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Using machine learning and demand sensing to enhance short-term forecasting
Implementing a short-term forecast is critical to understanding and predicting changing consumer demand and how factors such as weather, sales promotions, and other events can impact demand.
Short-term demand sensing allows manufacturers, retailers and CPG companies to predict and adapt to those changing consumer demand patterns.
Download this informative whitepaper on consumer demand sensing and its effect on supply chain efficiency.
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Demand sensing is a technique used in supply chain management to accurately forecast short-term consumer demand. It helps organizations predict and adapt to changes in consumer behavior due to factors like sales promotions, weather conditions, and unexpected events. By utilizing demand sensing, companies can improve product availability, enhance customer service levels, and ultimately increase sales revenue.
How does machine learning enhance forecasting?
Machine learning enhances forecasting by combining traditional time-series models with advanced algorithms to create more accurate weekly and daily demand forecasts. This approach allows analysts to leverage historical supply and demand data, resulting in improved accuracy across multiple lag periods. For instance, the new forecasting methodology showed significant accuracy improvements over existing methods, particularly when incorporating point-of-sale and customer inventory data.
What are the benefits of improved forecasting?
Companies that implement improved forecasting methods can experience several benefits, including increased sales revenue, better transportation planning, reduced inventory carrying costs, and enhanced customer service levels. By accurately predicting consumer demand, businesses can ensure that products are available when needed, thereby minimizing stock-outs and optimizing supply chain efficiency.
Using machine learning and demand sensing to enhance short-term forecasting
published by Douglas PC
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