Visit Our Sponsors
There are two major challenges companies face. First, companies need to ensure that they don't follow the hype but use the technology to meet their own competitive priorities in a cost-effective manner. Second, is to develop organizational processes to turn the huge amounts of data into business intelligence.
Recent interviews I conducted with 34 supply chain executives and managers confirm earlier findings that "big data" is a forecasting priority for the future. Although 71 percent expected to make some degree of technological forecasting software investment in the coming year only 12 percent were using it to some degree along all supply chain levers, and few (2 percent) in a coordinated manner. The majority identified barriers as being costs of the technology, leadership and organizational understanding. Further, the majority recognized the importance of using big data analytics across all supply chain levers. Specifically:
"¢ Marketing (Sell). This is the traditional supply chain lever seen with big data. The companies using big data analytics along this lever were using it for customer and market segmentation to drive their supply chains, location-based marketing, sentiment analysis, and in-store behavior analysis. Some were also using it for merchandising, particularly price and assortment optimization.
"¢ Distribution & Logistics (Move). A number of the companies report using big data analytics for routing and scheduling, and selection of transportation alternatives. A much smaller number is using it for vehicle maintenance at this time but see the potential for the future.
"¢ Manufacturing (Make). A large number of companies report using big data analytics for inventory management, optimization of stock levels, maintenance optimization, and some in facility location. Some are considering use in the workforce productivity evaluation as well as study of capacity constraints.
"¢ Source (Buy). Few companies report using big data to optimize sourcing channel options and integrate suppliers into data systems. Some mentioned future use in helping identify supplier characteristics and helping to inform their supplier negotiation.
Leading-edge companies will need to redesign internal processes in order to utilize these new information capabilities. Outsourcing analytical capability will continue to increase as many companies do not have the know-how to do it on their own or can keep up with the technology. Small and medium-sized companies will be especially vulnerable and must carefully target their technological selection and use.
Leading-edge companies are recognizing that the use of technology and big data analytics are needed along the entire supply chain. Although most are using big data analytics on the demand side of the supply chain, more will move to using it along the supply side, and to better coordinate supply with demand.
Enjoy curated articles directly to your inbox.