At a glance
A global manufacturer of semiconductor and microelectronics equipment modernized a 20-year-old refrigeration testing process. The process relied on static acceptance criteria and undocumented tribal knowledge instead of real equipment data.
SoftServe consolidated data scattered across more than ten disconnected systems into a single, reliable dataset. Machine learning models built on that dataset predicted pass/fail outcomes correctly in 73% of future-stage test cases. Eliminating unnecessary retesting cut total testing time by at least 6% and increased testing productivity by 10%–15%. The program also delivered 10%–15% cost savings and became the template for modernizing the manufacturer's other production lines.
Client background
The customer is a leading manufacturer of equipment and value-added services for the semiconductor and microelectronics industry. The product examined in this case study is a closed-cycle helium refrigeration system, used to cool laboratory experiments directly. The manufacturer designed its refrigeration testing process 20 years ago and needed to modernize it without disrupting production. Refrigeration units are tested in custom-built stands, so only a limited number can be tested at once, and each test can take up to four hours. The manufacturer selected SoftServe based on its track record delivering data analysis and predictive-maintenance solutions for manufacturing companies.
Business challenges
Before the engagement, the manufacturer's refrigeration testing process ran on outdated methods, fragmented data, and undocumented institutional knowledge, which made it impossible to analyze performance or predict failures with confidence. These structural gaps in data, documentation, and testing capacity had to be resolved before any modernization work could begin.
Specifically:
- Testing acceptance criteria and limits were based on partial data and assumptions
- The process lacked documented logic for many testing rules and decisions
- Only a few engineers fully understood how the testing process worked
- Refrigeration equipment history data was scattered across more than ten systems
- Custom test stands limited capacity, and each test took up to four hours
Activities & Solutions
SoftServe modernized the manufacturer's refrigeration testing process by consolidating fragmented data and building predictive models on top of it. The engagement began with a full data review that surfaced merging and reliability gaps across the manufacturer's systems, followed by cleaning, contextualizing, and integrating the data required for analysis. The resulting machine learning models reached 67% average prediction accuracy and correctly forecast pass/fail outcomes in 73% of future-stage test cases, giving engineers earlier visibility into likely equipment failures.
Refrigeration data was scattered across more than ten separate systems, files, and printouts. That is why the project followed a structured four-step methodology, split between onsite visits and offsite analysis, to move from raw data to a working model:
| Step | Onsite Visit | Offsite Work |
| 1 | Clarify and scope project goals | Analyze the collected data |
| 2 | Prioritize the hypothesis of testing-time reduction | Build data models |
| 3 | Gain main data flow details and data explanations | Identify data insights and data challenges |
| 4 | Transfer knowledge with the manufacturer's engineers | Prepare reports, models, and data flow suggestions |
The manufacturer viewed this project as its first step toward becoming a data-driven company built on applied data science, so the modeling had to be granular and trustworthy, not just accurate on average. SoftServe delivered a separate random forest model, a machine learning method that combines many decision trees for more accurate predictions for each refrigerator model, with the top-performing model reaching a 0.85 ROC AUC score, a standard 0–1 measure of prediction accuracy. These models classify suspicious refrigerators for further examination, and two additional models test whether the data can forecast the specific future point at which a unit is likely to fail.
To put those predictions in front of engineers, SoftServe also built a set of graphical interfaces. A sensor-reading chart tracks how each refrigerator behaves across every testing stage and flags any deltas significant enough to investigate. A separate model watches for early failure indicators in the first half of testing; when one appears, it flags the failure, halts the test, and routes the unit to diagnostics. A companion app shows engineers each refrigerator's predicted pass/fail outcome along with the features driving that prediction, and lets engineers confirm or dispute the result.
Value delivered
SoftServe's data and modeling work increased refrigeration testing productivity by 10%–15% and cut total testing time by at least 6% by eliminating unnecessary retesting. The manufacturer gained statistical proof of behavioral differences between refrigerators that reached full warranty life and those returned early, confirming some engineering suspicions and surfacing new failure causes. SoftServe delivered a predictive testing program that the manufacturer has since adopted as the roadmap for modernizing its other production lines.
| Metric | Before | After |
| Testing productivity | Manual, static-parameter process | +10%–15% |
| Total testing time |
No reduction from retesting | −6% or more from eliminated retests |
| Testing-related costs |
Baseline cost | −10%–15% |
| Pass/fail prediction accuracy |
Not measured | 73% correct in future-stage predictions |
| Failure-prediction model accuracy |
Not measured | 67% average; up to 0.85 ROC AUC |
Specifically:
- A data-driven redesign of refrigerator test stands is expected to cut testing time further.
- The prediction app and diagnostic dashboards give engineers a clearer, faster view of test results.
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