aRTi-D™ PM, Προβλεπτική Συντήρηση Βιομηχανικού Εξοπλισμού
Maintenance that leads to timely intervention — before a failure causes downtime
SEEMS shifts maintenance from a reactive and schedule-based model to a predictive one, based on operational data and actual equipment conditions.
Early detection of anomalies
Condition-based maintenance
Linking equipment to production
// 01 — THE PROBLEM
Why maintenance often comes too late
In many industries, maintenance is based on fixed schedules, triggered by a breakdown, or dependent on empirical judgment—with the result that breakdowns occur without warning.
82%
of companies have experienced unplanned downtime
at least once in the past 3 years— ServiceMax
-30%
to -50% in unplanned downtime
with predictive maintenance — McKinsey & Company
-18%
to -25% in maintenance costs
compared to traditional approaches— McKinsey & Company
TYPICAL SCENARIO
“The machine suddenly stopped in the middle of the shift. There were no warning signs. We lost 6 hours of production and needed replacement parts right away.”
Result: lost production, unexpected costs, and schedule disruption.
// 02 — THE APPROACH
What Sets SEEMS Apart
SEEMS bases its maintenance on actual operational data—identifying signs of deterioration before they develop into failures.
01
Operational Parameters
Real-time collection and analysis of machine status data
02
Behavioral Patterns
Identification of deviations from normal operation using historical data
03
Intervention Indication
Early warning — so maintenance can be performed when and where it is needed
Predictive maintenance complements preventive maintenance—it focuses on detectable changes in the actual condition.
// 03 — FEATURES
Τι προσφέρει στην πράξη
CONDITION-BASED MONITORING
● Real-time monitoring of equipment condition
● Detection of anomalies in operational behavior
● Continuous overview of the health of critical units — without manual inspection
PATTERN DETECTION & EARLY WARNING SIGNS
● Analysis of historical operational data
● Identification of deviations from normal behavior
● Early indication of the need for intervention — before a failure leads to downtime
LINKING MAINTENANCE TO PRODUCTION
● Correlating faults and interventions with production orders
● Understanding the actual impact on performance
●A basis for properly prioritizing maintenance actions
// 04 — BENEFITS
What you actually gain
⦿
Reduction in unplanned downtime
Through early detection of anomalies —before they lead to downtime.
✓
Targeted interventions
Maintenance when and where it’s needed—not based on a schedule.
▮
Better utilization of equipment
No unnecessary interruptions — maximizing available production time.
◎
Link to production performance
Maintenance is not an isolated event — it is linked to OEE, orders, and quality.
→ Maintenance is transformed from a cost center into a tool for ensuring production reliability and stability.
// 05 — ADJUSTMENT
For each type of production
DISCRETE MANUFACTURING
Equipment monitoring by machine and line — for job shops, make-to-order, and assembly.
CONTINUOUS PRODUCTION
Continuous monitoring of critical equipment in 24/7 environments — where failures have an immediate impact.
// 06 — INTEGRATED PLATFORM
Part of the SEEMS unified platform
Predictive Maintenance integrates with the other modules.
Predictive Maintenance — data-driven
CONNECTED TO
Production Analytics (OEE)
Correlation of failures and interventions with productivity losses.
PO Tracking
Impact of failures on the execution of production orders.
Quality Control
Correlation of equipment status with quality deviations.
Energy & Environment
Indications of anomalies through unusual energy consumption.
The status of the equipment is part of an overall production overview—with data from across the entire platform.
From Reactive to Predictive
