Date of Award


Publication Type

Doctoral Thesis

Degree Name



Mechanical, Automotive, and Materials Engineering

First Advisor

Zhang, Guoqing





Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.


The automotive industry is one of the world's most important economic sectors in terms of revenue and employment. The automotive supply chain is complex owing to the large number of parts in an automobile, the multiple layers of suppliers to supply those parts, and the coordination of materials, information, and financial flows across the supply chain. Many uncertainties and different natural and man-made disasters have repeatedly stricken and disrupted automotive manufacturers and their supply chains. Managing supply chain risk in a complex environment is always a challenge for the automotive industry. This research first provides a comprehensive literature review of the existing research work on the supply chain risk identification and management, considering, but not limited to, the characteristics of the automotive supply chain, since the literature focusing on automotive supply chain risk management (ASCRM) is limited. The review provides a summary and a classification for the underlying supply chain risk resources in the automotive industry; and state-of-the-art research in the area is discussed, with an emphasis on the quantitative methods and mathematical models currently used. The future research topics in ASCRM are identified. Then two mathematical models are developed in this research, concentrating on supply chain risk management in the automotive industry. The first model is for optimizing manufacturer cooperation in supply chains. OEMs often invest a large amount of money in supplier development to improve suppliers’ capabilities and performance. Allocating the investment optimally among multiple suppliers to minimize risks while maintaining an acceptable level of return becomes a critical issue for manufacturers. This research develops a new non-linear investment return mathematical model for supplier development, which is more applicable in reality. The solutions of this new model can assist supply chain management in deciding investment at different levels in addition to making “yes or no” decisions. The new model is validated and verified using numerical examples. The second model is the optimal contract for new product development with the risk consideration in the automotive industry. More specifically, we investigated how to decide the supplier’s capacity and the manufacturer’s order in the supply contract in order to reduce the risks and maximize their profits when the demand of the new product is highly uncertain. Based on the newsvendor model and Stackelberg game theory, a single period two-stage supply chain model for a product development contract, consisting of a supplier and a manufacturer, is developed. A practical back induction algorithm is conducted to get subgame perfect optimal solutions for the contract model. Extensive model analyses are accomplished for various situations with theoretical results leading to conditions of solution optimality. The model is then applied to a uniform distribution for uncertain demands. Based on a real automotive supply chain case, the numerical experiments and sensitivity analyses are conducted to study the behavior and performance of the proposed model, from which some interesting managerial insights were provided. The proposed solutions provide an effective tool for making the supplier-manufacturer contracts when manufacturers face high uncertain demand. We believe that the quantitative models and solutions studied in this research have great potentials to be applied in automotive and other industries in developing the efficient supply chains involving advanced and emerging technologies.