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3 Smart Strategies for Using Analytics in Smart Buildings
Once you understand the goals of your analytics project, the next step is to further develop a value proposition. To sign off on spending money to procure an analytics solution, management typically needs to understand, not only how existing problems can be solved, but how much money can be saved, or value can be gained. It doesn’t have to be hard to explain how installing and using a software will save a building money. Here are some lessons learned from past projects on how to most clearly convey the value of analytics:
1. Keep it simple and relatable. Create “win themes” or short summary sentences for the three most important parts of your value proposition, followed with some backup information. Some common examples are shown below.
• Shift from a reactive to proactive maintenance mindset. One benefit is to reduce the amount of time it takes to diagnose issues and shift that time to fixing the things that are broken. Analytics does not reduce maintenance hours, it shifts where the labor is used, from low-value activities like searching for issues to high-value activities like fixing issues.
What’s more, analytics uses existing, unused data from the digitally controlled mechanical and electrical systems of the buildings to make better decisions. Analytics also makes it possible to prioritize maintenance backlog based on the true cost of issues.
• Reduce energy consumption by 5 percent with simple payback within one year. The Department of Energy studied the outcomes of facility managers implementing analytics solutions in their buildings in 2017. The study — Synthesis of Year One Outcomes in Smart Energy Analytics Campaign — yielded highly promising results. “Campaign participants have made improvements to their buildings, achieving a median energy savings of 5 percent … with less than a one-year simple payback.”
Analytics makes it possible to identify previously unknown energy-saving opportunities. The solutions also track previous energy saving initiatives and verify true energy savings was achieved; if energy savings targets are not being met, you can take corrective action to correct energy waste quickly with greater visibility. And analytics enables monitoring-based commissioning and measurement and verification.
• Improve occupant comfort by 10 percent. You can’t manage what you don’t measure. The first step to improving occupant comfort is measuring it. Synthesizing the data with analytics allows the facility staff to pinpoint comfort issues and move towards fixing the causes. For example, temperature control can be improved by understanding the operation of equipment over long periods of time. Analytics software provides a means to perform better investigation into the operation of the mechanical equipment. The software also makes it possible to identify potential hot and cold calls before they happen by locating previously unknown mechanical issues and to identify root causes of problems that do occur, not just the symptoms.
2. Build a broad team and incorporate all stakeholders. Many analytics projects move forward for one or two of the reasons above. However, this is an opportunity to build a consensus across the entire facility management organization that new technology and organizational change are valuable. Many times, analytics projects can have a good ROI when only taking the potential energy savings into account. For this reason, it might be worthwhile to bring in those members of an organization responsible for the sustainable operation of the facility or energy spend, to gather their input and guidance during the early planning and decision making. Also, because an analytics project will involve installing software and transferring operational/equipment data between computers, the IT and cyber security team should be involved early on. The earlier in the process these individuals can be involved, the more likely the project will not be shelved or sunk at the last minute when someone is blindsided.
3. Define clear, measurable goals for the project. Take your best estimate at an ROI. Setting clear goals for the project, ones that can be easily measured at a later milestone, is an important part of the planning process. This will likely involve quantifying the “win themes” into energy savings targets, maintenance work order tracking, or a means of quantifying occupant comfort. Analytics projects, like many other types of projects, should have a quantifiable return of investment. Typically, an analytics project will pay for itself in fewer than three years. The estimated cost savings should be verified during the project.
Once the project gets the green light, there are still many hurdles still to come. To avoid problems, it’s important to create an unbiased team early on. It may be tempting to pick a favorite software early in the planning process to have a tangible end-result to help get stakeholders on board. However, there is a very wide range of analytics software options available on the market. Some you can customize yourself; some are not able to be changed. Some focus on energy, while others are better at identifying operational issues. Some are hosted in the cloud, and others are installed on a server in the facility. All these factors must be understood by all parties involved prior to a decision being made.
To understand the options you have, it’s essential to know the market. A comprehensive list of FDD software and service providers is listed on the Smart Energy Analytics Campaign website. The list is long and all providers are not created equal. However, this is a good place to begin researching all the options that are on the market today.
Clear the last hurdles
Another lesson learned from implementing analytics projects is that it’s important to understand the cost structure. Each software provider, service provider, and technology integrator prices their services differently. Some software has a high upfront cost, and a low on-going fee for maintenance. Some providers provide a high level of involvement along with their software-as-a-service (SaaS) model, such as monthly meetings to review progress and adjust programming.
As a result, implementation costs and levels of service provider involvement vary significantly across the industry. This reflects the variance in complexity of analytics projects. The key lesson learned here is that, if you are procuring these services, you need to clearly state the level of effort desired to be provided by the service provider in the RFP.
You also need to have a plan for fixing the issues that analytics will find. Who will have the authority and responsibility to take the analytics insights and direct that action be taken? Who will pay for the fixes? Who will be used to do the actual fixing (internal or external labor)? Who will verify the work is being completed correctly and fully?
Finally, it’s essential to make the process fair. Fully develop a scope and provide vendors a detailed account of what is required, and what is simply desired. Do not use an RFP template provided by software vendors. Every vendor has one and they are written to purposely to exclude their competition. It may be tempting to take a vendor RFP, but remember that the level of effort put in up front to clarify the vendor’s scope of work easily pays for itself in avoided future costs. If this level of technical planning seems daunting, reach out to a trusted service provider for help.
Facility managers have found that using commercially available analytics software can help them do their jobs better in a variety of ways. Analytics allows managers to make data-driven decisions and react faster and more reliably to on-going operational issues.
Matt Ernst (email@example.com), P.E., CEM, LEED AP, is a commissioning engineer with Burns & McDonnell. He manages and executes existing facility commissioning and new construction commissioning projects and has led over 100 building optimization and energy efficiency projects.
3 Smart Strategies for Using Analytics in Smart Buildings