Framework for assessing the impact of change on a factory by adapting learning behavior models

Abstract

Since the beginning of global networking, the environment of production systems has been considered a turbulent zone, in which the unpredictability of economic, technical and political trends characterizes a phase of sudden and high speed of change. These changes can lead to disturbances in factory operation resulting in economic losses. Therefore, each change requires time to adapt to reach and maintain a certain performance level. One element of a factory are the employees working on production processes, maintenance or further support functions. Employee specific learning behavior of production tasks is already investigated in detail and describes the constantly increasing performance based on a learning process. To reach a certain level of productivity, employees require a specific timeframe. The duration of the timeframe depends on further factors e.g. task complexity or individual learning ability. Learning behavior is similar to the adaption of factories to change events, which is why it offers a basis for describing the proven concept of changeability. Therefore, the goal of this paper is to relate the characteristics of learning curves to changeability of factories in order to develop a framework for assessing the impact of change. The framework contains starting points for influencing the learning process in terms of its speed or necessity, as well as for estimating the point in time at which a factory is ready for change again. For this purpose, possible influencing factors such as resilience or transformability, and the significance of production logistic key figures are examined in a more detailed analysis.