Understand how to capture the benefits of digital transformation rapidly, efficiently, and sustainably to achieve results in reducing energy consumption and increasing efficiency in your company.
But if the term “computerization” is already so old, why are we now seeing this new wave of “digital transformation?” We can draw on the greatest recent technological advances: mobility, processing power, storage capacity, and the vast availability of data. However, digital transformation fundamentally requires a cultural transformation facilitated or consolidated by digital technologies.
Organizations that incur a significant portion of their costs in energy and utilities – practically and increasingly all of them – can draw on efforts in processes, culture, and technology to identify inefficiencies, eliminate losses, and reduce emissions of waste and greenhouse gases, increase process efficiency and the predictability of operations, team productivity, and the effectiveness of their energy and utility supply contracts – all with a direct or indirect effect on costs.
The important thing is to be able to quickly, efficiently, and sustainably capture all of these benefits. Speed is relative but can confer significant competitive advantage in any segment; the efficiency of transformation actions should in turn be unquestionable; and the very culture of transformation and increase in efficiency should be sustainable in the short, medium, and long term. And that is precisely where digital transformation, as defined above, makes its greatest contribution.
Speed can come from two sources: from technology itself, embodied in high-performance devices, platforms, and systems; and organizational agility that is manifested in culture, people, and processes, inspired by the precepts of the Agile Manifesto.
At the intersection we can imagine, for example a software system for energy and utilities management that would be able to identify situations of abnormality in a given process – for example, consumption of an energy input significantly higher than expected for a given level and context of production – and the notification of the persons responsible so that they can identify the possible causes and take corrective actions. Even better, alerts like the example above could be generated based just on the trend toward abnormality so that the problem in question can be prevented.
In both cases the speed of response – whether for correction or prevention – confers direct gains for the organization and those responsible for the process.
Effectiveness can be supported by the great capacity that (good) technology solutions have for organizing data, facilitating analysis, and supporting more proactive decisions. In addition, it is up to the human to choose the right problems to be solved and the most appropriate tools to guarantee the effectiveness of the solution.
Perhaps the most frequent example in our experience is the development of models based on Machine Learning for the analysis and prediction of the energy performance of assets and processes.
Good energy efficiency prediction models should be built starting from good data sets (“good” in terms of quantity, quality, and diversity).
Prediction models built from imprecise or incomplete data are less likely to be effective. In the same way, good models are less likely to be effective if the initial data are of low quality.
In our context, sustainability goes beyond environment We are also talking about the capacity of an organization to grant sustainability and continuity, in the long term, to the transformation efforts and the consequent capture of value.
Such sustainability should be robust enough to tolerate uncertainties inherent in any initiative that contains some degree of innovation, whether in technology, its suitability to the challenge in question, or its effectivness.
Starting from the capture of energy consumption data and process variables that can explain the context in which a given asset operates, economic opportunities are expected to be able to be identified. The magnitude of these gains will depend on a variety of actors: the sophistication and complexity of the process, the level of instrumentation and control, and the maturity of the team responsible for its operation.
Thus it is common that different initiatives have different returns – including negative. A strategy that we often follow is to attempt to capture the “low-hanging fruit,” those problems that have low risk and high impact – for example, the consumption of useless energy in batch process intervals; the waste of water or process gases when the consumption analysis is made by assessment between areas of consumption and not by individual measurements; or even stratification of specific energy consumption of a certain process in terms of its context variables (mix of products, quality of raw material and energy inputs, operator training).
But as the energy management maturity of an organization increases, the good opportunities will be found in higher branches of our metaphorical tree. And reaching them can require exploration of opportunities where the chances of realizing their potential are not so clear – for example, identifying “golden batches” (those batches where everything worked as expected) starting from a large volume of historical data about a given production process, explaining the model, and reproducing its results; analyzing all of the decision parameters and variables (technical, energy, and financial) of a given process and optimizing their combined performance; and even exploring a large number of energy planning scenarios of an industrial operation and its exposure to the financial risks inherent in contracting in the free energy market.
A good digital transformation portfolio should balance initiatives with different levels of risk and potential for return. And sustaining the transformation movement even when there is risk of failure is fundamental – of course iterating the whole process of conception, experimentation, and learning so as to increase chances of creating positive impact in the short and long term.
When an organization is able to focus its digital transformation efforts with agility, efficiency, and sustainability, the results in reducing energy consumption and increasing efficiency will certainly be achieved. And with the support of a good platform for energy and utilities management, the scope and the chances of success of these initiatives will be maximized.