In higher ed we often find that the pace of decision making can be snail-like. While not always a bad thing, it is symptomatic of what the Higher Education culture embraces—making sure all the right data is in place before making the final decision. Thus, efficiency in decision making can become challenging because the institutional environment requires collaboration and every mind requires a different level of data satisfaction, due in large part to individual perspective. For example, facility engineers may need information and data that is much different than a financial business officer before moving forward with a potential project or even allow a change. Making sure everyone’s data needs and perspectives are satisfied is the critical tipping point to moving forward with a decision in higher education.
Compounding this difficulty is the multiplicity of performance measures relevant to higher ed. In private business decisions are measured nearly exclusively by their impact on profit, whereas in higher ed enabling the academic mission of the institution—research, teaching, operationally, financially, and socially—can all be the primary focus. With the many varying stakeholders, understanding what is important to change or influence in a particular decision BEFORE a decision is made is critical.
A Tool: Key performance indicators
Pulling different perspectives together by defining a set of Key Performance Indicators (KPI’s) that formalize what is important to the institution can catalyze the decision process. For some, the impact of a decision might be measured by the increase in tuition, or in financial aid, or increased use of the institution’s debt. A decision that deals with a proposed energy conservation effort might include these KPI’s but also descriptions of the required capital investment and resulting environmental impacts, building energy performance, and utility production capacities. Frequently in higher ed we have lots of options, but because we are slow to ‘sort and sift’ through them we continue to miss opportunities. Developing KPI’s – and the specific methodology for how they will be calculated – can quicken the pace of decisions.
Applying KPIs to Energy Management
The long term energy management needs of an institution (supply and demand management) are as critical as managing the ‘health care costs’ of higher ed. Without careful management of your institution’s energy portfolio, you will find yourself in the same place as many have found themselves in with steadily rising healthcare costs and soon you wonder how you got there.
Michigan State University was searching for a tool that could help change the decision culture, help more quickly identify and filter the best solutions, provide stakeholders the quick ability to see the trade-offs of each decision (if implemented) and measure the impact against a set of institutionally agreed upon KPI’s. MSU found and implemented an integrated energy and planning model, with the help of Confluenc, Inc., that has impacted university decision-making processes relating to resource allocation and strategic planning. Confluenc provides institutional energy planning distinguished by processes and analytical tools that facilitate complex decision-making.
The unique decision environment leverages a robust resource model of the campus energy supplies and demands to identify ‘missed opportunities and efficiencies’ to be gained in the context of KPI’s. Energy demand was addressed through several model components including a “building profile module” that enables the institution to scan all of its buildings to compare the impact of completing various energy conservation measure portfolios across a set of buildings or within buildings. Why is this important? At MSU the question was: if you invested $36 million into energy efficiency would you be better off investing a set of energy conservation measures across a set of buildings or better off completing a building for retro or re-commissioning? Don’t misunderstand, MSU had the ability to get the answer but to do so would take weeks to get the answer, now MSU can get the answer to this question and many variants in a matter of minutes.
Questions regarding the most effective deployment of capital dollars are also regularly asked relative to energy supply. Supply components of the model include renewable energy projects, fuel switching opportunities in the central utility plant, installation of new generation assets, and emerging energy technologies. MSU’s model allows the development of ‘what if’ scenarios using a web-based interface, the supply and demand modules, and data that already exist within the institution. A scenario comparison tool is used to compare these scenarios to various reference cases, including a business-as-usual (BAU) condition. The ability to begin this modeling with existing data sets – and augmenting only where the value of additional information is clear - distinguishes this methodology from any others used in the past.
The intangible benefits of the technology for MSU emerged during meetings with decision makers and constituents by showcasing and comparing multiple opportunities/scenarios against each other. Rapid response and visualization of ideas and proposals are easily viewed in a dashboard, allowing for in-depth discussion by individuals with different perspectives (administrators or students or facilities or academics). When multiple performance indicators are viewed simultaneously by a diverse group of stakeholders the tradeoffs associated with different scenarios become clear and understood and group insight is established.
The efficiency we have gained in decision making is phenomenal plus the ability to identify energy efficiencies more quickly and even more importantly those which provide the biggest bang for our buck is revealing and not always as intuitive as you might think.
Users can develop, and then compare multiple scenarios against multiple criteria:
- Fiscal criteria: debt capacity, capital expenditures (annual and cumulative), utility costs, and tuition impact
- Operational criteria: building efficiency, plant efficiency, and power capacity
- Environmental criteria: greenhouse gas emissions, renewable energy utilized, water conservation, or other institutional environmental goals
Institutions will likely have unique key performance indicators they want to measure, which can be easily incorporated into the platform and decision environment.
“The scenario analysis and visualization platforms used at MSU were purposefully built to be extremely flexible,” says Mike Walters, Confluenc principal. “As such, they can be readily used in any campus, college, or university.”
This is one of the most subtle educational and consensus building tools out there. It has changed how we make decisions. It has changed who is at the table when we make decisions and decisions are much more inclusive than ever. It has changed our decision environment and the pace of our decision making which is very hard to do in higher education.
Kathy Lindhal is vice president emeritus at Michigan State University.