How Big Data Is Disrupting the Logistics Industry
Big Data is disrupting the logistics industry by optimization logistics through greater intelligence, customer satisfaction, and last-mile efficiency.
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Big Data has changed everything from baseball to transportation, putting the power in the hands of those who know how to analyze and break down metrics.
Big Data has been especially crucial in the disruption of the logistics industry, allowing companies such as UPS and DHL to move packages faster and keep customers happy. It’s easy to see just how Big Data and the logistics industry were made for each other.
From the consumer side, it’s something you might not notice right away, but it is definitely happening. Routes are being optimized, based on years upon years of transportation data available.
Here are few ways Big Data has already disrupted the $1.1 trillion logistics industry.
Optimization Through Greater Intelligence
With the rise of Big Data, the logistics industry made the leap to being more of an information-driven vertical.
Instead of relying on outdated processes, today’s companies rely on hard data to make decisions. Companies all over the U.S. are stepping up and making significant plans in Big Data, as DHL notes that 60 percent of supply chain companies are investing in analytics platforms in the next five years.
Every day, data about weather conditions, origin, weight, height, destination and content is tracked. Instead of simply stockpiling that information, smart companies are making use of it to become more efficient.
This quest for greater logistics knowledge is an industry-wide trend. A recent study by the Council of Supply Chain Management Professionals shows that 98 percent of third-party logistics firms and 93 percent of shippers feel that data-driven decision making is key to supply chain activities. Additionally, 86 percent of third-party logistics firms and 81 percent of shippers expect analytics to become a core tenet of supply chain organizations.
When you have this much data, companies can anticipate busy and slow periods, as well as when they’d need to restock key supplies. This makes for a much more well-oiled machine, pleasing their customers with consistent on-time performance.
Big Data has also generated significant insight into the end-consumer base. Companies can tap into social sentiment data generated by the boatloads from platforms such as Facebook, Instagram, or Twitter to gain a deeper understanding of customers.
This way, companies can examine that sentiment, match with sales records, and anticipate a rise (or drop) in demand. Then, they can coordinate with their supply chain manager to make sure they’re not wasting money shipping superfluous units or leaving money on the table by not meeting demand.
But there are other ways logistics companies are using Big Data to make consumers happier. Companies know that the delivery of tangible goods often involves face-to-face contact with customers. DHL points out that companies are gathering all kinds of data about these touchpoints in order to make the process more user-friendly for the consumer.
Using Big Data means that companies now have unprecedented analytics into the preferences and buying behavior of their customers.
Often, the most difficult and expensive part of the shipping journey happens in the last part of the process — moving the package from the distribution center to the customer’s home. Congested roads, traffic, and varying consumer schedules lead to plenty of confusion.
Thanks to Big Data analytics, companies already know the best routes to take each day of the year. A clear road in April could be a snowbound one in December, meaning the same plans will not work year-round. By having this knowledge, logistics companies can make the final leg of the shipping journey a painless one for consumers and a productive one for themselves.
Recently, a team from MIT found a way to use Big Data and the Internet of Things to speed up the last mile process. The researchers used smartphones, GPS-enabled devices and IoT scanners and sensors to generate a better view of a shipping truck’s journey. From there, they were able to optimize routes, placing an emphasis on speed.
For instance, they were able to formulate a plan for customers who often aren’t home during the day to accept shipments and incorporate that into the supply chain.
By saving money and time on the last mile of the journey, companies can then invest those savings in more necessary areas. So, the next time you’re impressed with the speed and accuracy of a shipment, you’ll know that Big Data helped power that experience.
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