Designing and managing the supply chain : concepts, strategies, and case studies

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DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks (2017, background): “d enables data driven decision making. In retail for example, probabilistic forecasts of product supply and demand can be used for optimal inventory management, staff scheduling and topology planning [18], and are more generally a crucial technology for most aspects of supply chain optimization. The”
An integrated supply chain problem with environmental considerations (2015, background): “Assuming that the daily demands at each customer are uncorrelated over time and across customers, and approximating the Poisson demand distribution with a Normal distribution, which is a good approximation when the demands are sufficiently large [28,29], we find that the safety”
Drivers and Sources of Supply Flexibility: An Exploratory Study (2005, background): “total quantity to be produced), which are usually more precise than forecasts at the SKU level (Simchi-Levi et al., 2002).”
Contracting and Information Sharing Under Supply Chain Competition (2008, unknown): “Under continuous replenishment program and vendor-managed inventory, powerful manufacturers such as Campbell Soup Company and VF Corporation get sales data from the retailers for the production and distribution planning of the supply chains (Simchi-Levi et al. 2003).”