Total Quality Management in Higher Technical Education
RS Mishra1, Anshuman Pandey2, Aryan Rana3, Aakash Mehta4
1Prof. RS Mishra, Department of Mechanical Engineering, Delhi Technological University, Delhi, India.
2Anshuman Pandey, Department of Mechanical Engineering, Delhi Technological University, Delhi, India.
3Aryan Rana, Department of Mechanical Engineering, Delhi Technological University, Delhi, India.
4Aakash Mehta, Department of Mechanical Engineering, Delhi Technological University, Delhi, India.
Manuscript received on 11 May 2023 | Revised Manuscript received on 24 May 2023 | Manuscript Accepted on 15 June 2023 | Manuscript published on 30 June 2023 | PP: 29-39 | Volume-12 Issue-7, June 2023 | Retrieval Number: 100.1/ijitee.G95930612723 | DOI: 10.35940/ijitee.G9593.0612723
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The paper examines the behaviour of Indian Technical Education through Total Quality Management tools and techniques. To identify research gaps, we review existing research papers and propose solutions through an analytical data analysis process, with a significant focus on higher educational institutions. We will review the methodology currently used by universities and explore how it can be brought up to par with that of foreign universities. The reviewed papers will be used to identify the shortcomings of the research and will be taken into consideration when analysing it. Then we will be doing the quantitative data analysis of some collected data from google form and internet sources regarding the six TQM factors that influence the enrolment and hence the quality of institutions and determining the hypothesis result, then we will discuss the results and shortcomings of the analysis and will bridge those shortcomings by providing various possible solutions in that regards. Throughout the process, we have gathered data, analysed it, and provided a solution. We have used IBM SPSS AND AMOS software to construct TQM models and their covariance relations. The future aspect of this paper confirms that any industry can adopt the same procedure to analyse the quality of that organisation.
Keywords: Total Quality Management, Technical Institutions, AMOS & SPSS.
Scope of the Article: Artificial Intelligence