Old Approaches No Longer Work
Even before the successive waves of a global pandemic, demand forecasters were challenged to keep pace with rapidly evolving shopper preferences, competitive market entrants, and shifting market dynamics. For many retailers, it was feasible to limp along using data from past years to reflect seasonality, plus a bit of human guesswork to try to adjust for more recent market shifts. While a few forward-looking retailers sought AI-powered Demand Forecasting capabilities, many retailers deferred that investment in favor of other priorities.
Demand Forecast AI was developed by Symphony RetailAI to provide demand forecasting for every product in every store for a set of dates (e.g., next week, following week). Retailers use Demand Forecast AI to ensure adequate stock to avoid stock-outs and generate accurate input as the first link to the supply chain.
The Limits of Historical Data
Today, the historical data-driven approach is painfully and irrevocably broken. Since the shadow of Covid began reaching across different parts of the globe in late 2019 into early 2020, shopper demand has shifted sharply. Even historically technology-resistant consumer sectors shifted to online as store closures and fear of unwarranted exposure forced shoppers online. Consumption of food, toilet paper, and office supplies that formerly happened on a commercial scale in schools, offices or universities shifted to the home as remote learning and working became the norm.
Amid significant economic uncertainty, heightened price sensitivity drove shoppers to embrace store brands and more aggressively seek promotions before committing to purchase. Over time, particularly in markets where economic shocks were short-lived, many homebound shoppers began to seek luxury foods and alcohol as spending normally allocated for travel, dining out and entertainment left budgets flexible for more luxury-oriented home consumption.
The value of historical data in such times has dwindled from questionable to irrelevant, as has the accuracy of human guesswork. Fortunately, increasing numbers of retailers are seeing the value of AI-based demand forecasting that leverage machine learning
And of course, organic customer demand is only half the story for retailers to do effective forecasting and inventory planning. Complex dynamic modelling also needs to take into account how retailers’ pricing and promotions are impacting demand.
The Power of Dynamic AI-Based Predictions
Fortunately, leading Ai-based science is able to crunch complete historical and current information about promotions, stock-outs, seasonality, etc. The model learns from the historical data, of course, but reflects current conditions in its continually evolving machine-learning mode, and can also make surprisingly accurate predictions about the future. In forward-looking algorithms used to generate forecasts, the models factor in future data including calendar events and promotion plans. The result: timely, actionable projections that enable retailers to meet actual shopper demand, when and where it matters, and make optimal plans for supply chain management and optimization to keep enough – but not too many – items on hand to accommodate demand.
At a time of severe supply chain disruptions, accurate forecasting gives savvy retailers a critical edge in proactively managing their vendor partners, optimizing trade funds, and managing to financial and business targets effectively.
But Wait – There’s More!
I invite you to read other insights in this series that delve more deeply into the science behind Symphony RetailAI’s demand forecasting, and into what real-world users are looking for in AI-based forecasting solutions, today and in the future.
Want to learn more? Connect with a solution consultant.