AI of Things (X) 10-minute delivery: how Artificial Intelligence optimises delivery routes

Javier Coronado Blazquez    29 September, 2022
Photo: Mika Baumeister / Unsplash

Nowadays, speed and immediacy is a necessity for almost any company, especially for those in the logistics sector dedicated to the transport and delivery of goods. Due to the high volume of orders, it is essential to try to optimise the entire process, including physical delivery, and even react in real time to possible unforeseen events. This is possible with the Artificial Intelligence of Things (AIoT) platform, which combines Big Data and Artificial Intelligence.

Analytics as a planning tool

How many times have we taken the car and found ourselves stuck in an unexpected traffic jam in the city? Especially during rush hour or if there is an event in the area, it is quite possible that a 10-minute drive can turn into a frustrating half-hour give-and-take.

Now imagine that instead of going from point A to point B, we have to constantly move around the city, as would be the case for a transport company delivering goods. In this situation, possible delays would accumulate successively, seriously affecting our logistical planning.

We could fantasise about imitating films such as the remake of The Italian Job (2003), where, in order to get through the city in the shortest possible time, they hack the traffic lights so that they can turn green when we need them to. The dark side of this idea is also found in cinema: in The Jungle 4.0 (2007), a cyber-terrorist paralyses several cities by turning all the traffic lights green simultaneously, creating hundreds of accidents.

Optimizing delivery routes with Smart Mobility

While remaining within the law, there are different ways to try to optimise our routes, both in real time and for predicting possible delays, with so-called Smart Mobility. The first step if we want to work in real time is to sensor our delivery fleet, with the so-called Internet of Things (IoT).

With IoT sensors, the entire fleet status is always known and any incidents can be traced in real-time

In general, these sensors are simply and non-invasively connected to the vehicle’s OBD (On-Board Diagnostics) connector. In this way, we can know the status of our entire fleet at all times and have full traceability. If a delivery vehicle deviates from the route, runs out of battery, suffers a breakdown or exceeds the maximum speed, the system will send an immediate alert.

In recent years the costs of this IoT infrastructure have been drastically reduced. Today, the sensors themselves, the network connection and the information processing platform are very affordable at the enterprise level, with packaged solutions from leading cloud service providers.

Real-time tracking and tracing of all shipments

All this, moreover, with the highest standards of security and privacy, using technologies such as Blockchain. With this, we can have real-time tracking and tracing of any goods on their route, including environmental conditions (humidity, temperature, pressure, vibrations, etc.) with alerts if certain parameters are exceeded, as well as detecting possible tampering or opening.

The next challenge is to plan the route for each of these delivery vehicles. This is made possible by combining IoT and Artificial Intelligence (AI) in the Artificial Intelligence of Things (AIoT) platform. By combining IoT sensor data with advanced AI analytics, economic, operational and energy factors will be taken into account to increase operational efficiency.

The optimal route (i.e., the one with the shortest time/fuel consumption) does not necessarily have to be the shortest distance. For example, if tolls exist, the route with the lowest overall cost may be one that involves taking a small detour to avoid using the toll road. When assigning deliveries to the different vehicles and determining the best route, the AI will consider parameters such as the combination of packages to be delivered, delivery or collection times, product characteristics, load volume, vehicle type and information from its sensors, etc.

Since artificial intelligence makes decisions based on more information, the better predictions it can make with more quality data.

All this data is internal, i.e., information generated by the company itself. However, we can enrich it by incorporating external sources. This new knowledge can be critical when planning our route. In general, the more data we have (as long as it is relevant and of good quality), both in variety and extent, the better the prediction the AI can make, as it will use more information to make its decisions.

For example, we can add weather information, to predict whether there is going to be a big snowstorm or torrential rain potentially affecting the logistics chain. In such a case, the optimal route in terms of weather may be a large deviation from the base route.

Another important external source is the calendar of public holidays, events or incidents (road closures due to sporting events, demonstrations, festivals, etc.). Finally, statistical traffic data can be used to predict traffic jams, according to geography, time of year, time of day, etc. Thus, the AI will design the optimal route considering all these boundary conditions. Still, this only allows us to plan our route a priori, but we will not be able to react in real time to unforeseen events. Or will we?

AI reflexes

Let’s imagine now that we have our route perfectly designed and optimised, taking into account all relevant factors. However, if there is an accident blocking a street, or a major traffic jam that we didn’t expect, we would suffer an unforeseen delay. Is there a way to react to this in real time?

This is where services like Telefónica Tech’s Smart Steps come into play. With this technology, it is possible to geolocate mobile devices, either by location based on the mobile network or the WiFi network.

This makes it possible, for example, to see whether a shop or a street is very busy at the moment, by analysing the movement patterns of individual devices. Always with anonymised data, as it is only relevant in aggregate, it is possible to calculate footfall, using both streaming data and historical data.

This also makes it possible to estimate traffic density in real time. For example, if there is a major traffic jam, Smart Steps will detect how both devices are moving in fits and starts on the road, very slowly, generating a traffic jam alert.

With all this information, the AI can update the planning in real time, i.e. it can be prescriptive. Imagine, for example, that we are in a city centre making deliveries in neighbourhood A, but in a while we will be moving to neighbourhood B.

Artificial intelligence has the advantage of having all the information at its disposal, so it will make better decisions than a human.

If in the optimal pre-calculated route, we will be able to plan our route in real time. If an accident has occurred on the pre-calculated optimal route that has generated a traffic jam, AI will use all this information in real time to design a new itinerary on the go, modify delivery times, prioritise the order, send a message to the end customer with possible updates, etc.

The main advantage over a human reaction is that the AI has all the information available and will therefore make a better decision. In short, the AIoT platform offers a differential value to any company seeking to increase the operational efficiency of its logistics processes, with full traceability of its fleet of vehicles, optimisation of delivery routes and a system of real-time alerts in case of possible unforeseen events.

🔵 More content on IoT and Artificial Intelligence can be found in other articles in our series – the first article of which can be found here,

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