A Blended System Dynamics-Discrete Event Physics-Based Model for Anomaly Detection in Cyber-Physical Manufacturing Systems

Being able to quickly detect anomalies and reason about their root causes in critical manufacturing systems can significantly reduce the analysis time to bring operations back online, thus reducing expensive unplanned downtime. Machine learning-based anomaly detection approaches often need significant amounts of labeled data for training and are challenging to scale for manufacturing deployments. A robust blended system dynamics and discrete event simulation physics-based modeling methodology is proposed for the task of automated anomaly detection. The blended model consists of discrete event simulation (DES) components for the discrete manufacturing process modeling, and system dynamics (SD) components for continuous variables. The methodology strikes a balance between the computational overhead for online monitoring and the level of details required to perform anomaly detection tasks. The implementation of models takes an object-oriented approach, allowing multiple components of a smart factory to be robustly described in a modular, extendable and reconfigurable manner. The proposed methodology is applied to and validated by data collected from a real commercial manufacturing plant. A production line is modeled with DES components and heat transfer is modeled with SD. The blended model is then utilized for anomaly detection. It is demonstrated that the model-based approach is effective not only for detecting but also explaining particular types of anomalies in a commercial discrete manufacturing system.

Analysis of Factors Causing Wastes in FMCG Using Lean Tools

In this research paper, we have used lean tools that are applicable in various faster growing industries like FMCG, pharmaceuticals; hospitality, manufacturing etc are used. The tool that is given the priority over others in identifying the waste is Value stream mapping. Value stream mapping is a lean tool that is used to translate both information and flow of data through which those processes that are not adding any positive value in our study can be easily eliminated. This paper is an effective effort to find out the waste production during manufacturing of a perishable goods in discrete manufacturing system. Pareto chart has been used to categories the factors that are responsible for the 80 percent of waste. Here Ishikawa diagram has played a vital role for finding of their possible causes and effect. Other contemporary lean tools like Kanban, Kaizen, and FIFO are used for future state map processing. The result obtained through the future state value stream map states the changeover time of various processes, the cycle time near to the takt time, improvement in lead time and total cycle time of the manufacturing process