A Dhaka garment manufacturer could not see production problems coming. We built a forecasting system that gives planners days of warning on at risk orders and points to the likely cause on each line.
The factory ran dozens of production lines against tight shipment dates, but planning was reactive. Managers only learned that a line was behind or that defects were climbing once the damage was done, which meant missed dates and expensive last minute air freight to keep buyers happy.
They had years of production records, but the data sat in spreadsheets and a legacy system, unused for anything forward looking.
We started with a two week data assessment, cleaning and joining their production history into a single reliable dataset. From there we built a forecasting model that predicts daily output and flags orders likely to slip, days before a human would notice.
The output lives in a simple dashboard the planning team opens every morning. It ranks orders by risk, shows which lines are driving the risk, and surfaces the factors most associated with defect spikes so supervisors know where to look.
We treated this as a time series problem at the order and line level, combining classical forecasting with gradient boosted models for the risk scoring. Feature work drew on line, shift, product type, and historical defect patterns. Everything was validated against held back periods so the team could trust the numbers before they relied on them.
"For the first time we can see trouble on a line before it turns into a missed shipment. The team checks the forecast every morning, and it has already saved us from more than one expensive scramble."
Shamsul Islam, Operations Director