The Importance of Forecasting Inventory Availability in Retail and CPG

The Importance of Forecasting Inventory Availability in Retail and CPG

The Importance of Forecasting Inventory Availability in Retail and CPG


The retail and consumer packaged goods (CPG) industry is characterized by relatively low margins, high seasonality, demand variability, inventory risk, and influence of consumer sentiment.

These characteristics amplify the importance of predictive analytics for key activities such as sales forecasting, demand planning, supply chain management and inventory optimization.

To stress the importance of these activities, consider the following research about consumer perspectives at the start of the COVID-19 pandemic, conducted by BlueYonder:

  • 87% of consumers have experienced out-of-stock products, both in-store and online.
  • 79% percent of consumers were more likely to buy the same product from a different retailer if a desired product was out of stock.
  • 79% of consumers were more likely to buy a different brand of a product from the same retailer if their desired brand of that product was out of stock.

The bottom line — which affects your bottom line — is that inventory availability supersedes brand loyalty. Yet inventory availability is dependent upon numerous variable factors across the entire value chain (see animation below).


Value Chain Supply Chain

Each link in the value chain must serve a demand in the quickest possible way.


Reducing and accounting for this variability through better planning based on better forecasts (from better forecasting models) is the key to optimizing inventory, ensuring the right product is available at the right price at the right time, thus meeting customers’ needs.

Machine learning (ML) techniques provide the most up-to-date approach for accurate forecasting, but they can be time consuming to implement. TIM’s instant ML (InstantML) and real-time instant ML (RTInstantML) capabilities allow analysts to easily apply forecasting models to any time-series data and quickly iterate on planning scenarios.

Based on: Predictive Analytics for Time Series with InstantML, by L. Miller and Tangent Works (2021).


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