Demand & Inventory Optimization

Specialized engineers deliver production-grade ML systems that forecast demand, detect stockouts, and optimize pricing in your cloud.
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Premier-Certified Partner Expertise

Inventory Problems Compound Fast.

Disconnected systems, spreadsheet forecasts, and reactive planning make it difficult to see real demand, catch inventory gaps, or know where pricing is leaving margin behind.

  • Demand Planning Runs on Guesswork: Forecasts live in spreadsheets and depend on manual updates.
  • Inventory Systems Miss Reality: ERP, POS, and WMS data can say an item is available while the shelf is actually empty.
  • Stockouts Cost More Than One Sale: An unavailable SKU can push the entire basket, and eventually the customer, to a competitor.
  • Pricing Decisions Lack Demand Intelligence:
    Without SKU-level elasticity and promotion measurement, teams can't see where price changes create margin.

When Is DIO Right for You

Forecasting Still Depends On Excel

Planners manually update forecasts and models instead of relying on automated ML pipelines.

Stockouts Are Discovered Too Late

Reported inventory doesn't consistently reflect what's actually available to customers.

Pricing Is Based On Rules Or Instinct

Teams lack SKU-level elasticity signals to understand where price changes affect volume and margin.

Your Data Is Fragmented Across Systems

ERP, POS, WMS, product, sales, and external data need to become one operational ML pipeline.

Forecast Demand With Production ML

Replace manual forecasting with SKU-level models that continuously learn from your data and adapt as demand changes.
  • Model demand at the SKU level - Incorporate sales, product, and external signals
  • Automatically retrain as demand shifts - Explain what drives every forecast
  • Deliverables | Production forecasting pipeline, automated MLOps + drift detection, model registry + feature pipeline, forecast monitoring dashboard

Catch Stockouts Your Systems Miss

Detect gaps between reported inventory and actual shelf availability, then predict which SKUs are at risk next.
  • Establish expected demand by SKU and store - Detect silent stockouts from sales patterns
  • Predict next-day out-of-stock risk - Prioritize intervention by recoverable revenue
  • Deliverables | Counterfactual demand baseline, anomaly detection + OOS risk models, daily SKU/store risk scoring, backtesting + root-cause framework

Price Against Demand, Not Assumptions

Model how demand responds to price and promotions to protect volume, increase margin, and spend promotions more effectively.
  • Measure price sensitivity by SKU and segment - Identify critical price thresholds
  • Separate promotional lift from baseline demand - Quantify cannibalization and forward-buying
  • Deliverables | Demand elasticity engine, price-volume matrix, promotion impact pipeline, cannibalization analysis
Get Demand & Inventory Optimization
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Forecast demand at the SKU level
Detect inventory risk before revenue disappears
Measure how pricing actually changes demand

Proven At Enterprise Scale.

$63M

Recovered Revenue

+10%

Revenue Per Transaction

+15%

Conversion

Built Around Your Agent Stack

ERP

POS

WMS

Sales

Product

External Data

 XGBoost

Prophet

LSTM

MLflow

SHAP

Existing cloud infrastructure

Data warehouse/lakehouse

APIs

FAQs

What is Demand & Inventory Optimization?
Do we need all three modules?
What data do we need?
Does this replace our ERP?
Where does the solution run?
How long does implementation take?
Who builds the system?
Can our team operate it after launch?
Can Factored engineers stay embedded with us?
Put ML Behind Every Inventory Decision.
Forecast what customers will demand
Find inventory risk before it becomes lost revenue
Know where pricing creates or destroys margin
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