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We’ve already discussed how Big Data and Visibility are the two biggest buzzwords in supply chain. One question you may be wondering is, are these trends related?
Yes. Simply put, to obtain end-to-end visibility you need to solve a number of big data problems. The reason why there has been so little progress on the visibility front is that today’s supply chains are too sprawling, too outsourced, and too complex for traditionally architected systems to handle. Companies have abandoned vertical integration, instead outsourcing a large majority of the supply chain functions that they once managed in-house. The result is that supply chains have become incredibly complex global webs of trading partners scattered all over the world, each focusing on a narrow slice of the fulfillment or manufacturing process. They are filled with 1000s of suppliers, SKUs, and components.
Executives understand that their traditional enterprise management systems are not designed to manage activity beyond the four walls of their enterprise, and are certainly not equipped to deal with the big data problem. As supply chains become more tangled, with a greater number of far flung suppliers, managers are faced with risks that can crop up in dozens of countries. No wonder a recent Deloitte survey of 600 executives at manufacturing and retail companies found that 63% were highly concerned about risks within the extended supply chain comprising vendors and customers, ranking it among their top-two concerns..
In response, companies that have long used complex data sets to plan manufacturing to meet customer demand are now looking to combine data from external sources to better predict future risks. The problem is, if a single manufacturer alone can house 20 petabytes of data (as we already learned), how much more data must the rest of the supply chain contain?
If you’re interested in this topic, I suggest you read the recently released whitepaper, “Turning Big Data into Big Visibility“.
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