Better forecasts
SKU/location demand models fusing seasonality, promos and external signals.
Retail · Case Study
Demand & Inventory Intelligence
Demand and inventory intelligence that combines forecasting models with merchandising and supply workflows.
- 35%forecast accuracy improvement
- 28%stockout reduction
- 19%markdown reduction in two seasons
Delivered
What we built
Improve forecast accuracy by 35% for a retail & eCommerce leader.
Inventory health views
Stockout risk and overstock exposure visible before they bite.
Promo signal fusion
Campaign and event effects modeled instead of surprising the plan.
Planner copilots
Plain-language interrogation of any number the system produces.
Inside the build
Forecasts planners stopped overriding
An omnichannel retailer's planners overrode half of all system forecasts — usually making them worse. The fix wasn't a better model alone; it was explainability.
The challenge
A forecasting system nobody believed
The legacy system was a black box with mediocre accuracy. Planners overrode it constantly, adding bias on top of error. Stockouts and markdowns were both rising — the signature of forecasts nobody trusts.
- ~50% planner override rate, often harmful
- Promotions routinely surprising the plan
- Stockouts and markdowns rising together
−62%p95 latency
Before
3.4s
After
1.3s
Cost / 1k tasks
$41
Optimized
$14
The solution
Accuracy plus explainability, deliberately paired
New demand models fused promo calendars, weather, trends and pricing — and every forecast exposes its drivers. A planner copilot answers 'why is this number high?' in plain language, with the evidence.
- Signal fusion lifting accuracy 35%
- Driver attribution on every forecast
- Copilot Q&A that made planners partners, not overriders
99.9%Uptime
1.2sp95 latency
$0.021Cost / task
QualityCostLatencyDrift
The impact
Both failure modes fell together
Stockouts down 28%, markdowns down 19% across two seasons — the pattern only better forecasts produce. Override rates fell to 12%, concentrated where planners genuinely know more.
- Working capital freed from safety stock
- Overrides now rare and usually right
- Planning cycle time cut by a third
Groundedness
96%
Safety
100%
Task success
93%
Tone & format
91%
CI gate passedrelease promoted to production
Outcomes
What changed for the retailer
Availability
28% fewer stockouts on focus categories.
Margin
19% markdown reduction from better buys.
Working capital
Safety stock trimmed with confidence.
Planner productivity
Cycle time down, trust up, overrides rare.
Promo performance
Campaign lift predicted, inventory positioned.
S&OP quality
One explainable number driving the meeting.
Delivery
From challenge to outcome
We publish the architecture decisions, delivery approach and measurable results — so you can see how similar outcomes could apply to your enterprise.
- Measurable KPIs
- Architecture transparency
- Cross-industry patterns
- Repeatable playbooks
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1
Challenge
Business pressure, legacy process and constraints.
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2
Solution
Architecture, agents and integration design.
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3
Delivery
Build, evaluate, secure and launch.
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4
Impact
Efficiency, cost, quality or revenue outcomes.
Stockouts down 28%, markdowns down 19% — but the real win is cultural. Planners interrogate the forecast instead of ignoring it.
Under the hood
The stack behind the system
Built on the retailer's existing data platform, integrated with planning tools.
- Databricks
- Snowflake
- Python
- Anthropic Claude
- dbt
- Kafka
- Power BI
- AWS
- Airflow
- Kubernetes
FAQ
Common questions
Why did explainability matter more than accuracy?
Because unused accuracy is worthless. The 35% accuracy gain only translated into business results when planners stopped overriding — and they stopped when they could interrogate the number.
How do you handle new products with no history?
Attribute-based cold-start models borrow signal from similar products, with wider uncertainty bands shown honestly — planners see the confidence, not just the point estimate.
What data did this require?
Two years of sales history, promo calendars and product attributes — all data the retailer already had. External signals (weather, trends) were layered in during phase two.
Next step
Want forecasts your planners trust?
We'll backtest our approach against your last two seasons and show you the delta.