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To propose and test an augmented collaborative planning, forecasting, and replenishment (A‐CPFR) model in a retailer‐supplier context with a view to improving forecasting accuracy and then reducing the “bullwhip effect” in the supply chain.

After a literature review, the paper presents a real case in which the present authors provided assistance. The description of the case includes: case company background; an “as‐is” model analysis; a “to‐be” (CPFR) model analysis; and a description of the results and potential benefits. The paper then proposes an A‐CPFR model for the case and performs a simulation of the new model for comparison with the existing CPFR model.

The results show that the mean absolute deviation of forecasting and the inventory variance are both better in the proposed model than in the existing CPFR model. The proposed model can thus improve the accuracy of sales forecasting, reduce inventory levels, and reduce the “bullwhip effect”.

In addition to information provided by the retailer, a logistics supplier should also obtain competitors' promotional information from the market as another factor for forecasting – thus enabling timely responses to demand fluctuations.

The proposed model is an original and useful development on the existing CPFR model. It could become a reference model for the retail industry in implementing CPFR in the future.