Scaling Demand Forecasting with AI-Powered Predictive Analytics
This article is part of our Predictive Frameworks series, which explores the most effective use cases for predictive analytics.
In the past several years, reality has shifted for demand planning.
Consumers today expect higher quality products, and at the same time a seamless and instantaneous buying experience. From payment and banking to delivery and shopping platforms, buyers have quickly adapted to the fast-paced, multi-platform landscape that is online shopping.
As consumer data proliferates, shopping behaviors can seem increasingly erratic. New devices, regions, and protocols can make it appear the consumer is ever-changing, and impossible to pin down. Traditional methods of forecasting demand, which are rules-based, static formulas that only look at a set of contributing factors, are proving less and less effective.
But in this new reality, there is an unprecedented opportunity on the table for demand planners and supply chain analysts. Consumers are generating exponentially more data every year, and have shown through action they’re willing to trade more data for easier and more seamless experiences (see: Amazon).
The solution for demand planners to meet this opportunity combines the old and the new. It’s called predictive demand forecasting.
What is Predictive Demand Forecasting?
Predictive demand forecasting understands and predicts customer demand patterns to ensure supply meets demand at all the critical points along the supply chain. Unlike traditional demand planning, which is based on static rules, AI-based predictive demand forecast involves high resolution predictions that are based on complex statistical patterns that lie deep in the data.
The rewards are huge: demand planners and analytics teams can leverage predictive demand forecasting to increase sales, reduce inventory and manufacturing costs, and increase predictability across Supply Chain, Finance, and Risk Management.
In the past several decades, demand forecasting has become cemented as an integral operational component for any company with a supply chain, especially within high-volume output industries like CPG and retail.
But demand forecasting has evolved. Let’s look at the differences, and how predictive demand forecasting capitalizes on technological advances in data science and machine learning, as well as today’s data-rich landscape:
Traditional demand forecasting
- Rules-based modeling, and traditional regression-based 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
- Able to detect subtle and granular demand pattern at SKU and POS level
- Identify non-trivial and non-intuitive relationships within the data to unlock accuracy
- Leverage limitless data to base your models on, e.g. Sales, Marketing, Operations, Customer Transactions, Macroeconomics, Competitor Data
- Answers the “why” from multiple angles, from demographics to geography allowing for better personalization
- Connects Supply Chains to Marketing, Sales, and other orgs
KPIs to Forecast Success
While traditional demand forecasting was a battle just to get to accurate, relevant results, predictive demand forecasting allows you to automate parts of the process like data prep to ensure accuracy while keeping your forecast laser-focused on optimizing for business results.
The battle between relevant accuracy and scale of business impact can finally be settled, thanks to the advances in machine learning that enable predictive demand forecasting.
Regardless of what platform or team structure you’re using for predictive demand forecasting, it’s imperative to keep business outcomes front-and-center throughout the process. Demand forecasting that gives you an optimal supply chain should be able to predict and optimize for each of the following (and without weeks delay switching between models):
- Increased full-priced sales
- Higher sale volume
- Higher margins
- Preventing understock/stockouts
- Lower supply chain costs (e.g., reduce reverse logistics)
- Lower holding costs
Demand planning is in more demand than ever. But fighting the technological tide will make traditional demand forecasting methods seem increasingly difficult and ineffective.
At the same time, predictive demand forecasting that fails to keep KPIs front-and-center relegates this new-wave solution to just another AI vanity project.
If your demand planning team can combine the old and the new, with industry-savvy human beings wielding the power of AI and predictive analytics, there is untold potential for demand planning to become the most integral part of the supply chain—and the business.
Curious about more opportunities in predictive demand forecasting yourself? For more specific stories, read these case studies:
- An online grocer was able to accelerate predictive analytics time to market by 10x by building predictive models on Pecan’s; Pecan’s external data enrichment and feature engineering were able to outperform the existing predictive models
- A high tech manufacturer achieved a fully-trained and operational demand forecast model in just 14 days