Enter the challenge: how can we stop those CEOs from worrying? What are the tools and resources Information Technology can offer them in order to support energy-efficient as well as operationally-effective decisions?
Since the 90s Information technology has been trying to develop tools and methodologies to assess and improve energy performance for buildings and facilities. This gave birth to the field of Energy Informatics. Energy consumption meters were installed on buildings to monitor energy consumption accompanied by the necessary software. Managers had been offered the tools to monitor energy consumption and adopt their operational and strategic decisions accordingly. The keyword here is “monitor”. Monitoring alone however is not enough. What decision makers really need is “insights”. Monitoring on the one hand is based on numbers and process management: these form the foundation of management science. Insights on the other hand are based on data and produce knowledge: this implication captures the essence of what is known as data science.
Data science is the new buzzword floating around many domains and markets during the last couple of years. Numerous definitions and applications have been proposed from various sources. But what are its particular applications in the energy sector? What is the added value it offers to Managers and their organizations as a whole? First of of all it enables them to “travel in time”. Through predictive data analytics managers can get an estimation of the expected future energy consumption for a certain period of time. This can be a priceless tool in every manager’s toolbox as it offers a present knowledge of future costs. It enables them to experiment with what-if scenarios and sensitivity analysis reports, providing an opportunity to research various courses of actions along with the consequences, in a virtual setting without putting actual resources at stake.
They can perform association analysis to examine what specific variables and processes affect energy consumption the most and align their decisions accordingly. For instance, certain data analytics tasks may detect a strong correlation between a building’s energy requirements and the external weather conditions. This could trigger a sequence of intervention actions from the decision maker’s side, aiming to reduce the building’s exposure to sunlight. In an industrial case where operations are performed in a shift or group of production lines there may be a specific shift or production line that strongly affects the total energy consumption. In that case management should focus its efforts on reducing this “out-lier”‘s energy requirements. In extension, this may result to a wider effort of business process re-engineering leading to a potentially different and more efficient operations management approach.
These affect the organization or company itself. But this is only a narrow view. What about the organization’s external environment and its competition in particular? Comparative approaches lie at the heart of an effective competitive strategy. Energy analytics platforms can provide direct comparison with competitors in the field of energy efficiency. Especially in markets where energy is a primary production resource, energy efficiency could be a cornerstone of a successful competitive strategy. Managers can be immediately alerted for potential energy consumption peaks that are higher than the competition’s average. In that respect, they are given the opportunity to adjust their operational and strategic decisions in order to adjust their positioning against the competition.
That is only the surface of what data science and analytics can equip decision makers with. Predictive analytics, association analysis between operational Key Performance Indicators and energy consumption are only a few potential applications, but there are many more to come and stay. Stay tuned for upcoming posts to see how Intelen’s products help managers make better and greener decisions.