Since starting BUENO six years ago, we have implemented data-driven operational tools comprising a combination of the system integration, analytics, and workflow management technologies in more than 1,500 buildings across 8 countries. We want to share what we’ve learned from these implementations so that the industry at large can learn from past mistakes so that those only starting out in this space can slingshot their progress to having the benefits of implementing a mature data-driven solution without the pains of learning every aspect of the process from first principles.
Firstly though, we want to share how BUENO has got to its position as the market leader in this space.
I started my professional life as an energy consultant working for an engineering consultancy in Canberra, Australia. The consultancy was heavily involved in setting up the NABERS1 scheme which meant that I was very focused on maximizing the performance of buildings in operations regardless of the constraints of the existing systems (as opposed to more feature-based rating systems which are more heavily used in new build projects). We had deep engagements with one REIT client in particular to help support them to achieve the portfolio NABERS commitments that they’d made to their shareholders (and the public) and this focus on operational performance helped them to achieve very high (and in some cases world-leading) performance across almost all property-focused sustainability benchmarking benchmarks (GRESB, DJSI, CDP etc).
Seeing first hand, the achievement of such great results for our customers lead me to look at our highly embedded role with the customer and look for specific tasks that had the largest and most long-lasting impact.
As it turned out some of the most simple ones made the biggest impact. We could assign 15 percent - 20 percent energy savings to work with their contracting team to continuously improve the operation of their control strategies on their HVAC systems. Breaking that task down involved the following steps:
● Collecting data (if we were lucky via history files on the BMS or through screenshots and data entry by some poor graduate consultant).
● Analyzing the data using some basic spreadsheet tools to find faults or inefficiencies
● Facilitating the FM and contracting teams to work together to resolve as many of the issues as possible within their maintenance budgets
This process got great results, but not optimal for a number of reasons:
● It was retrospective - we were looking in the rearview mirror at data that was weeks or months old
● It wasn’t continuous, and it used a sampling methodology. It was too expensive to use human labor and spreadsheets to analyze every single piece of equipment, and it was too expensive to do the exercise more than every month (for critical sites) or quarterly (more typical, steady-state operation (i.e. it was calendar-based)
● It was a bolt on to the standard operational practice of the buildings. It involved extra work for all those involved.
So much skilled labor is expended in engineering-focused, but repetitive tasks. Furthermore, so much of the effort in the process is finding the problems in the first place before we even get to fixing them. This effort/impact imbalance is the problem that BUENO is solving.
While my personal experience was from the perspective of an energy consultant, it has turned out to be a much bigger problem than just delivering energy savings. All stakeholders (technicians, facilities managers, and consultants) engage via a scheduled calendar basis and they do it across all building services (HVAC, lighting control, vertical transport, fire systems, etc). An opportunity exists for the industry to utilize more sophisticated data technologies to act as algorithmically codified technicians/ engineers. These technologies can supersede much of the “calendar-ised” diagnostic work being conducted today, and we can re-purpose the effort associated with those tasks to provide continuous improvement within a data-driven operational framework.
Better adoption of technology can take many of these operational “commodities” and turn them into value-generating exercises that present opportunities for differentiation for savvy service providers.
There is the opportunity for a shift in the property sector to better adopt and utilize technology in how we operate our buildings. The reality is that the current market best practice operational model (“preventative”) is at its root nothing more sophisticated than using a calendar to manage the engineering systems in our buildings. Daily, weekly, monthly and quarterly checklists are accepted best practice which means that a huge amount of effort is expended every year, in every building, across every engineering service before we even “turn a wrench” to fix a single piece of equipment, let alone do something to improve the performance of the assets. There is a better way.
The data-driven operational model
To build this model shift into a business’ operational strategy there are a few ways to go. The approach that an organization would take would sit somewhere on the following spectrum:
● A centralized operations centre function that directs detailed activities. This type of approach is suited to portfolios with a high volume of sites, or sites with high complexity, or both.
● A distributed, self serve model which can supplement the existing workflows of the operational stakeholders to do various day to day tasks smarter. This is a lower overhead model that is more suited to lower volume or lower complexity properties.
The point here is that these types of solutions aren’t just for the premium grade assets, different solutions can be shaped to be appropriate. The barrier to entry is adjustable.
The first question that should be answered for this type of approach is “why?”. There aren’t many organizations in the world that have the resources to do a speculative exploration of this type of model, so it is important to establish success criteria and to do it before we start. The why can be different depending on the different pressures faced by an individual organization, but the general types of drivers that we all run into are:
● High energy costs or aggressive sustainability goals. Energy efficiency is still the number one reason that organizations make the initial investment into these types of solutions. Split incentives (passing on energy/ operational costs) can be barriers but many organizations (typically REITs) that suffer from split incentives have other pressures to satisfy sustainability objectives.
● High labor/maintenance costs. The business case for automation is best in countries with high labor costs. Where people can afford to staff their buildings with teams of operators manually controlling plant operation, the value case to justify data-driven operations doesn’t exist.
● Low skills availability. The business case for autonomous building operations is best in countries with a combination of high labor costs and low skills availability. Many countries have aging populations of facility managers (the average age in Canada, for example, is 60 years old) and the talent pool for new hires is typically non-technical (security guards, cleaning supervisors, concierges, etc) rather than what has historically been a trade heavy pipeline.
● High focus on occupant experience. Different facilities have different pressures on managing occupant experience. The vertical transport at a casino or entertainment venue is of high criticality, whereas thermal comfort can be more important in an office environment.
● A broader digitization strategy. Some organizations that we work with have a strategic mandate to digitize their operations to protect their leadership position or else to differentiate the product that they are selling (e.g. data-driven maintenance vs preventative maintenance).
How to build the strategy
Once the success criteria is defined, the next step is to look at various facets of your property portfolio to assess the feasibility of the different strategies that are available.
Important considerations are:
● Definition of the high value use cases. What are the top 2-3 tangible improvements that you are going to make to operational tasking? What is the value associated with these?
● Existing automation/instrumentation. How much existing instrumentation can be leveraged from the automation systems at the building? If the existing systems are older and proprietary, the data is more expensive to ingest, if there is no existing automation, then an IOT type of approach will be needed.
● Engineering standards. Data must be cleansed and structured before any value can be extracted. If the existing assets have strong engineering standards around their installation, then there is significantly less work to onboard them. This means that for a given budget you can get more results.
● IT infrastructure. The sophistication of the existing IT infrastructure (from managed networks to fully private networks) can provide opportunities to make the solution more scalable.
● Security. Not much needs to be said here other than that any organization going down this path should be assessing the cybersecurity risks of any solutions that they consider.
● Enterprise systems. The end goal for this type of approach is to integrate the data-driven “engine” with the existing workflows of an organization to minimize the barriers to action/value. A solution should be designed with ERP/CMMS integration in mind.
The most important thing is to test different approaches. Run multiple iterations of scopes and vendors to identify the best engagement model and use the evidence from these proof of concepts to inform your business case.
The property industry has been on a journey for the past 6 years. Tools that were once purely the domain of delivering energy services are now available for disrupting the core operational model of all engineering services within the property industry. At its root, the brain of the current best practice operational model is really just a calendar and some checklists, it’s time for the property industry to close the gap to the rest of the world.