For today’s logistics professionals, machine learning is more than a buzzword. If you’re shipping goods anywhere in the world, there’s a chance you’re already the beneficiary of machine learning technology – an innovation that is helping reshape the logistics and supply chain industry.
But you don’t have to be in the industry to experience machine learning. Every time you order from Amazon or watch a series on Netflix, you experience machine learning. Algorithms passively monitor your habits and serve up similar products and content with now familiar suggestions like “You might like this,” and “Recommended for you.”
“Machine learning uses massive computing power to recognize patterns in data that humans could never see, and then learns from every new piece of data it receives to get smarter and more accurate in real time,” says JOC.com. Machine learning is just one specialty within the broader universe of artificial intelligence.
In the logistics industry, we are using machine learning to make quicker and better decisions that help shippers optimize carrier selection, rating, routing, and quality control processes that save costs and improve efficiencies. With its ability to gather and analyze thousands of disparate data points, machine learning can help you solve a problem you don’t know is there. For example, if you’re looking at lane planning, a traditional analytical model would look at a fixed set of assumptions. Analytics based on machine learning can consider dynamic attributes like weather or traffic and self-evolve over time to recognize patterns that humans would not see.
The power of machine learning comes from leveraging data across multiple systems and data sets. We can combine all the data we have in our carrier network with outside data sources like GPS systems, historical pricing performance and FMCSA to help shippers more accurately predict demand, analyze trends in supply chains, monitor seasonal calendars, and track daily patterns within lanes.
To analyze multiple carriers and lane variations for thousands of companies, we use machine learning to create simulations that help determine the best combinations of carriers and lanes for delivering loads. Simulations tap into raw data and extract valuable information in near real-time, helping to improve operational efficiency, conflict avoidance and improved service levels.
Overall, this intelligence can help shippers lower risk, optimize routes and even learn new lanes at lightning speeds. No longer does it take six months to optimize a lane and work out all the kinks.
Natural language processing (NLP), another form of machine learning, is also drastically improving the efficiency of supply chains by speeding up data entry and auto-populating form fields.
When integrated with a transportation management system (TMS) and email, chat, text and voice communication, NLP systems monitor and learn from these exchanges. Over time, the system recognizes the behaviors of specific users and begins to anticipate what they want by auto-populating shipping orders, bills of lading, and other transactions, which saves the shipper valuable time.
The benefit of using natural language processing technology is that it’s always learning. This “unsupervised learning” also improves the classification accuracy of tracking status by analyzing inputs such as weather conditions and traffic patterns.
In an example of how you can apply sophisticated analytics through machine learning, at GlobalTranz we are helping a large manufacturing company, with multiple locations, track financial forecasts, pace and flow of production, and order processing. These data points, combined with deep insights into carrier capacity, results in a flexible plan optimized for both time and cost.
This information allows the company to answer real-time questions like “Are we operating within budget? “How much can we increase manufacturing without going over our freight budget?” or “How many more orders can we service within budget for a given set of lanes?” The company uses our GTZ Technology platform to access analytical tools, custom dashboards, and easy-to-understand data visualizations that help them make faster and better business decisions.
With so much data within reach, it’s much easier to predict what in the past has been unpredictable. As JOC.com said, “You can’t drive true efficiency unless you can predict every event and foresee every contingency.” These cutting-edge machine learning systems give us the tools and intelligence to make faster and more effective business decisions.
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