Abstract
The models presented in the previous chapters use knowledge of supply chain structure to represent the supply chain. Additionally, the parameters of the models were assumed as given and limited attention was devoted to estimation of these parameters. Data driven and statistical methods on the other hand can be used to uncover unknown structural relationships within the supply chain and provide methods for gathering and estimation of input data necessary for supply chain decision-making. Additionally, the data availability recently has increased dramatically making data driven approaches and attractive alternative for strategic supply chain analysis. That has opened a way to a range of new data gathering and supply chain analysis methods based on data integration from various sources. These methods follow a data driven approach implying that the primary means of analysis and decision-making are data processing operations. They are intricately intervened with technologies used for data integration and analysis.
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Chandra, C., Grabis, J. (2016). Data Integration Technologies. In: Supply Chain Configuration. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-3557-4_11
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DOI: https://doi.org/10.1007/978-1-4939-3557-4_11
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