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Operational intelligence and small data

  • Operational intelligence and small data

About the blog

Miguel Ángel Rodríguez Núñez
Operational Intelligence and SCI Technician at Emasesa. Master in Engineering and Maintenance Management. Master in Industrial Maintenance and Diagnostic Techniques.
Schneider Electric
Idrica

Themes

Developing a big data system from which to extract knowledge to improve our decision-making can be a real nightmare. If we do not know how to manage it properly, the much-vaunted digital transformation can lead us to design massive systems fed by huge amounts of data from multiple sources that require a large amount of resources, not only economic, to offer results of very disparate efficiency. By applying something as effective as lean thinking, we can avoid this extreme and focus on getting value from our processes.

As managers of the operations of full water cycle facilities, our objective must be aimed firstly at optimising decision making and finally at operational excellence. Moving towards this excellence entails not only designing an appropriate strategy, but also drawing up coherent operational plans that make it a reality and that at the same time are aligned with the company's general strategy. In this framework, the development of Operational Intelligence (OI) offers us an opportunity to optimise our operations and efficiently manage the useful life of our assets.

We can define OI as a system focused on continuous, real-time monitoring of the company's processes and operations, and on assisting in the optimisation of these ongoing activities and processes, which allows us to identify and detect undesired situations that could correspond to interruptions, failures or bottlenecks in daily operations. Through this systematic approach we will identify and detect situations categorised as inefficiencies, opportunities and threats. OI is an approach to data analysis that allows decisions and actions in operations to be based on real-time data as it is generated or collected by monitoring systems. For an optimal implementation of this type of operation, we must first define an Operational Intelligence model that adapts to the characteristics of our operations departments, in this way the adaptation phase will be less traumatic and the results will come more quickly.

The OI model must be supported by a coherent and robust infrastructure for the analysis of data from the operation of our assets. In this way, a SCADA system oriented towards obtaining defined metrics and KPIs of operation and processes in real time, a specialised environment for machine learning analysis of operational data and a coherent ICS automation and cybersecurity model are configured as the necessary elements that will give our model a stony consistency. Defining, developing and implementing this environment can be a goal of the ancient heroes of classical mythology given the current structure of our companies. It is therefore advisable to start with a partial implementation, as a germ for its subsequent global extension to the entire operating environment.

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