A new survey of 350 manufacturers conducted by the research firm IDC Manufacturing Insights found that respondents consider "big data" analysis to be the most important supply chain technology for their companies in the year ahead. Big data analysis even beat out such other well-hyped technologies as mobility and cloud computing for the top spot.
To anyone who pays attention to information technology trends, it should not be surprising that manufacturers are eyeing big data analysis as a way to improve their supply chain operations. Supply chain executives who work in any business segment or industry—but those in retail or wholesale distribution in particular—should have big data analysis at the top of their to-do lists.
Why? Companies of all types have been accumulating "big data"—large quantities of information about operational transactions—for many years. Every time a sensor sends a signal or a radio frequency identification (RFID) tag gets pinged, an electronic message is sent or an application issues an instruction, that notation is stored in a computer system, often in an unstructured database. Advances in software now make it possible for companies to examine those disparate repositories of information and analyze the pooled data.
Big data analysis could help companies improve their supply chain operations by enabling them to conduct a "path analysis" that looks for ways to move a product more effectively from the factory to the consignee, says consultant Marilyn Craig, managing director of the data science firm Insight Voices. Because big data analysis examines the trove of production, inventory, and distribution information that's stored in different computer systems and databases, it can help shippers find hidden cause-and-effect relationships. Companies could, to cite just one example, discover the connection between a particular production method or type of packaging and product damage that occurs during shipping.
Proponents of big data analysis advocate applying this approach even to information that's available on the Internet. Companies could, they say, review comments on social networks to determine correlations with sales. For example, social network chatter praising a new product could be a factor in determining manufacturing and replenishment plans for that product. Indeed, as companies move away from using forecasts to drive production and toward the adoption of a demand-driven model using point-of-sale (POS) data, the ability to spot demand trends by analyzing social media will become even more crucial to managing supply chains in support of sales growth.
By using insights gleaned from big data analysis, companies can more closely integrate procurement, production, and distribution to reduce costs and drive additional sales. That's reason enough for big data analysis to become an essential tool for every supply chain executive.
