Operations analytics is the idea of implementing digital and automatic processes which help collect relevant data which can then be used to:
Analyze the current situation
Design improved and more efficient future work processes
Manage existing resources through systematic measurement
And as a tool to ease daily and future decision-making
In order to decide how to adopt and shape the approach of implementing operational analytics, infrastructure corporations must begin by examining three key questions:
What important capabilities in infrastructure operation and maintenance do we want to develop?
What human and technological skills and resources do we have to support the process?
What practical decisions do we need to make - such as software requirements, number of processes and users involved, legal requirements and information security - before launch?
Then, beyond these questions, there are five key steps to take in order to produce a more accurate and realistic Operations Analytics plan:
1. Defining a value hypothesis
A value hypothesis is an approach in which we specify the value we want to verify or refute in a field known to us through operational analytics. For example, raising the value of "frequency of inspections of piping" will lead to a decrease in the "water depreciation" index. A value hypothesis will help us to pinpoint and demonstrate a result in a particular area familiar to us from the day-to-day operations of operations.
2. Technological Tool Selection
Identifying a suite of technologies that can help an organization understand and manage operations in a holistic way, based on its unique requirements. The goal is to implement a set of technologies that will help gather, analyze and generate insights from different sources using different technological systems.
For example, the Customer Relations Management (CRM) system, combined with a fault center and hazards, will be able to store data related to customers who consume our services, such as: customer details, location of the hazard, hazard intensity, hazard scheduling and hazard treatment satisfaction.
The CRM system, combined with the Geographic Information System -(GIS), will be able to visually display the location of the hazard and map the immediate and final hazard areas.
An example of a technological tool that can help with data analysis would be the Python programming language that will help process the data using built-in scripts (codes) and these will be transferred to the selected Business Intelligence (BI) tool.
3. Assimilation of the technological tools in a comprehensive procedural manner
The purpose of procedural insights is to present the complete picture and not necessarily to present an analysis of a specific case. The effectiveness of operational analytics is to examine the whole process, from beginning to end, in order to understand the whole picture.
For example, if we define a measurement of the output of pumping facilities and the intensity of activity in it, then through the definition of the maintenance processes of these facilities in the system and the amount and nature of faults, we can deeply understand the factors affecting increased equipment wear at the different stations.
4. Systematic data storage
Storing information from different information and operational systems is one of the main means of systematic data collection.
This can include basic data such as date, time and location of a fault event, the severity and nature of the damage or the consumption data flow frequency from different service and operation centers. Data can also include sensors and measuring instruments deployed throughout the infrastructure network.
The database also includes information such as data and manufacturer's instructions about the machine suppliers operating in the network and their maintenance protocols.
As the databases fill up, you can build queries and algorithms that are able to automatically analyze the data and provide insights - estimates of the time required to perform network maintenance, identifying patterns such as expected occurrence events according to consumption characteristics or environmental factors, expected investment in time and more.
5. Recruitment and training of skilled technological staff
Technological tools alone are not sufficient, since in order for the organization to be able to operate the technological systems and derive value from them, a skilled technological team from the field of information science (Data Science) is needed.
Data analysts, data engineers and computer engineers should be an integral part of the management and development of Operations Analytics, as they are required to perform the tasks at the programming and interface level with the systems.
6. Using process insights to increase (but not replace) expertise in the subject
There is a vast amount of information in systems and operational data. The approach and technologies of process insights are not a substitute for the knowledge and intuition of employees and experts. The purpose of the findings is to serve the professionals as supportive information in decision making as well as to confirm (or refute) hypotheses.
In conclusion, in order to successfully implement operational analytics processes and upgrade the quality of operations in the company, it is necessary to implement the six detailed steps with an emphasis on creating a high quality human-technological system which can effectively be assimilated into the corporate culture.
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