AI Solutions for Optimizing Retail

Boost on-shelf availability while cutting excess stock across store networks and ecommerce hubs with Lokad’s AI-automation.

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Worten advocates a digital strategy with stores and a human touch. Our partnership with LOKAD lets us digitalise and renew the intelligence of our management, ensuring that our stores are better prepared to give customers what they want. Ultimately, this partnership is a technological and management breakthrough in how we see and manage the supply chain.

Bruno Thiago Saraiva

Head of Stock, Worten

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Running a large-scale, custom bike business with out-of-the-box planning tools was not meeting the needs of our customers or team members. Lokad’s customized approach, leveraging the expertise of the Supply Chain Scientists, has transformed how we operate by automating some of the most time-consuming tasks for our analysts while also providing deeper insights into what is happening in the business. Now, our team is spending less time in Excel and more time making better decisions that help us provide incredible hospitality to our customers.

Dan Scharneck

Supply Chain Director | Project One, Trek

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Retail problems we fix

Chronic stockouts on high-velocity items that erode both sales and shopper loyalty.
Excess inventory and markdowns that tie up cash and crush margins.
Hours lost in spreadsheets trying to plan thousands of SKUs (and SKU-locations).
Disconnected store, DC and ecommerce flows that create costly transfers and phantom inventory.
Promotion, seasonal and new-item demand that traditional forecasting can’t handle.

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How we do it

Supply Chain Scientists (SCS)

Each initiative has its owns expert (or small team of experts) to partner clients from kickoff to go-live and into the continuous improvement phase. They monitor the automated pipeline, review performance, and adapt the solution as your supply chain evolves (new products, warehouses, or demand patterns).
Probabilistic forecasts

Our SCSs generate full demand distributions at the SKU-location level, capturing every plausible demand scenario. This replaces weak point forecasts and manual safety-stock tables with a clear, data-driven picture of uncertainty.
Evaluating risk

Every replenishment, allocation, and transfer is selected by simulating millions of futures and scoring each one in terms of dollars of impact. This drives higher availability and lower waste automatically.
Differentiable programming

Our SCSs crunch millions of SKUs, BOM levels, MOQs and price breaks in minutes, every night so that you have the best possible decisions ready each morning.
AI-automation

Optimized orders flow straight into your ERP or WMS with zero additional spreadsheet work. Planners regain days each week to focus on strategy, supplier collaboration, and growth.
Cloud native setup

Working with us does not require new hardware or ERP upheaval. Our Supply Chain Scientists' decisions are piped directly to your pre-existing software on a daily basis.
Rapid deployment

Full go-live in under 6 months (on average).

Project implementation

Common questions answered

How fast will we see results?

Most retailers notice meaningful drops in stockouts and inventory within 6–8 weeks of going live.

Can Lokad handle highly promotional or seasonal demand?

Yes. Probabilistic models capture promotion uplift, seasonality and even weather effects, so safety buffers flex automatically.

Do we need to replace our ERP or WMS?

No. Lokad layers on top of your existing stack, exchanging data and orders through flat files, APIs or EDI—no upheaval required.

Will planners still have control?

Absolutely. They can review, lock or override any recommendation while shedding routine spreadsheet work.

How is the solution priced?

A simple monthly subscription aligned to network size—no per-user fees or surprise costs.

What about new SKUs with little or no history?

Supply Chain Scientists infer demand from comparable lookalike items and catalogue attributes, producing credible distributions from day one.

The technical details

Probabilistic vs Point Forecasts

Every component, sub-assembly and finished good is forecast as a probability curve rather than a single number. These curves capture erratic, lumpy demand plus variable supplier lead-times, so the optimizer weighs the full risk distribution—measured in dollars—when deciding order sizes or production batch-starts.

Navigating sparse and exploding assortments

High SKU churn, short life cycles and one-shot items are modeled through attribute-based learning that borrows signal from similar products and channels, safeguarding availability even when history is thin. Cold-start SKUs inherit demand distributions from catalog attributes—brand, price, form-factor—while Bayesian shrinkage keeps the variance realistic, so buyers can launch long-tail products without overstock or lost sales.

Joint network optimization

Replenishment, allocation and transfer are solved together as one stochastic program, avoiding the siloed, suboptimal moves that occur when stores, DCs and ecommerce nodes are optimized separately. Lokad’s optimizer simulates thousands of demand and supply paths, scoring each candidate move in expected dollars of profit—including downstream handling costs—to steer the network toward maximum margin.

Multi-constraint solver

Lokad’s engine respects MOQs, shelf space, route capacity, labor windows and budget limits in a single pass. “What if” scenarios reveal the margin impact of tightening or relaxing any constraint before you commit, and new business rules—promo endcaps, delivery cutoffs—can be added through declarative constraints without rewriting code.

Differentiable programming for supply chain

Business logic is written in Envision, Lokad’s domain-specific language that compiles to autodifferentiable code. Gradients steer the optimizer, accelerating convergence on large-scale problems, while static analysis in Envision catches unit mismatches or null joins before anything reaches production. Because forecasting and optimization live in the same script, teams iterate in hours instead of months.

Cloud-scale compute and security

Workloads burst onto hundreds of Azure cores for the daily run and spin down afterward to keep costs low. Data remain encrypted end-to-end, backed by ISO 27001 controls, SSO and role-based access.

Customer Case Studies

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