In this advanced era of industry 4.0, many production and service processes are equipped with intelligent equipment, and their quality is monitored through count-based quality...y characteristics. Most of the time, count-based quality characteristics such as the defects/delays in a shipment are expected to be near zero, and the corresponding data will comprise a large number of zeros. Therefore, such near zero-defect/delay processes are termed high-quality processes. In literature, control charts, a well-known statistical process control tool, are widely used for real-time monitoring of the production/service process. For the high-quality processes, the traditional control charts will give high rate of false alarms due to the influence of large number of zeros in the data. This leads to misleading results, frequent stop up of processes, increased production costs and deprivation of resources, energy and workforce. Hence, the high-quality processes require more advanced control charts to monitor the count-based quality characteristics. Furthermore, most of the time, some covariates (additional linearly related information) are also present along with the count-based quality characteristics of a process. Hence, it is also needed to incorporate the additional information in developing the control charts to increase their power and obtain more valid conclusion based on them. In this project, we will design advanced control charts for high-quality processes to address the issue of high false alarms. The designed methods are not limited to monitoring quality characteristics, but we will also develop control charts, which can incorporate the linearly related extra information. We will run the simulation-based study to compare the performance of the proposed methods with existing ones. Moreover, we will implement the proposed structures on real-life datasets related to transportation, logistics, energy, air pollution, health and aviation to highlight the importance of the proposed study.
|Effective start/end date||5/06/22 → 4/06/23|
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