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Economic Feasibility of Adopting Additive Manufacturing for Low-volume Demand

Date Issued
May 1, 2020
Author(s)
Cokyasar, Taner  
Advisor(s)
Mingzhou Jin
Additional Advisor(s)
Sean P. Willems
Xueping Li
James Ostrowski
Permanent URI
https://trace.tennessee.edu/handle/20.500.14382/27179
Abstract

Additive manufacturing (also known as 3-D printing) is in its infancy. Although 3D-printers have been long existed as an alternative manufacturing technology to the conventional manufacturing (casting, molding, etc.), they were being used to prototype products rapidly hence the technology was referred to rapid prototyping. Recently, the AM technology has been adopted by enterprises to source low-volume demand products, such as spare parts and obsolete parts. Although the economical benefits of the technology for such products have not been theoretically measured, many enterprises started to adopt the technology. Under this research framework, we partially fill the gap in the literature and propose mathematical models and their sound solution methods to address the economic feasibility of AM adoption for low-volume demand. To this end, we first focus on a single enterprise and propose a mathematical model to determine the optimal amount of investment by partitioning products into two sourcing options, namely, AM option and inventory option. Then, we extend our research to answer more questions for a multi-facility enterprise. In this extended version, we determine the optimal location to deploy AM capacity while still assessing the viability and extent of the investment. Our study contributes to the literature by providing a generally applicable solution approach to mixed integer non-linear programs involving queueing non- linearity. On the other hand, we present useful managerial insights to aid practitioners in decision-making about AM adoption.

Subjects

Additive manufacturin...

supply chain manageme...

operations research

mixed-integer program...

Degree
Doctor of Philosophy
Major
Industrial Engineering
Embargo Date
May 15, 2023
File(s)
Thumbnail Image
Name

utk.ir.td_13642.pdf

Size

8.91 MB

Format

Adobe PDF

Checksum (MD5)

8f4dc3a38dfaccb04f626684381bc114


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