Measuring Quality Performance in a Garment Factory

Measuring garment quality performance

Amit, a quality manager in a garment factory, wants to know how to measure the quality performance of his factory. He wants to have some key quality reports that can show him the daily quality status and helps to bring the major quality issues in real-time. He wants to improve his factory’s quality standard and quality performance. 

The factory has quality checkers for the sewing floor and finishing section. They check each garment at the end-of-line. Quality checkers segregate passed pieces and defective pieces (garments that need repair work). How many pieces checked and how many defective pieces are not known to the checker and quality managers. The reason, the factory does not capture quality data. 

This might be true for many garment factories in India and in the global apparel manufacturing industry. 

They know how many pieces are passed from the production report. But the quality checker cannot tell how many pieces they have checked and how many defects and defective units found. They even recheck repaired garments. They don’t know the defect sources and frequency of defects in different defects types. 

This post covers what kind of information one needs to measure quality performance. 

Today, all the following information is important to know the factory’s quality performance and controlling the product quality on the shop floor.
  • How many garments are checked?
  • What kind of defects are found?
  • In which operations defects are found
  • How many defective pieces are found?
  • How many garments are rechecked by the checker?
  • Who is responsible for making defects (sewing operators, processes, or machines)?
  • Source of defects whether the defects are related to stitching, fabric defects or handling defects
  • Identification of rechecked garment.
  • Who will take action for getting rectified the defects?
  • Who will ensure the same issue will not appear again
  • Quality checker performance
  • Data and time of defect capturing and defect generation

Related Article: Existing quality checkpoints followed by garment factories

How to get all the information

When you capture garment checking data, include all the above in your data capturing format.

It is true that it would be a time-consuming task to write all this information in paper/printed formats. For finding the root cause and eliminating the defects generation you need to do all these steps, though it seems a cumbersome task. You can upgrade the quality control data capturing by introducing SQC applications.

There are a couple of good quality control applications (Mobile applications) you can use for your factory.

Template for data capturing

Yes, you need a template for data capturing. Whether you do it manually or using a mobile application, designing the data capturing format is crucial. Data capturing format must be easy to understand and easy to handle by users. Your object should be to reduce repetitive data entry.

Check the simple format and detailed form for shop floor data capturing.

If you go for app-based quality systems, you will get a pre-designed data entry template. It is assumed that your quality control application has the right formats. Don’t settle down with the data capturing format that you get from the software. Ask your software service provider to enhance and improve the data capturing template if you are not getting complete reporting.

Right data capturing is the first step for measuring quality performance.

Reporting and QMIS

Paperwork is important. Prepare the report of the floor quality performance. Your quality reports must include 3 things
  1. Details reports,
  2. Quality KPI and
  3. Presenting data in charts and graphs
Quality KPIs can include DHU reports trends, Defective percentage trends, Cost of quality, Major sources for defect generation, identifying problematics fabrics/trims (then tracking their suppliers), identifying critical internal processes, Identifying critical operations in each product. A lot of data needs to be analyzed.

Compare quality issues between lines. Compare quality issues between weeks and styles.

Make trend charts on quality performance. Benchmark your current quality performance and set a goal for improving shop floor quality.

Displaying quality performance data on the Floor

Data capturing is done. The report is prepared. What is next?

Bring it to the management and the shop floor team and operators. Let them know their quality performance by displaying daily quality KPIs on a display board. 

Keep one common display board for daily DHU trend charts and prepare separate display boards for each production line.


My suggestion to Amit for measuring and improving quality performance in his factory -

  • Explore how the top tire garment factories managing their product quality and set up their quality standards.
  • Decide what data and kind of reports you need to control product quality and improve the factory’s quality performance.
  • Develop the data capturing format (or use QMIS software)
  • Train your quality checkers and quality control team.
  • Captured data to get such reports.
  • Prepare quality reports using your existing tool (Excel spreadsheet) or Use garment quality control application to get reports prepared automatically with charts and graphs.
For the real-time shop floor quality data, you need to implement a quality control application.

Prasanta Sarkar

Prasanta Sarkar is a textile engineer and a postgraduate in fashion technology from NIFT, New Delhi, India. He has authored 6 books in the field of garment manufacturing technology, garment business setup, and industrial engineering. He loves writing how-to guide articles in the fashion industry niche. He has been working in the apparel manufacturing industry since 2006. He has visited garment factories in many countries and implemented process improvement projects in numerous garment units in different continents including Asia, Europe, and South Africa. He is the founder and editor of the Online Clothing Study Blog.

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