Using weather sensitivity analysis to predict business performance. (5th July 2019)

• Record Type: Journal Article
• Authors: Brown, Hannah; Lee, Malcolm; Steele, Edward; Neal, Robert; Chowienczyk, Katie
• Is Part Of: Weather. Volume 74:Number 7(2019)
• Page Start: 231
• Page End: 236
• Publication Date: 2019-07-05
• Subjects: Meteorology -- Periodicals; Weather -- Great Britain -- Periodicals; 551.1
• DOI: 10.1002/wea.3581
• Languages: English
• ISSNs: 0043-1656

Abstract

Abstract : For many businesses, the weather is a strong driver of performance. Here, we introduce two assessment tools for organisations wanting to identify these links, so that they may subsequently be used to predict future changes. By combining data collected by businesses with historical weather data, the assessment tools use a set of pre‐defined statistical analysis methods to quantify their particular sensitivities. This analysis is fast and flexible, to facilitate the ease of incorporation of meteorological effects into the corporate decision‐making process. The weather sensitivity analysis conducted includes both correlation and regression analysis and weather pattern analysis, providing results suitable for subsequent operational application within a real‐time forecast system. A demonstration is provided for bike hire data collected under the Santander Cycles scheme, published by Transport for London, wherein it is shown that 74% of the variability in the journeys undertaken may be explained by a simple statistical model involving only two weather variables (temperature and rainfall). Abstract : Ultimately, the goal for any corporate decision‐maker is to understand the key influences on their business performance in sufficient detail to enable them to predict it. Of the many possible influences, weather is frequently cited for its impact on operations and profits, affecting both supply (e.g. products) and demand (e.g. consumer behaviours). The application of weather sensitivity analysis techniques quantifies the effects of weather on business performance, which in turn can lead to the development of predictive demand models.