THE IMPORTANCE OF DATA ANALYTICS & BUSINESS INTELLIGANCE (BI) IN MODERN SUPPLY CHAIN:

How does supply chain analytics work?

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.

What are the types of supply chain analytics?

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, by using predictive analytics, you 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?”

Applying cognitive technologies:

• Supply chain analytics is also the foundation for applying cognitive technologies, such as artificial intelligence (AI), to the supply chain process. Cognitive technologies understand, reason, learn and interact like a human, but at enormous capacity and speed.
• This advanced form of supply chain analytics is ushering in a new era of supply chain optimization. It can automatically sift through large amounts of data to help an organization improve forecasting, identify inefficiencies, respond better to customer needs, drive innovation and pursue breakthrough ideas.

Why is supply chain analytics important in modern business?

Supply chain analytics can help an organization make smarter, quicker and more efficient decisions. Benefits include the ability to:

Reduce costs and improve margins:

• Access comprehensive data to gain a continuous integrated planning approach and real-time visibility into the disparate data that drives operational efficiency and actionable insights.

Better understand risks:

• Supply chain analytics can identify known risks and help to predict future risks by spotting patterns and trends throughout the supply chain.

Increase accuracy in planning:

• By analyzing customer data, supply chain analytics can help a business better predict future demand.
• It helps an organization decide what products can be minimized when they become less profitable or understand what customer needs will be after the initial order.

Achieve the lean supply chain:

• Companies can use supply chain analytics to monitor warehouse, partner responses and customer needs for better-informed decisions.

Prepare for the future:

• Companies are now offering advanced analytics for supply chain management. Advanced analytics can process both structured and unstructured data, to give organizations an edge by making sure alerts arrive on time, so they can make optimal decisions.

Key features of effective supply chain analytics:

The supply chain is the most obvious face of the business for customers and consumers. The better a company can perform supply chain analytics, the better it protects its business reputation and long-term sustainability.

?#. Key features of effective supply chain optimization include:

Connected:

Being able to access unstructured data from social media, structured data from the Internet of Things (IoT) and more traditional data sets available through traditional ERP and B2B integration tools.

Collaborative:

Improving collaboration with suppliers increasingly means the use of cloud-based commerce networks to enable multi-enterprise collaboration and engagement.

Cyber-aware:

The supply chain must harden its systems from cyber-intrusions and hacks, which should be an enterprise-wide concern.

Cognitively enabled:

The AI platform becomes the modern supply chain’s control tower by collating, coordinating and conducting decisions and actions across the chain. Most of the supply chain is automated and self-learning.

Comprehensive:

Analytics capabilities must be scaled with data in real time. Insights will be comprehensive and fast. Latency is unacceptable in the supply chain of the future.