I recently shared a blog called “The Power of Real-time Supply Chain Analytics” that discusses how manufacturers can use real-time supply chain analytics to reinvent their supply chain across sourcing, processing, and distribution of goods. Its focus is mostly on understanding events as they unfold in real-time. This time around, I’m providing an overview of using predictive analytics to understand what will happen in the future.
As a subset of artificial intelligence (AI), predictive analytics makes predictions about future outcomes using historical data. Predictive analytics also helps businesses transform their supply chain to increase efficiency, reduce risk, and grow revenue. Let’s look at four areas in which predictive analytics can revolutionize the supply chain: demand forecasting, procurement, supply chain risk management, and customer experience.
Demand forecasting in supply chain management refers to the process of planning or predicting the demand of materials to ensure delivery of the right products in the right quantities at the right time to satisfy customer demand, without creating excess inventory.
The benefits of being able to anticipate customer needs and buying behavior are tremendous. With this knowledge, manufacturers can better ensure that they have the right levels of inventory, plan for the optimal production schedule, and obtain the most cost-effective and efficient logistics. An accurate demand forecast can also help businesses determine the best price to charge for their product to make the most profit.
Manufacturers that implement intelligent procurement can gain better insight into and control over their spending and sourcing higher quality production components. Using predictive analytics, purchasing departments have a better understanding of what, where, and when to source based on their past purchases, commodity prices, and other industry trends.
Supply Chain Risk Management
Supply chain risk management is the implementation of strategies to manage everyday and exceptional risks along the supply chain based on continuous risk assessment with the objective of reducing vulnerability and ensuring continuity.
While there are many market disruptions that are typically unpredictable, such as natural disasters, pandemics, and cyber and terrorist attacks, there are many risks that can be forecast. Manufacturers can apply predictive analytics to their data for early detection and remediation of:
- Equipment and product issues at factories
- Capacity constraints at warehouses
- Late deliveries by logistics providers
- Financial distress of supplies and customers.
Delivering a great customer experience entails timely delivery of quality goods and keeping customers informed. The sooner a manufacturer can see a potential disruption to its supply chain, the faster it can react to avoid the interruption or at least lessen its impact. Even when the manufacturer can’t prevent the disruption, it can warn its customers of issues so that they aren’t blind sighted at the last minute.
Your Bottom-Line Deserves AI
A manufacturer does indeed need AI to optimize its supply chain. By using predictive analytics to optimize inventory levels, sourcing, transportation routes, and many other aspects of the supply chain, manufacturers can improve their bottom-line and provide better service to their customers.