Supply chain analytics refers to the processes organizations use to gain insight and extract value from the large amounts of data associated with the procurement, processing, and distribution of goods. Supply chain analytics is an essential element of supply chain management (SCM).
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Supply chain analytics brings together data from across different applications, infrastructure, third-party sources, and emerging technologies such as IoT to improve decision-making across the strategic, tactical, and operational processes that make up supply chain management. Supply chain analytics helps synchronize supply chain planning and execution by improving real-time visibility into these processes and their impact on customers and the bottom line. Increased visibility can also increase flexibility in the supply chain network by helping decision-makers to better evaluate tradeoffs between cost and customer service.
Supply chain analytics will continue to evolve in tandem with the evolution of analytics models, data structures and infrastructure, and the ability to integrate data across application silos. In the long run, advanced analytics will lead to more autonomous supply chains that can manage and respond to changes, much like self-driving cars are starting to do today. In addition, improvements in IoT, CEP and streaming architectures will enable enterprises to derive insight more quickly from a larger variety of data sources. AI techniques will continue to improve people’s ability to generate more accurate and useful predictive insights that can be embedded into workflows.
Applications:
- Healthcare and life sciences
- Manufacturing
- Automotive
- Retail and Consumer Packaged Goods
- High Technology Products
- Aerospace and Defense