A $3M Opportunity Hiding in Plain Sight
In a recent value pilot across a subset of SKUs, we uncovered over $3M in forecast-driven business impact — without investing any effort in model tuning.
The issue wasn’t forecast accuracy. It was what the forecast was optimized for.
For decades, forecasting teams have been told the same thing:
Improve forecast accuracy, and the business will improve.
So, organizations invest in better ML models, finer tuning, more overrides, and endless debates about which forecast is “right.”
Yet the outcomes remain stubbornly familiar: excess inventory, stockouts, expediting costs, unstable factory schedules, frustrated suppliers, and growing distrust across the board in the forecast itself.
The problem isn’t effort. The problem is the target.
Forecast accuracy is not the objective function of the business. Cost, risk, cash, service, and stability are.
At VYAN, we built AI Fusion forecasting around that reality.
Executive Summary — What Changed and Why It Matters
In a recent VYAN AI Fusion value pilot, we evaluated forecast performance across a subset of SKUs, spanning multiple historical cycles and lags. The evaluation intentionally combined:
· Traditional forecasting KPIs (error, bias)
· Direct business impact metrics, most importantly Cost of Forecast Error (COFE)
The results were unambiguous:
· The AI Consensus Forecast delivered:
o The lowest Cost of Forecast Error (COFE) across all alternative forecasts ($3M+ savings versus the consensus forecast baseline from just a subset of SKUs)
o Lower error than internal demand planner forecasts (45% error reduction)
o Lower bias than internal demand planner forecasts (41% bias reduction)
o A material reduction in forecast churn, producing a more stable demand signal (churn reduction by a factor of 16)
· Improvements demonstrated across traditional and business impact KPIs versus baseline approaches
· These gains were achieved without tuning individual models and without replacing planner or customer forecasts
In short, this was not a theoretical improvement. It was a measurable reduction in business pain.
The key insight:
The best forecast is not the most accurate one — it is the one that minimizes business impact while remaining stable enough to plan against.
Why Traditional Forecast Comparisons Miss the Point
Most forecast evaluations stop at metrics like:
· Error %
· Bias %
Those metrics matter — but they are not the objective function of the business.
The business absorbs forecast error through:
· Excess and obsolete inventory
· Stockouts and lost revenue
· Expediting and premium freight
· Cash tied up in working capital
· Supplier instability and schedule volatility
This is why two forecasts with similar accuracy profiles can lead to very different outcomes on margin, service, and cash.
VYAN reframes forecasting around a simple principle:
Forecasts should be optimized for economic impact, not statistical elegance.
COFE: Turning Forecast Error into an Optimization Problem
Cost of Forecast Error (COFE) explicitly quantifies the downstream cost of being wrong, capturing:
· Asymmetric penalties for over- vs under-forecasting
· Business-specific sensitivities (service vs inventory vs stability)
· The economic consequences of volatility, not just error magnitude
At VYAN, COFE is not a reporting KPI. It is a core component of the optimization objective function.
This allows forecast generation to align with business intent:
· Is avoiding stockouts more important than minimizing excess inventory?
· Is supplier and factory stability more critical than chasing short-term demand spikes?
· Is cash preservation more important than marginal service gains?
Different businesses — and different products — answer these questions differently. The forecast must reflect that.
The VYAN AI Consensus Process (How This Actually Works)
The pilot followed a two-round AI Fusion process, designed to be practical, scalable, and planner-friendly.
Round 1: AI Baseline Forecast — A Universal Starting Point
The AI Baseline Forecast is generated using AI Fusion across the entire forecast universe:
· All available statistical and ML models
· All hierarchy levels
· All time buckets
· Prior-cycle consensus forecast
· Current-cycle customer forecast (if available)
· Ensemble forecasts derived from multiple candidate signals
This is not a “single best model.” It is an economically optimized blend, designed to be accurate, unbiased, stable in sync with the business needs to reduce the impact from bad forecasts.
Critically, this AI Baseline is then shared as the starting forecast with:
· Demand planners
· Sales
· Marketing
· Operations
· Finance
No one starts from scratch. Everyone starts aligned.
Round 2: AI Consensus Forecast — Business-Aware Optimization
In Round 2, VYAN runs AI Fusion again — but with a narrower, more intentional scope.
Inputs include:
· AI Baseline Forecast
· Current-cycle Demand Planner Forecast
· Current-cycle Sales and Marketing Forecasts
· Current-cycle Customer Forecast (where applicable)
· Prior-cycle Consensus Forecast
The optimization objective places explicit weight on COFE, alongside traditional measures. This allows the system to:
· Reduce unnecessary volatility (forecast churn)
· Control bias without flattening meaningful signal
· Preserve planner intelligence rather than overwrite it
The output is the AI Consensus Forecast — the recommended baseline for demand review meetings.
This forecast is:
· Stable enough to plan against
· Economically aligned with business priorities
· Explicitly designed to minimize cost of error, not just error itself
What the Value Pilot Showed (Anonymized Results)
Across the pilot:
Forecast Quality
· AI Consensus delivered lowest forecast error, bias, churn compared to all alternative forecasts
Business Impact (COFE)
· AI Consensus achieved the lowest total COFE
· Outperformed:
o Naive forecasts
o Standalone AI baselines
o Planner-only forecasts
o Customer-only forecasts
In absolute terms, cost reductions exceeded 15%+ versus baseline approaches, translating directly into reduced expediting, improved service stability, and better inventory outcomes.
Signal Stability
· AI Consensus avoided overreacting to short-term noise
· Preserved meaningful trend shifts
· Improved downstream planning confidence (factory schedules, supplier commitments, customer promises)
The most important takeaway:
AI Consensus strengthened the Demand Planner signal — it did not replace or compete with it.
Demand Planner forecasts received the largest weight in the final consensus, but were corrected where they introduced removable error, bias or volatility.
Why This Works — and Why It Scales
This approach succeeds because it:
· Optimizes across multiple KPIs simultaneously
· Treats forecasting as a decision input, not an isolated artifact
· Allows business leaders to express intent through COFE weighting
· Avoids brittle model tuning and manual overrides
· Delivers results quickly, at portfolio scale
It is not about better math. It is about better alignment between forecasts and decisions.
From Pilot to Production: A Practical Path
At VYAN, we run AI Fusion value pilots that:
· Cover a meaningfully large portion of the product portfolio
· Complete in 2–4 weeks
· Compare results directly against existing baselines
· Quantify value using both forecast KPIs and business impact metrics
The outcome is not a slide deck. It is a working consensus forecast process that planners and executives can trust.
Final Thought
If your forecasting process still revolves around:
· Accuracy metrics alone
· ML Model Drift
· Manual overrides that work as predictable as a coin toss
· Post-hoc justification of results through gamed metrics
You are optimizing the wrong layer.
The future belongs to economically aware, stability-driven, decision-grade forecasting.
That’s what VYAN was built to deliver.
If you want to see what AI Fusion can deliver on a meaningful slice of your portfolio in 2–4 weeks, VYAN runs focused value pilots that measure success in business outcomes — not model metrics.
