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The ability to accumulate data has increased over recent years due to advances in digital technology, improved monitoring capability and ever-expanding fields within Asset Management systems and data bases. But what are we doing with it? It’s universally agreed that data is critical for the business to optimise these assets. But bringing it all together from disparate systems, presenting results in a valuable way to drive decisions and better business outcomes is more challenging.

The workforce is aging. Many older members of the community are leaving. Organisations are struggling to attract new talent, never mind top talent. So how are they going to cope with this transition, while at the same time they’re confronted with one of the most significant workforce challenges ever faced. The increase in automation and decarbonisation and climate targets reshaping the landscape of future job roles and required skill sets. Organisations must become more imaginative in their strategies to overcome these challenges.

There is a collective agreement about a drop in the quality of basic solid work management fundamentals. We must get back to basics, understand and adhere to maintenance planning, reporting, scheduling, work execution, spares management, and performance assessment and management. Compounding the situation, the declining integrity of maintenance work is leading to a deterioration in asset condition and health.

Decarbonisation is shaking things up for large industrial companies in the UK. With the government ramping up efforts to reduce carbon emissions, industries are under pressure to shift towards more sustainable practices. This transition isn't just about switching to renewable energy; it involves overhauling entire production processes, investing in new technologies, rethinking supply chains, and of course – planning for change in the processes and approaches to equipment maintenance and reliability.

Organisations are caught between two urgent challenges: finding skilled technical workers, and ensuring they capture and retain the institutional knowledge of retiring employees. These factors are central to both short-term operations and long-term business sustainability. A 2023 report from the National Skills Commission shows that approximately 50% of Australian employers are struggling to find skilled workers, particularly in trades like engineering, maintenance, and mechanical repair.

Maintenance and reliability leaders want to know how AI can enhance equipment performance, reduce downtime, and optimize asset management. They seek insights into AI-driven predictive maintenance, minimizing failures and costly repairs. Real-world case studies demonstrating AI’s impact on asset life and efficiency are highly valued. Integration with existing CMMS and EAM systems is a key concern, ensuring seamless adoption. Leaders also focus on AI’s reliability, data quality, sensor accuracy, and cybersecurity risks. ROI and cost-benefit analysis are critical, as they need to justify AI investments with measurable value. Additionally, they explore workforce implications, including upskilling teams to work with AI and balancing automation with human expertise. Understanding these factors helps them make informed decisions on AI adoption in maintenance and reliability strategies.

Maintenance and Reliability teams face difficult challenges in driving strategic change within their organisation. Defining the value of Asset Management, ROIC (return on invested capital), speaking the language your finance team understand, building relationships with consultants and advisors, and effectively employing the ISO 55000 standards are some of the main topics mentioned.

There is ongoing strong sentiment for the lack of attention to, and appreciation of, both the reliability function and the engineers responsible for equipment reliability. This extends to the reliability engineers themselves being often overlooked and misunderstood. This can be due to a lack of understanding of the value they bring or a failure to recognise their contributions. Rigorous reliability processes, role clarity, and clear accountability structures that align with the broader company strategy are essential components of reliability success. So is reliability engineering talent management and development.

There is consensus for the need to modernise IT and data systems to better align and integrate safety, risk, and Asset Maintenance plans. Without tight integration, different departments and teams operate in silos, leading to fragmented decision-making.

Maintenance and Reliability leaders often struggle to translate technical needs into business value propositions that resonate with C-suite executives. While experts in equipment reliability, there is often a mismatch in articulating ROI, risk mitigation, and strategic advantages in the financial language that drives executive decision-making and budget allocation.

Diversity within the workforce drives higher performance teams. It creates a more dynamic and resilient workforce, prepared to meet the challenges of the future, driving creativity, improving problem-solving, with low attrition and higher employee satisfaction and enhancing overall team performance. Maintenance and Reliability leaders want to play a greater role in promoting a successful EDI ethos in their workforce.

Maintenance and Reliability managers continue to struggle to translate high-level organisational objectives into executable asset management activities. While companies excel at creating strategic plans, they often fail at consistent, year-round execution. This disconnect leads to poor asset performance, increased lifecycle costs, and missed improvement opportunities.

As asset-intensive companies embrace digital transformation, technological breakthroughs in asset management are reshaping maintenance and reliability paradigms. In most cases, we need to respectfully temper the enthusiasm for the next shiny object and focus on getting bang-for-your buck or solving a pressing business need. It was generally agreed that great use cases and good business outcomes are the exception rather than rule.