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What is Supply Chain Analytics? And Its Types?

Understanding supply chain analytics

In supply chain analytics, organizations gain insight and extract value from large amounts of data associated with the procurement, processing, and distribution of goods. Analyzing the supply chain is an essential component of supply chain management (SCM).



What types of supply chain analytics are there?

There are different types of supply chain analytics, including:

Descriptive analytics: Provides a single source of truth that integrates internal and external data and systems across the supply chain.

Predictive analytics: Ensures an organization understands the most likely outcome or future scenario and how it will affect the organization's business. A predictive analytics approach, for instance, can help you anticipate disruptions and minimize risks.

Prescriptive analytics: Maximizes business value by helping organizations solve problems and collaborate. Reduces disruption time and effort by collaborating with logistic partners.

Cognitive analytics: Answers complex questions in natural language - the way a person or team of people would. By using this platform, companies can think through a complex issue or problem, such as “How could we optimize or improve X?”

Why are supply chain analytics so important?

Using supply chain analytics, an organization can make smarter, faster, and more efficient decisions. Among the benefits are:

Improve margins and reduce costs

Utilize comprehensive data to drive operational efficiency and actionable insights through a continuous integrated planning approach.

Understand risks better

By spotting patterns and trends throughout the supply chain, supply chain analytics can identify known risks and predict future risks.

Enhance planning accuracy

Supply chain analytics can help businesses predict future demand by analyzing customer data. Organizations can minimize products when they become less profitable or understand what customer needs will be after the initial order.

Ensure a lean supply chain

By monitoring warehouses, partner responses, and customer needs, companies can make better-informed decisions.

Future-proof your business

Supply chain management companies are now offering advanced analytics. Using advanced analytics, organizations can process both structured and unstructured data, ensuring alerts arrive on time so they can take optimal action. By building correlations and patterns among different sources, advanced analytics can minimize risks at little cost and with less sustainability impact.

Supply chain analytics: key features

A supply chain optimization strategy should include the following features:

Integrated: Accessing unstructured data from social media, structured data from the Internet of Things (IoT), and more traditional data sets available in traditional ERP and B2B integration tools.

Collaboration: Collaboration and engagement with suppliers is increasingly enabled by cloud-based commerce networks.

Cyber-savvy: Supply chain systems should be hardened against cyber-intrusions and hacks, which should be an enterprise-wide concern.

Embraced cognitively: By coordinating and conducting actions across the supply chain, the AI platform becomes the supply chain's control tower. There is a great deal of automation and self-learning in the supply chain.

Comprehensive: Scalable analytics capabilities must be based on real-time data. Fast and comprehensive insights will be provided. Future supply chains cannot tolerate latency.


READ MORE: Embracing Autonomous Supply Chain Planning For CPG Industry

Supply Chain Analytics

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