The modern world increasingly demands operational efficiency so that products reach consumers in the shortest possible time, ensuring quality, but also providing a best experience to these customers.
In this scenario, optimizing delivery routes is a challenge for companies in the logistics and supply chain sectors, where such requirements also exist.
A Brazilian study pointed out that route optimization can reduce by 23,35% the mileage traveled by the delivery person to end consumers.
In addition to being faster and more economical, optimized routes can boost sales and increase revenue, as satisfied customers tend to become loyal to the service and become fans of the brand.
Another positive point is increased competitiveness. In the United States, for example, Amazon managed to improve the delivery speed of its products by serving Orders 74% in distribution centers close to buyers.
This optimization was carried out with Artificial Intelligences (AIs) that already play an important role in improving operational efficiency and reducing costs. The estimate is that the savings margin for customers with transportation is between 10% and 40%.
Artificial Intelligence to improve food distribution
Distribution and delivery logistics can be a bottleneck, with high fuel consumption, long periods of vehicles stopped in traffic and underutilization of the capacity of the cargo transport system, among other operational issues.
The objective of route planning using AI is to contribute to the improvement of the entire supply chain, finding the most economical route for each delivery scenario.
When used to optimize routes, AI can reduce operational expenses and improve the quality of service provision.
The reduction in costs is related to the reduction in mileage driven, which reduces vehicle maintenance costs and fuel expenses, in addition to promoting greater use of the vehicles' load capacity.
Check out some possibilities for improvements that Artificial Intelligence allows.
Route optimization using AI
To determine the most efficient and cost-effective freight transportation route, AI can be programmed to analyze information such as traffic conditions, weather, and transportation restrictions such as road and construction closures.
Through advanced intelligent routing algorithms, AI takes these variables into account, which allows it to calculate the best routes for drivers.
With the use of Machine Learning and delivery history, AI can function as a continuous improvement tool, that is, periodically continue optimizing routes.
In this way, Artificial Intelligence can identify patterns and points for improvement, adjusting routing algorithms to achieve ever greater efficiency.
It is important to emphasize that reducing the time vehicles spend in transit saves fuel and also helps minimize environmental impacts.
Route optimization: real-time monitoring and tracking
One of the possibilities for applying AI in the supply chain sector is in monitoring and tracking food distribution operations in real time. The technology can provide more accurate and up-to-date location information by sending routing instructions to drivers' smartphones.
This prevents route errors, helps avoid congestion, minimizes delays and ensures deliveries are made efficiently.
Furthermore, the use of RFID (Radio Frequency Identification) sensors can provide real-time information on product conditions, such as temperature and refrigeration.
This type of monitoring ensures that products do not exceed their minimum storage temperatures and there is no waste. with the use of waste management.
Together, sensors and Artificial Intelligence, connected at all times, can issue alerts to drivers' mobile devices, offering greater safety, the ability to identify problems and take corrective actions quickly, in addition to improving communication with customers.
Demand and inventory forecast
AI can be applied to predict food demand and optimize product and input inventory through historical order data, customer location, traffic and consumption patterns, seasonality and market trend.
Again, machine learning is important for predicting future demand for logistics products and services. Predictability contributes to more efficient distribution, avoids waste and ensures the availability of food in adequate quantities. This is because companies can plan their operations based on data, avoiding excess stock or lack of products, for example.
Conclusion
There are many possibilities for applying Artificial Intelligence to make operations more efficient. There is no doubt that companies that invest in this technology are more prepared to face market challenges and to offer better experiences to customers.
At the food service sector, being competitive and meeting consumer demands for delivery efficiency and environmental responsibility is essential to consolidate.
Does your startup have technological solutions for the logistics area? You can be part of our innovation ecosystem. Send a pitch of your solution to iFood Labs and join a network of companies that care about the future!
