Earlier it was next to impossible for logistics service providers to convert huge amount of data into actionable information. On the contrary, in the current scenario, data forms the basis of logistics service operations. If data is the backbone for successful logistics operations, let us have a glance at the benefits it offers:
Improved operational efficiency
Real-time optimization of routes
Last-mile optimization of routes
Strategic network and capacity planning
Customer service improvement
Product innovation
Besides the pros mentioned above, there are some challenges associated with the huge volume of data. Although this data has the potential to transform logistics businesses, most of the time it gets overwhelming for logistics service providers to sort the information into useful and not so useful ones. Data stored in different formats or systems not able to create actionable insights is simply wastage of storage. This is exactly when implementable data-driven solutions come into the picture. In order to create implementable data solutions, it gets imperative to rely on industry data standards or logistics data standards. These data standards govern how exactly the data should be recorded, stored, and then shared.
The principle that dictates the standards is that the exchange of any important information must happen in a common format that would make collaborating and extracting valuable insights simple and convenient. Despite the well-known significance of data standards, it is not yet a very common practice for logistics organizations. For providing best end to end logistics services, it is imperative for the logistics companies to adapt this practice. The lack of data standards tends to complicate the exchange of information thereby confining innovation and providing an unclear picture of the issues that might otherwise be affecting the logistics scenario. Thus, standardization seems like a must for this industry. Let’s delve into some of the widely accepted standards that could be promising for this industry:
Analytics: Standardized data has common format for arrival timestamps, uniform data definitions, and security mandate in place. High-quality analytics can only work upon clean and organized data to convert them into meaningful and workable insights. With the help of analytics, the customers’ data is brought to high-quality standard which could then serve as a foundation for further predictive optimization.
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