Demand forecasting predicts future demand, examining past demand patterns and factors to improve demand predictions.

FREMONT, CA: Retailers and firms have traditionally used time-series modeling to predict future demand. These forecasts were changed using causal modeling and manual input. Modern corporations use machine learning to predict demand. Machine learning automates much of the effort and can incorporate external elements into the forecast. The effectiveness and transparency of a retailer's supply chain are crucial to the quality of the shopping experience for both the customer and the store. Retailers and consumer packaged goods companies using an omnichannel strategy must provide a positive customer experience across all channels.

AI improves demand forecasting

Machine learning improves demand projections, automates planner work, and can process huge data sets more than any human planner could. A system must be able to process a huge amount of data on the many variables that can affect demand to generate an accurate demand forecast. Modern demand planning systems can conduct millions of forecast computations in a minute, considering more variables than before. Variability in baseline demand patterns, internal company actions, and external factors like weather or local events affect demand.

Predicting business decisions

Promotions, price changes, and new product debuts affect sales volumes significantly. Because these actions may create so much unpredictability, they must be factored into forecasts. Machine learning algorithms integrate massive volumes of data into the baseline demand prediction by predicting the impact of business decisions. Accurate price elasticity modeling is vital for markdown optimization since it shows planners how to price markdown merchandise to sell while preserving the maximum margin.

Easy supply chain management

Effective supply chain coordination can accommodate pricing variations outside the organization's control in demand forecasting software. When retailers and CPG businesses share data regularly, both organizations have visibility into their end-to-end supply chain, which can help offset unexpected demand signal changes. Retailer decisions on store assortments, including product displays, promotions, and in-store events, impact store demand, which affects business demand. Firms can use retailer data to improve forecast accuracy and meet demand with efficient supply chain coordination.