AI Solutions for Fashion

Turn trends into profit. Not overstock.

Optimize the entire lifecycle of every item in your catalogue with our buy, allocate, rebuy, and markdown decisions. Balance risk vs. reward in money so you launch on time and stock winners at the right price.

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For over 180 years, La Redoute has succeeded in transforming and re-inventing itself along with our customers. Today, we are a worldwide leader in ecommerce, and our ambition is to become families' preferred lifestyle platform. Our work with Lokad is helping us further our transformation, by rethinking fundamental aspects of our business. Their team of driven and dedicated supply chain scientists challenge us to leverage cutting-edge tech in our everyday operations. Their attention to detail, AI expertise and deep understanding of our business make them ideal partners in our quest to drive higher performance and customer satisfaction.

Philippe Berlan

Deputy Managing Director, La Redoute

In video

Customer service, stock management and digitalisation are a high priority at Stanley/Stella. With Lokad, we have found a strong partner with high levels of technological and intellectual ambition. The partnership is allowing us to manage risks using advanced probabilistic forecasting techniques. The technical challenge is significant, but we very much enjoy it. Our interactions are always interesting, and we are essentially only limited by our imagination in what we can achieve. It is very motivating to work in a long-term partnership, with a product that can grow with the business and no hidden fees or surprises.

Stefan Crampton

Head of Planning and Inventory Management, Stanley & Stella

Fashion problems we fix

New product uncertainty, cannibalization and substitutions across styles, colors, and sizes.
Assortments that must have the right balance of sizes, colors, and shapes.
Store/DC allocation that misses size curves, clogs backrooms, and starves winners.
MOQs & long lead times that impact on-shelf availability.
Returns & loyalty programs that make true demand signals unclear.
Markdown prices & timings that leave too much margin on the table.
Siloed tools and spreadsheets that record the past but don't optimize the next move.

Fashion influencer reviewing clothing items

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How we do it

Supply Chain Scientists (SCS)

A dedicated SCS team encodes your economics (margin, MOQs, size curves, logistics, promo rules) and partners from kickoff to go-live and beyond. This lets the SCS supervise (and improve) your decision-making algorithm as your assets, vendors, and network evolve.
Decisions ranked in money

Every action (buy, transfer, rebuy, markdown) gets a financial score that blends working capital, obsolescence, freight, price breaks, and promo impact. This means you maximize ROI for every single decision.
Artificial intelligence

Your SCS team uses a unique coding language (Envision) and artificial intelligence to solve network-wide decisions nightly across stores, DCs, e-com, and suppliers.
Generate buy-in

We run "current process" vs "Lokad's process" side-by-side so you see the financial impact on uptime risk and operations costs before you switch.
Forecast for uncertainty

We model full probability distributions for demand, lead time, returns, and promo uplift. These tools are built for the unique volatility of fashion.
No new hardware or software

We layer on top of your ERP/WMS; flat files/APIs in, optimized decisions out. Typical go-live under 6 months.

Project implementation

Optimizing Inventory at Celio

“Before Lokad, our inventory teams were swamped with hundreds of calls from stores about stock issues. Now, those calls have disappeared – it just works. Lokad truly understands our business and lets us focus on our customers. Impressively, they kept us operational even during the unexpected challenges of the Covid-19 pandemic.”

David Teboul

CEO at Celio

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Common questions answered

How fast will we see results?

We start with a dual-run (current process vs Lokad’s process) so clients can see the financial difference with their own eyes. Once validated, automation frees up planners to focus on strategy. Typical results visible in 6-8 weeks (general estimate). Full go-live is typically within 6 months.

Can you handle new products, size curves, and assortments?

Yes. Our forecasts work at assortment level, automatically detect product similarity, and respect size curves in buys and allocation. Our algorithm can also handle multiechelon networks.

Can you optimize decisions for E-commerce + stores + DCs together?

Yes. Each night, Lokad performs a network-wide optimization that includes DCs, stores, and e-com under real constraints (capacity, bundles, labor, etc.).

Lead times and returns feel random—can you model them?

Absolutely. We generate probabilistic lead-time and return models that couple with demand.

Will planners still have control?

Absolutely. Your planners can review, lock, or override any recommendation we generate.

Do we need a new ERP?

No. Lokad’s decisions can be integrated into your ERP/existing enterprise software. Integration is done through simple flat file transfers or APIs, and follows our standard data-extraction pattern.

The technical details

End-to-end lifecycle, encoded in money

From range planning to clean exit, every feasible action (buy, pre-pack, allocate, rebuy, markdown) is scored in euros, blending margin, carrying cost, logistics, MOQs/price breaks, capacity, and loyalty effects. Decisions maximize expected financial return, instead of focusing on bad proxies for supply chain performance (such as service levels in isolation).

Assortment-aware, price-conditioned demand

We forecast full probability distributions for demand and make demand conditional on price and promo intensity at the assortment level (to capture cannibalization/substitution). Returns and lead times are forecast probabilistically and composed with demand. This is a much more granular and robust approach than using time-series forecasts and static parameters (e.g., for lead times) in an Excel spreadsheet.

MOQs & price breaks across layers

Fashion buys face overlapping constraints: per-SKU MOQs, fabric-by-color minimums (e.g., meters), supplier-level order floors, plus breakpoints. Our solver finds the most profitable envelope that satisfies all constraints.

Size curves, pre-packs, and capacity

Allocation and initial push respect size curves, store/DC capacity, labor handling limits, and carton/lot constraints. This means you avoid starving best-sellers while clogging backrooms.

Best-seller probes and early rebuy

We “probe” winners with small early quantities (often via e-commerce or a subset of stores), detect best-sellers vs. slow-movers fast, and trigger early replenishment or surgical markdowns.

Markdowns and loyalty interplay

Pricing optimization times and sizes markdowns to exit cleanly while protecting brand equity, and it accounts for store-wide loyalty discounts that stack with item-level promos.

Similarity learning for new items

No brittle manual “like-for-like” pairing. We use Artificial Intelligence (AI), particularly Large Language Models (LLMs), to auto-learn similarities from catalog attributes (type, family, size, color, fabric, style, price point, brand, etc.) to forecast new variants and entire capsules.

Lead time as a distribution

We forecast lead times probabilistically (seasonality, holidays like CNY, route effects), then compose them with demand to optimize buys and safety across sea/air mixes.

AI Solutions for Fashion

Fashion problems we fix

How we do it

Optimizing Inventory at Celio

Common questions answered

The technical details

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