Configuring your demand planning software for maximum value

  • December 15, 2020
Group of people working with Paperwork on a board room table at a business presentation

A demand planning application should generate value that goes beyond statistical forecasts. Sometimes this is easier said than done because it requires thoughtful system configuration and shared data insights.

With the initial public offering (IPO) of cloud-based data warehousing company Snowflake, the value proposition of demand planning software has changed. In R, Google OR-Tools or even Microsoft Excel, it’s no longer difficult to generate a statistical forecast. However, creating an accurate forecast that incorporates relevant business inputs remains a challenge for many companies.

The real value of demand planning tools

Demand planning tools are not only for producing statistical forecasts. They’re also excellent tools for creating value-added workflows and rule sets. Workflows allow you to manage your forecasts using an exception-based approach. It allows you to focus on the areas that have the most significance based on automatically generated metrics (for example, percent error). You can create workflows and rule sets for new products, supersessions or any other scenario that you can build into a monthly demand management cadence.

In addition to generating forecasts, demand planning tools such as those built by Vanguard, Logility or Blue Yonder allow for:

  • Adjustments and overrides. Use historical data to sharpen and add insight to your business forecasts.
  • Documentation. Set forecast checkpoints and track approvals by time, user and reason, which aids in your compliance with the Sarbanes-Oxley Act (SOX).
  • Dynamic forecast adjustment. Automatically apply a single adjustment across all linked forecasts.
  • Forecast aggregation. View your forecast at a higher level in the hierarchy and make adjustments, which in turn apply to other items in the hierarchy.

Does your demand planning tool play well with others?

Today, we need quick and easy access to advanced analytics. That’s why exporting data to a cloud-based data warehouse such as Snowflake is so important. With a data lake, Power BI or Tableau can become a one-stop business intelligence (BI) tool — a BI application that can pull in supply chain data plus information from other relevant sources such as SAP ERP or Salesforce. You can work from the same data set regardless of the BI tool in use, and it promotes an agreement on, and the accomplishment of, a unified set of business objectives.

From a demand planning software standpoint, this means that your goal should be generating best-in-class forecasts and workflows, leaving analytics to the BI application. Your analytics can then provide insight into market intelligence from various sources and give context to cross-functional business needs. This approach enables an environment that facilitates the creation of a cross-functional consensus forecast.

Machine learning-enabled forecasting and planning platforms are the ideal tools to manage your business comprehensively. By configuring these systems to fit the needs of your supply chain, you can build an accurate demand picture. And as a result, you’ll boost confidence in the output and secure management buy-in.

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