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How AI is Impacting 5 Demand Forecasting Roles
One person with one spreadsheet and thousands of SKUs isn’t scalable. That’s where artificial intelligence (AI) comes in. By automating repetitive tasks and surfacing strategic insights, AI frees up retail teams to focus on decision making rather than data wrangling.
As a result, key forecasting roles are evolving. Merchandise planners, allocators, buyers, sales and pricing teams are moving beyond manual processes to operate faster and with greater precision. AI brings structure to complexity, helping teams make smarter decisions that align with both the market and the business.
This article highlights some of the most common roadblocks in forecasting today. For a deeper dive into role-specific use cases, don’t miss the eBook From Reactive to Predictive: The AI-Driven Demand Forecasting Evolution.
Common roadblocks to accurate demand forecasting
Many brands still rely on outdated tools or siloed workflows that limit visibility and agility. These challenges are especially common in demand forecasting, where complexity has grown and data flows faster than ever.
Common pitfalls include:
- Heavy reliance on spreadsheets
- Incomplete or inconsistent data inputs
- Forecasting based only on internal history
- Delayed reactions to real-time shifts
These issues affect five core roles in retail forecasting:
- Merchandise planners and financial planners often have to forecast demand without full historical data or finalized assortments. This limits their ability to align plans with financial targets.
- Buyers, merchants and assortment planners face pressure to localize assortments by region or store cluster but often lack the tools to assess new styles without sales history.
- Allocators, inventory managers and replenishment planners are tasked with managing stock across locations, typically without a real-time view of evolving demand.
- Sales representatives and wholesale managers must make early commitments with limited insight into full-season sell-through, putting margin at risk.
- Pricing and lifecycle teams need to balance inventory, sell-through and margin targets, but struggle to adjust pricing dynamically based on demand signals.
Accurate, AI-powered forecasting connects every planning function—from pre-season buy planning to in-season replenishment and end-of-life pricing. It enables SKU-level precision, faster response times and confident decisions across every channel, leading to many business benefits.
Conclusion: Predict the products consumers want
With AI-powered forecasting from Centric Software®, teams can go beyond historical reporting and make smarter predictions about what consumers will buy next.
AI helps forecast which products will perform, what sizes and colors will sell, and when to adjust pricing or allocation strategies. Teams can run scenarios, model outcomes and stay better aligned across the business.
Learn about the breakdown of how AI demand forecasting supports these 5 key roles and more in the “From Reactive to Predictive: The AI-Driven Demand Forecasting Evolution” eBook, including details on the AI-driven platform that is transforming businesses with real results.