Machine Learning Brings Supply Chains into Ship Shape

Shippers use machine vision to improve logistics and workflow when getting goods to customers. Improving operations throughout the shipping and delivery processes results in cost and time savings.


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Image credit: Braingine

With supply chains and logistics taxed due to worker shortages, investing in advanced logistics software systems can have an immediate impact on a company’s profit margins. About 20 percent of shipping containers are transported empty, simply to be relocated for another shipment. Transporting “air” eats up profits, not to mention the negative impact it has on the environment.

Companies like Container xChange are using Internet of Things technologies to connect shippers with empty containers. The Container xChange platform maintains a database of vetted shippers in a variety of ports, and customers can buy, lease, or trade space on shipper owned containers (SOCs). It’s a win-win for shippers: owners can reduce or eliminate the repositioning of empty SOCs, and clients can buy space where and when they need it.

In addition to having available shipping containers, companies need to fill them effectively and ensure deliveries are expedient. With artificial intelligence and computer vision, companies can improve their logistics operations to reduce waste and inefficiencies throughout the process.

Intelligent Load Planning

One such solution is Braingine, from Mexico-based Expertos en Sistemas. Braingine uses artificial intelligence to develop effective load planning and end-to-end shipping strategies, so companies can transport items in the most cost-effective manner.

Load planning is a balancing act that considers weight, size, dimensions, domestic and international regulations, maximum stowage, package movement, and other package transport factors. The ultimate goal of load planning is to consolidate shipments to optimize the use of space within the shipping container, which reduces the dreaded transporting of air, leads to fewer vehicles on the road, and improves delivery times.

Load planning software exists to help companies best organize their wares for shipping, but irregular packages and human intervention can throw a wrench in the best laid plans. Extra space between packages cause items to shift during transport and pose a safety threat to employees as they unload. Improper or ineffective unloading can lengthen delivery time and delay distribution.

Video Vision

Beyond load planning, the Braingine solution assesses movements and other ways to reduce inefficiencies throughout the delivery process. The software connects to a company’s existing security cameras and is configured through a graphical user interface.

Braingine was developed using the Intel® Distribution of OpenVINO™ Toolkit, which supports deep learning and predictive analysis capabilities. Expertos en Sistemas has partnered with Axis Communications, which makes video and security solutions, for easy integration.

The data is collected through a mini NUC and is sent to the cloud, where the Braingine AI algorithms determine how to optimize the current operations to reduce time and cost. Simplifying or reducing movements can save time, so knowing where to park or minimizing idle time during deliveries can improve productivity and increase profits.

One of the most critical shipping concerns is time. Braingine looks at physical logistics, such as delivery time and how people move. Braingine monitors the time and efficiency of load and unload processes and watches for bottlenecks. It also tracks item placement, forklift-to-dock-door trips, and ramp congestion. It provides real-time information, so logistics managers can get alerts as problems arise rather than after the fact.

Braingine also monitors employee activity but only captures data about movements, rather than specific workers, to maintain privacy. Are people waiting around for approval before moving on? If quality assurance or order fulfillment is slow, the delivery cost per stop increases, eroding already slim margins.

Employing machine learning to analyze shipping logistics and employee workflow helps companies strip out inefficiencies, increase workplace safety, and capture profits.


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