Especially this time of the year with a record-setting $4.2 billion in online sales on Thanksgiving Day- a 14.5 percent increase from last year, according to Adobe Analytics. Implementing AI and machine learning is completely transforming and benefiting the logistics industry as a whole. Just a few years ago, same-day delivery was a unicorn type of concept-you barely heard about it. Now, it’s in high demand along with other trends that has the supply chain and logistics industries taking advantage of all the data AI brings to the table to improve operations.

Improving operations and warehousing

Using robots is commonplace for many warehouses, and for some logistics powerhouses, it’s a necessity to keep track of inventory efficiently within many warehouses in various locations. AI’s automation abilities increase the amount of productivity being done in a warehouse that just isn’t feasible with a limited number of human employees. Implementing AI into the logistics process also helps eliminate human error. Using data from the warehouse to managing a fleet of trucks is only enhancing the way logistics companies function and now with online sales at an all-time high, it’s a necessity to use AI technologies to meet demands that would otherwise be very costly. By collecting and using collected data based on how a logistics process or warehouse is running, decision-makers can make the necessary adjustments to solve those issues and evaluate delivery performance. Robots can now play a larger and more effective role in the fulfillment process. According to IHL Group, overstocking costs retailers about $470 billion annually and $630 billion in understocking costs. By using algorithms, predicting consumer demand can help decrease these costs. Better predictions can then of course improve inventory management and forecasting. According to Research and Markets, by 2022, robots in warehouses will have grown to a value of about $4.44 billion.

Tracking shipments

The Internet of Things (IoT) and AI technologies are not new concepts or technologies, but some applications are and it is widely being implemented to track and manage fleets. By having a network of devices in place that are connected with your fleet and share data, management can then make decisions based on that data to improve their entire operations whether it might be to increase supply-demand, track shipments, or managing drivers or vehicles that are on the road. According to a McKinsey report, shippers and carriers are using data and analytics to forecast demand and optimize their routes in ways we couldn’t imagine even a few years ago. Some shippers have lowered inventories by up to 75 percent, cut warehousing costs by 15 to 30 percent, and reduced administrative costs by 80 percent. The data that is collected is putting dollars back into the pockets of logistics companies, creating a massive amount of ROI for those implementing AI technologies.

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Nearshore IT Services

From ongoing staffing needs to a rich, 24/7 onsite/nearshore model, our Data Science premium support teams are tailored specifically to meet your needs. Hire an IT resource that’s relatively within the same time zone, cost-effective and easier to access than most offshore services.

Since we’re highly proficient Data Mining and Big Data specialists, our Nearshore team can extract the critical knowledge and insights you need from your structured and unstructured data.


Autonomous vehicles and vehicle safety

This might seem a little too out-of-this world, but believe it or not, the logistics industry is knee-deep in how they can take advantage of using AI with autonomous vehicles to also boost productivity and meet high consumer demands. According to a McKinsey report, about 5 percent of Amazon’s deliveries are same-day and by 2025, that figure is anticipated to grow to 15 percent. To meet these high demands for fast and free delivery, automating the delivery process is the answer. AI is making autonomous vehicles safer by being able to navigate complex scenarios and traffic. With a lot of variables to account for, there is a lot of machine learning that will come into play.

Autonomous driving aside, driving with AI enabled within a fleet of vehicles also has its advantages to increase safety measures, reduce costs, and maintenance. Using AI, a driver’s behavior can be evaluated, based on a risk profile and could impact the cost of insuring the logistic company’s vehicles. The data from each vehicle can also be analyzed for processing claims due to an accident or for predictive and prescriptive maintenance purposes.

Download Today:

Your Guide to Transportation & Logistics

Logistics is getting more complicated. In this easy-to-digest book, we will be covering how data creates competitive advantages and greater efficiency in transportation. We will also be looking at the top use cases and trends for both artificial intelligence and business intelligence in the logistics industry.


Last mile delivery

AI can improve the overall efficiency of delivering packages and on time. There’s nothing more frustrating than finding out that a package is late and with demand only increasing among consumers regarding shorter delivery/shipping periods, there’s little room for error. By using delivery and route management algorithms powered by AI, the last mile delivery issue is more of a streamlined process and opens up more opportunities to be smarter about warehousing and shipping, as previously mentioned.

Another issue with last-mile delivery is its impact on the environment and the level of carbon emissions that is impacting the air quality-it’s a big deal. AI has the power to manage routes more intelligently by using collected data to manage shipments more effectively and efficiently. By using machine learning tools, companies can make adjustments to routes, drivers, and schedules throughout the day based off of that data in response to varying conditions in order to reduce late deliveries and miles.

Anticipatory shipping and predictive analytics

AI makes things like anticipatory shipping and predictive analytics happen. Powerhouse companies that have a huge hand in offering fast deliveries (like Amazon) rely on AI to take advantage of predictive analytics. Based off of historical data, algorithms and machine learning are used to determine certain outcomes. Using these algorithms and user data will allow logistics companies to manage their fleet based off of that data. For example, data may help determine the number of delivery drivers needed at any given time or the amount of shipments typically delivered in a certain geographic location to improve delivery routes and scheduling based on historical delivery patterns.

“Anticipatory” shipping is a ship that is being led by Amazon. Before a consumer has even purchased an item, the idea is to use historical data and AI technology to predict future purchases thereby reducing delivery times. For example, if a particular location has a history of purchasing a particular item of clothing, an online retailer will use that data to make sure those warehouses are stocked full of that item that is closest to that location. AI has also learned about surges in particular products around the holidays and will ensure that several holiday lights, ugly Christmas sweaters and on-trend gifts are as close to your address as possible so the minute you confirm your purchase, your shipment is already halfway to its final destination.

Moving forward, the logistics industry is taking full advantage of AI and its capabilities in order to battle increasing costs and to find solutions to optimize operations.

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