Designing and Managing the Supply Chain Concepts, Strategies, and Case Studies

By C. Kuei. 2000

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Citations in the corpus

DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks (2017, background): “In retail for example, probabilistic forecasts of product supply and demand can be used for optimal inventory management, staff scheduling and topology planning [17], and are more generally a crucial technology for most aspects of supply chain optimization.”
Drivers and Sources of Supply Flexibility: An Exploratory Study (2005, unknown): “Volume uncertainty, however, is less affected by production schedule fluctuations, because volume decisions are based on aggregate forecasts (e.g. total quantity to be produced), which are usually more precise than forecasts at the SKU level (Simchi-Levi et al., 2002).”
Application of planning models in the agri-food supply chain: A review (2009, background): “These decisions can be classified as strategic, tactical or operational, depending on their effects to the overall supply chain (Simchi-Levi, 2003; Chopra and Meindl, 2003).”
A mathematical programming approach to supply chain models with fuzzy parameters (2006, methodology): “SCM has been extensively investigated (Arntzen et al. 1995, Berry and Naim 1996, Erengüc et al. 1999, Karabakal et al. 2000, Min and Zhou 2002, Smichi-Levi et al. 2003, Gunasekaran 2004, Gunasekaran and Chung 2004, Gunasekaran and Ngai 2004, Gunasekaran et al. 2004, Yusuf et al. 2004), and much…”
Benefits of IT in supply chain management: an explorative study of progressive companies (2005, background): “(For a detailed discussion of decision support systems for supply chain management see Simchi-Levi et al., 2003, pp. 293-318, and Swaminathan & Tayur, 2003 on the review of analytical models in the supply chain management literature).”
Business process management (BPM) standards: a survey (2009, background): “…of goods ordered; . the need for fast information transfer; . quick decision making; . the need to adapt to change in demand; . more international competitors; and . demands for shorter cycle times (Simchi-Levi et al., 2000) are challenging the profitability and survival of big and small companies.”
An entropy-based approach for measuring complexity in supply chains (2010, background): “Yao (1985) studied the entropy as a measure of flexibility.”
Information systems in supply chain integration and management (2004, methodology): “According to Simchi-Levi et al. (2000), SCM is a set of approaches utilized to eﬀectively integrate suppliers, manufacturers, warehouses, and stores, so that mer-chandise is produced and distributed at the right quantities, to the right locations, and at the right time, in order to minimize…”
Firm Performance Impacts of Digitally Enabled Supply Chain Integration Capabilities (2006, background): “These two measures assess operational excellence and are suggested to be impacted by supply chain integration (Rai, Bush, and Tiwana 2002; Simchi-Levi et al. 2000; Supply-Chain Council 2003).”
Relational Antecedents of Information Flow Integration for Supply Chain Coordination (2006, background): “and delivery, coordinate inventory stocking, and develop an accurate assessment of performance bottlenecks across the supply chain [138].”
Internet of things and supply chain management: a literature review (2019, background): “Products complexity is also increasing due to the high clock speed in many industries following the rapid changes in technology and the continuous introduction of new products to the market (Simchi-Levi, Kaminsky, and Levi 2003).”
How human behaviour amplifies the bullwhip effect. A study based on the beer distribution game online (2006, methodology): “Lead time of information and material as the primary reason for the bullwhip effect The lead time of information and material is the primary reason for the bullwhip effect [1, 2, 3, 4].”