5 Ways Big Data Analytics is Transofmring

We might be closer than you think to packages that deliver themselves.

Logistics companies are operating under enormous strain during the COVID-19 crisis, but its widespread adoption of big data—particularly when paired with powerful analytics applications—has quite likely kept the industry from imploding. 

As life returns to normal in the next few months, we’re likely to see a faster, smarter, and more capable worldwide logistics network, able to handle any challenge thrown its way. After all, it would be impossible for logistics companies to handle millions of combined daily orders, from all parts of the world, without technology embedded into every aspect of their operations. 

But these advancements are in no way being constructed overnight. Logistics companies have been using computers to optimize their network efficiencies for nearly as long as companies have been able to buy their own computers. Walmart and Fedex alone launched several changes in retail and package delivery during the 1970s on the backs of their computer systems.

But the industry’s early use of computing looks downright quaint compared to what’s happening in logistics today. If the 1970s brought computerization to logistics networks, the 2010s brought data—an absolute torrent of it.

This is why it’s become clear, the logistics companies set to thrive in a post-coronavirus world will be those that fully embrace big data analytics. 

We’ve worked in this industry for many years, helping some of the world’s top logistics companies modernize their tech stacks for the 21st century. Using the knowledge and experience we’ve gathered along the way, here are five of the ways we envision big data analytics transforming the logistics industry:

1. Smart demand forecasting

There are lots of expenses in the logistics industry, and accurately anticipating a logistics business’ need for certain expenses is often the difference between profitability and steep losses, particularly in turbulent economies.

There was once a time when forecasting shipment volumes was largely accomplished by manually combing through enormous spreadsheets, but today’s logistics businesses generate too much data for humans to track and analyze on their own. 

By using big data analytics, often operating in conjunction with (or entirely under the control of) artificial intelligence, today’s logistics companies can track and adapt to shifts in demand on a real-time basis―within reason, of course. Neither humans nor machines expected a viral pandemic to be the logistics industry’s biggest challenge in 2020. Such “black swan” events are virtually impossible to predict beforehand, but surging demand is easier to address profitably than a collapse in order volume.

2. Improved customer satisfaction and outcomes

Want to see where your package is? In 2020, it’s a breeze. Simply get the package’s tracking number and enter it in the delivery company’s website or mobile app. 

Most shipping companies now offer real or near-real-time status updates on millions of packages every day. This increased transparency, on its own, has had a major impact on customers’ satisfaction with their deliveries.

And yet, we rarely consider how much goes into real-time tracking. This feat requires the cooperation of thousands of people with thousands (or millions) of servers on a day-to-day, hour-by-hour, stop-by-stop basis. And it’s only one facet of the new data-driven approach to customer service.

For instance, if you need to contact support for your delivery, you’re likely to run through a series of automated prompts before reaching a human support agent. This is true of any type of transport and logistics service function in 2020, whether it involves checking the status of an inflatable swimming pool or a pizza delivery. Much of the customer support process is now automated at all manner of businesses, logistics and otherwise.

But since automated support is only as good as its outcomes, most large-scale customer support solutions continuously analyze every customer interaction with their systems or bots. Streamlining the support process and getting people their answers faster can add up to a lot of saved time and improved satisfaction when tallied up across millions of customers.

3. Better route planning

FedEx might have pioneered tech-driven shipping, but UPS has gone to extremes in pursuit of greater efficiencies from data analytics. 

The company’s trucks famously avoid left turns unless absolutely necessary, preferring to take three right turns to get to their destination. For a delivery company of UPS’ scale, this simple but somewhat counterintuitive result saves millions of gallons of fuel every year. Not to mention, left turns are also riskier than right turns, and the costs to insure and repair a big brown UPS truck are quite a bit higher than they are for regular vehicles.

In order to ensure drivers are compliant in this company policy, data from onboard GPS trackers flows into logistics companies’ data warehouses on a daily basis. Over time, this treasure trove of route data and driving history can help logistics companies avoid bad routes (such as left turns) and traffic, reduce travel times, and potentially save billions of dollars industrywide.

4. Automated recordkeeping and back-office functions

It’s not just package tracking and customer service that benefit from automation. The logistics industry also produces tremendous amounts of back-office paperwork, nearly all of which is a distraction to drivers, managers, warehouse employees, and other personnel between you and your packages.

Tracking fuel use, time on the road—even the hiring and payroll aspects of a logistics company—these activities once required regular human attention and input. 

Now, big data can help logistics companies and their employees automatically track their key metrics, manage their human resources activities, provide mandatory reports to government agencies, and conduct many other tedious administrative tasks. 

The software that handles these tasks can now automate many procedures, because it can accurately anticipate and record things based on prior data.

5. Fully automated deliveries (eventually)

It seems like we’ve been waiting for self-driving vehicles for decades, but the technology was virtually nonexistent and unknown a decade ago. Many analysts thought self-driving technology was a pipe dream or a lifetime from reality, but we’re already on the verge of widespread adoption thanks to billions of dollars in R&D investment from many of the world’s largest tech companies.

But aside from the obvious perks of driving around without actually having to drive, autonomous vehicles produce vast amounts of data with every mile driven. The technology operating these vehicles continuously self-assesses its performance against historical outcomes to produce a better and safer “driving” experience. 

This self-driving technology would simply be impossible without big data analytics and powerful artificial intelligence. And it’s not just the delivery trucks on the road using big data to work smarter.

The logistics warehouse of the future is looking increasingly autonomous as well. Amazon is a pioneer in the use of autonomous warehouse robots, which track down items and move them to the right place on the packing line so its human employees can focus on getting stuff out the door.

Virtually every logistics company now uses some form of automated or semi-automated warehouse management system incorporating robots, connected scanners and trackers, and big data analytics working on the backend to make sense of all the data. 

Automating the warehouse is a big driver of the decline in average time to delivery. Amazon cut its “click-to-door” time almost in half between late 2015 and early 2018. No big data, no automation, no two-day delivery. It’s as simple as that.

How will you use big data analytics?

The logistics industry is virtually unanimous in support for big data analytics. More than nine in ten shipping companies, and 98% of third-party logistics firms, believe data-driven decisions are critical to supply chain success. More than eight in ten of these companies expect big data analytics to become a core part of tomorrow’s supply chains.

In other words, every day you aren’t utilizing big data and artificial intelligence in your logistics operations is a day of unnecessary inefficiency and wasted effort. 

We’d love to help you augment your logistics business with big data. We’ve helped many logistics companies already, and we know what needs to be done to make the systems work within your day-to-day operation. Reach out and talk to us at RTS Labs today if you’re ready to bring your logistics into the 21st century.