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Instead of going to brick-and-mortar stores, people prefer buying products online: the number of orders placed at web shops keeps growing every year and does not show any signs of slowing down. As a result, to logistics companies it feels like it’s Christmas every day. But not in a good way: the end-of-year package frenzy is now taking place all year round. That means logistics companies are dealing with a lot of challenges. Hereby the four most important ones – and how to solve them.
1) Unpredictable service times
The first challenge is unpredictable ‘service time’: the minutes spent on queuing, unloading a delivery from the truck and waiting for the customer to accept it. Many logistics companies work with average service time estimates when they plan, but these are never accurate for a variety of reasons. At some customers, delivery trucks might have to wait longer than expected. Or, in the case of larger shipments, other suppliers might still be unloading. And every party will have their own proprietary procedures in place for receiving shipments. In some places, paper is still being used, while other customers opt for digital forms on tablets. All these factors can have significant impact on service time optimization. Analyzing historical data using machine learning, our system is able to accurately predict trends over longer time. It considers different factors and calculates the best time to go to a particular customer in order to reduce service time by a few minutes. This may sound marginal, but if ten minutes per customer can be saved on a total of ten deliveries, there are 100 minutes left. Many logistics companies do not realize it, but the advantages are significant.
2) Shorter time windows
It would be very convenient if logistics companies could just drop by at a time of their own choosing, allowing them to pick the shortest route possible, but most customers have different demands regarding time slots. That makes planning of routes a complicated affair, especially if this gets combined with traffic rules and temporary road restrictions for trucks. However, using evolutionary computing, our system is able to analyze all these time slots and still plan the best possible routes. The algorithms work irrespective of data patterns, which means they are able to respond counterintuitive and suggest route options that may sound illogical but turn out to be the best for the overall plan. With the click of a button, logistics companies can have ease of mind because they can be sure they are meeting all the requirements and wishes of all their customers.
3) Increased demand for drivers and tighter regulations
The same solution applies to the challenges caused by increased demand for drivers, and tighter health and safety regulations around maximum driving and regular rest times. Because the number of deliveries keeps growing, it becomes more difficult and increasingly expensive to find good drivers. However, using evolutionary computing to make a better planning decreases the demand for extra drivers – even after taking time-based road restrictions and required resting times into account.
4) Anomalies during execution
However, things do not always go according to plan. So even after the planning has been generated and optimized, there will be anomalies during execution. This could be related to vehicle malfunctions, human errors, traffic or weather issues, or delivery points that take longer than expected. That is why it is important to predict those and continuously monitor anything that could impact delivery for the rest of the day. Our system continuously looks at the present delivery status and all possible delay factors. It is able to predict, from a very early stage, if something is likely to go wrong and then send a warning. That allows organizations to call drivers or customers, rearrange their time slots, maybe cancel some orders or prioritize others. This helps to improve customer satisfaction and reduce the number of returns, which is currently a big problem in both e-commerce and presales order deliveries: if a delivery is late, the customer is more likely to change his mind or find alternatives, or place his orders with competitors in the future. If dealt with reactively, delays also cause significant headaches to the logistics management team. These are scenarios that can easily be avoided with latest technologies in prediction and machine learning.
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