Budget Forecast and Machine Learning Predictions

Understanding the problem

Following in-depth discussions with our clients, it became evident that their existing method of forecasting future expenses and free cash flow relied on an unstructured Excel document. This approach proved to be inefficient, as the document was disorganized, non-shareable, and prone to issues such as being quickly outdated or containing inaccuracies in the underlying formulas. Recognizing the need for a more robust solution, we collaboratively devised a two-phase approach to address these challenges.

The first phase involved the development and implementation of a sophisticated budget planning tool designed to serve as the definitive source of truth for expense forecasting and cash flow analysis. This tool aimed to streamline the budgeting process, eliminating the pitfalls associated with the Excel document, and providing a centralized and easily accessible platform for collaborative budget planning.

In the second phase, our focus shifted towards creating a digital visualization tool to depict the wear and tear patterns of a rail head over time. This visualization aimed to offer a comprehensive understanding of the rail's condition, empowering our clients to proactively plan maintenance activities. The overarching objective of this dual-phase approach was clear: to reduce maintenance costs significantly and extend the overall lifespan of the rail infrastructure.

By combining a reliable budget planning tool with a digital visualization solution, we sought to enhance our clients' decision-making processes, foster collaboration within their organization, and ultimately contribute to the overarching goal of achieving cost-efficiency and prolonged rail life. This collaborative and strategic approach exemplifies our commitment to delivering comprehensive solutions that address the specific needs and challenges faced by our clients.

User Research and Exploration

In my capacity as a Designer/Developer and Project Manager, I established close collaboration with the Maintenance Manager and a Customer Success Manager to deliver a comprehensive solution. Commencing with a thorough customer interview, I utilized journey mapping to gain insights into the existing workflow within the Excel document. Employing card sorting techniques, I organized inputs efficiently in the UI. Through an iterative process, I presented low-fidelity wireframes to test user hierarchy and flow, ensuring a seamless experience for both first-time users and those comparing different calculations.

Design Decisions

Choosing the right data visualization

One of the pivotal design decisions involved selecting a bar chart as the optimal data visualization method. This decision, informed by insights gained during the comparison phase, aimed to facilitate side-by-side value comparisons efficiently. The project's success was underscored by not only meeting the Maintenance Manager's requirements but also contributing to improved efficiency and user satisfaction. This multifaceted role allowed me to blend design expertise with hands-on project management, resulting in a solution that aligned precisely with the project's goals.

We choose a bar chart over a pie chart for several reasons. Bar charts excel at facilitating comparisons between different categories or groups, as users can easily assess the length of bars to understand relative values. Their clear representation of data simplifies interpretation, ensuring users can grasp information effortlessly. The linear nature of bars allows for precise measurements and comparisons, enhancing the accuracy of data understanding. Additionally, bar charts prove versatile by effectively displaying multiple data sets, particularly when grouped or clustered, making them suitable for presenting complex information in a comprehensible manner. The inherent strengths of bar charts in facilitating comparison, clarity, precision, and versatility make them a preferred choice for visualizing data in various contexts.

User Flow

A "Project View" is a feature designed to house various calculations or budget options that users can create. Within this view, users have the ability to create multiple calculators to compare values and assess life extension scenarios. This allows for a comprehensive and organized overview of different financial projections or scenarios within a single project, facilitating effective comparison and decision-making.

After initiating the project creation process, users are guided to establish a baseline set of values. This foundational baseline serves as a reference point that users can leverage to create additional calculations for comparative analysis. By having a well-defined starting point, users can systematically generate and assess various scenarios, enabling them to make informed decisions and comparisons within the project framework.

The culmination of the process is the Comparison View, consisting of a baseline model and up to two additional calculations. This comprehensive report serves as a valuable tool for users to share and present to their board, aiding in the decision-making process for their annual maintenance plan. The Comparison View provides a clear and concise overview of different scenarios, facilitating effective communication and informed decision-making based on the calculated data.

Machine Learning Predictions

In Phase 2 of the project, we introduced visual charts illustrating rail grinding processes, offering stakeholders, especially engineering teams, a dynamic view of how rail infrastructure wears over time. Complementing this visual representation, we implemented machine learning algorithms to predict and forecast how the rail would wear in the future. This advanced application of machine learning technology became an invaluable addition to the toolset, enhancing the proactive nature of maintenance planning.

The machine learning predictions enabled engineering teams to not only observe current wear patterns but also anticipate future wear, allowing for even more precise planning and resource allocation. This predictive capability empowered teams to take preemptive actions, optimizing preventive maintenance programs and extending the life of the rail infrastructure.

The visual charts, coupled with machine learning predictions, collectively transformed the maintenance planning process. Engineering teams gained a holistic understanding of the rail system's wear dynamics, enabling them to prioritize interventions effectively. Moreover, this approach contributed to the optimization of execution, financial planning, and the overarching goal of ensuring the safety and longevity of rail assets. Our integration of machine learning exemplifies our commitment to leveraging cutting-edge technologies to enhance the efficiency and effectiveness of rail maintenance programs.