Data driven manufacturing risk assessment for turbine engine programs
Author(s)Yadama, Sagar P.(Sagar Pandey)
Massachusetts Institute of Technology. Department of Mechanical Engineering.
Sloan School of Management.
David Hardt and Roy E. Welsch.
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Waste and uncertainty can be managed at all levels of a production process from a program level down to a single step in a manufacturing cell, and is done by assessing risk. Risk is defined as uncertainty in the ability to deliver the final product of a manufacturing process. The standard method for evaluating risk in aerospace is Manufacturing Readiness Level (MRL). However, these methods were developed for previous generations of turbine engines, and do not represent the capabilities of a modern manufacturing environment. The MRL process for managing overall manufacturing efficiency of an engine program is highly qualitative, and based on leveraging industry knowledge. The process requires experienced team members to implement, is highly time intensive, and is disconnected from the quantitative metrics that drive performance in the rest of the organization.In an effort to revamp the manufacturing risk assessment process for new turbine engine programs, Pratt & Whitney seeks to develop a standardized data driven risk assessment process to improve the accuracy and accessibility of risk management. This thesis develops a data driven risk assessment process to provide quantitative validation for the legacy risk evaluation method. Cost, quality, and delivery operations metrics are collected and analyzed to build a comprehensive measure of risk for each part in a turbine engine program that can be aggregated to determine total risk for the entire program. In three phases, this project addresses three key challenges faced by the risk management team while also building a comprehensive risk analysis process accessible to anyone in the production hierarchy. Phase one addresses automated identification of operations critical parts in an engine program resulting in complexity reduction of over 80%.Phase two focuses on development of an automated risk visualization dashboard that collects critical operations metrics into a central source and computes a comprehensive risk value for each part in the engine program. Phase three is the construction of standard risk mitigation management tools to transform output of the risk dashboard into useful information for individual manufacturing teams. Ultimately, this research shows that development of standard processes and tools for identifying, analyzing, and mitigating production risk using real time operations data significantly enhances the ability of management and manufacturing teams to understand and mitigate uncertainty in final engine delivery.
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, May, 2020Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, May, 2020Cataloged from the official PDF of thesis.Includes bibliographical references (pages 81-82).
DepartmentMassachusetts Institute of Technology. Department of Mechanical Engineering; Sloan School of Management
Massachusetts Institute of Technology
Mechanical Engineering., Sloan School of Management.