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Using Machine Learning and Demand Sensing to Enhance Short-Term Forecasting
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"Using Machine Learning and Demand Sensing to Enhance Short-Term Forecasting"

Effective supply chain management is critical to ensure the timely replenishment of products by properly managing the movement and storage of raw materials and finished goods from the point of origin to the point of consumption.

Implementing a short-term (one to eight week) forecast is critical to understanding and predicting changing consumer demand associated with sales promotions, events, weather conditions, natural disasters and other unexpected shifts (anomalies) in consumer demand patterns. Short-term demand sensing allows manufacturers, retailers and CPG companies to predict and adapt to those changing consumer demand patterns. Traditional time-series forecasting techniques that model patterns associated with trend and seasonality are typically used for demand forecasting at manufacturing companies, retailers and CPG companies. These models can uncover those two historical demand patterns and provide an estimate of demand into the future. In addition to the historical demand data, organizations can use other data feeds – such as point-of-sale (POS) information, future firm open orders and promotional events – to create a collective picture of the demand signal.

The key benefits for using demand sensing include:

  • Increasing sales revenue by improving sensing capability to drive an agile supply chain response to meet customer and  consumer demand needs.
  • Improving transportation planning with preferred carriers, cutting execution costs by reducing redeployment and lowering inventory carrying costs.
  • Improving customer service levels and on-shelf availability of products, ensuring consumers find the products that they want and need.
  • Improving revenue/profit through enhanced replenishment efficiencies and fewer stock-outs on shelf.

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