An Implementation of KPI-ML to a Multi-Robot Line Simulator
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Emergence of highly competitive markets have led to more deep and thorough evaluation of performances across the manufacturing industry to enhance the efficiency of production processes. Manufacturing industry across the globe have been using different performance indicators and measuring terminologies for performance evaluation. This diversity deters evaluating and comparing manufacturing industries performance on a global scale and thus limiting industry collaboration. To define key performance indicators and general terminologies that are applicable in manufacturing operations management level of manufacturing industries, the International Standards Organization (ISO) developed the ISO 22400 standard. The Manufacturing Enterprise Solutions Association (MESA) international, an international association for manufacturing solutions, takes forward the work done of defining Key Performance Indicators (KPIs) by developing a Markup Language (ML) that represents the data models for the KPIs defined in ISO 22400 standards in an Extensible Markup Language (XML) schemas format. This language is formally known as KPIML. This thesis implements several the key performance defined in ISO 22400 standards to monitor the performance and efficiency of a real-world production line. In addition, this research work demonstrates the visualization of the implemented performance indicators in the form of different graphs. This visualization aids the management to analyze and evaluate the performance of production line in run time. A knowledge-based system is designed on data models present in KPIML for the implemented KPIs, which can easily be extended. Moreover, keeping in view the varying nature of manufacturing industry, the implementation of this research work allows users to model their own KPIs, which are specifically applicable to their use case and able to visualize them in run time.