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sciencedirectForecast accuracy is a key stumbling block for manufacturing supply chains, as it continues to paralyze a variety of short-term operational decisions, such as production scheduling and inventory management.

This is especially true where there are multi-echelon complexities – including poor collaboration, costly allocation of inventory between locations, and a lack of visibility into sales data – which muddle the true picture of demand.

Seamless Data Collection and Demand Planning

Our recent partnership with ReSight highlights the importance of establishing a seamless sales data collection and demand planning value chain to create highly accurate wholesaler demand forecasts in these complex environments.

These advanced solutions improve the availability and reliability of consumer, retail and distribution data – empowering manufacturers to create highly accurate forecasts, eliminate manual planning and spreadsheets, and wipe out unnecessary risk, cost and inefficiencies in the forecasting process.

Not Convinced?

The strategy of using shared sell-through data to forecast demand is supported in a paper published by the European Journal of Operational Research. The paper

  • Explores empirical literature on the use of downstream data for forecasting
  • Investigates the use of sell-through data to improve short-term demand forecasts
  • Considers both time series methods and machine learning techniques
  • Uses information on both downstream demand and inventory positions
  • Discusses how improvements in forecast accuracy highlight the practical usefulness of sell-through data

Read the paper and then contact us to learn how Blue Ridge’s integration with ReSight and our Multi-Echelon Inventory Optimization (MEIO) solutions provide a number of features to help manufacturers drive profits through forecast accuracy, including:

  • Retail and distribution sell-through data collection;
  • Precise demand forecasting by item, customer and channel;
  • Demand planning collaboration;
  • Consensus planning and market trend analysis

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