Set-up based operator training method in automated manufacturing: A joint production engineering and competence theory approach
Productivity improvement strategies such as increased automation, implementation of work cells and introduction of flexible work organisations have changed the content of operator work and the preconditions for training manufacturing competence. Traditional industrial training approaches have become increasingly obsolete. Even though modern competence and learning theories provide adequate descriptions, they lack the precision of linking needed competences with productivity improvements. There is thus a need for learning and training methods that are adapted to manufacturing systems and organisations of today.
The purpose of this thesis is to develop and validate an operator training method for effective learning in automated manufacturing. The main research was done in two manufacturing lines within a multinational company. The collaborative action research method was selected as research approach.
The operator training method uses set-ups as the basis for training activities. The complete method includes of three parts: set-up training during pro-duction stops, instructor led training and video instructions as a preparation tool. The training was coordinated through production stops. These stops provided more concentration on the learning of the basic set-up sequence, which more appropriately prepared the operators for learning the set-ups in real production conditions. Instructor led training and video instructions also facilitated the learning process, mainly by providing multiple learning alternatives. The method improved the learning process and reduced set-up times both during training and normal production. Furthermore, the research indicated that learning the set-up task also served as a parallel way of learning to handle stops and disturbances. A relationship was observed between team competence and downtime losses. During the test period, the training method was able to reduce downtime and speed losses. However, these reductions were not significant. Remarkably, large variations in the measures used were observed. It is thus likely that the training method had an impact on productivity, although it was overshadowed by the great process variation.
It was concluded that operator-training methods that both train key work tasks such as set-ups and help to learn to handle disturbances are useful in automated manufacturing. Furthermore, this research indicates that competence and learning are necessary but not sufficient conditions for productivity improvements. This suggests that multi dimensional approaches such as joint production engineering and competence theory approaches are necessary for achieving an effective impact on productivity.