Digital management system and method for large commodity enterprises in clothing industry

Abstract

The invention discloses a digital management system and method for large commodity enterprises in the clothing industry, and relates to the technical field of clothing supply chain management. Comprising a data acquisition module, an enterprise analysis module, a production analysis module, a matching and screening module, a scheme generation module and an adjustment and correction module, data of industry chain enterprises of the clothing industry are production and operation data, management data and market feedback data of the enterprises in recent N years, and the technical key points are as follows: the production and operation data, the management data and the market feedback data of existing clothing production enterprises and clothing conveying enterprises are comprehensively analyzed; according to the method, the comprehensive evaluation value for reference of the costume design enterprise is generated, so that the costume design enterprise can quickly know the conditions of related enterprises, cooperative enterprises can be screened more reasonably, the use effect is good, and the method has a good use prospect.

Description

Technical Field

[0001] This invention relates to the field of apparel supply chain management technology, specifically to a digital management system and method for large-scale product planning in the apparel industry. Background Technology

[0002] In the era of global competitive economy, the competition is no longer between companies or brands, but between supply chains. Supply chain management is a key factor in a company's success and a major driving force for improving productivity and profits. However, driven by globalization, the originally simple supply chains have become more complex, with significantly increased uncertainty and increasing fragility.

[0003] The apparel industry is facing a fiercely competitive market environment. For customer-market-oriented companies, it is essential to deliver the right products to customers at the right time and place. Under this premise, the challenge lies in how to reduce costs. The supply chain needs to be more sensitive and flexible in order to improve efficiency and create a foundation for profitability in a competitive market.

[0004] To better help clothing design companies obtain designed clothing products more rationally, people have invented some clothing-related management systems, including the clothing supply chain management system.

[0005] The existing invention patent with patent authorization number "CN116307446B" and patent name "Apparel Supply Chain Management System" describes a method that first acquires the fabric procurement requirements and the detection images of the fabric to be evaluated; then, it maps the fabric procurement requirements and the detection images of the fabric to be evaluated into a high-dimensional feature space using a Clip model and a convolutional neural network model, respectively. By calculating the difference between the feature distributions of the two in the high-dimensional feature space, it determines whether the fabric to be evaluated meets the fabric procurement requirements. In this way, a fit analysis is performed on the fabric procurement requirements and the fabric to be evaluated in the high-dimensional feature space to determine whether the fabric to be evaluated meets the fabric procurement requirements. This method enables intelligent detection of the fabric to be evaluated, improving the accuracy and efficiency of quality inspection.

[0006] The central idea of ​​the aforementioned patent is to strictly monitor the fabric in the first stage of apparel supply chain management, ensuring that the apparel fabric matches the pre-set fabric, thereby guaranteeing the production quality of the apparel.

[0007] However, the above solutions are only applicable to ordinary apparel companies. Larger apparel manufacturers have long-term cooperative fabric suppliers and have strict supervision over fabrics. Therefore, the fabrics of their products can fully meet the requirements of apparel design companies. However, in the apparel industry chain, due to the differences in apparel design requirements and processes, the most crucial aspect is finding suitable manufacturers and suppliers. Regarding this, there is no scientific and complete solution in the existing technology. Therefore, we have developed a digital management system and method for large-scale product planning in the apparel industry. Summary of the Invention

[0008] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a digital management system and method for large-scale product planning in the apparel industry. It comprehensively analyzes the production and operation data, management data, and market feedback data of existing apparel manufacturers and suppliers, generating comprehensive evaluation values ​​for apparel design companies to reference. This allows apparel design companies to quickly understand the situation of relevant companies, enabling them to more rationally select partners. It has good usage effects and promising application prospects, solving the problems raised in the background technology.

[0009] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: A digital management system for large-scale product planning in the apparel industry, including modules for data acquisition, enterprise analysis, production analysis, matching and filtering, solution generation, and adjustment and correction: Data acquisition module: Acquires data from enterprises in the apparel industry chain, apparel design schemes, and apparel production requirements, and preprocesses the collected data; Enterprise Analysis Module: This module analyzes pre-processed data on enterprises in the apparel industry supply chain to obtain a comprehensive evaluation value for these enterprises. It also categorizes the production and operation data of these enterprises over the past N years to obtain agile response data and steady-state production data. Finally, it calculates individual evaluation values ​​for the agile response data and steady-state production data, evaluates and analyzes these individual evaluation values, and obtains attribute labels. Production Analysis Module: Analyzes garment production requirement data, determines garment production type and extracts demand data, then calculates demand assessment value, compares the demand assessment value with the individual assessment value in garment production type, and filters out the list of qualified enterprises. Matching and filtering module: Analyzes the attribute tags of companies that match the enterprise list, calculates the fit, and sorts them according to the fit, constructing production category lists and transportation category lists, and then displays the production category lists and transportation category lists. Solution generation module: Based on the production and transportation companies selected by the apparel design company and the apparel production requirements, construct an apparel production plan, calculate and mark the economic value of the apparel production plan, arrange the apparel production plans in order of economic value from low to high, and select the top K groups of apparel production plans to send to the apparel design company.

[0010] Furthermore, the data for enterprises in the apparel industry chain includes their production and operation data, management data, and market feedback data over the past N years. These enterprises include apparel manufacturers and apparel suppliers. The management data includes the enterprise's turnover, debt-to-asset ratio, net profit, enterprise size, and market share over the past N years. The market feedback data includes the satisfaction of partner enterprises and user satisfaction. The apparel production requirements data includes apparel technology, raw material data, quantity of apparel decorations, and decoration installation methods. The collected data undergoes preprocessing, including data cleaning, integration, and transformation.

[0011] Furthermore, the steps for organizing and analyzing data from enterprises in the apparel industry chain to calculate the comprehensive evaluation value of these enterprises are as follows: The company's management data and market feedback data are categorized and then arranged in chronological order. Calculate the annual rate of change data to obtain N-1 rate of change arrays, and then calculate the average of the N-1 rate of change arrays; The comprehensive evaluation value of enterprises in the apparel industry chain is calculated based on the average.

[0012] Furthermore, the steps to classify the company's production and operation data over the past N years to obtain agile response data and steady-state production data are as follows: Obtain order data from production and operation data, and extract time data from the order data; The extracted time data is compared with the preset time data; If the extracted time data is greater than the preset time data, the order data will be classified as steady-state production data. If the extracted time data is less than the preset time data, the order data will be classified as agile response data; Calculate the individual evaluation values ​​of agile response data and steady-state production data for garment manufacturing and garment transportation enterprises.

[0013] Furthermore, the steps for evaluating and analyzing individual assessment values ​​are as follows: Compare individual evaluation values ​​with the set standard evaluation values, and delete individual evaluation values ​​that are less than the standard evaluation values. Calculate the cost assessment value corresponding to the remaining individual assessment values; The comprehensive evaluation value, the remaining individual evaluation values, and the cost evaluation value will be labeled as attribute tags on enterprises in the apparel industry chain.

[0014] Further steps to determine the garment production type, extract demand data, and then calculate the demand assessment value are as follows: Obtain the time data recorded in the garment production requirements data, compare the time data with the preset time data, and determine the garment production type; Extract the raw material categories, production processes, quantity of decorative items, and installation processes required for calculating the demand assessment value; By comparing the raw material category, production process, number of decorations, and decoration installation process with the preset standards, the raw material grade, production grade, and number of decorations corresponding to each decoration difficulty level are obtained. The demand assessment value is calculated based on the compared data; The criteria for selecting companies that meet the criteria are those whose individual assessment value is greater than the demand assessment value.

[0015] Furthermore, before constructing the production category list and the transportation category list, the number of enterprises recorded in the enterprise list is obtained, and the number of enterprises is compared with the set standard number η. If the number of enterprises is greater than the standard number, then construct a production category list and a transportation category list, taking the top η enterprises based on their fit. If the number of enterprises is less than or equal to the standard number, then a production category list and a delivery category list are constructed for all enterprises whose fit is calculated.

[0016] Furthermore, the economic values ​​for the garment production plan were calculated as follows: Obtain data on selected apparel manufacturers, apparel delivery companies, and apparel production requirements; Obtain the time requirements, raw material grade, production grade, and number of decorations corresponding to each decoration difficulty level from the clothing production requirements data; The time requirement is divided into garment production time and garment transportation time, and economic values ​​are calculated based on the production time, transportation time and the acquired data.

[0017] Furthermore, the system also includes an adjustment and correction module: acquiring the scheme selected by the apparel design company and the actual economic data of the scheme implementation, and recalculating the cost assessment value based on the actual economic data to correct the attribute labels.

[0018] Furthermore, the digital management methods for large-scale product planning in the apparel industry include the following steps: We acquire data from companies in the apparel industry supply chain, including apparel design plans and apparel production requirements. The data from these companies includes their production and operation data, management data, and market feedback data for the past N years. Data from enterprises in the apparel industry chain is collected and analyzed to calculate the comprehensive evaluation value of these enterprises. Furthermore, the production and operation data of these enterprises over the past N years are categorized to obtain agile response data and steady-state production data. Individual evaluation values ​​for both agile response and steady-state production data are then calculated and compared with set standard evaluation values. Individual evaluation values ​​lower than the standard values ​​are deleted. The cost evaluation value corresponding to the remaining individual evaluation values ​​is calculated. The comprehensive evaluation value, the remaining individual evaluation values, and the cost evaluation value are then used as attribute labels to the enterprises in the apparel industry chain. Analyze the data on garment production requirements, determine the type of garment production and extract the demand data, then calculate the demand assessment value, compare the demand assessment value with the individual assessment values ​​in the garment production type, and screen out the list of qualified enterprises. The attribute tags of companies that meet the enterprise list are analyzed, the fit is calculated, and they are arranged according to the fit, thus constructing a production category list and a transportation category list, which are then displayed. Obtain the production and supply companies selected by the apparel design company, construct an apparel production plan based on the apparel production requirements data, calculate the economic value of the apparel production plan, mark the economic value on the apparel production plan, arrange the apparel production plans in order of economic value from low to high, and select the top K groups of apparel production plans to summarize and send them to the apparel design company. Obtain the solutions selected by the apparel design companies and the actual economic data of the solution implementation, and recalculate the cost assessment value based on the actual economic data, and correct the attribute labels.

[0019] (III) Beneficial Effects This invention provides a digital management system and method for large-scale product planning in the apparel industry, which has the following beneficial effects: 1. This invention provides a digital management system and method for large-scale product planning in the apparel industry. It comprehensively analyzes the production and operation data, management data, and market feedback data of existing apparel manufacturers and apparel suppliers to generate comprehensive evaluation values ​​for apparel design companies to refer to. This allows apparel design companies to quickly understand the situation of relevant companies, thereby enabling them to more rationally select cooperative companies. It has good usage effects and promising application prospects.

[0020] 2. This invention provides a digital management system and method for large-scale product planning in the apparel industry. It not only analyzes the enterprise from external perspectives and management practices, but also analyzes past order data to determine the types of apparel the enterprise excels at producing and its capabilities in different situations. These capabilities are then converted into individual evaluation values, enabling apparel design companies to quickly understand the production advantages and key areas of relevant enterprises. Simultaneously, it calculates corresponding cost evaluation values, facilitating apparel design companies to understand the required price levels for apparel production. This allows for better selection of appropriate production and supply enterprises, helping apparel design companies quickly find high-quality partners. The system demonstrates good performance and promising application prospects.

[0021] 3. This invention provides a digital management system and method for large-scale product planning in the apparel industry. It not only analyzes enterprises and provides a basis for screening, but also further filters based on the apparel design enterprise's requirements, calculates demand assessment values, reduces the number of candidate enterprises, and further combines the attribute tags of enterprises on the enterprise list for secondary analysis to calculate the enterprise suitability for this batch of apparel. Using the suitability score for further ranking, it helps apparel design enterprises clearly and intuitively understand the best partner enterprises, and can predict cooperation plans and their corresponding economic values. This helps apparel design enterprises quickly formulate cooperation plans. It is convenient to use, has good results, and has promising application prospects. Attached Figure Description

[0022] Figure 1 This is a flowchart of the digital management system for large-scale product planning in the apparel industry, as described in this invention. Figure 2 This is a flowchart of the adjustment and correction module in the digital management system for large-scale product planning in the apparel industry of this invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0024] Research and development concept: Existing apparel supply chain management systems focus on the first stage of apparel supply chain management, strictly monitoring fabrics to ensure that the fabrics match the pre-set specifications, thereby guaranteeing the quality of apparel production. The key is how to ensure product quality.

[0025] However, the above solution is only applicable to small apparel companies. For larger apparel manufacturers, they have long-term cooperative fabric suppliers and have strict supervision of fabrics. In addition, with the development of Internet technology, apparel companies are becoming more and more formalized and their requirements for fabrics are constantly increasing. Therefore, there is no need to supervise the quality of fabrics. Therefore, the fabrics used in existing garment production can fully meet the requirements of garment design companies. However, in the existing garment industry chain, due to differences in garment design requirements and production processes, the most critical issue is how to find suitable production companies and suitable transportation companies. In this regard, there is no scientific and complete solution in the existing technology.

[0026] Therefore, in the early stages of research and development, a relatively scientific and comprehensive system was developed to monitor and evaluate companies in the apparel supply chain. This would enable apparel design companies to quickly select suitable companies and develop more effective production plans. To this end, a scheme for calculating the comprehensive evaluation value of companies was developed.

[0027] However, in actual use, it was found that large enterprises with better financial performance generally ranked first in terms of comprehensive evaluation values. However, in the actual production process, large enterprises generally only accept stable clothing for long-term production. For short-term clothing (fashion clothing), due to the short production cycle, their supervision is not strict enough. Moreover, because large companies have many internal links and long production process adjustment time, their production speed is slower and does not meet people's requirements.

[0028] Therefore, during the mid-stage of research and development, a plan was developed to analyze the past order data of relevant companies. This would allow apparel design companies to quickly understand the production advantages and key areas of production of these companies, and at the same time calculate the corresponding cost assessment values. This would help apparel design companies understand the price levels required for producing garments, thereby enabling them to better select appropriate production and supply companies.

[0029] However, in actual use, it has been found that when considering the quality and strengths of enterprises, the cost of the top-ranked enterprises is often relatively high, making it difficult for clothing design companies to find suitable suppliers and to determine the cost of production plans, requiring a lot of manpower for assistance.

[0030] Therefore, analyzing companies in the later stages of R&D provides a basis for screening. Based on the clothing requirements of apparel design companies, further screening is conducted to calculate demand assessment values, reducing the number of candidate companies. Then, a second analysis is performed by combining the attribute tags of companies on the list to calculate the company suitability for this batch of clothing. The suitability is then used to further rank the companies, helping apparel design companies to clearly and intuitively understand the best partners. It also enables the estimation of cooperation plans and their corresponding economic values, thereby helping apparel design companies to quickly formulate cooperation plans. This method is quite convenient to use.

[0031] Example 1:

[0032] Please see Figure 1 This embodiment provides a digital management system for large-scale product planning in the apparel industry, which mainly consists of hardware and software components. The hardware components mainly include computers and servers, as well as other related equipment that support the operation of the software components.

[0033] The software section contains the following information: The software component includes a data acquisition module, an enterprise analysis module, a production analysis module, a matching and filtering module, a solution generation module, and an adjustment and correction module. The implementation of this system relies on sufficient enterprise data. To obtain enterprise data, it is necessary to establish relevant data upload channels, which includes the data acquisition module.

[0034] Data Acquisition Module: Used to acquire data from enterprises in the apparel industry chain, apparel design schemes, and apparel production requirements. The data from enterprises in the apparel industry chain includes their production and operation data, management data, and market feedback data for the past N years.

[0035] This system requires preliminary research on enterprises in the apparel industry chain to obtain annual report data, relevant management data, and order data. This data needs to be analyzed and extracted before the system can be implemented. Therefore, during implementation, the system needs to cooperate with enterprises, adopting a scheme where enterprises proactively upload orders and enterprise information, and personnel review the uploaded data. However, this step is not included in the system's data acquisition module. The data collected by the data acquisition module in this system has undergone manual review. The data acquisition module is responsible for entering all external information.

[0036] The apparel industry chain includes apparel manufacturers and apparel suppliers. The management data of these companies includes their turnover, debt-to-asset ratio, net profit, company size, and market share over the past N years. Market feedback data includes the satisfaction of partner companies and users. Apparel production requirements data include apparel technology, raw material data, quantity of decorations, and decoration installation methods.

[0037] The above data pertains to common clothing items. This system does not apply to clothing with special designs, special fabrics, or custom-made garments.

[0038] Once the relevant data is collected, it needs to be further analyzed to extract data related to the enterprise and assess the overall situation of the enterprise based on the enterprise-related data, so as to facilitate the selection of clothing design enterprises. This process is based on the enterprise analysis module.

[0039] Enterprise Analysis Module: This module is used to organize and analyze data from enterprises in the apparel industry chain, calculate the comprehensive evaluation value of these enterprises, and classify their production and operation data over the past N years to obtain agile response data and steady-state production data. It then calculates the individual evaluation values ​​for these data and compares them with set standard evaluation values. Individual evaluation values ​​lower than the standard values ​​are deleted, and the cost evaluation value corresponding to the remaining individual evaluation values ​​is calculated. The comprehensive evaluation value, the remaining individual evaluation values, and the cost evaluation value are then used as attribute tags to label the enterprises in the apparel industry chain.

[0040] The steps for organizing and analyzing data from companies in the apparel industry chain to calculate the comprehensive evaluation value of these companies are as follows: The company's management data and market feedback data are categorized and then arranged in chronological order. Calculate the annual rate of change data using the formula: rate of change. , The data is from the previous year, which is the base year. Using the baseline year data, we obtain N-1 arrays of rates of change, and then calculate the average of the N-1 arrays of rates of change. For example, if N=3, and the net profit data for the first three years are 10 million, 10.6 million, and 12 million respectively, the first set of data is not used as the base year in the calculation. If the base year is the second year with 10.6 million, then the year before the base year is the first year with 10 million. If the base year is the third year with 12 million, then the year before the base year is the second year with 10.6 million. Based on the above rules, two data points (the calculated data combinations form an array) are calculated, which are 6% and 13.2% respectively. Then the average is calculated. When calculating the average, only the same category is calculated. For example, the rate of change calculated based on the net profit data is independent of the rate of change data of other categories. An average is calculated for each category. The average calculated for the above data is 9.6%.

[0041] The formula for calculating the comprehensive evaluation value of enterprises in the apparel industry chain based on the average is as follows: In the formula, GE represents the comprehensive evaluation value of enterprises in the apparel industry chain, A represents the number of categories of enterprise management data, and B represents the number of categories of market feedback data. Let be the average of the i-th category in the company's management data. This represents the average of the x-th category in the market feedback data. Let be the correction ratio for the i-th category in the enterprise's management data. The weight of the i-th category in the enterprise's management data evaluation value; glqz represents the weight of the xth category in the market feedback data evaluation value, scqz represents the weight of the enterprise's management data evaluation value, and scqz represents the weight of the market feedback data evaluation value. pass Calculate the total assessment value for the company. The total assessment value based on market feedback is then weighted to obtain more accurate data.

[0042] in, The calculation formula is In the formula, The rate of change for the i-th category is calculated based on data from the previous year and the year before that. .

[0043] For example, if the annual profit in the previous year was 10 million, and the annual profit in the year before that was less than 10 million, it indicates that the company is growing, so the valuation can be appropriately increased. If the annual profit in the year before that was more than 10 million, it indicates that the company is growing less, so the valuation should be decreased. The valuation calculated in this way is more accurate.

[0044] This invention provides a digital management system and method for large-scale product planning in the apparel industry. It comprehensively analyzes the production and operation data, management data, and market feedback data of existing apparel manufacturers and suppliers to generate comprehensive evaluation values ​​for apparel design companies. This allows apparel design companies to quickly understand the situation of relevant companies, thereby enabling them to more rationally select partners. It has good results and promising application prospects.

[0045] The steps to classify a company's production and operation data over the past N years to obtain agile response data and steady-state production data are as follows: Obtain order data from production and operation data, and extract time data from the order data; The extracted time data is compared with the preset time data; If the extracted time data is greater than the preset time data, the order data will be classified as steady-state production data. If the extracted time data is less than the preset time data, the order data will be classified as agile response data; Research indicates that the peak sales period for fashionable apparel is 15-30 days. To ensure sales momentum, the total production and transportation time is typically one week. Under normal circumstances, the ratio of production time to transportation time is 5-6:1-2. Therefore, for ease of statistical analysis, the production time is set to 6 days and the transportation time to 2 days. Thus, for apparel manufacturers, orders with a production time exceeding 6 days are classified as steady-state production data, while those with a production time of 6 days or less are classified as agile response data. Similarly, for apparel transportation companies, orders with a production time exceeding 2 days are classified as steady-state production data, while those with a production time of 2 days or less are classified as agile response data.

[0046] The formulas for calculating the individual evaluation values ​​of agile response data and steady-state production data for garment manufacturing and garment transportation companies are as follows: In the formula, GEdc is the individual evaluation value of the garment manufacturing enterprise, GEds is the individual evaluation value of the garment manufacturing enterprise, and C is the total number of defect indicators reported in the order data. For the i-th group of defect index data, dt is the converted value of the defect index data of the i-th group, db is the number of days to complete the order ahead of schedule, 1<β≤1.2, and hsz is the converted value of the number of days to complete the order ahead of schedule.

[0047] Because garment production is prone to defects, while garment transportation is less likely to produce defects, garment manufacturers need to pay extra attention to production quality, while transportation companies only need to consider whether orders are completed on time.

[0048] Performing individual calculations makes subsequent evaluations more accurate and improves the overall effectiveness.

[0049] This invention provides a digital management system and method for large-scale product planning in the apparel industry. It not only analyzes the enterprise from external perspectives and management practices, but also analyzes past order data to determine the types of apparel the enterprise excels at producing and its capabilities in different situations. These capabilities are then converted into individual evaluation values, enabling apparel design companies to quickly understand the production advantages and priorities of relevant companies. Simultaneously, it calculates corresponding cost evaluation values, allowing apparel design companies to understand the required price levels for apparel production. This facilitates the selection of appropriate manufacturers and suppliers, helping apparel design companies quickly find high-quality partners. The system demonstrates good performance and promising future prospects.

[0050] The formula for calculating the cost assessment value corresponding to the remaining individual assessment value of garment manufacturing enterprises and garment transportation enterprises is as follows: In the formula, GosEc represents the cost assessment value for the garment manufacturing enterprise, GosEs represents the cost assessment value for the garment delivery enterprise, and E represents the number of order data recorded in the production data corresponding to the individual assessment value. The order amount recorded in the i-th order data. Let Ss be the apparel evaluation value recorded in the i-th order data, Ss be the quantity of apparel in that order, and e be a natural constant. , This represents the number of days it took for the order to be completed, as recorded in the data for the i-th order. Let Dyxs be the raw material grade recorded in the i-th order data, and Dyxs be the raw material grade weight. Let Djxs be the process level recorded in the i-th order data, and let Djxs be the process level weight. Let Dsxs be the number of decorations for the j-th installation difficulty level recorded in the i-th order data, and let Dsxs be the weight of the decoration difficulty level. This refers to the transport distance recorded in the i-th order data of the garment transport company.

[0051] The cost assessment value can reflect the basic cost. Since the vehicle size of general garment transportation companies is constant, there are vehicles of different sizes. Therefore, the cost of different vehicles is not considered separately.

[0052] If Ss > 1000 pieces, the data will be entered into the steady-state production data and agile response data. For garment manufacturing enterprises, this system is mainly for large-scale garment production.

[0053] When Ss≤1000 pieces, this part of the data is not entered into the steady-state production data and agile response data. For garments of a few hundred pieces, considering the costs such as pattern making, it is difficult to evaluate, so it is not applicable to the calculation of this cost evaluation value.

[0054] When a clothing design company needs to determine the cost of fewer than 1,000 garments, it can directly view similar order data and select the corresponding clothing manufacturers and suppliers, which is a more effective method.

[0055] By comparing the above cost assessment values ​​with the company's cost assessment values, clothing design companies can directly determine the company's pricing standards. Therefore, they can better assess the unit price of clothing, making it more convenient to use and more effective.

[0056] For example, if the cost assessment values ​​of two different garment manufacturers are calculated to be 78 and 81 respectively, then it is obvious that the cost assessment value of 78 is lower than that of the company with a cost assessment value of 81, thus making it easier for garment design companies to make a better choice.

[0057] After analyzing the relevant data of the enterprises, it is necessary to further analyze the requirements of the apparel design enterprises to determine whether the relevant enterprises can meet the requirements of the apparel design enterprises, and further screen the relevant enterprises to reduce the time for users to make selections. This step is based on the production analysis module.

[0058] Production Analysis Module: This module analyzes garment production requirement data, determines the garment production type, extracts demand data, calculates the demand assessment value, compares the demand assessment value with the individual assessment values ​​in the garment production type, and filters out a list of eligible companies.

[0059] The steps to determine the garment production type, extract demand data, and then calculate the demand assessment value are as follows: Obtain the time data recorded in the garment production requirements data, compare the time data with the preset time data, and determine the garment production type; Extract the raw material categories, production processes, quantity of decorative items, and installation processes required for calculating the demand assessment value; By comparing the raw material category, production process, number of decorations, and decoration installation process with the preset standards, the raw material grade, production grade, and number of decorations corresponding to each decoration difficulty level are obtained.

[0060] By using the methods described above, we can quickly extract the clothing design companies' needs for clothing. Only then can we further analyze the demand data, obtain demand assessment values, and achieve more accurate screening.

[0061] The demand assessment value is calculated based on the compared data, and the specific calculation formula is as follows: In the formula, Fzpg is the demand assessment value. For the raw material grade of the enterprise's pending processing orders, For the processing level of the orders to be processed by the enterprise, jsbl is the number of decorations with the i-th installation difficulty level recorded in the enterprise's pending processing order; jsbl is the conversion coefficient between the clothing assessment value and the demand assessment value. Companies that meet the criteria of having a single assessment value greater than the demand assessment value are selected from the list of eligible companies.

[0062] The demand assessment value calculated using the above method is used to screen garment manufacturers. Since garment manufacturers are the focus, further screening is required. Garment transportation companies, on the other hand, usually meet the requirements, with the difference being in price. Therefore, they do not need to be screened by calculating demand assessment values.

[0063] For example, if the calculated demand assessment value for agile response data is 75, and the individual assessment values ​​for agile response data of 5 apparel manufacturing companies are 71, 78, 86, 73, and 82 respectively, then after filtering out the companies corresponding to 71 and 73, two companies are reduced, which makes it easier for apparel design companies to select the number of companies they need to check, allowing them to quickly choose suitable partners, resulting in good performance.

[0064] Once a list of companies that meet the requirements for apparel design companies has been compiled, further evaluation and cost-effectiveness analysis are needed to make a more convenient selection process for these companies. This process is based on the matching and filtering module.

[0065] Matching and filtering module: This module analyzes the attribute tags of companies that match the enterprise list, calculates the fit score, and arranges them according to the fit score to construct production category lists and transportation category lists, which are then displayed. The formula for calculating fitness is as follows: In the formula, Fitc represents the fit of the garment manufacturing enterprise, Fits represents the fit of the garment conveying enterprise, gehs represents the conversion factor for converting the comprehensive evaluation value into the fit, gohs represents the conversion factor for converting the cost evaluation value into the fit, g is a constant, and gdhs represents the conversion factor for individual evaluation values.

[0066] The conversion factors for the compatibility were obtained through surveys. When users have different requirements, a certain conversion factor can be adjusted accordingly.

[0067] Under normal circumstances, the conversion factor is adjusted by 5% each time, with a maximum of two items adjusted, and each item adjusted a maximum of 3 times.

[0068] For example, if a clothing design company focuses on whether a company has extensive experience in producing fashionable clothing, it can adjust the conversion factor gdhs for individual evaluation values. If it focuses on scale, it can adjust the conversion factor gehs for the overall evaluation value. If it focuses on cost, it can adjust the conversion factor gohs for the cost evaluation value. This changes the degree of fit of the corresponding companies, making it easier for clothing design companies to quickly find suitable partners.

[0069] Once the companies are selected and further ranked, the next step is for the apparel design companies to choose apparel manufacturing companies and apparel delivery companies. After selecting the apparel manufacturing companies and apparel delivery companies, further solutions will be provided, and this process is based on the solution generation module.

[0070] This invention provides a digital management system and method for large-scale product planning in the apparel industry. It not only analyzes enterprises and provides a basis for screening, but also further filters based on the apparel design companies' requirements, calculates demand assessment values, reduces the number of candidate companies, and further combines the attribute tags of the companies on the list for secondary analysis to calculate the company suitability for the batch of apparel. Using the suitability score for further ranking, it helps apparel design companies clearly and intuitively understand the best partners, and can predict cooperation plans and their corresponding economic values. This helps apparel design companies quickly formulate cooperation plans. It is convenient to use, has good results, and has promising application prospects.

[0071] Solution generation module: It is used to obtain the production and supply enterprises selected by the apparel design enterprise, and construct the apparel production plan by combining the apparel production requirement data. It calculates the economic value of the apparel production plan, marks the economic value on the apparel production plan, and arranges the apparel production plans in order of economic value from low to high. It selects the top K groups of apparel production plans, summarizes them, and sends them to the apparel design enterprise. Before constructing the production category list and the transportation category list, first obtain the number of enterprises recorded in the enterprise list and compare the number of enterprises with the set standard number η; If the number of enterprises is greater than the standard number, then construct a production category list and a transportation category list, taking the top η enterprises based on their fit. For example, if η is 3, it means that both the production category list and the transportation category list have 3 entries, not that the total number is 3. If the calculated number of companies is 5, then the companies ranked last two will be deleted.

[0072] If the number of enterprises is less than or equal to the standard number, then a production category list and a delivery category list are constructed for all enterprises whose fit is calculated.

[0073] For example, if η is 3 and the calculated number of enterprises is 2 groups, then all enterprises will be displayed on the production category list and the transportation category list.

[0074] After selecting the garment manufacturing and transportation companies, it is still necessary to further analyze and judge the time ratio between the garment manufacturing and transportation companies.

[0075] For example, if the production time is set to 7 days, the possible scenarios are: 5 days for the garment manufacturer and 2 days for the garment conveyor; 5.5 days for the garment manufacturer and 1.5 days for the garment conveyor; and 6 days for the garment manufacturer and 1 day for the garment conveyor. Therefore, it is necessary to calculate the economic values ​​of the three scenarios to facilitate user selection.

[0076] Although under normal circumstances, the shorter the production time, the higher the cost, which is far higher than the increased cost for companies that shorten the garment delivery time.

[0077] However, there are cases where the normal production time for some garment manufacturing companies is 5.5 days. In this case, the optimal solution is 5.5 days for garment manufacturing companies and 1.5 days for garment transportation companies. Since the companies are different, it is still necessary to analyze and calculate the economic values ​​of the garment production plan separately.

[0078] The economic values ​​for the garment production plan are calculated as follows: Obtain data on selected apparel manufacturers, apparel delivery companies, and apparel production requirements; Obtain the time requirements, raw material grade, production grade, and number of decorations corresponding to each decoration difficulty level from the clothing production requirements data; The time requirement is divided into garment production time and garment transportation time. Based on the production time, transportation time, and acquired data, an economic value is calculated using the following formula: In the formula, AMOz is the economic value, T1 is the garment transportation time, T2 is the garment production time, and Sj is the distance between the production site and the warehouse.

[0079] The above can calculate the cost, which users can then choose from.

[0080] Reference Figure 2 The adjustment and correction module is used to obtain the solutions selected by the apparel design company and the actual economic data of the solution implementation, and to recalculate the cost assessment value based on the actual economic data and correct the attribute labels.

[0081] Before calibration, determine whether it meets the standard. If the difference between the actual economic value and the calculated economic value is within 5% of the set value, it will not be recalculated. If it exceeds 5%, it will be recalculated.

[0082] The cost assessment value is calculated by taking into account the actual economic situation and recalculating the cost assessment value based on the set of data with the longest actual economic change time for this order.

[0083] After the cost assessment value is recalculated, all subsequent analyses and calculations will use this cost assessment value, and the results of subsequent analyses will be different.

[0084] The weighting coefficients are determined using the coefficient of variation method, which assigns weights to each indicator based on the degree of variation between the current value and the target value. If the numerical difference of an indicator is large, clearly distinguishing each evaluated object, it indicates that the indicator has rich discriminative information and should therefore be given a larger weight. Conversely, if the numerical difference of each evaluated object on a certain indicator is small, then the indicator's ability to distinguish each evaluated object is weak, and therefore it should be given a smaller weight. This method directly utilizes the information contained in each indicator to calculate the weight of the indicator, thus possessing objectivity.

[0085] Example 2:

[0086] Based on Example 1, the digital management method for large-scale product planning in the apparel industry includes the following steps: We acquire data from companies in the apparel industry supply chain, including apparel design plans and apparel production requirements. The data from these companies includes their production and operation data, management data, and market feedback data for the past N years. Data from enterprises in the apparel industry chain is collected and analyzed to calculate the comprehensive evaluation value of these enterprises. Furthermore, the production and operation data of these enterprises over the past N years are categorized to obtain agile response data and steady-state production data. Individual evaluation values ​​for both agile response and steady-state production data are then calculated and compared with set standard evaluation values. Individual evaluation values ​​lower than the standard values ​​are deleted. The cost evaluation value corresponding to the remaining individual evaluation values ​​is calculated. The comprehensive evaluation value, the remaining individual evaluation values, and the cost evaluation value are then used as attribute labels to the enterprises in the apparel industry chain. Analyze the data on garment production requirements, determine the type of garment production and extract the demand data, then calculate the demand assessment value, compare the demand assessment value with the individual assessment values ​​in the garment production type, and screen out the list of qualified enterprises. The attribute tags of companies that meet the enterprise list are analyzed, the fit is calculated, and they are arranged according to the fit, thus constructing a production category list and a transportation category list, which are then displayed. Obtain the production and supply companies selected by the apparel design company, construct an apparel production plan based on the apparel production requirements data, calculate the economic value of the apparel production plan, mark the economic value on the apparel production plan, arrange the apparel production plans in order of economic value from low to high, and select the top K groups of apparel production plans to summarize and send them to the apparel design company. Obtain the solutions selected by the apparel design companies and the actual economic data of the solution implementation, and recalculate the cost assessment value based on the actual economic data, and correct the attribute labels.

[0087] In the application, the various formulas mentioned are all calculated by removing dimensions and taking their numerical values. The formulas are established by collecting a large amount of data and simulating the most recent real situation. Some coefficients or weights in the formulas are set by those skilled in the art according to the actual situation, so they will not be elaborated here.

[0088] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0089] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0090] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A digital management system for large-scale product planning in the apparel industry, characterized by: include: Data acquisition module: Acquires data from enterprises in the apparel industry chain, apparel design schemes, and apparel production requirements, and preprocesses the collected data; Enterprise Analysis Module: This module analyzes pre-processed data on enterprises in the apparel industry supply chain to obtain a comprehensive evaluation value for these enterprises. It also categorizes the production and operation data of these enterprises over the past N years to obtain agile response data and steady-state production data. Finally, it calculates individual evaluation values ​​for the agile response data and steady-state production data, evaluates and analyzes these individual evaluation values, and obtains attribute labels. Production Analysis Module: Analyzes garment production requirement data, determines garment production type and extracts demand data, then calculates demand assessment value, compares the demand assessment value with the individual assessment value in garment production type, and filters out the list of qualified enterprises. Matching and filtering module: Analyzes the attribute tags of companies that match the enterprise list, calculates the fit, and sorts them according to the fit, constructing production category lists and transportation category lists, and then displays the production category lists and transportation category lists. Solution generation module: Based on the production and transportation companies selected by the apparel design company and the apparel production requirements, construct an apparel production plan, calculate and mark the economic value of the apparel production plan, arrange the apparel production plans in order of economic value from low to high, and select the top K groups of apparel production plans to send to the apparel design company.

2. The digital management system for large-scale product planning in the apparel industry according to claim 1, characterized in that: The data for companies in the apparel industry chain includes their production and operation data, management data, and market feedback data over the past N years. These companies include apparel manufacturers and apparel suppliers. The management data includes the company's turnover, debt-to-asset ratio, net profit, company size, and market share over the past N years. The market feedback data includes the satisfaction of partner companies and users. The apparel production requirements data includes apparel technology, raw material data, quantity of apparel decorations, and decoration installation methods. The collected data undergoes preprocessing, including cleaning, integration, and transformation.

3. The digital management system for large-scale product planning in the apparel industry according to claim 2, characterized in that: The steps for organizing and analyzing data from companies in the apparel industry chain to calculate the comprehensive evaluation value of these companies are as follows: The company's management data and market feedback data are categorized and then arranged in chronological order. Calculate the annual rate of change data to obtain N-1 rate of change arrays, and then calculate the average of the N-1 rate of change arrays; The comprehensive evaluation value of enterprises in the apparel industry chain is calculated based on the average.

4. The digital management system for large-scale product planning in the apparel industry according to claim 3, characterized in that: The steps to classify a company's production and operation data over the past N years to obtain agile response data and steady-state production data are as follows: Obtain order data from production and operation data, and extract time data from the order data; The extracted time data is compared with the preset time data; If the extracted time data is greater than the preset time data, the order data will be classified as steady-state production data. If the extracted time data is less than the preset time data, the order data will be classified as agile response data; Calculate the individual evaluation values ​​of agile response data and steady-state production data for garment manufacturing and garment transportation enterprises.

5. The digital management system for large-scale product planning in the apparel industry according to claim 4, characterized in that: The steps for evaluating and analyzing individual assessment values ​​are as follows: Compare individual evaluation values ​​with the set standard evaluation values, and delete individual evaluation values ​​that are less than the standard evaluation values. Calculate the cost assessment value corresponding to the remaining individual assessment values; The comprehensive evaluation value, the remaining individual evaluation values, and the cost evaluation value will be labeled as attribute tags on enterprises in the apparel industry chain.

6. The digital management system for large-scale product planning in the apparel industry according to claim 5, characterized in that: The steps to determine the garment production type, extract demand data, and then calculate the demand assessment value are as follows: Obtain the time data recorded in the garment production requirements data, compare the time data with the preset time data, and determine the garment production type; Extract the raw material categories, production processes, quantity of decorative items, and installation processes required for calculating the demand assessment value; By comparing the raw material category, production process, number of decorations, and decoration installation process with the preset standards, the raw material grade, production grade, and number of decorations corresponding to each decoration difficulty level are obtained. The demand assessment value is calculated based on the compared data; The criteria for selecting companies that meet the criteria are those whose individual assessment value is greater than the demand assessment value.

7. The digital management system for large-scale product planning in the apparel industry according to claim 6, characterized in that: Before constructing the production category list and the transportation category list, first obtain the number of enterprises recorded in the enterprise list and compare the number of enterprises with the set standard number η; If the number of enterprises is greater than the standard number, then construct a production category list and a transportation category list, taking the top η enterprises based on their fit. If the number of enterprises is less than or equal to the standard number, then a production category list and a delivery category list are constructed for all enterprises whose fit is calculated.

8. The digital management system for large-scale product planning in the apparel industry according to claim 7, characterized in that: The economic values ​​for the garment production plan are calculated as follows: Obtain data on selected apparel manufacturers, apparel delivery companies, and apparel production requirements; Obtain the time requirements, raw material grade, production grade, and number of decorations corresponding to each decoration difficulty level from the clothing production requirements data; The time requirement is divided into garment production time and garment delivery time, and economic values ​​are calculated based on the production time, delivery time, and the acquired data.

9. The digital management system for large-scale product planning in the apparel industry according to claim 8, characterized in that: The system also includes an adjustment and correction module: it acquires the schemes selected by the apparel design company and the actual economic data of the scheme implementation, and recalculates the cost assessment value based on the actual economic data, and corrects the attribute labels.

10. A digital management method for large-scale product planning in the apparel industry, using the system described in any one of claims 1 to 9, characterized in that: Includes the following steps: We acquire data from companies in the apparel industry supply chain, including apparel design plans and apparel production requirements. The data from these companies includes their production and operation data, management data, and market feedback data for the past N years. Data from enterprises in the apparel industry chain is collected and analyzed to calculate the comprehensive evaluation value of these enterprises. Furthermore, the production and operation data of these enterprises over the past N years are categorized to obtain agile response data and steady-state production data. Individual evaluation values ​​for both agile response and steady-state production data are then calculated and compared with set standard evaluation values. Individual evaluation values ​​lower than the standard values ​​are deleted. The cost evaluation value corresponding to the remaining individual evaluation values ​​is calculated. The comprehensive evaluation value, the remaining individual evaluation values, and the cost evaluation value are then used as attribute labels to the enterprises in the apparel industry chain. Analyze the data on garment production requirements, determine the type of garment production and extract the demand data, then calculate the demand assessment value, compare the demand assessment value with the individual assessment values ​​in the garment production type, and screen out the list of qualified enterprises. The attribute tags of companies that meet the enterprise list are analyzed, the fit is calculated, and they are arranged according to the fit, thus constructing a production category list and a transportation category list, which are then displayed. Obtain the production and supply companies selected by the apparel design company, construct an apparel production plan based on the apparel production requirements data, calculate the economic value of the apparel production plan, mark the economic value on the apparel production plan, arrange the apparel production plans in order of economic value from low to high, and select the top K groups of apparel production plans to summarize and send them to the apparel design company. Obtain the solutions selected by the apparel design companies and the actual economic data of the solution implementation, and recalculate the cost assessment value based on the actual economic data, and correct the attribute labels.

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