Method and apparatus for providing optimal clothing production plan

Abstract

The present invention relates to a method by which a computing device provides an optimal clothing production plan, comprising the steps of: acquiring product information; determining a plurality of processes on the basis of the product information; acquiring production information acquired using at least one from among vibration pattern information, power consumption information, and input information generated in a machine; calculating process-specific standard work times on the basis of the production information; and establishing an optimal production plan on the basis of the process-specific standard work times calculated for each process.

Description

Method and device for providing an optimal clothing production plan

[0001] The present disclosure relates to a method for a computing device to provide an optimal garment production plan.

[0002] Typically, garment production progresses through the following stages: planning, sewing preparation, and sewing and finishing. The planning stage involves predicting market demand, determining the design concept, and then designing, including color and material planning. Patterns are created and prototypes are produced based on the design, followed by a product evaluation meeting before a decision is made on mass production. In the sewing preparation stage, mass production is decided upon during the planning stage. After purchasing raw and auxiliary materials, industrial patterns are created. Grading, marking, stretching, and cutting are then performed before the sewing process begins. After sewing, the finished garment undergoes finishing processing, inspection, and packaging before shipment. Once mass production is decided, a work instruction sheet is delivered to the factory where the sewing process will take place. This work instruction sheet provides detailed information on the process, including the processing sequence and working conditions, necessary for garment production.

[0003] In particular, the sewing process has a separate process suitable for the product to be worked on (pants, skirts, t-shirts, dresses, etc.), and once each process is decided, the production cost is determined based on the amount of manpower and raw materials required for the process.

[0004] Furthermore, the efficiency of connecting people may be the most crucial factor in the apparel industry. Because of the diverse range of designs, sizes, and patterns, and the difficulty in adjusting fabrics and accessories, workers are more important than robots or machines. Ensuring continuous work among workers may be the most crucial factor in increasing efficiency.

[0005] Accordingly, research is ongoing into how to produce with maximum efficiency in each process.

[0006] The purpose of the present invention is to provide a method and device for providing an optimal clothing production plan.

[0007] According to one embodiment of the present disclosure, an optimal clothing production plan for each production product can be provided.

[0008] Additionally, by utilizing existing collected data, the optimal production quantity and personnel for each process can be allocated to manage the balance of clothing production.

[0009] FIG. 1 is a drawing for explaining a computing device according to one embodiment of the present disclosure.

[0010] FIG. 2 is a general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

[0011] FIG. 3 is a schematic diagram illustrating steps of a method for providing an optimal garment production plan according to one embodiment of the present disclosure.

[0012] FIG. 4 is a schematic diagram illustrating an example of a process bottleneck according to one embodiment of the present disclosure.

[0013] FIG. 5 is a schematic diagram illustrating an example of a learning curve of a method for providing an optimal clothing production plan according to one embodiment of the present disclosure.

[0014] FIG. 6 is a schematic diagram illustrating an example of a monologue device according to one embodiment of the present disclosure.

[0015] FIG. 7 is a schematic diagram illustrating an example of calculating manufacturing cost according to one embodiment of the present disclosure.

[0016] One embodiment of the present disclosure for solving the above-described problem can provide a method for a computing device to provide an optimal clothing production plan, the method comprising: obtaining product information; determining a plurality of processes based on the product information; obtaining production information obtained by at least one of vibration pattern information generated from a machine, power consumption information, and input information; calculating a standard working time for each process based on the production information; and establishing an optimal production plan based on the calculated standard working time for each process.

[0017] In addition, the above production information includes working time data and loss time data, and the working time data is data on working time included in a time value obtained by multiplying a predetermined rate by a preset time value set for each process, and the loss time data is data on working time not included in a time value obtained by multiplying a predetermined rate by a preset time value set for each process, so that a computing device can provide an optimal clothing production plan.

[0018] In addition, the step of calculating the above standard working time can be calculated by applying the above loss time data to the advance time information for each unit time for the process.

[0019] In addition, the method of providing the optimal clothing production plan by the computing device may further include a step of obtaining information on the number of productions per unit time; and the step of establishing the optimal production plan may further include a step of selecting a process in which a bottleneck occurs due to an accumulation of work among a plurality of processes; and a bottleneck resolution step of determining a number of personnel to be added to the process selected as the bottleneck based on the information on the number of productions per unit time in order to resolve the bottleneck.

[0020] In addition, the step of establishing an optimal production plan may further include a step of applying the work time data to the standard work time for each process calculated above.

[0021] In addition, the step of establishing the above optimal production plan may further include a step of applying a learning curve that reflects a numerical change in production quantity based on the number of operations.

[0022] In addition, the vibration pattern information is acquired by a vibration pattern information acquisition device recognizing vibrations generated from a machine, and the input information is information on task completion input by a user into a task completion input device, and the vibration pattern information acquisition device and the task completion input device may be the same device.

[0023] In addition, the method for providing the above optimal clothing production plan may further include a step of calculating a manufacturing cost based on the clothing production volume calculated according to the above optimal production plan.

[0024] In addition, the step of selecting a process in which the bottleneck occurs may select a process in which the standard operation time is greater than a predetermined ratio compared to the previous process as the process in which the bottleneck occurs.

[0025] Additionally, the bottleneck resolution step may determine the number of personnel to be added to the selected bottleneck occurrence process based on the difference in standard working time between the selected bottleneck occurrence process and the previous process.

[0026] In addition, the bottleneck resolution step may further consider the production quantity information per unit time to determine the number of personnel to be added to the selected bottleneck occurrence process.

[0027] Additionally, the step of calculating the manufacturing cost can be calculated by further applying the defect rate.

[0028] Various embodiments are now described with reference to the drawings. In this specification, various descriptions are provided to facilitate understanding of the present disclosure. However, it will be apparent that these embodiments may be practiced without these specific details.

[0029] As used herein, the terms "component," "module," "system," and the like refer to computer-related entities, hardware, firmware, software, a combination of software and hardware, or an execution of software. For example, a component may be, but is not limited to, a procedure running on a processor, a processor, an object, a thread of execution, a program, and / or a computer. For example, both an application running on a computing device and the computing device may be a component. One or more components may reside within a processor and / or a thread of execution. A component may be localized within a single computer. A component may be distributed between two or more computers. Furthermore, these components may execute from various computer-readable media having various data structures stored therein. Components may communicate via local and / or remote processes, for example, by signals comprising one or more data packets (e.g., data from one component interacting with another component in a local system, a distributed system, and / or data transmitted to another system via a network such as the Internet via signals).

[0030] Furthermore, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or." That is, unless otherwise specified or clear from context, "X employs A or B" is intended to mean either of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, "X employs A or B" can apply to any of these cases. Furthermore, the term "and / or" as used herein should be understood to refer to and include all possible combinations of one or more of the associated items listed.

[0031] Additionally, the terms "comprises" and / or "comprising" should be understood to imply the presence of the features and / or components in question. However, it should be understood that the terms "comprises" and / or "comprising" do not exclude the presence or addition of one or more other features, components, and / or groups thereof. Furthermore, unless otherwise specified or clear from the context to refer to the singular form, the singular in the specification and claims should generally be construed to mean "one or more."

[0032] And, the term "at least one of A or B" should be interpreted to mean "if it includes only A", "if it includes only B", or "if it is combined in the composition of A and B".

[0033] Those skilled in the art should further appreciate that the various illustrative logical blocks, configurations, modules, circuits, means, logics, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application. However, such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0034] The description of the disclosed embodiments is provided to enable those skilled in the art to make or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art. The general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Therefore, the present invention is not limited to the embodiments disclosed herein. The present invention is to be construed in the widest scope consistent with the principles and novel features disclosed herein.

[0035] Producing a single garment requires multiple processes. For example, producing a shirt may involve cutting the material, sewing the shoulders and torso, attaching the sleeves, sewing the sides, attaching the collar, attaching the cuffs, creating buttons and buttonholes, and finishing processes. In this case, establishing a garment production plan, including the process sequence, the number of workers assigned to each process, and the movement path for performing the processes in a specific space, is extremely challenging. Furthermore, considering an optimal garment production plan for each unit and calculating the manufacturing cost for products manufactured according to the plan is also challenging. To address at least one of these issues, a computing device (100) according to one embodiment of the present disclosure can provide an optimized garment production plan.

[0036] In this case, the computing device (100) may provide a clothing production plan implemented in the form of software, may provide the clothing production plan by displaying it on a web page, or may provide the clothing production plan in a form stored on a non-volatile storage medium. Without being limited thereto, the computing device (100) may provide the clothing production plan in various forms of media.

[0037] Hereinafter, the present disclosure will be described with reference to the drawings.

[0038] FIG. 1 is a diagram for explaining a computing device according to one embodiment of the present disclosure, FIG. 2 is a general schematic diagram for an exemplary computing environment in which embodiments of the present disclosure can be implemented, FIG. 3 is a diagram schematically illustrating steps of a method for providing an optimal clothing production plan according to one embodiment of the present disclosure, FIG. 4 is a diagram schematically illustrating an example of a process bottleneck according to one embodiment of the present disclosure, FIG. 5 is a diagram schematically illustrating an example of a learning curve of a method for providing an optimal clothing production plan according to one embodiment of the present disclosure, FIG. 6 is a diagram schematically illustrating an example of a monolog device according to one embodiment of the present disclosure, and FIG. 7 is a diagram schematically illustrating an example of calculating a manufacturing cost according to one embodiment of the present disclosure.

[0039] FIG. 1 is a drawing for explaining a computing device (100) according to one embodiment of the present disclosure.

[0040] According to one embodiment of the present disclosure, a computing device (100) can provide an optimal garment production plan as described herein.

[0041] In one embodiment of the present disclosure, the computing device (100) may include other components for performing the computing environment of the computing device (100), and only some of the disclosed components may constitute the computing device (100).

[0042] A computing device (100) may include a processor (110), memory (130), and network unit (150).

[0043] The processor (110) may be configured with one or more cores, and may include a processor for data analysis and deep learning, such as a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), and a tensor processing unit (TPU) of a computing device. The processor (110) may read a computer program stored in the memory (130) and perform data processing for machine learning according to an embodiment of the present disclosure. According to an embodiment of the present disclosure, the processor (110) may perform operations for learning a neural network. The processor (110) may perform calculations for learning a neural network, such as processing input data for learning in deep learning (DL), extracting features from input data, calculating errors, and updating weights of a neural network using backpropagation. At least one of the CPU, GPGPU, and TPU of the processor (110) may process learning of a network function. For example, a CPU and a GPGPU can jointly process network function learning and data classification using network functions. Furthermore, in one embodiment of the present disclosure, processors of multiple computing devices can be jointly used to process network function learning and data classification using network functions. Furthermore, a computer program executed on a computing device according to one embodiment of the present disclosure may be a CPU, GPGPU, or TPU executable program.

[0044] According to one embodiment of the present disclosure, the memory (130) can store any form of information generated or determined by the processor (110) and any form of information received by the network unit (150).

[0045] According to one embodiment of the present disclosure, the memory (130) may include at least one type of storage medium among a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (e.g., SD or XD memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, and an optical disk. The computing device (100) may also operate in relation to web storage that performs the storage function of the memory (130) on the internet. The description of the above-described memory is merely an example, and the present disclosure is not limited thereto.

[0046] The network unit (150) according to one embodiment of the present disclosure can use various wired communication systems such as a public switched telephone network (PSTN), xDSL (x Digital Subscriber Line), RADSL (Rate Adaptive DSL), MDSL (Multi Rate DSL), VDSL (Very High Speed ​​DSL), UADSL (Universal Asymmetric DSL), HDSL (High Bit Rate DSL), and a local area network (LAN).

[0047] In addition, the network unit (150) presented in this specification can use various wireless communication systems such as CDMA (Code Division Multi Access), TDMA (Time Division Multi Access), FDMA (Frequency Division Multi Access), OFDMA (Orthogonal Frequency Division Multi Access), SC-FDMA (Single Carrier-FDMA) and other systems.

[0048] In the present disclosure, the network unit (150) may be configured regardless of the communication mode, such as wired or wireless, and may be configured as various communication networks, such as a personal area network (PAN) and a wide area network (WAN). In addition, the network may be the well-known World Wide Web (WWW), and may also utilize a wireless transmission technology used for short-distance communication, such as infrared (IrDA: Infrared Data Association) or Bluetooth.

[0049] FIG. 2 is a general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

[0050] Although the present disclosure has been described above as being generally implemented by a computing device, those skilled in the art will appreciate that the present disclosure may be implemented in combination with computer-executable instructions and / or other program modules that may be executed on one or more computers and / or as a combination of hardware and software.

[0051] Generally, program modules include routines, programs, components, data structures, and the like that perform particular tasks or implement particular abstract data types. Furthermore, those skilled in the art will appreciate that the methods of the present disclosure can be implemented with other computer system configurations, including single-processor or multiprocessor computer systems, minicomputers, mainframe computers, as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which may be operatively connected to one or more associated devices.

[0052] The described embodiments of the present disclosure can also be practiced in distributed computing environments, where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0053] Computers typically include a variety of computer-readable media. Computer-readable media can be any media that can be accessed by a computer, and includes both volatile and nonvolatile media, transitory and non-transitory media, removable and non-removable media. By way of example, and not limitation, computer-readable media can include computer-readable storage media and computer-readable transmission media. Computer-readable storage media includes both volatile and nonvolatile media, transitory and non-transitory media, removable and non-removable media implemented in any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer-readable storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be accessed by a computer and used to store the desired information.

[0054] Computer-readable transmission media typically includes any information delivery media that embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism. The term modulated data signal means a signal that has one or more of its characteristics set or changed so as to encode information in the signal. By way of example, and not limitation, computer-readable transmission media includes wired media, such as a wired network or direct-wired connection, and wireless media, such as acoustic, RF, infrared, or other wireless media. Combinations of any of the above are also intended to be included within the scope of computer-readable transmission media.

[0055] An exemplary environment (1100) implementing various aspects of the present disclosure is illustrated, including a computer (1102) comprising a processing unit (1104), system memory (1106), and a system bus (1108). The system bus (1108) connects system components, including but not limited to the system memory (1106), to the processing unit (1104). The processing unit (1104) may be any of a variety of commercially available processors. Dual processors and other multiprocessor architectures may also be utilized as the processing unit (1104).

[0056] The system bus (1108) may be any of several types of bus structures that may be additionally interconnected to a memory bus, a peripheral bus, and a local bus using any of a variety of commercial bus architectures. The system memory (1106) includes read-only memory (ROM) (1110) and random access memory (RAM) (1112). A basic input / output system (BIOS) is stored in non-volatile memory (1110), such as ROM, EPROM, or EEPROM, and includes basic routines that help transfer information between components within the computer (1102), such as during start-up. The RAM (1112) may also include high-speed RAM, such as static RAM, for caching data.

[0057] The computer (1102) also includes an internal hard disk drive (HDD) (1114) (e.g., EIDE, SATA) - which may also be configured for external use within a suitable chassis (not shown), a magnetic floppy disk drive (FDD) (1116) (e.g., for reading from or writing to a removable diskette (1118)), and an optical disk drive (1120) (e.g., for reading from or writing to a CD-ROM disk (1122) or other high-capacity optical media such as a DVD). The hard disk drive (1114), the magnetic disk drive (1116), and the optical disk drive (1120) may be connected to the system bus (1108) by a hard disk drive interface (1124), a magnetic disk drive interface (1126), and an optical drive interface (1128), respectively. The interface (1124) for implementing an external drive includes at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies.

[0058] These drives and their associated computer-readable media provide non-volatile storage of data, data structures, computer-executable instructions, and the like. In the case of the computer (1102), the drives and media correspond to storing any data in a suitable digital format. While the description of computer-readable media above refers to HDDs, removable magnetic disks, and removable optical media such as CDs or DVDs, those skilled in the art will appreciate that other types of media readable by a computer, such as zip drives, magnetic cassettes, flash memory cards, cartridges, and the like, may also be used in the exemplary operating environment, and that any such media may contain computer-executable instructions for performing the methods of the present disclosure.

[0059] A number of program modules, including an operating system (1130), one or more application programs (1132), other program modules (1134), and program data (1136), may be stored in the drive and RAM (1112). All or portions of the operating system, applications, modules, and / or data may also be cached in RAM (1112). It will be appreciated that the present disclosure may be implemented in various commercially available operating systems or combinations of operating systems.

[0060] A user may enter commands and information into the computer (1102) via one or more wired / wireless input devices, such as a keyboard (1138) and a pointing device such as a mouse (1140). Other input devices (not shown) may include a microphone, an IR remote control, a joystick, a game pad, a stylus pen, a touch screen, and the like. These and other input devices are often connected to the processing unit (1104) via an input device interface (1142) that is connected to the system bus (1108), but may be connected by other interfaces such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, and the like.

[0061] A monitor (1144) or other type of display device is also connected to the system bus (1108) via an interface, such as a video adapter (1146). In addition to the monitor (1144), the computer typically includes other peripheral output devices (not shown), such as speakers, a printer, and so on.

[0062] The computer (1102) may operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) (1148), via wired and / or wireless communications. The remote computer(s) (1148) may be a workstation, a computing device computer, a router, a personal computer, a portable computer, a microprocessor-based entertainment device, a peer device, or other conventional network node, and generally include many or all of the components described for the computer (1102), although for simplicity, only the memory storage device (1150) is shown. The logical connections shown include wired / wireless connections to a local area network (LAN) (1152) and / or a larger network, such as a wide area network (WAN) (1154). Such LAN and WAN networking environments are common in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which may be connected to a worldwide computer network, such as the Internet.

[0063] When used in a LAN networking environment, the computer (1102) is connected to a local network (1152) via a wired and / or wireless communication network interface or adapter (1156). The adapter (1156) may facilitate wired or wireless communications to the LAN (1152), which may also include a wireless access point installed therein for communicating with the wireless adapter (1156). When used in a WAN networking environment, the computer (1102) may include a modem (1158), be connected to a communications computing device on the WAN (1154), or have other means of establishing communications over the WAN (1154), such as via the Internet. The modem (1158), which may be internal or external and wired or wireless, is connected to the system bus (1108) via a serial port interface (1142). In a networked environment, program modules or portions thereof described for the computer (1102) may be stored in a remote memory / storage device (1150). It will be appreciated that the network connections depicted are exemplary and other means of establishing a communications link between the computers may be used.

[0064] The computer (1102) operates to communicate with any wireless device or object that is arranged and operates via wireless communication, such as a printer, a scanner, a desktop and / or portable computer, a portable data assistant (PDA), a communication satellite, any equipment or location associated with a radio-detectable tag, and a telephone. This includes at least Wi-Fi and Bluetooth wireless technologies. Accordingly, the communication may be a predefined structure as in a conventional network, or may simply be an ad hoc communication between at least two devices.

[0065] Wi-Fi (Wireless Fidelity) enables connections to the Internet and other devices without wires. Wi-Fi is a wireless technology that allows devices, such as computers, to send and receive data anywhere within the coverage area of ​​a base station, both indoors and outdoors, similar to cell phones. Wi-Fi networks use wireless technologies called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, and high-speed wireless connections. Wi-Fi can be used to connect computers to each other, to the Internet, and to wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in the unlicensed 2.4 and 5 GHz radio bands, at data rates of, for example, 11 Mbps (802.11a) or 54 Mbps (802.11b), or in products that include both bands (dual-band).

[0066] Those skilled in the art will appreciate that information and signals may be represented using any of a variety of different technologies and techniques. For example, the data, instructions, commands, information, signals, bits, symbols, and chips referenced in the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0067] Those skilled in the art will appreciate that the various illustrative logical blocks, modules, processors, means, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, various forms of programs or design code (referred to herein, for convenience, as software), or a combination of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0068] The various embodiments presented herein can be implemented as a method, apparatus, or article of manufacture using standard programming and / or engineering techniques. The term article of manufacture includes a computer program, carrier, or media accessible from any computer-readable storage device. For example, computer-readable storage media include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical disks (e.g., CDs, DVDs, etc.), smart cards, and flash memory devices (e.g., EEPROMs, cards, sticks, key drives, etc.). Furthermore, various storage media presented herein include one or more devices and / or other machine-readable media for storing information.

[0069] It should be understood that the specific order or hierarchy of steps in the presented processes is merely an example of exemplary approaches. It should be understood that the specific order or hierarchy of steps in the processes may be rearranged within the scope of the present disclosure based on design priorities. The appended method claims provide elements of various steps in a sample order, but are not intended to be limited to the specific order or hierarchy presented.

[0070] FIG. 3 is a schematic diagram illustrating steps of a method for providing an optimal garment production plan according to one embodiment of the present disclosure.

[0071] According to one embodiment of the present disclosure, a computing device (100) can obtain product information.

[0072] For example, the computing device (100) can obtain information on how to generate an optimal clothing production plan for a certain product. In this case, the product information may be information on the type of clothing, such as a shirt, pants, a dress, or a T-shirt.

[0073] According to one embodiment of the present disclosure, product information may include various information necessary for manufacturing a product. For example, product information may include, but is not limited to, at least one of product type information (e.g., shirt, pants, dress, etc.), size information (e.g., medium, large, etc.), and process information (e.g., operation 1, operation 2, operation 3, etc.).

[0074] The computing device (100) can receive product information from a user. In addition, the computing device (100) can receive product information from another device (not shown) via a network, and is not limited thereto, and can obtain product information in various ways.

[0075] According to one embodiment of the present disclosure, the computing device (100) can determine multiple processes based on product information.

[0076] According to one embodiment of the present disclosure, product information acquired by the computing device (100) may include process information of the product.

[0077] Clothing is manufactured through multiple processes. For example, when making a shirt, there's a process of cutting prepared material according to a pattern. Furthermore, the cut pieces can be sewn together to complete the shirt. In this case, the sewing process may include sewing the shoulders and torso, attaching sleeves, sewing the sides, attaching a collar, attaching cuffs, creating buttons and buttonholes, and finishing. Thus, numerous processes are required to manufacture a single garment, and product information can include information about these processes.

[0078] The computing device (100) can determine multiple processes based on product information. For example, if the product information acquired by the computing device (100) represents a shirt, the computing device (100) can determine that a cutting process, a process for sewing the shoulders and torso, a process for attaching sleeves, a process for sewing the sides, a process for attaching a collar, a process for attaching cuffs, a process for creating buttons and buttonholes, and a finishing process are necessary for manufacturing the shirt.

[0079] In this case, the computing device (100) can obtain process information from product information. In addition, the computing device (100) can obtain process information about the product from an external device via a network, and determine multiple processes based on the obtained process information.

[0080] According to one embodiment of the present disclosure, the computing device (100) can obtain production information obtained by at least one of vibration pattern information generated from the machine, power consumption information, and input information.

[0081] According to one embodiment of the present disclosure, production information may include information generated during the production of a product for each process.

[0082] The computing device (100) can obtain production information based on at least one of vibration pattern information, power consumption information, and input information generated from the machine.

[0083] In the garment manufacturing process, workers may use a variety of machines to perform their tasks. For example, workers may use sewing machines to perform sewing tasks.

[0084] According to one embodiment of the present disclosure, when workers use a sewing machine, vibrations are generated in the sewing machine, and the computing device (100) can obtain production information based on the generated vibration pattern information. In this case, the computing device (100) can obtain information about vibrations from a vibration pattern information acquisition device (e.g., a mono-log device, etc.), but is not limited thereto and can obtain vibration pattern information in various ways.

[0085] According to another embodiment of the present disclosure, when workers use a sewing machine, the power consumption of the sewing machine changes, and the computing device (100) can obtain information on the changed power consumption of the sewing machine and obtain production information based on the obtained power consumption information. In this case, the computing device (100) can obtain power consumption change information directly from the sewing machine, or from a power measurement device (not shown), but is not limited thereto, and can obtain power consumption information in various ways.

[0086] According to another embodiment of the present disclosure, when workers use a sewing machine, they can input that they have completed a task after performing a task, and the computing device (100) can obtain production information based on the input information. In this case, the workers can input that they have completed a task into a task completion input device (e.g., a mono log device, etc.), and can input that they have completed a task directly into the sewing machine (e.g., when the sewing machine is equipped with an input device), and the input that they have completed a task can be performed in various ways without being limited thereto.

[0087] According to one embodiment of the present disclosure, the computing device (100) can acquire production information based on at least one of acquired vibration pattern information, power consumption information, and input information. For example, the computing device (100) can generate production information using vibration pattern information and power consumption information to increase the accuracy of the production information, and can generate production information using vibration pattern information and input information. However, the present invention is not limited thereto and can generate production information using various methods.

[0088] According to one embodiment of the present disclosure, the aforementioned vibration pattern information acquisition device and task completion input device can be implemented by a single machine. For example, the machine illustrated in FIG. 6 can be placed on a table on which a sewing machine is installed. This machine can detect vibrations generated by the sewing machine via the table and generate vibration pattern information based on the detection results. In addition, this machine is equipped with an input button, so that when a worker completes a task, they can input task completion by pressing the input button. This machine can provide the generated information (e.g., vibration pattern information and / or input information) to a computing device (100) via a wireless (or wired) network.

[0089] According to one embodiment of the present disclosure, production information may include work time data and loss time data.

[0090] Working time data refers to data on the time a worker actually spends performing a task. For example, to obtain data on a worker's work performance using vibration pattern information and / or input data, it is necessary to classify data on the time a worker does not work. Data on the time a worker does not work can be classified as lost time data.

[0091] To reiterate, work time data may be the actual time a worker performed work, and loss time data may be the time a worker was away from his / her seat or took a break.

[0092] According to one embodiment of the present disclosure, the working time data may mean data on the working time included within a time value obtained by multiplying a predetermined ratio by a preset time value set for each process.

[0093] For example, the computing device (100) may retain preset time values ​​set for each process. The preset time values ​​set for each process may be time information regarding the existing work time for each process. For example, if it takes 50 seconds to complete a single product task and 10 seconds to prepare for the next task, the process time for one product may be 1 minute.

[0094] Additionally, the predetermined ratio may be a ratio (e.g., 120%, 130%, 150%, etc.) by which the process operation time is multiplied. For example, this may be to exclude cases where the average process operation time differs significantly.

[0095] As described above, it may be possible to classify the work time data and loss time data by comparing the actual process work time with the time value obtained by multiplying the process work time by a predetermined ratio.

[0096] Specifically, if the actual process operation time falls within the time value obtained by multiplying the process operation time by a predetermined ratio, it may be classified as work time data. For example, if the process operation time is 50 seconds and the predetermined ratio is 1.5, if the actual process operation time is within 75 seconds based on the product of the two, the task may be classified as work time data, and if it takes more than 75 seconds, it may be classified as loss time data. For example, if a worker's work speed is 50 seconds on average but it consistently takes 60 seconds, the worker's work speed may be judged to be slow and the task may be classified as performed. In addition, if the work time is significantly slower than the existing work time, it may be included as loss time data when the worker did not perform the task, such as leaving the seat or taking a break.

[0097] According to one embodiment of the present disclosure, the computing device (100) can calculate a standard working time for each process based on production information.

[0098] For example, the computing device (100) can calculate a standard working time for each process by reflecting production information in the preset time value set for each process. Specifically, the computing device (100) can calculate a standard working time by applying loss time data to the preset time information for each unit time for the process.

[0099] For example, the computing device (100) can obtain a loss time ratio for a specific process. For example, the computing device (100) can obtain that the loss time ratio for a specific process is 5%. In this case, the computing device (100) can obtain the information by at least one of the vibration pattern information, power consumption information, and input information described above, or can obtain the information from an external device (not shown) via a network, and is not limited thereto, and can obtain the information by various methods.

[0100] The computing device (100) can obtain unit-time advance time information for a process. For example, the computing device (100) can obtain information that the process of attaching the shoulders and torso to a shirt takes 3,000 seconds if performed 100 times. Unit-time advance time information for a process can be derived from data actually performed in a factory and stored as a preset value.

[0101] In this case, the computing device (100) can obtain different unit time advance time information for each factory. For example, the computing device (100) can obtain different unit time advance time information for each factory even for the same process.

[0102] The computing device (100) can calculate the standard working time for the process by applying the loss time data to the acquired unit time-based advance time information. Specifically, if the loss rate of the process of attaching the shoulder and the torso to the shirt is 5%, the computing device (100) can remove 5% of 3000 seconds, determine that 2750 seconds is the actual working time, and calculate that the standard working time of the process of attaching the shoulder and the torso to the shirt is 27.5 seconds (2750 / 100=27.5).

[0103] In this case, the standard working time for each process may vary across manufacturing plants. For example, the standard working time for the first process (e.g., attaching the shoulder and torso of a shirt) in Plant 1 may differ from the standard working time for the first process (e.g., attaching the shoulder and torso of a shirt) in Plant 2.

[0104] Additionally, the standard working hours for each process may vary depending on the worker. The computing device (100) can provide a highly reliable and optimal clothing production plan by calculating the standard working hours for each process based on the actual work performed.

[0105] According to one embodiment of the present disclosure, the computing device (100) can establish an optimal production plan for clothing production based on the standard working time for each process calculated for each process.

[0106] For example, the computing device (100) can establish an optimal production plan by adjusting the manpower input for each process of producing a product based on the standard working time for each process.

[0107] Specifically, the computing device (100) can establish an optimal production plan by assigning relatively more workers to a process with a long standard working time per process than to other processes.

[0108] To explain in detail, let us assume that the standard working time of the first process is 30 seconds, the standard working time of the second process is 90 seconds, and the standard working time of the third process is 60 seconds. In this case, the computing device (100) can establish an optimal production plan by assigning more workers to the second process than to the first and third processes.

[0109] According to one embodiment of the present disclosure, the computing device (100) can obtain information on the number of units produced per unit of time. Furthermore, it can select a process among multiple processes where a bottleneck occurs due to an accumulation of work. Furthermore, to resolve the bottleneck, the number of personnel to be added to the process selected as the bottleneck can be determined based on the information on the number of units produced per unit of time.

[0110] For example, the computing device (100) can obtain information on the number of shirts produced per unit time. Specifically, the computing device (100) can obtain information that 1,000 shirts must be produced per hour.

[0111] The computing device (100) can select multiple processes necessary for producing a shirt, determine the time required to produce one shirt based on the standard working time for each process, and determine that additional workers are needed to produce 1,000 shirts per hour based on the determined time.

[0112] In this case, the computing device (100) can select a process among multiple processes in which a bottleneck phenomenon occurs due to an accumulation of work.

[0113] For example, the computing device (100) may select a process in which the standard operation time is greater than a predetermined ratio of the previous process as a process in which a bottleneck occurs. Specifically, if the standard operation time of the first process is 30 seconds and the standard operation time of the second process is 90 seconds, and the predetermined ratio is 200%, the computing device (100) may select the second process as the process in which a bottleneck occurs.

[0114] In this case, the computing device (100) can determine the number of personnel to be added to the selected bottleneck process based on the difference in standard working time between the bottleneck process and the previous process. Specifically, if the standard working time of the first process is 30 seconds and the standard working time of the second process is 90 seconds, two more personnel can be added to the second process. Additionally, if the standard working time of the second process is 120 seconds, three more personnel can be added to the second process.

[0115] Additionally, the computing device (100) can select a process in which a bottleneck occurs by selecting at least one process in descending order of standard operation time from among a plurality of processes. Specifically, the computing device (100) can select the top three processes in which standard operation time is long from among a plurality of processes as the processes in which a bottleneck occurs.

[0116] In this case, the computing device (100) may determine the number of personnel to be added to the selected bottleneck process based on the difference in standard working hours between the processes that are not selected as bottlenecks and the processes that are not selected as bottlenecks. Specifically, the computing device (100) may determine the number of personnel to be added based on the difference between the average of the standard working hours of the processes that are not selected as bottlenecks and the standard working hours of the selected bottleneck process. In addition, the computing device (100) may determine the number of personnel to be added by comparing the minimum standard working hours among the processes that are not selected as bottlenecks with the standard working hours of the bottleneck process, and the computing device (100) may determine the number of personnel to be added to the bottleneck process in various ways without being limited thereto.

[0117] According to one embodiment of the present disclosure, the number of personnel to be added to a process selected as a bottleneck can be determined by considering information on the number of units produced per unit time.

[0118] For example, if the computing device (100) can satisfy the production quantity per unit time information by adding a predetermined number of workers to a process where a bottleneck occurs, it may not add additional workers. Specifically, if the computing device (100) can satisfy the production quantity per unit time information by adding one worker to a process where a bottleneck occurs, it may not add additional workers.

[0119] According to one embodiment of the present disclosure, the computing device (100) can re-evaluate whether the unit-hourly production quantity can be met after adding workers to meet the unit-hourly production quantity information. Furthermore, based on the re-evaluated results, the computing device (100) can determine whether to deploy additional workers to resolve bottlenecks.

[0120] For example, the computing device (100) can add a worker to a process where a bottleneck is occurring. Furthermore, the computing device (100) can re-evaluate whether the hourly production quantity information is met by calculating the number of workers added. If the re-evaluated result indicates that the hourly production quantity information is not met, additional workers can be added, and after adding workers, whether the hourly production quantity information is met can be re-evaluated.

[0121] The computing device (100) can generate an optimal clothing production plan by alternately adding workers and determining whether the production quantity per unit time can be met.

[0122] Below, the bottleneck phenomenon according to one embodiment of the present disclosure is described again.

[0123] A bottleneck generally refers to a congestion caused by a sudden narrowing of a wide path, like the neck of a bottle. However, in this disclosure, each process is designated as having a sequence, with the preceding process having to be completed before the next process can proceed. Accordingly, if the amount of material sent from a preceding process increases, the subsequent process may experience a backlog of work, which can be referred to as a bottleneck.

[0124] Specifically, referring to FIG. 4, information on the number of units produced per unit time for each process can be obtained. Here, the information on the number of units produced per unit time may be information on the time required to produce one product per process.

[0125] Accordingly, information about the time required for each process can be obtained, and processes that take a long time can be selected as processes that cause bottlenecks.

[0126] Additionally, it may be necessary to determine additional personnel to address the process selected as a bottleneck.

[0127] Here, adding additional personnel to a process experiencing a bottleneck can be a solution. While this increases production time per product, it also improves productivity. By adding additional personnel, the workload in that process can be quickly transferred to the next process.

[0128] According to one embodiment of the present disclosure, the step of selecting a process in which a bottleneck occurs may be to select a process in which a standard operation time is greater than a predetermined ratio of a previous process as a process in which a bottleneck occurs.

[0129] Here, the standard working time may be a standard working time calculated based on the above production information.

[0130] At this time, it may be possible to select a process that causes a bottleneck based on the production time per product per process based on the standard working time produced.

[0131] For example, if it takes 10 seconds to produce one product in process 1 and 20 seconds to produce one product in process 2, then process 2 may be selected as the process that causes the bottleneck because one worker in process 1 produces 6 products in 1 minute and one worker in process 2 produces 3 products in 1 minute, resulting in the accumulation of 3 products.

[0132] According to one embodiment of the present disclosure, the bottleneck resolution step may determine the number of personnel to be added to the selected bottleneck occurrence process based on the difference in standard working time between the selected bottleneck occurrence process and the previous process.

[0133] Here, the bottleneck resolution step is a necessary step to resolve the bottleneck in the process selected as the bottleneck process. This can involve methods such as assigning additional personnel or reducing the number of personnel in the previous process. Preferably, the additional personnel can be used to increase product production efficiency.

[0134] Specifically, a bottleneck process requires more time per product than previous processes, resulting in a buildup of product. This may necessitate the addition of additional personnel to the process.

[0135] For example, if the work in process 3 requires 10 seconds per product and the work in process 4 requires 20 seconds per product, twice as many workers may be assigned to process 4 as to process 3 because it takes twice as much time. Also, if the work in process 4 requires 20 seconds per product and the work in process 5 requires 60 seconds per product, three times as many workers may be assigned to process 5 as to process 4 because it takes three times as much time. Accordingly, if one worker is assigned to process 3, two workers may be assigned to process 4, and six workers may be assigned to process 5.

[0136] According to one embodiment of the present disclosure, the bottleneck resolution step may determine the number of personnel to be added to the selected bottleneck occurrence process by further considering the information on the number of units produced per unit time.

[0137] Here, the production quantity per unit time may be the number of products that can be produced per hour, determined by each process based on the standard operating hours. For example, if Process 1 can produce 1,000 products per hour with one worker, the production quantity per unit time may be 1,000 products.

[0138] Referring to Figure 4, the number of units that can be produced per unit time for each process is the largest for process 4, and process 5 after process 4 may have a significantly lower production volume than process 4. Accordingly, process 5 may not be able to process all of the quantities coming from process 4, but if the production volume of process 5 is greater than the total production volume within a preset period, additional personnel may not be assigned.

[0139] Specifically, considering production volume information, additional personnel can be determined for processes experiencing bottlenecks. For example, if Process 3 requires the production of 15,000 units over five days, one worker in Process 3 can produce 1,000 units per hour. If each worker works five hours a day to produce 5,000 units, the required 15,000 units can be produced in three days. This could provide an optimal production plan that allows production to proceed without additional personnel even if Process 3 experiences a bottleneck due to the high volume of production delivered from Process 2.

[0140] As mentioned above, by using the information on the number of units produced per unit time, bottlenecks can be predicted in advance and an optimal clothing production plan can be provided by using additional personnel.

[0141] According to one embodiment of the present disclosure, the step of establishing an optimal production plan may further include a step of applying the work time data to the calculated standard work time for each process.

[0142] The computing device (100) can obtain process-specific work efficiency data based on work time data. In this case, the work efficiency data can be determined for each factory. Additionally, the work efficiency data can be determined for each worker. The work efficiency data can be calculated as a ratio. Specifically, the work efficiency data can be calculated as a ratio of 110%, 90%, 85%, etc.

[0143] The computing device (100) can reflect work efficiency data in the standard work time. For example, if the standard work time for a specific process is 100 seconds and the work efficiency data is 90%, the computing device (100) can determine the standard work time for the process as 90 seconds.

[0144] By further incorporating work efficiency data into standard working hours, the computing device (100) can generate a more accurate optimal production plan for each factory. Furthermore, by further incorporating work efficiency data into standard working hours, the computing device (100) can generate an optimal production plan that reflects the characteristics of each worker.

[0145] According to one embodiment of the present disclosure, the step of establishing an optimal production plan may further include the step of applying a learning curve that reflects a numerical change in production quantity based on the number of operations.

[0146] A learning curve is information that shows how the time and effort required for an individual or organization to learn and master a new skill, knowledge, or task changes over time. Initially, learning occurs rapidly, but after reaching a certain level of proficiency, the benefits of additional learning may diminish.

[0147] Referring to Figure 5, it can be seen that the production rate increases rapidly until the 5th day, and that the production rate increases gradually after the 5th day.

[0148] According to one embodiment of the present disclosure, when calculating a standard operation time, the computing device (100) may further apply a learning curve based on the number of operations. For example, for a specific process, the standard operation time for a production quantity of 10,000 units may be shorter than the standard operation time for a production quantity of 1,000 units. This calculation is made in consideration of the fact that, if a manufacturer continuously repeats the same process by applying a learning curve, the operation speed will increase.

[0149] According to one embodiment of the present disclosure with reference to FIG. 6, a computing device (100) can acquire information about vibration from a vibration pattern information acquisition device (e.g., a mono log device, etc.). The vibration pattern information acquisition device includes a sensor that detects vibration and can detect vibration. The computing device (100) can acquire vibration pattern information from the vibration pattern information acquisition device through a wireless network (or wired network), but is not limited thereto and can acquire vibration pattern information in various ways.

[0150] According to one embodiment of the present disclosure with reference to FIG. 6, the computing device (100) can obtain production information based on input information. In this case, workers can input that a task has been completed into a task completion input device (e.g., a mono log device, etc.). Specifically, the task completion input device may have a physical button, and workers can input that a task has been completed by clicking the button each time a task is finished. In addition, the computing device (100) can be connected to the task completion input device via a wireless (or wired) network, and can obtain input information about the task completion from the task completion input device. In addition, workers can input that a task has been completed directly into a sewing machine (e.g., if the sewing machine is equipped with an input device), but are not limited thereto, and can input that a task has been completed in various ways.

[0151] According to one embodiment of the present disclosure, a vibration pattern information acquisition device and a task completion input device can be implemented as a single device.

[0152] For example, referring to FIG. 5, a single device may include both a button and a vibration detection sensor. In this case, if the device is placed on a worker's work table, the device can detect vibrations, and the user can input the completion of a task by pressing a button when the task is completed.

[0153] According to one embodiment of the present disclosure, the method for providing the optimal clothing production plan may further include a step of calculating a manufacturing cost based on the clothing production volume calculated according to the optimal production plan.

[0154] Manufacturing cost can refer to the amount of money required to produce a single product. For example, manufacturing cost can be calculated by dividing the number of products by the total line usage cost, which includes the cost of raw materials, factory usage, worker wages, and the number of line days.

[0155] When calculating manufacturing costs, the number of products produced and the production time can contribute significantly. For example, manufacturing costs may be lower when producing 1,000 units than when producing 100 units. Furthermore, manufacturing costs may be lower when producing 1,000 units in 10 days than when producing 1,000 units in one day.

[0156] According to one embodiment of the present disclosure, by calculating the manufacturing cost according to an optimal clothing production plan, a relatively low manufacturing cost can be provided compared to other companies.

[0157] In one embodiment of the present disclosure, when calculating manufacturing costs, the computing device (100) may additionally apply a defect rate to calculate the manufacturing cost. Specifically, the computing device (100) may set the production quantity of products to be greater than the preset production quantity, taking into account the occurrence of samples and defective products, thereby providing an optimal clothing production plan.

[0158] Referring to Figure 7, for example, if 1,000 products are produced and the defect rate is 4.2%, the manufacturing cost can be calculated assuming that 1,042 products are produced.

[0159] The description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the embodiments disclosed herein, but is to be construed in the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. In a method for providing an optimal clothing production plan using a computing device, Steps to obtain product information; A step of determining multiple processes based on the above product information; A step of acquiring production information obtained by at least one of vibration pattern information generated from a machine, power consumption information, and input information; A step of calculating standard working hours for each process based on the above production information; and A step of establishing an optimal production plan based on the standard working time for each process produced; including, How a computing device provides an optimal garment production plan.

2. In paragraph 1, The above production information is Includes working hours data and loss hours data, Working time data is data on working time included within the time value obtained by multiplying the preset time value set for each process by a predetermined ratio. Loss time data is data on working time that is not included in the time value obtained by multiplying the preset time value set for each process by a predetermined ratio. How a computing device provides an optimal garment production plan.

3. In paragraph 2, The steps for calculating the above standard working hours are: By applying the above loss time data to the unit time advance time information for the process, How a computing device provides an optimal garment production plan.

4. In paragraph 1, The method of providing an optimal clothing production plan by the above computing device is as follows: A step of obtaining information on the number of units produced per unit time; further comprising: The steps for establishing the above optimal production plan are: A step for selecting a process among multiple processes in which a bottleneck phenomenon occurs due to accumulated work; A bottleneck resolution step that determines the number of people to be added to the process selected as a bottleneck based on the production quantity information per unit time to resolve the bottleneck; including, How a computing device provides an optimal garment production plan.

5. In paragraph 2, The steps to establish an optimal production plan are: A step of applying the above working time data to the standard working time for each process calculated above; including more, How a computing device provides an optimal garment production plan.

6. In paragraph 2, The steps for establishing the above optimal production plan are: A step of applying a learning curve that reflects the numerical change in production volume based on the number of operations; including more, How a computing device provides an optimal garment production plan.

7. In paragraph 2, The above vibration pattern information is, The vibration generated from the machine is recognized and acquired by the vibration pattern information acquisition device, The above input information is, Information about the completion of a task entered into the task completion input device by the user. The above vibration pattern information acquisition device and the above task completion input device are the same device. How a computing device provides an optimal garment production plan.

8. In paragraph 2, A method for providing the above optimal clothing production plan, A step of calculating manufacturing cost based on the clothing production volume calculated according to the above optimal production plan; further comprising; How a computing device provides an optimal garment production plan.

9. In paragraph 4, The step of selecting the process where the above bottleneck occurs is: Select a process as a bottleneck process if its standard operation time is more than a predetermined percentage of the previous process. How a computing device provides an optimal garment production plan.

10. In paragraph 9, The above bottleneck resolution steps are: Determine the number of personnel to be added to the selected bottleneck process based on the difference in standard working time from the previous process for the selected bottleneck process. How a computing device provides an optimal garment production plan.

11. In paragraph 10, The above bottleneck resolution steps are: By further considering the production quantity information per unit time, the number of people to be added to the selected bottleneck occurrence process is determined. How a computing device provides an optimal garment production plan.

12. In paragraph 8 The steps for calculating the above manufacturing cost are: Applying more defect rates, How a computing device provides an optimal garment production plan.

Images