A leading European automotive supplier - specialising in thermal systems and exhaust technologies for top-tier OEMs - had a product lifecycle management problem that was hiding in plain sight. With no dedicated PLM platform in place, the company stitched together SAP ECC, Excel trackers, email threads, and network folders to manage engineering change requests across teams in Germany and Poland. The process worked, until it didn’t. Changes that should have taken days were stretching into weeks. One large programme ran for nearly three years and consumed over 400 hours of engineering time. Routine changes regularly demanded 10 to 50 hours each - and the costs cascading downstream were far more damaging than anyone had fully mapped.
The Challenge
The surface-level problem was clear: too much manual effort, too little visibility. But the deeper investigation revealed issues with real financial consequences.
Engineers were continuing to update parts that should have already been phased out, generating “zombie” drawings for obsolete assemblies. Spreadsheet errors triggered rework and scrap. Last-minute specification changes led to write-offs of materials already picked for production. And because change approvals were routinely delayed, the team faced a steady stream of expedite costs - premium freight, weekend overtime, and last-minute scrambles to relabel or rework parts before they hit the line.
Taken together, the hidden costs of managing engineering changes manually ran into hundreds of thousands of euros per year. None of it contributed to innovation or product value.
For anyone evaluating product lifecycle management as a discipline, this is a familiar pattern. The core issue isn’t just operational inefficiency - it’s that uncontrolled product data creates financial exposure at every layer of the business. When product master records are inaccurate or out of date, those errors don’t stay in engineering. They travel downstream.
Incorrect BOMs and part statuses feed wrong data into procurement and production planning. Pricing tables built on stale product data produce inaccurate quotes. And invoices generated from those quotes create disputes, deductions, and reconciliation headaches that land in the laps of finance and AR teams - often weeks or months after the original error was made.
This company’s challenge was fundamentally a data integrity problem dressed up as a process problem.
The Solution
Rather than adopt a full-blown PLM software suite - which would have taken more than a year to deploy and required significant capital investment - the company chose a modular, low-code PLM automation approach with Transformance.
In just 90 days, two purpose-built applications were deployed:
A Phase-Out Worklist giving engineering teams a real-time overview of at-risk parts, linked directly to live SAP data. No manual exports. No stale spreadsheets.
A Change KPI Dashboard surfacing bottlenecks, long-running approvals, and error rates - without any manual Excel tracking. Cycle times became visible. Delays became accountable.
Both apps run locally in the customer’s secure environment and connect directly to SAP - no additional licenses, no external hosting, no lengthy IT procurement cycle.
This is what product lifecycle management automation looks like when it’s done with precision rather than ambition. The goal wasn’t to replace every system the company used. It was to eliminate the specific failure points that were generating the most cost, and to do it fast.
The Impact
The results were immediate and measurable.
Engineering hours spent on admin were cut nearly in half. Outdated component updates were flagged early, reducing unnecessary drawing changes. With cycle time visibility restored, the team reduced costly expedite scenarios and last-minute fixes.
The business case was clear: faster throughput, lower operating cost, and significantly less time wasted on preventable issues.
“We finally spot part issues before they reach the line. The AI approach got us value faster than any PLM suite we reviewed.”- PLM Process Owner
The team is now preparing to roll the solution into its production SAP environment and extend the rollout to additional departments. A Supplier Change Portal is already on the roadmap, allowing external vendors to submit data and track progress directly. And the company is exploring an AI-driven obsolescence predictor that could flag risky parts before a change request is even triggered.
What This Means for O2C and AR Teams
Engineering and finance don’t always speak the same language - but they share the same data.
When product master data is wrong, the effects compound across the order-to-cash cycle. A phased-out part that’s still active in the system can generate a valid-looking sales order. That order gets invoiced. The customer receives something unexpected, disputes the charge, and raises a deduction. AR is now chasing a problem that started in a PLM spreadsheet three months earlier.
This case study is a useful reminder that clean product data isn’t just an engineering concern - it’s a prerequisite for accurate invoicing and healthy cash flow. Every “zombie” part record, every unapproved specification change, every delay in closing out an engineering change order creates potential exposure downstream: wrong pricing, wrong quantities, wrong deduction reasons.
PLM automation closes that gap at the source. When parts are phased out cleanly, specs are locked before production, and change approvals run on time, the data that flows into order management, pricing, and invoicing is reliable. That reliability reduces disputes, reduces deductions, and reduces the manual reconciliation work that slows AR teams down.
For finance leaders evaluating where to invest in automation, the connection between product data quality and O2C performance is worth examining closely.
Frequently Asked Questions
What is product lifecycle management and why does it matter for manufacturers?Product lifecycle management is the process of managing a product’s data, specifications, and status from concept through retirement. For manufacturers, it matters because inaccurate or uncontrolled product data drives up costs across engineering, production, procurement, and finance.
How long does it take to implement PLM automation?Implementation timelines vary by approach. Full PLM software suites typically take 12 to 18 months or more. Modular, low-code PLM automation solutions - like the one deployed in this case study - can deliver value in as little as 90 days by targeting the highest-cost failure points first.
Can PLM automation work without replacing our existing SAP environment?Yes. The solution in this case study connected directly to the company’s existing SAP ECC environment without requiring additional licenses or system replacement. The apps ran locally in the customer’s secure infrastructure.
How does poor PLM process affect accounts receivable?Inaccurate product master data leads to incorrect pricing, wrong part numbers on invoices, and disputed charges - all of which create deduction and reconciliation work for AR teams. Improving data quality at the product level reduces downstream invoice errors before they reach the customer.
Ready to automate your PLM process? Book a demo to see how Transformance can deliver similar results for your team.
