From Digital Work Orders to Full Asset Intelligence

From Digital Work Orders

to Full Asset Intelligence

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A Research Report on CMMS Maturity for Water and Wastewater Utilities

Prepared for Public Utility Maintenance Professionals

February 2026

Executive Summary

Most water and wastewater utilities today own a Computerized Maintenance Management System. Far fewer are using it to its potential. Industry data consistently shows that approximately 70% of CMMS implementations underperform or stall, and roughly 80% of CMMS users do not leverage the full capabilities of their software. For many utilities, the CMMS has become what industry professionals bluntly describe as "the place where data goes to die" — an expensive digital filing cabinet that merely replicates paper-based processes on a screen.

This report examines the journey from that baseline state to full asset intelligence. Drawing on established maturity frameworks from IBM, the Global Forum on Maintenance and Asset Management (GFMAM), the Society for Maintenance and Reliability Professionals (SMRP), and Gartner’s enterprise asset management research, it maps the stages organizations move through and identifies the practical steps, organizational changes, and technology decisions that enable progress at each level.

The report is structured around six themes: CMMS maturity models and frameworks; the "digitized paper" problem and why organizations get stuck; what full CMMS utilization looks like in practice; integration with enterprise systems including Microsoft 365 and ERP; the practical state of AI, machine learning, and IoT in maintenance; and real-world case studies from water and wastewater utilities that have achieved measurable improvements.

The central finding is that CMMS maturity is not primarily a technology problem. It is an organizational capability problem. The utilities that have successfully climbed the maturity curve share common traits: executive sponsorship, defined asset management frameworks aligned with standards like ISO 55000, disciplined data governance, sustained training investment, and a phased approach that builds on demonstrated wins rather than attempting wholesale transformation.

1. CMMS Maturity Models and Frameworks

1.1 The Maintenance Maturity Model

The maintenance industry has converged on a five-level maturity model that, while expressed in slightly different terminology by different organizations, follows a consistent progression. The foundational concept derives from the Capability Maturity Model (CMM) originally developed by Carnegie Mellon’s Software Engineering Institute in the late 1980s for the U.S. Department of Defense. That framework’s five-level structure — from ad hoc and chaotic to continuously optimizing — has been adapted extensively for maintenance and asset management.

The five recognized stages of maintenance maturity are Reactive, Preventive, Condition-Based, Predictive, and Prescriptive (sometimes termed "World-Class" or "Optimizing"). Each stage builds on the previous one, and organizations cannot meaningfully skip levels. Industry experience suggests that progressing from one level to the next typically requires 18 to 24 months of sustained effort.

Table 1: The Five Levels of Maintenance Maturity

1.2 IBM/Maximo Asset Management Maturity Model

IBM positions its Maximo Application Suite along an asset management maturity model that has evolved significantly as technology has advanced. According to IBM’s published framework, the journey proceeds through stages they label Innocence, Awareness, Understanding, Competence, and Excellence. In the early stages (Innocence through Understanding), the enterprise asset management system fundamentally serves as a preventive maintenance program, transitioning the organization away from reactive maintenance and driving down the cost of unplanned repairs.

To reach the Competence level, Asset Performance Management (APM) capabilities take the lead. The focus shifts toward uptime and business value by preventing failures. IoT-based connectivity enables condition-based maintenance and health monitoring. At the Excellence level, organizations achieve financially optimized maintenance, balancing the cost of maintaining, refurbishing, and replacing assets using tools like Maximo Health and Maximo Predict.

A critical insight from the Maximo community is that the core Maximo Manage application, which most clients use today, only supports progress to condition-based maintenance. Advancing to real-time condition-based, predictive, and prescriptive maintenance requires the broader Maximo Application Suite components including Maximo Monitor (IoT data), Maximo Health (asset health scoring), Maximo Predict (failure prediction), and Maximo Visual Inspection (AI-powered inspections). Importantly, these higher levels require good quality foundational data, which should be collected at lower maturity levels to position the organization for future advancement.

1.3 GFMAM and SMRP Frameworks

The Global Forum on Maintenance and Asset Management (GFMAM), of which SMRP is a member, published its Asset Management Maturity Position Statement to establish consensus on maturity assessment. The GFMAM framework recognizes that maturity extends beyond conformance with ISO 55001 and identifies characteristics of mature organizations including being adaptive, innovative, and learning-oriented.

The GFMAM framework uses a scale developed by the Institute of Asset Management (IAM) ranging from Level 0 (Innocent — the organization has not recognized the need) through intermediate levels of awareness, developing competence, and competence, up to levels of optimization and excellence. A key distinction is that this framework assesses maturity across multiple dimensions: people and culture, processes and structures, and objects and technology. It emphasizes that higher maturity levels are context-dependent and place increasing emphasis on leadership, culture, and organizational behaviors rather than purely technical capabilities.

SMRP provides complementary guidance through its Body of Knowledge and its best practice metrics. World-class maintenance organizations, as defined by SMRP benchmarks, typically allocate less than 10% of maintenance effort to reactive work, 25–35% to preventive maintenance, and 45–55% to predictive maintenance. PM completion rate is the most commonly tracked KPI across the industry, used by 56% of facilities, followed by work order backlog tracking at 53%.

1.4 Gartner’s Perspective

Gartner analyzes the enterprise asset management software market through its Market Guide for Enterprise Asset Management Software and its Peer Insights reviews. Gartner defines EAM as a business application used by asset-intensive industries to optimize maintenance and repair of industrial plants and equipment, and distinguishes CMMS as typically consisting of smaller-scale, single-site applications with less functionality around parts management and resource scheduling.

Gartner’s market research emphasizes that EAM platforms should support on-premises, cloud, or hosted deployments, and should be designed to integrate with other relevant enterprise applications such as ERP, procurement, and HR. Their assessment criteria look at functional completeness, scalability, APM capabilities including condition-based monitoring, predictive forecasting, and reliability-centered maintenance, as well as mobile workforce enablement and IoT integration. The direction of the market, per Gartner’s analysis, is clearly toward platforms that combine traditional maintenance management with AI-driven asset performance management on unified, cloud-native architectures.

2. The “Digitized Paper” Problem

2.1 How Common Is the Problem?

The data on CMMS underutilization is stark. Industry research consistently reports that up to 80% of CMMS implementations fail to achieve their intended outcomes. Perhaps not coincidentally, approximately 80% of CMMS users do not take advantage of all the features their software offers. A 2020 Plant Engineering survey found that 60% of manufacturing companies were still performing reactive maintenance as a primary strategy, and 47% of manufacturing businesses continued using internal spreadsheets for maintenance schedules despite having a CMMS available. Only 9% of organizations had fully integrated mobile devices into their CMMS and IoT systems.

The pattern is clear: organizations invest significant resources in CMMS technology, then use it primarily as an electronic work order system — essentially digitizing their paper-based processes without leveraging the analytical, planning, or predictive capabilities that justify the investment. The CMMS becomes what Dr. Anthony Kenneson-Adams of The Project7 Consultancy has described as a "data graveyard" rather than a data mine.

2.2 Symptoms and Consequences

Organizations stuck at the digitized-paper stage exhibit recognizable symptoms. Work orders are created and closed but contain minimal detail: no failure codes, no labor hours, no parts consumption data. The asset register is incomplete or flat, lacking the hierarchical structure needed for meaningful analysis. Preventive maintenance schedules exist but are calendar-based copies of the previous paper schedules, never reviewed or optimized. Reporting is limited to basic counts — how many work orders were opened and closed — rather than analytical outputs such as mean time between failures, mean time to repair, or cost-per-asset trends.

The consequences are substantial. Without failure coding and analysis, organizations cannot identify recurring problems or perform root cause analysis. Without accurate labor and parts tracking, they cannot calculate true maintenance costs per asset or justify capital replacement decisions with data. Without condition monitoring integration, they remain locked in time-based maintenance strategies that either over-maintain (wasting resources) or under-maintain (risking failures). A study published in the International Journal of Production Research noted that organizations operating at this level typically experience maintenance costs 12–18% higher than those utilizing preventive strategies effectively, and the U.S. Department of Energy has documented that predictive maintenance approaches can reduce costs by up to 40% compared to reactive maintenance.

2.3 Why Organizations Get Stuck

Research identifies several interconnected factors that trap organizations at the digitized-paper stage:

Inadequate training and sustained investment. CMMS training is frequently treated as a one-time event during implementation rather than an ongoing program. FieldCircle’s research indicates that improper utilization of a CMMS contributes to inefficiencies and failed adoption in over 70% of cases. When training stops, users revert to the simplest functions they understand, and the system’s advanced capabilities go unused.

Resistance to change and cultural inertia. Maintenance staff who have decades of experience with paper-based or informal processes often view the CMMS as an additional administrative burden rather than a tool that improves their work. Internal resistance was identified by 43% of organizations as the biggest obstacle to adopting digital technologies. Without visible executive sponsorship and clear communication of benefits, this resistance calcifies.

Absence of an asset management framework. Many organizations implement a CMMS without first defining their asset management strategy, service levels, risk tolerance, or governance structure. As West Yost’s asset management practice observes, without a defined framework, a CMMS risks becoming just a work order database. The software is a tool in service of the broader mission — when no broader mission is articulated, the tool has nothing to serve.

Poor data quality and governance. When organizations migrate from paper or spreadsheets, they frequently populate the CMMS with flawed data. Without data entry standards, required fields, and regular audits, data quality degrades further over time. Garbage in, garbage out becomes a self-fulfilling prophecy that erodes confidence in the system and discourages analytical use.

Lack of defined roles and accountability. When no one is specifically accountable for CMMS data quality, system configuration, report generation, and continuous improvement, the system stagnates. The research consistently shows that organizations with a dedicated CMMS administrator or system champion achieve significantly higher utilization.

3. What Full CMMS Utilization Looks Like

Moving beyond the digitized-paper stage requires deliberate, structured effort across multiple dimensions of the CMMS. This section describes what mature utilization looks like in each core functional area.

3.1 Asset Hierarchy Design

A well-designed asset hierarchy is the structural foundation of a mature CMMS. In IBM Maximo implementations, best practice follows the 12 Steps to Asset Management Maturity framework, which begins with identifying asset systems, establishing criticality rankings, defining location hierarchies, and building parent-child relationships among assets.

For water and wastewater utilities, the asset hierarchy typically follows a structure of System (e.g., Water Distribution) to Facility (e.g., Pump Station 4) to Asset System (e.g., Raw Water Pumping) to Equipment (e.g., Pump P-401) to Component (e.g., Mechanical Seal). The primary location hierarchy often serves simultaneously as the geographic address system, with latitude/longitude coordinates enabling map-based asset visualization and mobile crew navigation. Maximo supports multiple concurrent hierarchies, allowing different roles to navigate assets by geography, by functional system, or by criticality classification without conflict.

Mature organizations classify assets using standardized taxonomies. ISO 14224 provides classification standards for the oil and gas industry; water utilities increasingly adopt similar structured approaches, defining asset types, classes, and attributes consistently. Asset templates in Maximo allow organizations to define standard classifications, attributes, meters, spare parts, and master PMs for each asset model, then propagate this configuration across all instances of that asset type.

3.2 Failure Coding and Analysis

Failure coding is the single most underutilized capability in most CMMS implementations, and simultaneously the capability with the greatest analytical payoff. A mature failure coding system captures three elements for every corrective work order: what failed (the failure class), how it failed (the failure mode), and why it failed (the root cause).

When failure codes are captured consistently over time, patterns emerge that enable targeted interventions. Organizations can identify which asset types experience the most failures, which failure modes are most common, which root causes are driving costs, and where redesign, replacement, or different maintenance strategies would yield the greatest return. This data directly feeds reliability-centered maintenance (RCM) analysis and supports evidence-based capital planning decisions.

In Maximo, failure codes are organized in a hierarchical structure of Failure Class, Problem, Cause, and Remedy. The key implementation step is making failure coding mandatory on corrective work orders through system configuration — if the system allows work orders to be closed without failure codes, they will be. Mature organizations also train their workforce not just on how to enter codes but on why the data matters, connecting the individual data entry task to organizational outcomes like reduced breakdowns and justified equipment replacements.

3.3 Preventive Maintenance Optimization

Mature PM programs go far beyond copying paper-based inspection schedules into the CMMS. They employ both time-based and meter-based (usage-based) triggers, with the trigger selection driven by failure analysis data. For assets where run-time correlates more strongly with failure risk than calendar time, meter-based PMs ensure maintenance occurs at the right interval regardless of actual utilization.

PM optimization involves periodically reviewing PM effectiveness by analyzing whether PM tasks are actually preventing the failures they target, whether frequencies are appropriate (not too frequent, wasting resources; not too infrequent, allowing failures), and whether task lists include the right inspections and measurements. Organizations with mature CMMS use track PM completion rate (the percentage of scheduled PMs completed on time), with world-class targets exceeding 90%. They also track the planned-to-reactive maintenance ratio, targeting less than 10% reactive work.

3.4 Planning and Scheduling Workflows

A mature planning and scheduling process distinguishes between the planner role (defining what work needs to be done, what parts and tools are required, and how long it should take) and the scheduler role (determining when work will be performed and by whom). Organizations that follow best practices in planning and scheduling can increase productive wrench time to 60–65%, compared to the 25–35% typical of organizations without structured planning.

In a mature CMMS implementation, work orders progress through defined status workflows: from Request to Approved to Planned (with job plans, parts, and labor estimates attached) to Scheduled to In Progress to Complete. Each status transition triggers appropriate actions and validations. The system enforces that work orders cannot be closed without required data — actual labor hours, parts used, failure codes for corrective work, and meter readings where applicable.

3.5 Inventory and Procurement Integration

Effective inventory management directly affects maintenance execution. When parts are unavailable, planned work is delayed, and organizations revert to reactive emergency procurement at premium cost. The cost ratio between planned and unplanned work is approximately 1:5 — unplanned work can be five times more expensive than planned work.

Mature CMMS implementations maintain accurate bills of materials (BOMs) for critical assets, enabling planners to identify all required parts when creating job plans. Automated reorder points ensure stocking levels are maintained for critical spares. Integration with procurement systems enables purchase requisitions to be generated directly from work orders and routed through approval workflows. Organizations using the inventory management capabilities of their CMMS consistently report improvements in parts availability and reduced repair times.

3.6 Mobile Field Access

Mobile access has become essential for maintenance operations, particularly for utilities with geographically distributed assets. Research shows that 93% of organizations with their entire staff using a mobile CMMS solution realize strong-to-maximum improvements in labor efficiency.

For water and wastewater utilities, mobile functionality must include offline capability, since crews frequently work in locations without reliable connectivity — treatment plant basements, remote pump stations, and underground vaults. Mobile CMMS access enables technicians to receive work assignments, access asset history and documentation, capture completion data (including photos), record meter readings, and scan asset QR codes or barcodes directly in the field. This eliminates the delay and data loss inherent in returning to the office to enter information.

3.7 Reporting, Dashboards, and KPIs

The reporting layer is where CMMS data transforms into organizational intelligence. Mature organizations move beyond basic work order counts to track a suite of key performance indicators:

Table 2: Core Maintenance KPIs for Utility CMMS

Real-time dashboards make these KPIs visible to maintenance managers, operations leadership, and executive stakeholders. Integration with business intelligence tools such as Power BI enables more sophisticated analysis, trend visualization, and automated report distribution.

3.8 Predictive Analytics

Predictive analytics represents the frontier of CMMS utilization. In platforms like Maximo, the Predict module uses machine learning algorithms trained on historical failure data and real-time sensor inputs to estimate remaining useful life (RUL) for critical assets. Health scores provide at-a-glance assessment of asset condition, enabling risk-based prioritization.

The prerequisite for effective predictive analytics is high-quality historical data — which reinforces why disciplined data capture at earlier maturity levels is so critical. Organizations that have been consistently recording failure codes, maintenance actions, meter readings, and condition assessment data for several years possess the training data needed to build meaningful predictive models. Those that have treated their CMMS as a work order ticketing system find they lack the data foundation for analytics.

4. Integration with Enterprise Systems

4.1 The Integration Imperative

A CMMS operating in isolation delivers a fraction of its potential value. Leading organizations integrate their CMMS with ERP systems, geographic information systems (GIS), supervisory control and data acquisition (SCADA) systems, document management platforms, business intelligence tools, and communication platforms. Each integration point amplifies the value of maintenance data and eliminates manual handoffs that introduce delays and errors.

4.2 CMMS and ERP Integration

The CMMS-ERP integration is the most critical enterprise connection for financial visibility and procurement efficiency. Modern CMMS platforms integrate with ERP systems like Microsoft Dynamics 365, SAP, and Oracle through APIs and middleware solutions. The key data flows include: purchase requisitions generated from CMMS work orders flowing to ERP procurement; actual maintenance costs flowing from the CMMS to ERP general ledger and cost centers; employee and labor rate data flowing from ERP HR modules to the CMMS; and inventory transactions synchronized between CMMS storerooms and ERP financial inventory.

As Plant Engineering has observed, ERP maintenance modules often fall short in ease of use and quick implementation compared to dedicated CMMS platforms. The recommended approach is to use the CMMS as the system of engagement (where maintenance teams perform their daily work) while the ERP serves as the system of record (where financial transactions are consolidated). API-based integration keeps both systems synchronized without requiring maintenance staff to navigate complex ERP interfaces.

4.3 Microsoft 365 Integration

For organizations using the Microsoft ecosystem, several integration patterns have proven valuable:

SharePoint as Document Repository. Standard operating procedures (SOPs), equipment manuals, engineering drawings, safety data sheets, and regulatory permits can be stored in SharePoint document libraries and linked to CMMS asset records. This eliminates duplicate document storage, ensures all users access the current version, and leverages SharePoint’s version control and access management capabilities. When a technician opens an asset record in the CMMS, they can navigate directly to the associated documentation in SharePoint.

Microsoft Teams for Maintenance Communication. Teams channels dedicated to maintenance operations can receive automated notifications from the CMMS — critical work order assignments, PM due reminders, or equipment alarm escalations. Power Automate (formerly Microsoft Flow) serves as the integration bridge, triggering Teams messages based on CMMS events. This keeps dispersed maintenance teams informed in real-time without requiring everyone to be logged into the CMMS.

Power BI for Maintenance Analytics. Power BI connects directly to CMMS databases to create interactive dashboards and reports that go far beyond the CMMS’s native reporting capabilities. Maintenance managers can build visual dashboards tracking KPIs, trending asset health, comparing facility performance, and presenting executive summaries. Power BI dashboards can be embedded in SharePoint sites or Teams tabs, making maintenance intelligence accessible to stakeholders who never log into the CMMS directly.

Power Automate for Workflow Orchestration. Power Automate enables complex cross-system workflows. For example, a work request submitted through a Microsoft Form can automatically create a CMMS work order, notify the assigned planner via Teams, and store any attached photos in SharePoint. When a work order is completed, Power Automate can trigger approval workflows, update SharePoint tracking lists, and distribute completion notifications.

4.4 GIS and SCADA Integration for Utilities

For water and wastewater utilities, GIS and SCADA integration are arguably more critical than ERP integration for operational effectiveness. GIS integration (typically with Esri ArcGIS) provides map-based visualization of assets, enabling crews to locate mains, hydrants, valves, manholes, and lift stations without cross-referencing paper maps. CMMS platforms like Cityworks, Cartegraph (OpenGov), and Brightly are built with GIS at their core, while platforms like Maximo and MentorAPM offer GIS integration through connectors.

SCADA integration enables the most valuable automation pattern for utilities: using SCADA alarms or run-hour totals to automatically generate preventive maintenance work orders. A pump that trips due to high vibration or temperature can immediately create a CMMS work order with the relevant SCADA data attached, shortening the time between detection and response and ensuring the event is logged with full context. Linking historical SCADA trends to maintenance records strengthens root-cause analysis and supports the transition from time-based to condition-based maintenance.

5. AI and Emerging Technology in CMMS

5.1 What Is Real and Practical Today

The practical application of AI in maintenance management in 2025–2026 is moving from experimental pilots to embedded capabilities, but adoption remains early-stage. According to recent research, only 32% of maintenance teams have fully or partially implemented AI, while 65% plan to adopt AI in the next 12 months. Maintenance leaders identify knowledge capture and sharing as the most valuable AI use case, with 39% citing it as their top priority.

The AI capabilities that are delivering measurable results today include:

Anomaly detection using unsupervised learning. Machine learning models trained on normal operating patterns can flag deviations in sensor data (vibration, temperature, pressure, acoustic signatures) that indicate developing faults. These models use algorithms such as Isolation Forests, One-Class SVMs, and Autoencoders, and they work even without labeled failure data, making them accessible to organizations with limited failure history.

Remaining useful life (RUL) estimation. Where organizations have sufficient historical failure data linked to sensor readings, supervised learning models (Random Forests, Gradient Boosting, LSTMs for time-series data) can estimate when an asset is likely to fail. IBM Maximo Predict and similar platforms commercialize this capability.

Natural language processing for work orders. Generative AI can convert technician voice observations into structured work orders, auto-populating asset codes, parts lists, and safety steps. Early adopter pilots report reducing parts-picking errors by approximately 28% through AI-generated spare parts recommendations.

AI-assisted maintenance planning. Large language models can draft maintenance procedures, estimate time requirements, and surface troubleshooting steps at the point of work. Some implementations have achieved a 19% cumulative parts-spend reduction over three years.

5.2 What Remains Aspirational

Several AI capabilities frequently discussed in vendor marketing remain more aspirational than practical for most water utilities:

Fully autonomous maintenance scheduling, where AI independently prioritizes and schedules all maintenance work without human oversight, remains uncommon. Most successful implementations use AI to recommend actions that human planners review and approve. Digital twins — comprehensive virtual replicas of physical assets that simulate behavior under varying conditions — are being piloted at leading utilities like Hampton Roads Sanitation District (HRSD), but require substantial investment in modeling and data infrastructure. Prescriptive maintenance, where AI not only predicts failures but recommends specific interventions optimized for cost and risk, exists in commercial products but requires mature data foundations that most organizations have not yet built.

5.3 Practical Guidance: Positioning Without Chasing Hype

The most important action a utility can take to prepare for AI capabilities is not to purchase AI tools — it is to build the data foundation those tools will eventually require. This means:

Ensure failure codes are captured consistently on every corrective work order.

Record actual labor hours, parts consumption, and costs against every work order.

Maintain accurate meter readings (run hours, flow volumes, cycle counts) on critical assets.

Establish condition assessment records with standardized rating scales.

Integrate SCADA data to build historical operating profiles for critical equipment.

Maintain clean, complete asset registers with accurate attributes and relationships.

Organizations that build these data habits now will have the training datasets needed when AI tools mature to the point of practical deployment. Those that wait to start collecting quality data until they are ready to implement AI will face a multi-year delay while they build a sufficient data history.

5.4 IoT Considerations

IoT sensor deployment for maintenance has become significantly more accessible and affordable. The most common and immediately valuable sensors for water utilities monitor vibration (for pumps and motors), temperature (for electrical systems and bearings), pressure (for distribution systems), and flow (for process monitoring and leak detection). Modern IoT platforms support hybrid edge-cloud architectures, where edge devices handle real-time anomaly detection locally while cloud platforms perform deep analytics and long-term trend analysis.

The key to successful IoT integration is starting with critical assets where the cost of failure is highest and where sensor data provides the clearest signal. Instrumenting every asset simultaneously is neither practical nor necessary. A phased approach that demonstrates ROI on high-value assets builds organizational support for broader deployment.

6. Water and Wastewater Utility Case Studies

6.1 City of Portsmouth, New Hampshire

The City of Portsmouth’s Public Works Department manages three wastewater treatment facilities, three drinking water facilities, and over 30 remote sites. Prior to CMMS implementation, the city had no preventive maintenance schedule for its assets. Maintenance records resided on Excel spreadsheets, which limited the team’s ability to use real-time data, schedule maintenance based on time or condition, or track parts and inventory.

As part of a $92 million upgrade project to its Peirce Island water treatment facility, Portsmouth implemented Fiix CMMS (a Rockwell Automation product). The implementation followed a structured approach: reviewing maintenance objectives, documenting current processes, defining reporting requirements, and using those insights to define an implementation plan.

Measurable outcomes: The city projected approximately $20,000 in first-year savings through more efficient maintenance planning, reduced downtime, and reduced overtime. Weekly scheduling replaced daily ad hoc assignment, and custom reports distributed automatically to each facility every Friday morning enabled better planning. Parts procurement became proactive rather than reactive, and work order tracking enabled accurate year-end reporting of time and cost by facility.

6.2 Inframark (Multi-State Water/Wastewater Operator)

Inframark provides water and wastewater services to municipal and commercial clients across 16 states with over 1,500 employees. The company needed CMMS software powerful enough to standardize maintenance operations across all facilities while providing a centralized, system-wide view of asset management activities.

After evaluating several systems, Inframark selected eMaint CMMS and executed a phased rollout. They began with a single pilot site in Danville, Virginia, then built out a master system configuration before rolling out in seven phases across all facilities. Critical success factors included having the same eMaint trainer for all implementations (ensuring consistency), developing a customized training guide, establishing clear roles and accountability, and holding people accountable to timelines.

Measurable outcomes: Inframark customized system fields to prioritize work orders based on asset criticality rankings, generated comprehensive backlog reports for early issue identification, and created a combined account to track KPIs and maintenance progress across all sites. The company used CMMS data to guide clients on capital budget expenditure decisions. Inframark subsequently developed a CMMS assessment tool for setting annual system-wide goals with minimum requirements and best practices embedded.

6.3 City of Reidsville, North Carolina

Reidsville utilizes enterprise CMMS software (iMaint) to manage a broad range of municipal assets: vehicles, equipment, facilities, streets, water lines, sewer lines, a water treatment plant, a wastewater treatment plant, parks, and thousands of spare parts. The system provides employees with up-to-date asset information, preventive maintenance schedules, and repair management for each piece of equipment.

Measurable outcomes: A particularly notable benefit emerged in regulatory compliance and risk management. When claims were made against the city’s no-fault sewer backup policy, Reidsville was able to provide detailed CMMS records demonstrating that sewer lines were properly maintained. This documentation capability transformed the CMMS from a maintenance tool into a legal and regulatory compliance asset.

6.4 Sacramento Regional Wastewater Treatment Plant

The Sacramento Regional Wastewater Treatment Plant (Regional San), serving approximately 1.6 million people, is undergoing an estimated $1.7 billion capital expansion (the EchoWater Project). Recognizing the need for long-term, cost-effective asset management, the facility implemented Reliability Centered Design (RCD) and Reliability Centered Maintenance (RCM) principles from the front-end engineering design phase through commissioning, with support from Pinnacle Reliability.

The approach involved building the complete asset hierarchy, preventive maintenance plans, job plans, standard operating procedures, risk-based spare parts strategies, and operator round duties before the new facilities came online, then populating all this data directly into the CMMS. RAM (Reliability, Availability, Maintainability) modeling was used to simulate future performance of the process design.

Measurable outcomes: The facility estimated $100 million in cost savings over the lifecycle of the facility by embedding reliability and maintenance strategies from day one rather than retrofitting them after commissioning. The approach ensured that the new facilities started up with a complete reliability and maintenance program already in place.

6.5 Hampton Roads Sanitation District (HRSD), Virginia

HRSD, serving the Hampton Roads, Virginia region, represents one of the most technology-forward water utilities in the United States. As part of its $1 billion Sustainable Water Initiative for Tomorrow (SWIFT) program, HRSD developed comprehensive BIM (Building Information Modeling) guidelines and created intelligent BIM models containing operation and maintenance data, then developed a bridge to connect BIM data directly to its CMMS system.

HRSD’s approach included developing standard project templates for model elements, families, and parameters to ensure consistent data capture across multiple facilities designed by different consultants. A bridging software was selected to facilitate the integration of BIM equipment data with CMMS asset attributes. This means that asset data captured during the design and construction phases flows directly into the maintenance system, eliminating the traditional gap where operational knowledge is lost during the handoff from construction to operations.

Significance: HRSD’s approach demonstrates the leading edge of CMMS maturity for utilities: leveraging available technologies to prepare for the fourth industrial revolution with AI, machine learning, and IoT, while ensuring that foundational asset data is captured systematically from the point of design. The utility explicitly positions its technology strategy as building capability for future generations.

6.6 City of Savage, Minnesota

The City of Savage, a growing Minnesota community, faced challenges managing over 2,100 assets and 10,500 water accounts using outdated manual maintenance tracking. Frequent unplanned service calls and operational inefficiencies highlighted the need for a more proactive approach.

Measurable outcomes: By implementing a CMMS with automated maintenance scheduling, the city introduced predictive maintenance strategies and streamlined work order management. The transition significantly reduced unplanned breakdowns, optimized resource allocation, and improved accountability through detailed expenditure tracking.

7. Recommendations: A Phased Roadmap

Based on the research and case studies examined in this report, the following phased approach is recommended for water and wastewater utilities seeking to advance their CMMS maturity:

Phase 1: Foundation (Months 1–6)

Conduct a maturity assessment using SMRP-aligned or ISO 55000-aligned criteria to establish your baseline.

Define your asset management framework: service levels, risk tolerance, and governance structure before making technology changes.

Clean and complete the asset register: verify the asset hierarchy, ensure all maintainable equipment is recorded, and establish consistent naming conventions.

Assign a dedicated CMMS administrator or system champion with time and authority to manage the system.

Implement mandatory fields on work orders: failure codes on corrective work, actual labor hours, and parts used.

Phase 2: Operational Discipline (Months 6–18)

Optimize PM schedules: review every PM task for relevance, frequency, and effectiveness based on failure history data.

Implement planning and scheduling workflows with distinct planner and scheduler functions.

Deploy mobile CMMS access to field crews, prioritizing offline-capable solutions.

Establish bills of materials for critical assets and integrate inventory management.

Begin tracking and reporting KPIs: PM compliance, planned vs. reactive ratio, MTBF, MTTR, and maintenance cost per asset.

Phase 3: Integration and Analytics (Months 12–30)

Integrate CMMS with GIS (ArcGIS), SCADA, and ERP systems.

Connect SharePoint for document management, Teams for communication, and Power BI for analytics dashboards.

Implement condition-based maintenance on critical assets using SCADA data and initial IoT sensors.

Begin failure mode and effects analysis (FMEA) on highest-criticality assets using accumulated failure data.

Develop executive dashboards that connect maintenance performance to organizational service levels.

Phase 4: Intelligence and Optimization (Months 24–48)

Evaluate predictive analytics capabilities based on accumulated data quality and volume.

Pilot AI-assisted maintenance planning on a subset of critical assets.

Expand IoT sensor deployment based on demonstrated ROI from Phase 3 pilots.

Implement risk-based capital planning using CMMS data on asset condition, failure history, and remaining useful life.

Pursue alignment with ISO 55001 Asset Management System certification if organizational goals warrant it.

The journey from digital work orders to full asset intelligence is neither quick nor simple. But it is achievable, and the water utilities that have made the journey report not just operational improvements but a fundamental transformation in how maintenance is perceived within their organizations — from a cost center to a strategic function that protects public health, extends infrastructure life, and stewards ratepayer investment.

References and Sources

1. IBM Think Insights, "The Journey to a Mature Asset Management System" (April 2025).

2. Maximo Secrets, "IBM Maximo Application Suite Overview" and "The 12 Steps to Asset Management Maturity" (2020–2025).

3. Global Forum on Maintenance and Asset Management (GFMAM), "Asset Management Maturity: A Position Statement" (First and Second Editions).

4. UpKeep, "Maintenance Maturity Model" — Five-level framework with SMRP metrics (2024–2025).

5. Gartner, "Market Guide for Enterprise Asset Management Software" (2022, 2024 editions).

6. Plant Engineering, "Maintenance Study" (2018, 2020 survey results).

7. Chemweno et al., "Asset Maintenance Maturity Model: Structured Guide to Maintenance Process Maturity," International Journal of Production Research (2015).

8. Dr. Anthony Kenneson-Adams / Project7 Consultancy, "The CMMS: Where Data Goes to Die," Maintenance World (2023).

9. Rockwell Automation / Fiix, "City of Portsmouth Water Utility CMMS Case Study" (2021).

10. eMaint / Fluke Reliability, "Inframark Water and Wastewater Case Study" (2025).

11. DPSI / iMaint, "City of Reidsville, NC CMMS Implementation" (2023).

12. Pinnacle Reliability, "Sacramento Regional Wastewater Treatment Plant RCM Case Study" (2024).

13. Hazen and Sawyer / HRSD, "Bridging the Gap Between BIM Data and CMMS for SWIFT" (2024).

14. LLumin, "Utility Asset Management: CMMS for Power and Water Infrastructure" and "City of Savage, MN Case Study" (2025).

15. West Yost Associates, "How CMMS Strengthens Strategic Asset Management for Water Utilities" (2025).

16. MaintainX, "Why 70% of Reliability-Centered Maintenance Programs Fail" (2024).

17. ClickMaint, "CMMS Implementation Mistakes" and "CMMS Training Is Critical" (2024–2025).

18. Accruent, "Maintenance Management Benchmarks and Best Practices Report" (2021–2025).

19. IFS Ultimo, "From CMMS to EAM: Full Cycle Maintenance and Asset Management" (2023).

20. Siemens, "The True Cost of Downtime" Report (2024).

21. U.S. Department of Energy, Predictive Maintenance Cost Savings Studies.

22. McKinsey & Company, Maintenance Operations Research (workforce, cost ratios, RCM benchmarks).

23. NIST, Predictive Maintenance Benefits for Manufacturing (inventory, planning time, material cost impacts).