From sports to health to retail, Big Data has had an impact on everything imaginable. However, its most significant impact will probably be felt in the logistics industry.
This is because they’re both perfectly suited to each other. The logistics industry already has a wealth of data that is continuously generated. This creates a great opportunity to analyze the data to optimize operations, identify new revenue streams, and scale intelligently.
However, leveraging data in the logistics industry won’t be easy. This is because as logistics management and transportation networks scale and become more complex, the data that has to be processed and managed will also become highly complex.
At the same time, it’s worth the effort as advanced data analytics can help consolidate an industry that has been traditionally fragmented. With the Internet of Things (IoT) making its presence felt in the logistics sector, it’s not hard to perceive Big Data as the underlying “fuel” that drives the industry forward.
How Is Big Data Used in the Logistics Industry?
When it comes to efficiently managing supply chain activities, data-driven decisions make the difference. According to research, as much as 93% of shippers and 98% of third-party logistics companies believe that data analytics is critical to making intelligent decisions.
Big data and analytics can play an important role in solving operations challenges and reduce resource consumption. For example, by taking advantage of RF tags and smart sensors, inventories can also be better managed with intelligent forecasts.
By deriving insights on how demand will evolve, you can anticipate shifts to better manage inventory shortages. This will also help the industry better manage staff by avoiding overtime, rush periods, and exhaustion.
This approach can go a long way to help cut unnecessary overhead expenses and make logistics firms more competitive. It will also be vital going forward as the modern customer has grown to expect the same level of service provided by giants like Amazon (who offer extremely low shipping costs and flexible return policies).
As a result, if the logistics sector doesn’t embrace IoT and Big Data analytics immediately, they risk becoming irrelevant in an industry that’s going through a period of significant digital transformation.
Barriers to Adopting of Big Data and Analytics
For big data and analytics to work, companies need to be able to draw the right conclusions from the data. This means that they have to find innovative methods of deriving real value from oceans of data.
It’s important to always take a scientific approach and not be quick to make assumptions that can be costly. It’s no secret that there’s a huge lack of big data talent, so hiring data scientists and engineers (and keeping them) can be expensive and a major barrier to the adoption of data analytics in the logistics industry.
So if you can’t find top big data talent, it will be important to look beyond your borders to attract data scientists and engineers. However, whether you do it in-house or outsource this function, security should always be at the forefront.
Security is a huge concern in the age of ransomware attacks as it can bring the whole business down to its knees. So to avoid disruptions and significant expenses, the industry needs to take significant steps to ensure that the data generated is always protected at every level.
This can be a challenge as you will have many different players in the supply chain and some of them might have very different ideas when it comes to maintaining enhanced security.
Furthermore, all logistics and related systems need to be able to work together seamlessly to make all of this possible.