Blogs > World's First Demand Forecasting Engine Powered by Julia, Designed for Large-Scale Operations
World's First Demand Forecasting Engine Powered by Julia, Designed for Large-Scale Operations
Sun Apr 23 2023
Author
Noah Xiao
An accurate and responsive demand forecast can make all the difference in confidently automating decisions in retail—from adjusting order plans due to weather changes to setting the right price on products about to be marked down. We recently launched our game-changing forecasting SaaS product, jahanForecast. Many factors contribute to making jahanForecast accurate and robust enough to handle such scenarios, so let's dive deeper under the hood and focus on one aspect: Julia, the programming language that powers this engine.
What is jahanForecast?
jahanForecast is a holistic demand forecasting engine designed not only to produce accurate outcomes but also to enable optimal decisions for retailers and manufacturers across entire value chains.
🎯 Prediction Accuracy: At the heart of the product, jahanForecast achieves 25% better accuracy than other solutions. We've accomplished this by integrating retail subject matter expertise with the latest in machine learning algorithms and architecture to deliver the best outcomes.
🌐 Integration and Holistic Planning: Forecasting enables a more comprehensive view of the retail value chain. With seamless integration with all our other products in jahanVerse, it allows for extensive simulations across processes including promotions, range planning, long-range planning, new product development, replenishment, workforce planning, and more.
The Julia Factor
Julia is an open-source programming language that integrates seamlessly with top-tier forecasting, machine learning, and deep learning algorithms. Known for its elegance and performance, Julia is one of the many reasons why we love it.
🏎️ Speed: A large retailer can generate millions of transaction records in the blink of an eye. Forecasting customer demand for all products and locations is a compute-intensive job. Julia has demonstrated quicker runtime during processing, achieving 70% faster speeds for both training and scoring.
💰Cost Reduction: For some of our clients, Julia effortlessly handles over 20 million time-series forecasts. This enables us to run more experiments while developing the optimal forecast and significantly reduces operational cloud costs for training and scoring machine learning models by up to 90%!
More Julia Use Cases
Forecasting is at the centre of retail and supply chain operations. Beyond jahanForecast, we are leveraging Julia for our other retail optimisation products—including promotions, pricing, assortment, space planning, supply chain, and operations. Watch this space!
At jahan.ai, we are passionate about the latest in AI in retail. Want to know more or discuss all things retail and AI?