How Consolidating Duplicate PMs Can Unlock Hidden Efficiency Gains

According to the U.S. Department of Energy, nearly 30% of preventive maintenance tasks are redundant or inefficient. This can be a costly drain on time and resources in industrial settings. In many EAM systems, whether using IBM Maximo, HxGN EAM, or Oracle EAM, this often translates to duplicate PMs. These duplicates burden maintenance teams and dilute strategic focus.

The Hidden Cost of Duplication

Duplicate preventive maintenance (PM) work orders arise when multiple instructions are created for similar or identical functional locations (FLOCs), equipment, or tasks. Over time, these redundancies accumulate unnoticed. This issue is especially common in organizations that have undergone mergers, system migrations, or EAM transitions. Whether a company migrated to SAP PM, adopted modern CMMS tools, or moved from legacy systems, such inefficiencies are frequent consequences.

The effects are far from trivial. When multiple PMs are scheduled for the same task or equipment, the result is fragmented technician effort, excessive parts inventory, and redundant data entries. Moreover, this often leads to compliance issues and errors in KPIs such as uptime, mean time between failures (MTBF), and scheduled maintenance adherence.

From Clutter to Clarity: Data-Driven Consolidation Strategies

A compelling example of the power of consolidating duplicate PMs comes from Nordic Paper, a leading Scandinavian producer of kraft and greaseproof paper. After implementing IFS Applications across its production facilities, the company discovered that years of organic system growth and manual PM entry had caused a buildup of duplicate and overlapping tasks.

To address this, Nordic Paper used IFS’s asset management and maintenance planning tools to conduct a thorough review of its PM programs. The analysis uncovered numerous instances where similar PMs were assigned to the same or closely related equipment. This had caused overlapping technician schedules and avoidable work orders.

The results were substantial. Technician workloads became more balanced. Production cycles saw fewer interruptions. Resources were also allocated more effectively across maintenance operations. Most importantly, the clarity achieved through PM consolidation allowed planners to refocus on safety-critical equipment and high-priority reliability initiatives.

The Role of AI and Modern EAM Systems

Modern EAM platforms, often powered by artificial intelligence and machine learning, make detecting PM duplication easier than ever. Predictive analytics can highlight anomalies in PM frequency, part usage, or equipment grouping. As a result, reliability engineers can consolidate PMs effectively while still protecting safety-critical equipment. The goal is not to reduce PMs, but to make them smarter and more impactful.

In addition, digital twins are increasingly integrated into maintenance strategies. These allow organizations to simulate the impact of PM consolidation on asset performance. Consequently, teams can avoid the risk of over-consolidation and ensure that asset risk remains well managed.

Improved Production and Reliability

Ultimately, consolidating duplicate PMs does more than improve resource use. Additionally, it contributes to higher asset uptime, better workforce efficiency, and stronger production reliability. In industries such as oil and gas, where unplanned downtime can cost up to $260,000 per hour, even modest improvements through PM optimization can produce significant financial returns.

Therefore, PM optimization efforts, particularly during data migration or system integration phases, should include duplicate detection and standardization. This step, though often overlooked, is critical for building a strong foundation for future-ready maintenance operations.