  Price forecasting for used construction equipment is a challenging task due
to spatial and temporal price fluctuations. It is thus of high interest to
automate the forecasting process based on current market data. Even though
applying machine learning (ML) to these data represents a promising approach to
predict the residual value of certain tools, it is hard to implement for small
and medium-sized enterprises due to their insufficient ML expertise. To this
end, we demonstrate the possibility of substituting manually created ML
pipelines with automated machine learning (AutoML) solutions, which
automatically generate the underlying pipelines. We combine AutoML methods with
the domain knowledge of the companies. Based on the CRISP-DM process, we split
the manual ML pipeline into a machine learning and non-machine learning part.
To take all complex industrial requirements into account and to demonstrate the
applicability of our new approach, we designed a novel metric named method
evaluation score, which incorporates the most important technical and
non-technical metrics for quality and usability. Based on this metric, we show
in a case study for the industrial use case of price forecasting, that domain
knowledge combined with AutoML can weaken the dependence on ML experts for
innovative small and medium-sized enterprises which are interested in
conducting such solutions.
