Using pairwise associations for multi-item markdown optimisation

ABSTRACT

Forecasting is an essential task conducted regularly by competitive retailers around the world. Most retail decisions particularly markdowns are made based on the demand forecasts which may or may not be accurate in the first place. In this study, we propose a framework for forecasting weekly demands of retail items via linear regression models within multi-item groups that incorporate both positive and negative item associations. We then utilise dynamic pricing models to optimise markdown decisions based on the forecasts within multi-item groups. Grouping items can be considered as a form of variable selection to prevent the overfitting in prediction models. We report regression results from multi-item groupings besides results from single-item regression model on a real-world data-set provided by an apparel retailer. We then report markdown optimisation results for the single items and multi-item groupings that multi-item forecasting models are built upon. The results show that the regression models provide better estimates within multi-item groups compared to the single-item model. Moreover, the overall revenue achieved in multi-item markdown optimisation across all grouping schemes are higher than the total revenue yielded by single-item markdown optimisation scheme.

Keywords:

• Retail demand forecasting
• constrained clustering
• association mining
• k-means clustering
• markdown optimisation

Acknowledgements

A significant part of this work was originally supported by the Turkish Scientific Research Council under Grant TUBITAK 107M257. I would like to thank my colleagues Professors Ufuk Kula, Gürdal Ertek and Tankut Atan for their helpful comments on this work. I also would like to thank to merchandising managers of a leading apparel retailer in Turkey for providing the data and sharing their domain knowledge.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. http://tinyurl.com/cu5txlg

Additional information

Funding

Turkish Scientific Research Council [grant number TUBITAK 107M257].