It is really a
great challenge to find Standard Allowed Minutes (SAM) from any garment images
because the image cannot provides all proper information needed
SAM is a crucial metric in the garment manufacturing industry, used to determine the standard time required to produce a garment. Though it has some difficulties but it is sometimes needed to find out SAM to do some pre-production activities like costing, order dealing etc.
Why finding SAM from garments images is difficult:
1.
Complexity of Garment Features
Different garment
types: Garments can vary widely in design, style, size, and complexity from
product to product. And operation can also vary from each other even design to
design. Each variation affects the time required for production, which cannot
be accurately assessed just from an image without understanding the
construction and details of each piece. Beside some hidden activities also
remain in processing which is difficult to find and assuming the required
making time exactly.
Detailed
construction processes: SAM involves detailed production steps, from cutting to
stitching, which are influenced by design features (e.g., pleats, embroidery,
trims) that can be difficult to capture visually.
2. Lack of Contextual Information
Missing workflow
context: Only single images cannot
provide information about the specific production environment or manufacturing
process. SAM depends on specific conditions such as worker efficiency,
equipment, and factory setup, which cannot be inferred from an image.
Scale and
positioning of garments: Garment images might not reflect the actual size or
scale of the pieces in relation to the final production process (e.g., how it
will be sewn, folded, or cut). This can lead to inaccurate SAM assessments.
3. Variation in Manufacturing Methods
Different stitching
techniques and machinery: The time it takes to produce garments can vary
significantly depending on the machinery and techniques used (e.g., hand
stitching vs. machine stitching, single-needle vs. multi-needle stitching).
These details are often not visible in the garment image.
Use of automation:
In factories that use a combination of manual and automated production methods,
the specific equipment used can drastically affect the time it takes to
manufacture the garment. Images don’t provide this insight.
4. Texture and Fabric Material
Fabric type and
texture: The type of fabric used can have a significant impact on the
manufacturing time. For instance, working with heavy fabrics like denim or
leather typically takes more time than working with lighter fabrics like cotton
or polyester. These differences may not be obvious from an image alone.
Fabric behavior:
Materials that stretch, crease, or behave differently during processing might
require additional steps that an image cannot indicate. These are critical when
determining time for processes like ironing, shaping, or fitting.
5. Lighting and Image Quality
Low-quality images:
Image clarity, resolution, and lighting can significantly affect the accuracy
of garment feature extraction. Poor quality images may hide key garment details
such as stitching patterns or intricate designs, leading to inaccurate SAM
estimation.
Lighting
variations: Inconsistent lighting can distort the true colors or textures of a
garment, leading to incorrect assumptions about the type of material or
construction process.
6. Recognition of Non-Visual Features
Hidden design
elements: Some design features (e.g., internal linings, pockets, or pleats) may
not be visible in a flat image, even if they significantly impact manufacturing
time.
Invisible
operations: Certain operations such as cutting, ironing, or quality checks that
take place off-screen or behind the scenes are not detectable from an image,
but can be crucial to the SAM calculation.
7. Difficulty in Measuring Process Time
Time estimation for
specific actions: SAM is based on time studies for specific operations in
different departments like cutting, sewing, and finishing departments. These
actions depend on the complexity of each task, which can't be easily quantified
just by observing the garment's appearance.
Variability in
worker speed: The SAM values are influenced by the efficiency of the worker,
but this is difficult to distinguish from just an image, as it depends on
individual skill, experience, and external factors (like factory ergonomics) or
environment.
8. Machine Learning Limitations
Accuracy of image
recognition: Automated systems based on machine learning and AI need large
datasets of labeled garment images to learn effectively. However, the
complexity and variety of garment features can lead to errors, and there is
often a lack of sufficient labeled data.
Lack of detailed
annotations: For accurate SAM determination, each part of the garment (like
sleeves, collar, button, etc.) must be identified and labeled properly. Without
detailed annotations, AI models may struggle to correctly estimate SAM.
9. Data Integration and Standardization
Inconsistent
definitions of SAM: Different industries, factories, or countries may have
slightly different definitions of SAM or use different standard procedures.
This inconsistency can complicate the use of garment images to universally
determine SAM values.
Limited data on garment lifecycle: SAM depends on the entire production process, not just the design phase. Without data on the full lifecycle of the garment (including material sourcing, cutting, assembly, and finishing), determining SAM from an image becomes incomplete.
Conclusion
While advancements in AI and machine learning are gradually addressing some of these challenges, accurately determining SAM from garment images remains difficult due to the complex nature of garment production. It requires more than just image recognition—it involves understanding a wide range of factors like fabric type, stitching techniques, and factory processes.
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