Smarter Operations for Apparel
Turning operational data into forecasts, dashboards and decisions that hold up in a review meeting.
7 published articles across 5 sub-categories
A buyer in Europe places an order on a Tuesday morning. By Tuesday afternoon in Dhaka, a merchandiser has booked the fabric, a planner has slotted it against next month's…
If the supply chain dashboard from Part 1 is the part of the business looking out the window at the buyer, the operations dashboard is the part standing inside the engine room,…
To be able to build a multiple regression model from your own batch records that predicts a dyeing recipe's chemical cost before the batch runs, and know which two or three variables actually drive that cost.
You'll be able to design a factory KPI dashboard supervisors keep opening past week four, by starting from the five decisions it must support instead of the data that happens to be available.
A planner will be able to pick the right smoothing model for a given SKU, tune alpha, beta and gamma against a holdout period instead of by feel, and know which forecast error metric to track so bias gets caught before it fills a warehouse.
A QA head or IE will be able to pick the right control chart, set limits correctly, tell centring problems from variation problems using Cp and Cpk, and stop tampering with a process that is actually in control.
A practical decision framework for factories with an ERP, several disconnected machine systems, and a finance team that lives in Excel. Spoiler: the answer is rarely the most impressive tool.
