A 2012 Steelwedge webinar survey of 160 supply chain leaders found that companies captured 70% more data (compared to 2011) to manage their organizations, yet 77% of these businesses were not leveraging this data in their sales and operations planning (S&OP) processes. As companies grapple with the supply chain implications of big data, they’ve become paralyzed with inaction, leaving blind spots in their decision-making processes around critical supply/demand tradeoffs.

In a recent Information Week article, IBM CEO Ginni Rometty said, “Many more decisions in your company or entity will be based on predictive analytics, not gut instincts or experience…because they can be based on all this [big] data.”

This applies directly to supply chain planning, where the explosion of data requires advanced S&OP technology that marries both analytics and planning: backward-looking metrics suited for aggregation and forward-looking metrics for simulation. Only by accessing the nuances of big data volume, velocity and variability can organizations reliably power the ad hoc decision making that is required by today’s volatile business environments.

Compared to traditional S&OP solutions (which focus on unit-based planning), big data S&OP incorporates units/revenue/margin, links departments and time horizons, and provides insight into multiple global supply chain scenarios. Enhancing S&OP processes with big data capabilities enables companies to gain greater visibility into information such as order fulfillment rates, global inventory levels, forecast accuracy, order to deliver lead-times, inventory levels and cash-to-cash cycle times. Moreover, this information is communicated on a real-time, exception basis as well as on a regular cycle and delivered via mobile devices and tablets.

Utilizing large amounts of data requires massive amounts of storage, which creates difficulties in identifying patterns and driving improved decisions. Next-generation, big-data driven S&OP incorporates fast-moving data from multiple sources, resulting in cost savings, efficiencies and improved operational performance.

Take, for example, a billion-dollar global leader in semiconductor manufacturing. This organization needed a better way to manage extreme cycles of surging or rapidly falling demand and to ensure forecast accuracy for their configure-to-order business. The company turned to S&OP technology to create a “single source of truth” reflecting sales, demand planning, build and finance. It used S&OP to analyze big data, relying on top-down and bottom-up data aggregation that was driven by business rules and scenario evaluation of insights. This helped the manufacturer to quickly respond to exceptions and changes in the business, and improve demand management.

As you prepare your business to better leverage big data in your decision making, consider the following recommendations for how to adapt bit data into S&OP processes: