As the field of requirements engineering for AI-based systems is currently being extensively researched, explainability requirements are one of the most focused issues of that research. Authors have written about the problems in requirements for AI, and a few researchers have also provided numerous frameworks to address these challenges. Researchers have pointed out that the available state of the art solutions for the explainability of an AI based system are not precisely focused on the requirements of the system’s end users. The requirement engineering to know the explainability requirements of the system’s users is a prerequisite in order to enhance the explainability of an AI based system. In this thesis, we are implementing an AI-based monitoring system for an already developed and deployed Enterprise resource planning system (ERP) in a midsize automotive parts manufacturing company. During this development process, it is necessary to conduct thorough work on the explainability requirements for such an AI-based system. We are undertaking a case-study based practical experiment to give support to the concept of explainability. Along with the development of AI-based software according to the users’ requirements and specifications, we are using knowledge graphs and HyperText Markup Language (HTML) tables to document the explanations of the system’s outputs.We are elaborating on how knowledge graphs and HTML tables can be used to explain the outputs of the AI-based system differently for different users or stakeholder groups in different contexts. Finally, the validation of our work is done with the help of the system end user’s feedback.