Fuzzy logic: intelligence, control, and information
Fuzzy logic: intelligence, control, and information
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Transaction cost theory has been used to explain the governance of the organization supply chain for decades. However it can not be used to explain the governance change of the organization supply chain in Taiwan's hi-tech industry. Based on contingency theory, a precious study then adopted two factors to explain the governance changes between upstream and upstream players in a supply chain. The two factors proposed in the study are relative profitability and technology complexity. The previous used a binary value (either 1 or 0) to describe the factors. It however can not describe the gradual changing value between 0 and 1, e.g., 0.12, 0.23, 0.85, etc. To overcome this problem, this study uses fuzzy logic to deal with these gradual change values and proposed a fuzzy-inference-based expert system to determine the governance controllability among the organizations in a supply chain. In the proposed fuzzy expert system, the input fuzzy variables are relative profitability and technology complexity. The output fuzzy variable is governance controllability. Nine fuzzy rules are used in the fuzzy expert system. We use Java programming language to implement the system. We also conducted 20 simulation cases to validate the proposed system and discuss the simulation results. Concluding remarks are finally provided at the end of this paper.