Organizations across manufacturing, energy, utilities, chemicals, and production-intensive industries face mounting pressure to improve operational efficiency, reduce costs, and maintain supply chain reliability.
Yet, despite billions invested in ERP systems and digital transformation initiatives, many enterprises continue to struggle with data quality, MRO inefficiencies, and fragmented procurement processes.
Structured MRO and operational data strategies are increasingly critical for reducing downtime, optimizing inventory, improving workforce productivity, and unlocking strategic procurement opportunities.
Data-Driven MRO Optimization
Effective management of MRO data is critical for operational continuity and cost efficiency. Dispersed and unstructured data often results in duplicate orders, stock-outs, and inefficient maintenance workflows.
Verdantis’ research across hundreds of industrial organizations demonstrates that adopting structured MRO data practices can yield significant financial and operational improvements, including cost avoidance, optimized inventory, and enhanced procurement efficiency.
Traditional approaches have forced organizations to choose between reactive operations – which risk costly downtime – and preventive measures that may increase planned downtime and overhead. Modern, data-driven MRO practices provide a balanced approach, minimizing both operational disruptions and unnecessary expenditures.
In a recent 18-month study of nearly 1,900 senior executives across Mining, Oil & Energy, Utilities, and Manufacturing sectors, Verdantis identified a billion-dollar efficiency gap in asset-intensive operations. Key findings include:
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51% of respondents highlighted data-quality issues in MRO operations.
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49% reported inconsistencies in supplier master data.
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26% noted gaps in supply chain visibility and integration.
Nearly 60% of respondents agreed that clean, reliable data, systematically documented processes, and software systems are essential to drive operational improvements across maintenance management and operational excellence functions.
Furthermore, 56% believe AI-powered systems can play a corrective role, with 51% specifically citing AI agents as impactful in rectifying production-related data inaccuracies.
These findings underscore the urgent need for structured data strategies to optimize maintenance, procurement, and inventory operations.
Report Purpose
Organizations across industries are under increasing pressure to manage costs, improve operational efficiency, and enhance supply chain reliability. MRO data, often dispersed across multiple systems and formats, is frequently a source of inefficiency rather than insight.
This report presents the findings of Verdantis’ research initiative aimed at quantifying the impact of structured MRO data practices on cost avoidance, inventory management, maintenance efficiency, and procurement optimization. By providing evidence-based benchmarks, the report helps organizations understand how data governance, classification, and analytics-driven approaches contribute to measurable improvements in operational performance.
The report’s primary objectives are:
- To establish industry benchmarks for MRO data-driven cost savings.
- To highlight areas where improvements in data governance and accessibility yield the highest returns.
- To support decision-making in digital transformation initiatives, ERP upgrades, and supply chain optimization.
- To empower business and technical leaders with data-backed strategies for sustainable operational excellence.
Survey Research and Methodology
The findings presented in this report are grounded in Verdantis’ deep expertise, extensive industry experience, and validated data gathered through structured surveys and operational audits. Our research is informed by partnerships with over 100 satisfied clients and 1700+ industry experts, spanning diverse sectors such as manufacturing, energy, chemicals, utilities, food & beverage, and more. Through years of experience implementing data-driven solutions, we have developed frameworks, patterns, and methodologies that consistently yield measurable operational and financial improvements.
The findings presented in this report are based on a combination of structured surveys, interviews, and operational audits conducted with a diverse set of industrial organizations. Data collection methods included:
- Client Surveys: Quantitative surveys distributed to maintenance, procurement, and operations teams across industries.
- Operational Audits: Review of inventory levels, downtime logs, and procurement records to identify inefficiencies.
- Interviews: Discussions with senior executives and technical leaders to understand challenges, practices, and data governance models.
- Benchmark Analysis: Cross-industry comparisons to validate trends and patterns observed in the data.
All data was anonymized and aggregated to ensure confidentiality. The survey and audit instruments were reviewed by subject matter experts within Verdantis and validated through feedback from client advisory panels.
Research Methods
The research approach combined both qualitative and quantitative methodologies to ensure robustness and practical relevance:
- Data Triangulation: Combining survey responses, audit data, and expert interviews to confirm findings and reduce bias.
- Scenario-Based Modeling: Applying variable input ranges (e.g., 5–7% hit rate, downtime costs) to simulate potential improvements under real-world operational conditions.
- Trend Analysis: Identifying patterns across multiple clients, industries, and regions to establish scalable benchmarks.
- Benchmarking: Comparing client-specific outcomes to industry averages and validated operational standards.
- Continuous Validation: Regular review of methodologies and results to ensure alignment with evolving industry needs and emerging technologies.
Demographics
The survey captured responses from a wide range of organizational roles, industries, and geographical regions to ensure the findings are representative and actionable.
Geographic Distribution
United States: 68%
Europe: 15%
Middle East & Africa: 8%
Asia-Pacific: 10%
Organizational Size
Less than $500 million revenue: 23%
$500 million – $1 billion: 19%
$1 billion – $5 billion: 24%
Over $5 billion: 33%
Length of Engagement
More than 5 years: 40%
2–5 years: 35%
Less than 2 years: 25%
Roles of Participants
Role | Percentage |
Gestión de activos | 8.87% |
Compliance & Risk Management | 9.92% |
Data Governance / MDM | 8.72% |
Engineering | 10.28% |
IT / Digital Transformation | 8.52% |
Maintenance & Operations | 8.62% |
Operations Excellence | 7.82% |
Otros | 9.07% |
Procurement & Sourcing | 9.42% |
Quality & Assurance | 9.12% |
Supply Chain & Logistics | 9.62% |
Industries Represented
Industria | Percentage |
Agriculture | 5% |
Automotive Ancillary | 6% |
Materiales de construcción | 6% |
Chemical Manufacturing | 6% |
FMCG | 6% |
Alimentación y bebidas | 5% |
Industrial Machinery | 6% |
Fabricación | 6% |
Meat Processing | 6% |
Metal, Mining & Minerals | 6% |
Oil, Gas & Energy | 6% |
Otros | 6% |
Paper, Plastic & Packaging | 7% |
Pet Products | 6% |
Tires & Rubber | 5% |
Servicios | 6% |
Wire & Cable | 6% |
The diversity of industries ensures that the findings are applicable across a broad spectrum of operational environments, supply chain models, and data maturity levels.
Key Research Findings
- Inventory Cost Avoidance – Reducing Waste and Improving Availability
Inventory mismanagement leads to unnecessary purchases and excess stock holding. Our research reveals:
- Average annual MRO spend per organization: $750 million
- Duplicate purchases due to poor data accuracy: 5-7%
- One-time savings potential from inventory avoidance: $37.5M – $52.5M
- Overhead savings in stores management: $3M – $6.3M annually
Insights:
Organizations that improved classification and visibility into existing stock reduced duplicate orders significantly. Improved inventory hit rates led to quicker sourcing from existing warehouses, lowering both costs and procurement time.
- Inventory Holding Cost Reduction – Optimizing On-Hand Stock
Excess inventory results in tied-up capital and storage costs. Our analysis shows:
- Average inventory valuation: $1.5 billion
- Reduction opportunities: 5-7% of stock levels
- One-time reduction savings: $75M – $105M
- Recurring overhead savings: $7.5M – $15.75M annually
Insights:
Better data-driven classification enabled organizations to retire obsolete parts, streamline reorder points, and shift toward demand-driven stocking strategies, reducing carrying costs without compromising service levels.
- Reduced Stock-Out Downtime – Enhancing Operational Continuity
Unplanned downtime is one of the largest hidden cost drivers in industrial operations. Our research uncovered:
- Average stock-outs per year: 52
- Downtime per event: 1-2 hours
- Cost per hour of downtime: $50,000
- Annual downtime cost exposure: $26M – $52M
- Downtime reduction potential: 25-50%
- Annual savings from reduced downtime: $6.5M – $26M
Insights:
Better data accuracy and classification helped maintenance teams predict stock-out risks and proactively address replenishment needs. Companies that invested in data-driven alerts and inventory visibility experienced lower downtime and improved equipment availability.
- Improved Materials-Related Personnel Productivity – Reducing Time Waste
Engineering and maintenance teams often lose valuable hours searching for parts or reconciling inaccurate data.
- Labor time wasted annually: 1,200 – 2,000 hours
- Hourly labor cost: $70 – $85
- Productivity savings potential: $84K – $170K annually
Insights:
Organizations that adopted structured data workflows and centralized spare parts information systems reduced time spent on manual searches and reconciliations, leading to higher operational efficiency and workforce satisfaction.
- Strategic Sourcing – Unlocking Procurement Synergies
Procurement inefficiencies, especially around off-catalog purchases and unstructured demand aggregation, lead to higher costs and supplier mismanagement.
- Influenceable off-catalog spend: 10-15%
- Improvement opportunities: 15-20% reduction
- Demand aggregation potential: 10-15%
- Savings from better sourcing: $22.5M – $45M annually
Insights:
With improved data governance and visibility, organizations aggregated demands, identified strategic sourcing opportunities, and reduced reliance on high-cost emergency purchases, thereby optimizing supplier negotiations and contracts.
Consolidated Financial Impact
Zona | Savings Type | Minimum Savings | Maximum Savings |
Inventory Avoidance | One-Time | $37.5M | $52.5M |
Inventory Overhead | Recurring | $3M | $6.3M |
Inventory Reduction | One-Time | $75M | $105M |
Inventory Holding | Recurring | $7.5M | $15.75M |
Downtime | Recurring | $6.5M | $26M |
Productivity | Recurring | $0.08M | $0.17M |
Aprovisionamiento estratégico | Recurring | $22.5M | $45M |
Total Year 1 Savings | Mixed | $671M | $1.12B |
In addition to inventory optimization, downtime reduction, and sourcing improvements, our research revealed several underlying challenges that organizations face in managing MRO operations. These challenges create significant cost, efficiency, and compliance risks – and highlight why structured, data-driven approaches are essential.
Leveraging Smart Technologies for MRO
Structured MRO data strategies leverage technology and analytics to provide actionable, real-time insights across operations:
Inventory management platforms and analytics tools
Maintenance management systems
Procurement and sourcing platforms
IoT sensors, telematics, and asset tracking
By transforming fragmented, manual processes into integrated, data-driven workflows, organizations can:
Predict stock-outs and proactively manage inventory
Optimize maintenance schedules to reduce unplanned downtime
Streamline procurement and identify off-catalog spend
Enhance workforce productivity by centralizing MRO data
Key Areas of Impact
1. Inventory Optimization – Reducing Waste and Improving Availability
Duplicate purchases and excess inventory tie up capital and increase costs. Verdantis research shows that improved classification and visibility can reduce duplicate orders and improve sourcing efficiency.
2. Downtime Reduction – Enhancing Operational Continuity
Unplanned downtime due to MRO inefficiencies remains a significant cost driver. Survey data indicates:
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50.78% of respondents experiencia 25–50% or more downtime due to MRO data quality issues.
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Structured MRO data strategies enable predictive maintenance and proactive replenishment to minimize production disruptions.
3. Supplier and Procurement Data – Reducing Risk and Cost
Fragmented vendor data leads to cost overruns and supply chain inefficiencies:
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49.17% of respondents frequently encounter supplier record inconsistencies.
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26% report lack of supply chain integration as a barrier to operational efficiency.
Structured, validated supplier data allows organizations to consolidate demand, negotiate better contracts, and reduce emergency procurement costs.
4. Workforce Productivity – Streamlining Tasks
Engineering and maintenance teams often waste hours reconciling inaccurate records or locating materials. Centralized MRO data reduces manual effort and enables faster decision-making, improving both operational efficiency and employee satisfaction.
5. AI Adoption – Correcting Data and Enabling Insights
Despite challenges, AI adoption is accelerating:
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56% of respondents have either implemented AI or are piloting AI systems for operational improvements.
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51% rely on clean data to drive AI-enabled decision-making.
AI-powered analytics and automation help correct data errors, identify anomalies, and optimize maintenance and procurement processes.
Challenges and Opportunities
Mala calidad de los datos
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Downtime: 50.78% experience serious MRO-related downtime due to data issues.
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Supplier Records: 49.17% face frequent errors in vendor master data.
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Supply Chain Visibility: 26% lack integrated data across operations.
Procesos manuales
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En 60% of maintenance teams still rely on spreadsheets or paper for work orders.
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Manual tracking leads to delays, errors, and repeat work.
AI and Digital Transformation Barriers
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Fragmented or unstructured data limits AI effectiveness despite widespread interest and pilot programs.
Operational and Workforce Impact
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60% report improved efficiency when clean, structured data is applied.
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Poor data access frustrates 58% of technicians and engineers, negatively affecting productivity and morale.
Strategic Benefits of Data-Driven MRO Strategies
| Zona | Key Impact | Survey Insight |
|---|---|---|
| Optimización de inventarios | Reduced duplicate orders and excess stock | 51% cite MRO data issues; improved classification reduces waste |
| Reducción del tiempo de inactividad | Lower unplanned downtime and higher asset availability | 50.78% affected by poor MRO data; predictive systems reduce downtime |
| Supplier Data Accuracy | Improved procurement and cost efficiency | 49.17% frequently encounter vendor data errors |
| Workforce Productivity | Streamlined operations, reduced manual reconciliation | 58% technicians report frustration due to poor data access |
| AI Adoption | Predictive insights, anomaly detection, automation | 56% of respondents use AI in operations; 51% rely on clean data for AI efficacy |
Workable Approach to Solving the Billion-Dollar Efficiency Problem
Implementing a structured MRO data strategy and driving operational improvements requires a pragmatic, stepwise approach that combines governance, technology, and organizational alignment. Verdantis recommends the following roadmap:
1. Assess Current Data and Processes
Conduct an operational audit to identify MRO, inventory, and procurement data gaps.
Quantify the impact of duplicate orders, stock-outs, and downtime on costs.
Prioritize areas where clean data can deliver the highest ROI.
2. Establish Governance Frameworks
Standardize master data across MRO, supplier, and procurement records.
Implement validation rules, classification systems, and stewardship protocols.
Create a single source of truth for asset, material, and vendor data.
3. Leverage Technology and Analytics
Deploy predictive inventory management and maintenance platforms.
Integrate AI-enabled tools to detect anomalies, correct errors, and forecast demand.
Use IoT sensors and smart asset tracking for real-time operational visibility.
4. Pilot and Scale Initiatives
Begin with high-impact assets or facilities to test predictive maintenance and inventory optimization workflows.
Monitor outcomes against KPIs such as downtime reduction, inventory hit rates, and procurement savings.
Gradually expand successful pilots to all relevant operational areas.
5. Enable Workforce Adoption
Provide teams with centralized access to MRO and supplier data.
Reduce manual reconciliation and paper-based workflows.
Train staff to leverage analytics dashboards and AI-driven alerts for decision-making.
6. Measure, Iterate, and Improve
Establish continuous feedback loops integrating operational data with maintenance and procurement systems.
Track both quantitative and qualitative benefits, including cost savings, downtime reduction, and employee satisfaction.
Refine data governance, AI models, and operational workflows based on outcomes.
