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In Pursuit of Predictability

image credit: ©2019 Avineon, Inc. All rights reserved.
Anil Jayavarapu's picture
Director, Software Solutions Avineon, Inc

- Operational experience leading teams through the development, deployment, and support of web, mobile, and desktop applications for enterprise customers- Consulting experience assisting...

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  • Jan 25, 2019 7:12 pm GMT
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This item is part of the Special Issue - 2019-01 - Predictions & Trends, click here for more

“Customers understand service delays and disruptions but won’t tolerate unpredictability,” is something I learned years ago from a senior executive with tenure at a large utility company. It is not so much about a service outage, delayed construction project, or equipment failure. The concern is about the consistency and accuracy with which service commitments can be made and delivered. It is also about the innate capabilities to design precisely and forecast variances, and the ability to render preventive actions based on equipment condition and prediction of failure.

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All of these demand predictability, achieving which requires meticulous collection, integration, analysis, visualization, and sharing of enterprise (systems, networks, work, finances, personnel, processes, regulatory requirements) data and events. The choice is between instituting systems and methods that enable this predictability to occur naturally within the ethos of the organization or hoping for random victories that happen at the whim of individuals.

At most utility companies, the digital footprint is a collection of Information Technology (IT) and Operational Technology (OT) systems. Assets such as vaults, voltage regulators, switches, transformers, fuses, cables, and their configuration within a circuit are represented digitally with varying degrees of detail in separate digital systems used for establishing standards, engineering design, purchasing, estimation, construction, operations, maintenance, and cost accounting. In most cases, the data are collected with varying levels of detail, accuracy, and frequency depending on the functional needs of each enterprise system, and stays at rest.

Investments into newer technologies such as Automated Distribution Management System (ADMS), Distributed Energy Resource Management System (DERMS), Robotics Process Automation (RPA), drones, and blockchain, can create more data islands in an enterprise’s digital landscape. The flow of data from smart meters, smart grid, microgrids, smart cars, smart homes, and smart cities will further increase this digital footprint.

The energy industry is unforgiving. Changes such as grid modernization, distributed energy resources, regulations, mergers, policy changes, and raising customer expectations make business and operational predictability very challenging. All the while, the bars for delivering safety, reliability, and cost reduction continue to rise.

A well-architected digital model that can continuously integrate the digital footprint at a consistent level of detail will empower employees to predict and act, rather than react at every turn. Therefore, for every investment made into IT and OT projects, transformative or otherwise, the imperative for leadership is to define and align with a high fidelity digital model. Such a foundation enables the business to connect data islands and identify patterns through analytics for predictability.

On the other hand, any further divergence towards disintegrated systems lowers an enterprise’s potential for business and operational predictability. Every disintegrated step takes the enterprise further away from realizing a digital model optimizing for the demands of the ever-changing energy industry landscape.

Unpredictability may be intolerable today, but the absence of a strategy towards high fidelity digital model may risk business failure.

Anil Jayavarapu's picture
Thank Anil for the Post!
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