December 2025
By The FSO INSTITUTE
Reliability Programs & Case Studies
SESSION OVERVIEW
The December MHRT session concluded the 2025 calendar year and featured a deep dive into asset management, reliability excellence, and maintenance transformation, led by David Mierau, PE, Vice President of the Commercial Division at Life Cycle Engineering (LCE). The discussion blended strategic frameworks, real-world case studies, and open peer dialogue, reinforcing MHRT’s focus on practical, plant-level execution tied to enterprise value.
1. Asset Management as a Business System
David Mierau framed asset management not as a maintenance function, but as an enterprise system spanning the full asset life cycle—from concept and design through operation and eventual disposal. The ISO 55000 framework was presented as a practical foundation for manufacturers seeking alignment without necessarily pursuing formal certification.
Key points:
- Assets exist to deliver value to customers and stakeholders, not simply to produce output.
- More than 80% of total cost of ownership is determined during early life-cycle decisions (concept and design).
- Leading indicators (availability, reliability, maintenance effectiveness) must be elevated alongside traditional lagging metrics such as OTIF, EBITDA, and sales.
2. Reliability Must Start Before the Asset Arrives
A recurring theme was the late involvement of maintenance and reliability in capital projects. Mierau emphasized that reliability outcomes are largely “designed in” long before equipment reaches the plant floor.
Key takeaways:
- Selecting lower-cost assets without understanding lifecycle implications often leads to disproportionate downtime and lost revenue.
- Reliability and maintenance teams must be engaged early in capital planning, specification, and vendor selection.
- Most organizations spend over 90% of an asset’s life in the operate/maintain phase—yet underinvest in preparation for it.
3. What MHRT Members Are Struggling With
The session validated that many participants face shared, systemic challenges, not isolated site-level issues.
Common challenges highlighted:
- Weak or incomplete preventive and predictive maintenance programs
- Poor quality or missing maintenance job plans
- Limited downtime windows for inspections and corrective work
- Maintenance not embedded in capital project execution
- Aging assets and skills gaps among technicians
- Ongoing friction between Operations and Maintenance
These challenges were grouped across Reliability, Maintenance Work & Materials Management, and Organizational Change Management (OCM)—underscoring that technical fixes alone are insufficient.
4. People, Process, and Culture Drive Reliability Outcomes
Beyond tools and models, the discussion strongly emphasized culture and leadership behaviors.
Notable insights:
- High-quality maintenance execution requires trained technicians, proper tools, available parts, and clear instructions.
- Maintenance planners are most effective when staffed by experienced technicians, not entry-level roles.
- Operations must be treated as asset owners, accountable for long-term performance—not just short-term output.
- Leaders need to be visible on the floor, actively supporting maintenance events and learning from frontline feedback.
5. Case Studies: Reliability at Scale vs. Site-Led Transformation
Two contrasting but complementary case studies illustrated different deployment models.
Global Reliability Transformation – Pfizer
- Enterprise-wide Center of Excellence (COE) model
- Implemented across 60+ global sites
- Integrated reliability with OpEx and Lean
- Delivered hundreds of millions (potentially billions) in avoided capital and downtime
Site Transformation – US Sugar
- Grass-roots, pilot-site-led approach
- Focused on modernizing a 100+ year-old facility
- Significant reductions in emergency work and safety risk
- Tens of millions in sustained financial value with expansion underway
6. AI in Maintenance: Opportunity with Caution (Roundtable Q&A)
In the open discussion, participants explored AI’s role in maintenance and reliability.
Where AI is showing promise:
- Drafting and improving maintenance job plans using LLMs (with expert review)
- Advanced predictive analytics when strong, structured data foundations exist
Key cautions:
- AI outputs require expert validation—models can confidently produce incorrect recommendations
- Data integration and governance remain the biggest barriers
- AI must be deployed as a program, not a standalone tool or vendor experiment
These points were strongly endorsed by MHRT members with firsthand experience, including those from large pharmaceutical organizations.
