Analytics represent the ability to make data-driven decisions, based on a summary of relevant, trusted data, often using visualization in the form of graphs, charts and other means. Supply chains typically generate massive amounts of data. Supply chain analytics helps to make sense of all this data — uncovering patterns and generating insights.
Different types of supply chain analytics include:
Descriptive analytics. Provides visibility and a single source of truth across the supply chain, for both internal and external systems and data.
Predictive analytics. Helps an organization understand the most likely outcome or future scenario and its business implications. For example, using predictive analytics can project and mitigate disruptions and risks.
Prescriptive analytics. Helps organizations solve problems and collaborate for maximum business value. Helps businesses collaborate with logistic partners to reduce time and effort in mitigating disruptions.
Cognitive analytics. Helps an organization answer complex questions in natural language, in the way a person or team of people might respond to a question. It assists companies to think through a complex problem or issue, such as “How might we improve or optimize X?”
In the recent times, supply chain optimization has had a major role to play when it comes to uncertainty that we face currently. Supply chain optimization makes the best use of technology and resources like blockchain, AI and IoT to improve efficiency and performance in a supply network. An organization’s supply chain is a critical business process that is crucial for a successful customer experience. A high-performing supply chain enables business efficiency and responsiveness, so customers get what they want, when and where they want it — in a way that is both profitable for the organization and contributes to supply chain sustainability.
An article from IBM…