ABSTRACT

The basic idea of process mining is to extract knowledge from event logs recorded by an information system. Until recently, the information in these event logs was rarely used to analyze the underlying processes. Process mining aims at improving this by providing techniques and tools for discovering process, organizational, social, and performance information from event logs. Hence to gain competitive advantage, dyeing unit try to streamline their processes. In order to do so, it is essential to have an accurate view of the workers work load under consideration. In this paper, we apply process mining techniques to obtain meaningful knowledge about these flows, e.g., to discover typical paths followed by particular groups of colors and assigned workers to do work. This is a nontrivial task given the dynamic nature of dyeing unit processes. The paper demonstrates the applicability of process mining using a real case of a worker work load process in Emerald Dyeing Unit, to discover the social network analysis. Using a variety of process mining techniques, we analyzed the dyeing unit process from three different perspectives: (1) the control flow perspective, (2) the organizational perspective and (3) the performance perspective. In order to do so we extracted relevant event logs from the dyeing unit information system and analyzed these logs using the ProM framework. This paper only deals about organizational perspective of the dyeing processes. Therefore the results show that process mining can be used to provide new insights that facilitate the improvement of existing workers work load.