Optimal dynamic pricing in the retail industry
The fashion industry is largely confronted with products having seasonal demand. This brings forth a risk that the supply is not totally cleared at the end of the season. Hence, near the end of the season, the retailer is forced to markdown the overstocked supply. Since there is always a gap between the demand that is expected and the demand that is actually realized, it is necessary to find the optimal markdown path during the season. The central question is when and how much to markdown in order to optimize the expected total profit given the available supply.
In this project, we focus on dynamic pricing policies that determine when to apply a markdown (based on the observed inventory levels) and how much to markdown (based on price elasticity which is estimated from sales data).
The model for deriving optimal dynamic pricing policies builds upon models from econometrics, statistics, operations research, and revenue management. The sales data provides input to price elasticity models from econometrics. By using Cox regression from statistics, one can obtain a model for the lead time of products. The two previous models are combined into a revenue management model with demand unconstraining. This is then used in a Markov decision model from operations research to determine optimal policies. The model has proven its success in a real retail environment.
Revenue management, optimal markdown policies, Markov decision processes, inventory management, dynamic pricing, censored data