Q&A: Enroute AI wants to build delivery’s ‘fingerprint of the city’

MODERN SHIPPER. How did the idea for Enroute AI come about?

FERNANDES. The idea for Enroute came about when I was working as an analyst at a supply chain consulting firm run by an MIT professor. From the various projects that I had done, I realized that most companies were struggling with their last-mile operations, including the Fortune 500s who had access to state-of-the-art routing software. The software would plan routes that were not achieving the desired results in the real world. Almost none of the deliveries ended up being on time. The routes were being planned without considering traffic conditions, or the actual time it takes to make a delivery.

While developing Enroute AI, I knew it had to be super easy to use and intuitive. I didn’t want our customers to have to go through long implementation cycles, or significant training. In developing the product, I sat with dispatchers and rode on delivery trucks for months to see what exactly was causing delays and how we could build something that a driver and dispatcher could easily use.

As I started working with small and medium businesses, I realized that their struggles with last-mile deliveries were even worse than the bigger companies.

MODERN SHIPPER. Can you tell me a little about Enroute AI’s approach to last-mile logistics?

FERNANDES. Our philosophy while planning delivery routes is to make them very close to reality. Our ultimate goal is to be able to plan routes such that our predicted ETAs for stops on the route are within a couple of minutes of the actual delivery. This means that we have to predict traffic better, understand infrastructure limitations like one-way streets and road closures, and allow room for last-minute surprises. We also have to be really good at estimating how long it actually takes to complete the delivery once the driver gets to the drop-off location. For example, a delivery to the front porch of a house in the suburbs might take a minute or two.  However, a delivery to the 43rd floor at 500 Wall Street is going to take much longer.

We strongly believe that software should be easy and intuitive to use. Most last-mile software has a long implementation time and a steep learning curve. It’s not built for drivers and dispatchers who are usually not technically savvy. Most last-mile software is built by software engineers with little to no real-world logistics background. However, we understand the nuances of the delivery workflow. We designed Enroute AI to be intuitive, easy to use, and to automate as many tasks for the drivers as possible.  

Finally, we built EnrouteAI to be dynamic and flexible. Exceptions happen every day in the last-mile world. We make it easy for dispatchers and drivers to respond to last-minute customer requests or disruptions in the schedule. This dynamic route optimization capability is a key differentiator for us.

MODERN SHIPPER. You’ve spoken with end customers, delivery drivers and supply chain professionals, what are their biggest pain points with current technology?

FERNANDES. Each of these parties has specific pain points with current routing technology.Customers don’t like it when they have to take time off work to receive a delivery and they are unsure of when it will actually arrive. Almost everyone we talked to prefers a late delivery with good visibility to an uninformed delivery where they are kept in the dark.

I did not fully appreciate how hard a delivery driver’s job is until I started riding along with them on delivery routes. They have to worry about a ton of things simultaneously: traffic, parking, finding the exact entrance to the building, knowing where to leave the package once they are inside the building etc. Their biggest pain point was that their existing routing technology was static and not responsive to real-world conditions leading them into areas of high traffic, going down roads that are closed to traffic, or asking them to make unsafe turns on busy roads. Other frustrating things for them were that it would route them to the leasing office of an apartment complex instead of the actual apartment, or the reception of an office building instead of the loading dock, which in rush hour downtown traffic could cost the driver half an hour. Also, most routing apps only work when a driver is connected to the internet. Their routing app stops working when they are downtown, inside high rises, basements, or in remote locations.

Shippers who operate their own fleet have trouble understanding the true cost of a delivery. Their routing software is not able to give them an estimate of how much a particular stop on the route costs them, which results in them losing money on some stops. One of our customers uses our software to decide which stops should be offloaded to a third-party carrier since we provide visibility into per stop costs.

Another pain point for our clients is visibility and billing issues when dealing with 3rd party carriers. Many of our clients use a mix of internal and external fleets for their deliveries. This combination helps them scale and expand to new geographies. However, their route planning software does not give them visibility into external carrier’s deliveries. This lack of visibility costs them valuable customer support time when customers call support asking for ETAs. It also results in them overpaying their carrier for all the missed and late deliveries. We integrate with many carriers to solve the visibility issue.

MODERN SHIPPER. Technology providers are always trying to solve for all possible conditions, but even the most robust and intelligent solutions run into real-world challenges. How has the Enroute AI technology been adapted to meet these ever-changing needs? 

FERNANDES. We realized pretty quickly that each customer’s way of running their business is different. We did not want to be stuck with a platform that needs to be re-architected for each new customer or vertical market. Also, from a software perspective, things can get messy when maintaining multiple codebases for different customers. What we settled on is building a core that is common to all customers. Things like validating and geocoding addresses, optimizing the sequence of stops, notifications etc. Everything else is built as plugins (adaptors). This enables us to cater to each customer’s unique needs.

We also know that software and AI can only get you 90% there, it can’t account for every single eventuality that may happen when making pickups and deliveries. We try to mitigate some of that by making it very easy to modify routes by dragging and dropping markers on a map, to move stops between vehicles, automating notifications to the customers etc. All along our AI is learning and planning better routes with each delivery.

MODERN SHIPPER. You’ve mentioned that Enroute AI’s artificial intelligence is able to capture small details such as which driveway is best to use when entering a location. How are you able to collect this type of granular data?

FERNANDES. Yes, we gather and process a lot of data that captures the nuances of making a delivery. We call that our “fingerprint of the city.” We noticed that a driver who is familiar with a delivery location can complete a delivery in the fraction of time it takes a new driver. This is because the experienced driver knows the best place to park, which building entrance to take, and where specifically the package needs to be dropped off. None of the routing software I’m aware of captures this information. Making location insights available to drivers is a key factor in ensuring deliveries happen on time. For example, we work with a company making deliveries in Manhattan. They were using a routing solution that sent the driver to the Google address of the building which happens to be the front desk, instead of the loading dock. With one-way streets and rush-hour traffic, this can cause delays of up to half an hour. 

We use multiple providers for location data; however, our biggest source of data is our mobile app for drivers. We constantly collect GPS pings from our mobile app for drivers and use machine learning to recognize patterns from that information. We are able to learn where the driver usually parks, what the traffic patterns look like in the neighborhood, where the exact entrance of the building is, etc. We are also experimenting with collecting WiFi and Bluetooth network information to improve location accuracy within buildings and dense urban environments where only relying on the GPS can sometimes show your location as being a block away.

Data is only half of the story; our secret sauce lies in processing this information to make sense out of it. If you only look at raw GPS pings in downtown for example, you might think that your driver is driving through buildings and flying across blocks. We need to filter out those outlier data points and only rely on points that make sense. When you deal with AI algorithms, garbage in equals garbage out. We spend a lot of effort making sure we feed our algorithms the right data.

MODERN SHIPPER. Last-mile drivers face any number of obstacles each day – traffic congestion, missed delivery windows, weather, etc. What approach does Enroute AI take with its solution to minimize disruptions due to real world, often unforeseen situations to ensure deliveries are made on time and drivers and vehicles remain efficient?

FERNANDES. You’re right, last-mile delivery is a hard job. As a delivery driver you deal with multiple unforeseen obstacles every single day. I still regularly ride along with delivery drivers during their daily routes. Every time they find something hard to do, I start thinking of ways that we can simplify the job for them.

Here are a few things we do to minimize disruptions and make it easier on delivery drivers:

• Drivers hate being stuck in traffic, we try to help them by taking into account predictive traffic when planning routes.  
• For unpredictable events such as accidents, we continuously monitor real-time traffic patterns and suggest a better sequence way ahead of time. For instance, EnrouteAI has customers in the Boston area. One of the key things about Boston is its infrastructure is quite old. You have to match the characteristics of your fleet equipment to the infrastructure constraints of the city. For example, several times a year, a driver will try to take a 12-foot-high truck through a 10-foot-high bridge on Storrow Drive.
• Missed delivery time windows is a tricky situation that is sometimes unavoidable. We take a proactive approach, unlike the major carriers who inform you that your delivery is late after the time window has actually passed. Most of the time, we know well ahead of time when a driver isn’t going to make it within the time window. In those situations, we proactively notify customers and managers that this particular stop is going to be late.
• Dynamic routing is a key differentiator for Enroute AI. Existing solutions are great for planning out a route. When the day starts and the drivers have started their routes, changes occur. We can handle real-time changes to traffic patterns.
• Our client’s customers have a tracking screen to see where their package is in real time along with the latest ETA. With other current solutions, the customer calls customer support who in turn has to call the driver, who looks up the map and estimates the ETA, disrupting the usual course of making deliveries.   We avoid that situation through tracking.
• We make it easy to rebalance routes in the middle of the day. We make it super simple to move a pickup stop from one driver to another.
• If the customer isn’t at home you end up paying twice as much for the delivery in addition to all the customer support overhead that goes with rescheduling. Our automated notifications confirm that the customer will be ready when the driver arrives.

MODERN SHIPPER. Last-mile delivery efficiency starts long before a package is loaded into a vehicle and requires coordination across a number of different departments and businesses. What obstacles exist to the integration of these disparate systems?

FERNANDES. Yes, from the outside it’s not apparent how many things have to come together to make an on-time delivery possible. It looks and sounds simple, until you start to understand all of the systems (ordering, billing etc.) involved. It’s a very daunting and complex problem. There needs to be integration with many systems to ensure an efficient delivery. For example, we had to build an integration with a furniture company’s e-commerce ordering system to make sure they were not promising customers a delivery on days when they had no capacity.

This problem also extends downstream into the billing systems as well. Making sure that payments are made according to the contract is pretty daunting: fuel surcharges, waiting times, premium for white-glove services etc. Billing was such a big pain point for our clients that we started integrating our software into their billing system to process rules on the rate card automatically (time, distance, number of packages, fuel surcharge, etc.).

The biggest problem we face is that many carriers and shippers run on legacy platforms that have an integration layer (they don’t support API integration). Getting data in and out of these systems involves a convoluted process of exporting data from their system and importing them into our system. This process often requires manual intervention and can be error prone. We also encounter clients who use TMSs and order management systems that support integrations, but these systems lock down that functionality to thwart competition.

What makes integration even more confusing, is that we deal with carriers and shippers using different terminology for the same process, depending on their industry. Even within the same industry carriers and shippers use different terminology. For example, some shippers refer to white glove service as premium service, whereas some carriers refer to it as special deliveries. As a result, we can’t build a one size fits all integration. We need to customize the integration depending on the shipper and carrier combination, which can be pretty daunting.

Our approach to deal with complexity of integration is by architecting our software to be flexible and extensible. As mentioned earlier we have core functionality that is common to all our clients. All the customizations and one-off integrations are built as plugins (adaptors). When we notice that the plugin is used by enough people, we integrate that into our core functionality.