Data Analyst
Golden Eyes Garments
- Location Tirupur, Tamil Nadu, India
- Duration July 2022 – July 2024
- Type Full-time
Golden Eyes Garments is a vertically integrated knitwear manufacturer and exporter in Tirupur, the knitwear hub of South India. It covers design, yarn, fabric, and finished garments across men's, women's, children's, and sustainable lines, and serves international buyers on tight delivery windows.
When I joined, the data side of the business was still manual. Production counts were written on paper at each stage, then entered into Excel by supervisors at the end of each shift. Inventory was tracked in hand-updated stock registers and planned by intuition, and order data sat in separate files with the merchandising team. Weekly management reports were stitched together by hand. Nothing was connected, so by the time a problem showed up in a report, it had already cost time, fabric, or a shipment window.
I spent my first few months on the floor before touching the data, talking to production supervisors, sitting with the merchandising team, and following orders from cutting to packing. The costliest problems lived in the handoffs between stages, in the gap between what buyers ordered and what inventory could support, and in the hours spent compiling reports.
The foundation. I created standardized data collection templates for the floor and the merchandising team, so every shift, order, and stock movement was recorded the same way. I consolidated the scattered sheets into one dataset and used Python for the cleanup and transformation that would have taken weeks by hand. Trusted data made every later project possible.
Production output. I built stage-by-stage production tracking and used Python to join and analyze the logs, which surfaced bottlenecks that were not obvious from the floor, especially between stitching and checking. With the supervisors, we adjusted scheduling and line balancing, and over the following months output rose 12–15% and on-time delivery improved from 78% to 91%.
Inventory. Excess yarn and fabric were tying up working capital and creating waste at the end of every season. I built a demand forecasting model combining historical order patterns, seasonality, and the current order book, with Python for the analysis and Excel as the merchandising team's interface. Once they trusted it, excess inventory dropped 15–20%, freeing about $50K–$75K in working capital.
Reporting. I rebuilt the weekly management reporting in Tableau and Excel, pulling directly from the centralized dataset so updates flowed through automatically. That cut manual reporting time by 8–10 hours per week and gave the management team a single, trusted view of the operation.