Online Retailer Assortment Planning and Managing under Customer and Supplier Uncertainty Effects Using Internal and External Data

Abstract

One of the main significant and challenging decisions for online retailers is assortment planning (AP). This decision become even more complex while considering demand and supply uncertainties in the AP planning. However, this lead to more efficient results in today's uncertain markets. Online retailers of late have access to massive amounts of internal and external data which they can leverage their power for tackling the inherent demand uncertainty and supplier uncertainty for assortment planning. This paper propose an AP framework for declaring how to use that data in different stage of decision making. Demand function in the framework is augmented using Google Trends (GT) and Google Correalte (GC) data which improve its accuracy. Using GT and GC increase the power of demand function extrapolability. Feature based modeling has been proposed to this end which allows us to use the GT data more easily. The final assortment decisions are then weighted against the supplier uncertainties to adjust for considering the variety and lead-time supplier effect. Techniques such as operations research methods and web science model will be utilised to develop the required approaches. While assortment planning is the combination of marketing and operations research techniques, in this work for the first time we incorporate web science techniques as the third edge of this important process.