Picture of Open Access badges

Discover Open Access research at Strathprints

It's International Open Access Week, 24-30 October 2016. This year's theme is "Open in Action" and is all about taking meaningful steps towards opening up research and scholarship. The Strathprints institutional repository is a digital archive of University of Strathclyde research outputs. Explore recent world leading Open Access research content by University of Strathclyde researchers and see how Strathclyde researchers are committing to putting "Open in Action".


Image: h_pampel, CC-BY

Extended revenue forecasting within a service industry

Whitfield, Robert and Duffy, Alexander (2013) Extended revenue forecasting within a service industry. International Journal of Production Economics, 141 (2). pp. 505-518. ISSN 0925-5273

Full text not available in this repository. (Request a copy from the Strathclyde author)


Revenue forecasting is an important topic for management to track business performance and support related decision making processes (e.g. headcount or capital expenditure). It focuses on how a business recognises operating revenue, which can differ from the point at which a sales order is won. Whilst there are many publications detailing forecasting theory, in a business context these largely focus on sales order recognition alone. This paper describes the development of a revenue forecasting tool appropriate for service provision. The organisation involved in the development of the revenue forecasting tool will remain anonymous for commercial reasons but will be referred to as “Organisation A”. The targeted outcome was to extend the forecast window from one month to three months with an error rate of no more than ±10%. The tool was required to consolidate supporting data, adopt appropriate analysis/projection techniques and extend the forecast window in a specific and complex business environment. The resulting tool returned high level results that were aligned to the original targets, and was developed with three components using a combination of projection approaches appropriate to the operating environment. Whilst limited to a specific service industry as a trial, the paper provides a useful reference point for revenue forecasting in complex service businesses and provides a basis for further research opportunities for extended revenue forecasting and business analysis approaches within other service industries.