Introduction: Why Forecasting Is Retail’s Real-Time Superpower
In modern retail, getting demand wrong is more than an inconvenience, it’s a costly misstep. Overstocking ties up capital and leads to markdowns. Understocking means missed sales, frustrated customers, and lost loyalty. The real challenge? Predicting the right product, in the right store, at exactly the right time—across thousands of SKUs and hundreds of locations.
Demand forecasting is no longer just a back-office routine; it’s become a strategic advantage.
To meet this challenge, retailers need more than spreadsheets or outdated tools. That’s why 0to60.ai, powered by Databricks, brings together real-time data, advanced time series models, and intelligent orchestration—transforming demand planning into a scalable, adaptive business capability.
Where Forecasting Falls Short (and What Retailers Can’t Afford to Ignore)
Even with automation, many forecasting tools fall behind when it matters most. Common limitations include:
- Multi-SKU, Multi-Store Complexity
Brand-level forecasts don’t cut it when planning must happen at the SKU-store-week level. - Lack of Contextual Awareness
Events like promotions, weather, holidays, and market disruptions often go unnoticed by static models.
- Scalability Gaps
Legacy systems struggle to process hundreds of thousands of forecast combinations in real time. - Outdated Forecast Cycles
Monthly updates don’t keep pace with shifting demand signals—forecasts arrive late and lack impact.
Retailers don’t just need new models—they need an operational engine that delivers fast, contextual, and scalable forecasts across the business.
Choosing the Right Model
At 0to60.ai, we tailor the modeling approach to fit business use cases. Here’s how we choose the right tools:
- Prophet (by Meta)
Ideal for products with strong seasonal patterns and campaign-driven volatility. Fast to deploy and easy to interpret—great for short to medium-term forecasts. - ARIMA
Reliable for stable, low-variance demand patterns. Useful for regulatory or baseline SKUs where explainability is critical.
- LSTM (Long Short-Term Memory Networks)
Best suited for complex, high-variance scenarios. LSTM models can handle multiple influencing signals—like weather, promotions, or competitor pricing—and learn patterns over time.
Most deployments use hybrid ensembles, selecting the most effective model per SKU, region, or category.
Why Databricks: More Than Just a Forecasting Platform
Forecasting at enterprise scale isn’t just about better models—it’s about better systems. Databricks offers the performance, governance, and flexibility needed to bring forecasts into real-world operations.
- Delta Lake
Centralizes batch and streaming data across POS, inventory and external sources with versioning and schema evolution to ensure quality at scale. - MLflow
Enables experiment tracking, version control, and model management with full visibility for both data science and business teams.
- Databricks Workflows
Automate reforecasting on a daily or weekly schedule with no manual runs and no missed updates. - GPU Clusters for Deep Learning
Run high-performance LSTM models with minimal latency. - Seamless BI Integration
Push forecasts directly to tools like Power BI, Tableau, or Looker for instant visualization and decision-making.
Conclusion: Let Forecasting Drive Retail Forward
In retail, forecasting is no longer a back-office process. It’s a frontline differentiator.
With 0to60.ai and Databricks, retailers unlock the ability to forecast not just what customers want, but when and where they’ll want it. From optimizing inventory and aligning promotions to improving margins and agility, we turn forecasting into a powerful business enabler.
Let your data lead the way – from planning to shelf and from insight to impact.
