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Big Data Analytics: Changing the Logistics Industry

The widespread adoption of big data analytics has likely protected the logistics industry from collapsing due to recent changes and stresses.
The widespread adoption of big data analytics has likely protected the logistics industry from collapsing due to recent changes and stresses.

The widespread adoption of big data analytics has likely protected the logistics industry from collapsing due to recent changes and stresses.

During the COVID-19 crisis, logistics companies are under tremendous pressure. However, life has been returning to something like normal in recent months. Therefore, we should expect to witness a faster, smarter, and more capable global logistics network. With the help of big data analytics, it should be one that is capable of dealing with any problem.

After all, logistics companies would be unable to handle millions of daily orders from all over the world if they did not include technology in every element of their operations.

These advancements, on the other hand, are not being built overnight.

Corporations have been able to acquire their own computers for a long time. Furthermore, logistics organizations have been employing computers to maximize their network efficiencies for as long as there have been computers.

During the 1970s, Walmart and Fedex alone used their computer systems to pioneer major innovations in retail and package transportation. However, when contrasted to today’s logistics, the industry’s early usage of computing appears antiquated.

If the 1970s saw the introduction of computerization to logistics networks, the 2010s saw the introduction of data — a deluge of it. Big data analytics is the child of both of these movements.

This is why it’s become evident that the logistics companies that completely embrace big data analytics will flourish in a post-coronavirus future. Therefore, here are five ways we see big data analytics impacting the logistics business.

1. Analytics enables accurate demand forecasting.

In the logistics industry, there are many expenses. Therefore, precisely estimating a logistics company’s requirement for certain expenses can be significant. It can mean the difference between profitability and significant losses, especially in uncertain economic times.

Forecasting cargo quantities used to be mostly done by manually searching through massive spreadsheets. However, today’s logistics organizations generate far too much data for humans to follow and analyze on their own.

Today’s logistics organizations can analyze and adjust to variations in demand in real-time by using big data analytics. This is commonly used in conjunction with (or totally under the control of) artificial intelligence.

However, no one predicted a viral pandemic would be the logistics industry’s largest issue in 2020 by either humans or machines. Such “black swan” situations are impossible to forecast. However, an increase in demand is easier to respond to than a drop in order volume.

2. Customer satisfaction and outcomes have improved thanks to big data analytics.

Do you want to find out where your package is? Simply obtain the tracking number for the parcel and enter it into the delivery company’s website or mobile app.

Every day, most shipping companies provide real-time or near-real-time status updates on millions of items. This enhanced transparency has had a significant influence on consumer satisfaction with deliveries.

Despite this, we rarely consider the amount of effort that goes into real-time tracking. This work is done on a daily, hourly, stop-by-stop basis. Therefore, this feat necessitates the cooperation of thousands of individuals with thousands (or millions) of servers. Additionally, it’s only one aspect of the new data-driven customer service approach.

However, automated support is only as good as the results it produces. Therefore, most large-scale customer care solutions assess every client interaction with their systems or bots on a regular basis.

Millions of customers are involved. Therefore, streamlining the support process and delivering individuals their answers faster can add up to a lot of time saved. In addition, it means enhanced satisfaction.

3. Big data analytics provide more precise route planning.

UPS has taken the lead in its quest for more efficiencies from big data analytics. It’s true that FedEx pioneered tech-driven shipping. However, UPS has gone to extremes in its pursuit.

The company’s trucks are known for avoiding left turns until absolutely essential. They prefer to travel to their destination through only right turns. This is a straightforward and perhaps unexpected solution. However, it saves millions of gallons of gasoline every year for a delivery firm of UPS’s size.

Additionally, left turns are riskier than right turns. Therefore, they also save on the costs of insuring and repairing their big brown UPS trucks.

Additionally, data from onboard GPS trackers feeds into logistics businesses’ data warehouses on a daily basis. This guarantees drivers follow this company’s guidelines.

Over time, this gold mine of route data and driving history can assist logistics companies to avoid undesirable routes. Furthermore, it helps them avoid gridlock. It also helps shorten travel times and save billions of dollars.

4. Recordkeeping and back-office functions are increasingly automatic.

Automation benefits more than just delivery tracking and customer service.

The logistics sector also generates a lot of back-office paperwork. Almost all of this paperwork is a source of distraction for managers, drivers, warehouse workers, and other people who are better employed making your deliveries.

Keeping track of time spent on the road and tracking fuel use necessitates human input. Even the hiring and payroll components of logistics firms require regular human attention and input.

However, nowadays big data can assist logistics organizations and their staff in tracking critical indicators. In addition, they can help in managing human resources activities, supply chain optimization, and submitting required reports to regulatory authorities. Furthermore, they can assist in completing a slew of other time-consuming administrative procedures.

Big data analytics can reliably anticipate and record things based on prior data. Therefore, the software that handles these duties can now automate many procedures. This saves both time and money.

5. Big data analytics will completely automate delivery…eventually.

Without big data analytics and advanced artificial intelligence, self-driving technology would be impossible.

Big data isn’t simply in use by delivery trucks on the road to improve their efficiency. The logistics warehouse of the future appears to be becoming increasingly self-contained.

Amazon is a forerunner in the use of autonomous warehouse robots. They track down items and move them to the proper location on the packing line. After that, human workers can focus on getting things out the door.

Almost every logistics firm now employs some type of automated or semi-automated warehouse management system. This includes connected scanners and trackers, robots, and big data analytics on the backend to make sense of the data.

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