This manufacturer of precision machined components serving the automotive and aerospace industries operates multiple facilities globally. Facilities are highly machine dependent, with high mix, low volume production lines. The manufacturer was struggling to evolve past reactive maintenance practices, and the engineering team lacked access to real-time, contextualized data needed to drive continuous improvement across production and maintenance processes.
Fragmented factory data is roadblock to actionable insights
On the factory floor, machines were equipped with sensors, yet engineers and technicians were overwhelmed by fragmented data streams some siloed in legacy MES systems, others buried in spreadsheets, and much of it completely inaccessible when and where it was needed most. Despite a wealth of information, teams couldn’t normalize, interpret, or act on the data in a timely or meaningful way. Critical difficulties included:
Delayed root cause analysis: Weeks to determine failure patterns due to disjointed data access.
Redundant maintenance routines: Preventive schedules were based on time, not usage, resulting in both over-maintaining and missing critical issues.
Underutilized data: Engineers lacked a centralized interface to explore trends across machines, materials, and operator shifts.
Ineffective collaboration: Maintenance and production teams worked off different data sets, limiting shared understanding of risks and opportunities.
Enabling bilateral, contextualized data access
On Time Edge was brought in to help the manufacturer shift its maintenance strategy from reactive to predictive and prescriptive an essential leap to unlock the full potential of Maintenance 4.0. The approach centered around one principle: Data only becomes powerful when it’s normalized, contextualized, and made available to the right people at the right time. The On Time Edge team delivered:
Bilateral data normalization — On Time Edge created a unified data model, structured around the ISA-95 framework, bringing together sensor data, maintenance logs, operator feedback, and real-time machine status. This model acted as the single source of truth, accessible both upstream (for capital planning and engineering optimization) and downstream (for operators and maintenance crews in the field).
Data contextualization at the point of use — Through lightweight edge integration and visualization layers, On Time Edge ensured that engineers weren’t just viewing raw data; they were seeing machine performance in the context of part numbers, operator shifts, work orders, environmental conditions, and historical maintenance actions.
Smart, actionable dashboards — Dashboards weren’t static. OTE designed them with feedback loops in mind empowering engineers to ask questions like:
- “What is the correlation between spindle temperature variance and surface finish quality across operators A and C?”
- “Which machines exceed vibration thresholds only after part changeovers, and what does that imply?”
Enablement of predictive and prescriptive maintenance — Leveraging AI driven analytics, the system flags impending anomalies and suggest optimal times and methods for intervention significantly reducing unscheduled downtime.
Results of empowering Maintenance 4.0 with context-aware data
- Reduced downtime across critical machining centers
- Faster root cause identification due to real-time contextual data access
- Fewer preventative maintenance hours through condition and usage-based scheduling
- Improved cross functional alignment between engineering, maintenance, and production teams
- Enhanced decision making for capital equipment replacement based on actual asset performance trends
This MVP delivered more than operational improvement it creates a new operating rhythm where data is the connective tissue between people, machines, and decisions. By empowering engineers with bilateral, actionable insight, the manufacturer transitioned from “repairing” to predicting, prescribing, and optimizing—the essence of Maintenance 4.0.
On Time Edge’s professional services were not just about deploying technology—they were about transforming how data is used to unlock human intelligence, build resilience, and drive performance at scale. Maintenance is no longer a cost to manage it is a lever for competitive advantage.
