Pearson on Excellence: Strengthen the supply chain with Big Data analytics

Big Data analytics is a big deal, particularly in supply chain management where this technology is broadly applicable but often underutilized. This month, I’ll present a brief overview ofBig Data analytics, discuss its burgeoning role in supply chain management, and examine some recent research.

For many years, supply chain decision makers have used analytics to help design facility networks, determine economic order quantities, and define safety stock parameters. However, these are not Big Data efforts. They are generally narrow and mostly piecemeal, and most are applied situationally rather than systemically.

Big Data analytics is different.On the one hand, it involves collecting and analyzing unprecedented large quantities of data in real time or close to real time. And, it’s also about handling data collected across time and in myriad contexts.

Think of these differentiators as the Three Vs: volume, velocity, and variety. Big Data analytics also differs from traditional analytics because of the many techniques and leading-edge technologies it requires: statistics, mathematics, econometrics, simulations, and optimizations.

Not surprisingly, there are plenty of ways that Big Data analytics can help supply chain decision makers. According to recent Accenture research, the most frequent benefits cited were better customer service, more effective fulfillment, faster responses to supply chain problems, and increased end-to-end efficiency.

但同样的基于“增大化现实”技术的研究说,这些好处e only enjoyed by a small percentage of companies. And, there’s little consensus about the best way to develop, structure, and deploy most analytical capabilities. Respondents worry about security, privacy, executive support, and the cost and complexity of implementation. Just 37 percent said Big Data analytics has been embedded in their key processes.

However, that’s about to change. The research indicated that more companies seem ready to invest. Here are three best practices companies should consider:

• Create an enterprise-wide strategy. An enterprise-wide analytics strategy has the greatest potential to help companies use Big Data to drive business value. A supply-chain-specific strategy will probably be less effective, but it’s still preferable to a strategy that focuses only on a few processes. More than 60 percent of surveyed companies that have implemented an enterprise-wide strategy reported shorter order-to-delivery cycle times and increases of 10 percent or more in supply chain efficiency.

• Embed Big Data analytics into operations. Companies that fold analytics into their day-to-day supply chain operations often generate more significant and farther-reaching benefits than companies whose analytics efforts are applied more narrowly or sporadically. Among researched companies that took this route, roughly 60 percent reported shorter order-to-delivery cycle times, better overall efficiency and improved demand management.

• Hire talent with a mix of deep analytics skills. An independent team of data scientists—dedicated to Big Data analysis on an ongoing basis—is an important success determinant. According to our research, companies that assemble such teams are three to five times more likely than those that don’t.

最后一点,尽管大数据分析ers tremendous potential to improve business performance, it nonetheless is a sizeable investment whose sheer scale and complexity lessen its attainability for some companies. Strong talent is in short supply. Analytical tools are complex and rapidly evolving. Technology vendors come and go quickly. For all these reasons, some companies’ best course may be to outsource the development and management of a Big Data analytics program.

In addition to leveraging best-of-breed expertise and tools, this approach could help companies launch or enhance their analytics capabilities quickly and cost-effectively, and to test-drive various approaches before bringing them in-house.

Regardless of whether you build or outsource your analytics program, the key to success is the same: identify the foremost supply chain benefits that Big Data analytics can offer and rigorously pursue the implementation path that aligns most fully with your organization’s priorities.