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# Drones in manufacturing: Exploring opportunities for research and practice

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Although the industrial application of drones is increasing quickly, there is a scarcity of applications in manufacturing. The purpose of this paper is to explore current and potential applications of drones in manufacturing, examine the opportunities and challenges involved and propose a research agenda. This paper is Open Access. Download at: https://www.emerald.com/insight/content/doi/10.1108/JMTM-03-2019-0099/full/html
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Post-print. This is the Authors’ Original Manuscript of an article accepted for publication in the
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Drones in Manufacturing: Exploring Opportunities for
Research and Practice
Omid Maghazei and Torbjørn H. Netland
Chair of Production and Operations Management, D-MTEC, ETH Zurich, Switzerland
Citation:
Maghazei, O. and T. H. Netland (2019). Drones in Manufacturing: Exploring Opportunities for
Research and Practice. Journal of Manufacturing Technology Management (Forthcoming).
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Drones in Manufacturing: Exploring Opportunities for
Research and Practice
ABSTRACT
Purpose
Although the industrial application of drones is increasing quickly, there is a scarcity of applications in
manufacturing. The purpose of this study is to explore current and potential applications of drones in
manufacturing, examine the opportunities and challenges involved, and propose a research agenda.
Research design
The paper reports the result of an extensive qualitative investigation into an emerging phenomenon. The
authors build on the literature on advanced manufacturing technologies (AMT). Data collected through in-
depth interviews with 66 drone experts from 56 drone vendors and related services are analyzed using an
inductive research design.
Findings
Drones represent a promising AMT that is expected to be used in several applications in manufacturing in
the next few years. This paper proposes a typology of drone applications in manufacturing, explains
opportunities and challenges involved, and develops a research agenda. The typology categorizes four types
of applications based on the drones’ capabilities to “see,” “sense,” “move,” and “transform.”
Research implications
The proposed research agenda offers a guide for future research on drones in manufacturing. There are many
research opportunities in the domains of industrial engineering, technology development, and behavioral
operations.
Practical implications
Guidance on current and promising potentials of drones in manufacturing is provided to practitioners.
Particularly interesting applications are those that help manufacturers “see” and “sense” data in their
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factories. Applications that “move” or “transform” objects are scarcer, and they make sense only in special
cases in very large manufacturing facilities.
Originality
The application of drones in manufacturing is in its infancy, but is foreseen to grow rapidly over the next
decade. This paper presents the first academically rigorous analysis of potential applications of drones in
manufacturing. An original and theory-informed typology for drone applications is a timely contribution to
the nascent literature. The research agenda presented assists the establishment of a new stream of literature
on drones in manufacturing.
Keywords: unmanned aerial vehicles; drones; advanced manufacturing technologies; factory operations
1 INTRODUCTION
Recent advancement in technologies challenge the way companies manufacture and deliver products. For
example, additive manufacturing technologies change the processes used to manufacture customized
products, collaborative robot technologies enable new assembly processes, augmented reality technologies
offer new ways to train operators, and artificial intelligence replaces or assists human operators in customer
service processes (e.g., Deradjat and Minshall, 2017, Fox, 2010, Hedelind and Jackson, 2011, Steenhuis and
Pretorius, 2017). Another promising technology is the unmanned aerial vehicle (UAV), which is commonly
known as a drone. Over the past decade, the capability of drone technology has improved, its price has
plummeted, and its availability has greatly increased. Hence, many manufacturers have begun to consider
the benefits of drone technology for their businesses. This paper contributes to the manufacturing literature
by presenting the first academically rigorous analysis of current and potential applications of drones in
manufacturing. We examine the opportunities and challenges involved and proposes a research agenda.
A breakthrough in the use of drones in industry occurred in 2006 when the US Federal Aviation
Administration issued the first commercial drone permit. Coincidentally, the Chinese drone company SZ
DJI Technology was founded in the same year. This company now holds about three quarters of the global
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consumer drone market (The Economist, 2017). Since then, interest in the professional application of drones
has grown rapidly. In 2018, the research and advisory company Gartner Inc. described drones as an
“emerging technology that will become a source of competitive advantage over the next decade” (Panetta,
2018). Nevertheless, drones have hardly found any profitable applications in manufacturing. Why?
There is a rich research on the technical capabilities of drone technology (e.g., robotics, control, and
computer vision), but much less research on the practical application of drones in industry. Currently, there
is a gap between the technological developments of drones (“what drones can do”) and the profitable
applications they can offer to manufacturing (“what it makes sense they do”). This is in part, due to the
novelty of the technology and in part due to a focus of drone development for other purposes than
manufacturing. The current industrial applications of drones are mainly in the outdoors. Their profitable
applications in industries such as agriculture, construction and infrastructure, energy, logistics, and mining
(see, Goldman Sachs, 2016, Mazur and Wiśniewski, 2016) take advantage of their ability to fly quickly and
safely at high altitudes to places that are difficult, hazardous, or expensive to reach. Manufacturing
operations, on the contrary, are almost exclusively indoors.
In manufacturing facilities, drones compete with conventional technologies that can be mounted to
fixed installations (such as floors, pillars, walls, or ceilings) or moving installations (such as cranes,
conveyors, or vehicles). While outdoor drones can use conventional global positioning systems (GPS) for
localization, positioning, and routing, indoor drones require complex technologies, such as laser
rangefinders (e.g., simultaneous localization and mapping [SLAM]), ultra-wideband radio signals (a form
of “indoor GPS”), or more expensive technologies, such as motion capture systems (e.g, Khosiawan and
Nielsen, 2016). Safety, noise, and privacy also remain of considerable concern. Moreover, doors, cables,
cranes, equipment, and people limit the maneuverability of drones, and the confined spaces in manufacturing
facilities can create turbulence. However, indoors also have advantages. Indoor settings are not subject to
governmental legislation regarding open air flight (Floreano and Wood, 2015) and weather conditions are
irrelevant.
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This study aims to bridge the gap between the current capabilities of drone technology and its
potential applications in manufacturing, as well as to set the stage for the future of drones in manufacturing.
To do so, we explore the potential use of drones in manufacturing by drawing on rich qualitative data
collected in interviews with experts. We start with three research objectives. The first research objective is
to develop and propose a typology of drone applications in manufacturing. The second research objective is
to evaluate the related opportunities and challenges. The third research objective is to use the insights gained
in this study to propose a research agenda. We enter the field informed by the knowledge from the rich
literature on advanced manufacturing technology (AMT).
2 THEORETICAL BACKGROUND
Although the application of drones in manufacturing has not received much attention in the operations
management literature, the application of comparable manufacturing technologies has of course been
studied. The drone technology can be classified as an AMT. An AMT is a computer-based technological
innovation used in manufacturing processes (Udo and Ehie, 1996, Gouvea da Costa and Pinheiro de Lima,
2008). AMTs include a range of modern technologies used in manufacturing, such as computer numerical
control (CNC) machines, industrial robots, flexible manufacturing systems (FMS), automated storage and
and automatic guided vehicles (AGV), among others (Jonsson, 2000, Boyer et al., 1997).
Drones as an advanced manufacturing technology
An AMT is a technological system that consists of hardware, software, and support processes (Bessant and
Buckingham, 1989, Cagliano and Spina, 2000, Chung and Swink, 2009) (see Appendix 1, Figure A-1, for a
high level architecture of a drone system). The hardware of a drone system includes the aircraft, the remote
controller, the installed payload (cameras, sensors, carriers, etc.), local navigation support systems, energy
supply system, and the information technology (IT) infrastructure. The software consists of the programs
and algorithms that control the drone’s flight and payload tasks, as well as communicate with the controllers,
navigation system, and IT systems. The support processes cover a range of drone operations that involve
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humans, such as manual piloting or establishing and maintaining the infrastructure required for automatic
or autonomous flights, and interpreting the data collected by the drone.
An AMT application can be “stand alone” or it can be linked or integrated with other technologies
(Meredith and Suresh, 1986, Small and Yasin, 1997, Small, 2007). Most current drone applications are
stand-alone designed to do a specific task. Examples include the inspection of hard to reach equipment using
video and thermal cameras in the oil and gas industry, aerial photogrammetry for developing three
dimensional modelling during factory planning, and delivering spare parts during maintenance operations
(see, Barth and Michaeli, 2018, Maghazei and Netland, 2018, ZF Friedrichshafen AG, 2018). Linked AMTs
conduct their own tasks, but also communicate and coordinate with other technologies. An example of a
linked drone system is applications used in inventory cycle counting to automatically update inventory
records in a warehouse management system. Integrated AMTs are dependent on the tasks of other
technologies. Only very few current drone applications qualify as integrated AMTs.
Physical versus analytical capabilities of advanced manufacturing technologies
AMTs can be classified according to their physical and analytical capabilities (e.g., Bessant and
Buckingham, 1989, Kotha and Swamidass, 2000, Kotha, 1991, Steenhuis and Pretorius, 2016). The physical
capability of an AMT is its ability to conduct physical tasks. For example, radio frequency identification
(RFID) readers and sensors have relatively low physical capabilities and computer numerical control (CNC)
machines, conveyor systems, AGVs, and industrial robots have relatively high physical capabilities.
Following Kotha (1991) and Kotha and Swamidass (2000), we define the analytical capability of
an AMT as its ability to process data. What separates high from low analytical capability is not necessarily
associated with tedious programming but is decided by the degree of data processing during use. For
example, a simple special-purpose CNC machine has a high programming setup, but the machine usually
simply runs its program during use and is therefore an AMT with low analytical capability. An advanced
FMS, however, needs high analytical capability to synchronize the real-time flows between machining
processes, machine tools, and materials.
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The capabilities of AMTs are improving quickly due to the advancement of complementary
technologies such as sensors, robotics, cloud computing, big data and analytics, to name a few (Guo and
Qiu, 2018, Frank et al., 2019). Such technological advancements are improving both the physical and the
analytical capabilities of AMTs. For instance, Steenhuis and Pretorius (2016) discuss that the growth of
consumer 3D printing is due to the advances of both its physical capabilities (e.g. extruder movement and
speed), as well as its analytical capabilities (e.g. using open source software packages). In a study of the
evolution of automated guided vehicles (AGV) in flexible manufacturing systems (FMS), Buyurgan et al.
(2007) show how the physical capabilities of AGVs are enhanced with analytical capabilities such as
autonomous travel without guided paths.
The separation of analytical capabilities versus physical capabilities of AMTs makes intuitively
sense for drone systems. A drone with low analytical capability only captures input data without processing
it; examples are simple photography or filming. A drone with high analytical capability converts the input
data to other forms of data or information. For instance, a drone system that is equipped with a thermal
camera receives input data and produces thermogram images of temperature radiation. A drone with low
physical capability is not able to perform any physical operation other than flying. A drone with high
physical capability has the ability to conduct physical tasks such as move objects (in addition to the mounted
payload) (e.g., parcel-, part-, or tool delivery), or to perform a physical operation in addition to flying (e.g.,
spraying chemicals or repairing scratches).
Using the separation of analytical capabilities versus physical capabilities, we derive a conceptual
framework from the AMT literature (Figure 1). The combination of low and high for these two capabilities
suggests that AMTs can be classified into four different types: respectively, low-low, high-low, low-high,
and high-high configurations of analytical versus physical capabilities.
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Figure 1. Classifying AMTs as a combination of physical and analytical capabilities.
A conceptual framework explains graphically “the main things to be studied” and can “evolve as the
study progresses” (Miles et al., 2014, p. 20). We use the theory-informed framework in Figure 1 as a
conceptual underpinning when we go to the field to study the applications of drones as a new form of AMT
in manufacturing.
3 RESEARCH METHODOLOGY
In this study, we explore an emerging phenomenon. Due to the novelty of drone applications in
manufacturing, there is little empirical evidence in the operations management literature. Qualitative
research is “particularly oriented toward exploration, discovery, and inductive logic,” which allows us to
determine the emergence of meaningful categories or dimensions (Patton, 2015, p. 64), and to establish a
knowledge base for the phenomenon studied (Karlsson, 2016, Patton, 2015). When a field of research is
nascent or unexplored, qualitative research is helpful for identifying promising propositions, hypotheses,
and theories, which can be tested with quantitative methods at a later stage (Barratt et al., 2011).
We selected the qualitative research method of interviewing experts (Bogner et al., 2009), which has
been used extensively in industrial sociology, educational research, policy research, and political science
Analytical
capabilities
Physical
capabilities
Low
High
Low
High
High-Low
Low-Low
High-High
Low-High
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(Meuser and Nagel, 2009), but less so in the operations management literature. According to Bogner et al.
(2009, p. 5), “expert interviews are, of course, not only a popular way of gathering information; they are
also a totally legitimate method for some forms of research.” Moreover, interviewing experts is an
appropriate and complete method in research that aims to reconstruct explicit expert knowledge
(Pfadenhauer, 2009). We also followed the general advice regarding the analysis of qualitative data in
operations management (Meredith, 1998, Ketokivi and Choi, 2014).
3.1 Selection of experts
We searched for informants who possessed technical knowledge, procedural knowledge, and interpretive
knowledge in the field of industrial drone applications (Bogner and Menz, 2009). These experts were
required to have first-hand experience in applying drones in industrial settings because they would then be
“in a position to actually put their own interpretations into practice” (Bogner et al., 2009, p. 7). Identifying
experts using objective measures (such as years of experience or number of flight hours) was infeasible
because of the unavailability of data. Therefore, we selected informants using the purposeful sampling
strategy combined with intensity sampling, reputational sampling, and snowball sampling (Patton, 2015).
First, we visited locations where drone experts gather, such as drone exhibitions, technical conferences, and
drone seminars. Second, we searched the Internet for media articles, white papers, and commentaries written
by, or mentioning, drone experts. Finally, we used word-of-mouth to access the network of drone experts
We focused the data collection procedure on drone vendors and drone service providers. Although
we searched intensively for informants about cases of drone implementation in manufacturing, we could
only identify a few companies that had run experiments with drones in that industry. Moreover, most of
these companies were reluctant to share their experiences, which we speculated was for reasons of
confidentiality, concerns about intellectual property rights, or the failure of experiments.
3.2 Data collection
The expert interviews were conducted during the European Drone Expo held in March 2017 and March
2018 in Brussels, Belgium; the UAV Expo in June 2017 in Brussels, Belgium; and the UAV Expo in April
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2018 in Amsterdam, the Netherlands. Other interviews were carried out in Switzerland and Germany, and a
few were conducted in video conferences (see Table 1). Almost all the expert interviews were conducted
face-to-face, which was recommended by Christmann (2009). In total, we conducted 66 interviews with
drone experts from 56 companies headquartered in 13 countries worldwide. We decided to stop conducting
new interviews when we reached the saturation of information (Eisenhardt, 1989). The companies included
drone manufacturers, drone service providers, drone insurance companies, regulatory bodies, training
centers, software developers, and part suppliers. Typical in this industry, most of the companies were small-
and medium-sized, which enabled us to communicate with C-level executives and the founders of many of
the firms.
Table 1. Place and time of interviews
Place and time No. of interviews
European Drone Expo 2017, Brussels, Belgium, 1012 March 2017
19
UAV Expo 2017, Brussels, Belgium, 2022 June 2017
10
European Drone Expo 2018, Brussels, Belgium, 911 March 2018
17
UAV Expo 2017, Amsterdam, the Netherlands, 1112 April 2018
7
Zurich, Switzerland, 17 May 20174 September 2018
5
North Rhine-Westphalia, Germany, 2223 May 2017
4
Online video conference, 12 May 201715 August 2018
4
Total
66
We conducted semi-structured interviews. We first designed an interview guide based on the
guidelines of Bryman and Bell (2015). It was organized into four different topics: evaluation of drone
applications; implementation of drone applications; results of drone applications; and lessons learned from
experiments and implementation. We used the same version in the interviews conducted between March
2017 and March 2018. After conducting more than half of the interviews, we conducted a preliminary
analysis and reduced the survey to questions that had not yet reached saturation in their responses. We used
this second version in the interviews conducted after March 2018. Versions of both the early and the late
interview guide are included in Appendix 2.
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The experts’ perceptions of the interviewer’s technical, procedural, and interpretive knowledge
could have affected the quality of the interviews (Bogner and Menz, 2009). Because we are experts in
manufacturing, we qualified as an “interviewer as an expert from a different knowledge culture.” However,
we were laypersons regarding drones. During the initial interviews, we therefore assumed the role of a
“knowledgeable layperson” (Froschauer and Lueger, 2009). We researched drone technologies, attended
introductory courses for drone pilots, attended drone exhibitions and seminars, and acquired a set of drones.
As the interviews progressed, we became accustomed to drone technology, terminology, and applications.
After conducting about 25 interviews, we classified ourselves as “co-experts” (Bogner and Menz, 2009).
To increase the validity of our findings, we collected complementary data from other sources. For
example, we analyzed 39 keynote presentations and 28 recorded keynote talks given at the first and second
FAI International Drones Conference and Expo in Lausanne in Switzerland in 2017 and 2018, respectively.
We also tape recorded 13 keynote addresses and panel discussions at the UAV Expo 2018 in Amsterdam.
Another important source of data was obtained in our review of 474 pages of catalogs and manuals from
over 35 different companies, which we collected from the exhibitions, seminars, and visits. We also
reviewed 57 white papers and industrial reports amounting to 830 pages of text; we read more than 100
media articles; and we viewed online videos about drone technology and its applications.
3.3 Data analysis
We followed the six-step analytical approach to analyzing expert interviews, which was recommended by
Meuser and Nagel (2009): 1) transcription, 2) paraphrasing, 3) coding, 4) thematic comparison, 5)
sociological conceptualization, and 6) theoretical generalization. This approach is similar to thematic
analyses aimed at identifying, analyzing, and reporting patterns (themes) within data with high flexibility,
which allows researchers to interpret various aspects of the research topic (Braun and Clarke, 2006). Steps
1 through 4 involve objective processes, whereas Steps 5 and 6 rely on subjective interpretation and
ingenuity. As usual in explorative qualitative research, this process was iterative (Eisenhardt, 1989)
First, we transcribed 47 of our interviews and took notes on 19 interviews, which resulted in more
than 600 double-spaced pages of raw text. We were not able to tape record and transcribe 19 interviews
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mainly because of the noisy environment and in a very few cases, because no tape recording was allowed.
For these exceptions, we took notes during and immediately after the interviews. All interview recordings
and notes were stored in a research database. Second and third, we used the software package ATLAS.ti to
paraphrase and code the text from the interviews by using the closest possible language to the data (Strauss
and Corbin, 1990). In paraphrasing, the text was sequenced according to thematic units. The open coding
resulted in a list of 409 codes, of which 142 codes described use cases that corresponded to the first part of
the interview guide. In the fourth step, we performed thematic comparisons by classifying similar codes into
57 empirical themes.
In the fifth step, we started to extract new knowledge from the sorted and coded data. In sociological
conceptualization, “the specific characteristics of the commonly shared knowledge of experts are condensed
and categorizations formulated” (Meuser and Nagel, 2009, p. 36). We aggregated the empirical themes into
17 conceptual categories. The final step was to “arrange the categories according to their internal relations”
(Meuser and Nagel, 2009, p. 36) seeking generalization by developing a typology of industrial drone
applications that builds on the conceptual framework developed through the review of the literature. A
typology is a “conceptually derived interrelated sets of ideal types” (Doty and Glick, 1994) that assist the
theoretical progress of a field by breaking it down into subparts with distinct characteristics (Miller, 1996).
After developing the typology, we drew on the rest of the data from the interviews to discuss both
opportunities and challenges in drone applications.
4 FINDINGS
4.1 Industrial applications of drones
Table 2 shows our data reduction structure for the industrial applications of drones (e.g. Gioia et al., 2013,
Ramus et al., 2017). The left column shows the empirical themes that describe the current applications of
industrial drones, which directly emerged from the transcription, paraphrasing and coding of interview data.
The middle column shows the conceptual categories that emerged from the thematic comparison of the
empirical themes. We summarized empirical themes (i.e. drone applications) into generic concepts (i.e. areas
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of application) based on similarities through a process of abstraction (Flick, 2014, p. 404). For example,
drone applications for gas detection and noise monitoring were summarized into the area of hazard
identification.The right column shows our theory-informed generalizations of activities that a drone can
provide: see, sense, move, and transform. We derived the four aggregate dimensions by elaborating on the
relations between concepts through further abstraction. For instance, we aggregated all the application areas
of drones in relation to visual capabilities into “see”.
Table 2. Data reduction structure for industrial applications of drones
Empirical themes
Conceptual categories
Aggregate dimensions
Visual inspection of equipment, such as flare
stacks, silos, boilers, chimneys, pipelines,
etc.
Visual inspection
See
Visual inspection of transportation
Visual inspection of power lines, high-
voltage electricity pylons,
telecommunication masts, etc.
Monitoring the safety of staff
Monitoring
Monitoring human factors and ergonomics
Regulatory compliance
Security patrols
Traffic management
Aerial imaging
Photography and filming
Filming
Photography
Media
Thermal inspection of equipment, such as
flare stacks, boilers, chimneys, pipelines, etc.
Thermal inspection
Sense
Thermal inspection of solar panels
Thermal inspection of wind turbines
Thermal inspection for search and rescue
Thermal inspection for surveillance
Gas detection
Hazard identification
Noise monitoring
Dry film thickness measurement
Non-destructive testing (NDT)
Ultrasonic thickness measurement
Corrosion detection (e.g., corrosion in
conductors)
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Cycle counting
Inventory management
Tracking
Finding lost pallets and slots
3D factory planning
3D mapping
Process mapping
Stock measurement in open-pit mining
Volume measurement
Measuring containers and gaps between
containers in the ship industry
Land surveying
Remote sensing
Topographic map
Photogrammetry
Terrain mapping
LiDAR scanning
Archeological research
Multispectral imaging
Hyperspectral imaging
Computing vegetation
Calculating environmental-
science indices
Tree study in forestry
Wildlife management
Mapping crops
Precision agriculture
Mapping fertilizers
Mapping pesticides
Last-mile logistics
Delivery
Move
Intra-logistics
Transportation of medicines
Transportation of blood samples
Spraying for firefighting to support HSE
Manual spraying
Spraying crops
Order picking
Warehouse management
Transform
Order sorting
Carrying tools and repair
Maintenance management
Carrying spare parts and assembling
3D printing spare parts and assemble
Spotting drowning victims and providing
rescue kits
Lifesaving and disaster
management
Spotting victims and delivering emergencies
in volcanic events, hurricanes, flooding and
major storms, and earthquakes
Bringing emergency supplies and delivering
medical kits, CPR, etc.
“See” is the capability of collecting visual data; often in the forms of images and videos. In the
manufacturing industry, examples are the visual inspection of equipment, such as gas flare, silos, boilers,
drums, tanks, chimneys, and pipelines (both above and below ground). These are common tasks in many
process industries (e.g., petrochemical industry, offshore and onshore oil platforms). Drones that “see” are
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also used to monitor the safety of staff, such as during maintenance operations where fixed cameras are not
economically feasible. Some large plants apply drones to monitor security instead of closed-circuit
television (CCTV) or human patrols. Drones are also tested in applications used to monitor of safety,
ergonomics, and regulatory compliance.
“Sense” is the capability of collecting data and transforming it into the other forms of data or
structured data (i.e., information) without performing additional physical operations. Some relevant
examples in manufacturing include the following: the thermal inspection of equipment, machines, chimneys,
and stacks; gas detection and noise monitoring to identify hazards in the oil, gas, and petrochemical
industries; non-destructive tests such as measuring the thickness and detecting corrosion of equipment; cycle
counting, tracking and trace, and finding lost pallets and slots for inventory management; 3D factory
planning and process mapping for the optimization of factory layouts and material flows.
“Move” is the ability of a drone system to grasp and carry objects or perform physical operations
(e.g., spraying). A typical example in manufacturing consists of intra-logistics operations, such as delivering
light components, spare parts, or tools especially during maintenance operations. Drones can also be used
to spray paint on the corrosion in equipment and buildings and to spray foam during fires.
“Transform” is the ability of a drone system to collect data and transform them into information
while performing physical operations (e.g., carrying objects). It combines the capabilities of see, sense, and
move. Current examples of “transform” in industry are scarce, but a few promising pilot studies are
underway. For instance, a drone system with a camera can simultaneously inspect equipment and perform
simple repair operations using mounted tools (e.g., patching, painting, and sealing). Drones can perform
pick up operations in a warehouse. Both examples are technically complex and not economically feasible in
the current state of the technology. For example, in e-commerce warehouse management, order picking and
order sorting require advanced drones that grasp items and carry them reliably. This operation also requires
multiple sensors (e.g., barcode-, data matrix-, or RFID readers) to manage inventory and update warehouse
management systems in real time. An efficient operation would require a swarm of autonomous drones with
the capability of recognizing obstacles and applying avoidance algorithms.
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Building on the theory-informed classification of AMTs in Figure 1, we can now use the empirical
findings to propose a typology of drone applications in manufacturing. It is illustrated in Figure 2. Seeing is
a low analytical and low physical capability. Sensing involves a high analytical capability and low physical
capability. Moving represents high physical capability and low analytical capability. Transforming requires
high analytical and high physical capabilities. We use this typology to discuss the current state of drone
applications in manufacturing, propose a research agenda, and propose implications for practitioners.
Figure 2. Typology of industrial drone applications
4.2 Potential benefits of drones
We asked all interviewees about the potential benefits of using drones in manufacturing. Although the real
benefits are related to specific use cases and contexts, the data analysis showed that the potential benefits
1. Cost savings
3. Safety improvements
4. Efficient data collection
5. Public relations (PR) and marketing
Analytical
capabilities
Physical
capabilities
Low
High
Low
High
Sense
Move
See
Transform
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First, drones can increase productivity and hence reduce the costs of manufacturing. In particular,
in manufacturing plants in inspection-intensive process industries, drones can bring a significant cost saving.
Inspections carried out by drones reduce the amount of labor-intensive work and eliminate the need for
scaffolding. Regarding an extreme non-manufacturing example, one interviewee reported that in an
inspection project on one of the biggest oil platforms in the North Sea, the introduction of drones reduced a
700 person-day inspection of 14 objects to 28 person-days. Furthermore, the inspection of flare exhausts
required a shut down in which time was an extremely precious resource valued at USD 7 million per day.
Another frequently reported example was the use of drones to count stocks in large warehouses. The cost
savings in this application were derived from replacing human work, eliminating rework due to human
errors, and improving order fill rates, thus increasing customer satisfaction and decreasing safety stock
levels. Similar findings were reported by Hoffmann (2017), in which an estimated annual operating cost
savings of USD 300,000 was derived in scanning 1,000,000 barcodes per year in a warehouse of 500,000
square feet.
A related potential benefit is the increased speed of performing tasks. Using drones for the
inspection of hard-to-reach equipment and installations speed up the operations because of the shorter setup
time and higher maneuverability compared to traditional processes involving scaffolding, ladders, and rope
access. Shorter setup times and higher maneuverability can also increase the frequency of inspections,
allowing for the faster detection of incidents such as gas leakages. Another example is the use of drones for
the inventory management of bulk raw material, in which light detection and ranging (LiDAR) scanning
with drones can increase the speed and efficiency of inventory counting compared with handheld scanners.
Another example was provided by an interviewee who explained that drones can speed intra-logistics
operations:
Imagine an assembly line in the automotive sector where parts are not working or are
missing. The normal process is then that a human being is running or biking to get the
part from the warehouse. This could take between 10 and 15 minutes in normal cases.
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With the drone, you could fly over infrastructure and by that, you could do it in 3 to 4
minutes. These are real numbers we measured in an automotive factory.
Safety improvement was the most frequently mentioned benefit of drones. According to the
president of a global drone association and the CEO of a drone start-up, “Dull, dirty, and dangerous, those
are the jobs that drones improve on.” Drones can reduce hazardous tasks in many operations. In particular,
drones can replace manual human inspection of hard-to-reach equipment and hazardous areas. Moreover,
drones can be a supportive tool in conducting health, safety, and environment (HSE) activities, such as
sniffing for contamination and gas leaks or search and rescue operations during emergencies in large
manufacturing plants. Drones can also film emergency drills to improve the responsiveness of HSE teams
during evacuations.
A fourth benefit is that drones can increase data collection efficiency and assist acquisition of data
that has not been collected before. For example, one interviewee explained, “A drone can get high quality,
more consistent, and repeatable datasets, and that’s important because if you inspect the same structure
many times, you see trends.” This capability is particularly promising in maintenance operations in process
industries. Drone users can also increase the capability of data collection using multiple sensors. For
example, drones can be used to provide digital 3D models of factory floors to support layout planning and
redesign (Barth and Michaeli, 2018, Melcher et al., 2018). In general, the increased amount of accurate data
collected by drones can be used to support managerial decision-making. An interviewee shared, “We use a
drone to inspect and with all that data you can make decisions on what you do. Do I fix, do I inspect it again,
or do I do nothing?” Drones that include complementary software packages for data analysis can provide
decision makers with meaningful reports in easy-to-understand formats.
A fifth and more subtle benefit of drones is their use in PR stunts. Media outlets and newspapers
have been quick to report on pilot studies of drones in factories. Consistent with the findings of previous
works, press coverage and media attention is usual for companies that are early adopters of robots and other
AMTs (Meredith, 1987). A few recent examples of press coverage for drones are reports of applications
used in cycle counting in Mercedes warehouses (e.g., Banker, 2016), intra-logistic applications in ZF
19
Friedrichshafen (Dellinger, 2018), and inspections of hard-to-access equipment in a Ford factory (Hatt,
2018), the Pilsner Urquell brewery (Margaritoff, 2018), and Royal Dutch Shell’s oil and gas facilities
(Castellanos, 2018). Companies that use drones may be perceived as innovative and future-oriented, which
can have positive effects on recruitment, public goodwill, and brand value.
4.3 Challenges for drone applications
We identified five generic categories of challenges and drawbacks related to the use of drones in
manufacturing:
1. Technological challenges
2. Operational challenges
3. Organizational challenges
4. Legislative challenges
5. Societal and mental challenges
It is not surprising that major challenges to the industrial application of drones are related to
technological limitations, the most frequently mentioned of which related to constraints in current battery
technologies. The limited battery capacity implies that drone users must balance flight endurance with
payload. As of 2019, commercially available industrial drones will have a flight time between 2 and 25
minutes. After the mission, the batteries must be replaced or recharged. Recharging often takes 45 minutes
or longer. One interviewee observed, “If the battery technology gets better or we can find a way to make
something lighter, then we have more range.” Another solution is to eliminate the need for batteries by using
tethered drones, which have a direct power supply and use wired data transmission. Tethered drones are a
promising solution in applications that require high flight endurance and low hovering capabilities (e.g.,
inventory management). Other technological challenges include indoor navigation, reliable data transfer and
communication, danger of explosion, safety mechanisms, and noise. For example, indoor drones may need
a combination of positioning systems, object recognition and collision avoidance algorithms, SLAM
algorithms, as well as a combination of multiple sensors, including an inertial measurement unit (IMU).
20
The second challenge relates to the operation of the drone. Most current drone applications are
manual pilot operations that are flown within the line of sight. The alternatives are automatic or autonomous
flights. All operation modes pose a range of challenges. Manual operations require alert and skilled pilots.
In long operations, pilot fatigue can quickly become a source of human error. Automatic and autonomous
flights require a continuously maintained navigation infrastructure. In both cases, drone flights need to be
reliable and safe, especially around people. Redundant systems, such as parachutes, extra propulsion, and
safety algorithms in autonomous flights, can make drones failsafe. Furthermore, the current drone
technology is a poor fit in factory environments that are at risk for explosions or are sensitive to electrostatic
discharge (ESD). The gates, doors, pillars, ventilation, fire protection installations, cranes, utility gateways,
and large machines in factory environments are challenging to navigate even by experienced pilots.
The organizational challenges include the need for skilled drone pilots, who not only must be able
to fly drones safely but also must have a deep understanding of the tasks and missions involved. Human
issues such as workers’ knowledge and technical experience, training, and involvement in planning are key
determinants for the success of technology adoption (Chung, 1996, Walton, 1987, McCutcheon and Wood,
1989, Pagell et al., 2000). The data collected in the interviews revealed that human error is a greater problem
than technological error in drone operations. For example, pilots need to be trained in the use of a drone as
an inspection device as well as to collect and deliver useful data. However, the use of autonomous drones
may overcome the challenge of training drone pilots and keeping them alert. According to one interviewee,
“autonomous drones are safer than drones with human pilots.” Other organizational challenges of adopting
drones are related to developing a convincing business case that provides an acceptable return on investment.
This is similar to the debate on measurable benefits of adopting AMTs in manufacturing industries (Swink
and Nair, 2007, Udo and Ehie, 1996). On one hand, it is difficult to specify the potential savings that drones
can provide in manufacturing. The costs, on the other hand, are visible to everyone. Therefore, risk averse
managers often do not invest beyond trials. Yet, the results from trials can help managers to set expectations
and to develop a risk profile for drone programs in their settings (Hottenstein and Dean Jr, 1992).
Furthermore, organizations that plan to invest in drone operations face the “make-or-buy” dilemma. The
21
data collected in the interviews indicated that this decision should depend on the availability of internal and
external skills and the sensitivity of both the processes and the data. An additional challenge concerns
dealing with the data that are collected. In many firms, drones are only a small part of their “digital
transformation.” Often, the use of drones must wait for the slow preparation involved in digitalization and
data management. The co-founder of a leading drone service provider shared, “It’s not that drones can’t fly,
it’s the fact that digitization of these big industries is difficult and lengthy.” This has also been pointed out
in the AMT literature, which suggests a prevalence of stand-alone AMT applications and islands of
automation with limited integration (Sun, 2000).
The fourth challenge concerns legislative rules and regulations. Although the number of drone
applications is increasing, the regulations concerning their use is lagging. A main benefit of using drones in
indoor applications is that the regulations are more relaxed compared with outdoor applications. There are
large variations between countries in terms of drone legislation. The licenses (or the lack of them) define
how, where, and what applications the manufacturer can use drones. As in many emergent technologies, it
has been difficult to regulate drones, which is because of the rapid improvement of the technology, safety
and security issues, the lack of clarity of who should draft the regulations, and the lack of knowledge about
many real applications (Khanna, 2018). For instance, flying beyond the visual line of sight (BVLOS) is
prohibited in many countries, which reduces the applications of drones as well as the areas of coverage in
outdoor applications; however, some countries make exceptions for flying BVLOS.
Finally, there are societal and mental challenges related to the use of drone applications in
manufacturing. For example, the common use of drones as a military weapon affects public opinion. Many
members of the public have negative perceptions of drones as a new technology. People are also concerned
about the safety of drone technologies, the intimidating appearance and noisiness of drones, and the invasion
of personal data. In a case in Australia, drones were used to monitor staff behavior, but the practiced was
stopped because it violated workers’ privacy (Opray, 2016). In operations that use heavy drones or payloads,
safety concerns are justified.
22
Interestingly, only two of our 66 interviewees mentioned price as a drawback. This finding was
surprising. Only few years ago, price would have been a major challenge. The recent affordability of drone
technologies is because of the mass production enabled by SZ DJI Technology and other manufacturers of
drones in China (Khanna, 2018). However, when manufacturers need solutions that are tailored to sense,
move, or transform capabilities, the price of drones, consulting, and infrastructure will increase significantly.
5 DISCUSSION
In our proposal of a research agenda for industrial applications of drones in manufacturing, we first
summarize the current state of drones in manufacturing, and then we discuss the findings in light of the
AMT literature. We also discuss the implications for practice.
5.1 Current state of drones in manufacturing
The following observation by a drone vendor serves to illustrate the current state of drones in manufacturing:
Everybody wants a flying Swiss-army knife. But drones aren’t capable of doing
everything. It’s really about trying to give the customer real expectations and tailored
capabilities; it’s about trying to figure out their primary goals and what they are trying
to accomplish with the data.
In 2018, there were few established applications of drones in manufacturing. Many companies are
now experimenting with the use of drones in different applications, and a few manufacturers have already
begun to use drone applications in warehouse operations and inspection tasks. Nevertheless, there is a
significant potential for further drone applications. As drone technology continues to develop during the
next 510 years, we expect a range of new use cases to emerge across many manufacturing industries. Figure
3 provides a summary of current drone applications used in manufacturing industries.
23
Figure 3. Current drone applications in manufacturing
The majority of the current applications of drones are the “see” and “sense” types. The first drone
experiments by most manufacturers involve off-the-shelf commercial drones with high-definition cameras
for photos and videos. Many manufacturers employ at least one drone enthusiast who brings his or her
interest and expertise to producing aerial photos and videos of the facilities. These applications are
inexpensive and simple, and they do not require specialized drone technology or consultation. Learning-
by-doing with inexpensive drones helps accumulate knowledge and enables incremental innovation (Bourke
and Roper, 2016, Sohal et al., 2006). For manufacturers with large facilities, tanks, hazardous areas, cranes,
conveyors, or high machines that require regular inspections, the next stop is to consider whether drones
could replace manual inspections. In many cases, drones are an economical alternative to traditional
inspections. These “see” capabilities could be enhanced to “sense” capabilities by integrating advanced
sensors and software. Standard video cameras could be replaced by thermal cameras to detect heat loss from
machines and buildings. Gas-sniffing sensors could be used to detect gas leaks. Laser or ultrasound sensors
could be used to conduct non-destructive testing in hard-to-reach areas. Barcode or RFID readers could be
used to identify objects on high shelves. LiDAR scanners could be used in volume measurement and the
Analytical
capabilities
Physical
capabilities
Low
High
Low
High
Sense
Move
See
Transform
Remote maintenance ops.
Corrosion protection ops.
Warehouse picking
Intralogistics
Part delivery to line
Thermal inspection
Hazard identification
Non-destructive testing
Cycle counting
Volume measurement
3D mapping
Layout photography
Visual inspection
Monitoring
Filming
24
indoor 3D mapping of factories, which could help in designing layouts and in factory redesign projects.
There are examples of all these drone applications in current manufacturing practices.
It is harder to improve the physical capabilities of drones than it is to improve their analytical
capabilities because of their physical limitations, especially their payload and battery capacity restrictions.
In “move” or “transform” tasks, which require high physical capabilities, drones are typically inferior to
tools, sensors, or cameras that are mounted to grounded infrastructure, AGVs, cranes, walls, or ceilings.
“Move” operations, such as intra-logistics and part delivery applications, are rare, and their use is practical
only in small, light, and urgent “emergency” deliveries. A non-manufacturing outdoor example is the
delivery of blood samples from hospitals to test laboratories, which already is in daily operation for example
in Lugano and Zurich in Switzerland (Müller, 2018). Indoor applications are more difficult to justify. Even
in the case of missing parts on an assembly line (see quote in section 4.2); the better option is to remove the
root cause of the error. “Transform” operations are rare not only in manufacturing but also in all industries.
Some applications are in the experimental stage in the oil and gas industry and the construction and
infrastructure industries. In manufacturing, remote maintenance operations and corrosion protection may be
promising business cases in nuclear power plants, metal smelting plants, shipyards, petrochemical plants,
and other large process industry plants. However, the risk of explosion remains a technical hurdle for the
full adoption of drones in these contexts.
5.2 A research agenda
Our insights suggest that by 2025, drones will be applied in many manufacturing plants. This should provide
a rich opportunity for empirical research in this area. In the support of an efficient development of a literature
on drones in manufacturing, we call for three broad streams of research: 1) operations management and
industrial engineering issues; 2) technology development and customization for manufacturing; 3) socio-
cultural and behavioral issues.
We call for conducting both descriptive-oriented and solution-oriented research on the applications
of drones in manufacturing industries (see, van Aken, 2005). Operations management scholars could extend
the literature on the evaluation, implementation and measuring effects of adopting drones in manufacturing.
25
As a starting point in particular, scholars can build frameworks for supporting “make-or-buy” decision-
making. Industrial engineering scholars could study specific-use cases and develop design guidelines for
different applications. The typology presented in this paper could help focus attention on these areas on
research. Because the majority of applications in manufacturing will continue to be of the “see” or “sense”
types, we suggest focusing on “sense” as the most promising type of application. Because this field of
research is in the early stage, we suggest that qualitative empirical methods should be used to advance the
research on drones in manufacturing. Expert interviews, which were used in the present study, is a promising
method for explorative research. Action research and design science research can support the development
of solution-oriented theories and practical artifacts. Modeling and simulation can help explore and evaluate
opportunities in virtual environments. As the number of cases in industry continues to increase, case research
and survey research will allow for the exploration of the tangible benefits and challenges of the
implementation of drones in manufacturing. The theoretical literature on AMT is a valuable starting point.
For instance, reviewing AMT literature shows the potential of survey research to determine the industries
that are more likely to benefit from each type of applications, as well as elaborating on the contingency
factors (such as proximity to know-how, size of the companies, and organizational culture) that influence
There is also much work to be done in the engineering sciences and in product development
regarding the application of drones in manufacturing. The further development of drone technology, such
as the capability of carrying heavier loads, conducting long missions (e.g., drones with hydro engines), and
improving the technology of BVLOS with advanced object recognition and object avoidance algorithms,
will increase the number of future applications. Research could also help improve battery life,
communication and control processes, safety systems, the availability of sensors, and other technological
aspects. Wu et al. (2015) suggested that cloud-based design and manufacturing could increase the
productivity of the design, prototype, and production of future drones, thus enabling faster reactions to
market needs.
26
The most promising technological research in terms of manufacturing applications is the
development of automatic drones. Replacing manual work by a piloted drone produces only marginal
benefits. Replacing manual work by automatic drones is a much better business proposition. Automatic
drones require complementary technologies, such as ground stations, multiple sensors for object recognition
and avoidance, and algorithms that control flight. The next step after automatic flight is autonomous flight
by artificially intelligent drones that make decisions in changing environments (Floreano and Wood, 2015).
One interviewee from a leading provider of drone-based inspection stated, “We are long way away from
autonomy, but we are close to automation.Autonomous micro aerial vehicles is another research area with
promising potentials in manufacturing operations (Kumar and Michael, 2012). Furthermore, the “swarming
drone” technology could offer unprecedented opportunities to scale drone applications for inventory
management and material handling (Khosiawan et al., 2018a). However, this technology would require
robust wireless communication, three-dimensional trajectory data, precise flight control, and scheduling task
execution (Khosiawan and Nielsen, 2016, Khosiawan et al., 2018b), all of which is worthy of future
research.
Drones differ from many other AMTs because they are negatively portrayed by media. In addition,
drones exhibit animal-like behavior, they are noisy, and they can be hard to see until they are close to their
target. People know that drones collect data and can potentially film them while they are working, which
poses serious questions about personal data protection rights. In addition, drones and robotics in general
evoke the fear that people will lose their jobs to machines (Stewart, 2015). In short, drones involve a trust
problem that is more serious than that involving many other AMTs. To establish trust for drone applications
in manufacturing, past studies on AMT advice managers to develop an innovation-supportive culture that
supports experiments with new technologies (Khazanchi et al., 2007). Because drones are quite different
from other “grounded” AMTs, socio-cultural and behavioral aspects of drone implementation represents a
particularly promising research area. Such research on behavioral aspects could be based on ethnographic,
field experiment, interview, or survey methodologies.
27
5.3 Implications for practice
Drones are a new form of AMT that will be applied in many manufacturing industries, especially in large,
technology-intensive facilities in process industries. The overview of current use cases shown in Figure 4
could provide manufacturers with a perspective on what is possible today. An important point is that current
drone applications are mainly “see” and “sense” types of applications.
Where should manufacturers start? As the arrows shown in Figure 4 indicate, manufacturers could
start with simple experiments related to the “see” capabilities. From there they could move to “sense”
applications, “move” applications, or both. However, the transition to “move” applications is currently the
most challenging. By following this advice, manufacturers could start running experiments with off-the-
shelf drone technologies. Such actions could foster learning and champion drone technology through
familiarization and promotion (Dimnik and Johnston, 1993, Kolb, 1976). That would help in discerning
opportunities and challenges, as well as justifying investment (Boyer, 1999, Kolb, 1976). As suggested in
previous work on AMT, learning from the experiences of other manufacturing companies can help managers
avoid common mistakes and assist them during the planning phase of a drone program (Sohal, 1996).
Manufacturers that have gained experience in using drones in “sense” or “move” tasks could consider further
integrating technology to make their drones capable of performing “transform” tasks. Evidence from the
adoption of other AMTs imply that such integration needs financial and strategic justification, readiness for
organizational change, investment in infrastructure, and support from top management (see, Small, 2007,
Gouvea da Costa and Pinheiro de Lima, 2008, Dean Jr et al., 1992, Zammuto and O'Connor, 1992, Boyer et
al., 1997, Percival and Cozzarin, 2009, Bessant, 1994).
6 CONCLUSION
In the present study, we explored the current and potential uses of drone technologies in manufacturing. We
proposed a typology of drone applications, discussed the related benefits and challenges, and recommended
a research agenda. The proposed typology separates four types of applications based on the combination of
the physical and analytical capabilities of drones: “Seeapplications have a low analytical capability and a
low physical capability. Senseapplications have a high analytical capability and a low physical capability.
28
“Move” applications have a low analytical capability and a high physical capability. Finally, “transform”
applications are characterized by a high analytical capability and a high physical capability.
We conclude that drones are on the verge of being adopted for use in many manufacturing industries.
Particularly promising and cost-efficient applications are those that help manufacturers “see” and “sense”
data in their factories. Examples are the inspection of hard-to-reach areas or in hazardous areas, the detection
of gas leaks in large plants, and cycle counting in large warehouses. Applications that “move” or “transform”
objects are scarcer, and they make sense only in special cases in very large manufacturing facilities. Our
findings show that drones could have higher potential in process industries than in discrete manufacturing.
We present a research agenda that promotes research within three domains. First, operations
management and industrial engineering scholars could develop descriptive and normative knowledge about
drone applications in manufacturing. Expert interviews, simulation and modelling, action research, design
science research, and survey research offer good opportunities to explore and explain the dynamics of using
drones in manufacturing. Second, scholars from engineering sciences and product development should
continue the development of drone technologies in order to improve the physical and analytical capabilities,
improve design and drive down cost. The most promising current areas of technological development are
concerned with the development of automatic drones, autonomous drones using artificial intelligence, micro
aerial vehicles, and swarming technology. Third, socio-cultural and behavioral research perspectives on
drone applications in manufacturing are needed in order to ensure technology acceptance. In short, drones
offer rich opportunities for future research.
Despite the great amount of technological development during the past decade, there are still
technological, organizational, and regulatory challenges to the implementation of drones. Drones will not
revolutionize manufacturing alone, but they have the potential to radically improve the efficiency of certain
tasks in manufacturing. In 2025, drones are likely to be a much more common sight in manufacturing
facilities than they are today.
29
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33
APPENDIX 1: HIGH LEVEL ARCHITECTURE OF A DRONE SYSTEM
Figure A-1. High-level architecture of a drone system
Ground control
Communication
module
Actuators and
sensors
Unmanned Aerial Vehicle
infrastructure
Energy
supply
On-board
computing
Remote control
Off-board
computing
Aircraft hardware
(e.g., frame, motors, rotors)
Operator
34
APPENDIX 2: INTERVIEW GUIDES
Original interview guide (March 2017 to March 2018)
Part I The interviewee and the company
responsibilities.
Part II Applications of drones in your company
1) What applications does your company provide for its customers? Which industries? Indoor or
outdoor?
2) What are the benefits for industries of using drone technology?
3) What challenges in using drone technology limit its wider use and application?
4) How do you measure the performance of drones for your customers? What performance indicators
do you use?
5) What capabilities do your drones have?
Implementation phase
1) How many flying hours have you recorded so far? How many pilot projects has your company
implemented?
2) What were the main challenges during the implementation?
3) How does your company coordinate implementation processes with customers? Which business
functions are involved? What is the involvement of customers in your projects?
4) Do you implement the project during normal working hours? Are the employees and workers
present during the implementation? What are the employees’ reactions?
5) How do you collect data from drone(s)? How do you analyze the data?
6) What safety measures are used during the implementation?
7) How does your company deal with the regulations and laws regarding implementation?
8) How do you deal with the characteristics of countries, governments, and organizations in the
implementation?
9) If you do not mind sharing this information, in which companies and industries have you
Service specifications
1) Do you provide standardized solutions or do you customize solutions? What is the ratio? What
customization criteria do you consider?
35
2) Is your pricing regime based on customized products and missions or it is standardized?
3) What is the degree of autonomy in conducting drone projects? To what degree are they fully
autonomous?
4) What are the payloads and speed?
5) Do you include checklists in the missions or manuals for your products? Do you provide training?
Implications
1) What are the main best practices for the application of drones in your company?
2) What are the lessons learned? To what degree is this knowledge transferable?
3) Did the drone application change the business process of your customer’s company? How?
4) Can you describe the organizational structures?
5) How does drone technology impact employees, organizations, and societies?
Part III Future applications
1) Do you plan to expand the use of drone technology to a wider range of applications?
2) What other future applications do you envision for drone technology? What are the barriers?
3) Will drones be used in the manufacturing industry?
4) Will drones be used in indoor applications?
Part IV Open question
Based on your experience in this area, what are the potential application of drones and in which
industry will they be used the most?
36
Reduced interview guide (after March 2018)
Part I The interviewee and the company
responsibilities.
Part II Current applications of drones
1) What are the current top five applications of drone technology? In which industries?
2) What are the top three benefits of using drone technology for industries? Please provide an
example.
3) What are the top three challenges in using drone technology? Please provide an example.
4) How do you collect the data from drone(s)? How do you analyze these data?
Part III Future applications in manufacturing operations
1) What are the top five applications that you envision for the use of drone technology in
manufacturing operations within five years?
2) What will be the top three main reasons for using drones instead of existing technologies?
3) What will be the top three challenges?
4) Will drones be used in indoor applications?
a. What applications will be used in warehouse operations?
b. What applications will be used for inspections in confined spaces?
Part IV Open questions
What applications do you predict for drone technology if there are no limits to technological,
social, and regulations?
Open discussion: Typology of the industrial applications of drones
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