AI Solutions for Automotive Aftermarket

Reduce breakdown hours. More uptime for every €/$ invested.

Shrink immobilizations and backorders across your catalog. Turn fitment complexity and volatile lead times into daily, money-ranked decisions: what to buy, where to stock, how to price, and when to move.

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In terms of procurement, replenishment, and inventory management, our partner Lokad has shown to be revolutionary. We have achieved unprecedented levels of operational efficiency because of their highly skilled team of data scientists and their potent machine-learning predictive software. Tokić Group reduced its inventory investments while increasing revenue and enhancing service quality. Only these kinds of technological advancements can help businesses grow sustainably by transforming obstacles into opportunities.

Ivan Šantorić

CEO, Tokic Group

In video Read the case study

We’ve been using Lokad daily for over 2 years to calculate our sales prices. It’s truly a tailored solution, especially given that our combined catalogs take in to account the 20 countries where we operate. This choice in technology has really helped us to take our ability to generate value via our pricing to a whole new level, thanks to Lokad’s algorithmic models based on Big Data. As well as being very powerful, Lokad’s solution gives us speed and reactivity, two elements that have become essential for any e-commerce.

Mathieu Pajot

Commercial and Pricing Director, Mister Auto

In video

Aftermarket problems we fix

Immobilization from "one missing part" despite high inventory elsewhere."
Fitment complexity and long-tail demand that safety-stock rules fail.
MOQs and price breaks that tie up capital in the wrong echelons.
Lead times causing expensive stockouts and expedites.
Pricing vs. inventory decisions causing lost margin or lost sales.
Firefighting across DCs, hubs, and stores that buries planners in spreadsheets.
Siloed tools and spreadsheets that record the past but don't optimize the next move.

Check our modules

Automotive aftermarket warehouse worker checking inventory

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

Millions of future production scenarios are simulated; each decision is scored in dollars/euros to balance stock-out risk versus holding cost.
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?

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 our fitment database at scale?

Yes. We model part-vehicle compatibility as a graph with ~millions of edges and use it directly in demand prediction and stocking policy.

Do you optimize pricing together with inventory?

Yes. Prices and stocks are co-optimized because they drive each other in the aftermarket.

Can you handle uncertain, long lead times?

We forecast lead-time distributions and compose them with demand distributions to size buys and buffers. This maximizes availability while minimizing spend.

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

Objective function: hours of breakdown avoided per €/$ invested

We rank every feasible action (buy, transfer, dispatch, re-price) by expected financial impact: margin uplift and avoided immobilization minutes net of carrying, obsolescence, freight, and handling. The engine then selects the best pack of actions under budget, MOQ, truckload, and capacity constraints (no service-level proxies). Purchase priorities are computed directly from probabilistic outputs (not reorder points), yielding a money-ranked to-do list planners can execute or batch.

Fitment as Bipartite Graph

We ingest the part-vehicle compatibility matrix (typically ~100M+ lines) and learn demand at the unit of need—the vehicle—not just the SKU. This captures substitutes/supersessions and prevents false “low demand” signals caused by catalog fragmentation. Those graph-oriented models drive superior purchasing, allocation, and pricing by forecasting need per vehicle family and mapping it back to interchangeable parts.

Probabilistic Forecasts

Demand and lead time are modeled as full distributions; we optimize on demand-over-lead-time (DOLT), not point estimates. This makes decisions robust to intermittency and long tails. Lokad’s forecasting stack evolved from quantile grids to a production probabilistic engine (built to turn uncertainty into measurable gains).

Multi-echelon optimization

We optimize the whole network (OEM → DC → hub → store/garage), explicitly valuing opportunity costs across locations instead of “fixing” each SKU/site in isolation. The solver enforces nonlinear realities such as MOQs, truck capacities, and site limits. Under uncertainty, we use Lokad’s modern paradigms (Stochastic Discrete Descent and Latent Optimization) to compute network-wide decisions that actually respect those constraints.

Joint pricing–assortment–inventory

Prices, stocks, and assortment are co-optimized because price shifts demand and demand justifies stock. We generate price moves only where supply can follow, and we stock SKUs whose price-realized ROI beats alternatives. For aftermarket specifics, see how we tackle pricing with fitment complexity and erratic demand—without divorcing pricing from supply.

Delivery & governance

Everything runs in Envision, our DSL compiled to machine code; scripts are strongly typed with compile-time checks, making assumptions explicit, testable, and versionable. Role-based access and SSO support segregation of duties; dashboards accept inputs for planner-in-the-loop reviews and what-if scenarios before exporting orders back to your systems.

Why tech matters

Automotive aftermarket is exactly where our stack was forged: the “vehicle as customer” mindset, fitment graphs, and hours-of-breakdown KPI are first-class citizens in the platform (not retrofits). Our technology roadmap (Quantile → Probabilistic → Stochastic/Latent Optimization) exists to solve these messy, constraint-heavy problems at scale.

AI Solutions for Automotive Aftermarket

Aftermarket problems we fix

How we do it

Common questions answered

The technical details

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