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Surfboard Warehouse is Australasia’s largest surfboard manufacturer with a distribution network encompassing six owned channels across associated retail outlets, and supply contracts with some of the region’s largest sporting goods retailers. The company designs, builds and offers its customers its own range of exclusive brands and has a strong online presence.
As some of the key steps in the surfboard production process involve hand finishing, lead times of up to a year are common for this business. Raw materials like resin are often required well in advance and a seasonal demand pattern contributes to the risk of out-of-stocks across key SKUs. This then leads to increased costs associated with relocating items in order to fulfill orders at a given point of sale.
The company wanted to understand “true demand” for their range (i.e. hypothetical sales if SKUs had remained in stock in all locations). They also needed a better short-term (6 month) forecast to facilitate more balanced demand and supply planning for regional distribution channels. And a more accurate long-term (18 month) forecast to enable better portfolio planning and lifecycle management, with a specific emphasis on business casing new product launches. Finally, they needed a simple, intuitive portal to review, manage and report on forecast accuracy.
“I need my range to stay relevant year to year and more accurately replenish my DC warehouses and retail sites, to avoid running out of stock during periods of heavy demand.”
Sean Kennedy, CEO and Founder of The Surfboard Warehouse
Quantiful ingests the The Surfboard Warehouse’s weekly sales and inventory data directly from the customer into QU, our AI/ML forecasting platform, via an automated, low-touch process. This transactional data is combined with QU search data to generate a 6 month forecast which can be viewed, and if necessary, adjusted in the QU portal. QU used search data from Google in initial models, and continually improves forecasts by combining this with search data from surf forecasts as well as consumer searches for the customer’s brands and products.
Systemic delivery of an 18 month forecast at a weekly frequency for over 1200 SKUs across 8 geographic locations, including the machine learning correlation of 5 years of sales transactions with QU’s search and social data.
