ABSTRACT

TRANSFORMING INDUSTRIAL MAINTENANCE FROM INSIGHTS TO IMPACT

In the dynamic landscape of industrial operations, digitization has emerged as a transformative force. Beyond mere automation, it creates an environment that transcends traditional reactive approaches and propels us toward proactive and even prescriptive methodologies. Let us explore this evolution:

1. From Reactive to Proactive:
   • Historically, maintenance was reactive—a fire-fighting endeavor triggered by equipment failures.
   • Digitization changed the game. Real-time data streams from sensors, IoT devices, and interconnected systems allow us to anticipate issues before they escalate.
   • Predictive maintenance emerged, enabling us to schedule interventions based on early warning signs. Downtime decreased, and asset longevity improved.
2. The Rise of Proactive Maintenance:
   • Proactive maintenance leverages historical data, statistical models, and machine learning algorithms.
   • We analyze patterns, detect anomalies, and predict when components might fail.
   • Armed with this foresight, we intervene proactively—replacing a worn bearing before it seizes, adjusting parameters to optimize performance, or cleaning filters to prevent clogs.
3. Prescriptive Maintenance: The New Frontier:
   • Now, we stand at the threshold of prescriptive maintenance.
   • Imagine a scenario: Our system detects a subtle deviation—an irregular vibration pattern in a critical pump.
   • Instead of stopping there, we delve deeper. We trace the root cause—an impending bearing failure due to lubrication issues.
   • The system prescribes specific actions: Re-lubricate the bearing, adjust the flow rate, and monitor closely.
   • We act preemptively, preventing downtime and safeguarding quality.
4. Data as Our Ally:
   • The driving force behind this evolution is data—big data, smart data, and contextual data.
   • Machine learning algorithms sift through terabytes of information, discerning meaningful trends.
   • We no longer wait for alarms; we anticipate them. We address deviations before they ripple through our processes.
5. Challenges and Opportunities:
   • Prescriptive maintenance isn’t without challenges:
     • Data quality and integrity.
     • Algorithm accuracy.
     • Human expertise to interpret recommendations.
   • Yet, the rewards are immense:
     • Enhanced reliability.
     • Cost savings.
     • Sustainable operations.

In this digital era, we move beyond reacting to events—we prescribe actions, shaping outcomes before they materialize. Our journey continues, fueled by data, innovation, and a commitment to excellence.

BIO

Johnny Stieger

Background:
Production Manager
Business Development & Quality Manager Service
Maintenance Manager SKF Gothenburg site
Global Strategic Account Manager
Global Manufacturing Reliability Manager (develop and implement how SKF should work with reliability/maintenance within our own factory - Globally
Performance Solutions Manager CoE EMEA - condition monitoring, contract management, field service, reliability engineering, partner set up, assessment, training, projects, standards, infrastructure, digitalization. 

Has a strong background in maintenance, has worked as Production Manager, as Quality Manager, as Maintenance Manager for SKF's factories in Gothenburg, from there to take the responsibilities for how SKF will work with reliability in its own factories globally.  Since the last 1,5 year heading up Performance Solutions EMEA, where the entire SKF offering regarding how we help our customers towards condition-based maintenance is included, everything from bearing assembly/disassembly, customer training, maintenance assessment to how we set up reliably cloud solutions for our customers and digitize all data from an "asset" perspective.  These digital solutions are both for SKF's own organization as for customers.