New last mile infrastructure is about doing more with less. Our resources in terms of vehicles and delivery personnel are limited and would not scale as fast as the demand grows. Cloud-based optimization is the load-balancing platform which would help logistics and supply chain companies satisfy the fluctuations in demand through better planning and scheduling of orders and parcels.

Many retail and e-commerce companies are vertically integrating with logistics partners to streamline their last mile deliveries. Late in the year, Target acquired Shipt for the same reason. Shipt is a fresh-food delivery aggregator which has more than 21,000 drivers optimized for on-demand deliveries. Shipt will boost Target’s same-day delivery capabilities. Target had previously acquired Grand Junction for the same reason. Localized ground-level intelligence would show more such acquisitions and consolidations soon.

Orders Returns to be a Necessity Feature

Parcel returns were a luxury not a few years back, but 2018 would see returns become a necessary feature for all retail and e-commerce companies. Returns add the assurance of post-sales service and make it easier for customers to fulfill their buying decision. While returns become a necessary feature, the feasibility of reverse logistics would have to be simultaneously improved. Promising quick and east returns is one thing, executing them on the ground is another.

Here again, cloud-based optimization would be the driving force. Dynamic schedule planning can allocate pick-up requests to the delivery personnel nearest to the return’s origin point. The delivery person can pick-up the returned parcel directly from the home of the customer and drop it back at the hub. These reverse logistics processes can function in parallel to the last mile delivery schedules. The balancing between both schedules would be done through dynamic route optimization where an algorithm suggests the ideal sequence and pick-ups and deliveries to be done to ensure maximum efficiency.

In a nutshell, reverse logistics market is going to get a huge uplift through the growth in returns.

Growth of Freight-as-a-Service Markets

Freight management industry is an upswing across the world. However, due to resource constraints such as shortage of skilled drivers, or road-infrastructure bottlenecks, the freight management industry would adopt more technology-backed optimization in the near future.

A key segment that would see more growth is the Freight-as-a-Service (FaaS). Aggregated shipments give these companies the economies of scale which can be passed back to the clients in terms of competitive freight rates. Specialized FaaS companies would be better equipped to localized and skilled drivers leading to more efficient freight movement.

This is not to say that companies would not prefer to use their own fleets for their shipments. They would just be more comfortable than before regarding partnering with FaaS companies.

Logistics Application of Machine Learning

As we have seen in some of the above points, the upcoming trends would be either influenced or centered on technology, especially cloud-based technology. With internet penetration increasing across all major countries, manufactures, logistics partners, retailers, and customers are all much better connected than before. This interlinking of communication and visibility of operations is best orchestrated through cloud-based optimization technology.

When you look into this technology you would find the most beneficial applications of machine learning. Machine learning has been talked about for years, but right now we are at the juncture of realizing its potential. The year 2018 would see more and more applications of machine learning.

For each of the above-mentioned cases, machine learning backed algorithms could make the processes much more optimized. For FaaS providers, machine learning would streamline their delivery points to maximize their carrying capacity while complying with all service level agreements with their clients. For the returns market, machine learning would optimize the address locating mechanism to ensure that the delivery personnel doesn’t waste time in locating new pick-up points. Within the new last mile infrastructure and the omnichannel shift for companies, the applications of machine learning are more profound. Machine learning would power the smart stores and the connected last mile infrastructure to enable faster deliveries and better customer interactions.

The future holds bright for logistics, and next year would embrace more than we ever did before. The tech adoption rates would sour, and customer-centered logistics and supply chain movement would become a priority for many companies. It’s time to realize this future, now.