Over $1.7M in Annual Savings with better CMMS Data Documentation

In asset-intensive industries, maintenance teams rely heavily on accurate, consistent, and comprehensive asset information. Poor-quality master data can undermine even the most advanced reliability strategies, resulting in inefficiencies and rising costs.

Asset Master Data refers to the structured information about equipment, spare parts, and Bill of Materials (BOMs) used to support maintenance data management, inventory, and operations. It is the foundation for digital transformation and data-driven decision-making. 

Find out how our client saves $1.7M annually by reducing carrying costs and minimizing both planned and unplanned downtime. 

“Master Data is the DNA of your asset”

Problem Statement

The company previously faced the following challenges due to poor master data management:

• High downtime caused by inefficient asset and part identification, which directly impacted Mean Time to Repair (MTTR), leading to prolonged repair times and decreased operational efficiency.
• Excess, obsolete, or duplicated inventory, resulting in unnecessarily high carrying costs and complicating inventory management across the organization.
• Lack of data standardization, which hindered effective performance monitoring and reliability analysis (e.g., MTBF and RCFA), preventing the company from accurately assessing asset performance and identifying areas for improvement.

Financial Benefits Analysis

Downtime Reduction

Prior to the master data management project, the plant was considered a bottom performer in maintenance and reliability. It exhibited many of the typical symptoms associated with this category:

• High levels of planned and unplanned downtime
• Incomplete or inconsistent asset master data
• Limited visibility into spare parts and equipment structure
• Maintenance work that was more reactive than planned

At the time, the plant experienced approximately 30 days of downtime per year. With an average operational cost of $5,000 per hour of downtime, this translated into an estimated $3.6 million in annual downtime costs.

These costs stem from:

• Inaccurate or duplicated asset data, which slows down diagnostics and work order preparation
• Missing or incomplete equipment BOMs (bill of materials), delaying part identification and repair readiness
• Inefficient maintenance workflows, resulting in longer Mean Time to Repair (MTTR)

Projected Improvement with Master Data Management

By transitioning from bottom performer’s practices to a higher data maturity level, specifically, by implementing a clean and standardized asset master data structure, the client has had a 20% reduction in total downtime.

That equates to saving 6 days per year, reducing downtime to 24 days annually, and generating $720,000 in savings.

This improvement is driven by:

• Clear asset hierarchies’
• standardization enabling faster diagnostics
• Complete and accurate BOMs allowing for better kitting and preparation
• Reduction in reactive work through improved planning
• Quicker technician response due to better asset and part identification

By aligning the plant’s data quality and asset structure with best-in-class practices, Spartakus Technologies helps bridge the gap between reactive maintenance and proactive, performance-driven operations.

Inventory Carrying Cost Reduction

Before the implementation of Spartakus’ Master Data Management project, the plant maintained a highly inflated spare parts inventory. The root cause was poor inventory data quality: duplicate items, inconsistent naming conventions, obsolete parts, and a lack of linkage between assets and their required components.

This situation led to significant excess stock, parts that were either unnecessary, incorrectly ordered, or not visible to maintenance planners, driving up annual carrying costs.

Carrying costs, such as warehousing, insurance, capital tied up in stock, and depreciation, can account for as much as 20% of a part’s value annually.

The plant’s Replacement Asset Value (RAV) stands at approximately $1 billion. Prior to the project, there was no clear standard for inventory sizing, and the volume of stocked items exceeded optimal levels. Through Spartakus’ data cleansing and inventory rationalization efforts, the spare parts inventory was reduced by 0.5% of RAV, equivalent to $5 million in unnecessary inventory value.

By removing duplicate entries, eliminating obsolete items, and standardizing the structure of the master data, the plant has now achieved an ongoing annual saving of $1 million.

Non-Financial Benefits

Beyond direct cost savings, high industrial data quality enables long-term strategic advantages:

• Improved KPI tracking: Accurate asset and failure data enhance reporting on maintenance effectiveness (cost control, MTBF, RCFA, bad actor analysis).
• Faster work execution: Streamlined work orders reduce time spent by technicians interpreting instructions or searching for parts.
• Standardization across plants: Common data rules facilitate multi-site consistency and scalability.
• Foundation for predictive maintenance: Reliable data enables Data driven insights, condition monitoring, and failure prevention.

Conclusion

The implementation of Spartakus’ Asset Master Data Management initiative has delivered measurable and recurring benefits, including over $1.7 million in annual savings through reduced downtime and optimized spare parts inventory.

Beyond these financial gains, the project has established a solid data foundation that now supports more effective maintenance planning, improved KPI tracking, and a transition toward proactive, performance-driven operations.

Learn more about our approach to Master Data Management.

Raphael Tremblay,
Spartakus Technologies
[email protected]