Many industries today have begun to leverage big data in surprising ways, all to enhance their business processes as well as to improve the way they engage with their customers and address their needs. Some analyze their big data in order to find and iron out kinks in their operations, while others employ data integration via log-based change data capture to ensure that their data is consistent and updated throughout the system.
One industry in particular—the logistics industry—has benefitted massively from big data analysis. By integrating data that is generated by multifarious assets engaged in the flow of goods and commodities from source to consumers, logistics organizations are able to prevent delays and snarls in the supply chain that would otherwise have tremendous economic costs. To illustrate this, here are some concrete exampleson how efficient use of data can improve logistics.
The last leg of deliveries can be accomplished much more quickly
In logistics, the last part of any delivery job—that being when the delivery person must physically move the product to the hands of the customer or recipient—is usually regarded as the slowest, costliest and most inefficient link in any logistical process. This is because there are often many unforeseen elements involved in the physical delivery of the package, ranging from the recipient’s location being very remote or hard to get to, to even the recipient being absent at the time of the delivery. These can not only cause time-consuming delays in the completion of the delivery but also costly returns and replacements.
Fortunately, data integration can resolve this. By analyzing data coming in from multiple streams during the delivery process—for instance GPS and location data, traffic data, weather information, and so on—logistics companies can track down significant pain points in last-leg deliveries and immediately formulate immediate workarounds or long-term solutions.
For example, by tracking deliveries made to a remote area, a shipping company can decide if it’s worth opening up a satellite branch or office in that location. Opening an outlet in this remote area will not only make the deliveries there much faster—by way of having personnel familiar with that area to finish the delivery—but it will also provide recipients a location where they can collect their packages in case they miss the deliveries.
Delivery routes can be optimized
Another way that logistics companies have gained efficiency from big data analysis is through the optimization of delivery routes. Before the digital age, logistics companies had to rely on their drivers and personnel to manually learn the geography of their designed delivery areas and to find the quickest routes from point A to point B. This learning process, of course, takes a lot of time and resources in the form of on-the-job training as well as fuel consumption. Delays in delivery would also be inevitable during this period of learning.
However, by employing GPS and smart mapping technologies, logistics companies can equip their drivers and delivery personnel with the necessary navigational knowledge from the get-go. Going further than this, they can also utilize data from real-time traffic feeds and weather reports in order to view alternative routes that are safer and quicker, particularly when there are vehicular accidents, traffic jams, and inclement weather conditions to avoid.
Time-sensitive or fragile goods can be shipped more efficiently and carefully
For any logistics firm, the safe and efficient shipping of sensitive goods—especially perishables—has always been a stiff challenge. Not only can these goods be easily damaged en-route, necessitating an increased amount of care from the porters and drivers involved in their shipping, but the smallest delay could also render the entire cargo worthless. This can easily translate to huge losses not just for the client but also for the logistics company itself.
Thankfully, big data—along with the Internet of Things—provides a way to make shipping this particular sort of cargo a bit more uniform and efficient. One example scenario where this can be realized is through the installation of smart temperature sensors in a truck carrying raw, freshly-caught fish. Computer-aided sensors can periodically keep track of the temperature of the truck interior so that it can be automatically adjusted when needed, thus preventing spoilage. The shipping company can supplement use of this data with use of traffic data in order to avoid disruptive events on the road, thus further expediting the delivery process.
Many enterprises have yet to really see the benefits of big data analytics, simply content in letting their voluminous data rot away in their archives. However, just by looking at how the logistics industry has become revitalized by leveraging their data in multiple ways, it’s undeniable how much businesses and organizations stand to gain by putting their data to good use.