Improving Forecasting and Pricing for a Major Food Manufacturer

Improved Forecasting for a major food manufacturer
  • May 19th, 2018


Balancing sales across distribution channels is a critical area for manufacturers due to its impact on brand image, product value and strength of distribution channel. A leading food manufacturer wanted to assess the impact of the pricing tactics adopted by its retail channel partners.

The Problem

The retail partners of our client – a Fortune 500 food manufacturer set their own prices for the manufacturer and competitor products, including discounts, and changing prices across markets.
Since the manufacturer had no visibility into these price changes, it was challenging to plan for the impact on volume sales through the retail channels, resulting in a significant loss of revenue opportunities. The existing pricing algorithms did not incorporate the cross-price elasticity for price changes implemented by the retail partners for their products as well as competitor products.

The Solution

Mu Sigma helped the Pricing Managers improve volume forecasts by understanding the impact of retailer pricing decisions. The solution had three components that helped quantify the impact of retailer pricing decisions on volume forecasts:

•   Cross Price Elasticity Models (at a product-market level) which quantified the impact of price changes
•   Price Threshold Models that identified the price bands beyond which the sales showed a significant drop in volumes
•   Price Gap Models that factored the price difference between their products and competitor products

These models were developed for each of the major retail channel partners. They helped the Pricing Managers respond better to retailer led price changes and promotions and incorporate the impact of these events into the forecasting process.

These models were codified in an easy-to-use dashboard which is currently being used by the Pricing Managers to evaluate alternative scenarios and develop better volume forecasts by retail channel partners on an ongoing basis.

The Impact

This solution framework delivered 35 – 40% improved volume forecast accuracy and helped the client better estimate the impact of retailer led pricing decisions on their volume sales.

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