Today, many organizations are making strategic decisions using only historical data, which is like driving a car using only the rear-view mirror. Predictive analytics expands their visibility to the front as well as the rear.

The applicability to industries such as technology, in which demand can be extremely volatile, is particularly powerful. Imagine if business management could access real-time sales data and related costs by region and product at any time. Then, based on this information—as well as data from external sources—dynamically adjust production schedules, marketing initiatives, sales promotions, inventory positions, stock locations and transportation routing. The result is a predictive enterprise better able to capitalize on market volatility while avoiding excess inventory, improving service, shrinking product obsolescence and improving profitability.

Big Data and the Supply Chain

Studies of organizations that have used data effectively, documented numerous benefits, including higher revenue, improved customer service, more successful product launches and higher quality.
Most significantly, companies that do a better job predicting demand can improve margins by 1-2 percent.

In any global company, the supply chain is one of the largest sources of big data. It carries and produces information that affects almost every other area of the business. That should put the supply at the center of any organization’s big data initiative.

By layering analytic techniques and tools onto their existing descriptive information architecture, organizations in dynamic markets can reduce inventory, start to sense and shape demand, streamline networks, improve agility, responsiveness, and generally get out ahead – not just of their supply chain but of their business as a whole. For example, a manufacturer of consumer electronics with access to a real-time sales analysis by region, along with shipping costs and sales projections, could dynamically adjust production schedules, sales promotions, stock locations and transportation routing to capture sales, avoid excess inventory, improve service, shrink product obsolescence and improve the bottom line.

This requires going beyond the internal borders of the company to include external sources of data, both structured and unstructured, that can influence demand and distribution. One example is social media. Supplementing real-time sales data with industry trends and breaking news provides a more complete perspective on demand, as when a celebrity shows up at a major event using a distinctive product or a new product introduction generates unexpected media coverage. When that data can be captured and factored into the analysis, it enables more accurate and dynamic predictive modeling.

According to a Deloitte and the Material Handling Institute (MHI) study, which surveyed 400 supply chain professionals from across industry verticals, less than 25 percent of respondents have adopted predictive analytics to date, although that number is expected to climb to 70 percent over the next three to five years. While only 24 percent of surveyed companies currently use these types of systems, 38 percent cited them as a source of competitive advantage. Clearly, now is the time to begin to evolve toward the predictive supply chain.

Enabling the Evolution

Data mining and other analytics are forming an emerging field of supply chain data science that has the potential to drive an evolution toward predictive operations.To take advantage of this opportunity, supply chain professionals need to ensure they have a descriptive supply chain solidly in place while plugging into organizational big data initiatives and getting smart about the new generation of business intelligence, data visualization and artificial intelligence tools. The result promises to be more powerful than a better supply chain. It is the ability to create profit, growth and value on a sustained basis.

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