GLOBAL FIELD SERVICE ATLAS
AI-powered field diagnostics
for large-scale industrial equipment.
Pre-dispatch diagnostic accuracy improved threefold
Industry
Heavy industry (large-scale industrial equipment)
Target area
After-sales service and field engineering departments
Implementation period
10-week PoC → 9-month phased rollout
FIELD DIAGNOSTIC MAP
Thirty years of maintenance records were consolidated into AI, structurally improving field diagnostic accuracy.
North America
Europe
Southeast Asia
Company-wide search
BACKGROUND
Turning maintenance records into diagnostic assets usable at sites around the world
Company K’s core business is after-sales service for large-scale industrial equipment.
When equipment problems occurred, on-site response costs were enormous, and cases such as “we went there but had the wrong parts” or “we went there but it could have been handled locally” were not uncommon.
The after-sales service business contributed significantly to company-wide profit, so improving its profit margin had long been a management priority.
With CLAVI Mining, the company consolidated 30 years of maintenance records into AI and structurally improved the accuracy of field diagnostics.
BEFORE
Challenges Before Implementation
FIELD BOTTLENECK 01
After receiving a trouble report from a customer, it took an average of 1.5 days to determine the required parts, tools, and dispatch personnel, and damage often expanded during that time. The amount of time a customer’s production equipment was stopped directly affected compensation and reputational risk.
FIELD BOTTLENECK 02
Past maintenance records were managed in separate systems by region and site, making it virtually impossible to search company-wide for “cases with the same symptoms.” As a result, knowledge accumulated at the North American site did not reach the Southeast Asian site, and different sites repeatedly solved the same problems independently.
FIELD BOTTLENECK 03
Veteran field engineers were dispersed around the world, and knowledge sharing did not progress, creating multiple situations where “only that person knows.” The travel frequency of those veterans also exceeded normal levels, reaching a point where impacts on their health and family life could no longer be overlooked.
WHY CLAVI
Reason for Selection
KNOWLEDGE LAYER
Utilizing 30 years of maintenance records by equipment model and generation.
SELECTION FACTOR 01
What Company K prioritized in selection was the ability to learn from 30 years of maintenance records as-is without reorganizing them, and the ability to prevent incorrect answers caused by mixing up equipment models. General-purpose AI sometimes confused models, which was judged to be a safety risk.
SELECTION FACTOR 02
CLAVI Mining was selected for its patented hallucination-suppression technology and high search accuracy by equipment model and generation. During the PoC, verification was conducted in parallel at three sites: North America, Europe, and Southeast Asia. Since diagnostic accuracy before dispatch improved significantly at all sites, the decision to proceed to full deployment was made quickly.
SELECTION FACTOR 03
Another major advantage in the selection process was its standard support for legal compliance at overseas sites, including GDPR and each country’s data protection laws, which was essential for global deployment.
AFTER
Results After Implementation
FIELD RESULT 01
3x
Pre-dispatch diagnostic accuracy
[Pre-dispatch diagnostic accuracy] Classification accuracy improved threefold. Cases of “we went there but it was different” decreased by 72%, dramatically improving the quality of preparation.
FIELD RESULT 02
4 hours
Parts arrangement lead time
[Parts arrangement lead time] Reduced from an average of 1.5 days to 4 hours. Improved pre-arrangement accuracy greatly shortened customer downtime and significantly reduced lost production opportunities on the customer side.
FIELD RESULT 03
Approx. ¥320M reduction
Travel costs
[Travel costs] Reduced by approximately ¥320 million per year. The profit margin of the after-sales service business improved by 4.1 percentage points.
FIELD RESULT 04
72%
Independent response ratio
[Field engineer independence] The percentage of mid-level engineers able to handle cases independently rose from 38% to 72%. Veteran engineers’ travel frequency dropped significantly, giving them more time for family and next-generation training.
EXECUTIVE COMMENT
Comment from the Head of After-sales Service: “Thirty years of maintenance records were our greatest asset, but we had not been able to use them. With CLAVI, they moved as an ‘asset’ for the first time. We plan to expand this further by linking it with predictive maintenance data.”
FIELD ENGINEER VALUE
AI is a key element of talent strategy
The motivation of field engineers engaged in maintenance work also improved. As the experience of “solving customer problems based on past cases” became part of daily work, the retention rate of young engineers rose above the industry average. AI also became an important element of the company’s talent strategy.
INSIGHTS / NEXT
Insights from This Case and Future Development
SERVICE STRATEGY 01
Company K’s case reconfirms the reality that maintenance records are one of the greatest management assets, but their value is zero if they are not used. Not only in heavy industry but also for manufacturers with long-term maintenance operations, making past maintenance knowledge usable through AI is a strategic initiative that can transform both after-sales profitability and customer satisfaction.
SERVICE STRATEGY 02
Starting with CLAVI Mining use across field engineering organizations worldwide, Company K is developing a new customer-facing service called an “AI-assisted maintenance contract.” The strategy is to include downtime reduction through AI diagnostics in contractual SLAs and increase the added value of maintenance services.
SERVICE STRATEGY 03
By building a feedback loop from maintenance knowledge to the equipment design department, the company is establishing an end-to-end structure that reflects market quality in design. This directly contributes to improving the reliability of next-generation equipment. Maintenance AI is becoming a core technology in the servitization strategy of manufacturing.