In today’s tech-driven society, big data is everywhere. Leveraging that data for supply chain management can reap tremendous rewards.
The digitization of just about every facet of the supply chain means that massive amounts of data are produced in real-time, and that it can all be collected, analyzed, and turned into actionable solutions. 64% of supply chain executives consider big data analytics an important disruptive technology, and 97% believe that related tools can benefit their supply chain. Still, 83% have not yet integrated analytics into their systems, signaling a disconnect between insight and action. Here are four ways that big data is critical to supply chain management:
1) The Backbone of Knowledge-Sharing Supplier Networks
On the most basic level, supply chain management involves overseeing multiple relationships with suppliers, buyers, and customers. Big data can give companies important insights into these relationships, both within their own supply chains and outside of them. Better understanding your vendors, and with whom and how they conduct business, establishes a knowledge-sharing network that builds trust and improves efficiency well beyond a simple transactional relationship.
In the same way, big data can give you a much more comprehensive view of your customers (and your relationships with them). Statistically, 90% of customers will not return to a business that failed to meet their expectations -- so understanding their needs is critical. Analytics help you predict their preferences so you can tailor a unique brand experience.
2) Improved Visibility
Leveraging big data gives leaders much greater visibility (and improves traceability) throughout the entire supply chain by providing real-time updates on inventory. Locating a problem accounts for 90% of the total time it takes to make a repair, so knowing exactly where and why a problem is taking place dramatically reduces the “Mean-Time-to-Know,” or the amount of time it takes to address those issues. Manually managing, assessing, and integrating massive product databases could take thousands of manhours; analytics take the guesswork out of the equation entirely.
According to a report by Deloitte, there is great promise in the application of big data to the optimization of supply chain tools. Geo-analytics, for example, can help a company streamline distribution and logistics by modeling the merger of delivery networks. The subsequent consolidation can significantly improve accuracy and service time. Similarly, optimization algorithms can help your company identify patterns and correlations between your supply chain decisions and the big data that they yield.