When we talk about Industry 4.0, the first thing to know is that it is one of the most important changes of this era: the "fourth industrial revolution". The first, in the 18th century, was that of steam engines. The second, in the 19th century, was represented by the assembly line. The third, in the 20th century, was fuelled by the development of electronics and information technology. And here we are today: the fourth revolution, anticipated by the Hannover Fair 2011 and based on four pillars. The first is the massive use of data. The second is analytics, artificial intelligence and machine learning which put to use the enormous amount of data we are accumulating. The third is the interaction between man and machine; touch interfaces that allow us to communicate with the new robotics systems. The fourth is represented precisely by robotics, digital manufacturing, 3D printing.
Every industrial revolution started from a different and more advanced use of energy and from this point of view Enel Energia it is also the protagonist of this change. In industry 4.0 , all performance must be optimised in line with the numbers and companies need an energy partner that can provide the ideal solution in terms of costs and consumption. Nothing can be left to chance. Our approach, in dialogue with the world of business and companies, is data driven, guided by the numbers and their analysis and understanding. The data communicates decisive information, we make it talk and we put it at the service of companies.
The numbers we collect on energy performance allow us to have an agnostic approach free from preconceptions about the profiles, needs and behaviours of our customers, with the ultimate goal of providing a service that is increasingly suited to the times and the complex challenges facing companies. This primary source of information on energy consumption is the basis of our knowledge, but the tool that allows us to make it «talk» and turn it into digital energy is artificial intelligence and the machine learning. The models we build to assess the status of the customer, the effectiveness of satisfying them and the impacts on economic performance are interpretable, simple and above all actionable: in this way they can be transformed both into instruments for making decisions and into general business strategies. The big data and the machine learning in short allow us to move with the customer from the reporting (the monitoring of indicators) to predictability: anticipating the energy needs of the companies and acting accordingly.
One of the main expressions of this change within Enel Energia is the project of churn prevention. In marketing, «churn» means leaving, the moment in which a customer terminates the relationship with the company. Socio-demographic data on consumption, billing and credit, as well as reasons for contact and complaint, help us to understand the processes that lead to the customer leaving, to prevent its causes and to continue the relationship together with mutual satisfaction. Information is always processed and managed with respect for privacy as a guiding compass: this work of understanding serves exclusively to bring optimal service to customers. In the relationship with the customers, in fact, as in every relationship, to help each other you have to understand each other and to understand each other you need to know each other. With the project churn prevention we are able to collect, analyse, clean, collect and optimise all this data on a single and transversal data lake to the whole customer journey, on which we build predictive and prescriptive models, which then help us to anticipate all the failure points that the customer may experience and to intervene accordingly with targeted actions, prescribing what we consider to be most effective for the individual customer. All these actions always start from the same point: knowledge. Extracting “objective” knowledge means knowing how to link together and interpret the data generated in the individual processes and vice versa, also knowing how to map the customer's life phases, generating, in each significant step, the most useful data to describe these business phenomena, allowing us to then work in an iterative process and on an incremental basis on the improvement of the company-customer relationship.
According to the McKinsey report, Notes from the frontier: Modeling the impact of AI on the world economy, today we are still in the "slow-burning" phase, but this type of data-based approaches machine learning will lead to the creation of 13 thousand billion dollars of global value and an additional growth of 1.2% of world GDP within 2030. The companies that will have invested in this direction will be able to benefit from a 6% growth, those left behind will experience a decline of up to 20%: in short, right now working together on Artificial Intelligence allows Enel Energia our customers to be advantageously positioned when it comes to the momentous changes we will soon be facing. And this also applies to the labour market: according to research The Future of Jobs 2018 World Economic Forum, the professional balance of this industrial revolution will be positive: the new professions will grow by 27% by 2022, industrial employment will increase by 58 million units over the next three years. The most sought after profiles? Data scientist and Business translator, the same people who, together with our business experts, are driving change even within Enel Energia.
