Manufacturers can prevent backorders and keep their inventory on track by predicting customer demand, otherwise known as using predictive analytics.

Instead of manually tracking inventory data or using real-time analytics, manufacturers who use big data to forecast demand — which can vary according to the market, weather, and a host of variables, ultimately helps manufacturers to ensure their stock levels meet — not exceed — demand.

This reduces overstocks and backlogs and gives manufacturers a clearer view of their finances. 

Predictive inventory analytics also empowers manufacturers to better know and streamline their operations. They are prompted to organize their stock, learn appropriate production procurement levels, resolve supply chain disruptions; find the best transportation routes; minimize waste and loop in marketing to react to trends — which is better for business anyway. Ultimately, these improved processes minimize waste, boost relationships and better the bottom line.

Predictive analytics in practice

So how does it work?

Predictive analytics relies on historical data to lead to information about trends. Manufacturers integrate their sales data with a machine learning analytics platform, which delivers insights and helps manufacturers better understand their processes. Predictive analytics is helpful way to learn what impacts manufacturers’ outcomes. It also makes predictions and help optimize processes — modern machine learning algorithms are known for high predictive accuracy.

The right predictive software helps manufacturers make data-driven decisions. User-friendly statistical software can enable manufacturers to glean true insights from their data and ultimately improve results.