Quarterly sales analysis using linguistic fuzzy logic with weather data

Abstract

Reliable estimation of customer demand for products and services constitutes a key aspect of financial planning in every company. When estimating future sales, as a proxy to demand, in addition to pure economic quantities, a large selection of (exogenous) variables specific to a given product can be considered. Potential impact of weather conditions on sales has been known for very long time, though the research using weather data has been mostly focused on energy sector. As concerns retail, several authors have started to analyze this issue only recently. This paper proposes a novel approach studying (quarterly) sales using weather data and is inspired by fuzzy natural logic. The method is based on modeling the influence of average temperatures on sales by fuzzy linguistic IF-THEN rules. The proposed methodology is applied to real data of quarterly ice-cream sales and compared with a standard approach. The results are promising especially when monthly average temperatures are considered.