not designed to last for long hours
in order to cover a full working day. Alternative
solutions with extra batteries carried by operators
can be cumbersome.
Processors overheating and slowing down after
long periods of use or when complex computing is
required can affect the physical process.
Many wearable, AR devices available are not de-
signed for long period of continuous use which can
cause comfort problems: screen latency can cause
headaches, spectacles need to be worn by some
users, non-central view creates eye tiredness, heavy
devices are hard to wear.
Using head-mounted devices, certain operations can
be very slow compared to hand-held devices (e.g.
checking multiple incoming items).
Programming environment/languages are not stan-
dardised, thus making it hard for practitioners to
experiment with devices, develop their own appli-
cations and link devices with existing systems.
With user interfaces being very important for
acceptance, simple and intuitive ways to interact
with the device are needed to avoid confusion.
Screens might not automatically adapt to change
of light (e.g. from moving in and out a building).
Acceptance Some users are not willing to wear a device with a
camera and mic at all times due to privacy issues.
Confidentiality issues might arise from the fact
that AR devices can capture photos or videos from
proprietary operations and data.
Cost The total cost of ownership is still quite high
especially if wearable devices are considered to be
personal equipment (i.e. due to hygiene issues) .
Alternative IT solutions for warehouse manage-
ment can be significantly cheaper and with well-
established benefits.
Internal IT teams cannot easily maintain and ex-
tend AR solutions thus causing extra costs.
though augmented reality is not consider to be a new
technological development, its usage in manufacturing and
logistics operations is still significantly behind compared
to other industries like retail and gaming. In this study
we identified some of the reasons this is the case which
are mainly related to the maturity of the technology for
this type of operations and the benefits it can provide
compared to existing, well-adopted solutions.
Even though the barriers seem to be significant enough at
the moment, with the development of new technologies and
the improvement of existing ones, augmented reality might
soon be seen in many industrial deployments, perhaps
focusing at first at small parts of longer processes. Solution
providers and researchers need to keep working closely to
the end-user to identify suitable cases augmented reality
could be implemented first in order to show real benefits
over cheaper alternatives.
(2012). The 16th International Symposium on Wearable
Computers. Newcastle Upon Tyne, UK.
Azuma, R.T. (1997). A survey of augmented reality.
Presence: Teleoperators and virtual environments, 6(4),
Cirulis, A. and Ginters, E. (2013). Augmented reality in
logistics. Procedia Computer Science, 26, 14–20.
Davarzani, H. and Norrman, A. (2015). Toward a relevant
agenda for warehousing research: literature review and
practitioners’ input. Logistics Research, 8(1), 1.
Giannikas, V., Lu, W., Robertson, B., and McFarlane, D.
(2016). An interventionist strategy for warehouse order
picking: evidence from two case studies. Under review.
Glockner, H., Jannek, K., Mahn, J., and Theis, B. (2014).
Augmented reality in logistics: Changing the way we see
logistics - a DHL perspective.
Gorecky, D., Garcia, R.C., and Meixner, G. (2012). Seam-
less augmented reality support on the shopfloor based on
cyber-physical-systems. In 14th International Confer-
ence on Human-computer Interaction with Mobile De-
vices and Services. ACM.
Gu, J., Goetschalckx, M., and McGinnis, L.F. (2007).
Research on warehouse operation: A comprehensive
review. Eur. J. Oper. Res., 177(1), 1–21.
Guo, A., Raghu, S., Xie, X., Ismail, S., Luo, X., Si-
moneau, J., Gilliland, S., Baumann, H., Southern, C.,
and Starner, T. (2014). A comparison of order picking
assisted by head-up display, cart-mounted display, light,
and paper pick list. In ACM International Symposium
on Wearable Computers, 71–78. New York, NY.
Khalid, C.M.L., Fathi, M.S., and Mohamed, Z. (2014).
Integration of cyber-physical systems technology with
augmented reality in the pre-construction stage. In 2nd
International Conference on Technology, Informatics,
Management, Engineering, and Environment, 151–156.
Leitao, P., Colombo, A.W., and Karnouskos, S. (2016).
Industrial automation based on cyber-physical sys-
tems technologies: Prototype implementations and chal-
lenges. Computers in Industry, 81, 11–25.
Lu, W., McFarlane, D., Giannikas, V., and Zhang, Q.
(2016). An algorithm for dynamic order-picking in
warehouse operations. European Journal of Operational
Research, 248(1), 107–122.
Ong, S.K. and Nee, A.Y.C. (2013). Virtual and augmented
reality applications in manufacturing. Springer Science
& Business Media.
Real, J. and Marcelino, L. (2011). Augmented reality
system for inventorying. In 6th Iberian Conference on
Information Systems and Technologies, 1–9.
Reif, R. and G¨unthner, W.A. (2009). Pick-by-vision:
augmented reality supported order picking. The Visual
Computer, 25(5), 461–467.
Schwerdtfeger, B., Reif, R., Gunthner, W.A., Klinker, G.,
Hamacher, D., Schega, L., Bockelmann, I., Doil, F., and
Tumler, J. (2009). Pick-by-vision: A first stress test.
In 8th IEEE International Symposium on Mixed and
Augmented Reality, 115–124.
Schwerdtfeger, B. and Klinker, G. (2008). Supporting
order picking with augmented reality. In Proceedings of
the 7th IEEE/ACM International Symposium on Mixed
and Augmented Reality, 91–94. Washington, DC.
Tumler, J., Doil, F., Mecke, R., Paul, G., Schenk, M., Pfis-
ter, E.A., Huckauf, A., Bockelmann, I., and Roggentin,
A. (2008). Mobile augmented reality in industrial appli-
cations: Approaches for solution of user-related issues.
In 7th IEEE/ACM International Symposium on Mixed
and Augmented Reality, 87–90. Washington, DC.
Van Krevelen, D.W.F. and Poelman, R. (2010). A survey
of augmented reality technologies, applications and lim-
itations. International Journal of Virtual Reality, 9(2).
Weaver, K.A., Baumann, H., Starner, T., Iben, H., and
Lawo, M. (2010). An empirical task analysis of ware-
house order picking using head-mounted displays. In
Conference on Human Factors in Computing Systems,
1695–1704. ACM, New York, NY.
White, J., Clarke, S., Groba, C., Dougherty, B., Thomp-
son, C., and Schmidt, D.C. (2010). R&D challenges and
solutions for mobile cyber-physical applications and sup-
porting internet services. Journal of Internet Services
and Applications, 1(1), 45–56.
... For instance, IAR can help to minimise inventory costs by facilitating warehouse management [79]. This can be achieved by reducing the error rate, increasing flexibility, improving reliability, speeding operations, improving the safety of staff, and improving the engagement of operators [80]. ...
... This is why secure systems are particularly important in Industry 4.0. Acceptance of IAR from potential users is also a common problem [80,84] that can be addressed emphasising the advantages of the system and giving adequate training. However, training delivery may be an important disadvantage of IAR, as this costs time and money. ...
... Hardware and software have also been identified as significant limitations of AR that require further research and development. In particular, the user interfaces and user interactions must be improved so AR becomes more widely adopted in the industry [78,80]. Improvements in this area are also expected in the near future, as research progresses to optimise IAR systems. ...
Full-text available
Food Logistics 4.0 is a term derived from Industry 4.0 focusing on all the aspects of food logistics management based on cyber-physical systems. It states that real-time information and the interconnectivity of things, supplemented with novel technologies will revolutionise and improve the way food logistics is carried out. It has tremendous potential in terms of bringing transparency, swift delivery of food at reduced cost, flexibility, and capability to deliver the right quality product at the right place and at the right time. This paper discusses the vital technologies within Food Logistics 4.0 and the opportunities and challenges in this regard. It focuses primarily on food logistics, including resource planning, warehouse management, transportation management, predictive maintenance, and data security. Internet of Things, Blockchain, Robotics and Automation and artificial intelligence are some of the technologies discussed.
... By implementing the system, companies can thus collect structured data and train neural networks in a facilitated manner, thereby enabling real-time object recognition. One promising application area is the domain of logistics, where high-level object recognition can be employed for quality control of picking processes [62]. ...
Conference Paper
Convolutional neural networks (CNNs) offer great potential for business applications because they enable real-time object recognition. However, their training requires structured data. Crowdsourcing constitutes a popular approach to obtain large databases of manually-labeled images. Yet, the process of labeling objects is a time-consuming and cost-intensive task. In this context, augmented reality provides promising solutions by allowing an end-to-end process of capturing objects, directly labeling them and immediately embedding the data in training processes. Consequently, this paper deals with the development of an object labeling application for crowdsourcing communities following the design science research paradigm. Based on seven issues and twelve corresponding meta-requirements, we developed an AR-based prototype and evaluated it in two evaluation cycles. The evaluation results reveal that the prototype facilitates the process of object detection, labeling and training of CNNs even for inexperienced participants. Thus, our prototype can help crowdsourcing communities to render labeling tasks more efficient.
... Neurophysiological sensors such as functional Near-Infrared Spectroscopy (fNIRS), which have become widely available, are lightweight, noninvasive, and offer improved spatial resolution compared to other technologies. Similarly, Augmented Reality Head-Mounted Displays (HMDs) are becoming lightweight, untethered, and consumer-grade, and in fact are already being deployed in a variety of industrial fields, such as warehousing [48]. There has been recent work on neurophysiological control of robots [e.g., 8,18,36], or neurophysiologically informed adaptations in robot behavior [49]. ...
... Additionally, Stoltz (2017) listed the several potentials of the implementation of Augmented Reality glasses could bring to warehouse operations. It could enhance the routines of human operator in order picking, by decreasing error rates and increasing the execution of operation. ...
Full-text available
Industry 4.0 is not a new idea and it has long been the focus of the academic community who approached it in many different ways but until now researchers did not agree on one definition. Furthermore, this concept is more and more accepted in the industrial society where companies intend to implement those new technologies to their business models to increase their competitiveness. Therefore it is crucial for companies to primarily understand the content of the Industry 4.0 to successfully transform to digital manufacturing. It is the role of the researchers to study the nine technologies of Industry 4.0 in order to facilitate the implementation of the later in the companies’ processes. To this matter, this paper intends to present a comprehensive literature review in order to establish a clear definition of Industry 4.0, studying independently each of the nine technologies to understand their respective functioning. It then permits us to identify the impacts of the Industry 4.0 nine technologies in manufacturing, logistics and stores. However, experts have agreed that most related theorems and definitions of Industry 4.0 are not mature enough to be implemented in real-life industrial scenarios. This is why in this paper we will also look at the practical implementations of those nine technologies in order to determine and correct the gap between the impacts identified by the academic community and reel-life practitioners. Lastly, many people regard new technologies as a threatening tool whose functioning goes beyond their scope of understanding; they fear that smart devices and systems will take over the control of their lives. This is why explaining the impacts that they will have on businesses and consumers, will increase awareness of Industry 4.0 and the adoption rate of its nine technologies and ultimately make the world a better place to live.
... If a technology is not regarded as easy to use and easy to understand, it will not enhance the user´s performances and process at hand and will not be accepted by the users [6]. User interface is described as "does not require special knowledge to use, user friendly and easy to use, with minimum interactions required", and has been identified as one important feature for the future adoption of AR [11]. A consequence of evaluating AR with operators is that they might spend more time to complete the task in total, especially in the first steps, because they don't have previous experience with the technology and therefore might need time to understand instructions given in the AR-system [9]. ...
Augmented Reality (AR) is seen as a key technology for the development of smart manufacturing. Despite the many possibilities and affordances of this emerging technology, it is a fairly new technology in industry without widespread adoption. Research indicates that there are many affordances of using this technology in industry as well as some challenges. However, there seems to be a lack of research on how to evaluate AR in industry. Based on literature and a case study, we propose guidelines for evaluating AR based on identified dimensions.
This study investigates the potentials and challenges of applying augmented reality (AR) smart glasses in logistics and supply chain management (SCM). A systematic literature review on AR smart glasses was conducted to provide a comprehensive synthesis of what has been published in the literature to capture the dynamics surrounding this technology and identify areas deserving of further academic attention. To summarise the latest developments in this field, eighty-two (82) publications were selected and thoroughly analysed. In terms of research findings, four main clusters of potential benefits were identified: visualisation, interaction, user convenience, and navigation. In contrast, the challenges posed by smart glasses in the logistics field were clustered around technical, organisational, and ergonomic considerations. The accumulation of knowledge and actionable insights in this study will benefit both academics and practitioners interested in this emerging wearable technology segment. As the first attempt to explore the importance of smart glasses in logistics and SCM activities, this study offers significant contributions to the literature by codifying extant knowledge on AR smart glasses and setting forth an agenda for future research.
Full-text available
Very well into the dawn of the fourth industrial revolution (industry 4.0), humankind can hardly distinguish between what is artificial and what is natural (e.g., man-made virus and natural virus). Thus, the level of discombobulation among people, companies, or countries is indeed unprecedented. The fact that industry 4.0 is explosively disrupting or retrofitting each and every industrial sector makes industry 4.0 the famous buzzword amongst researchers today. However, the insight of industry 4.0 disruption into the industrial sectors remains ill-defined in both academic and nonacademic literature. The present study aimed at identifying industry 4.0 neologisms, understanding the industry 4.0 disruption and illustrating the disruptive technology convergence in the major industrial sectors. A total of 99 neologisms of industry 4.0 were identified. Industry 4.0 disruption in the education industry (education 4.0), energy industry (energy 4.0), agriculture industry (agriculture 4.0), healthcare industry (healthcare 4.0), and logistics industry (logistics 4.0) was described. The convergence of 12 disruptive technologies including 3D printing, artificial intelligence, augmented reality, big data, blockchain, cloud computing, drones, Internet of Things, nanotechnology, robotics, simulation, and synthetic biology in agriculture, healthcare, and logistics industries was illustrated. The study divulged the need for extensive research to expand the application areas of the disruptive technologies in the industrial sectors. 1. Introduction In the second decade of the twenty-first century, the world stands on the cusp of industry 4.0 paradigm which has remarkably become global emergence with a core of industrial transformation, revitalization, and development [1]. Simply put, industry 4.0 is the integration of cyber and physical worlds through introduction of new technologies in the industrial fields [2, 3]. In other words, it is a technological revolution in every production system including operator and maintenance [4], which is quite unique from the previous revolutions as shown in Table 1 [5–9]. Industry 4.0 is the digitization of the industrial value chain which has become unexampled for economic and social development in the recent years [10–12]. On the one hand, it allows high-wage countries to maintain their business responsiveness and competitiveness [13]. On the other hand, research and development units are organizationally, personally, and methodically being aligned for innovation competitiveness [14, 15]. Transition Industry Operator Maintenance Level 1 Industry 1.0Mechanical production, rail road, steam power Operator 1.0 Manual and dexterous work Machine tools Maintenance 1.0Visual inspection Level 2 Industry 2.0 Mass production Assembly line Electrical power Operator 2.0 Assisted work Numerical control Maintenance 2.0 Instrument inspection Level 3 Industry 3.0 Automated production Electronics, computers, and IT First PLC system Operator 3.0 Cooperative work Industrial robots Maintenance 3.0 Real-time condition monitoring Level 4 Industry 4.0 Fusion of virtual, physical, digital, and biological sphere (CPPS) Convergence of technologies: AI, IoT, VR/AR, big data, etc. Operator 4.0 Work-aided human-CPS Maintenance 4.0Predictive maintenance Use of big data Statistical analysis Smart sensors and IoT Use of digital twins IT: information technology, PLC: programmable logic controller, CPPS: cyber-physical production system, AI: artificial intelligence, IoT: Internet of Things, VR: virtual reality, AR: augmented reality, and CPS: cyber-physical system.
Full-text available
Zusammenfassung Die vorliegende Studie vergleicht ein Pick-by-Watch (PbW)-System mit dem Pick-by-Paper (PbP)-Verfahren im Hinblick auf fünf objektiv und sieben subjektiv gemessene Dimensionen von Kommissionierleistungen in einem Laborexperiment mit 55 Teilnehmern. Uni- und multivariate Analysen sprechen dafür, dass (1) die Kommissioniergeschwindigkeit bei PbW im Vergleich zu PbP signifikant niedriger ausfällt und (2) Fehler bei PbW nicht seltener auftreten als bei PbP. Hingegen schneidet PbW bei subjektiven Messungen von vier Reaktionsaspekten (empfundene Anforderungen, Leistungszufriedenheit, Unterstützung, Nutzerfreundlichkeit) besser ab als PbP. Aus diesem Ergebnismuster werden Schlussfolgerungen für Unternehmenspraxis und die Forschung abgeleitet. Praktische Relevanz Anstelle von herkömmlichen Kommissionierlisten auf Papier (Pick-by-Paper; PbP) testen Unternehmen in der Lagerbewirtschaftung zunehmend informationsdarstellende digitale Assistenzsysteme, da sie sich hiervon Verbesserungen von Kommissionierleistungen versprechen. Eine Variante solcher Assistenzsysteme, Pick-by-Watch (PbW), verwendet vernetzte Armbanduhren mit berührungsempfindlichen Bildschirmen (Smart Watches). Überraschenderweise mangelt es aber bislang an methodisch soliden empirischen Analysen zum Ausmaß, in dem mit PbW-Systemen tatsächlich bessere Kommissionierleistungen erzielt werden als mit papierbasierten Kommissionierlisten. Derartige Befunde sind jedoch erforderlich, um Praktiker dabei zu unterstützen, wirksamere Auswahlentscheidungen bezüglich vernetzter digitaler Assistenzsysteme in der manuellen Kommissionierung zu treffen.
Conference Paper
Augmented reality applications are computationally intensive and have latency requirements in the range of 15-20 milliseconds. Fog computing addresses these requirements by providing on-demand computing capacity and lower latency by bringing the computational resources closer to the augmented reality devices. In this paper, we reviewed papers providing custom solutions for augmented reality using the fog architecture and identified that the ongoing research trends towards balancing quality-of-experience, energy, and latency for both single and collaborative multi-device augmented reality applications. Furthermore, some works also focus on providing architectures for fog-based augmented reality systems and also on the training of machine learning algorithms in the fog layers to improve user experience. Based on these findings, we provide some challenges and research directions that can facilitate the adoption of fog-based augmented reality systems.
Full-text available
As the role of the customer becomes more important in modern logistics, warehouses are required to improve their response to customer orders. To meet the responsiveness expected by customers, warehouses need to shorten completion times. In this paper, we introduce an interventionist order picking strategy that aims to improve the responsiveness of order picking systems. Unlike existing dynamic strategies, the proposed strategy allows a picker to be intervened during a pick cycle to consider new orders and operational disruptions. An interventionist strategy is compared against an existing dynamic picking strategy via a case study. We report benefits both in terms of order completion time and travel distance. This paper also introduces a set of system requirements for deploying an interventionist strategy based on a further case study.
Full-text available
Cyber-Physical Systems (CPS) is an emergent approach that focuses on the integration of computational applications with physical devices, being designed as a network of interacting cyber and physical elements. CPS control and monitor real-world physical infrastructures and thus is starting having a high impact in industrial automation. As such design, implementation and operation of CPS and management of the resulting automation infrastructure is of key importance for the industry. In this work, an overview of key aspects of industrial CPS, their technologies and emerging directions, as well as challenges for their implementation is presented. Based on the hands-on experiences gathered from four European innovation projects over the last decade (i.e. SOCRADES, IMC-AESOP, GRACE and ARUM), a key challenges have been identified and a prioritization and timeline are pointed out with the aim to increase Technology Readiness Levels and lead to their usage in industrial automation environments.
Conference Paper
Full-text available
Wearable and contextually aware technologies have great applicability in task guidance systems. Order picking is the task of collecting items from inventory in a warehouse and sorting them for distribution; this process accounts for about 60% of the total operational costs of these warehouses. Current practice in industry includes paper pick lists and pick-by-light systems. We evaluated order picking assisted by four approaches: head-up display (HUD); cart-mounted display (CMD); pick-by-light; and paper pick list. We report accuracy, error types, task time, subjective task load and user preferences for all four approaches. The findings suggest that pick-by-HUD and pick-by-CMD are superior on all metrics to the current practices of pick-by-paper and pick-by-light.
Full-text available
Abstract Warehousing has been traditionally viewed as a non-value-adding activity but in recent years a number of new developments have meant that supply chain logistics have become critical to profitability. This paper focuses specifically on order-picking which is a key factor affecting warehouse performance. Order picking is the operation of retrieving goods from specified storage locations based on customer orders. Today’s warehouses face challenges for greater responsiveness to customer orders that require more flexibility than conventional strategies can offer. Hence, dynamic order-picking strategies that allow for changes of pick-lists during a pick cycle have attracted attention recently. In this paper we introduce an interventionist routing algorithm for optimising the dynamic order-picking routes. The algorithm is tested using a set of simulations based on an industrial case example. The results indicate that under a range of conditions, the proposed interventionist routing algorithm can outperform both static and heuristic dynamic order-picking routing algorithms.
Full-text available
We are on the verge of ubiquitously adopting Augmented Reality (AR) technologies to enhance our perception and help us see, hear, and feel our environments in new and enriched ways. AR will support us in fields such as education, maintenance, design and reconnaissance, to name but a few. This paper describes the field of AR, including a brief definition and development history, the enabling technologies and their characteristics. It surveys the state of the art by reviewing some recent applications of AR technology as well as some known limitations regarding human factors in the use of AR systems that developers will need to overcome.
Full-text available
The main purpose of this research is to provide an agenda for future warehousing research relevant for both academic development and practitioners’ needs. In order to suggest a practically relevant future research agenda, first a comprehensive literature review was performed to identify research areas covered in the literature. Then, 15 warehouse managers and senior consultants were interviewed to add empirical input to the development of potential future research areas. The literature review reveals gaps, both methodology- and topic-wise. A considerable methodological imbalance is observed. Some of the highlighted managerial concerns have been investigated in the literature extensively, but the managerial concerns emphasized mostly do not belong to the most researched categories. While most of the practitioners’ concerns relate to supportive aspects of warehousing business, a relatively high number of the reviewed studies highlight operational problems. The suggested future research agenda highlights the importance of supportive aspects of the warehousing business, employment of real data in analysis and empirical research methods. The insights from practitioners stress the expected trends of business environment such as more volatile demand, higher desire for customized services and more expansion of e-commerce.
Conference Paper
Full-text available
Cyber-Physical Systems (CPS) is a computerized networking system that integrates with physical processes. The lack of theoretical foundation in CPS has resulted with this study, which aims to provide a holistic view of Cyber-Physical Systems and its integration with Augmented Reality (AR) as a decision support tool in the pre-construction stage. It will then discuss the problems that arise and propose the use of location aware mobile augmented reality handheld system as a support tool to provide users with an intuitive information visualization of local network infrastructures.
Full-text available
This paper describes the basic elements of logistics and pays special attention to improvement possibilities in packaging, handling, storage and transportation phases where manpower and personnel also play an important role. To decrease the error rate of object selection and decision-making time it is necessary to simplify natural logistics element execution and make it more humane where human resources are involved. Modern technologies can improve those processes by taking care of stressful situations and depressing warehouse worker routines. Augmented reality (AR) offers a key technology to solve these problems by allowing to make decisions based on computer generated visualizations and 3D model projections. The successful use of AR technologies in various industries and some experimental approbation in warehouse environments confirms the potential and perspectives. The use of instructions in three dimensional space instead of text and image based guides is a general improvement which is also introduced in this paper.
Conference Paper
Augmented Reality (AR) is a fast rising technology and it has been applied in many fields such as gaming, learning, entertainment, medical, military, sports, etc. This paper reviews some of the academic studies of AR applications in manufacturing operations. Comparatively, it is lesser addressed due to stringent requirements of high accuracy, fast response and the desirable alignment with industrial standards and practices such that the users will not find drastic transition when adopting this new technology. This paper looks into common manufacturing activities such as product design, robotics, facilities layout planning, maintenance, CNC machining simulation and assembly planning. Some of the issues and future trends of AR technology are also addressed.
Conference Paper
This article presents and describes the implementation of an augmented reality system to assist in the task of warehouse inventory. The proposed system integrates an augmented reality engine (NyARToolkitCS) with a QR Code library. This integration enables users to capture product codes add the product's information to the view of the product itself. This article also presents the challenges for developing this system, the adopted architecture and the tested threshold algorithms. These algorithms were evaluated under real environment conditions.