How much money will Amazon save by using drones for deliveries?

When Amazon CEO Jeff Bezos first talked about drones delivering packages in December 2013, naysayers were quick to dismiss his ambitions as a mere publicity stunt. Today, as we near Bezos’ 2018 timeline for the coveted project, drone deliveries have become an obsession with the tech world.

Let’s face it. Drones are the ultimate disruptive technology. But its many uses notwithstanding, for a logistics company, it all boils down to the dollars and cents it would be saving using drones. So, does it actually make sense for Amazon and other companies to use drones for deliveries?

According to a new report by Skylark Drone Research, it absolutely does. In fact, the authors make that clear right on the front page of the report: “The economics of using commercial UAS for package delivery are so compelling, companies in the package delivery market will either need to adopt this new disruptive technology or face declining market power in the face of a new industry model.”

Here’s how they arrived at the conclusion that using drones would ultimately prove cheaper than the ground methods in use today: Amazon’s purported cost of last-mile delivery with USPS is $2.50, and this figure has been taken as the benchmark in the report. The researchers conducted extensive interviews with 25 drone operators and took numerous factors like UAV cost, battery life, electricity cost, insurance, labor, etc., into account for their calculations. They deduced that if a large online retailer was to use its own drones for deliveries, not only would it save $0.76 per delivery, in the absence of a third-party delivery company like FedEx, it would also have more control over its distribution channel.

Every day, more than 100 million products are sold online. Of this, almost 90% come in the weight range that a drone can deliver economically. As such, the research forecasts 8 million daily drone deliveries in a pessimistic view and 50 million daily operations as a midpoint range. Accordingly, the annual savings for a logistics company is projected to be at least $2 billion in the pessimistic forecast and $10 billion in savings for the midrange forecast. So, we can easily expect Amazon to save a lot more.

Now, as promising as these numbers are, the report acknowledges that several questions remain unanswered. What type of a system can be scaled to handle this much traffic? Who will manage it? What is the trade-off between automation and human interaction? Who will finance this system?

What are your thoughts?

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Massive Autonomous Vehicle Sensor Data – What Does It Mean?

It was reported by Ford that connected car sensors generate 25 gigabytes of data per hour, and then by the WSJ that a typical autonomous vehicle generates 4 terabytes of data in 90 minutes, and then by Intel of 45 terabits per hour.  All are massive numbers, but why so different?  It points to a bandwidth problem.  The raw data is beyond any auto OEM’s ability to manage, even in 5G, and so the amounts reported could be raw data or some prioritized data, that facilitate core vehicle diagnostics and operating behavior improvements.  

One mitigating factor is local bandwidth.  In-vehicle computing offers the potential for sharing the computational workload.  Sensor systems like Mobileye’s onboard camera are integrating sophisticated machine-learning vision algorithms.  Likewise, Lidar innovator Innoviz is building a software stack which has a similar strategy.  One also can speculate a future where non-critical, near-redundant data capturing is efficiently crowdsourced.

Nevertheless, today there are in excess of 100 sensors onboard, and the auto sensor market is expected to grow over 100% per year, and reach nearly 200m units in 2021.  Advances in the more powerful sensors (camera, lidar) will produce richer data, and require more bandwidth.  Furthermore, physical world updating in real-time, and sensor-fusion for collating and acting will add more bandwidth demands.  Taken together, we can be sure bandwidth issues will continue as a challenge for OEMs.

So, what does it mean for the OEM?

 In an autonomous vehicle future, OEMs will differentiate by onboard data processing.  OEMs become vehicle operators and software companies, and their success metrics will lean on navigation performance and incidence rates.  Theoretically, as software companies, with massive real-time data, they can choose to optimize data processing between vehicle operating behavior and the environment beyond the vehicle.  Practically, OEMs will be compelled to prioritize vehicle operating behavior.

So, what does it mean for real-time third party applications?

 Even strong third party applications may be deprioritized.  Weather is a good example.  Weather modeling today is coarse and heavily interpolated.  With onboard environmental and weather sensors, the ability to mine very detailed and very real-time data would enable ultra-precise weather models, of interest to utilities, infrastructure companies, smart cities.  Interestingly, Continental has been testing vehicle swarm crowdsourcing to capture hyper-local weather sensor data.

So, what does it mean for AV technology companies?

While AV technology companies will continue to innovate and differentiate based on performance and system architecture, they will also increasingly be tested on bandwidth consumption.  Here are some companies to watch, and their bandwidth strategies:

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