Babel Buster Network Gateways: Big Features. Small Price.
– What Problem Are We Trying To Solve?
Global Workplace Solutions
Global Energy & Sustainability
has been a hot topic in our industry for the past three or four
years. In that time, numerous analytics products have appeared
and much has been written and presented about the power, and benefits
of analytic solutions.
all the new technologies, players, and attention analytics has
received, the actual adoption by clients has been slow and somewhat
painful. There are success stories to be sure, but widespread
adoption of analytics has been slow and sporadic. This article is
a first step in identifying underlying causes for this and developing
new and more effective ways to positon analytics and their value
proposition. To do this, let’s begin by addressing a couple of
common perceptions about analytics.
Analytics, fault detection, automated diagnostics, continuous commissioning, or whatever you prefer to call it, saves money.
Analytics by themselves does not save money.
For a couple of reasons; first, it’s not about the technology; it’s about people and process. Technology is merely the enabler. Second, analytics is a tool, not the remedy. Think of it this way; you don’t feel well so you go to the doctor and the doctor orders an MRI. The MRI costs money. However, the MRI does not cure your ailment; it simply gives the doctor insight as to what is wrong. The cure could be relatively simple (low cost) in the form of a prescription, or it could be major (high cost) in the form of surgery. Either of these remedies (corrective actions) will cost money to fix your ailment. The MRI is analytics; you need to spend money on it, but by itself, it will not save you money.
Similar to the MRI, meaningful bottom-line results from an analytics implementation is the result of people and process. While it’s true that you need good technology, technology is only an enabler of the solution; it is not the solution in itself.
We are selling analytics to save energy.
it’s certainly true that analytics can help save energy, if we only
consider energy as the justification for analytics then we are missing
the bigger issue.
Let’s consider this from a building owner’s or manager’s perspective: in a typical building, energy represents a $1 - $9 per square foot cost item. Lease/maintenance and operations represent a $10 - $99 per square foot cost, and people (occupants) represent a $100 - $999 per square foot cost.
Clearly, focusing on and addressing occupant experience issues have two orders of magnitude greater impact on business costs. In a tight labor market, this can be the difference in employee attraction and retention, in healthcare, this could have a significant impact on quality of patient care, in education, this could have an impact on student enrollment, and in corporate offices, this can have an impact on employee productivity and engagement.
It is logical to assume that if we focus on occupant experience, we will also have a positive impact on operations cost and energy cost as well. However, if we only focus on energy cost, that doesn't necessarily mean we will be addressing the most significant business cost; people.
As the largest FM services provider in the world, we know that approximately 50 to 60% of work orders our technician's process are for hot/cold complaints. This is a direct correlation to occupant experience and while it is true that you will never make everyone happy all the time, focusing on the cause of these hot/cold complaints will likely result in a better occupant experience.
Addressing hot/cold complaints and therefore one aspect of the occupant experience, requires more than just technology, however. It fundamentally requires changing our attitude towards maintenance and building operations.
We should not be discussing innovation and new technologies (analytics, IoT, etc.), if we’re not willing to have a discussion about taking care of the fundamentals of proper building operation and maintenance; specifically, deferred maintenance and maintenance strategy.
It is common sense that identifying issues through tools such as analytics that are enabled by low-cost sensors (IoT) won’t save money or address energy, operations or occupant experience issues, unless you fix the issue that’s been identified. Identifying it and then deferring it will not lead to an outcome that will ultimately result in a better occupant experience or help reduce costs.
though it is a relatively new technology, analytics has primarily been
viewed as a technology that is attached to a building automation system
and other devices controlling a building, and its primary justification
has been based on energy savings identified as a result of fault
detection algorithms. Admittedly, this has led to much debate
over how to determine things like cost impact values for each rule and
the fact that you cannot show real savings until you fix the identified
issue or fault.
For this reason, it has been difficult to get widespread adoption of analytics technology because frankly, decision makers are not convinced it can produce the results to justify its cost. This is the correct thinking if we are not going to change our attitude (and budgets) toward fixing and maintaining equipment in the building. As previously stated, analytics do not save money; they are a tool that provides insight and without an organization’s commitment to fixing and maintaining and providing the budgets to support that, no hard savings can be realized.
on this reality, it’s time to look at implementation and justification
of analytics from a different perspective based on what has been
learned over the past few years. We need to look at analytics as
a business tool and like other business tools, it will give us greater
insight into the state of our business, in this case as it relates to
the buildings in our portfolio.
we consider analytics as a business tool, the question then becomes
what problem are we trying to solve for; is it the 1x, 10x, or 100x
problem? Let’s assume that the area of focus is the 100x problem
(which makes logical sense), then before we consider an analytics
implementation we need to address some fundamental questions that have
little to do with technology but are critical to a successful outcome.
is the current condition of the equipment and is there an active
program for maintenance and improvements? Once the analytics
solution is implemented, what is it likely to find? A building or
portfolio of buildings whose equipment is well maintained and has a
regular replacement cycle in place will likely result in fewer “major”
issues discovered by the analytics solution.
applying analytics to equipment that has not been properly maintained
will likely result in significant findings that will likely include
major repairs. This needs to be considered when planning an
analytics solution because the findings could be overwhelming.
This is not to say that it shouldn’t be done or that equipment that has
not been maintained properly is not a good candidate for an analytics
solution. Quite to the contrary, these are excellent situations
for analytics and the opportunity for meaningful results is
significant. However, there needs to be a clear understanding that the
problem wasn’t created overnight and it won’t be corrected
Implementing analytics should be done with the understanding that analytics is not a discrete project. Rather, it is a continuous process where issues are identified and corrected in a prioritized manner, within the limits of staff and budget. When those tasks are completed, another level of issues is identified and addressed. As each successive layer of issues is resolved, the process narrows its focus and moves to greater levels of detail, which continues to drive greater levels of efficiency and ultimately cost savings. Keep in mind though there is no “silver bullet” to achieve success. It’s on-going commitment to continuous improvement.
much money is allocated for corrective action? This may seem
obvious, but all the money spent on analytics won’t return a penny to
the bottom line if the issues identified by the solution are not
addressed. It is frequently assumed that simply implementing an
analytics solution will produce results. If an organization’s
maintenance strategy is “run to failure,” it is virtually impossible
for any analytics solution to achieve the desired results.
Sufficient money and resources must be allocated to correct the issues
identified by the solution. And this amount is inversely
proportional to the current condition as stated above; if the current
condition is poor, the amount of money required to improve will be
greater, than if the current condition is good. Given the amount
of deferred maintenance that exists in most organizations today (a
number is that is growing daily), we need to ensure that clients
understand the realities of the on-going care of their building systems
and assets. There is no “free lunch,” nor are there any silver
bullets. New technology will not fix this; only a commitment of
money and resources will solve this problem.
action needs to be implemented in the context of a clearly defined
process. The key to a successful implementation, one that
is aligned with an organization’s budget and resources, is to build a
process around the technology. The process manages the rate at
which corrective action is taken to reflect that which the organization
can effectively support. Change management is critical in this
regard. An organization must be willing to change its processes
and procedures to achieve successful outcomes from an analytics
implementation. Like the need to commit money to fixing and
maintaining assets, resources need to adopt new workflows and
procedures to embrace and utilize the data. To be sure, the data needs
to be accurate and credible, but without an organizational commitment
to change, success will not be achieved.
Beyond the BAS and Meter Data
we consider analytics as a business tool, then we should consider the
data it acts on. Most analytics implementations are rooted in the
idea of fault detection and diagnostics and to do so requires a
“connection” to real time or near real time data. But suppose we
don't constrain our application of analytics technology to fault
detection? What if we consider analytics as a business tool to
help us manage our buildings to address the 10x and 100x problems
rather than just the 1x problem?
For example, we should consider analyzing CMMS data. When you consider that almost 60% of work orders are related to hot/cold complaints, and that there is likely a very strong correlation between hot/cold calls and the 100x problem, analysis of this data and the subsequent corrective actions (again, we need to make sure we do something with the insight), could yield results that surpass the 1x problem most implementations currently focus on.
it is clear that analytics can address the 1, 10 and 100x issues,
current implementations have largely focused only on the 1x
issue. We need to expand our thinking and ask ourselves what
problem are we trying to solve and what matters.
We also need to address the issue of deferred maintenance. Clients need to commit resources, primarily financial, to fixing identified issues and dealing with deferred maintenance. It should not be acceptable to enter into discussions about new technologies, IoT, analytics, etc. unless there is a firm commitment to funding corrective action, eliminating deferred maintenance backlogs and an organization commitment to change management.
Considering the amount of under-utilized technology in buildings today, it makes no sense to “pile on” more technology unless clients are committed to addressing the fundamentals of proper building and asset management.
is a professional services firm focused on making buildings perform
better. As part of CBRE’s Global Workplace Solutions group, the world’s
largest provider of real estate services, and with multi-faceted
expertise and best-in-class technology, CBRE|ESI delivers solutions to
reduce cost, reduce risk, and improve the occupant experience. For more
information please visit www.cbre.com.
About the Author
Oswald is Managing Director of CBRE|ESI. Paul has over 30 years of
experience in building automation, system integration and energy
management. His experience includes product strategy and development,
business and channel development, and services.
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