Estimating the fleet investment demand for primary production vehicles and machines

Abstract

This thesis examines what are the forecasts, trends, and uncertainties associated with renewal of the machinery and equipment fleet in Finnish primary production industries. The aim of this thesis is divided into two overlapping parts. The first objective is to explore how investment demand for Finnish agriculture and forestry machines can be estimated by using available databases. The second objective is to assess the state of agriculture and forestry fleets in Finland and form an estimate of the investment demand in the chosen vehicle groups.

The thesis is conducted as an exploratory study and uses both quantitative and qualitative methods. The results indicate that by utilizing public Finnish databases, investment demand can be estimated with quantitative methods only for certain agricultural vehicles and for forestry vehicles investment demand can be estimated only on an aggregate level. Qualitative opinions of experts can provide value in supplementing the forecasting process by assessing the quality of the data used in the forecasts. The results show which methods are most feasible to estimate investment demand for forestry vehicles and tractors. The ARIMA method yielded the best results for forestry vehicles and damped trend DES model for tractors. The most important factors causing uncertainty to future investment demand were seen to be, the level of interest rates, geopolitical changes, price level of machines, changes in the global economy, and regulation.

If companies opt to estimate investment demand for primary industry vehicle market based on public data sources, they should utilize Traficom open-source data as it enables analysis on a more detailed level than other sources of available data. To maximize the value the open-source data can provide, companies should develop a separate database based on the open-source data. This separate dataset would more accurately reflect the historical demand of different vehicles as the open-source data changes due to unactive vehicles get removed from it which impacts the validity of the open-source data.

The findings of this study apply only to specific vehicle groups and therefore the results cannot be generalized to other industries or vehicle groups.