This study investigates the challenges Chinese manufacturing suppliers face in global value chains (GVCs) and how they respond to the challenges through GVC upgrading facilitated by e‐commerce. A multiple case study approach on nine case companies of three company size categories in the Chinese nonwoven fabric product industry was employed. The findings reveal four categories of internal challenges (i.e., a product development challenge, a human resource challenge, a financial resource challenge, and an intellectual resource challenge) and two categories of external challenges (i.e., a market challenge and a macroeconomic challenge) faced by the Chinese suppliers in the e‐commerce context. Furthermore, strategic responses undertaken by Chinese manufacturers are identified and are further related to various types of economic, environmental, and social upgrading by applying the GVC framework. Unlike most extant research on the phenomenon of sourcing from China that approaches it from the viewpoint of global buyers, this study examines the phenomenon from the perspective of Chinese suppliers. Through the theoretical lens of GVC analysis and its core concept of upgrading, this study contributes to GVC research by shedding light on the impact of e‐commerce on suppliers' GVC upgrading practices.

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The Authors has Commented the Complexity of Supply Chain Networks in Manufacturing Industry Based on Complex Network Theory.Complex Supply Chain Networks in Manufacturing Industry has Characteristics of Small-World and Scale-Free. the Authors has Discussed Various Uncertainties of Complex Supply Chain Networks in Manufacturing Industry. and has Analyzed Spread and Control of Uncertainty of Complex Supply Chain Network in Manufacturing Industry. the Research Showed that Shorten Delivery Lead Times and Strengthen the Exchange of Information can Effectively to Improve Enterprises Cope with Uncertainty Ability, which has Important Role to Control Uncertainty Occurrence in Manufacturing Manufacturing Complex Supply Chain Networks in Manufacturing Industry.

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Enterprise often face to limit financial resources but also have to consider how to invest effectively on a number of projects in the various factors of the risks and benefits in different periods. In order to assure the optimal investment results of capital investment, this paper has established dynamic programming model which is multi-dimensional and multi-objective and fuzzy optimization, dynamic programming and genetic algorithm is combination to solve investment decision of enterprise. At last, this paper through an example to verify the validity of dynamic programming model.