Using the Stochastic Approach Framework to Model Large Scale Manufacturing Processes

Modelling manufacturing process of complex products like electronic ships is crucial to maximize the quality of the production. The Process Mining methods developed since a decade aims at modelling such manufacturing process from the timed messages contained in the database of the supervision system of this process. Such process can complex making difficult to apply the usual Process Mining algorithms. This paper proposes to apply the Stochastic Approach framework to model large scale manufacturing processes. A series of timed messages is considered as a sequence of class occurrences and is represented with a Markov chain from which models are deduced with an abductive reasoning. Because sequences can be very long, a notion of process phase based on a concept of class of equivalence is defined to cut up the sequences so that a model of a phase can be locally produced. The model of the whole manufacturing process is then obtained with the concatenation of the model of the different phases. The paper presents the application of this method to model the electronics chips manufacturing process of the STMicroelectronics Company (France).