The Devil is in the Data: One Oil & Gas Company's EAM Realization

Posted by Katie Mowery on July 18, 2018

Enterprise asset management (EAM) is a strategy to provide an optimal approach for the management of an organization’s physical assets to maximize value. It covers items such as the design, construction, commissioning, operations, maintenance and decommissioning/replacement of equipment and facilities.  By managing assets across the facility, organizations can improve utilization and performance, reduce capital cost, reduce asset-related operating cost, extend asset life and subsequently improve return on assets (ROA).

Asset intensive industries often face the harsh realities of operating in highly competitive markets and dealing with high value facilities and equipment where each failure is disruptive to the business and costly. At the same time, they must also adhere to stringent occupational safety, health and environmental regulations. Maintaining optimal availability, reliability and operational safety of plant, equipment, facilities and other assets is therefore essential for an organization’s competitiveness.

Maintenance organizations have worked hard to keep pace with ever changing technology. This has taken away from the primary function of daily and preventative maintenance tasks and responsibility. Too often the requirement for managing the system precludes the ability to effectively manage the accountability, performance and efficiency of the maintenance organization. All industries are being faced with increasing pressure to reduce costs related to their maintenance programs. This has led them to take a close look at EAM to ensure there is a positive impact on total revenue generation.

The case study below explores the findings and insights gained from an EAM data analysis for a large global oil and gas company. On the outset, the client sought to optimize their EAM processes, reduce inventory and improve plant maintenance and operations and found that key business processes were compromised because of incomplete and inaccurate data in the company’s production enterprise resource planning (ERP) systems. The knowledge gained provided opportunities for process and business optimization as well as benefits realized from better business insights and more accurate decision-making.

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Topics: EAM, Data Quality, Asset Data