Pros of Using Predictive Analytics in Supply Chain

By Logistics Tech Outlook | Wednesday, February 17, 2021

The supply chain is using predictive analytics for understanding consumer trends and develop proper pricing strategies. 

FREMONT, CA: Predictive analytics techniques enable companies to detect trends and patterns embedded in their data to understand consumer trends, identify demand, and build suitable pricing strategies.

What is Predictive Analytics?

As its name suggests, predictive analytics is about forecasting future patterns like demand for sales, exchange rates, and other significant metrics of the supply chain. To assess and understand trends and formulate future trends, the methodology depends on applying statistical modeling and regression analysis to historical data.

Such methods are not new, as they have been in use since computers were born. But what has changed is the capacity of computers, together with sophisticated data mining methods, to rapidly analyze vast volumes of data, making it possible to analyze structured and unstructured data.

How Are Supply Chain Managers Using Supply Chain Predictive Analytics?

There are several examples of where professionals in the supply chain benefit from predictive analytics. It includes forecasting demand, predictive pricing techniques, inventory management, logistics, and predictive maintenance.

Demand forecasting

For companies, understanding and predicting, demand continues to be a challenge. Demand is never linear and is impacted by various factors, some of which are beyond the company's control. Predictive analytics enables organizations to increase demand forecasting by evaluating past and present patterns and forecast demand trends along with business intelligence and economic forecasts.

Predictive pricing strategies

Predictive pricing strategies can overcome the drawbacks of historical cost-plus pricing strategies or those that use a fixed margin. However, it is possible to dynamically adjust prices to what the market wants by predicting demand for the commodity. A good example of predictive pricing is Uber's pricing approach, other commute firms, and even some airlines.

Shipping and logistics

A large portion of the overall product price is mostly provided for shipping and transport expenses. It is possible to assess optimum shipping frequency and quantity utilizing predictive analytics to meet demand while reducing downtime. The predictive-route-planning can decide the fastest routes by considering traffic congestion, distance, weather, and delivery points. In addition, smart sensors can track the fuel consumption, tire pressure, style of driving, and condition of the car.

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