Digital business model development and validation for real-time monitoring solution for electrical power transformers
Author(s)Kahawatte, Nalaka Kanishka Bandara.
Sloan School of Management.
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering.
Leaders for Global Operations Program.
Thomas Roemer and David Simchi-levi.
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Currently, the health of high-value power transformers is primarily evaluated by visual inspections, oil sample analysis conducted by a third-party lab, and measurements taken by a mechanic physically present at the transformer. Furthermore, the maintenance philosophies followed by Transformer Asset Managers are primarily reactive and preventive. Therefore, understanding the current health using real-time data monitoring, trends, and predictive models presents a significant opportunity for Transformer Asset Managers. These insights can be utilized to optimize maintenance scheduling, order necessary parts ahead of time, reduce downtime, increase service availability of the grid, improve asset utilization, and reduce maintenance costs. Maschinenfabrik Reinhausen(MR) has developed a Minimum Viable Product (MVP) for a Transformer Asset Monitoring solution named TESSA® Fleet Monitoring and is currently field-testing TESSA® Fleet Monitoring with several utility partner companies.One of the goals of this project was to develop a viable business model that would create tangible value to Transformer Asset Managers, enable MR to capture some of this value as profit, allow for growth of MR's new Digital Venture in Automation (AV) business unit, and sustain profits and growth for an extended period. To achieve this goal, the project activities included deep dives into asset management philosophies, customer segmentation, understanding the jobs, pains, and gains for the customers, partnering strategies, competitor analysis, product differentiation, revenue models, and cost structures. The business models developed were validated with customer interviews and further research. Another goal of the project was to survey the current data available on transformers that MR tap changers are installed on. A clustering algorithm was used to gain insights into common characteristics among transformers, end-users, and the types of transformers utilized by each of the end-users.Such a tool would allow MR to tailor maintenance services and trace any common failures among specific batches of components.
Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, May, 2020Thesis: S.M., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, in conjunction with the Leaders for Global Operations Program at MIT, May, 2020Cataloged from the official PDF of thesis.Includes bibliographical references (pages 134-137).
DepartmentSloan School of Management; Massachusetts Institute of Technology. Department of Civil and Environmental Engineering; Leaders for Global Operations Program
Massachusetts Institute of Technology
Sloan School of Management., Civil and Environmental Engineering., Leaders for Global Operations Program.