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About the Author

Glen Margolis
Founder and Chief Executive Officer
Steelwedge Software

Glen Margolis, CEO of Steelwedge Software, has led the industry in the development and delivery of technology to power planning agility. Steelwedge is the global market leader of cloud-based integrated business planning (IBP) and collaborative S&OP solutions trusted across the world’s best manufacturers including Lenovo, Jaguar Land Rover, Canon, Sony, Tyco, Dow and Emerson.

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Supply Chain Comment

By Glen Margolis, Founder and Chief Executive Officer, Steelwedge Software

May 9, 2013

Adapting Big Data into Agile Supply Chain Planning


Results of a 2012 Webinar Survey Found the Majority of Businesses were Not Leveraging Data in Their Sales and Operations Planning (S&OP) Processes

Margolis Says:

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.
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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:


Create a unified data model, and tear down silos of information

  Bring financial (e.g. profitability, on-hand inventory, revenue) planning, as well as operational (e.g. constrained supply plan) and strategic (revenue forecast) planning together in a single environment
  Make S&OP planning and reporting a global, corporate-wide process with the ability to create global, supply/demand scenarios
  Define forecasting process and ownership
  Incorporate S&OP data into regular decision making cycles. This can be tactical from both the technology element (How do you make it all play nicely together?) as well as the human element (and then, once it’s all playing nicely, what do you DO with it to make it work for you?)
  Incorporate pricing, inventory, risk, and capital trade-offs into planning
  Ensure real-time integration of unit, margin and revenue planning across common time horizons with back-end systems
  Leverage robust predictive analytics and what-if scenario planning that incorporates top-down, middle-out, and bottom-up planning

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