How Predictive Analytics Helps Supply Chain Demand Forecasting
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How Predictive Analytics Helps Supply Chain Demand Forecasting

By CIOReview | Thursday, October 29, 2020

Predictive analytics can enhance supply chain forecasting accuracy by formulating and optimizing a cost function for the predictions.

FREMONT, CA: Keeping the right amount of products in stock is vital to any business. Having too few products can cause customers to buy from elsewhere. But having too much product can force the business to pay unnecessarily high costs for storage and inventory management. So, finding a middle ground between these two is vital in running a business. In the world of supply chain management, it is done by forecasting. There are numerous instances where supply chain professionals are winning with predictive analytics, including demand forecasting, predictive pricing strategies, inventory management, logistics, and predictive maintenance. Read on to know more.

Knowing and forecasting demand accurately is a crucial challenge for organizations. Demand is never linear and is impacted by numerous variables that are outside the organization's control. Predictive analytics enables organizations to enhance demand forecasting by analyzing past and present trends and market intelligence and economic forecasts, to assume demand. Predictive device monitoring can identify when servicing is necessary and provide early warning of component failure. This data may be used to order parts when needed, allowing organizations to reduce spare inventory holdings and eliminate unplanned equipment outages.

Predictive analytics also allows organizations to decide optimal inventory levels to satisfy demand while reducing stock. Using sophisticated models, predictive analytics enables supply chain managers to determine complex inventory needs by region, location, and usage. Safety stock levels can be mitigated, and inventory placed where needed. This ability is useful when firms have multiple distribution points, as it helps supply chain managers decide whether the stock should be held centrally or at regional facilities.

The growing demand to customer behavior analysis and demand forecasting is driven by increasing market competition and the surge in supply chain digitization practices. Predictive analytics can make a lot of guesswork in planning and decision support processes. Unlike traditional analytics that only looks into the past, supply chain predictive analytics enables supply chain leaders to assume and prepare for the future.