The past few years have been transformative for the American labor market, and the utility industry is no exception. Veteran storm bosses and decision-makers are retiring en masse, taking decades of valuable experience in storm response with them. Their younger replacements find themselves in a highly competitive and mobile job market, making it unlikely that they will remain in any one position for very long. The resulting vacuum of institutional knowledge around these crucial, experience-driven roles is putting additional strain on utility companies, who are already under immense pressure to effectively respond to storms and restore power rapidly.
StormImpact’s machine learning models help utility decision-makers understand the location and magnitude of outages and damage days in advance, allowing utilities to adequately prepare for storms and optimize restoration timelines. A native feature of machine learning models is that they are “trained” on years’ worth of storm events, meaning that they know the outcome of the event and understand the storm parameters that caused the greatest impact. Therefore, our models capture the experience from all of the storm events within the historical record, giving newer utility decision-makers insight that may have taken their predecessors years to develop, and giving more experienced decision-makers tools for detailed analysis of the expected event and another layer of data to act on.
In this blog post, we will delve into why standardizing decision-making processes through machine learning models is crucial for consistently addressing storm-related challenges, ensuring reliable power supply, and addressing the pressing issue of retaining long-term employees with valuable experience in storm response, ultimately contributing to the success of electrical utility companies.
Machine learning has revolutionized how electrical utility companies make critical decisions regarding storm preparedness and power outage response. By analyzing extensive datasets, machine learning models can offer valuable insights to optimize resource allocation, improve response times, and enhance the overall reliability of power distribution. However, the effectiveness of these models hinges on the quality and consistency of the data used for training and deployment.
Many businesses have recently struggled to retain talented employees with valuable experience, and the utility industry is no exception. With an aging workforce, many utility companies face the impending loss of employees who have accumulated years of experience in effectively managing storm-related challenges. The departure of such experienced personnel can leave utility companies in a precarious position, as their knowledge and expertise are difficult to replace. Their young replacements are mobile and in-demand, making it difficult for utilities to retain them even well before they reach retirement age. Standardizing decision-making practices through machine learning modelling is therefore becoming a key industry practice in the 21st century.
Standardizing decision-making practices through machine learning models is of paramount importance in the context of electrical utility companies for the following reasons:
1. Consistency in Storm Preparedness and Outage Response: Standardized data and models yield consistent and reliable results, allowing decision-makers to have confidence in the insights and recommendations provided. This consistency leads to better-informed decisions when it matters most and helps bridge the gap left by departing experienced employees.
2. Operational Efficiency: Standardized practices streamline the development and deployment of machine learning models for storm preparedness and outage response. This reduces the time and resources required to implement data-driven solutions, enabling utility companies to respond to storm-related challenges more swiftly and effectively, even in the absence of experienced personnel.
3. Enhanced Collaboration: Standardization promotes collaboration among various teams and departments by ensuring that data and model-related practices are uniform across the organization. This collaborative approach enables the transfer of knowledge and expertise from experienced employees to newer team members, mitigating the impact of workforce turnover.
4. Employee Satisfaction and Retention: Demonstrating a commitment to consistency and accuracy in decision-making during storm events enhances employee confidence and job satisfaction. This, in turn, contributes to higher employee retention rates, preserving the invaluable experience within the organization.
5. Compliance and Risk Mitigation: In the regulated environment of electrical utilities, standardized practices are essential for meeting compliance requirements and managing the risks associated with data handling and decision-making processes. Consistency in practices helps ensure compliance even when experienced employees retire or change roles.
To standardize decision-making practices in machine learning models, electrical utility companies should consider the following steps:
1. Data Governance: Establish clear data governance policies and procedures to ensure data quality, security, and accessibility, with a specific focus on storm-related data such as historical outage records and weather data. Such data initiatives have historically provided utilities with transformative insights into their organization.
2. Model Development Standards: Define best practices for developing machine learning models tailored for storm preparedness and outage response. This includes data preprocessing, feature engineering, model selection, and performance evaluation metrics specific to storm-related challenges.
3. Cross-Functional Training: Provide comprehensive training and resources to ensure that employees from different departments are well-versed in the standardized practices related to machine learning for storm management. This training can include knowledge transfer from experienced employees.
4. Regular Auditing: Conduct routine audits to assess adherence to standardized practices and identify areas for improvement, especially in the context of storm preparedness and response. Audits can also help identify knowledge gaps that need to be addressed.
5. Feedback Mechanism: Establish a feedback loop where employees can contribute insights and suggest improvements to the standardized practices, fostering a culture of continuous improvement in storm management and knowledge sharing.
In an era where reliable power supply is essential, standardizing decision-making practices in machine learning models is not just a strategic choice but a necessary institutional practice for electrical utility companies looking. Consistency in storm preparedness and outage response not only leads to improved reliability but also addresses the pressing issue of retaining long-term employees with valuable experience. By investing in standardized practices and knowledge transfer, electrical utility companies can establish a culture of excellence, empower their workforce to excel in effectively managing storm-related challenges, and preserve the invaluable experience within the organization, ensuring uninterrupted power supply to customers.
Copyright © 2023 StormImpact. All rights reserved.
