Skip to Content
Author's profile photo Dan Everett

Drive Return on Assets by Governing Asset Data

For capital intensive industries like Oil & Gas, Utilities, Telecommunications, and Manufacturing return on assets (ROA) is a critical performance metric, as it measures how efficiently management is using its assets to generate earnings. Maximizing ROA depends on optimal equipment productivity throughout the entire equipment lifecycle. The utilization of capital equipment is largely determined by the duration and frequency of planned maintenance and unscheduled plant stoppages due to equipment failure.

Poor data quality can reduce ROA in many ways including;

  • Lost revenue due to unplanned downtime,
  • Reduced profitability because insufficient spare parts force expedited procurement orders
  • Regulatory fines, potentially on multiple occasions
  • High Days Working Capital due to excessive spare parts inventories

In each of these cases the opportunity costs or real losses can run into millions of dollars every day. Moreover, the effects of production disruptions can irreparably damage a company’s brand image when orders aren’t delivered as promised.

    Information governance can help in several areas;

  • Efficient and accurate collection of equipment data from multiple, diverse sources
  • Data cleansing, standardization and validation based on policies/rules for quality
  • Enrichment of asset data, such adding warranty information, or geo-locations attributes
  • Simplified processes for data movement and synchronization between systems
  • Connecting structured data with EAM content such as engineering drawings and instrumentation diagrams

EAM Data.png

EAM Data Sources and Domains That Need to be Governed

One company who started an information governance initiative for asset data found they had eight functionally equivalent ball bearings from seven different suppliers, with prices ranging from $2.50 to $19.00 and lead times ranging from 13 to 84 days. One supplier was not identified, and two suppliers were duplicates and the duplicate suppliers were charging different prices. The lack of complete, consistent and trusted data was also causing excess ordering, which drove up inventory carrying costs. Although ball bearings may seem small this was just one example across hundreds of thousands of materials.

With a clear business case across inventory, procurement, warehousing and plant maintenance the company implemented standards and procedures for material master data, ensuring accurate equipment records and bill of materials, and information governance policies and processes. The results

  • Better managed spare part inventories, which reduces working capital
  • A simplified process for spend analysis and strategic sourcing which increased profitability by capturing better discounts based on global volume.
  • Increase equipment uptime because maintenance personnel were spending nearly 40% less time searching for asset information needed for the work


Learn More

Webinar February 26th, 2015

SAP Master Data Governance for EAM Solution Brief

Assigned Tags

      1 Comment
      You must be Logged on to comment or reply to a post.
      Author's profile photo Former Member
      Former Member

      After 20 years of predictive modeling - the greatest danger is that we assume that we actually know the cause behind the correlation. We spot something that looks interesting - does it really tell us what our customers are like? Should we embark on a new business division? More often than not, the machine-created connections are no more than noise. The danger is in allowing the machines to draw the conclusions without asking for greater explanation. Keep the people in the loop.

      Best, ace writers editor.