Industry overview
What is shaping industrial robots & automation
Industrial automation and robotics are moving from large plants to mid-sized manufacturers. Adoption depends on clear payback, integrator support and confidence in the technology.
- Plant heads need a clear business case before investing
- System integrators are central to successful adoption
- Skills gaps slow down deployment
Client background
An industrial robot service provider, working in Japan & Taiwan. Client identity is withheld to protect confidentiality.
- Organisation
- An industrial robot service provider
- Sector
- Industrial robots & automation
- Team engaged
- IT & Development
- Services
- Data analytics & dashboards, AI & automation solutions, Cloud & DevOps services
Client challenge
The provider responded to breakdowns as they happened, leading to unplanned downtime for customers and costly emergency visits. Engineers often travelled long distances for faults that might have been prevented, and customers were questioning the value of their service contracts.
Research objectives
- Deliver on the brief: predicting maintenance needs from robot service data.
- Design around the real needs of installed robots.
- Define requirements and success criteria the solution would be tested against.
- Deliver a reliable, secure solution the team can run and extend.
Target audience
The work focused on 1,200 installed robots.
- installed robots1,200
Audiences we typically reach in industrial robots & automation
Plant heads and ownersProduction engineersSystem integratorsAutomation distributorsMaintenance teams
Geographic coverage
Coverage: Japan & Taiwan. Markets: Japan and Taiwan.
- Japan
- Taiwan
Research methodology
An iterative delivery approach: discovery, design, build and test in short cycles, with the client reviewing each step.
- 1Brief
- 2Service data analysis
- 3Predictive dashboard
- 4Insight
We analysed service records, error logs and usage data to identify patterns preceding failures, then built a dashboard flagging robots likely to need maintenance, with alerts for service planners. Service engineers reviewed the early warning signals with us, helping to separate meaningful patterns from noise in the error logs.
Data analytics & dashboardsAI & automation solutionsCloud & DevOps services
Sampling strategy
- installed robots
- 1,200
- Users and stakeholders from every affected team took part in discovery workshops and acceptance testing.
Data collection
- Requirements were gathered through workshops, process walk-throughs and a review of existing systems and data.
Data quality & validation
- Peer code review on every change
- Automated and manual testing
- User-acceptance testing with the client
- Release checklist and rollback plan
Data analysis
- Usage and performance monitoring after launch
- Defect and support-ticket trends
- Process time before and after
Key findings
- Customers on planned maintenance contracts received monthly health reports, which strengthened renewals and opened conversations about upgrades.
Historical service data already held the early warning signs.
Recommendations
- Build on the core insight: historical service data already held the early warning signs.
- Focus effort where the evidence was strongest: customers on planned maintenance contracts received monthly health reports, which strengthened renewals and opened conversations about upgrades.
- Re-measure after the changes to confirm the effect.
Business impact
- The provider shifted part of its work from emergency repairs to planned maintenance, improving customer uptime and making service schedules more predictable.
What changed in the business as a result of the work. Impact is described qualitatively.
Deliverables & next steps
- Interactive dashboard
- Deployed solution with documentation and handover
- Support and maintenance plan
- Repeat the measure after changes are in place to track progress.
- A natural follow-on question: what stops plants from automating?
Client identity is withheld to protect confidentiality.