Ralston Fitler

Case Study: Enhancing Inventory Prediction Accuracy for Ford Parts

Project Overview: as a UX Designer on the team responsible for improving the Ford Parts website (https://parts.ford.com/), my primary role was to conduct in-depth stakeholder interviews to identify key requirements, pain points, and areas for improvement. The ultimate goal was to refine inventory management processes and ensure a seamless user experience for stakeholders interacting with the website.

The Challenge: The Ford Parts website serves as a critical platform for sourcing and managing automotive parts. However, stakeholders expressed frustration due to parts frequently being out of stock, a problem largely attributed to inaccurate inventory predictions. This issue led to inefficiencies for part planners, delays for technicians, and logistical challenges for inventory managers. These pain points were impeding the platform’s ability to effectively support Ford’s operations and customer satisfaction.

Research Methodology: To address these challenges, I conducted qualitative research by interviewing eight key stakeholders whose roles were directly impacted by inventory prediction issues:

Participants:

• Part Planners (4 participants)
• Inventory Managers (3 participants)
• Parts Technicians (1 participant)

Objectives:

• Understand their workflows and how they interacted with the platform.
• Identify pain points in the inventory management process.
• Gather requirements for an improved system.

Interview Highlights: The Ford Parts website serves as a critical platform for sourcing and managing automotive parts. However, stakeholders expressed frustration due to parts frequently being out of stock, a problem largely attributed to inaccurate inventory predictions. This issue led to inefficiencies for part planners, delays for technicians, and logistical challenges for inventory managers. These pain points were impeding the platform’s ability to effectively support Ford’s operations and customer satisfaction.

• The part planners relied heavily on predictive analytics to determine stock levels. They encountered frequent inaccuracies in demand forecasting, leading to stock shortages or overstock.
• Inventory managers struggled to maintain balance due to lack of real-time updates and data transparency. They found it difficult to communicate stock levels effectively to other departments.
• Parts technicians experienced delays in completing repairs due to unavailable parts. The technician felt disconnected from the inventory planning process.

Findings and Insights: After synthesizing the interview data, several themes emerged.

• Inaccurate demand predictions. The forecasting algorithm lacked the sophistication to account for seasonal demand shifts and emergent trends.
• Limited data transparency. Stakeholders expressed a desire for more visibility into inventory data, including historical trends and real-time updates.
• Role-specific needs. Part Planners required better tools for analyzing demand. Inventory Managers needed streamlined communication tools for cross-department collaboration. Parts Technicians sought a system that provided clearer timelines for restocking.

Proposed Recommendations: Based on these findings, the following recommendations were made to enhance the platform.

• Improved forecasting algorithm by integrating machine learning models capable of leveraging historical and real-time data for more accurate predictions.
• Data transparency dashboards. Develop role-specific dashboards providing actionable insights tailored to part planners, inventory managers, and technicians.
• Implement tools to facilitate real-time notifications and cross-department communication.
• Communication pipeline established. Continuously gather user feedback to refine the platform and address evolving needs.

Impact: While this project was focused on research, the insights gathered formed the foundation for actionable improvements to the Ford Parts website. By addressing the core issues of inventory prediction accuracy and data transparency, these recommendations aimed to reduce stock shortages and overstock incidents. Enhance efficiency across roles. Improve overall user satisfaction and operational effectiveness.

Conclusion: This case study underscores the importance of user-centered research in identifying and addressing complex challenges within a large-scale platform. My role as a UX Designer allowed me to advocate for the needs of stakeholders and deliver insights that informed strategic decisions for improving the Ford Parts website. Although I did not contribute to the UI design, my research played a critical role in setting the stage for impactful solutions that align with Ford’s commitment to excellence.