Engineering knowledge is relevant to the field in manufacturing and logistics processes. Every change and high-level decision depends on it. Sunstone proposes a data-driven process of model discovery to support engineering knowledge and to keep it up-to-date,
Process mining is a form of process management that supports the analysis of business processes based on event logs. During process mining, specialized data mining algorithms are applied to event log data to identify trends, patterns and details contained in event logs recorded by the manufacturing execution system (MES). Process mining aims to improve process efficiency and the understanding of processes.
Process simulation technique
A huge amount of data is generated by a MES, and it becomes harder to understand the overall processes using data analysis techniques. Process mining can extract process-oriented knowledge from event logs in the MES, to exploit big data and provide an accurate view of the manufacturing process. Process mining provides a manufacturing process model, which is valuable as it provides insight into actual manufacturing processes. Based on the model, discrete-event process simulation is proposed to process improvements and to answer the “what if…” questions.
Simulation in manufacturing systems is the use of software to analyse them and thereby obtain important information. It is easy to understand the difference made by changes in the local system, but it is very difficult or impossible to assess the impact of this change in the overall system. Simulation gives us some measure of this impact.
The Sunstone system provides the following data for the process-simulation software:
- Parts produced per unit time
- Time parts spend in the system
- Time parts spend in the queue
- Time spent during transportation from one place to another
- In time deliveries made
- The build-up of the inventory
- Inventory in process
- Utilization percentage of machines and workers.