How a global industrial engineering firm replaced reactive counter-offers with predictive intelligence, saving over $1.7M in specialist replacement costs in the first year.
| Employee / Role | Risk | Eng. | OT/mo | vs Mkt | |
|---|---|---|---|---|---|
| M. KowalskiSr. Mechanical Eng. | High | 3.8 | +41h | −14% | |
| A. NowakProcess Engineer | High | 4.1 | +28h | −8% | |
| P. WróbelMfg. Engineer | High | 4.4 | +9h | −11% | |
| K. JankowskiQA Specialist | Med | 5.2 | +34h | +2% | |
| J. WiśniewskaR&D Engineer | Med | 5.7 | +12h | −17% | |
| T. GrabowskiAutomation Eng. | Med | 5.9 | +22h | −6% |
This case story illustrates a common challenge for industrial and engineering organizations losing specialized talent. The solution demonstrates our proven approach to building a predictive people analytics platform. Are your best engineers already halfway out the door? Let’s build your Flight Risk Dashboard →
For a global industrial engineering firm with 500 specialists across five production sites, employee turnover was a silent, compounding crisis. Replacing a senior engineer costs up to 200% of their annual salary — not just in recruitment fees, but in lost project continuity, safety risk, and the overtime burden placed on remaining colleagues.
The HR team had no early-warning system. Performance data lived in one system, engagement surveys in another, payroll in a third. By the time a high-performer handed in their notice, it was already too late. Counter-offers rarely worked, and the institutional knowledge walked out the door anyway.
The moment of truth came when a lead process engineer — a 12-year veteran with unique knowledge of a critical production line — resigned with two weeks’ notice. The backfill took six months and cost €190,000 in recruitment, onboarding, and productivity loss.
A post-mortem review revealed his exit survey had flagged engagement concerns four months earlier. Nobody had acted. The data existed — it was simply trapped in a silo, invisible to the people who needed it most.
We used Azure Data Factory to build pipelines that extracted and consolidated data from the HRIS, payroll system, ATS, performance management tool, and pulse survey platform into a single Azure Data Lake.
In Azure Synapse Analytics, we engineered predictive features — engagement score trends, overtime accumulation, compa-ratio to market, tenure bands, and manager stability — and trained a gradient boosting model achieving 88% prediction accuracy.
We delivered a Power BI Flight Risk Dashboard surfacing at-risk employees 30–90 days before a likely exit, with driver analysis and a curated intervention playbook mapped to each risk pattern.
We architected a modern, governed people analytics platform that transformed scattered HR transactions into a predictive early-warning system for talent retention.
Azure Cloud
Power BI
Data Factory
Synapse Analytics“We’d find out someone was leaving in the exit interview. By then, it was too late — the decision had been made months ago.”
“Now I walk into a manager meeting knowing exactly who is at risk and why. We intervene early — and it actually works.”
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Learn About Power BI ServicesA complimentary workshop to map your current HR data landscape, identify integration points, and quantify the cost of your current turnover rate.
We select one high-risk specialist group and deliver a working Flight Risk pilot dashboard in 6–8 weeks — complete with model scores and driver analysis.
We expand coverage to your full population and integrate flight-risk insights into your regular talent reviews, succession planning, and workforce planning.
“You can’t retain people you can’t see. The data to predict who will leave almost always exists — it’s simply trapped in silos. We break down those walls and surface the signal early enough for managers to act humanely, not desperately. Proactive retention beats a counter-offer every time.”
Our team combines deep expertise in industrial HR processes with cutting-edge data architecture. They’ve helped dozens of manufacturing and engineering clients move from reactive talent firefighting to proactive, analytics-driven retention strategies — protecting institutional knowledge and reducing the hidden costs of specialist turnover.
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