A novel lean manufacturing mathematical assessment tools using set theory in energy sector

Lean manufacturing assesments have become fundamentally significant for companies that seek to identify problems, identify the sources of the problems, and present effective ways through which these problems can be solved through practices of lean manufacturing. Thus, the present study aimed at developing an initial step that contributes to the transformation of lean applications, and assessments of the logic of artificial intelligence through set theory with the aim of facilitating the identification of practices and their actual impact. The study also amined at extending the application of lean manufacturing practices assessments in the energy sector, especially in thermal power plants, renewable power plants, nuclear power plans, electric transmission companies, and electric distribution companies. Therefore, the study was conducted based on the main objectives which are: I) To develop mathematical logic for improving the lean manufacturing assessment tools. ii) To design a novel lean manufacturing mathematical methods and set theory. iii) To test the validity and reliability of a novel lean manufacturing mathematical assessment through a real-life case study of the Jordanian energy sector. iv) To identify the types of wastes and sources of wastes in the energy sector and appropriate LMPs and the weight of their effect on the energy sector. The goals were achieved by using the "Set Theory" to develop the mathematical logic that can facilitate the efficiency and accuracy of the design process of assessments. The main hypotheses of the study were developed based on the identification of wastes, source of waste, and lean manufacturing practices that could be used for waste disposal in the energy sector. The mathematical logic was based on an association between quality, quantity and the hierarchical analysis in different processes in energy systems.This logic was then applied in the process of designing the assessment of practices in the real-life case study. The effectiveness of the mathematical logic was confirmed by the precise results obtained, whereby the results showed that the mathematical model is able to identify waste, source of waste and lean manufacturing practices accurately. Based on the study findings, a comprehensive description of practices that can be used in the energy sector was presented. More specifically, the findings demonstrated a clear difference between the nature of waste and lean manufacturing practices depending on the different operations of companies within the energy sector. Another difference is in the actual value of the impact of the application of lean manufacturing practices according to the type of lean manufacturing practice. In addition, the mathematical logic that was developed can be applied in the evaluation process in other sectors with ease and clarity to ensure more effective results. These results can be used to the design smart assessment systems through software programs such as Python and Matlab applications in future studies.