Last mile deliveries have always been a logistics nightmare, but with the growth of companies that deliver their products directly to their customers’ homes, they’re becoming more critical than ever. These last mile deliveries are often the most expensive part of a company’s supply chain. How can data analytics help to improve last mile logistics?
The Problem of Last Mile Logistics
Why are last mile logistics so problematic? Consumers have become used to instant or near-instant gratification when it comes to online shopping. Amazon Prime offers two-day, same-day and even same-hour deliveries, depending on your location. Your package’s journey, from the moment it leaves the warehouse to the moment it arrives at your home, is considered a package’s last mile.
Currently, last mile deliveries are incredibly inefficient, especially in some areas of the country where you might have miles between homes. It’s also expensive, for both the company and the consumer. The last mile uses up roughly 53 percent of the cost of shipping an item.
This inefficiency and high cost are leaving companies struggling to improve last mile delivery, and many are turning to data analytics to find their solutions.
What Are Data Analytics?
The term “data analytics” is defined as “qualitative and quantitative techniques and processes used to enhance productivity and business gains.” What that means to anyone who isn’t in the industry is that these companies use specially programmed computers to sort through their data, finding patterns and behavioral information that can be used to make various processes within the company more efficient.
These collected data points are then used to improve efficiency, tracking customer demographics and even predicting future industry trends if they can gather enough information. How can this tool be applied to last mile logistics?
Using Analytics to Improve Last Mile Deliveries
Data analytics and logistics may seem like two entirely different animals, but they’re two sides of the same coin. By analyzing everything from delivery data to customer demographics, companies can make the changes necessary to improve their last mile logistics.
Three different aspects of last mile logistics include:
- Improving performance and the quality of deliveries
- Improving operational efficiency
- Improving service levels
Before implementing an analytics program, it can be challenging to determine the best way to enhance a logistics system. You can study the collected data every day for months and still miss minute patterns that an analytical system could easily spot. These patterns can help the company reduce operating costs by making small shifts — such as handing a portion of the shipments in-house rather than hiring contractors — that won’t change the majority of the company’s logistics.
Delivery pattern data is valuable when it comes to improving operational efficiency. Studying GPS tracking data from previous deliveries can provide the tools you need to make decisions that will enhance operational efficiency. One study found that 86 percent of companies lowered their fuel costs by switching to field management software. If a specific neighborhood or city experiences higher order volumes during certain times of the year, you can prepare for these additional demands in advance instead of waiting for them to sneak up on you.
Finally, there are improved service levels. Customers have particular expectations when it comes to their last mile deliveries. This expectation is primarily why people spend so much time tracking their packages, from the moment they’re picked up to the minute they arrive. Improved service levels mean happier customers, which translates to increased sales for the company. Data analytics is a new tool in the last mile logistics toolbox, but adopting this technology could help companies keep up as the equipment and software continues to evolve. Installing a data analytics system is an investment of both time and money, but it has one of the highest ROIs (returns on investment) in the industry.
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EuroScientist journal 
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