Crew Management Application

Industry

Electric Utilities, Enterprise

Client

One of the nation’s largest electric utilities companies in the USA.
Total revenue: $3,964 billion

Context

The client’s organization was in the process of implementing organizational changes, prioritizing safety above all else. The executive team was on the lookout for a solution capable of anticipating and flagging high-risk activities among field workers that could result in severe injuries or fatalities.

Product

Proof of Concept for a Safety-Driven Crew Management Application - a digital tool featuring an integrated safety predictive analytics machine learning model. The new tool integrates real-time data accessibility and clear risk presentation, incorporating decision-support features to empower field supervisors to make more informed decisions.

Year

2020-2021

Disclaimer

This case study respects the non-disclosure agreement, excluding visual design features and certain project specifics. It focuses on my contributions, design activities, and the impact made during the Discovery phase, leaving out data and technology aspects to maintain the confidentiality of the work.

The Value of Journey Mapping

The importance of user-centricity demands a broad-based view of where design can make a difference. McKinsey & Company researchers studied the design practices of 300 publicly listed companies across various countries and industries over five years. They gathered over two million financial data points and tracked over 100,000 design actions. Among the four clusters of design actions analyzed, breaking down barriers between physical, digital, and service design showed the most correlation with improved financial performance. In practice, this often means mapping a user journey.

The following table from the N/N Group details the impact and success rating of six key areas of journey mapping's impact on the product development process. It shows that journey mapping is crucial for establishing a shared understanding and alignment among the team and educating them about the users' pain points and needs.

The Role of Design in ML-Powered Products

Design plays a crucial role in seamlessly integrating AI and Machine Learning into product design, unlocking significant value for users and enterprises. By incorporating intuitive user interfaces and interactive features, we can enhance user experiences with ML-powered products, making them more user-centric and efficient.

For enterprises, thoughtful design solutions of well-designed AI/ML integrations mean continuously optimized products based on user feedback and data, leading to adoption rates, customer satisfaction, and market competitiveness. The symbiotic relationship between AI and design drives innovation, adding value for the business by creating products that are not only functional but also highly engaging and profitable.

The primary objective of the client's organization was to make significant progress toward eliminating serious injuries and fatalities. One purpose of the Safety Predictive Model (SPM) developed by the client's Data and analytics team was to pinpoint risks in work orders for field personnel, aiding in hazard mitigation and injury prevention.

My Contribution:

ML Model Integration Risk Assessment
I collaborated with the product owner and data team on the risk assessment of integrating a Safety Predictive Model (SPM) into the Digital Daily Morning Shuffle workflow, which was critical for business and data team leaders. I conducted a comprehensive analysis of the model's key components to align it with the organization's objectives and the specific operational hurdles faced by the management in that district.

SPM Impact Analysis
I created an SPM Impact comparative analysis of the current and future states of the working process with and without the incorporation of SPM. This visual presentation helped the project owner effectively communicate with key decision-makers, enhancing their understanding of complex ideas.

Integrating ML into product design
I developed a design proposal for integrating a Risk Score Calculator featuring an integrated safety predictive analytics machine learning model, into the digital experience. My strategy for seamlessly integrating the Risk Calculator into the digital user interface, encompassing visual and functional solutions, received positive feedback from the teams during interactive prototyping and user-testing sessions.

Key
Learnings

Challenges
Due to time constraints and a siloed environment within the clients' walls, I encountered initial pushback to user research and limited access to field workers and crew leaders. I insisted on conducting user interviews, which provided valuable insights for the executive team to identify knowledge gaps regarding employee challenges.

Resolution
Working with the main users helped me strategize the digitization of a risk-based framework with a user-centric approach. By prioritizing user needs, I designed a solution that empowers field supervisors, enabling them to make more informed decisions and ultimately reducing the risk of injuries and fatalities among the crew.

Impact
It is crucial to incorporate user research and gather user data during experimentation to facilitate innovative product ideation and achieve overall success effectively.

By emphasizing the importance of user research, I ensured that business acquired valuable insights into the challenges faced by their employees at an early stage. This understanding allowed businesses to allocate their investments towards solutions that fulfilled their objectives and aligned with employees' needs.