4 steps for using predictive analytics in demand planning

4 benefits of using predictive analytics in demand planning

Using predictive analytics for demand planning can improve inventory management, reduce company costs, improve forecasting accuracy and improve supply chain resilience. Learn more.

Improved inventory management

Predictive analytics tools can analyze sales data, seasonal trends and other factors, then suggest the stock levels that a company should maintain in order to meet future demand.

These capabilities help companies plan better for their busier times of year and make sure that they possess enough inventory to fulfill orders.

Greater understanding of storage and waste

Better forecasts mean less waste and lower costs. The data from predictive analytics enables companies to reduce their storage and warehousing costs because they won't store extra inventory.

Predictive analytics can also help optimize routes and capacity when transporting products, which can reduce costs because companies can potentially cut down on their gas usage and number of transport vehicles.

Improved customer service

Companies need to keep customers happy, and predictive analytics identifies future customer needs so that supply chain leaders can make sure that products are available and the company can deliver them quickly.

Possessing the right amount of stock helps reduce customer complaints.

Improved supply chain resilience

International supply chains can be fragile, so correctly managing the flow of orders and products is essential. Predictive analytics can forecast potential issues and enable companies to plan accordingly.

As soon as a company is notified of a delay or disruption, supply chain leaders can adjust orders so that their organization can still get goods into the marketplace.

4 steps to get started with using predictive analytics in demand planning

Learn how to get started integrating predictive analytics into your organization's demand planning processes.

Step 1. Understand current demand planning processes

Many companies have already implemented rudimentary demand planning processes. A demand planning strategy could be as simple as ordering three times as many products for November and December because of holiday demand.

Supply chain leaders should determine how their company will integrate predictive analytics into their current demand planning processes as well as their organization's current tech capabilities.

Step 2. Establish predictive analytics goals

Supply chain leaders should define what their company wants to achieve from using predictive analytics for demand planning.

Hoped-for outcomes might include any or all of the benefits discussed above. Establishing KPIs will help supply chain leaders evaluate the success of the predictive analytics tools or lack thereof.

Step 3. Ensure data quality

Examples of data sources that predictive analytics might use include historical sales trends, desired inventory levels, changes in the marketplace, customer demographics and promotional activities.

The data will need to be analyzed and sanitized to make sure that it will provide high-quality forecasts.

Step 4. Choose the right predictive analytics tool

Companies need to choose the right predictive analytics software for their specific organization. The software purchasing team should understand the capabilities of each candidate and determine whether the software is right for their organization's specific industry.

Predictive analytics software must also integrate with existing systems and business processes and be able to scale if needed.

Paul Maplesden creates comprehensive guides on business, finance and technology topics, with expertise in supply chain and SaaS platforms.