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NSF Awards Smart Software with SBIR Grant

Belmont, Mass.Nov. 28, 2012—With a received Phase I Small Business Innovation Research (SBIR) grant from the National Science Foundation (NSF), Smart Software Inc. will investigate new statistical methods to forecast intermittent demand to help enterprises worldwide reduce inventories by 10’s of billions of dollars.

“Any organization that builds or supports capital equipment experiences intermittent demand for some portion of its inventory,” said Nelson Hartunian, President of Smart Software. “This grant is a terrific opportunity to impact one of the biggest forecasting challenges facing these organizations—accurately forecasting parts and optimizing inventories. Ultimately, the goal is to have the right part at the right place at the right time. The research we are undertaking will make this goal more achievable.”

A provider of demand forecasting, planning, and inventory optimization solutions, Smart Software’s new research will build upon its patented solution for forecasting slow-moving or intermittent demand—developed with the support of a previous NSF grant. The current method—commercialized as part of the company’s flagship product, SmartForecasts—evaluates historical demand for each item and establishes the optimum level of inventory that will be required to achieve service level objectives. The new research seeks to extend demand forecasting beyond individual products and parts, identifying and interpreting interactions across clusters of items whose demands fluctuate together.

The new forecasting capabilities will benefit customers in the following ways:

  • A more dynamic statistical model of parts will enable forecasts to better reflect a variety of external factors that include part usage by itself or in combination with other products, as well as the impact of macroeconomic and environmental factors.
  • Research results will provide planners with a dynamic model of item usage, enabling planners to develop functional maps of the interrelationships of large numbers of parts. Knowing which parts have demands that co-vary can be useful in at least two ways. First, item managers can be assigned to work with coherent clusters rather than arbitrary collections of miscellaneous parts, and second, parts can be co-located in warehouses for more efficient storage and retrieval.
  • Another benefit from this new approach will be improved forecasts of "aggregates" where intermittent demand is present, such as all items in a product line, or all items at a particular warehouse. Better forecasts of aggregate demand across groups of parts will also be useful for raw materials purchasing, as well as for financial planning when parts are a source of revenue.

 

The SBIR grant program from NSF is extremely competitive, as more than 1,000 companies compete in a two-stage screening: one for intellectual merit, and the other for commercial potential.

This Phase 1 grant is the third Smart Software has received.

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