Addressing critical equipment replacement in data centers is a strategic exercise for data center owners
Planning for the ultimate demise of your data center infrastructure – or “End of Life (EOL) Planning,” provides data center owners with a roadmap or blueprint for investment decisions and a clear-eyed view of the challenges they will face.
Enabled Energy explored the concept and needs in detail in our previous blog post – “End of Life Planning in Data Centers.” As described previously, the steps in EOL planning include:
The first three steps of the process are academic in nature – they are a function of internal data collection and research from manufacturers and industry benchmarks. In general, these steps are going to be consistent across data center owners and operators – as they are a function of equipment type and age.
The fourth step – decision criteria, in contrast, is subjective and a function of the specific goals of the data center owner’s organization and leadership team. Elements of the criteria can be ‘science-based’ or quantified, but ultimately the data center operator needs to make determinations of the relative importance of these factors based on their own organization’s mission, goals, and constraints.
Making end of life analysis fit with your business
Enabled Energy (EE) has worked with data center operators on these criteria, and found that the relevant categories are consistent to include:
- Criticality
- How does the failure relate to outages or core building operation?
- Current operating issues
- Is the equipment already creating problems or operating challenges?
- Redundancy
- Does the building have options in case of failure of this equipment?
- Service & support
- Can the equipment be upgraded or maintained to extend life? Is the manufacturer discontinuing service?
- Age
- How old is the equipment in relation to its expected life or maximum life?
- Site priority
- Is the site a priority long-term (if part of a larger portfolio)?
- Efficiency & maintenance
- Will a replacement create energy or operational savings as a result, and to what magnitude?
EE has also found that a quantified, numerical weighting system works as an algorithm to build a decision model. This includes a matrix-type approach whereby:
- Categories are weighted against each other in relative importance
- For example, criticality may be more important than age for an operator that has a strong ongoing maintenance culture, or site priority may be far more important than all other factors for a property located in an ideal geography.
- Within each category, assets are given a rating in relation to the criteria
- For example, UPS systems may be deemed as more critical to operations than CRAC units due to the data center design and ability to handle failures. Or, a 6-year old battery should be flagged as a higher EOL priority than a 15 year old generator due to service life issues.
In terms of a numerical weighting algorithm, rating categories and assets on a standard scale, typically 1-100 – provides a method to mathematically weight the relative importance. It also provides a method to scale the weighting over time as assets age to build a dynamic model over a five-to-ten-year projection. For example, an owner may determine that batteries go from a 75 to 100 weighting between years 5 and 6, while chillers have a more gradual EOL rating that increases from 50 to 100 from year 15 to 25.
Data center operators must remember that an EOL tool is just that – a support structure for weighting and making decisions
The operator must employ discipline and critical thinking, and resist the temptation to rate everything as “99 out of 100” or “Red – critical.” The other point to remember is that items that may be rated relatively lightly, e.g. efficiency or current operating conditions, will factor into the total weighting of a given asset in comparison. For example, if two assets are both at a critical failure point, but one has a far greater option for efficiency or some lower-weighted factor, the asset with the better efficiency opportunity will be rated above that of the less efficient option.
The final step in the exercise is to apply the criteria and weighting to the replacement costs built in “Step 3” of the EOL exercise. Assets that reach a threshold for replacement – generally a weighting above 80 on a 100 point scale or the like – rise to the top of the list on an annual basis. The operator can then evaluate the total liability looming in the future or evaluate the relative EOL costs in one facility vs. the next.
The owner or operator then has a quantified peek into the future of the overall data center enterprise. The reality for most owners is that capital is limited and competitive, and options may exist to divest vs. re-invest, build vs. re-power, or expand to take advantage of a location.
EOL planning tools provide a heuristic model to guide capital to where it is most valued
An EOL tool, however, is only effective if the organization is willing to make logical, brutally honest, and politically courageous decisions in its criteria weighting. The tool will likely provide difficult answers on the magnitude of the re-investment liabilities and may also expose vulnerable sites or locations that appear more resilient on the surface.
How EE can help
Enabled Energy has assisted data center owners in developing, implementing, and optimizing EOL planning models across large and small enterprises. We have complex, pre-developed models that can be customized on criteria and site data – regardless of data platform. EE also has assisted large owners in site inventory data collection and inventory management, including sending technicians to audit data centers across North America.
EE recognizes that every data center owner has their unique challenges, so we work with our clients to leverage our experience and extensive tool set to customize any EOL analysis and plan to drive the required business results.
info@enabledenergy.com | 303-761-9890