Supply Chain Demand Forecasting
Stockouts cost you sales; overstock costs you capital. We replace static Excel forecasts with advanced Time-Series AI models (like Prophet or Temporal Fusion Transformers) that predict future product demand with hyper-accuracy, factoring in seasonality, marketing spend, and macroeconomic weather.
Core Features
Hyper-Accurate Time Series
Moving beyond basic moving averages. Our models capture complex weekly, monthly, and yearly seasonalities simultaneously.
External Variable Integration
Enhancing forecasts by feeding the model external data: upcoming weather patterns, planned marketing ad spend, and local holidays.
SKU-Level Granularity
Generating unique forecasts not just for categories, but for tens of thousands of individual SKUs across different geographic warehouse locations.
Safety Stock Optimization
Calculating the mathematical probability of supply chain delays and automatically adjusting 'safety stock' recommendations to prevent stockouts.
Our Process
Historical Sales Ingestion
Week 1-2Gathering 2-5 years of historical sales data, cleaning anomalies (like Covid-19 spikes), and resolving missing data points.
Exogenous Feature Gathering
Week 3Connecting APIs to pull in the external variables that impact your demand (Weather data, Google Trends, Holiday calendars).
Model Architecture & Training
Week 4-6Training modern forecasting models (Facebook Prophet, ARIMA, or deep learning Transformers) on the historical data.
Backtesting & Validation
Week 7Testing the model against historical data it hasn't seen (e.g., training on 2020-2022, testing on 2023) to prove its accuracy against your old methods.
ERP Integration & Dashboarding
Week 8-9Pushing the final forecast numbers directly back into your ERP (SAP/NetSuite) to automate purchase order generation.
Technologies We Use
FAQ
How do you handle unpredictable events, like a viral TikTok video?
Our past data is ruined by the pandemic. Can you still forecast?
Do we need to replace our current ERP system?
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