The Truth About Deploying Global Maintenance Strategies for Like Assets
On the surface, developing best-in-class strategies for like assets and deploying these across an asset base seems a logical way to improve consistency and efficiency of maintenance while reducing cost and risk. However, many organizations have attempted this approach and failed due to four main challenges:
Generic strategies which are developed at the wrong level (e.g. system, maintainable item, equipment, lowest repairable unit) – This issue can introduce inconsistencies in structure and strategy for similar components within different maintainable items.
Adjustments to generic strategies which are made to reflect specific operating context – Once these adjustments are made, it is difficult to maintain the connection between the adjusted asset strategy and the generic asset strategy
Adjustments to generic strategies based on regional differences – Strategies may need adjusting to account for labor category differences, regulatory differences, and other regional requirements.
Inconsistent task grouping across the organization – This results in additional effort required to group tasks into different Master Data structures dependent upon the specific site
For many, these challenges seem insurmountable and devalue the effort dedicated to generating best-in-class equipment type strategies. Also, without any ongoing connection between generic and modified strategies, there’s no foundation for ongoing governance and continuous optimization.
The good news is that these challenges can be addressed with the right process and technology:
With appropriate technology, you can establish a data architecture that allows component level, reliability-based strategies to be deployed across all maintainable items, assets and systems. This removes duplication of data and makes it easier to create and maintain strategies across all assets.
With the right process and data architecture, you can maintain a connection between generic strategies and those strategies which are adjusted based on specific operating context. You can also report on the differences between a best-in-class strategy and a specific asset strategy.
Having a multi-tiered connected data architecture allows you to maintain generic best-in-class strategies, adjusted strategies with regional or site differences, and then strategies adjusted for specific asset differences. Again, the differences in strategies across an enterprise can be reported, assessed and monitored.
While there is great benefit in having standard task grouping and Master Data construct rules, many organizations don’t get that prescriptive. To counter this, task grouping rules can be created to cater for each region or site as required. This rule-based task grouping does not then disrupt the reliability strategy content data architecture.
The benefits of this more sophisticated data architecture is that development, deployment and review of strategies is much more efficient. Confidence is gained around the quality of strategy across the organization and, perhaps most importantly for an organization’s competitive future, a solid foundation is established for ongoing governance and optimization.
In addition, having the right data architecture enables you to efficiently deploy any learnings or reliability improvements on specific assets, right across the whole asset base where relevant. This delivers cost savings and risk reduction in real-time and solidifies the benefits of this approach.
As the pioneer of Asset Strategy Management – a process enabled by people, data, and technology to sustain a reliability-driven approach to improving asset performance – Jason has paved the way for ARMS Reliability’s impressive growth trajectory.