Every time a sensor is pinged or a bar code scanned, bits of data are recorded, and that information keeps piling up in computer systems. That's why "big data" analysis has become a hot topic of late in the world of information technology. "Big data" analysis offers the promise of gleaning insights from the millions of bits of information stored in dozens of computers and on the Internet. For logistics managers, the analysis could ultimately lead to improvements in their operations.
Advances in computer hardware and software have made it possible to sift through piles of data, both structured (information residing in conventional databases) and unstructured (data lying in information repositories such as e-mail accounts or social media networks). "Think of pulling information from texts like e-mails or comments left on a warehouse management system," says Aditya Naila of DreamOrbit, one of the vendors developing software for this purpose. "Because this is unstructured data, traditional tools couldn't make much of them."
Although traditional data analysis couldn't do the job, new tools from a number of software vendors can. Well-known software developers such as IBM and SAP are actively working on applications in this area, as are newcomers like DreamOrbit, Opera Solutions, and PatternBuilders.
For logistics managers, there are a number of operational areas where "big data" analysis could yield valuable insights—insights that lead to improvements. Take radio-frequency identification (RFID) data, for example. Gartner analyst John Hagerty notes that an analysis of RFID data could bring about an understanding of a product's location at any point in time, leading to changes in supply chain execution to facilitate more efficient delivery.
Another area of logistics where the impact of "big data" analysis could be felt is "cold chain" movements, or temperature-controlled shipments. Companies are starting to place sensors on pallets, and that's allowing for tracking analysis. "Sensors built in a pallet can call home via cellular GPS (global positioning systems) and tell a manufacturer or logistics company exactly where it is sitting, anywhere on the planet, and what condition it's in," says consultant Marilyn Craig, who is managing director of the firm Insight Voices. "A 'big data' analysis of that data would allow logistics managers to examine that pile of information to make changes ensuring that shipments do not vary from their desired state"—in other words, that they don't become too hot or too cold, or encounter too much vibration in transit.
Finally, "big data" analysis could be useful to managers looking to perform a "path analysis" of the supply chain to examine ways to move a product more effectively from manufacturer to consignee, notes Craig. That's because data from sensors and RFID could be merged together with information from enterprise resource planning (ERP) systems, warehouse management systems (WMS), and transportation management systems (TMS) into a common pool for analysis. "Information coming from a number of sources throughout the supply chain can help a manufacturer and [its] logistics partners understand exactly how product is flowing, where things are getting hung up, where value is added, and the location where damage or expenses occur," she explains.
For logistics managers, Hagerty says, "big data" analysis could be an "eye-opening experience." That's because it would allow them to make use of data that's already being captured and get a better understanding of what's going on in their distribution operations in order to make improvements. "It allows you to dig into data to figure out what's causing what," Hagerty says.
