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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:
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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
Maghazei, O. and T. H. Netland (2019). Drones in Manufacturing: Exploring Opportunities for
Research and Practice. Journal of Manufacturing Technology Management (Forthcoming).
Drones in Manufacturing: Exploring Opportunities for
Research and Practice
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.
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
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
factories. Applications that “move” or “transform” objects are scarcer, and they make sense only in special
cases in very large manufacturing facilities.
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
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
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
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).
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
retrieval systems (AS/RS), radio frequency identification (RFID), additive manufacturing (“3D printing”),
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
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.
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.
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.
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
(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
whom we had already interviewed.
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
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
UAV Expo 2017, Brussels, Belgium, 2022 June 2017
European Drone Expo 2018, Brussels, Belgium, 911 March 2018
UAV Expo 2017, Amsterdam, the Netherlands, 1112 April 2018
Zurich, Switzerland, 17 May 20174 September 2018
North Rhine-Westphalia, Germany, 2223 May 2017
Online video conference, 12 May 201715 August 2018
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.
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
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.
4.1 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
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,
Visual inspection
Visual inspection of transportation
infrastructure, such as roads, bridges,
railroads, etc.
Visual inspection of power lines, high-
voltage electricity pylons,
telecommunication masts, etc.
Monitoring the safety of staff
Monitoring human factors and ergonomics
Regulatory compliance
Security patrols
Traffic management
Aerial imaging
Photography and filming
Thermal inspection of equipment, such as
flare stacks, boilers, chimneys, pipelines, etc.
Thermal inspection
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
Cycle counting
Inventory management
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
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
Transportation of medicines
Transportation of blood samples
Spraying for firefighting to support HSE
Manual spraying
Spraying crops
Order picking
Warehouse management
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
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
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.
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
fell into five broad categories:
1. Cost savings
2. Task speed
3. Safety improvements
4. Efficient data collection
5. Public relations (PR) and marketing
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
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.
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
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
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).
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
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.
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.
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.
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
Remote maintenance ops.
Corrosion protection ops.
Warehouse picking
Part delivery to line
Thermal inspection
Hazard identification
Non-destructive testing
Cycle counting
Volume measurement
3D mapping
Layout photography
Visual inspection
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.
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
drone adoption in manufacturing companies.
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.
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
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.
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).
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.
“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
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Figure A-1. High-level architecture of a drone system
Ground control
Actuators and
Unmanned Aerial Vehicle
Remote control
Data link
Aircraft hardware
(e.g., frame, motors, rotors)
Original interview guide (March 2017 to March 2018)
Part I The interviewee and the company
Briefly introduce yourself, your company, your position, your experience, and your
Part II Applications of drones in your company
1) What applications does your company provide for its customers? Which industries? Indoor or
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?
6) What are the advantages of your drones and services compared with your competitors?
Implementation phase
1) How many flying hours have you recorded so far? How many pilot projects has your company
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
9) If you do not mind sharing this information, in which companies and industries have you
implemented your drone technology?
Service specifications
1) Do you provide standardized solutions or do you customize solutions? What is the ratio? What
customization criteria do you consider?
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
4) What are the payloads and speed?
5) Do you include checklists in the missions or manuals for your products? Do you provide training?
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?
Reduced interview guide (after March 2018)
Part I The interviewee and the company
Please briefly introduce yourself, your company, your experience, your position, and your
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
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
... In addition, drones can effectively be used in Search and Rescue (SAR) operations in the aftermath of disasters such as hurricanes, explosions, and earthquakes in a timely and efficient manner [3,4]. UAVs have also found applications in mapping and observations [5,6], inspecting buildings and bridges [7,6], logistics [8,6], maneuvering around building sights [9,6], and exploring harsh environments unfit for humans [10]. ...
... However, with recent developments allowing visually aided navigation, precise awareness sensors, and more precise attitude control, drones are safer than ever to use indoors [14]. Consequently, drones have found many indoor applications such as performing light-weight part and material delivery between workstations in manufacturing plants [15], detecting problems in manufacturing equipment [16], conducting routine inspections in areas that are difficult to reach [10], detecting gas leaks, overheating machinery, and fire [10,16], and providing surveillance of manufacturing facilities [10]. ...
... However, with recent developments allowing visually aided navigation, precise awareness sensors, and more precise attitude control, drones are safer than ever to use indoors [14]. Consequently, drones have found many indoor applications such as performing light-weight part and material delivery between workstations in manufacturing plants [15], detecting problems in manufacturing equipment [16], conducting routine inspections in areas that are difficult to reach [10], detecting gas leaks, overheating machinery, and fire [10,16], and providing surveillance of manufacturing facilities [10]. ...
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Recently, drones have become a useful tool in training and practicing the core of industry 4.0 for applications ranging from machine diagnostics to surveillance and detection of air leaks. In this work, train-the-trainer workshops were organized to train primarily STEM educators from Two-year higher education and secondary education institutions on Smart Manufacturing (SM) technologies. The hands-on activities during these workshops included assembling and coding drones. Four workshops were held between 2019 and 2021 with 114 participants from 20 states across the United States. The workshops included research, industry speakers, and hands-on activities with assembling and coding drones with Arduino, Python, or Blockly. The effectiveness of using drones for training in SM workshops was evaluated using retrospective surveys. Most participants reported that their knowledge of coding and smart manufacturing increased and that the knowledge gained from the workshops is applicable to their work. In addition, using statistical tools, 7,182 students ± 1,903 were exposed to the smart manufacturing concepts using drones six months after the workshops with a confidence level of 90%.
... Obwohl die gesetzlichen Vorschriften für den Betrieb von Drohnen innerhalb von Gebäuden deutlich überschaubarer sind als im Falle von Freiluftflügen, herrscht dennoch enorme Unsicherheit über die Gesetzeslage [3]. Zum einen existieren tiefgreifende Unterschiede in den Gesetzestexten über Landesgrenzen hinaus, welche Unklarheiten für international agierende Betriebe nach sich ziehen, und zum anderen mangelt es grundsätzlich an klaren Richtlinien, die den innerbetrieblichen Betrieb von Drohnen regeln [11,14]. Gleichermaßen ungewiss ist bislang die Haftungsfrage im Falle von Kollisionen mit Menschen und betrieblicher Infrastruktur. ...
... Ebenfalls in das Entscheidungskalkül einer möglichen Drohnennutzung miteinzubeziehen ist der menschliche Faktor. Es könnte zu Produktivitätseinbußen kommen, wenn Mitarbeitende durch den Luftverkehr abgelenkt werden, Sicherheitsbedenken entstehen oder die Sorge vor Arbeitsplatzverlust zu Technologieablehnung führt [14]. Eine breite Akzeptanz der Technologie ist Grundvoraussetzung, damit die neue Lösung auch genutzt wird und bewusste oder unbewusste Technologiewiderstände reduziert werden [21]. ...
... "Everybody wants a flying Swiss-army knife. But drones aren't capable of doing everything" [14]. Diese Aussage trifft so auch in der Intralogistik zu. ...
Die Innovationsfähigkeit von Unternehmen bestimmt deren Wettbewerbsfähigkeit und wirtschaftlichen Erfolg. Dies trifft insbesondere auf innerbetriebliche logistische Prozesse zu, die einen direkten Einfluss auf Durchlaufzeiten und Kundenzufriedenheit haben. Innovationen im Rahmen einer „Logistik 4.0“ versprechen Prozessverbesserungen durch Automatisierung und durch die technische Unterstützung des Menschen bei manuellen Tätigkeiten. Eine dieser vielversprechenden Technologien sind unbemannte Luftfahrzeuge (Drohnen). Der vorliegende Beitrag konsolidiert den Stand der Forschung zu flugfähigen Drohnen in der Intralogistik und untersucht Potenziale und Barrieren. Es zeigt sich, dass grundlegende Veränderungen der Intralogistik durch Drohnen bislang ausgeblieben sind. Drohnen weisen aber vielfältige Potenziale auf, insbesondere in der Automatisierung spezifischer intralogistischer Prozesse, vor allem hinsichtlich der innerbetrieblichen Lieferung eiliger Güter und der Inventur, aber auch der Inspektion von Fertigungsanlagen.
... In this collaboration, drones take off from the truck to deliver the package to customers and then return to the truck for the next delivery. In addition drones have enormous applications in the field of agriculture [?], healthcare [13], defense and disaster response [9], resource monitoring and assessment [14], manufacturing [10], and many more. Challenges: In spite of the broad application of drones, it has certain limitations. ...
... Then all the update operations corresponding to S i , W i , P i , M and M take constant time. Hence, total running time for step (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15) ...
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The coordination among drones and ground vehicles for last-mile delivery has gained significant interest in recent years. In this paper, we study \textit{multiple drone delivery scheduling problem(MDSP) \cite{Betti_ICDCN22} for last-mile delivery, where we have a set of drones with an identical battery budget and a set of delivery locations, along with reward or profit for delivery, cost and delivery time intervals. The objective of the MDSP is to find a collection of conflict-free schedules for each drone such that the total profit for delivery is maximum subject to the battery constraint of the drones. Here we propose a fully polynomial time approximation scheme (FPTAS) for the single drone delivery scheduling problem (SDSP) and a $\frac{1}{4}$-approximation algorithm for MDSP with a constraint on the number of drones.
... The main scope of using UAVs during the de-icing process is the accurate registration and mapping of ice to ensure safety and cost-effectiveness (Castro et al., 2005). The integration of AR technology is to fill the virtual gaps and provide feedback to the operator through a user-friendly and interactive 1. INTRODUCTION Recent technological developments are increasingly impacting the way industrial companies are manufacturing and delivering their products (Maghazei & Netland, 2020;Panagiotopoulou et al., 2022;Papacharalampopoulos et al., 2021). Unmanned Aerial Vehicles (UAVs) are widely considered as a promising advancement, that is expected to be used in several industrial applications due to the significant prospects and new capabilities, particularly in remote sensing and real-time monitoring and control (Rubin, 2014). ...
... Recent technological developments are increasingly impacting the way industrial companies are manufacturing and delivering their products (Maghazei & Netland, 2020;Panagiotopoulou et al., 2022;Papacharalampopoulos et al., 2021). Unmanned Aerial Vehicles (UAVs) are widely considered as a promising advancement, that is expected to be used in several industrial applications due to the significant prospects and new capabilities, particularly in remote sensing and real-time monitoring and control (Rubin, 2014). ...
During the past few years, industry has begun to recognize the operational and economic value of Unmanned Aerial Vehicles (UAVs) operations. Internet of Things (IoT), Augmented Reality (AR), UAVs etc. are being continuously applied within Industry 4.0 applications due to their major capabilities on the optimization of modern manufacturing concepts. The scope of this paper is the development of an AR application to support the inspection process performed by UAVs during the de-icing procedure on aircrafts before take-off. A mixed reality interface is being designed for providing virtual feedback to the operator regarding the position and extension of ice on the aircraft. The main objective is to significantly reduce the inspection time and increase the accuracy of detecting ice through an immersive environment, exceeding the standard time consuming pre-flight methods relying on visual inspection. To broaden applicability, two different development strategies are being implemented: for Android devices and for Microsoft HoloLens Head-Mounted Display. Both scenarios have been tested in a mixed reality to evaluate the performance of the Augmented Reality application.
The use of custom-designed Unmanned Aerial Vehicles (drones) to transfer parts weighing up to 2kg in manufacturing systems is investigated. The components that will make up the drone are selected using an existing online tool. Accordingly, an existing drone simulation model is updated and suitably modified in order to examine the dynamic response of the drone in the main three Cartesian axes for ten scenarios corresponding to takeoff, landing, normal flight on a plane, change of flight planes, with and without load as well as response to an instantaneous perturbation. The results confirm that deviations from nominal trajectory are tolerable even in the landing case, where requirements are strictest.
Full-text available
Edge computing leverages computing resources closer to the end-users at the edge of the network, rather than distant cloud servers in the centralized IoT architecture. Edge computing nodes (ECNs), experience less transmission latency and usually save on energy while network overheads are mitigated. The ECNs can be fixed or mobile in their positions. We will focus on mobile ECNs in this survey. This paper presents a comprehensive survey on mobile ECNs and identifies some open research questions. In particular, mobile ECNs are classified into four categories, namely aerial, ground vehicular, spatial, and maritime nodes. For each specific group, any mutual basic terms used in the state-of-the-art are described, different types of nodes employed in the group are reviewed, the general network architecture is introduced, the existing methods and algorithms are studied, and the challenges that the group is scrimmaging against are explored. Moreover, the integrated architectures are reviewed, wherein two different categories of the aforementioned nodes jointly play the role of ECNs in the network. Finally, the research gaps, that are yet to be filled in the area of mobile ECNs, are discussed along with directions for future research and investigation in this promising area.
While autonomization and digitalization solutions appear beneficial within last mile delivery process, the literature on these solutions remain fragmented and distributed across different themes among various research papers. This paper aims to assemble some of the prominent solutions and outline their key characteristics to guide our researchers in future studies. To do so, this paper first extensively investigates the available literature and presents the most prominent solutions in a prioritization and categorization method (PCM) approach. Where these solutions currently stand in the perspective of inclusive last mile 4.0 transition is then discussed in our findings.KeywordsAutonomizationDigitalizationLast mile deliveryLast mile 4.0Big dataLogistics 4.0
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page:423 Big Data Analytics for Large Scale Wireless Body Area Networks; Challenges, and Applications
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Although disruptive “Industry 4.0” technologies often lack a clear business case, vendors are advocating and companies are actively exploring their use in operations settings. The technology management literature suggests that successful adoption derives from an appropriate fit between the specific technology and (1) economic and strategic factors, (2) operational and supply chain factors, and (3) organizational and behavioral factors. Through a five‐year research project, we explore how drones—an archetypal emerging technology supported by a thriving vendor ecosystem—transitioned from early ideas to experimental applications to full adoption in daily operations. We analyze a range of data, including exploratory interviews with drone ecosystem actors, a secondary dataset, and case studies of drone applications in Geberit and IKEA. Key findings relate to our observation that technology adoption patterns for emerging technologies do not always follow the traditional linear logic of technology fit. We find that emerging technologies are characterized by a dynamic interaction between technology push from a thriving ecosystem and market pull from companies exploring meaningful operational and business value using the concept of “use case.” Based on these findings, we contribute to the technology management literature with an alternative technology adoption framework for emerging “Industry 4.0” technologies. The notion of “use case” is at the center of “Industry 4.0” technology adoption. The challenge is to turn a use case into a business case. Drones are starting to find profitable niche applications in operations; for example, IKEA is scaling drones for inventory control.
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Industry 4.0 has been considered a new industrial stage in which several emerging technologies are converging to provide digital solutions. However, there is a lack of understanding of how companies implement these technologies. Thus, we aim to understand the adoption patterns of Industry 4.0 technologies in manufacturing firms. We propose a conceptual framework for these technologies, which we divided into front-end and base technologies. Front-end technologies consider four dimensions: Smart Manufacturing, Smart Products, Smart Supply Chain and Smart Working, while base technologies consider four elements: internet of things, cloud services, big data and analytics. We performed a survey in 92 manufacturing companies to study the implementation of these technologies. Our findings show that Industry 4.0 is related to a systemic adoption of the front-end technologies, in which Smart Manufacturing plays a central role. Our results also show that the implementation of the base technologies is challenging companies, since big data and analytics are still low implemented in the sample studied. We propose a structure of Industry 4.0 technology layers and we show levels of adoption of these technologies and their implication for manufacturing companies. Keywords: Industry 4.0; smart manufacturing; digital transformation; manufacturing companies.
Full-text available
The development of information, materials science, and technology has led to profound changes in manufacturing. Many new ideas and technologies have emerged in recent years, such as cloud manufacturing and 3D printing. Cloud manufacturing (CM) is a new type of networked manufacturing mode proposed in 2010, and 3D printing is an example of new manufacturing methods. CM and 3D printing both have significant influence in the manufacturing mode and manufacturing method and are already research hotspots in the field of advanced manufacturing technology. What kind of innovations will be produced when they integrate? To explore this issue, this paper first analyses the characteristics of cloud manufacturing and 3D printing. Second, the combination research of cloud manufacturing and 3D printing in recent years is studied, including 3D printing cloud platform (3D–PCP) service architectures, 3D printing resource access technology, 3D printing service optimized configuration technology, 3D printing service control and monitoring technology, and 3D printing service evaluation technology. There are several research achievements and commercial application cases of 3D–PCP, but a true sense of 3D printing cloud ecology from the perspective of a manufacturing system has not been formed to date. On the basis of the survey and analysis, this paper highlights the shortcomings in current research and proposes development suggestions of the combination of 3D printing and cloud manufacturing, including the potential research direction of 3D–PCP system modes, the Internet of Things in 3D printing, and the 3D printing service process control and evaluation.
This study investigates the complex relationships among strategy, advanced manufacturing technology (AMT) and performance using survey responses from 160 U.S. manufacturing firms. In contrast to previous studies that emphasize only the flexibility dimension of AMT, this study adopts a multidimensional view of AMT by stressing the information processing capability inherent in AMTs. The study found support for four dimensions of AMT: information exchange and planning technology (IEPT), product design technology (PDT), low‐volume flexible automation technology (LVFAT), and high‐volume automation technology (HVAT). The results found also indicate empirical support for the study's major premise that a fit between certain strategy–AMT dimensions will be associated with superior performance. Using the findings, the study discusses the implications of the findings and suggests several avenues for future research.
This article investigates the relationships between adoption of various advanced manufacturing technologies (AMT), the way that firms plan for and implement them, and their eventual performance. Data obtained from 125 manufacturing firms in the U.S. is used to test several hypotheses which were derived from an extensive review of the AMT implementation literature. The four research questions that drive this study are: (1) What types of planning and installation activities have firms utilized to support their AMT adoptions?, (2) do differences in the level of effort applied to these activities have any impact on the eventual performance of the systems?, (3) are firms that are using integrated technologies, such as FMC/FMS and CIM, applying higher levels of effort on these planning and installation activities than other firms?, and (4) Are these firms achieving higher levels of success than firms that are not using the integrated technologies. The results indicate that firms adopting integrated technologies had exerted significantly higher levels of effort on strategic planning and team‐based project management and had also achieved higher levels of performance across a wider range of performance factors than other firms. In addition, firms that had exerted higher levels of effort on developing human factors appeared to be achieving more of the benefits of AMT than their counterparts. The overall results and the research and practical implications of this study are discussed.
This paper deals with the role of advanced manufacturing technologies (AMT) within the context of changes in the basic principles to organize and manage manufacturing systems. In particular, the use and effectiveness of various technologies and their computer‐based integration are investigated in the light of three emerging principles: (i) strategic multi‐focusedness, (ii) process integration across functions, and (iii) process ownership. Together, these principles are referred to as Strategically Flexible Production (SFP). In an international sample of 392 manufacturing units from the metal‐working industry, the use of AMT is analyzed in three groups: core adopters, partial adopters or non‐adopters of SFP. Data show that while core adopters do not use stand‐alone AMT more than the other groups, they have a higher level of computer integration (CI), in particular in their forefront departments. However, the use of integrating technologies varies much within the core adopters, suggesting that SFP does not necessarily require massive information technology (IT) support. This is further confirmed by the analysis of performance improvements. The mere adoption of stand‐alone AMT per se does not provide companies with superior improvements in performance. On the contrary, SFP alone or combined with a higher level of integration of stand‐alone AMT fosters increased time responsiveness.
This research examines whether investments in advanced manufacturing technologies (AMTs) such as flexible manufacturing systems (FMS), computer aided design (CAD), computer aided manufacturing (CAM), robotics, etc., are more likely to lead to improved performance if they are supported by improvements in the manufacturing infrastructure of the company. This question is evaluated using data gathered from 202 manufacturing plants chosen from industries generally considered to have relatively high investments in technology. Multiple item scales are developed and adapted from sources in the literature to measure investments in technology, infrastructure, and the performance of the plant. Evidence supporting the reliability and validity of these scales is provided. Hierarchical regression is used to analyze the relationship between technology, infrastructure, and performance. The results suggest that there is an important interaction between the adoption of advanced manufacturing technologies and investments in infrastructure. Firms that invest in both AMTs and infrastructure perform better than firms which only invest in one or the other. Separate analyses on sub‐samples of firms with the highest and lowest investments in AMTs show that infrastructural investments have a stronger relationship with performance in the high investment group. Thus, the data indicate that infrastructural investments provide a key to unlocking the potential of advanced manufacturing technologies.
Case and field research studies continue to be rarely published in operations management journals, in spite of increased interest in reporting such types of studies and results. This paper documents the advantages and rigor of case/field research and argues that these methods are preferred to the more traditional rationalist methods of optimization, simulation, and statistical modeling for building new operations management theories. In the process of describing the constructs of inference and generalizability with reference to case research, we find the existing definitions inadequate and thus extend and refine them to better discriminate between alternate research methodologies. We also elaborate on methods for increasing the generalizability of both rationalist and case/field research studies. A major conclusion is that these alternate research methods are not mutually exclusive and, if combined, can offer greater potential for enhancing new theories than either method alone.
Kurzfassung Digitale 3D-Modelle können die Fabrikplanung deutlich vereinfachen. Die digitale Datengrundlage – in Form eines 3D-Modells des Fabrikgeländes – ist jedoch häufig nicht vorhanden und muss erst manuell erfasst und digitalisiert werden. Drohnenunterstützte Luftbilderfassung in Kombination mit kommerziell erhältlicher Photogrammetriesoftware ermöglicht hingegen eine schnelle und detaillierte Ist-Aufnahme des Fabrikgeländes. Die anschließende Visualisierung verschiedener Planungsvarianten in einem realistischen Kontext vereinfacht deren Bewertung.
Conference Paper
The purpose of this paper is to investigate the potentials of using drone technology in petrochemical industry. Evidences from different industries reveal that industrial applications of drones are growing fast. Many petrochemical plants have started to use drones for visual inspections, and it seems that drones might become a source of technological innovation in the petrochemical industry. We conduct a multiple case study in the petrochemical industry. We collect and synthesize the opinions of 18 managers from three petrochemical companies. We describe potential use cases of drones in this industry and discuss perceived benefits and challenges.
Kurzfassung Strukturierte Fabrikplanung stellt einen Schlüssel zur Erhaltung der Wettbewerbsfähigkeit in Hinblick auf den ständig steigenden Druck durch Globalisierung und die hohe Marktdynamik dar. Die Durchführung von Fabrikplanungsprojekten wird jedoch von Unternehmen gescheut, da diese Projekte mit hohem Aufwand verbunden sind. Aus diesem Grund sollen die Prozesse der Fabriklayoutaufnahme und -auswertung durch neue Techniken ergänzt, teilautomatisiert und damit effizienter gestaltet werden.