Demand Forecasting Using Time Series Models on Databricks: How Retailers Are Driving Inventory Intelligence with Scalable Forecasting

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To meet this challenge, retailers need more than spreadsheets or outdated tools.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.

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• Outdated Forecast Cycles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.

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.

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.