01 The Problem
Three failures show up in every demand forecast we audit:
- The model can’t see what moves your demand. Blind to tariffs, commodity prices, and demand shocks, it stays wrong on the 30 to 50% of your catalog that’s lumpy or seasonal.
- No insight into why the forecast moved. Planners are told the number changed, not the reason they need to make a better call and defend it.
- Overrides change the number, never the reasoning. Signal and bias get patched in by hand and lost, so the same miss compounds.
The result is about 5% of your lumpy-SKU inventory sitting idle as excess safety stock, which for a $300M company is roughly $1M of working capital tied up and $250K a year in carrying cost.
02 Why Synthefy
Three things Synthefy does that most models can’t.
Accuracy on the SKUs your model gets wrong.
SKU-specific external signals lift accuracy on the half of your catalog that was stuck.
Forecast trust your team can defend.
Every forecast ships with a signal-by-signal breakdown plus a confidence score.
A model that learns from your whole team.
Each override teaches the next forecast. The loop compounds.
Methodology Illustrative for a mid-market company at $300M revenue. Scales with your inventory load and team size. McKinsey, APQC, ISM, BLS.
03 How Synthefy Compares
Synthefy works on top of the tools you already run. Here is what it adds.
Synthefy augments your current stack with a sharper forecasting layer, your existing tools stay in place.
| Capability | Synthefy | Blue Yonder | Kinaxis | StockIQ | Excel |
|---|---|---|---|---|---|
| Time to first forecast | Hours, API-native | 12 to 24 months | Months | Weeks to months | DIY |
| Finds which connected signals causally drive each SKU | |||||
| Finds cross-product correlations across the whole catalog automatically | |||||
| Per-forecast numerical signal attribution with a confidence score | |||||
| Turns each planner override into a training signal | |||||
| Cost | $$ | $$$$ | $$$$ | $$$ | $$ |
Synthefy works from the signals you connect, and only where external forces actually move demand.
We don’t promise perfect accuracy. We deliver a defensible forecast that significantly outperforms your incumbent stack, with confidence scores planners can act on.
04 Customer Case Study
“Huge under-forecasting on lumpy items. The factory is always lagging behind.”
Head of Supply Chain · Global industrial OEM
- The customer
- A multi-billion dollar industrial OEM with a global supply chain across six product lines and 160 countries.
- The challenge
- 200,000+ active SKUs and over 1M product-country combinations. Half the catalog lumpy and intermittent, stuck at 40 to 45% accuracy on the incumbent stack.
- The approach
- Phase zero deployment. Added external signals (copper, commodity ETFs, country macros, news, currency, cross-product), zero-shot.
- The outcomes
Metric Baseline With Synthefy Accuracy, lumpy SKUs 40 to 45% ~60% Time to live Months of training and tuning 3 days, zero-shot Working capital release Baseline ~$145M one-time Three-year FCF impact Baseline ~$255M cumulative Methodology McKinsey 5% accuracy-to-inventory bridge applied to roughly half of customer’s $5.8B year-end inventory (lumpy SKU portion). APQC and ISM carrying cost benchmarks.
Our model uniquely found some of these correlations
A copper-price signal cut overprediction nearly 6×.
The model found a strong causal link between a key SKU and copper prices — an external input the incumbent never used. Feeding in that single signal collapsed overprediction from +11% to +1.8%.
Cross-product learning caught Turkey’s October spike.
By borrowing demand patterns from similar products, the model detected geo-specific October seasonality the incumbent missed. It predicted 3.6K against 13.8K actual; Synthefy called 12.3K.