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• W2912133196 abstract "A supply chain performance prediction system aims to estimate lagging metrics based on leading metrics so as to predict performance based on causal relationships. Two studies in the literature propose a supply chain performance prediction system based on metrics suggested by the SCOR® (Supply Chain Operations Reference) model. However, a limitation of both systems is the difficulty of adjusting them to the environment of use, since their implementation and updating require manual parameterization of many fuzzy decision rules. To overcome this difficulty, this study proposes a performance prediction system also based on the SCOR® metrics but using artificial neural networks (ANN), which enables adaptation to a specific environment by means of historical performance data. Computational implementation of the ANN models was made using MATLAB. The method of random subsampling cross-validation was applied to select the network topologies. Results showed that the values of the correlation coefficient evidence that there is a high positive correlation between the expected and predicted performance values for the SCOR® level 1 metrics by all the ANN models. Statistical hypothesis tests showed that multilayer perceptron neural networks are adequate to support performance prediction of supply chains based on the SCOR® model. The proposed system promotes rational decision-making through a prospective diagnosis of the supply chain performance. By comparison between the predicted value and the target defined for each level 1 metric, managers can simulate whether improvement plans can lead to objectives; it can also help to identify areas that have performance problems and may need improvements." @default.
• W2912133196 created "2019-02-21" @default.
• W2912133196 creator A5029956237 @default.
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• W2912133196 date "2019-06-01" @default.
• W2912133196 modified "2023-10-11" @default.
• W2912133196 title "Predicting supply chain performance based on SCOR® metrics and multilayer perceptron neural networks" @default.
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• W2912133196 doi "https://doi.org/10.1016/j.ijpe.2019.02.001" @default.
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