Optimizing logistics in a lean international supply chain

• Main Author: Frys, Peter
• Other Authors: Christopher L. Magee and Stephen C. Graves.
• Format: Others
• Language: English
• Published: Massachusetts Institute of Technology 2009
• Subjects: Sloan School of Management.; Mechanical Engineering.; Leaders for Manufacturing Program.
• Online Access: http://hdl.handle.net/1721.1/50094

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2009. === Includes bibliographical references (p. 86-87). === Since the signing of NAFTA in 1993, North American automotive OEMs have moved final assembly and other manufacturing operations from domestic locations to international locations. Mexico provides a relatively inexpensive labor force and is within geographic proximity of the US. Tier One suppliers have also relocated some operations to Mexico, such as American Axle & Manufacturing (AAM) and its Guanajuato Gear & Axle (GGA) facility, where this study was performed. While the proportion of GGA's inbound material sourced in Mexico has increased, this still represents a small fraction of GGA's supply base. Numerous efforts have been made at localization of suppliers to Mexico, both through existing suppliers relocating and the development of Mexico-based suppliers. For the suppliers remaining in the US and Canada, there are numerous possible transportation solutions, including FTL, stacktrain, LTL, and milkruns. The crux of this thesis lies in the hypothesis that GGA would be better able to optimize logistics, if it had the ability to choose mode and frequency on a real time basis after having a more precise understanding of inbound material flow. A case study was then performed on the optimal manner in which to ship empty returnable containers to suppliers, which is established to be per container demand at the supplier site. Then, a model is developed and tested that takes as input the forecast of raw material shipments from GGA's entire supply base and outputs a set of packing lists that minimizes logistics cost while meeting supplier demand for empty containers. === (cont.) The model outputs are tested on a limited basis, but full implementation has not been conducted at the time of writing. Based on preliminary calculations, it is expected that implementation would have a significant impact on GGA logistics expense. === by Peter Frys. === S.M. === M.B.A.