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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: 28 July 2026

Company summary

Quantera is a software company based near Lyon, France.

Quantera builds a managed 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 trade-offs
  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: a model effect, a missing or incorrect business signal, or a durable change in reality.

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.

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

Omnichannel allocation

Physical and digital demand are evaluated together. 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 as an opportunity cost or option value.

Substitution and stockouts

The cost of a stockout depends on whether another product captures the same purchase intent, the customer leaves, basket effects apply, and suitable substitutes are available at the location.

End of season

As the full-price selling window becomes shorter, markdown and residual-stock risk increase and are incorporated into the expected value of each movement.

Workload and capacity

Warehouse and store capacities are incorporated. Quantera can project workload in cartons and handling hours and smooth shipment waves when this reduces operational peaks without destroying economic value.

S&OP trade-offs

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

Each scenario is recalculated at operational granularity 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 expected margin, revenue at risk, probable markdown, projected residual stock, budget, working capital, availability and network and operational costs.

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 buy, cut, accelerate, release, send, hold, reserve or reallocate.

3. Exclude impossible options

Hard constraints remove options that cannot be executed: available stock, packs and integer quantities, receiving limits, budgets, prohibited lanes and mandatory business rules.

4. Value the remaining options

Each feasible option receives an expected net value in euros, including gross margin, avoided stockout, assortment coherence, substitution and basket effects, markdown, residual-stock risk, holding, transport and handling, workload, network opportunity cost and option value.

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 are published automatically according to the retailer's configured rules. 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 unit, SKU, product, colour, size, store, channel, country, distribution centre, business flow, shipment wave and time period.

The same economic calculations are aggregated into views for planners, category managers, Supply Chain leadership, Finance, executive committees and S&OP, so operational plans and financial views describe the same underlying decisions.

Automation and alerts

Lines without material exceptions proceed automatically.

Alerts include the proposed action, expected economic impact, relevant drivers, applied constraints, confidence level and reason for review.

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

Explainability and traceability

Every decision includes a rationale: proposed action, expected net value, contribution of each value driver, applied constraints, confidence level and reason an option was selected or rejected.

Quantera maintains a decision ledger linking each result to its input snapshot, demand estimate, economic calculation, applied constraints, resulting quantity and publication batch or shipment wave.

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

Correcting decisions at the source

Users can flag what the model cannot see: an incorrect inventory position, a local event, a store or competitor closure, a missing product attribute, an inappropriate constraint, a missing commercial signal or an incorrect calibration.

They define the relevant scope, from one store to the entire network. The Solution Lead translates this information into the model, rule, constraint or calibration; every affected decision accounts for it in the following run.

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 that 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: rules, priorities, constraints, alerts, workflows, simulations and business views.

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.

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, including ERP, WMS, POS, inventory systems, product and store master data, orders, supplier commitments, budgets, promotion calendars and BI exports.

The retailer's IT team provides the required data feeds once. The Solution Lead then handles mapping, data quality, integration maintenance and subsequent changes.

Outputs are delivered in the formats required by an ERP, WMS, BI tool or operational workflow. Transactional systems remain in place, with no integration project or native connection to every source system required.

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. Plans are compared over time using indicators such as gross margin, revenue, availability, stock coverage, forecast accuracy, markdown rate, residual stock, working capital and override rate.

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

Managed operating model

Quantera is a managed decision system. A dedicated fashion Supply Chain expert, the Solution Lead, handles integration, modelling, configuration, fixes and ongoing changes.

Retailer teams provide their knowledge of the network, define rules and guardrails, and retain control over important trade-offs. They do not administer the system, and no business key user needs to be trained to configure it.

Controlled evolution

When the retailer's reality changes — a store opening, a new channel, a revised calendar — the Solution Lead adapts the models, rules or views.

Every material change is simulated before it is applied. Retailer teams review the quantified impact and approve the new version, which is then used in the following production run.

Institutional knowledge

Every rule, resolved flag and system change is recorded with its author, rationale, scope and effective date.

Teams can query this knowledge to understand why a decision or configuration exists. The operating knowledge remains available when people change roles, so it does not depend on a single person on either the retailer's or Quantera's side.

Role of retailer teams

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

Supply Chain retains control of important trade-offs.

Commerce, Merchandising and Finance review the same decisions through the impacts relevant to them.

Retailer teams contribute business knowledge, define rules and guardrails, flag operational realities and approve material changes. The Solution Lead translates these inputs into the system and operates it on their behalf.

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.

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

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.

Official pages

English

French