Automating Device Ageing Analysis: Power BI Modernization for an Australian Mobile Renewable Company

An Australian company specializing in mobile device renewables relied on manual Google Sheets to track the ageing of returned devices. The process was time-consuming, error-prone, and lacked visibility. I developed an automated Power BI solution that integrated with their existing Google Sheets data, categorized device ageing intelligently, and provided actionable insights—transforming their operations from reactive to proactive.

In the mobile refurbishment and resale industry, understanding how long devices sit in inventory is crucial for minimizing loss and maximizing resale value. The client faced several bottlenecks:

• Manual Google Sheet Workflows:Calculations were performed manually, increasing the risk of error.
• No Standardized Ageing Buckets:Device ageing was inconsistently tracked, making performance reviews unclear.
• Lack of Real-Time Insights:Reporting was delayed and often outdated by the time decisions were made.
• Low Visibility into Stock Movement:Filtering by device brand, model, or location was difficult.
• Scaling Challenges:As the business grew, their spreadsheet model could no longer keep up.

To address these challenges, I designed an automated Power BI dashboard solution that:

• Integrated directly with Google Sheets for seamless data refresh.
• Created dynamic ageing buckets (e.g., 0–30, 31–60, 61–90, >90 days).
• Enabled drill-downs by warehouse, brand, model, and return date.
• Automated calculations to eliminate manual intervention.
• Presented insights in a visually intuitive, executive-friendly format.

• Google Sheets Data Integration:Connected to shared sheets via Power BI’s online connector.
• Power Query Transformation:Cleaned and structured raw data to standardize date formats and return timelines.
• Ageing Logic & DAX Measures:Calculated the number of days each mobile had aged and categorized them into defined buckets using robust DAX.
• Interactive Visuals:Used Power BI's built-in visuals to enable filtering by warehouse, model, return status, and device brand.
• KPI Cards & Trends:Displayed high-level metrics on ageing distributions and slow-moving stock.

• 70% Reduction in Report Preparation Time:From hours to minutes.
• Improved Accuracy:Fully eliminated manual calculation errors.
• Real-Time Insights:Enabled instant visibility into stock movement and ageing.
• Actionable Reporting:Helped the team prioritize devices for resale based on ageing.
• Scalable & Reusable Model:Easily adaptable to future data sources and categories.