Supply chain management is a field where Big Data and analytics have obvious applications. Until recently, however, businesses have been less quick to implement big data analytics in supply chain management than in other areas of operation such as marketing or manufacturing.
Of course, supply chains have for a long time now been driven by statistics and quantifiable performance indicators. But the sort of analytics which is revolutionizing industry today – real-time analytics of huge, rapidly growing and very messy unstructured datasets – were largely absent.
Relying on traditional supply chain execution systems is becoming increasingly more difficult, with a mix of global operating systems, pricing pressures and ever-increasing customer expectations. There are also new economic impacts such as rising fuel costs, the global recession, supplier bases that have shrunk or moved off-shore, as well as increased competition from low-cost outsourcers. All of these challenges potentially create waste in your supply chain. That’s where data analytics comes in.
Data analytics is the science of examining raw data to help conclude information. It is used in many industries to allow companies and organization to make better business decisions and in the sciences to verify (or disprove) existing models or theories.
In the past few years,the impact of the use of data analytics in the supply chain & logistics function is being felt.
Experts, like Robert St. Thomas believe that the day for real-time supply chain practices has come and it’s on the verge of being more mainstream, thanks to a multitude of cloud data management tools and increased corporate adoption of new supply chain software platforms coming to market. However, there’s also anacknowledgment that a necessary foundation for moving efficiently at real-time speed — supply chain analytics — is still very much at the beginning stages of development at many companies, and will take time to build out.”
There’s an increased interest in the subject, especially in the areas of improved forecasting and S&OP (Sales and Operations Planning), but that “upgrades and tool implementation take time” and that the data has to be both accurate and accessible to perform data analytics with its complex algorithms.
All businesses with a supply chain devote a fair amount of time to making sure it adds value, but these new advanced analytic tools and disciplines make it possible to dig deeper into supply chain data in search of savings and efficiencies.
The supply chain is a great place to use analytic tools to look for a competitive advantage, because of its complexity and also because of the prominent role supply chain plays in a company’s cost structure and profitability. Supply chains can appear simple compared to other parts of a business, even though they are not. If we keep an open mind, we can always do better by digging deeper into data as well as by thinking about a predictive instead of areactive view of the data.
So while advanced supply chain analytics is promising regarding making our supply chain leaner, it may not be “ready for prime time,” at least not for the masses for awhile.