Claims:1. A system (100) for production on demand of fashion apparel comprising:
a processing subsystem (105) hosted on a server (108), wherein the processing subsystem (105) is configured to execute on a network (115) to control bidirectional communications among a plurality of modules comprising:
an apparel design receiving module (110) configured to receive a plurality of images of a plurality of apparel designs created by one or more fashion designers;
an apparel image processing module (120) operatively coupled to the apparel design receiving module (110), wherein the apparel image processing module (120) is configured to:
process the plurality of images of the plurality of apparel designs using an image processing technique;
identify a type of one or more apparels from plurality of processed images of the plurality of apparel designs; and
predict positions of one or more functional key points defined on the one or more apparels identified;
an apparel pattern extraction module (130) operatively coupled to the apparel image processing module (120), wherein the apparel pattern extraction module (130) is configured to extract one or more patterns of each of the one or more apparels based on the positions of the one or more functional key points predicted using a trained neural network model; and
a work allocation module (140) operatively coupled to the apparel pattern extraction module (130), wherein the work allocation module (140) is configured to:
determine a stitching process associated with designing of each of the one or more apparels based on combination of each of the one or more patterns extracted;
recommend a plurality of stitching associated parameters for designing each of the one or more apparels based on the stitching process determined; and
allocate tailoring work to one or more tailors for facilitating the production on demand of each of the one or more apparels based on the stitching process determined and the plurality of stitching parameters recommended.
2. The system (100) as claimed in claim 1, wherein the plurality of images are received in at least one of a joint photographic expert group format, a portable network graphics format or a combination thereof.
3. The system (100) as claimed in claim 1, wherein the image processing technique comprises rescaling of the plurality of images, correcting illumination of the plurality of images, detecting edges of the plurality of images, mathematical morphology calculation of the plurality of images, evaluation and ranking of segmentation technique for the plurality of images.
4. The system (100) as claimed in claim 1, wherein the type of the one or more apparels comprises a bottom wear or a top wear.
5. The system (100) as claimed in claim 1, wherein the one or more functional key points comprises at least one of cutline, hemline, corners of sleeves, neckline, cuff, panel of a pant, a J-piece at waist or a combination thereof.
6. The system (100) as claimed in claim 1, wherein the one or more patterns comprises approximately thirty-five patterns.
7. The system (100) as claimed in claim 1, wherein the stitching process comprises atleast one of an aggregation of each of the one or more patterns extracted, right stitching, overlock, interlock or a combination thereof.
8. The system (100) as claimed in claim 1, wherein the plurality of stitching associated parameters atleast one of a type of stitch, number of stitches, needle type, and location, orientation and extent of stitches, type of fabric, usability of apparel or a combination thereof.
9. A method (300) comprising:
receiving, by an apparel design receiving module of a processing subsystem, a plurality of images of a plurality of apparel designs created by one or more fashion designers (310);
processing, by an apparel image processing module of the processing subsystem, the plurality of images of the plurality of apparel designs using an image processing technique (320);
identifying, by the apparel image processing module of the processing subsystem, a type of one or more apparels from plurality of processed images of the plurality of apparel designs (330);
predicting, by the apparel image processing module of the processing subsystem, positions of one or more functional key points defined on the one or more apparels identified (340);
extracting, by an apparel pattern extraction module of the processing subsystem, one or more patterns of each of the one or more apparels based on the positions of the one or more functional key points predicted using a trained neural network model (350);
determining, by a work allocation module of the processing subsystem, a stitching process associated with designing of each of the one or more apparels based on combination of each of the one or more patterns extracted (360);
recommending, by the work allocation module of the processing subsystem, a plurality of stitching associated parameters for designing each of the one or more apparels based on the stitching process determined (370); and
allocating, by the work allocation module of the processing subsystem, tailoring work to one or more tailors for facilitating the production on demand of each of the one or more apparels based on the stitching process determined and the plurality of stitching parameters recommended (380).

Dated this 14th day of March 2022

Signature

Jinsu Abraham
Patent Agent (IN/PA-3267)
Agent for the Applicant , Description:BACKGROUND
[0001] Embodiments of the present disclosure relate to a technology-based system for manufacturing industry and more particularly to a system and a method for production on demand of fashion apparel.
[0002] Fashion industry has for long followed the traditional assembly line practices of production and manufacturing. There are seasonal launches, designs are finalized, and sent for production. A pre-defined quantity is manufactured for each design and each size and sent to the warehouse. Designs are made by fashion designers and a sample is created. Thereafter, huge numbers of tailors are employed to make bulk production of those styles. Every tailor follows their own approach of cutting, stitching, finishing, etc. Globally, 15% of fabric used while manufacturing gets wasted due to the traditional approach followed by different tailors towards apparel manufacturing. Also, more than 25% apparel manufactured, never gets sold and is wasted, burnt, or put to land pits which further leads to increase in cost of apparels. because of such issues. For apparel inventory management and warehousing various systems are developed which helps in creating apparels based on requirement and helps in avoiding wastage of materials.
[0003] Conventionally, the system designed for apparel manufacturing involves managing of diverse range of tasks such as designing, fabric gathering, cutting, stitching, finishing and the like. However, such conventional systems for manufacturing the apparel relies upon certain aspects which is time consuming and requires coordination of many different geographically dislocated suppliers, vendors, manufacturers, and retailers. Also, such conventional systems for identifying unique patterns of apparels from each designs provided by the designers involves manual intervention and thereby make the process error prone. Furthermore, such conventional systems are incapable of identifying the steps involved in manufacturing of the apparels based on the input images provided by the designers.
[0004] Hence, there is a need for an improved system and a method for production on demand of fashion apparel in order to address the aforementioned issues.

BRIEF DESCRIPTION
[0005] In accordance with an embodiment of a present disclosure, a system for production on demand of fashion apparel is disclosed. The system includes a processing subsystem hosted on a server. The processing subsystem includes an apparel design receiving module configured to receive a plurality of images of a plurality of apparel designs created by one or more fashion designers. The processing subsystem also includes an apparel image processing module configured to process the plurality of images of the plurality of apparel designs using an image processing technique. The apparel image processing module is also configured to identify a type of one or more apparels from plurality of processed images of the plurality of apparel designs. The apparel image processing module is also configured to predict positions of one or more functional key points defined on the one or more apparels identified. The processing subsystem also includes an apparel pattern extraction module configured to extract one or more patterns of each of the one or more apparels based on the positions of the one or more functional key points predicted using a trained neural network model. The processing subsystem also includes a work allocation module configured to determine a stitching process associated with designing of each of the one or more apparels based on combination of each of the one or more patterns extracted. The work allocation module is also configured to recommend a plurality of stitching associated parameters for designing each of the one or more apparels based on the stitching process determined. The work allocation module is also configured to allocate tailoring work to one or more tailors for facilitating the production on demand of each of the one or more apparels based on the stitching process determined and the plurality of stitching parameters recommended.
[0006] In accordance with another embodiment of the present disclosure, a method for production on demand of fashion apparel is disclosed. The method includes receiving, by an apparel design receiving module of a processing subsystem, a plurality of images of a plurality of apparel designs created by one or more fashion designers. The method also includes processing, by an apparel image processing module of the processing subsystem, the plurality of images of the plurality of apparel designs using an image processing technique. The method also includes identifying, by the apparel image processing module of the processing subsystem, a type of one or more apparels from plurality of processed images of the plurality of apparel designs. The method also includes predicting, by the apparel image processing module of the processing subsystem, positions of one or more functional key points defined on the one or more apparels identified. The method also includes extracting, by an apparel pattern extraction module of the processing subsystem, one or more patterns of each of the one or more apparels based on the positions of the one or more functional key points predicted using a trained neural network model. The method also includes determining, by a work allocation module of the processing subsystem, a stitching process associated with designing of each of the one or more apparels based on combination of each of the one or more patterns extracted. The method also includes recommending, by the work allocation module of the processing subsystem, a plurality of stitching associated parameters for designing each of the one or more apparels based on the stitching process determined. The method also includes allocating, by the work allocation module of the processing subsystem, tailoring work to one or more tailors for facilitating the production on demand of each of the one or more apparels based on the stitching process determined and the plurality of stitching parameters recommended.
[0007] To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:
[0009] FIG. 1 is a block diagram of a system for production on demand of fashion apparel in accordance with an embodiment of the present disclosure;
[0010] FIG. 2 is a schematic representation of an exemplary embodiment of a system for production on demand of fashion apparel of FIG. 1 in accordance with an embodiment of the present disclosure;
[0011] FIG. 3 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure; and
[0012] FIG. 4 is a flow chart representing the steps involved in a method for production on demand of fashion apparel of FIG.1 in accordance with an embodiment of the present disclosure.
[0013] Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.
DETAILED DESCRIPTION
[0014] For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.
[0015] The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or sub-systems or elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, sub-systems, elements, structures, components, additional devices, additional sub-systems, additional elements, additional structures or additional components. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.
[0016] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.
[0017] In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.
[0018] Embodiments of the present disclosure relate to a system and a method for production on demand of fashion apparel. The system includes a processing subsystem hosted on a server. The processing subsystem includes an apparel design receiving module configured to receive a plurality of images of a plurality of apparel designs created by one or more fashion designers. The processing subsystem also includes an apparel image processing module configured to process the plurality of images of the plurality of apparel designs using an image processing technique. The apparel image processing module is also configured to identify a type of one or more apparels from plurality of processed images of the plurality of apparel designs. The apparel image processing module is also configured to predict positions of one or more functional key points defined on the one or more apparels identified. The processing subsystem also includes an apparel pattern extraction module configured to extract one or more patterns of each of the one or more apparels based on the positions of the one or more functional key points predicted using a trained neural network model. The processing subsystem also includes a work allocation module configured to determine a stitching process associated with designing of each of the one or more apparels based on combination of each of the one or more patterns extracted. The work allocation module is also configured to recommend a plurality of stitching associated parameters for designing each of the one or more apparels based on the stitching process determined. The work allocation module is also configured to allocate tailoring work to one or more tailors for facilitating the production on demand of each of the one or more apparels based on the stitching process determined and the plurality of stitching parameters recommended.
[0019] FIG. 1 is a block diagram of a system (100) for production on demand of fashion apparel in accordance with an embodiment of the present disclosure. The system (100) includes a processing subsystem (105) hosted on a server (108). In one embodiment, the server (108) may include a cloud server. In another embodiment, the server (108) may include a local server. The processing subsystem (105) is configured to execute on a network (not shown in FIG. 1) to control bidirectional communications among a plurality of modules. In one embodiment, the network may include a wired network such as local area network (LAN). In another embodiment, the network may include a wireless network such as Wi-Fi, Bluetooth, Zigbee, near field communication (NFC), infra-red communication (RFID) or the like.
[0020] The processing subsystem (105) includes an apparel design receiving module (110) configured to receive a plurality of images of a plurality of apparel designs created by one or more fashion designers. In one embodiment, the plurality of images may be received in at least one of a joint photographic expert group (jpeg) format, a portable network graphics (png) format or a combination thereof. In such embodiment, the plurality of images of approximately five thousand stock keeping units (SKUs) may be received. Once the design and construction is accomplished high contrast wire frames from image formats like the png, and the jpeg are extracted.
[0021] The processing subsystem (105) also includes an apparel image processing module (120) which is configured to process the plurality of images of the plurality of apparel designs using an image processing technique. The image processing technique processes the plurality of images upon rescaling the plurality of images, correcting illumination of the plurality of images, detecting edges of the plurality of images, mathematical morphology calculation of the plurality of images, evaluation and ranking of segmentation technique for the plurality of images.
[0022] The apparel image processing module (120) is also configured to identify a type of one or more apparels from plurality of processed images of the plurality of apparel designs. In one embodiment, the type of the one or more apparels may include a bottom wear. In such embodiment, the bottom wear may include, but not limited to, a trouser, a track pant, a jogger, a jeans, a palazzo, a leggings, a salwar, a skirt and the like. In another embodiment, the type of the one or more apparels may include a top wear. In such embodiment, the type of the top wear may include, but not limited to, a top, a shirt, a kurta, a jacket, a t-shirt, a pullover and the like.
[0023] The apparel image processing module (120) is also configured to predict positions of one or more functional key points defined on the one or more apparels identified. In one embodiment, the one or more functional key points may include at least one of cutline, hemline, corners of sleeves, neckline, cuff, panel of a pant, a J-piece at waist or a combination thereof.
[0024] The processing subsystem (105) also includes an apparel pattern extraction module (130) configured to extract one or more patterns of each of the one or more apparels based on the positions of the one or more functional key points predicted using a trained neural network model. As used herein, the term ‘trained neural network model’ is defined as a computational model implemented using deep learning technology and trained with plurality of images. In one embodiment, the trained neural network model may include a trained convolutional neural network model. In some embodiment, the one or more patterns may include approximately thirty-five patterns extracted from approximately 5000 images of the plurality of apparels. In such embodiment, the one or more patterns may include, but not limited to, a sleeve pattern, a neck pattern, a single piece pattern, a two-piece pattern, a gated pattern, a multi-piece pattern, a match plate pattern, a skeleton pattern, a sweep pattern, a loose piece pattern and the like.
[0025] The processing subsystem (105) also includes a work allocation module (140) configured to determine a stitching process associated with designing of each of the one or more apparels based on combination of each of the one or more patterns extracted. For example, Style 1 = Pattern X + Pattern Y + Pattern Z. Similarly, Style 2 = Pattern A + Pattern B and so on. In a specific embodiment, the stitching process include atleast one of an aggregation of each of the one or more patterns extracted, right stitching, overlock, interlock or a combination thereof.
[0026] The work allocation module (140) is also configured to recommend a plurality of stitching associated parameters for designing each of the one or more apparels based on the stitching process determined. In some embodiment, the plurality of stitching associated parameters may include atleast one of a type of stitch, number of stitches, needle type, and location, orientation and extent of stitches, type of fabric, usability of apparel or a combination thereof. The work allocation module (140) is also configured to allocate tailoring work to one or more tailors for facilitating the production on demand of each of the one or more apparels based on the stitching process determined and the plurality of stitching parameters recommended. Once, the stitching process is recommended, the system allocates the tailoring work to huge numbers of the one or more tailors who are employed to make bulk production of those styles. Every tailor follows their own approach of cutting, stitching, finishing, etc. and thus avoids human error coming in at every stage.
[0027] FIG. 2 is a schematic representation of an exemplary embodiment of a system (100) for production on demand of fashion apparel of FIG. 1 in accordance with an embodiment of the present disclosure. Considering an example, where the system (100) is utilized in fashion industry by an apparel manufacturing company for production of fashion apparels. Generally, in the fashion industry, apparel inventory management and warehousing, liquidation and return management is a major issue for all apparel brands, not to mention the wastage and the effect of environment due to apparel being put to land pits or being burnt. In order to overcome such issues, the system (100) is utilized which breakdowns an apparel design to their most basic cuts/patterns, and feed in the pattern combinations of a variety of apparel into a tech driven backend, to look at apparel production in the light of pattern cuts and stitching jobs.
[0028] For on demand production of the fashion apparels, an apparel design receiving module (110) of the system (100), at first receives a plurality of images of a plurality of apparel designs (102) created by one or more fashion designers. In the example used herein, the plurality of images of the plurality of fashion apparels are received in at least one of a joint photographic expert group (jpeg) format, a portable network graphics (png) format or a combination thereof. Here, the apparel design receiving module (110) is located on a processing subsystem (105) which is hosted on a cloud server (108). The processing subsystem (105) is configured to execute on a network (115) to control bidirectional communications among a plurality of modules. For example, the network as used herein for communication with various modules includes a wireless communication network.
[0029] Once, the plurality of images are received, an apparel image processing module (120) processes the plurality of images of the plurality of apparel designs using an image processing technique. The image processing technique processes the plurality of images upon rescaling the plurality of images, correcting illumination of the plurality of images, detecting edges of the plurality of images, mathematical morphology calculation of the plurality of images, evaluation and ranking of segmentation technique for the plurality of images. Upon processing of the plurality of images of the plurality of apparel designs, a type of one or more apparels from plurality of processed images of the plurality of apparel designs is identified. For example, the type of the one or more apparels may include a bottom wear which may include, but not limited to, a trouser, a track pant, a jogger, a jeans, a palazzo, a leggings, a salwar, a skirt and the like. Similarly, the type of the one or more apparels may also include a top wear such as a top, a shirt, a kurta, a jacket, a t-shirt, a pullover and the like.
[0030] Suppose for an example, if a jogger image is selected and processed, then from such image the apparel image processing module (120) predicts positions of one or more functional key points defined on the one or more apparels identified. In the example used herein, the one or more functional key points may include at least one of a panel of a pant, a J-piece at waist and the like.
[0031] Based on the positions of the one or more functional key points identified, an apparel pattern extraction module (130) extracts one or more patterns of each of the one or more apparels. For example, the one or more patterns may include, but not limited to, a sleeve pattern, a neck pattern, a single piece pattern, a two-piece pattern, a gated pattern, a multi-piece pattern, a match plate pattern, a skeleton pattern, a sweep pattern, a loose piece pattern and the like. In the example used herein, approximately 35 unique patterns are extracted for different type of apparels.
[0032] Further, a work allocation module (140), determines a stitching process associated with designing of each of the one or more apparels based on combination of each of the one or more patterns extracted. For example, Style 1 = Pattern X + Pattern Y + Pattern Z. Similarly, Style 2 = Pattern A + Pattern B and so on. In a specific example, the stitching process include atleast one of an aggregation of each of the one or more patterns extracted, right stitching, overlock, interlock or a combination thereof. The work allocation module (140) also recommends a plurality of stitching associated parameters for designing each of the one or more apparels based on the stitching process determined. For example, the plurality of stitching associated parameters may include atleast one of a type of stitch, number of stitches, needle type, and location, orientation and extent of stitches, type of fabric, usability of apparel or a combination thereof. The work allocation module (140) is also configured to allocate tailoring work to one or more tailors for facilitating the production on demand of each of the one or more apparels based on the stitching process determined and the plurality of stitching parameters recommended. Once, the stitching process is recommended, the system allocates the tailoring work to huge numbers of the one or more tailors who are employed to make bulk production of those styles. Every tailor follows their own approach of cutting, stitching, finishing, etc. and thus avoids human error coming in at every stage. Thus, the system (100) enables companies to have an accurate control on sizing and stitching quality, reduce wastage drastically, breakdown the apparel manufacturing process to molecular job level splits, thereby reduce time taken to stitch an apparel, and reduce dependency on the craftsmanship of the tailors
[0033] FIG. 3 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure. The server (200) includes processor(s) (230), and memory (210) operatively coupled to the bus (220). The processor(s) (230), as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof.
[0034] The memory (210) includes several subsystems stored in the form of executable program which instructs the processor (230) to perform the method steps illustrated in FIG. 1. The memory (210) includes a processing subsystem (105) of FIG. 1. The processing subsystem (105) further has following modules: an apparel design receiving module (110), an apparel image processing module (120), an apparel pattern extraction module (130) and a work allocation module (140).
[0035] The apparel design receiving module (110) is configured to receive a plurality of images of a plurality of apparel designs created by one or more fashion designers. The apparel image processing module (120) is configured to process the plurality of images of the plurality of apparel designs using an image processing technique. The apparel image processing module (120) is also configured to identify a type of one or more apparels from plurality of processed images of the plurality of apparel designs. The apparel image processing module (120) is also configured to predict positions of one or more functional key points defined on the one or more apparels identified. The apparel pattern extraction module (130) is configured to extract one or more patterns of each of the one or more apparels based on the positions of the one or more functional key points predicted using a trained neural network model. The work allocation module (140) is configured to determine a stitching process associated with designing of each of the one or more apparels based on combination of each of the one or more patterns extracted. The work allocation module (140) is also configured to recommend a plurality of stitching associated parameters for designing each of the one or more apparels based on the stitching process determined. The work allocation module (140) is also configured to allocate tailoring work to one or more tailors for facilitating the production on demand of each of the one or more apparels based on the stitching process determined and the plurality of stitching parameters recommended.
[0036] The bus (220) as used herein refers to be internal memory channels or computer network that is used to connect computer components and transfer data between them. The bus (220) includes a serial bus or a parallel bus, wherein the serial bus transmits data in bit-serial format and the parallel bus transmits data across multiple wires. The bus (220) as used herein, may include but not limited to, a system bus, an internal bus, an external bus, an expansion bus, a frontside bus, a backside bus and the like.
[0037] FIG. 4 is a flow chart representing the steps involved in a method (300) for production on demand of fashion apparel of FIG.1 in accordance with an embodiment of the present disclosure. The method (300) includes receiving, by an apparel design receiving module of a processing subsystem, a plurality of images of a plurality of apparel designs created by one or more fashion designers in step 310. In one embodiment, receiving the plurality of images of the plurality of apparel designs may include receiving the plurality of images in at least one of a joint photographic expert group (jpeg) format, a portable network graphics (png) format or a combination thereof. In such embodiment, the plurality of images of approximately five thousand stock keeping units (SKUs) may be received.
[0038] The method (300) also includes processing, by an apparel image processing module of the processing subsystem, the plurality of images of the plurality of apparel designs using an image processing technique in step 320. In some embodiment, processing the plurality of images of the plurality of apparel designs may include processing the plurality of images upon rescaling the plurality of images, correcting illumination of the plurality of images, detecting edges of the plurality of images, mathematical morphology calculation of the plurality of images, evaluation and ranking of segmentation technique for the plurality of images.
[0039] The method (300) also includes identifying, by the apparel image processing module of the processing subsystem, a type of one or more apparels from plurality of processed images of the plurality of apparel designs in step 330. In one embodiment, identifying the type of the one or more apparels from the plurality of processed images may include identifying the type of the apparel which may include a top wear or a bottom wear.
[0040] The method (300) also includes predicting, by the apparel image processing module of the processing subsystem, positions of one or more functional key points defined on the one or more apparels identified in step 340. In some embodiment, predicting the positions of the one or more functional key points defined on the one or more apparels may include predicting at least one of cutline, hemline, corners of sleeves, neckline, cuff, panel of a pant, a J-piece at waist or a combination thereof.
[0041] The method (300) also includes extracting, by an apparel pattern extraction module of the processing subsystem, one or more patterns of each of the one or more apparels based on the positions of the one or more functional key points predicted using a trained neural network model in step 350. In one embodiment, extracting the one or more patterns of each of the one or more apparels may include extracting the approximately thirty-five patterns from approximately 5000 images of the plurality of apparels. In such embodiment, extracting the one or more patterns may include, but not limited to, extracting a sleeve pattern, a neck pattern, a single piece pattern, a two-piece pattern, a gated pattern, a multi-piece pattern, a match plate pattern, a skeleton pattern, a sweep pattern, a loose piece pattern and the like.
[0042] The method (300) also includes determining, by a work allocation module of the processing subsystem, a stitching process associated with designing of each of the one or more apparels based on combination of each of the one or more patterns extracted in step 360. In some embodiment, determining the stitching process associated with designing of each of the one or more apparels may include determining the stitching process including atleast one of an aggregation of each of the one or more patterns extracted, right stitching, overlock, interlock or a combination thereof.
[0043] The method (300) also includes recommending, by the work allocation module of the processing subsystem, a plurality of stitching associated parameters for designing each of the one or more apparels based on the stitching process determined in step 370. In one embodiment, recommending the plurality of stitching associated parameters for deigning of each of the one or more apparels may include recommending atleast one of a type of stitch, number of stitches, needle type, and location, orientation and extent of stitches, type of fabric, usability of apparel or a combination thereof. The method (300) also includes allocating, by the work allocation module of the processing subsystem, tailoring work to one or more tailors for facilitating the production on demand of each of the one or more apparels based on the stitching process determined and the plurality of stitching parameters recommended in step 380.
[0044] Various embodiments of the present disclosure provide a system which follows an extremely scalable, efficient and accurate production on demand methodology that would help brands to create an apparel in less than 5 mins, while ensuring accuracy in sizing and fitting for every apparel stitched.
[0045] Moreover, the present disclosed system has all the combinations for a certain number of apparel designs, and the pattern details as well, and it allocates work at a pattern level which not only makes the process of apparel designing efficient but also formulaic approach for the apparel design improves scalability.
[0046] Furthermore, the present disclosed system follows a unique, scalable and smart production on demand methodology through process-based innovation and tech-based innovation, that helps companies create apparel in a few minutes, with almost zero wastage, in a scalable yet efficient way.
[0047] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the disclosure and are not intended to be restrictive thereof.
[0048] While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person skilled in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.
[0049] The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, the order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.