Assess Before You Invest in Maintenance Technology
Across asset intensive industries, maintenance leaders are under growing pressure to reduce unplanned downtime, improve technician productivity, optimize spare parts usage and make better decisions with asset data, while also improving asset reliability, reducing operating costs and managing aging equipment, labor shortages and increasing production demands. Many organizations respond by investing in new technology: mobile maintenance applications, advanced scheduling tools, asset performance management platforms, condition monitoring and predictive maintenance solutions. While these technologies can deliver significant value, they rarely solve the root causes of poor maintenance performance on their own. Sustainable improvement depends on the maturity of the processes, data, governance and organizational behaviors that support them.
A company may already have a CMMS like SAP PM, or another enterprise maintenance system in place, but still struggle with reactive work, poor master data, inconsistent work order execution, weak planning discipline, low technician adoption or fragmented processes across sites. In these cases, implementing additional tools can simply digitize existing inefficiencies.
That is why an Asset Management Maturity Assessment should come before major technology investment. It helps organizations understand where they are today, what is holding them back and which improvements should be prioritized before scaling a CMMS, including its mobile and planning capabilities and more advanced functions for Asset Performance Management and Predictive Maintenance.
The goal is not to delay transformation. The goal is to make transformation more practical, effective and value-driven.
The Pressure to Modernize Maintenance is Increasing
Unplanned downtime remains one of the largest hidden costs in asset-intensive operations. According to Siemens’ 2024 True Cost of Downtime report, unplanned downtime costs the world’s 500 largest companies nearly $1.4 trillion annually, equivalent to approximately 11% of their revenues. The same report states that an average large plant still loses 27 hours per month to unplanned downtime.
At the same time, manufacturers and industrial operators are being asked to do more with less. Maintenance teams face aging assets, labor shortages, rising production demands, supply chain constraints and increasing expectations for operational visibility. The result is a growing need for better planning, better data, better execution and better reliability decision-making.
This is why digital asset management technologies are becoming increasingly important. SAP Field Service and Asset Management helps technicians access and capture asset data, work orders and documentation from mobile devices, provides visibility into maintenance workload and available capacities, supports asset health, performance, risk evaluation, reliability methodologies and predictive maintenance use cases.
However, the most important question is not, “Which technology should we implement?” Instead, organizations should ask, “Are we mature enough to capture the value from that technology?”
Technology Can Accelerate Performance, But Only When the Foundation Is Ready
Many organizations begin their asset management transformation with a technology-first mindset. They want mobile work orders, advanced scheduling, dashboards, analytics or predictive maintenance models. These are valid goals, but they depend on a strong operational foundation.
If the asset hierarchy is incomplete, technicians may not be able to record work against the correct equipment. If BOMs and spare parts are not linked properly, repair execution becomes slower and less predictable. If failure codes are inconsistent, reliability engineers cannot trust the data for root cause analysis. If preventive maintenance plans are outdated, the organization may continue to over-maintain some assets while under-maintaining critical ones. If planners and schedulers lack clear processes, even the best scheduling tool will not produce consistent execution.
In other words, technology amplifies the maturity of the organization around it. When the foundation is strong, digital tools accelerate performance improvement. When the foundation is weak, digital tools can expose gaps but rarely fix them by themselves.
McKinsey has noted that predictive maintenance can reduce machine downtime by 30–50% and increase machine life by 20–40%. However, realizing those benefits requires more than technology alone. McKinsey emphasizes that advanced analytics creates value when it is embedded into day-to-day operations and supported by data management, process redesign and change management. This is the core reason why maturity assessment matters.
Source: McKinsey & Company, "Manufacturing Analytics Unleashes Productivity and Profitability.
What an Asset Management Maturity Assessment Should Evaluate
A strong Asset Management Maturity Assessment should look beyond the system configuration. It should evaluate how maintenance actually works across people, process, data and technology.
1. Process Maturity
The assessment should evaluate the end-to-end maintenance lifecycle, from work identification and prioritization through planning, scheduling, execution and continuous improvement.
Key questions include:
- Are corrective, preventive and improvement work processes clearly defined?
- Are notifications and work orders used consistently?
- Is planning performed before execution, or is work mostly reactive?
- Are schedules realistic and aligned with production constraints?
- Is backlog visible, categorized and actively managed?
- Are shutdowns and planned downtime events prepared with enough discipline?
- Are maintenance KPIs used to drive behavior, or only reported after the fact?
This helps identify whether the organization has the process discipline needed to benefit from mobile execution, scheduling and analytics.
2. Data Maturity
Maintenance master data is often the most important enabler, or constraint, of maintenance transformation. The assessment should evaluate whether the organization has reliable data structures to support planning, execution, reporting and reliability improvement.
Key data areas include:
- Functional locations and equipment hierarchy
- Equipment criticality
- Bills of material and spare parts linkage
- Task lists and maintenance plans
- Measuring points and counters
- Failure codes, damage codes and cause codes
- Work centers, activity types and labor planning data
- Historical work order quality
Without strong data, the organization may struggle to generate meaningful insights. Predictive maintenance, APM and reliability analytics require reliable historical and condition data. If the foundation is incomplete, the output of advanced tools may be misleading or difficult to act on.
3. System Maturity
The assessment should also evaluate how current systems are being used. Many companies already have SAP PM or another CMMS, but utilize only a fraction of its available capabilities.
Key questions include:
- Is SAP PM or the CMMS used as the system of record?
- Are users following standard processes or relying on workarounds?
- Are mobile tools already in place?
- Are spare parts and inventory processes integrated with maintenance?
- Are scheduling tools used to balance workload and capacity?
- Are dashboards and KPIs trusted by the business?
- Are OT, MES or condition monitoring systems connected to maintenance processes?
- Is the organization ready for SAP APM or predictive maintenance?
The goal is to identify whether the existing system landscape supports the desired business outcomes or whether process, data and integration gaps need to be addressed first.
4. Organizational Maturity
Technology adoption depends heavily on the people and governance model around it. The assessment should evaluate whether roles and responsibilities are clear across maintenance, production, reliability, engineering, procurement, IT and leadership.
Key questions include:
- Are planners, schedulers, technicians and supervisors clear on their responsibilities?
- Is there ownership for master data quality?
- Are reliability engineers actively involved in maintenance strategy improvement?
- Is there a governance model for global standards and local deviations?
- Are technicians equipped and trained to capture high-quality field data?
- Does leadership use maintenance performance data to make decisions?
This is especially important for multi-site organizations. A global maintenance template can create consistency, but local site realities must be understood before standardization is imposed.
5. Reliability Maturity
A mature, high-performing maintenance organization does more than just execute work -- they continuously learn from it.
The assessment should evaluate whether maintenance history is used to improve asset strategies over time. This includes:
- Root cause analysis
- Failure mode and effects analysis
- Reliability-centered maintenance
- Criticality assessment
- Maintenance plan optimization
- Bad actor analysis
- Feedback loops from technicians to planners and reliability engineers
This is where asset management moves beyond compliance and execution toward continuous performance improvement.
Aligning Global Standards with Local Requirements
One of the most common challenges in multi-site organizations is balancing global standardization with local operational realities. While global processes and data standards create consistency and scalability, local facilities often have unique equipment, regulatory requirements and workforce constraints. A maturity assessment helps identify where standardization creates value and where flexibility is required, ensuring that future technology investments support both enterprise objectives and site-level execution.
From Assessment to Roadmap
The ultimate value of a maturity assessment is not the diagnosis itself. It is a prioritized roadmap that aligns business objectives, operational improvements and technology investments into a realistic transformation plan.
A typical roadmap may include phases such as:
Phase 1: Stabilize the Maintenance Foundation
This phase focuses on core SAP PM or CMMS usage, process standardization, work order discipline, basic reporting and master data cleanup. The objective is to create a reliable operational baseline.
Phase 2: Improve Mobile Work Execution
Once the core process is stable, organizations can introduce or expand mobile maintenance execution. Mobile maintenance can help technicians access work orders, capture confirmations, record findings, consume materials and update asset information directly in the field.
Phase 3: Strengthen Planning and Scheduling
With better data and execution discipline, companies can improve scheduling and capacity planning. Resource Scheduling can support workload visibility, schedule simulation, work center utilization and backlog management.
Phase 4: Activate Reliability and Asset Performance Management
As maintenance history becomes more reliable, organizations can introduce Asset Performance Management solutions to support risk-based strategies, reliability methodologies, recommendations, asset health monitoring and predictive maintenance use cases.
Phase 5: Integrate Machine Data and Predictive Maintenance
Only after the process, data and system foundation is mature enough should companies scale advanced condition monitoring, OT integration and predictive models. At this stage, machine data can be connected to maintenance strategies, alerts, recommendations and work execution.
This phased approach reduces risk because each step builds the foundation for the next. It also helps leadership understand which investments are required now, which can wait and which dependencies must be resolved first.
A Practical Example
Consider a global manufacturer operating multiple facilities, each with different maintenance practices, varying levels of system adoption and inconsistent data quality. Some sites use SAP PM consistently, while others rely heavily on spreadsheets, 3rd party CMMS, local workarounds or paper-based execution. Preventive maintenance plans exist, but many are outdated. Spare parts are not always linked to equipment. Technicians complete work, but findings are not captured consistently enough to support root cause analysis.
In this scenario, implementing predictive maintenance immediately may create limited value. The organization first needs to standardize core processes, improve master data, establish a global template considering mobile execution, strengthen planning and build a feedback loop between technicians, planners and reliability engineers.
An Asset Management Maturity Assessment helps separate ambition from readiness. It does not tell the organization to slow down. It shows the organization how to move forward in the right sequence.
Why Leading Organizations Assess Before They Invest
Assessing maturity before investing in technology creates several benefits:
- It reduces implementation risk by identifying gaps early.
- It creates alignment between maintenance, production, reliability, IT and leadership.
- It helps prioritize initiatives based on value and readiness.
- It prevents companies from digitizing inefficient processes.
- It improves user adoption because the roadmap reflects operational reality.
- It creates a stronger business case for any technology investment
Most importantly, it helps organizations focus on business outcomes rather than technology features.
The end goal is not simply to implement a new system. The end goal is to reduce downtime, improve asset availability, increase technician productivity, strengthen maintenance planning, improve spare parts readiness and create a reliable data foundation for continuous improvement.
Conclusion
Successful asset management transformation begins with a clear understanding of current capabilities, business priorities and organizational readiness. Technology plays an important role, but lasting results come from aligning processes, data, people and systems around a common reliability strategy. Before investing in mobile maintenance, advanced scheduling, APM or predictive maintenance, organizations need to understand their current maturity across process, data, systems, organization and reliability practices. A maturity assessment provides that foundation. It helps companies identify the real barriers to performance, prioritize the right initiatives and build a roadmap that connects technology investment to measurable business value. For asset-intensive organizations, the question is no longer whether maintenance should become more digital. The question is whether the organization is ready to make digital maintenance deliver real operational impact.
