Smart Maintenance is a data-driven, digitally supported maintenance approach that combines three dimensions:
Digital maintenance using MES and CMMS, mobile devices, and sensors
Predictive Maintenance based on condition and process data
Lean Maintenance with waste-free, standardized processes
The goal is to keep equipment stable, reduce unplanned downtime, and focus maintenance resources on value-adding work instead of reactive firefighting.
Lean Maintenance applies Lean and TPM principles to maintenance operations.
Key elements include:
Elimination of waste such as waiting, searching, over-maintenance, and duplicate work
Standardized workflows, 5S, and clear roles and responsibilities
Prioritization of critical assets instead of treating all equipment equally
This turns maintenance from a cost center into a lever for availability, OEE, and stable material flow.
Lean Maintenance provides the process backbone of Smart Maintenance: standards, workflows, and continuous improvement.
The second pillar of Smart Maintenance is Predictive Maintenance (PdM).
Typical setup:
Sensors capture vibration, temperature, current, pressure, cycle counts, and runtime data
Statistical methods and machine learning models detect anomalies and wear patterns
Maintenance is planned based on actual condition instead of fixed intervals
Key benefits:
Fewer unplanned breakdowns and secondary damage
Better-planned maintenance windows
Longer lifetime of critical components
Direct impact on availability and OEE
In a Smart Maintenance approach, Predictive Maintenance is not a standalone data science project. It is embedded in clear Lean Maintenance processes that define who reacts, how, and when.
Many manufacturers use a CMMS for maintenance planning. For Smart Maintenance that combines Lean and Predictive approaches, this is not sufficient.
Without MES, critical elements are missing:
Production context such as OEE, downtime structure, FPY, and scrap
Order, product, and shift context
Live machine and process data in a unified model
An MES fills this gap by:
Collecting real-time machine data, downtime reasons, and OEE
Linking maintenance events to orders, materials, lines, and shifts
Providing dashboards and alerts for production and maintenance teams
Integrating predictive insights into daily operations through automated actions
This embeds Smart Maintenance directly into the Digital Shopfloor instead of creating another isolated system.
A Cloud MES like SYMESTIC provides a practical foundation for Smart Maintenance in mid-sized manufacturing.
SYMESTIC captures cycle times, downtime, process, and quality data in real time and links it to assets and orders. This data forms the basis for condition-based and predictive models.
OEE analysis shows where availability losses actually occur. Combined with predictive insights, this turns abstract downtime into concrete actions tied to specific assets and components.
When a potential failure is detected, the MES can automatically trigger alarms, maintenance orders, planned downtime, order blocking, or load balancing.
Transparency into failure types, response times, MTBF, MTTR, recurring issues, and spare part usage makes waste visible. Lean Maintenance principles such as standardization and asset prioritization can be implemented based on data instead of assumptions.
Smart Maintenance becomes an operational lever for reliability and OEE, not just a theoretical concept.
Typical triggers include:
High unplanned downtime on a small number of critical assets
Limited transparency into failure causes and maintenance costs
Maintenance teams overloaded with reactive work
Existing or planned MES implementation
A proven step-by-step approach:
This turns Smart Maintenance from a buzzword into a measurable, MES-driven maintenance strategy with real impact on equipment reliability and operational performance.