Big Data = Big Changes in Logistics, Transportation
One of the highlights of the annual Council of Supply Chain Management Professionals (CSCMP) conference is the unveiling of the new Third-Party Logistics Study. According to this year’s survey, released in late September at the organization’s Atlanta meeting, big data is going to play a bigger role moving forward among shippers and their logistics providers.
Among the findings: 98 percent of 3PLs and 93 percent of shippers believe data-driven decision-making is essential to supply chain activities; 86 percent of 3PLs and 81 percent of shippers expect analytics to become a core competency of supply chain organizations; 71 percent of 3PLs believe that big data improves process quality and performance.
Amid these glowing reviews there was one cautionary note: just 35 percent of shippers believe 3PLs could support their big data initiatives—down from 44 percent in 2014.
It’s not that anyone is against collecting data—both sides rely upon it. The issue stems from ongoing evaluation on how it can be optimally merged in a way that delivers the best results and the best value.
Where industries like marketing and manufacturing are already maximizing the use of big data and analytics, implementation has been slower in supply chain management.
Given all the factors that can impact this industry, from weather conditions to vehicle usage to automation, access to real-time information can be pivotal to improve efficiency.
Inventory management is already being transformed, thanks to automated alerts triggered to replenish stock levels when necessary. Forecasting data, compiled in part through links to cameras in warehouses, can anticipate when resupplies are necessary before the stock has a chance to run short.
Those curious as to how such obvious data applications will affect the industry, outside of making it more effectual, should ask themselves what happens to the employees now charged with the jobs that data can do better.
Structured and Unstructured Data
The most significant growth area will be in unstructured data, according to industry experts. It’s been slower to develop because it deals in fields that are less clearly defined.
For instance, structured data deals in hard numbers—how many products are in a warehouse, and how many have been ordered—that are stored in defined databases fields. Unstructured data delves into issues such as the impact of visibility of product brands and logos on store shelves, and how display space is allocated—and typically derive from non-database material such as text—which might appear on social media—or even audio and video.
While supply has traditionally been easier to measure than demand, supply chain forecasting analytics may be changing that as well. By tracking what people are saying about a product on Facebook and other social media platforms, it becomes easier to predict potential appeal.
The interest is there, the technology is there, and the enthusiasm for putting it all together is getting there. Chances are it won’t be long before traditional supply chain data monitoring will be joined with more predictive analytics solutions, providing a clearer picture into every aspect of operations.
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