What’s really stopping manufacturers from turning insight into action?
By Daniel Shearly, Chief Product Officer Syncron
Manufacturers have already invested heavily in data platforms, analytics teams, and increasingly, AI. But most of that investment stops short of the aftermarket. Pricing, inventory, warranty, and service decisions — the ones with direct impact on margin, working capital, customer retention, and uptime — are still being made without the benefit of that infrastructure. Closing that gap requires more than moving data into a warehouse. It requires a route for insight to travel back out into the systems where aftermarket decisions get made.
Technology isn’t usually the constraint. Most manufacturers already have the pipes, the platforms, and increasingly the models. The challenge is ensuring enterprise analytics teams and aftermarket teams are working toward the same operational outcomes. Analytics teams are typically measured on model accuracy, data quality, and time-to-insight. Aftermarket teams are measured on service levels, inventory turns, and margin. An integration can be technically flawless and still change nothing on the shop floor if no one on either side owns the connection between those two sets of measures. Everything that follows here — the architecture, the write-back path, the automation — only pays off once that alignment exists.
As customers hold onto equipment longer and focus more heavily on uptime and total cost of ownership, the aftermarket becomes one of the clearest places for manufacturers to protect relationships and build recurring value.
That makes aftermarket data more valuable than many organizations have historically treated it. Parts histories, installed base data, service demand, supplier information, pricing inputs, and warranty claims help manufacturers understand how assets are performing in the field, where customer needs are changing, and which decisions will have the greatest impact on availability, margin, and service outcomes.
Simply exporting that data into an analytics environment isn’t enough on its own. A dashboard full of installed-base trends doesn’t lower inventory costs or protect margin by itself. The real value only shows up when insight makes it back into the workflow that sets a price, positions a part, or resolves a warranty claim.
Many manufacturers have already invested heavily in Snowflake, Databricks, or their own analytics platforms, providing the environment for data governance, model development, reporting, and increasingly advanced analytics across the business.
Aftermarket decisions bring a different type of complexity. Setting a parts price may involve competitive position, cost movement, demand history, stock availability, customer commitments, and commercial strategy. An inventory call may depend on service levels, failure rates, location, lead times, dealer behaviour, and the cost of a part being unavailable when a customer needs it. In each case, the logic lives with the aftermarket application, not the data warehouse.
A manufacturer may build a model in its enterprise analytics environment — an inventory optimization model, say, combining parts history with external demand signals, equipment usage data, fleet activity, or lead-time information. But that model only pays off if its output can reach the aftermarket workflow that acts on it, without creating another manual handoff for someone on the team to manage.
This is where AI adds urgency to the problem rather than solving it by itself. Many manufacturers are now building AI capabilities into their enterprise data platforms, generating sharper predictions and recommendations than ever before. But a prediction that stays in the data platform doesn’t move a part, adjust a price, or resolve a claim. AI models only create value in the aftermarket when their outputs can be fed back into the operational systems that put them into practice.
“It’s a symbiotic relationship, rather than a one-way street where we just send the data off and have no idea what happens with it.”
— Daniel Shearly, Chief Product Officer, Syncron
Manufacturers need access to their aftermarket data, in whatever environment best fits their enterprise architecture. Providing that data to customers in some form is already table stakes for most mature software platforms.
Feeding enriched data back into the aftermarket system is where far fewer vendors have anything real to show — and it’s the half of the equation that moves the needle on margin, inventory efficiency, and service performance.
With automated, bidirectional data exchange, a manufacturer can take aftermarket data into its own analytics environment, enrich it with other data or its own models, then pass the result back into the aftermarket system to guide the next action. Without that return path, data leaves the system, insight gets created somewhere else, and someone still must work out how to translate it into a stocking decision or a price change. That translation gap is where value leaks out of otherwise sound analytics investments.
A bidirectional model closes that gap. The aftermarket system stays connected to whatever wider data estate the customer already runs, while enriched outputs return to the workflow where that call actually gets made — improving parts availability, sharpening pricing, tightening control of warranty exposure, and freeing up the manual effort teams currently spend reconciling insight with execution by hand.
One global construction equipment manufacturer is already using this model for inventory replenishment. The organization connects its analytics environment to its aftermarket inventory solution, runs its own optimization models and additional data sources, then writes the results back into the application that supports stocking decisions.
In many aftermarket environments, decisions about which parts to stock and where still require substantial manual effort — teams working through logic location by location and part by part, adjusting inputs based on experience and whatever information happens to be at hand. When enriched data flows back into that replenishment workflow, manufacturers can automate far more of that decision-making, with human expertise focused where it adds the most value rather than spent reconciling spreadsheets.
For manufacturers with large parts portfolios, distributed service networks, and long asset lifecycles, these improvements compound across thousands of daily decisions. The result is faster analysis, better control over working capital, and stronger service performance — the metrics that determine customer trust and retention.
It’s easy for this to become a platform debate — Snowflake versus Databricks, build versus buy. Those choices matter, but only in the context of the operating model. Enterprise data platforms are the right home for analytics, governance, and model development. Aftermarket systems are where pricing, inventory, and warranty decisions get managed and executed.
Manufacturers need an architecture that assigns work to each layer by function, not by which vendor happens to sit there. That also means the aftermarket system should fit into whatever a manufacturer has already built, rather than asking them to build around it. A Snowflake shop, a Databricks shop, and a customer-hosted environment should all get the same result. The architecture question matters, but only once the organizational one is settled.
Manufacturers have already made significant investments in enterprise data strategy, and increasingly in AI. The aftermarket needs to be connected into that strategy in a way that reflects how pricing, inventory, and warranty calls get made, with a return path built for insight to reach execution as much as a route for data to leave.
Competitive advantage in the aftermarket will come down to how short the distance is between insight and execution — how quickly a model’s output becomes a stocking decision, a price change, or a resolved warranty claim. The manufacturers that close that distance first are the ones who will turn their data investments into margin, uptime, and customer loyalty.
Not usually. The technology exists. The harder problem is organizational — getting enterprise analytics teams and aftermarket teams aligned around the same operational outcomes, so a model’s output actually reaches someone accountable for acting on it.
Aftermarket decisions depend on parts behaviour, service levels, installed base context, pricing rules, warranty exposure, and customer commitments. Those factors require deep domain knowledge as well as data access.
Write-back means enriched data or model outputs can be returned to the aftermarket system so they can influence operational decisions, such as pricing, replenishment, or warranty actions.
No. The aim is to make those platforms more useful in the aftermarket by connecting them to the systems that execute aftermarket decisions.
Manufacturers with complex parts portfolios, long asset lifecycles, distributed service networks, and significant aftermarket revenue exposure have the clearest opportunity.
About the Author:
Daniel Shearly is Chief Product Officer at Syncron. Over 18 years in product and design leadership, including senior roles at GfK, Sainsbury’s, Sky, and O2, he has built and launched award-winning SaaS and machine learning products across retail, telecom, and analytics. At Syncron, he leads product strategy across the full service lifecycle, with a focus on connecting aftermarket data to the systems that turn it into action for global manufacturers.
