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- W4285284571 abstract "In this chapter, we define and conceptualize the emerging practice of “Digital Supply Chain Surveillance (DSCS)” as the proactive monitoring of digital data that allows firms to track, manage, and analyze information related to a supply chain network using available data and information sources. DSCS has potential applications in risk management, supplier performance management, production planning, inventory optimization, quality management, supplier financing, and cost reduction in supply chains. Artificial Intelligence (AI) is potentially a key enabler and may facilitate a step change in DSCS. We present a framework, SDAR (Surveillance, Detection, Action, Response), to support the design of effective business processes for supply network surveillance. We outline the most important types of AI algorithms and models and discuss their applicability to a range of questions that arise in DSCS. By linking different surveillance data sources and systems, appropriate AI techniques can make surveillance easier, more informative, and scalable. However, AI-based DSCS gives rise to significant technical, ethical, and managerial challenges. These include the decomposition and reintegration of surveillance data and analyses, data imbalances, mitigation of biases in data, algorithms and statistical estimations, and the challenge of embedding DSCS in effective supplier monitoring and auditing processes." @default.
- W4285284571 created "2022-07-14" @default.
- W4285284571 creator A5052681293 @default.
- W4285284571 creator A5066517252 @default.
- W4285284571 creator A5075872953 @default.
- W4285284571 creator A5079201432 @default.
- W4285284571 date "2022-01-01" @default.
- W4285284571 modified "2023-09-25" @default.
- W4285284571 title "Digital supply chain surveillance" @default.
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