Reaching 28–93% cost savings compared to conventional delivery methods

Low investment and operating costs (for single drones)

Improving efficiency by shortening travel distance/time and reducing reliance on fuels

Lowering drivers cost by reducing work shifts

Lowering storage cost by relieving amassed inventory volumes

Lowering cost of delayed/failed deliveries through speedy/timely deliveries

Difficulty in considering a myriad of factors impacting economic viability (e.g. scale economies, maintenance and depreciation rates, payload-to-energy ratio, time-window penalties, service coverage, population density, labor cost, battery charging/replacement, insurance, regulatory compliance, facility operation)

Large investment cost in drone fleets, depots, charging stations, and operating systems

Adopting a “system-thinking” approach for cost estimation

Sharing drones across multiple warehouses (via “sharing economy” schemes)

Operating drones with autonomous ground vehicles

Syncing drones with trucks along delivery routes

Increasing flexibility by offering a variety of truck-drone configurations (Figure 8)

Lowering traffic congestions, transport cost, and emissions through distributing loads between drones and trucks

Enabling senders to choose suitable delivery configurations based on the LMD context at hand (e.g. trucks carrying drones to furthest launch points for humanitarian missions)

Challenge in selecting the right truck-drone configuration as it relies on several factors (e.g. cost, urgency of delivery, existent infrastructure)

Need of capital to invest in truck-drone fleets, their operating systems, and associated depots

Lack of policies to guide structuring warehouses, fleets, inventory allocation, and battery management

Deficient infrastructure to accommodate drone-truck setups

Utilizing deep learning (e.g. Q-learning) to aid the selection between drones and trucks

Selecting multiple truck-drone configurations to optimize the LMD process

Adopting airborne fulfillment centers (“flying warehouses”) to reduce dependency on land infrastructure

Drones' flying capacity enables avoiding buildings, traffic, rivers, and other geographical/physical barriers

Improving LMD's overall capacity (e.g. speed, range, accessibility, payload) when combined with trucks

Enabling utilizing the capacity of each delivery mode (e.g. drones: reaching inaccessible zones; trucks: carrying heavier loads)

Limited capacity of drones (in terms of travel range, speed, battery, payload, and extreme weather resistance)

Difficulty in balancing between competing capacity tradeoffs (e.g. speed vs travel range, travel range vs battery capacity, battery capacity vs payload)

Meticulous planning requirements for boosting capacity (e.g. charging consumes times, replacing batteries demands human access)

Operating in tandem with trucks

Deploying battery charging/swapping points (or docking stations) along routes

Charging on trucks carrying drones

Hitchhiking on private/public vehicles

Scheduling deliveries based on drones' capacity

Adopting airborne fulfillment centers (“flying warehouses”)

Having multiple drones carry the payload

Equipping drones with multiple propellers or mini jet engines

Reaching 60–79% reductions in delivery time compared to conventional delivery methods

Drones' ability to avoid barriers (e.g. buildings, traffic, rivers) facilitates time reductions

Achieving time reductions is possible using drones only or in combination with trucks

Delivering vital items (e.g. blood products, organs, vaccines, drugs) to those in need in record time

Attaining substantial health benefits and success rates of urgent missions through speedy deliveries

Enhancing customer satisfaction in e-commerce by speedy deliveries (especially for consumables such as food)

Shortening delivery times requires operating dedicated drones for individual orders (which can increase LMD cost by increasing the number of drones and delivery centers)

Drones' limited payload/battery capacity can restrict time-savings to light-weight items and nearby receivers

Difficulty in balancing between several variables to achieve optimal time reductions (e.g. travel distance, weather conditions, geographical coverage, item's weight, drone's capacity)

Using simultaneous pick-up and delivery setups to reduce time and cost

Sharing workloads among drones based on their capacities

Allotting deliveries between drones and trucks

Using relaxed/strict delivery time slots based on item perishability and urgency of delivery

Applying penalty charges for exceeding delivery time slots to warrant arriving on time

Drones can skip physical barriers (e.g. mountains, hurricanes, poor transport infrastructure) to reach receivers in hard-to-access zones

Drones' reach potential can be enhanced in drone-only deliveries and drone-truck setups

If supplied with the right tools (e.g. AI), drones hold potentials to deliver to unknown delivery points

Limited applications in urban areas due to deficient landing space (especially amid high-rise buildings)

Restricted flights to rural areas to avoid interfering with other aircrafts or creating risks to residents

Most countries limit drone flights to VLOS zones (hence creating a need for human intervention)

Inability to reach people within no-fly-zones (e.g. near airports)

Installing “common delivery zones” in urban areas

Utilizing algorithms to reach receivers between no-fly-zones

Using different landing (on, e.g. ground, balconies, rooftops) and drop-off methods (by, e.g. cable, parachute) to increase accessibility

Supplying drones with AI, zooming and thermographic cameras to increase reach capacity

Equipping drones with fiducial markers, satellite/street imaging and precision drop algorithms to enhance accuracy

Preserving items from perishability due to substantial savings in delivery time (especially medical items)

Ability to provide and monitor special temperature requirements using box attachments

Lowering wastage of medical items

Risk of damaging items due to drones' airborne maneuvers

Preservation remains limited to small/lightweight items due to drones' limited payload/battery capacity and restricting policies

Need to deliver close to depots for time-sensitive items

Placing items in reinforced boxes to lower damage risk

Using wet/dry ice, polystyrene foams and pre-calibrated thermal packs to maintain temperature requirements

Utilizing quick-release systems to expedite item detachments for time-sensitive deliveries

Using smart capsules with sensors for live monitoring of carried items

Governing the airspace and reconciling competing interests

Ensuring safe drone operations (through specifying altitudes, proximity to people/property, maximum weight, flight zones, etc.)

Protecting privacy of individuals through laws for data collection and data use

Standardizing drone guidelines across operational, technical, infrastructural, risk, safety and environmental issues

Promoting innovations and investments in drones for LMD

Policies steered independently in each country (creating dissimilar/conflicting rules)

Drone registration processes can get tedious

Restricted drone flights to certain zones (e.g. VLOS) limits their utility

Difficulty in sponsoring overarching infrastructures (with comprehensive laws, UAV-dedicated frequencies, etc.)

Challenge in resolving competing interests of involved parties

Loopholes in current laws to accommodate drone deliveries

The EU passed a uniform set of rules across its 27 states to streamline drone delivery guidelines

The FAA started granting commercial companies licenses to operate drone deliveries in the US

Many countries (e.g. US, UK, China, Australia, Rwanda) are relaxing their aviation policies to accommodate drone deliveries over their territories

Incubating drone deliveries by integrating live data into holistic transport systems (e.g. airborne traffic, number of people/objects on ground, geofences, physical obstacles, weather forecasts)

Promoting safe and collision-free drone operations through utilizing data transmitted by drones and surrounding objects

Ensuring uninterrupted drone-to-drone and drone-to-pilot communications

Instituting fairness to all parties involved

Challenge in expanding UAV-dedicated frequencies across large areas of land

Difficulty in maintaining uninterrupted signals in complex environments with high interferences

Challenge in gathering and streamlining live data from all involved units (e.g. drone operators, airports, weather forecast centers, satellites, etc.)

Most cities' infrastructures are unprepared to accommodate drone deliveries

Having drones act as a means of delivery and data transmission simultaneously

In absence of signal: utilizing deep learning to aid drones auto allocate deliveries using visual information

Adopting pre-flight conflict detection and resolution systems

Sponsoring the adoption of digitized automated control systems (e.g. UTM, U-Space)

Supporting and expediting drone adoption in LMD (especially for urgent applications such as medical deliveries)

Shedding light on critical considerations such as safety, privacy, security, sustainability and usefulness

Needed to circumvent chaos upon launch

Public skepticism about the need for the technology and its usefulness

Safety, privacy and noise pollution concerns are voiced extensively by the public (especially in urban areas)

Challenge in alleviating the “stigma” of drones after misguided military applications

Drivers' fear of losing their jobs to the technology

Defining clear guidelines and codes of ethics

Enforcing strict aviation safety measures

Educating and training pilots

Applying stringent violation penalties

Familiarizing the public with drones' usefulness via various channels (e.g. word of mouth, marketing campaigns, TV/radio channels)

Minimizing road accidents by substituting traditional delivery vehicles

Reducing health risks from air- and noise pollution associated with traditional vehicles

Enabling “contactless” deliveries to limit spread of disease

Saving lives of patients/endangered persons due to substantial savings in delivery times

Preserving delivered items from theft, loss, or damage

Creating physical/mental stress to societies through overcrowding the airspace

Accidents can happen both in-flight (drone crash; package falling) and take-off/landing events (exposed propellers; drone crash)

Drone accidents can harm people, animals and objects

Intensified safety risks in urban areas (esp. at lower altitudes)

Susceptibility to communication interference, computer disturbances, operator errors and drone component failures

Equipping drones with redundant systems (e.g. additional motors, sensors) to avoid accidents

Adopting collision free paths based on space congestion and battery status

Utilizing deep learning to allocate safe landing spots based on remaining battery level

Installing event-based emergency detection systems

Ordaining dedicated airways and standardized routing protocols

Drones' recording of videos and images of public areas during flights could help preventing crime and reducing reliance on potentially more intrusive surveillance methods (e.g. police patrols, fixed cameras)

Using drones in rescue missions can lower the need for potentially more intrusive search methods (e.g. helicopters, dogs)

Drones capture large amount of data (e.g. locations, identities), posing privacy concerns if shared with third parties without their consent

Capturing videos and images via drones' cameras can make them a means of undesired surveillance to people and their private space

Risk of accessing, stealing, or tampering with drones' collected data by malicious actors through cyberattacks

Operating drones in LMD might violate laws that prohibit recording the interiors of private property

Adopting secure data-encryption methods (e.g. blockchain) for drone-related transactions

Giving landowners the rights to allow, lease, or prohibit drones from entering their private airspace (especially at low altitudes)

Incorporating the case of drone deliveries under national privacy laws (e.g. Data Protection Act, GDPR)

Relieving traffic congestion and emissions through substituting traditional vehicles

Reducing air- and noise pollution associated with traditional vehicles

Lowering CO₂ emissions due to drones' reliance on electric batteries

Reducing energy consumption due to drones' light weight

Most promising environmental performance in rural areas

Challenge to lower emissions in urban areas due to stricter policies, circumventing buildings, need for depots and higher receiver density

Drones' limited payload/battery capacity make them always in need of traditional vehicles (along with their emissions)

Tradeoffs between lowering CO₂ emissions and costs (in terms of, e.g. equipment, charging, insurance)

High energy consumption during drones' production phase

Drones can interfere with wildlife (especially birds)

Emitting debris from potential drone collisions

Installing depots closer to receivers in urban areas to increase environmental friendliness

Lowering number of stops the drones make

Relying on clean energy courses (e.g. solar, wind) for charging drones

Operating drones through underground subways to alleviate environmental challenges in urban areas

Integrating drones with (electric) trucks in LMD