Recent rapid development of machine learning and artificial intelligence (ML/AI) have led to the development of fundamental questions for humans: how are we to understand and trust those more and more complex software systems. From ML approach of a data-based black box and earlier developments of knowledge and/or ontology based expert systems, the need to be able to explain ML/AI procedure has been identified and research area of explainable/responsible AI has been born. This paper applies the methodology for evaluation of AI systems for explainability and transparency on a use case of a traditional rule-based system for intelligent manufacturing planning. The methodology from the literature that addresses four components of explainability (namely, aspects, contexts, addressees, and explainers) has been utilized for the purpose of the case study. Overview of the methodology is briefly discussed in the paper before the analysis for the case study is performed. The analysis focuses on transparency of several aspects of explainability, like purpose, inputs, data, processing, and outputs with identification of which aspects are given by the systems, and which are still under purview of humans. The illustration of transparent rule-based procedures for process selection and process sequencing in the planning system have been provided. The paper can serve as a guideline or template for performing explainability studies of classical and modern AI systems.