Descriptive analytics mines historical data to identify trends and relationships. Examples include identifying excess inventory and late deliveries.

Diagnostic analytics examines trends and correlations between variables to determine the root cause of a supply chain event. This type of analytics can diagnose events such as why there was too much stock and why deliveries were late.

Predictive analytics uses supply chain data to predict future outcomes, such as forecasting demand or anticipating possible transportation bottlenecks.

Prescriptive analytics uses data to prescribe the best course of action, such as decreasing production or using alternative shippers.

Answering these types of questions provides a myriad of benefits. Below are just a few of them:

• Improved efficiency and cost savings: Through using supply chain analytics to streamline processes, reduce waste and optimize operations. Examples include optimizing routes and schedules, reducing manufacturing downtime, using less fuel and better sourcing of materials, and many more opportunities.
• Increased visibility and transparency: Allow organizations to identify potential problems early on and take proactive measures to address them.
• Better risk management: By highlighting interdependencies and uncovering areas along the supply chain where disruption can lead to failure.
• More accurate planning: Gain better insight into sourcing, manufacturing, and distribution to meet customer demand.
• Better customer experience: Real-time insights into customer demand can improve how you manage inventory levels and ensure that products are in stock when customers want them.
• Less environmental impact: Normalize analyzing energy consumption, waste, and other sustainability factors.