Forecasting replenishment orders in retail: value of modelling low and intermittent consumer demand with distributions

Abstract

In retail, distribution centres can forecast the stores’ future replenishment orders by computing planned orders for each stock-keeping-unit. Planned orders are obtained by simulating the future replenishment ordering of each stock-keeping-unit based on information about the delivery schedules, the inventory levels, the order policies and the point-estimate forecasts of consumer demand. Point-estimate forecasts are commonly used because automated store ordering systems do not provide information on the demand distribution. However, it is not clear how accurate the resulting planned orders are in the case of products with low and intermittent demand, which make up large parts of the assortment in retail. This paper examines the added value of modelling consumer demand with distributions, when computing the planned orders of products with low and intermittent demand. We use real sales data to estimate two versions of a planned order model: One that uses point-estimates and another that uses distributions to model the consumer demand. We compare the forecasting accuracies of the two models and apply them to two example applications. Our results show that using distributions instead of point-estimates results in a significant improvement in the accuracy of replenishment order forecasts and offers potential for substantial cost savings.

Keywords:

• forecasting
• replenishment orders
• planned orders
• retail supply chain
• low and intermittent demand

Acknowledgements

We would like to thank the four reviewers and the associate editor for their comments and suggestions that helped us to improve this paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This paper was supported financially by the HSE Foundation and the Aalto University School of Business.