For decades, aftersales was the function nobody fought over. Prime equipment got the strategy deck; service got whatever headcount was left over after the sale. That hierarchy is now backwards.
Aftersales carries the margin, and the market knows it
Manufacturers with a genuine services business earn gross margins up to four times higher than they get from the original equipment sale, according to McKinsey’s research on industrial aftermarket economics. Over a 15-year study of more than 50 industrial companies, McKinsey found that the ones with a high service focus delivered 1.7 times the total shareholder returns of companies that stayed product-led. That is not a rounding error. It is a different business model wearing the same logo.
The problem is that most aftersales organizations are still built to defend margin, not generate intelligence. Parts planning runs on moving averages built for finished goods, not the lumpy, intermittent demand that spare parts actually produce. Warranty audits sample a fraction of claims by hand. Dealer performance gets reviewed quarterly, on a lag. None of that is a people problem. It’s an architecture problem, and it is starting to cost real customers.
Customers noticed first
McKinsey surveyed 250 aftermarket customers across the US and Europe and found that 70% report being no more satisfied with service performance than they were a decade ago. That dissatisfaction has a price attached: 35% of those customers say they would pay more for access to better field talent, and 70% say they would pay more for faster resolution. Customers have priced the gap. Most OEMs haven’t. And in a business where the contract renews rather than the machine, that gap is where retention leaks - a customer who has stopped expecting better service has already started shopping the next one.
Where intelligence actually earns its keep
The shift underway is from “plan and hope” to “sense and respond” - asset telemetry, machine learning, and agentic workflows woven through parts, field execution, dealer networks, and warranty governance, rather than bolted onto each in isolation.
Three places this shows up concretely:
- Parts forecasting. Spare parts demand is sporadic by nature with long stretches of nothing punctuated by spikes, which is exactly what traditional statistical forecasting handles worst. Probabilistic, telemetry-driven models are built for that pattern instead of fighting it, replacing static SKU categories with dynamic, criticality-weighted stocking. The inventory consequence is the point: the same service level held on materially less working capital, because stock follows predicted failure rather than historical consumption.
- Warranty and dealer governance. Claims fraud and inflation have historically escaped simple rule-based checks because they accumulate in small increments across decentralized dealer networks. Machine learning models that compare claims against peer benchmarks and vehicle telematics in real time close that gap, turning warranty audit from a sampling exercise into full coverage.
- Demand sensing at scale. Gartner projects that 70% of large organizations will adopt AI-based supply chain forecasting to predict demand by 2030, moving away from forecasts that require constant manual correction toward ones that self-adjust as conditions change.
The governance leaders can’t skip
None of this argues for handing planning fully to a model. It argues for deciding, deliberately, what runs untouched, what a person reviews, and what escalates automatically, before volume forces the decision by default. Low-risk, low-variance recommendations should move straight through. Ambiguous or high-value exceptions belong in front of a planner or claims adjuster, with the model’s reasoning attached, not hidden. Anything touching a contractual SLA or a pattern of dealer anomalies belongs in front of a leader.
The metrics have to move too. Call-handling time and inventory turns measure the old business. Contractual uptime, first-time-fix rate, and renewal rate measure the one customers are actually paying for.
The real shift
Aftersales was never just a cost center, pretending otherwise was the old mistake. The new mistake would be treating it as a margin center and stopping there. The organizations pulling ahead are treating it as an intelligence business: one where every service event, every claim, and every dealer interaction is a data point that makes the next decision sharper. That compounding is the actual moat. Equipment can be copied. A decade of connected service intelligence cannot.
Ferfier partners with aftersales and service organizations to build exactly this kind of connected intelligence, across parts, warranty, and dealer performance. If you want to see where your own aftersales data stands today, talk to us.
