Machine Learning Spurs Change at Southern California Edison
By: Mit Ide
Tags: Machine Learning, Utilities, Analytics, AI, Organizational Change

Machine Learning Spurs Change at Southern California Edison

The utility found that the whole organization has to adapt in order to maximize AI benefits

Moment Editorial/Getty Images

By Tom Davenport*

Analytics are typically viewed as an exercise in data, software and hardware. However, if the analytics are intended to influence decisions and actions, they are also an exercise in organizational change. Companies that don’t view them as such are likely not to get much value from their analytics projects.

One organization that is pursuing analytics-based organizational change is Southern California Edison (SCE). A key focus of their activity is safety predictive analytics — understanding and predicting high-risk work activities by the company’s field employees that might lead to a life threatening and/or life altering incident causing injury or death. Safety issues, as you might expect, are fraught with organizational peril-politics, lack of transparency, labor relations, and so forth. Even reporting a close call runs counter to typical organizational cultures. These organizational perils are a concern to SCE as well, but the company has created an approach to address them. SCE hasn’t completely mastered safety predictive analytics and the requisite organizational changes, but it’s making great progress.

A Structure for Analytical Change

Key to the success of the SCE approach is the structure of the analytical team that is addressing safety analytics. It is small, experienced, and integrated. Two of the key members of the team are Jeff Moore and Rosemary Perez, and they make a dynamic combination. Moore is a data scientist who works in the IT function; Perez works in Safety, Security, and Business Resiliency, and is a “Predictive Analytics Advisor.” In effect, Moore handles all the analytics and modeling activities on the project, and Perez, who has many years of experience in the field at SCE, leads the change management activities.

Steps to manage organizational change started at the beginning of the project and have persisted throughout it. One of the first objectives was to explain the model and variable insights to management. Outlining the range of possible outcomes allowed Perez and Moore to gain the support needed for a company-wide deployment. Since Perez had relationships and trust in the districts, she could introduce the project concept to field management and staff without the concern about “Why is Corporate here?” Perez noted that it’s important to be transparent when speaking with the teams.

That trust has resulted in the district staff’s willingness to listen and share their ideas on how best to deploy the model, to address missing variables and data, and to drive higher levels of adoption.

The team took all the time needed to get stakeholders engaged. Moore came into the project in the summer of 2018, and he was able to get a machine learning model up and running in a month or so, but presenting it, socializing it, and gaining buy-in for it took far longer. Moore and Perez met with executives of SCE in November and December of 2018. Within days of these meetings the safety model analytics project became a 2019 corporate goal for SCE. Safety was the company’s number one priority, and it was willing to try innovative ideas to move it forward. For such a small team to have their work made into a corporate goal is unusual at SCE and elsewhere.

The Risk Model

SCE now has an analytical risk-based framework, and risk scores for specific types of work activities and the context of the work. The model draws from a large data warehouse at SCE with work order data, structure characteristics, injury records, experience and training, and planning detail. All those factors were not previously linked, and there was — as is often the case with analytics — considerable data engineering necessary to pull together and relate the data.

The machine learning model scores activities that teams in the field perform, like setting a new pole or replacing an insulator. Each activity may be more or less dangerous depending on the time of year, day of the week, weather, crew size and composition, and so forth. Replacing a pole, for example, may be only a moderate risk task in itself, but when done on the side of a hill in the rain with a crane it becomes very high risk.

Instead of generic safety messages to employees, SCE can now get much more specific by describing the risk of particular activities they perform on the job in a particular context.

As the model learns it will recommend specific approaches to reduce the risk of a job, like altering the crew mix or crew size, requiring additional management presence, using specific equipment or rigging to perform the work, or creating a longer power outage in order to do the job more slowly. The latter recommendation runs counter to the culture of not inconveniencing customers, but if the model specifically recommends it, then the teams will discuss the contributing factors as well as their years of experience to mitigate the risk before executing the work.

The project has led to several more general findings, which are of greatest interest to SCE executives. For example, management has long been interested in using data to understand changing safety risk profiles of the field teams over time as a result of increasing/decreasing workloads or as weather patterns change. While the predictive model considers more than 200 variables, the findings from the model have been summarized into the top fifteen distinct drivers of serious injury and fatality. Some shifting of variables is expected over time, but there has been great interest in better understanding the initial set of risk factors.

Deploying the Model

Moore and Perez are in the early stages of deploying the model; they’ve rolled it out to six of 35 districts thus far. Each district has a unique personality, and they don’t want cookie-cutter answers on how to deploy in their district.

Moore, whose primary role was to create the model, said he has realized that safety analytics are not just about a model. “I started out thinking it was about an algorithm, but I realized many other factors were involved in improving safety.” Moore said that he gets some pressure to move on to analytics in other parts of the business, but “in order to see your models come to life you have to go through this kind of process.” And everyone at SCE believes the safety work is critical.

Perez, whose primary focus is change management, listed some of the organizational changes in deployment. “There might be training issues — not only on analytics, but also communication, leadership and ownership. There might be process concerns--how we plan and communicate work. There may be technology concerns in using the system.”

Perez also says the process of working with a district is critical. “You can’t just walk into a district and disrupt their work flow for no reason,” she says. “They want to know your purpose and your objective. We try to connect, show transparency, and build trust that we are here to help, that we are here to observe how they mitigate risk, to share our findings, and to see how the findings might be integrated into their work practices. We hope they will help us understand the complexity they face every day.”

Both team members say they learn something every time they visit a district. Moore notes, “You can only see the data you can see in the data warehouse — time sheets, work orders, etc. But when you talk to the people who do the work, you learn a lot about how the data is created and applied.

With each visit I understand the drivers better and the complexity of the work. I can also speak the language better with each district visit, and I understand the process and the equipment better as well.”

With the findings from the model, Moore and Perez are beginning to work with another partner at SCE — the HR organization. It is responsible for defining work practices, training needs, standard operating procedures, and job aids. Each of these is potentially influenced by findings about safety risks, so the goal is to incorporate analytical results into the practices and procedures.

The team is already working to modify the model to incorporate new factors-one of which, not surprisingly given the situation in California, involves the risk of wildfires. Moore and Perez are also trying to create more integration of the risk scores with the work order system. They also plan to try to incorporate the risk model into other SCE business functions like Engineering, which might be able to lower the risk in the planning and construction of the electric grid. All in all, using data and analytics to improve safety is a time-consuming and multifaceted process, but what could be more important than reducing injury and fatality among SCE employees and work crews?