Forecasting Chargeable Hours at a Consulting Engineering Firm - Applicability of Classical Decomposition, Holt-Winters & ARIMA

Abstract

Reinertsen, a Swedish consulting engineering firm, is dissatisfied with the accuracy of its qualitative forecast of chargeable hours. This thesis investigates whether classical decomposition, Holt-Winters, or ARIMA can perform more accurate forecasts of chargeable hours than the qualitative method currently used at Reinertsen. This thesis also attempts to explain why or why not these forecasting methods improve the forecasting accuracy at Reinertsen. The purpose of this thesis is twofold: (1) to identify a suitable manpower forecasting method for Reinertsen; and (2) to contribute to previous literature on forecasting by further assessing the performance and the applicability of the chosen forecasting methods. The data applied was monthly numbers of chargeable hours which covered the period between 2007 and 2011. The first 48 monthly observations were used to generate the forecasts while the remaining 12 monthly observations were used to evaluate the forecasts. The data contains trend and strong monthly fluctuations. The results indicate that ARIMA and classical decomposition are inappropriate forecasting methods to forecast chargeable hours at Reinertsen. The relatively poor performance of classical decomposition and ARIMA is believed to be attributable to these methods inability to forecast varying fluctuations. The results also show that Holt-Winters yield the most accurate forecasts amongst the evaluated forecasting methods. The forecasted time series fluctuates much and the Holt-Winters method, which focuses on recent observations, might be better suited to capture these fluctuations. Consequently, the Holt-Winters method has the potential to improve the forecasting of chargeable hours at Reinertsen.