Cybernetis AI for Lead Time Visibility

Challenges

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A mid-sized home goods and lifestyle retailer manages thousands of SKUs through a network of stores and distribution centers. Forecasting inbound delivery lead times was based on a 12-week rolling average and aggregated at the distribution center level, without considering SKU, supplier, or carrier specifics. When demand shifted or supply chain disruptions occurred, analysts manually adjusted forecasts or placed expensive expedited orders. This often resulted in warehouse backlogs, emergency shipping fees, and higher labor costs from overtime.        

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Additional challenges included:

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• Delay signals were hard to detect due to invalid timestamps and inconsistent carrier data, making AI models less effective.
• External indicators such as AIS shipping data, weather, and port congestion were not integrated, leaving supply risks invisible.
• No unified interface existed to monitor or segment models; forecasts defaulted to network-wide averages.
• ETA updates were handled manually and often came too late to act.

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‍Result: backlogs at ports and distribution centers, increased costs for expedited shipments, and excess labor costs from firefighting operations.

Approach

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The Cybernetis AI solution used two types of models:

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• E2E prediction at PO creation – estimated delivery date to the retailer’s distribution center.
• Daily ETA updates – recalculated expected arrival times for containers already in transit, with risk factors included.

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The pilot covered major import hubs, representing 70% of inbound volume. Models were validated against both pre-pandemic and recent datasets, with consistently strong performance.A multi-screen user interface was developed, including geospatial route maps, order milestones, predictive risk alerts, and explainability packages for every AI-generated recommendation.

Project Highlights

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• Integrated 3.5 years of historical data (~300M rows) from 10 enterprise tables and 3 external sources.
• Built a scalable data model with 25+ logical objects.
• Developed 120+ time-series analytics for ML and application UI.
• Configured and tested 250+ model permutations.
• Deployed Cybernetis AI interface to supply chain managers and analysts.