AI integrated Drone Based Optimized Supply Chain

Abhinav Dhaundiyal • September 10, 2021
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With the tremendous increase of internet dependency, especially in the current crisis, people are dependent on online shopping more than ever. The situation demands a well-organized and fast delivery system. Over time, most online players have managed to tackle the delivery demands via the existing transportation system. Even though the current method is irreplicable for inter-city/state deliveries, various government bodies are trying to reduce vehicle congestion within the city limits. Moreover, with the increasing pollution due to traffic, highest ever fuel prices, and the demand for same-day deliveries, there is a dire need for a technologically proven system to address these obstacles. 

An AI-integrated optimal drone-based delivery system has been found as a potential solution to the problem. Integrating AI with drones makes the process effective and efficient to such an extent that it allows the machines to work for multiple days in a row and not requiring any rest or taking any sick or paid leaves. Drones are already excelling in areas like providing humanitarian aides, conducting rescue operations, and taking up tasks that are life-threatening to us humans. Coming up with a scalable solution for drones to be used in a supply chain industry does not sound far-fetched.


In a typical product warehouse, the number of consignments a single drone can pick up at a time would depend on the size of drones, their weighing capacity, and feature enable for multiple consignments. For simplicity, in this article, we will consider only one consignment at a time for an AI integrated drone to pick up and deliver. To build an automated AI system, there are multiple key factors to be considered. The image below gives a birds-eye view of the same:

Once the customer places an order, the nearest warehouse (within the same city) is identified and checked for drone availability. Assuming the product is in stock, the system will assign a drone to that order ID. Factors such as product demography, the shortest route to travel, etc. will be explicitly handled by the in-house analytics server. The server will also manage the commands to be shared with a drone and ensuring the product reaches the right customer within the committed period. Once the item is delivered, the system will then analyze the drone’s health condition and create a new set of commands, either for the next delivery or to head for a charging station.

When an AI-powered drone delivery system is developed, the timid turnaround time for deliveries will be a game-changer for some. Given how recent times have been like, a new way of hygienic, secure, and untouched delivery process will ensure maximum safety of the customers. The reduction of human intervention will ultimately decrease the operational time consumption noticeably and in-house algorithms can be used to further minimize resource consumption to increase efficiency.

Even so, there are several roadblocks to consider while implementing such a drone service. Avoiding customer malpractice has always been an issue but with the help of an authorization process, this problem can be minimized at first and eliminated with further use. The drones also should be robust enough to withstand extreme weather conditions and smart enough to avoid harming the wildlife and other drones within the proximity. Power management and emergency landing algorithms will have to be carefully put in place to ensure pedestrian safety while keeping the drone intact and operational with no/minimal damage as the technology is not yet cost-effective.

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