For many years I worked in the sales department of one of the arguably 1 or 2 largest HVAC controls and equipment companies in the world. One of my greatest frustrations was the constant focus and demand to generate, in my case, over 60% of all annual revenue from my existing customer base. This is not surprising for large, often Fortune 1000 companies chasing ever-increasing quarterly revenue and earnings goals. As new large customers don’t grow on trees, it became increasingly difficult to attain goals each year for the large HVAC controls and equipment vendors. The result of all this for the end user was limited flexibility and innovation of service and parts offerings at the expense of profits.
Today, long-term agreements for ongoing equipment, controls, parts, service and maintenance place the commercial HVAC customer in an untenable position. Despite having large energy plants capable of far more efficient operation, new equipment and controls are regularly recommended for purchase by the vendor. These traditional approaches were dictated by a variety of financing options, but without the correct tools to manage it, due to the constant plant dynamics changing daily, your commissioning is out of date the moment the ink is dry.
With the evolution of “born-on-the-cloud” products today, building efficiency can be achieved through a variety of tools available to maximize your plant performance prior to new product purchases. The introduction of objective and 3rd part efficiency measurement into the market has made it possible to identify peak performance levels and objective kw per square foot metrics. The dependence upon an equipment vendor’s definition of efficiency “potential” and prospective energy cost reductions is simply very difficult to establish without the independent verification that these SaaS-based energy analytics applications can provide. Only through cloud-based analytics, which allow for a real time view into efficiency, can you get the true view into how your building consumes energy and how to reduce costs. The use of data science and computational analytics validating the date and being powered through 1000’s of virtual machines, is impossible to be replicated at the local PC/BMS platform level.
Simply, rather than insisting a customer continue to buy new equipment and controls, and regardless of corporate revenue and earnings targets, let’s start making what we have more efficient through real-time analytics and constant commissioning, lower the costs of running an energy plant and reduce the monthly operational energy costs for our customers. We owe it to them; that’s what they were told the goal was when they bought their plant