Future of Air Delivery

Production Scalability in aerospace with Digital Transformation: Case of the Drones

Imagine the time in the future when you notice your dog’s tail wag during a Superbowl commercial, and you place an order for the advertised soon-to-be the favorite toy and it gets delivered by a UPS or a FedEx drone in 15 mins from a nearby warehouse. Now because it’s Superbowl, it’s not just you but millions of dog owners ordering in a similar way, whose soul is content at the accelerated tail wags. Can this be the real cause of customer satisfaction? If so, what’s it going to take to operate a fleet of drones to deliver goods and supplies from point A to point B in the time you would take to walk your dog? Enter Digital Transformation for high scale production for the Aerospace Industry where a shopfloor is integrated with the latest software stack, digital technologies, and connectivity.


Let’s cook up a case where the logistics companies mentioned previously are operating in a business model wherein drones are docked at stations in multiple locations in every zip code in the US to complete point A to B deliveries. For these companies to have a strong presence in the market they would need to be present in at least 41,683 zip codes in the US. That boils down to 85 square miles per zip code (approximate to a 9*9 miles square). If a drone can travel an average of 3 miles on a single charge with the payload, we would require around 16 different stations where the drones can be docked. That gives us an approximate 600,000 stations, which need at least 4 drones in each station eventually tallying up to an operational capacity of 2.4 Million drones to cater to logistical needs.


For manufacturers to deliver 2.4 million high-quality, highly durable drones to their customers the production floor would look no less than a Tesla gigafactory. Managing this feat would require the manufacturing firms to be equipped with a shopfloor that is transformed into an information-intensive digital manufacturing plant. This means that data originating from point sources are utilized to analyze the manufacturing cycles and processes to maintain a higher level of consistency in quality, monitor machines for highly optimized performance output, and support manual processes with the right data to make decisions. This applies to every unique part of the assembled drone ranging from the propellers to the wiring harness and the cargo compartments.

How to realize Value?

The current state of the industry is dragged by the high overhead of equipment manufactured by OEMs to achieve a narrow set of operations on a single machine maintained by multiple operators and minimal flexibility to implement customized systems without placing the production line at risk. Because of the rigid blueprint of how these equipment functions, each of these acts as a single source of failure. Just like how microservices transformed the software development domain by breaking down the siloed monolithic architecture, Manufacturing Industry will soon need to adapt internet-enabled smaller machines with a central control system to monitor and deploy operational requirements in a more sentient way across the entire value stream of the production cycle.

Where’s the Value?

Here are some of the key functions and the value proposition behind Industry 4.0 realization,

Design: AR/VR-based 3-D modeling and simulation software enables the design team to be equipped with realistic parameters that back the manufacturability and scalability of systems. AI-based Recommendation systems that suggest changes to the part design in lieu of high scale production while accounting for the business strategies could play a major role in the planning phase. For our use case, a propellor shaft and blade’s material composition can be designed by an AI-recommended design accounting for the varying environmental conditions spanning the latitude of the deployed geography for efficient flights.

Additive manufacturing: Rapid prototyping has been a game-changer in the industry for quite some time now with additional capabilities in terms of high tensile materials and metal fusion process, 3-D printers can enable Just-in-time production based on varying demands. This enables modular and decentralized manufacturing of parts required for the air vehicles for repair, maintenance, and custom configurations. For example, a 3-D printer deployed in the maintenance depot can produce components as and when required thereby reducing the maintenance lifecycle time.

Quality Management: As is the focus of every other industry, Quality management is the core of an organization’s operational efficiency. Collecting data from manufacturing cycles and controlling the dependent parameters that could potentially go out of tolerance limits results in a higher production throughput with minimal wastage of the resources available. This will help reorient the various workarounds that have been employed to offset the losses triggered by poor quality and defect-prone manufacturing cycles.

Control mechanisms: Going back to the robot birds, the current algorithmic approach toward managing the specifications of the drones (for example, changing the power modes to save battery consumption, rpm of rotors, etc.) is quite efficient to manage the flights. But an AI engine at the edge of the technology gives rise to semi-supervised control over its parameters. Imagine a drone trained to account for the weather forecasts in the upcoming flight path and making its own alterations to the flight route, changing the tensile flex of its arms to account for the payload being carried, or the way a 3-D printer changes the structural design of the props based on the failure modes from a previous flight, These kinds of feedback loops and control mechanisms disrupt the entire industry and the approach towards complete autonomy over a Product’s production and operational lifecycle.


Be it mini drones, eVTOLs, commercial aircraft, or even flying cars, the best solution to realize higher yield from the aerospace industry is to scale the production efforts at a higher efficiency than current processes by leveraging the capabilities of Edge/decentralized computing/processing, low-latency connectivity, and cloud-enabled development and monitoring solutions.