In the infrastructure industry, operations are at the core of the business.
Once the infrastructure itself has been established, it immediately begins being used, which brings with it regular maintenance and repair of faults. These ensure that the infrastructure fulfills its purpose and satisfies the needs of the residents in a continuous and satisfactory manner.
As such, this is where most of the corporations' resources are invested - be it allocating budgets, directing the corporation's quality manpower to handle the operational tasks, and being in contact with contractors to perform these tasks.
However, despite the centrality and importance of the field of operations, the method of handling it remains in many cases outdated and traditional, without the use of innovative technologies and approaches created to support such complex processes.
Studies and in-depth surveys by leading companies such as McKinsey and Deloitte point to how companies in areas defined as traditional industries, including organizations and infrastructure corporations, can benefit from the assimilation of data collection and analysis technologies in industry management.
Operations analytics implements computing and automation processes that can, through systematic measurement and data collection, assist in analyzing the existing situation, designing improved and more efficient future work processes, and managing existing resources.
So what return will the organization receive in the short and long term as a result of implementing these processes?
In the short term, operational analytics will help make the best decisions in real time in the event of a breakdown in the maintenance chain, and can even assist with implementing agreements, contracts and managing accounting with contractors and suppliers.
Moreover, its application brings with it consistent and systematic data storage alongside reliance on regulated protocols for information analysis.
This reduces dependence on certain expert employees who are in key positions and will reduce the loss of organizational memory and harm to the corporation in the event that such an employee leaves.
Beyond these immediate benefits that allow for inefficiency treatment, bottleneck troubleshooting, optimal fault handling, identification of areas of activity that have been neglected over time and formulation of best practices - the operations analytics approach supports longer-term strategic solutions.
Predict expenses and thus optimize the planning of future tasks.
As part of a more long-term vision, infrastructure corporations, through operational analytics, will be able to gain a more accurate perspective on strategically valuable work such as long-term cost savings, future engagements, improved auctions and employment contracts, and improving the accuracy determining the exact characteristics of each type of position in the organization.
This reveals the complexity of processes and therefore has an impact on decisions about the nature of outsourcing, automation or redefinition of processes or any activities. The consequent insights into these processes can then help define accurate performance metrics for workflows, which can help organization managers make better decisions about how to drive and manage their teams, such as defining the level of education and skill of the human capital required to perform the variety of tasks.
Moreover, the ability to imagine and predict how work will change over time can help companies assess the impact of process improvement and automation efforts.
The very process of analyzing the data and gaining insights will contribute to building a more technological workforce among the employees involved and eventually change the “low-tech” paradigm, as today most corporate employees are end users of systems and not system developers.
Operations workers will be able to move away from manual, repetitive tasks with no added value and perform more productive work, such as identifying ways to improve their day-to-day work or upgrading the overall process.
The construction of the Operations Analytics system is divided into several stages, which we will expand on in the next article.
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