Rethinking aftermarket parts: How AI and robotics turn inventory cost into service

If you manage a parts network, the choice isn’t between hoarding inventory and hoping forecasts are right. 

The automotive aftermarket lives in a constant trade-off: keep more stock to guarantee service, or cut inventory and risk unhappy customers. For many original equipment manufacturers (OEMs) and dealer networks that trade at over 99% service levels, the result has been bloated inventories and poor cash efficiency.

Why the old playbook breaks down

Traditional demand planning for parts is brittle. Forecasting routines tend to be single-model, low-dimensional, and manual-heavy. Planners routinely spend more than half their time patching forecasts by hand. 

The consequence: safety stock recommendations that overshoot actual needs, “one-size-fits-all” inventory rules that strangle capital, and slow reaction to pulses from promotions, new model introductions, or real-world outages. In short, the system is optimised for certainty that no longer exists.

A different approach: layered intelligence and human-in-the-loop design
The answer is pragmatic and platform-driven. Rather than chasing the myth of perfect prediction, five capabilities are stitched together into an operationally viable system:

Smarter forecasting via model races: Multiple algorithms—time series, classic machine learning, and deep networks—run in parallel. A “race” mechanism selects the best performer for each stock-keeping unit (SKU) and context, and models learn continuously from new data. This immediately cuts forecast error and reduces the need for manual fixes.

Dynamic inventory strategies: Instead of blanket rules, each SKU gets a tailored policy that factors in sales velocity, value, supplier reliability and seasonality. The system re-optimizes inventories continuously to protect service levels whilst trimming excess stock.

Human + AI decisioning: Predictive systems output recommendations; experts review, adjust, and intervene where context requires. Visual dashboards explain drivers—so planners can trust and tweak outcomes rather than override them blindly.

Digital twins and “what-if” simulation: Before committing to new stocking rules or autonomous flows, we rehearse scenarios in high-fidelity simulations. This reduces rollout risk, quantifies cost-vs-carbon tradeoffs, and identifies safe corridors for automation.

Composable automation and orchestration: From automated replenishment workflows to AR-assisted picking and AMRs for bulk moves, automation is modular. That keeps the system flexible as tech, rules and demand patterns evolve.

These aren’t theory experiments. We’ve deployed more than 800 intelligent logistics projects across over 28 countries and bring that operational muscle to auto parts supply chains. 

The results are compelling: high-frequency parts forecasting accuracy improves significantly (up to ~20%), mid-frequency SKUs see even larger gains (~30%), inventory turnover can rise by 25% to 35%, and total inventory levels may fall by roughly 20% to 25%. 

On the operations side,  automated ordering can exceed 95% of transactions, cutting manual intervention by roughly 60%. Those numbers translate into lower working capital, fewer stock-outs, and better customer satisfaction.

Beyond forecast and stock: the wider AI opportunity
The same platform logic extends well past forecasting and inventory. AI can optimise pricing and dynamic promotions tied to parts availability, drive smarter supplier selection and capacity planning, and automate quality-control signals from returned parts. 

Crucially, omnichannel inventory optimisation—balancing stock across dealers, warehouses, and e-commerce—lets networks serve customers faster with less capital. 

Finally, customer segmentation and demand analytics enable personalised service offers: priority fulfillment, predictive maintenance recommendations, and tailored warranty logistics.

The automotive aftersales market is only getting more complex: vehicle parc growth, extended warranties, and a fragmented service ecosystem amplify demand uncertainty. 

If you manage a parts network, the choice isn’t between hoarding inventory and hoping forecasts are right. The real option is to redesign the supply chain so models, optimisers, simulations, and expert judgment work together. 

That’s how inventory becomes advantageous, not overhead—and how aftermarket parts providers win the next decade.