Hierarchical Task Analysis (HTA) for Application Research on Operator Work Practices and the Design of Training and Support Systems for Forestry Harvester

Abstract

Highly mechanized forestry operations are essential for efficient timber harvesting. Therefore, the skills of harvester operators appear to be key to productive and sustainable use of the machines. Recent research has revealed a knowledge deficit regarding the work practices of forest machine operators. This urges systematic research into forestry machine handling and a corresponding refinement of analytical methods. Current analyses of operator tasks in forestry are less formalized and focus predominantly on machine efficiency and overall performance, but not so much on the human-related conditions of work performance and workload. Therefore, the objective of this paper is to introduce hierarchical task analysis (HTA) into forestry science. HTA is a versatile, formalized human-factors method that can be used to describe the work objectives of forest machine operators. HTA is suitable, for example, for describing (in)efficient work practices and thus as a basis for designing machine operator training and for systematically evaluating assistive technologies. The task analyses in this paper draw on a recently published empirical approach to analyzing work practices, workflows, and machine operator behavior for optimal human–machine collaboration in forestry application research. Specifically, the main work methods of clearcutting and thinning stand in European forestry were considered, with examples from Scandinavian and German method application. The process of HTA is described and a prototypical approach to HTA for both working methods provided. As a result, this work could show that a single work practice affects operator goals within different work elements and sets out how inefficient work practices can be described in terms of operator goals. With the introduction and exemplary application of HTA, a structured task definition in human-centered approaches is encouraged to analyze work practices, workflows, and machine operator behavior for optimal human–machine collaboration in forestry application research.