As e-commerce continues to boom worldwide, customers expect timely and accurate delivery of their orders. According to a survey made by a delivery company, 60% of customers consider knowing the delivery date important when making an online purchase.
However, shipping can be complex, with multiple carriers, transportation modes, and unpredictable events affecting the delivery time. Fortunately, A parcel tracking tool combined with AI has the potential to optimize and automate the process, improving the customer experience and reducing costs for businesses by simply reading the parcel’s tracking number and extracting and transcribing all the information from it.
Importance of Accurately Predicting Delivery Dates
Predicting the delivery date accurately is crucial for customers and retailers alike. For example, if a package arrives late, customers may lose trust in the retailer and leave negative reviews or return the item.
Conversely, if a package arrives early, customers may not be at home, leading to missed deliveries and increased costs for the carrier. Therefore, retailers need to balance speed, cost, and reliability to meet customer expectations while avoiding losses.
AI can help by analyzing various data sources, such as historical shipping data, weather conditions, traffic patterns, and carrier performance, to predict the delivery date more accurately. For instance, UPS, one of the largest logistics companies in the world, has implemented AI-powered algorithms to optimize its delivery routes and reduce fuel consumption. By analyzing millions of data points, including the package weight, size, destination, and priority, UPS can estimate the delivery date within a one-hour window, reducing the number of missed deliveries and improving customer satisfaction.
Similarly, FedEx, another major logistics company, has developed an AI-based predictive model that uses machine learning algorithms to estimate the delivery time for each shipment. By analyzing more than 3 million packages per day, FedEx can predict delays due to weather, traffic, or mechanical issues and reroute the shipments accordingly. As a result, FedEx can deliver more than 97% of packages on time.
Moreover, AI can also help retailers optimize their inventory management and order fulfillment. By predicting the demand for each product and optimizing the shipping routes, retailers can reduce shipping costs and improve delivery time.
For example, Amazon, the world's largest e-commerce company, uses AI-powered algorithms to manage its inventory and predict the demand for each item. By analyzing the purchase history, search queries, and browsing behavior of millions of customers, Amazon can optimize its product offerings and reduce the shipping time. As a result, Amazon can deliver some products in as little as two hours, thanks to its Prime Now service.
Challenges in Using AI Algorithms in Logistics
Predicting the delivery date accurately is not without challenges. For example, unexpected events, such as natural disasters, strikes, or customs delays, can disrupt the shipping process and affect delivery time. Moreover, privacy and security concerns may arise when handling sensitive data, such as customer addresses, phone numbers, and credit card details. Therefore, AI-powered logistics systems need to comply with strict regulations and data protection laws, such as the EU's General Data Protection Regulation (GDPR).
To address these challenges and help businesses implement AI-powered logistics systems, several companies have developed software solutions, such as Ordertracker. Ordertracker is a cloud-based platform that uses AI and machine learning algorithms to predict the delivery time for each shipment, optimize the shipping routes, and provide real-time tracking updates to customers.
AI and machine learning algorithms to predict the delivery
By integrating with various carriers and e-commerce platforms, Ordertracker can streamline the logistics process and improve the customer experience. Moreover, Ordertracker can also generate insights and reports to help businesses monitor their performance, identify bottlenecks, and optimize their operations. With the increasing demand for fast, reliable, and cost-effective shipping, the adoption of AI-powered logistics systems is expected to grow in the coming years.
This article was written in cooperation with independent tech journalist Esther Howell