Client Success Story
Skulicity
With Nixtla's TimeGPT, Skulicity transformed a compute-heavy forecasting pipeline into a scalable, production-ready system, enabling faster replenishment decisions across dynamic retail supply chains without replatforming its Microsoft Fabric environment
50%Reduction in Forecast Runtime
Achieved a 50% reduction in forecast runtime on selected workloads, completing forecasts in under an hour
500KUnique IDs per Pipeline Run
Processing 500,000 unique IDs per pipeline run with 3× weekly forecasting cadence at production scale
50%Reduction in Compute Costs
Lowered cloud costs by 50% in a time-based billing environment while expanding forecasting capabilities
About
Skulicity is a predictive analytics and forecasting platform built for demanding retail supply chains. They help businesses selling to major big-box retailers. Skulicity supports complex planning and replenishment needs across horticulture, perishable grocery, consumer packaged goods, and retail categories where demand volatility, short shelf life, and timing precision are critical
Skulicity is a predictive analytics and forecasting platform built for demanding retail supply chains. They help businesses selling to major big-box retailers. Skulicity supports complex planning and replenishment needs across horticulture, perishable grocery, consumer packaged goods, and retail categories where demand volatility, short shelf life, and timing precision are critical
The Challenge
For Skulicity, forecasting is a core operational function rather than a back-office exercise. The company generates highly granular forecasts, by store, by SKU, by day, that directly inform replenishment decisions for customers operating in fast-moving retail categories. In environments such as horticulture and perishables, where products have short shelf lives and demand is sensitive to external factors, forecast timing is just as important as accuracy
- · Microsoft Fabric's 300 MB limit on model artifacts made deploying modern deep learning models difficult
- · Growing runtimes of 2-4 hours per client slowed replenishment decisions in time-sensitive retail categories
- · Bespoke code maintenance increased operational risk for a lean team managing complex forecasting pipelines
The Solution
The team evaluated TimeGPT against its existing pipeline using cross-validation across three criteria that mattered most in production: forecast accuracy, compute time, and overall solution complexity. TimeGPT outperformed the internal approach on all three dimensions. It delivered stronger accuracy, ran significantly faster, and required far less bespoke code to operate at scale. Based on these results, Skulicity replaced its internal forecasting pipeline with TimeGPT
- · Deployed TimeGPT with custom compressed model artifacts and lazy execution strategy to work within Microsoft Fabric constraints
- · Expanded from ~10 to nearly 40 input features without requiring additional infrastructure or longer runtimes
- · Standardized forecasting workflows using Nixtla's APIs, reducing technical debt and making pipelines easier to maintain
A collaborative approach to adapt TimeGPT to Microsoft Fabric constraints
Implementation Timeline
[Evaluation]
Cross-Validation Testing
Evaluated TimeGPT against existing pipeline across forecast accuracy, compute time, and solution complexity—TimeGPT outperformed on all three dimensions
[Deployment]
Microsoft Fabric Integration
Worked directly with Nixtla's technical team to implement custom compressed model artifacts and lazy execution strategy within Fabric's constraints
[Expansion]
Feature Enhancement
Expanded from ~10 to nearly 40 input features, improving forecast quality while maintaining production performance
[Ongoing]
Continuous Improvement
Rolling out multivariate forecasting from TimeGPT 2.1 release to enable time series to learn from related signals across datasets
Transforming forecasting performance and accelerating time to market
Business Outcomes
[01]
Faster Forecasting Cycles
- Training and inference reduced from 2-4 hours to under 1 hour for select workloads
- 3× weekly forecasting cadence at production scale
[02]
Reduced Infrastructure Costs
- 50% reduction in compute costs through optimized runtime
- Better resource utilization with expanded feature sets
[03]
Accelerated Time to Market
- Reduced technical debt with standardized workflows
- Easier handoffs between team members without specialized time-series expertise
"The biggest breakthrough for us was overcoming our deployment constraints without changing our environment. That flexibility made it possible to scale forecasting without rebuilding everything from the ground up"
Data Engineer Intern