Scaling Demand Forecasting with AI-Powered Predictive Analytics

This article on demand forecasting with predictive analytics is part of our Predictive Frameworks series, which explores the most effective use cases.

In the past several years, reality has shifted for demand planning.

Consumers today expect higher-quality products and want a seamless and immediate buying experience. From payment to delivery, buyers have quickly adapted to online shopping’s fast-paced, multi-platform landscape.

As consumer data grows, customer behaviors can seem increasingly erratic. New devices, regions, and protocols can make it appear the consumer is impossible to pin down. Traditional methods of forecasting demand use rules-based, static formulas that only look at a set of factors from historical data. These methods are proving less and less effective.

But in this new reality, there’s an unprecedented opportunity for demand planners and supply chain analysts. Consumers generate more data every year. They’ve shown they’re willing to trade their data for more effortless, seamless experiences. You can improve their experience by ensuring they can get the products they want, when and where they want them.

The solution for demand planners to meet this opportunity combines the old and the new. It’s called predictive demand forecasting.

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Actuals vs. Predictions, 1 Week Forecast Comparing NY and LA Locations

What is Predictive Demand Forecasting?

Predictive demand forecasting understands and predicts customer demand patterns to ensure supply meets demand at critical points along the supply chain. Unlike traditional demand planning based on static rules, AI demand forecasting provides granular, nuanced predictions. Those predictions are based on complex statistical patterns deep in the data.

The rewards are enormous. Demand planners and analytics teams can leverage AI-powered demand forecasting to increase sales and reduce inventory and manufacturing costs. Their forecasts can improve consistency across the supply chain, finance, and risk management.

In the past several decades, demand planning has become an integral component of any company with a supply chain. That’s especially true in high-volume industries like CPG and retail.

But forecasting has evolved. Let’s look at the differences and how predictive demand forecasting builds on technological advances and today’s data-rich landscape.

Traditional demand forecasting

  • Rules-based modeling, and traditional time-series modeling
  • Limited data points
  • Requires strong relationships between variables to determine causation
  • Informs supply chains what to order

Predictive demand forecasting

  • Uses AI and ML models to find and optimize predictions automatically
  • Reduces forecast error through constantly updated models based on current data
  • Able to detect subtle and granular demand patterns at SKU and POS level
  • Identify non-trivial and unexpected relationships within data sets to unlock accuracy
  • Leverage limitless data to build models, e.g., sales, marketing, operations, customer transactions, economic, competitor data
  • Answers the “why” from multiple angles, from demographics to geography, allowing for better personalization
  • Connects supply chain to marketing, sales, and other orgs
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Predictions Table with Confidence Highlighted

KPIs to Forecast Success

To be sure, traditional demand forecasting was a battle just to get accurate, relevant results. In contrast, predictive demand forecasting allows you to automate parts of the process, like data prep. Automation ensures demand forecast accuracy while optimizing for business results. This approach can finally settle the battle between accuracy and scale of business impact.

However you approach predictive demand forecasting, it’s imperative to keep business outcomes front and center. Demand forecasting for ideal inventory management should be able to provide accurate forecasts and optimize for:

  • Increased full-priced sales
  • Higher sale volume
  • Higher margins
  • Preventing understock/stockouts
  • Lower supply chain costs (e.g., reducing reverse logistics)
  • Lower holding costs

Forecasting Demand with AI

Undoubtedly, accurate demand forecasting and planning are in more demand than ever. But machine learning and forecasting innovations will make traditional methods seem increasingly difficult, inefficient, and ineffective.

At the same time, predictive demand forecasting can become another AI vanity project if KPIs aren’t the primary focus.

Your demand planning team can combine the old and the new. They can blend their data and business savvy with the latest innovations to maximize efficiency and results. All in all, there’s untold potential for demand planning to become the most integral part of the supply chain—and the business.

Read More About AI-Powered Demand Forecasting

Curious about more opportunities in predictive demand forecasting for yourself? For more specific stories, read these case studies:

An online grocer accelerated predictive analytics time to market by 10x by building predictive models with Pecan. Pecan’s external data enrichment and feature engineering outperformed the existing predictive models.

A high-tech manufacturer achieved a fully trained demand forecast model ready for production in 14 days.

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