When you become quality conscious, you might look at improving existing quality standards at various stages of garment manufacturing process. Producing a good quality product is a result of combined efforts of management, employees and workers by developing system, implementing good practices in shop floor and setting up standards.
Following 17 tips will help you to improve garment quality at production.
- Communicate the importance of quality production to your employees and shop floor workers, and explain quality expectations by the management.
- Maintain a clean and dry workplace, including storage rooms and shipping areas.
- Select and utilize appropriate equipment in cutting, sewing and finishing processes.
- Provide appropriate tools, machines and equipment to each department.
- Provide on-the-job training to workers.
- Plan an ongoing program for machine maintenance.
- Establish agreed-upon quality standards with all fabric suppliers before purchase, including procedures for rejecting/returning unacceptable goods.
- Follow 100 per cent inspection of all incoming fabrics.
- Allocate a trained quality inspector for visual inspection.
- Compare actual fabric width and length against reported figures (by supplier) and required length and width.
- Return fabrics to supplier that doesn’t meet agreed-upon quality standards.
- Follow 100 per cent inspection of value-added processes, such as panel printing, machine embroidery, hand embroidery etc.
- No defective panels or components should be sent to stitching section. Defective components can be accepted after corrective measures have been taken.
- Cutting quality is the second-most important area. Checking is to be performed for the cut components, such as matching cut panels with original patterns, shade variations, fabric-related defects etc.
- Check 100 per cent of the garments after stitching and in the finishing section.
- Record defects by garment production lot, source of defects (fabric, cutting or assembly), type of defects etc.
- Analyse inspection report data to identify sources of quality problems.