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Project-based manufacturing : an approach for quote development

Author(s)
Montgomery, Dante Edward.
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Other Contributors
Sloan School of Management.
Massachusetts Institute of Technology. Department of Mechanical Engineering.
Leaders for Global Operations Program.
Advisor
Hermano Igo Krebs and Roy Welsch.
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MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. http://dspace.mit.edu/handle/1721.1/7582
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Abstract
Project-based manufacturing presents unique challenges that highly automated and repetitive production lines do not face. Highly automated production lines do not have much variation in the types of products that are made, and these products are usually manufactured at high volume. This makes it relatively easy for the business to know its marginal cost of production and to assist with pricing strategies. Project-based manufacturers, on the other hand, tend to specialize in manufacturing products that are highly customized. These environments have much more variability and products are usually produced in low volume. Predicting the cost of production in this scenario is difficult and usually requires a dedicated team that focuses on cost estimating and proposal development for competitive bids. Cost estimating and proposal development is a time-consuming and costly overhead activity and inaccurate proposals can have significant impact to business performance.
 
This project focuses on how data analytics can be used to streamline the estimating process for project-based manufacturers resulting in reduced proposal cycle time and improved accuracy and precision metrics. This pilot project focuses on one product type with a privately owned project-based manufacturer. Although the scope of this project focuses on one product type, each project is unique as it relates to dimensions, surface contour shapes, technical specifications, and other unique features. The methodology for this project analyzes historical technical data and labor durations and implements a lasso regression model to predict the number of labor hours required for a future project with a given set of technical inputs. While this project focuses on producing a fit-for-purpose solution for estimating one product type for one company, the process and methodology can be applied more holistically.
 
The findings from this research can be applied to accomplish the same objectives for products across an array of project-based manufacturing industry verticals.
 
Description
Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, May, 2020
 
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, in conjunction with the Leaders for Global Operations Program at MIT, May, 2020
 
Cataloged from the official PDF of thesis.
 
Includes bibliographical references (pages 64-65).
 
Date issued
2020
URI
https://hdl.handle.net/1721.1/126912
Department
Sloan School of Management; Massachusetts Institute of Technology. Department of Mechanical Engineering; Leaders for Global Operations Program
Publisher
Massachusetts Institute of Technology
Keywords
Sloan School of Management., Mechanical Engineering., Leaders for Global Operations Program.

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