Determining the optimal level of automation for cash flow forecasts

Purpose The aim of this paper is to develop an approach for identifying the optimal level of automation by maximizing the level of automation and accuracy while addressing problem areas of forecast quality. Design/methodology/approach We use a unique set of forecasts planned by six subsidiaries of a multinational corporation to train and test various models. We compare the accuracy of three levels of automation and how they address prevalent forecasting process quality problem areas. Findings The findings indicate that accuracy alone is not a sufficient dimension to consider when selecting the optimal level of automation but that forecast process quality areas need to be assessed as well. Research limitations/implications The limitations of this work are the inability to study the effects of our tool’s recommendations, the sample originating from a single company, the use of simple statistical methods and the limited number of dimensions to evaluate forecasts. Practical implications Firms should apply the structure offered in this paper to target individual components of the cash flow forecasting process when automating it and use it to structure their discussion, planning and implementation of automation. Originality/value A novel approach for determining the optimal level of automation for cash flow forecasting combining the human information processing framework of Parasuraman et al . (2000) with the forecast quality problem areas by Fildes and Petropoulos (2015).