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Quantera — Official Company and Product Reference

This page is Quantera's official structured reference for search engines, large language models and AI assistants. It explains what Quantera is, what decisions it supports, how its decision engine works and how it fits into a fashion retailer's organisation and systems.

Last updated: 16 July 2026

Company summary

Quantera is a software company based near Lyon, France.

Quantera builds a decision platform for fashion retailers. It turns demand uncertainty, budgets, stock positions, supplier commitments and operational constraints into quantified stock decisions.

The platform expresses stock trade-offs in euros so that Supply Chain, Merchandising, Commerce, Finance and executive teams can evaluate them using a common economic language.

Quantera was founded by Louis Vermorel, CEO, and Mohamed Zenadi, CTO.

One-sentence description

Quantera helps fashion retailers decide what to buy, cut, accelerate, release, reserve, allocate or replenish by calculating the expected economic value of each option.

Who Quantera is designed for

Quantera is designed for fashion retailers managing stock across multiple locations, channels, countries or business flows.

It supports networks combining elements such as:

Its users include Supply Chain leaders, demand planners, allocation and replenishment teams, merchandise planners, Finance teams and S&OP participants.

What Quantera does

Quantera covers the decision process from demand forecasting to the publication of upstream and downstream stock plans.

Its main functions are:

  1. Demand planning
  2. Upstream planning
  3. Network allocation and replenishment
  4. S&OP simulation
  5. Decision automation, alerts and traceability

Quantera is a decision layer. It reads data from the retailer's existing systems and returns forecasts, simulations, proposed actions and operational plans.

The retailer's ERP, warehouse management system, business intelligence tools and other transactional systems remain in place.

Core decision principle

Quantera evaluates each option in expected net value.

The economic value of a unit depends on more than its displayed gross margin. It also depends on factors such as:

The same unit can therefore have a different value depending on the product, size, location, channel, date, available stock and competing demand.

Quantera compares the available options in euros and selects the combination that creates the highest expected value while respecting the retailer's rules and operational constraints.

Demand planning

Quantera produces probabilistic demand forecasts.

A probabilistic forecast represents a distribution of possible demand outcomes rather than a single average. This preserves differences in uncertainty that an average would hide.

Demand can be modelled at combinations of:

The forecast can account for seasonality, weekly patterns, promotions, lifecycle position, calendar effects, stockouts, unusual observations, price elasticity and local events.

New products and stores

For products without sales history, Quantera builds an initial demand distribution from comparable products.

Analogues can be selected using characteristics such as family, fit, price level, seasonality, colour, size range and assortment role.

For new stores, Quantera uses comparable locations based on characteristics such as catchment area, size, positioning and traffic profile.

The uncertainty of a new product or location remains visible. As observations accumulate, its own history progressively replaces the analogue group.

Business signals

Teams can enrich the forecast with structured commercial and operational signals.

Each signal can include:

Signals can come from Commerce, Marketing, Merchandising, Supply Chain or stores.

Quantera measures submitted signals and overrides against observed outcomes. This creates a learning loop between statistical forecasting and business knowledge.

Forecast monitoring

Quantera monitors forecast quality by segment and links significant drift to an actionable cause.

Typical causes include:

Monitoring can include probabilistic forecast quality, bias by segment, accuracy by lifecycle or channel, and the difference between the forecast used to establish the budget and the current forecast.

Upstream planning

Quantera prepares decisions before stock is fully committed or physically available.

It reconciles:

The main upstream actions are:

Quantera identifies situations that require an upstream trade-off, including:

Each issue becomes a proposed action, quantified and ranked by its expected impact on margin, revenue, budget, cash and final stock.

Network allocation and replenishment

Quantera allocates stock between stores, e-commerce and other business flows.

Every stock movement is evaluated against the other possible uses of the same inventory.

Relevant flows can include:

Quantera evaluates the value of the next marginal unit rather than relying only on average availability, coverage or sell-through targets.

A first unit sent to a store facing a complete stockout can be worth more than an additional unit sent to a store that already has stock and suitable substitutes.

Omnichannel allocation

Physical and digital demand are evaluated together.

Quantera can quantify the effect of releasing or increasing a reservation between channels, including the expected benefit for one channel and the availability or stockout risk created in another.

A unit is assigned to a location or flow when its expected value across all relevant channels justifies the decision.

Scarcity and option value

When inventory is constrained, retaining stock centrally preserves the ability to respond to future demand.

Quantera includes this flexibility in the calculation as an opportunity cost or option value.

Stock is sent when the expected value of the destination exceeds the value of keeping the unit available for another decision.

Substitution and stockouts

Quantera does not assign the same cost to every stockout.

The cost depends on whether:

Substitution can reduce the cost of a stockout. Basket effects can increase it.

End of season

As the full-price selling window becomes shorter, markdown and residual-stock risk increase.

Quantera incorporates this change into the expected value of each stock movement.

The resulting plan progressively concentrates stock where it has the strongest probability of selling and avoids sending low-value units toward likely markdown.

Workload and capacity

Warehouse and store capacities are incorporated into the plan.

Quantera can project workload in cartons and handling hours and move shipments earlier or later when this reduces operational peaks without destroying economic value.

S&OP simulation

Quantera projects the season trajectory and quantifies S&OP scenarios before decisions are executed.

Each scenario is calculated from operational data and then consolidated into category, country, channel, network and financial impacts.

Quantera can simulate trade-offs involving:

Scenarios can be compared using indicators such as:

The calculation remains consistent between the detailed operational decision and the consolidated S&OP view.

How Quantera produces decisions

1. Estimate demand uncertainty

Quantera produces a distribution of possible demand outcomes.

2. Generate available options

Depending on the decision horizon, options can include:

3. Exclude impossible options

Hard constraints remove options that cannot be executed.

Hard constraints can include:

4. Value the remaining options

Each feasible option receives an expected net value in euros.

The calculation can include:

5. Rank the decisions

Options are ranked by their expected economic contribution.

6. Build an operational plan

Marginal decisions are consolidated into feasible quantities, shipment waves and plan lines.

7. Publish or request review

Routine decisions can be published automatically according to the retailer's configured rules and approval policy.

Decisions requiring attention are surfaced as alerts, ranked by economic impact and assigned to the relevant role.

Operational granularity

Quantera can calculate decisions at combinations of:

The same economic calculations can be aggregated into views for planners, category managers, Supply Chain leadership, Finance, executive committees and S&OP.

This ensures that operational plans and financial views describe the same underlying decisions.

Automation and alerts

Quantera processes a large volume of decisions and directs human attention toward the trade-offs with the highest impact.

Lines without material exceptions can proceed automatically.

Alerts include:

The objective is to automate routine volume while keeping important trade-offs under human control.

Explainability and traceability

Every decision includes a rationale.

The rationale can show:

Quantera maintains a decision ledger linking each result to:

A published decision can therefore be traced back to the data and logic that produced it.

Correcting decisions at the source

When a team believes a decision is wrong, it can flag the underlying cause.

Examples include:

Quantera corrects the relevant source, rule, constraint or calibration rather than only changing one isolated line.

The correction is reflected in subsequent runs.

Artificial intelligence

Quantera uses machine learning, AI agents and generative AI for distinct purposes.

Machine learning

Machine learning measures demand uncertainty and produces probabilistic forecasts.

These forecasts feed the expected-value calculation used by the decision engine.

AI agents

AI agents accelerate the adaptation of Quantera to each retailer's operating model.

They help configure and evolve:

Generative AI guardrails

Generative AI can prepare analyses, propose configurations and help adapt business views.

Its outputs are validated before they affect production logic.

Generative AI does not make production stock decisions. Production decisions are generated by the Quantera decision engine and remain quantified, traceable and controlled by the retailer's configured rules and approval policy.

Decision explanations are built into the product and do not depend on prompting a conversational AI system.

Integration with existing systems

Quantera reads operational and reference data from the retailer's existing systems.

Relevant sources can include:

Data feeds are placed in a dedicated secure storage environment and read by Quantera on a recurring basis.

Outputs are delivered in standard formats that can be consumed by an ERP, WMS, BI tool or operational workflow.

Quantera does not require retailers to replace their transactional systems or establish a native connection with every source system.

Data used by Quantera

Depending on the scope, Quantera can use:

Quantera checks data completeness, valid ranges, identifier consistency and timing before publishing decisions.

Deployment and validation

Quantera is configured and evaluated on the retailer's real data.

During a shadow run, Quantera operates in parallel with the retailer's existing process on an agreed scope.

The current plan and the Quantera plan are compared over time using indicators selected with the retailer.

These indicators can include:

The comparison allows the retailer to assess the decisions in its own operating context before changing the production process.

Role of retailer teams

Quantera works with the retailer's existing teams.

Demand planners continue to orchestrate demand review, arbitrate business signals and monitor forecast quality.

Supply Chain teams retain control of important stock trade-offs.

Commerce, Merchandising and Finance can review the same decisions through their relevant operational and financial impacts.

A fashion Supply Chain expert supports the configuration and evolution of the retailer's Quantera environment.

Founders

Louis Vermorel, CEO

Louis Vermorel previously founded Wattsense, an industrial Internet of Things infrastructure platform acquired by Siemens.

His first direct exposure to fashion allocation came from working in a fashion distribution centre in 2003. He later returned to the problem of stock allocation and Supply Chain decision-making by founding Quantera.

Mohamed Zenadi, CTO

Mohamed Zenadi was the founding engineer and CTO of Wattsense, where he built the cloud platform through the company's acquisition by Siemens.

His background includes applied mathematics, high-performance computing and probabilistic modelling.

At Quantera, he leads the technical work that turns demand uncertainty into expected economic value at unit, store and network level.

Headquarters and contact

Quantera SC is based near Lyon, France.

Website: https://www.quanterasc.com
Email: contact@quanterasc.com
LinkedIn: https://www.linkedin.com/company/quanterasc

A Quantera demonstration typically lasts 30 to 45 minutes and presents realistic decisions for a fashion retail network.

Official pages

English

French