Demand forecasts keep readers engaged | SAS

Demand forecasts keep readers engaged

If you've ever stopped by your local public library to grab the smash new title from your favorite best-selling author, then you probably know the letdown of adding your name to a wait list instead.

Unless you live in Singapore. Public libraries span the island. Located in the heart of many residential town centers, libraries in Singapore are popular social spaces for families, students and individuals. The NLB houses more than 8 million reference books, novels, magazines, audio visuals and audio books at its 25 public libraries and one National Library.

Our librarians' experience combined with our expertise in analytical insights have increased our ability to make acquisition decisions with greater accuracy.

Colin Seow
Manager of Resource Management

That's because Singapore's National Library Board (NLB) uses advanced analytics to make sure none of its 2 million registered patrons walk away empty-handed. The NLB uses SAS® Demand-Driven Forecasting to know precisely which books, new and old, to stock at each of its 25 public libraries.

"We want to provide our library patrons access to literature from around the world and meet their unique reading needs," says Colin Seow, the NLB's Manager of Resource Management. "Using international sources, we bring together a flourishing, vibrant and up-to-date collection of new and existing titles."

Forecasting with SAS® for better decisions

Innovative IT solutions streamline workflows, eliminate redundancy and make library operations more efficient. The result is a better experience for staff and patrons alike.

NLB uses SAS® Demand-Driven Forecasting to ensure materials stay up to date, relevant and engaging. The staff makes better procurement decisions through guided forecasting based on past data and statistics. The solution analyzes NLB's loan data to generate unrestricted, rolling forecast numbers for both new and existing titles and for unmet demands when patrons left empty-handed. Each forecast is calculated using statistically optimized parameters to provide up-to-date projections.

For its forecasting processes, the NLB identified and quantified factors that could affect demand analysis, including loans, book categories, renewals, reservations, authors and titles. The analysis was a success, resulting in a significant 60 percent accuracy rate when forecasting patron demand for existing titles and titles by category.

"Through the SAS Demand-Driven Forecasting solution, we are able to analyze past patron and circulation data, and turn these data into useful insights to guide our acquisition decisions," Seow says. "Our librarians' experience combined with our expertise in analytical insights have increased our ability to make acquisition decisions with greater accuracy."

Smooth integration with other systems

The NLB deployed SAS across its network of libraries. The board integrated SAS forecasting with two other library systems – the electronic selection and acquisition system and the collection planning system, which manage and oversee the NLB's collection throughout the island.

Although the other applications are written in a different programming language, SAS integration was executed smoothly using a standard Web-based protocol – the SAS 9.2 SOAP module, which allows disparate systems to communicate.

The successful deployment of SAS Demand-Driven Forecasting is the result of an intensive six-month planning phase to finalize system requirements and design before carrying out integration and testing.

Today, the solution is commissioned across NLB's libraries. It is fully automated and pulls data from different systems, allowing NLB staff to provide acquisition and collection-planning recommendations with greater precision.


  • Create a better library experience for patrons, staff.
  • Keep materials up to date, relevant, engaging.
  • Ensure the right mix of books for each segment.
  • Forecast demand for popular titles.


SAS® Demand-Driven Forecasting


  • Acquisition recommendations are more precise.
  • Past patron and circulation data informs useful insights.
  • Significant accuracy rate (60%) when forecasting demand for existing titles, titles by category.

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