Commodity enterprise-drawing operation management system

Abstract

The invention discloses a commodity enterprise-drawing operation management system. The system comprises a factor model module, a product model module, a style model module and a commodity operation module. Wherein the factor model module is used for configuring and managing influence factors of commodity enterprises, and forming a commodity operation plan through operation maintenance; the product model module is used for defining basic attribute fields and contents of products; the style model module is used for defining style attribute fields and contents of a product and providing planning of a style model; the commodity operation module is based on the factor model module, the product model module and the style model module, and combines actual operation data to carry out continuous operation. According to the invention, the problems that a traditional clothing commodity enterprise-drawing operation management system generally lacks a data model, an enterprise-drawing result does not accord with a real demand, the enterprise-drawing result is difficult to fall to the ground, replenishment is not timely, and allocation and distribution are disordered can be solved.

Description

Technical Field

[0001] The present invention relates to the field of merchandise planning and operation management, and particularly to a merchandise planning and operation management system. Background Art

[0002] Merchandise planning is to integrate resources purposefully and plannedly and optimize the allocation according to the strategic development direction of an enterprise, give full play to all positive factors of human, material, financial, social and information resources related to the planning goal, make them form a resultant force, create the maximum value at the lowest cost, so that the enterprise can find its own position in the fierce competition and gain an advantageous position.

[0003] The existing merchandise planning and operation management systems generally adopt the futures planning technical solution, and conduct operation management through several major modules such as formulating brand planning, production plans, analyzing the production structure, formulating sales plans, confirming the order model, and operations such as returns, replenishments, transfers, and promotions. Most of them need to analyze the existing merchandise sales and inventory situations and then make corresponding plans and corresponding operation plans, which may have certain differences from the actual needs of the stores. The technologies of the current merchandise planning and operation management systems have the following disadvantages:

[0004] 1) The factors affecting merchandise sales and inventory are not refined into digital factors, resulting in most of the planning being affected by the subjective judgments of planners;

[0005] 2) Most of the planning process relies on the existing sales and inventory data, without fully starting from the market demand, and focusing on analyzing customer group needs and market demand;

[0006] 3) The planning cycle is generally long and cannot match the rapidly changing market demand;

[0007] 4) Most of the merchandise operations rely on the experience judgments of operation personnel and lack the support of relevant data models. Summary of the Invention

[0008] In view of the deficiencies of the prior art, the present invention provides a merchandise planning and operation management system, which solves the problems commonly faced by traditional clothing merchandise planning and operation management systems, such as lack of data models, inconsistent planning results with actual needs, difficulty in implementing planning results, untimely replenishments, and chaotic transfers.

[0009] The present invention is realized through the following technical solutions:

[0010] A merchandise planning and operation management system, comprising:

[0011] A factor model module, which is used to configure and manage the influencing factors of merchandise planning and form a merchandise operation plan through operation and maintenance;

[0012] Product model module, which is used to define the basic attribute fields and content of the product;

[0013] Style model module, which is used to define the style attribute fields and content of the product and provide the planning of the style model;

[0014] Commodity operation module, which is based on the factor model module, product model module and style model module, and combines the actual business data to conduct continuous operation.

[0015] Furthermore, the factor model module includes:

[0016] Model parameter configuration, which is used to provide product level configuration and store level configuration, and provide a parameter basis for the calculation of the factor model module;

[0017] Satisfaction rate factor model, which is configured with satisfaction rate factors that affect the satisfaction rate of each category in each store, and is used to finally calculate the satisfaction rate plan of each category in each store;

[0018] Turnover factor model, which is configured with turnover factors that affect the turnover of each category in each store, and is used to finally calculate the turnover plan of each category in each store.

[0019] Furthermore, the factor model module also includes:

[0020] Operation plan, which provides a calculation model and uses the calculation model to generate planning results. The planning results include satisfaction rate plan results and turnover plan results, and are used for subsequent prediction of procurement plans and operation regulation during the process;

[0021] Satisfaction rate operation sub-plan, which is created based on the satisfaction rate factor model according to the business cycle, and the satisfaction rate operation sub-plan of each business cycle calculates the satisfaction rate plan result according to the calculation model provided by the operation plan;

[0022] Turnover operation sub-plan, which is created based on the turnover factor model according to the business cycle, and the turnover operation sub-plan of each business cycle calculates the turnover plan result according to the calculation model provided by the operation plan.

[0023] Furthermore, the calculation model includes:

[0024] Category satisfaction rate calculation model, which is used to calculate the satisfaction rate plan result, and the category satisfaction rate calculation model = ∑ product ability coefficient * product configuration coefficient * (category benchmark satisfaction rate + store level value + regional difference value + weather change coefficient) * marketing / discount value * store exception coefficient;

[0025] Category turnover plan calculation model. The category turnover plan calculation model is used to calculate the turnover plan result, and the category turnover plan calculation model = (category standard turnover * product capacity coefficient * product configuration coefficient + store level value + regional difference value) * weather change coefficient * marketing / discount value * store anomaly coefficient.

[0026] Furthermore, the product model module includes:

[0027] Brand agreement template, which is used to determine the content of the operating brand;

[0028] Product line agreement template, which is used to determine the content of the product line;

[0029] Category agreement template, which is used to define the categories under different brands and product lines;

[0030] Size group agreement template, which is used to define the sizes corresponding to the clothing and the corresponding size widths;

[0031] Size group management module, which is used to uniformly manage size groups and size details.

[0032] Furthermore, the style model module includes:

[0033] Style model management, which is used to define product style fields and content;

[0034] Style data module, which is used to collect and manage style data provided by each business department according to multiple data sources. Among them, the multiple data sources include supply market conditions, competitor environment, customer group analysis, fashion trends, store feedback, and sales trends;

[0035] Style planning management, which includes style planning rules and style planning models. The style planning rules are used to define the ranking rules, segmentation rules, screening rules, and matching rules for style planning. The style planning model generates style planning model data based on the style planning rules and style data. Among them, the ranking rules and segmentation rules are used to match style data, the screening rules are used to filter data that do not meet the conditions, and the matching rules are used to specify the matching order;

[0036] Planning model analysis, which is used to optimize and adjust the style planning model and provide basic data support for the fulfillment rate factor model in the next business cycle.

[0037] Furthermore, the style model management includes:

[0038] Quarterly style model, which defines the style attribute content under different categories and is used to limit the optional values of each style attribute;

[0039] Style category rules are used to set the definition rules for clothing categories.

[0040] Quarterly category planning defines the category content for different periods and is used to limit the data that can be collected from multiple data sources in style data management.

[0041] Furthermore, the style data module includes:

[0042] Style data collection is used to gather style data provided by multiple data sources.

[0043] Style data management is used to uniformly manage the style data provided by multiple data sources.

[0044] Furthermore, the planning model analysis includes:

[0045] Brand agreement analysis is used to analyze the difference between the actual proportion of each brand in each store and the range set in the brand agreement template.

[0046] Product line agreement analysis is used to analyze the difference between the actual proportion of each product line in each store and the range set in the product line agreement template, as well as the actual gross profit margin and actual turnover.

[0047] Category agreement analysis is used to analyze the difference between the actual fulfillment rate and the planned fulfillment rate of each category in each store, as well as the gross profit margin and turnover of each actual category.

[0048] Style model analysis is used to analyze the turnover, gross profit margin, and sales during a period of each style category product.

[0049] Furthermore, the merchandise operation module includes:

[0050] Operation rule management is used to define operation rules for different business scenarios. Different business scenarios include new product launches, order chasing, replenishment, transfer, and promotion. The operation rules are created based on the planning results and style planning model data.

[0051] Operation analysis management is used to analyze the current merchandise operation status and store operation status.

[0052] Operation order management is used to automatically or manually create operation orders according to operation rules. Operation orders include new product launch orders, order chasing orders, replenishment orders, transfer orders, and promotion orders.

[0053] Supply chain integration is used to connect the generated operation order information to the supply chain system.

[0054] Warehouse logistics integration is used to connect the generated operation order information to the warehouse logistics system.

[0055] Compared with the prior art, the advantages of the present invention are as follows:

[0056] 1. By setting up a factor model module and a style model module to construct a data model, the workload of planners for constructing the data model is reduced, and the efficiency of product planning is improved.

[0057] 2. By establishing a factor model module, a style model module and a product operation module, weekly rolling planning can be realized, and routine product operations such as automatic replenishment and transfer can be achieved according to the operation rules.

[0058] 3. By refining the factors affecting product sales and inventory into digital factors, the planning becomes more scientific and objective, avoiding being affected by the subjective judgment of planners.

[0059] 4. By setting up supply chain integration and warehouse logistics integration, the order information generated by the operation order management is connected to the supply chain system and the warehouse logistics system, realizing seamless data transfer and making the business operation smoother. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a schematic structural diagram of a product planning operation management system according to an embodiment of the present invention;

[0061] Figure 2 It is a schematic structural diagram of the factor model module;

[0062] Figure 3 It is a schematic structural diagram of the satisfaction rate factor model;

[0063] Figure 4 It is a schematic structural diagram of the turnover factor model;

[0064] Figure 5 It is a schematic structural diagram of the satisfaction rate operation sub-plan;

[0065] Figure 6 It is a schematic structural diagram of the turnover operation sub-plan;

[0066] Figure 7 It is a schematic structural diagram of the product model module;

[0067] Figure 8 It is a schematic structural diagram of the style model module;

[0068] Figure 9 It is a schematic structural diagram of the product operation module;

[0069] Figure 10 It is a flowchart of the factor model module;

[0070] Figure 11 Flow chart of the style model module;

[0071] Figure 12 Flow chart of the product operation module.

[0072] Label description: 1. Factor model module; 11. Model parameter configuration; 111. Product level configuration; 112. Store level configuration; 12. Fulfillment rate factor model; 121. Benchmark fulfillment rate factor; 122. Merchandise allocation level factor; 13. Turnover factor model; 131. Standard turnover factor; 132. Turnover level factor; 14. Fulfillment rate operation sub-plan; 141. Benchmark fulfillment rate sub-plan; 142. Merchandise allocation level sub-plan; 15. Turnover operation sub-plan; 151. Standard turnover sub-plan; 152. Turnover level sub-plan; 16. Operation plan; 171. Marketing discount factor; 172. Regional difference factor; 173. Weather change factor; 174. Store anomaly factor; 175. Product configuration coefficient; 176. Product ability coefficient; 181. Marketing discount sub-plan; 183. Weather change sub-plan; 185. Product configuration sub-plan; 186. Product ability sub-plan; 2. Product model module; 21. Brand agreement template; 22. Product line agreement template; 23. Category agreement template; 24. Size group agreement template; 25. Size group management module; 3. Style model module; 31. Style model management; 311. Quarterly style model; 312. Style item class rules; 313. Quarterly item class planning; 32. Style data module; 321. Style data collection; 322. Style data management; 33. Style planning management; 331. Style planning rules; 332. Style planning model; 34. Planning model analysis; 341. Brand agreement analysis; 342. Product line agreement analysis; 343. Category agreement analysis; 344. Style model analysis; 4. Product operation module; 41. Operation rule management; 42. Operation analysis management; 43. Operation order management; 44. Supply chain integration; 45. Warehouse logistics integration. Detailed implementation method

[0073] The technical solution of the invention will be further described in detail below in conjunction with the preferred embodiments and their accompanying drawings in a non-limiting manner. In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. is based on the orientation or positional relationship shown in the accompanying drawings. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of the present invention, "a plurality" means at least two, such as two, three, etc., unless otherwise specifically defined. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0074] As Figures 1 to 12 shown, a merchandise planning and operation management system according to an embodiment of the present invention includes a factor model module 1, a product model module 2, a style model module 3, and a merchandise operation module 4. Among them, the factor model module 1 is used to configure and manage the influencing factors of merchandise planning and form a merchandise operation plan through operation and maintenance; the product model module 2 is used to define the basic attribute fields and contents of products; the style model module 3 is used to define the style attribute fields and contents of products and provide the planning of the style model; the merchandise operation module 4 is based on the factor model module 1, the product model module 2, and the style model module 3 and combines the actual business data to perform continuous operation. In this embodiment, the product model module 2 constitutes the basis of the system. The merchandise information involved in the factor model module 1 all originates from the product model module 2, and the style model module 3 further expands the style attributes of products on the basis of the product model module 2; in addition, the factor model module 1, the product model module 2, and the style model module 3 together constitute the basis of the merchandise operation module 4. By establishing the factor model module 1, the style model module 3, and the merchandise operation module 4, weekly rolling planning can be achieved and automated replenishment, allocation, and other regular merchandise operations can be configured according to the operation rules.

[0075] As Figures 2 to 6As shown in the figure, the factor model module 1 includes a model parameter configuration 11, a satisfaction rate factor model 12, a turnover factor model 13, a satisfaction rate operation sub-plan 14, a turnover operation sub-plan 15, and an operation plan 16. Further, in this embodiment, the influencing factors of merchandise planning include a benchmark satisfaction rate factor 121, a stocking level factor 122, a standard turnover factor 131, a turnover level factor 132, a marketing discount factor 171, a regional difference factor 172, a weather change factor 173, a store anomaly factor 174, as well as a product configuration coefficient 175 and a product ability coefficient 176. By refining the factors affecting product sales and inventory into digital factors, the planning becomes more scientific and objective, avoiding being affected by the subjective judgment of planners.

[0076] Specific reference Figure 2 , the model parameter configuration 11 is used to provide a product level configuration 111 and a store level configuration 112, and provide a parameter basis for the calculation of the factor model module 1. Among them, the product level is divided according to the quarterly sales amount and sales volume ratio of different products, and is divided into 10 levels in total, and each level corresponds to a level value; the store level is divided according to the average turnover of different brands, product lines, and categories in different stores, and is divided into 9 levels in total, and each level corresponds to a level value.

[0077] Further reference Figure 3 , the satisfaction rate factor model 12 is configured with satisfaction rate factors that affect the satisfaction rate of each category in each store, and is used to finally calculate the satisfaction rate plan of each category in each store. Among them, the satisfaction rate refers to the space fullness of a single store with the display rack as the display carrier, and the satisfaction rate of the store is determined by the business rhythm of the store.

[0078] In this embodiment, the satisfaction rate factors include a benchmark satisfaction rate factor 121, a stocking level factor 122, a marketing discount factor 171, a regional difference factor 172, a weather change factor 173, a store anomaly factor 174, a product configuration coefficient 175, and a product ability coefficient 176. Among them, the benchmark satisfaction rate factor 121 is a benchmark satisfaction rate value given to each category of each store by the business department based on the satisfaction rate data of each category in each store for 52 weeks over the years, after being approved by the strategic development department and excluding the remaining influencing factors, that is, the category benchmark weekly satisfaction rate. In this factor model, the remaining influencing factors include a stocking level factor 122, a marketing discount factor 171, a regional difference factor 172, a weather change factor 173, a store anomaly factor 174, a product configuration coefficient 175, and a product ability coefficient 176. And, the stocking level factor 122 determines its stocking level coefficient according to the level of different stores. The stocking level is generally divided into 9 levels from T0 to T8, and different levels are set with different coefficients.

[0079] Further referenceFigure 4 The turnover factor model 13 is configured with turnover factors that affect the turnover of each category in each store and is used to finally calculate the turnover plan for each category in each store. Among them, turnover is a unit used to reflect the efficiency of goods and is generally used for calibration of the fulfillment rate.

[0080] In this embodiment, the turnover factors include a standard turnover factor 131, a turnover grade factor 132, a marketing discount factor 171, a regional difference factor 172, a weather change factor 173, a store anomaly factor 174, a product configuration coefficient 175, and a product capacity coefficient 176. Among them, the standard turnover factor 131 is a standard turnover value given to a single category in each store by the business department based on the turnover data of each category in 52 weeks of each store over the years, after being approved by the strategic development department and excluding other influencing factors, that is, the category standard turnover. In this factor model, the other influencing factors include the turnover grade factor 132, the marketing discount factor 171, the regional difference factor 172, the weather change factor 173, the store anomaly factor 174, the product configuration coefficient 175, and the product capacity coefficient 176. And, the turnover grade factor 132 determines its turnover grade coefficient according to the grades of different stores. The turnover grade is generally divided into 9 grades from T0 to T8, and different grades are set with different coefficients.

[0081] Furthermore, the marketing discount factor 171 refers to the estimation of the fulfillment rate or turnover fluctuation of each category when a marketing scenario occurs, and analyzes and calculates the impact of each marketing method on the sales plan. When setting the marketing / discount value, it is necessary to complete the selection of the marketing activity and the activity date or cycle.

[0082] The regional difference factor 172 is mainly due to current logistics restrictions or the setting of the change in the number of people in certain regions at fixed times, and is generally set in the basic parameters of the store, that is, the regional difference value, which is mainly divided into two categories. The first category: advance fulfillment type, that is, the change in the fulfillment rate of this store is due to logistics lag and needs to be increased and decreased in advance. The second category: the fulfillment rate increases or the turnover decreases at fixed cycles. This increase only occurs at certain fixed nodes. In a specific embodiment, a certain store is fixed on the 18th day, and due to the membership day activity in the shopping mall where it is located, the passenger flow will increase, and it is necessary to increase the fulfillment rate or decrease the turnover value.

[0083] The weather change factor 173 manages the influence coefficient of weather change on the fulfillment rate or turnover. Here, an influence coefficient is defined for abnormal weather, and the proportion of abnormal weather within the required planning period is judged to obtain the cycle influence coefficient, that is, the weather change coefficient, so as to affect the fulfillment rate and turnover. Among them, abnormal weather includes various scenarios such as rain, snow, wind, and thunder.

[0084] The store anomaly factor 174 generally indicates that the store is unable to operate normally due to objective reasons, and different levels are divided to correspond to different store anomaly coefficients.

[0085] The product configuration coefficient 175 is used to judge the percentage of satisfaction rate or turnover coefficient according to the temperature matching degree of different categories in different stores, and the coefficient is set according to the clothing life cycle from the introduction period, growth period, maturity period, decline period to the delisting period.

[0086] The product ability coefficient 176 is to set different satisfaction rate ability coefficients or turnover ability coefficients for product categories with different product abilities, which is used to control the quantity and proportion of different products.

[0087] Furthermore, the operation plan 16 provides a calculation model and uses the calculation model to generate the planning results, including the satisfaction rate plan result and the turnover plan result, which are used for subsequent prediction of the procurement plan and operation control during the process.

[0088] Such as Figure 5 shown, the satisfaction rate operation sub-plan 14 is created based on the satisfaction rate factor model 12 according to the business cycle, specifically including the benchmark satisfaction rate sub-plan 141, the distribution level sub-plan 142, the marketing discount sub-plan 181, the weather change sub-plan 183, the product configuration sub-plan 185 and the product ability sub-plan 186. And the satisfaction rate operation sub-plan 14 of each business cycle calculates the satisfaction rate plan result according to the calculation model provided by the operation plan 16. By setting the operation plan 16 to calculate the planning results through the calculation model, it provides deeper data support for product planning and formulates more scientific product operation rules and operation strategies.

[0089] Such as Figure 6 shown, the turnover operation sub-plan 15 is created based on the turnover factor model 13 according to the business cycle, specifically including the standard turnover sub-plan 151, the turnover level sub-plan 152, the marketing discount sub-plan 181, the weather change sub-plan 183, the product configuration sub-plan 185 and the product ability sub-plan 186. And the turnover operation sub-plan 15 of each business cycle calculates the turnover plan result according to the calculation model provided by the operation plan 16.

[0090] In this embodiment, the calculation model provided by the operation plan 16 includes the category satisfaction rate calculation model and the category turnover plan calculation model. Among them, the category satisfaction rate calculation model combines 7 factors in the satisfaction rate factor model 12 and is used to calculate the turnover plan result, and its mathematical expression is:

[0091] Category satisfaction rate calculation model = Σ Product ability coefficient * Product configuration coefficient * (Category benchmark weekly satisfaction rate ++ Store level value Regional difference value + Weather change coefficient) * Marketing \ Discount value * Store anomaly coefficient

[0092] Among them, the product ability coefficient is set with different satisfaction rate ability coefficients for category products with different product abilities; the product configuration coefficient is to judge the proportion value of its satisfaction rate according to the temperature matching degree of different categories in different stores.

[0093] The category turnover plan calculation model combines the 7 factors in the turnover factor model 13 and is used to calculate the turnover plan result. Its mathematical expression is:

[0094] Category turnover plan calculation model = (category standard turnover * product ability coefficient * product configuration coefficient + store level value + regional difference value) * weather change coefficient * marketing / discount value * store anomaly coefficient

[0095] Among them, the product ability coefficient is set with different turnover ability coefficients for category products with different product abilities; the product configuration coefficient is to judge its turnover coefficient according to the temperature matching degree of different categories in different stores.

[0096] For further reference Figure 10 , during the business operation process, the business department configures each factor parameter in the factor model module 1 according to the established business rules and historical data. Subsequently, according to the factor parameters and combined with the business cycle, each factor sub-plan is constructed, and these sub-plans can be officially enabled only after being approved. After all sub-plans are successfully created, the corresponding influence factors will be selected according to the calculation model, and then the specific values of the satisfaction rate plan and the turnover plan will be calculated.

[0097] Such as Figure 7 shown, the product model module 2 includes the brand agreement template 21, the product line agreement template 22, the category agreement template 23, the size group agreement template 24, and the size group management module 25.

[0098] Among them, the brand agreement template 21 is used to determine the content of the operating brand. Specifically, the brand agreement template 21 mainly defines the target proportion of different brands. In a specific embodiment, the target proportion of men's clothing is 25.4% - 27.4%.

[0099] Among them, the product line agreement template 22 is used to determine the content of the product line. Specifically, the product line agreement template 22 mainly defines the target proportion of different product lines under different brands. In a specific embodiment, the target proportion of the children's clothing simple and versatile product line is 18% - 22%.

[0100] The category agreement template 23 is used to define categories under different brands and product lines. Specifically, the category agreement template 23 mainly defines categories under different brands, genders, ages, and product lines, as well as the bands, marketable temperatures, and thicknesses corresponding to different categories. In a specific embodiment, women's clothing - women's - youth - simple and versatile includes thermal underwear, suits, base underwear, leggings, dresses, shorts, casual pants, vests, hoodies, sweaters, shirts, T-shirts, vests, cotton clothes, down jackets, coats and other categories.

[0101] The size group protocol template 24 is used to define the sizes and size widths corresponding to the clothing. Specifically, it mainly defines the size groups and size widths corresponding to different brands, genders, ages, product lines, categories, bands and styles. In a specific embodiment, the size group corresponding to women's clothing - women's - teenagers - simple and versatile - down jackets - drainage styles is the regular size group for women's tops.

[0102] The size group management module 25 is used to uniformly manage size groups and size details. In a specific embodiment, the conventional size group of women's tops includes sizes S / 155, M / 160, L / 165, XL / 170, 2XL / 175, 3XL / 180, 4XL / 185, and 5XL / 190.

[0103] like Figure 8 As shown, the style model module 3 includes style model management 31, style data module 32, style planning management 33 and planning model analysis 34. By setting the factor model module 1 and the style model module 3 to build the data model, the workload of the planners for building the data model is reduced, the efficiency of product planning is improved, and a fast and short-cycle rolling planning and a quick response operation mode are realized.

[0104] The style model management 31 is used to define product style fields and content. Further, the style model management 31 includes a quarterly style model 311 , a style category rule 312 and a quarterly category planning 313 .

[0105] Among them, the quarterly style model 311 defines the style attribute content under different categories, which is used to limit the optional values ​​of each style attribute; among them, the style attributes specifically include style, color, version, element, material, series, etc. The style category rule 312 is used to set the definition rules of clothing categories in order to improve the accuracy of planning data analysis; the quarterly category planning 313 defines the category content of different periods, which is used to limit the data that can be collected from multiple data sources in the style data module 32.

[0106] Specifically, the quarterly style model 311 defines the content of style attributes corresponding to different brands, genders, ages, product lines, categories, and seasons. Although there are many options for each style attribute, certain restrictions are set in different quarters, and it is necessary to select the attribute content suitable for this quarter from the options. After the quarterly style model 311 is set up, it is necessary to set the style item class rules 312 to merge clothes of the same style and collectively call them "item classes". The merging rules for different style attributes are different, and the style item class rules 312 are set to define the rules for item class merging. When the quarterly style model 311 and the style item class rules 312 are defined, the business department sets the item classes for this quarter as the quarterly item class plan 313 according to the changes in each quarter, which is used to limit the data that can be collected from each data source in the style data module 32.

[0107] The style data module 32 is used to collect and manage style data provided by each business department according to multiple data sources. Among them, the multiple data sources include supply market conditions, competitor environment, customer group analysis, fashion trends, store feedback, and sales trends.

[0108] Furthermore, the style data module 32 includes style data collection 321 and style data management 322. Among them, the style data collection 321 is used to collect style data provided by multiple data sources; the style data management 322 is used to uniformly manage the style data provided by multiple data sources.

[0109] The style planning management 33 includes style planning rules 331 and a style planning model 332. The style planning rules 331 are used to define the ranking rules, segmentation rules, screening rules, and matching rules for style planning. The style planning model 332 generates style planning model data based on the style planning rules 331 and style data. Among them, the ranking rules and segmentation rules are used to match style data, the screening rules are used to filter data that does not meet the conditions, and the matching rules are used to stipulate the matching order.

[0110] The planning model analysis 34 is used to optimize and adjust the style planning model 332 and provide basic data support for the fulfillment rate factor model 12 in the next business cycle.

[0111] Furthermore, the planning model analysis 34 includes brand agreement analysis 341, product line agreement analysis 342, category agreement analysis 343, and style model analysis 344.

[0112] Among them, the brand agreement analysis 341 is used to analyze the difference between the actual proportion of each brand in each store and the interval set in the brand agreement template 21, so as to adjust the parameters of the fulfillment rate factors under different brands in the factor model module 1 in the next business cycle.

[0113] Product line agreement analysis 342 is used to analyze the differences between the actual proportions of each product line in each store and the intervals set in the product line agreement template 22, as well as the actual gross profit margin and actual turnover, so as to adjust the parameters of the fulfillment rate factors under different brands in the factor model module 1 in the next business cycle.

[0114] Category agreement analysis 343 is used to analyze the differences between the actual fulfillment rates of each category in each store and the planned fulfillment rates, as well as the gross profit margins and turnover of each actual category, so as to adjust the parameters of the fulfillment rate factors under different brands in the factor model module 1 in the next business cycle.

[0115] Style model analysis 344 is used to analyze the turnover, gross profit margin, and sales during the period of each style and product category, so as to assist in judging the collection of style data in the next business cycle.

[0116] As Figure 9 shown, the commodity operation module 4 includes operation rule management 41, operation analysis management 42, operation order management 43, supply chain integration 44, and warehousing and logistics integration 45.

[0117] Among them, the operation rule management 41 is used to define the operation rules for different business scenarios. Further, different business scenarios include new product launches, order chasing, replenishment, transfer, and promotions, and the operation rules are created based on the planning results and style planning model data.

[0118] Specifically, the operation rules include minimum order quantity rules, data extraction rules, order calculation rules, order issuance rules, demand matching rules, and arrival matching rules. Among them, the minimum order quantity rule is the starting order quantity for different categories of goods, and goods with insufficient predicted quantities will be restricted.

[0119] Further, the operation rules for batch new product launches include minimum order quantity rules, data extraction rules, order calculation rules, order issuance rules, and demand matching rules; the operation rules for replenishment include data extraction rules, order calculation rules, and order issuance rules; the operation rules for transfer include data extraction rules, order calculation rules, and order issuance rules; the operation rules for promotions include data extraction rules and order issuance rules; the operation rules for order chasing include data extraction rules, order calculation rules, order issuance rules, and arrival matching rules.

[0120] The operation analysis management 42 is used to analyze the current commodity operation status and store operation status, so as to better assist operation decisions and provide corresponding data and indicator support.

[0121] The operation order management 43 is used to automatically or manually create operation orders according to operation rules, so as to quickly support business operations. Further, the operation orders include new product launch orders, order chasing orders, replenishment orders, transfer orders, and promotion orders.

[0122] Supply chain integration 44 and warehousing and logistics integration 45 are used to dock the generated operation order information with the supply chain system and the warehousing and logistics system, so as to achieve seamless data transfer and make the business operation smoother.

[0123] As Figure 12 shown, during the execution of the product operation module 4, first, based on the planning results calculated by the factor model module 1, including the fulfillment rate plan and the turnover plan, and the style planning model data generated by the style model module 3, the basic data required to create operation rules is obtained. Subsequently, these basic data are reviewed, and after the review is correct, the corresponding product operation rules are established accordingly.

[0124] In the scenario of new product listing, first, a batch listing order is generated, the stores to be listed are selected, the style data and the fulfillment rate plan data are synchronized, and the order is calculated according to the established rules. After the calculation result is generated, corresponding instructions are sent through the integrated supply chain system, and the supply chain system performs the procurement order placement operation accordingly.

[0125] The replenishment operation is usually automatically executed daily to generate replenishment orders, automatically calculate the replenishment demand according to the established rules and the selected stores, and perform a matching calculation with the existing inventory to obtain the final result. Then, the demand result is pushed through the integrated warehousing and logistics system, and the warehousing and logistics system decomposes and executes according to the instructions.

[0126] Additional orders usually occur when the product sales performance is good. The business department initiates and creates additional orders. First, the relevant stores are selected and the products to be added are screened, the additional quantity is calculated according to the established rules, and after the demand result is obtained, corresponding instructions are sent through the integrated supply chain system, and the supply chain system performs the additional procurement order placement operation accordingly.

[0127] The transfer operation is usually initiated and a transfer order is created by the business department according to the actual demand. After selecting the transfer stores, the transfer-in and transfer-out requirements are calculated according to the established rules, and the demand result is pushed through the integrated warehousing and logistics system. The warehousing and logistics system decomposes and executes according to the instructions.

[0128] Promotion activities are usually triggered according to demand. The business personnel create promotion orders and select the promotion stores. The system automatically calculates the promotable products according to the established rules, and then pushes the information to the store system.

[0129] A merchandise planning operation management system according to an embodiment of the present invention can solve problems commonly faced by traditional clothing merchandise planning operation management systems, such as the lack of a data model, the inconsistency between the planning results and actual demands, the difficulty in implementing the planning results, untimely replenishment, and chaotic allocation. By setting up a factor model module 1 and a style model module 3 to construct a data model, the workload of planners for constructing the data model is reduced, the efficiency of merchandise planning is improved, and an operation mode of fast short-cycle rolling planning and quick response is realized. By refining the factors affecting merchandise sales and inventory into digital factors, the planning becomes more scientific and objective, avoiding being affected by the subjective judgment of planners. By establishing a factor model module 1, a style model module 2, and a merchandise operation module 4, weekly rolling planning can be realized, and conventional merchandise operations such as automatic replenishment and allocation can be achieved according to the configured operation rules. By setting up a supply chain integration 44 and a warehousing and logistics integration 45, the order information created and generated by the operation order management is docked into the supply chain system and the warehousing and logistics system, realizing seamless data transfer and making the business operation smoother. By setting up an operation plan 16 to calculate the planning results through a calculation model, deeper data support is provided for merchandise planning, and merchandise operation rules and operation strategies can be formulated more scientifically.

[0130] The above-described embodiments merely represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention should be subject to the appended claims.

Claims

1. A product planning and operation management system, characterized in that, including: a factor model module (1), which is used to configure and manage the influencing factors of product planning, and form a product operation plan through operation and maintenance; a product model module (2), which is used to define the basic attribute fields and contents of products; a style model module (3), which is used to define the style attribute fields and contents of the products, and provide the planning of the style model; a product operation module (4), which is based on the factor model module (1), the product model module (2) and the style model module (3), and combines the actual business data to carry out continuous operation.

2. The merchandise planning operation management system according to claim 1, wherein The factor model module (1) includes: a model parameter configuration (11), which is used to provide a product level configuration (111) and a store level configuration (112), and provide a parameter basis for the calculation of the factor model module (1); a satisfaction rate factor model (12), which is configured with satisfaction rate factors affecting the satisfaction rate of each category in each store, and is used to finally calculate the satisfaction rate plan of each category in each store; an inventory turnover factor model (13), which is configured with inventory turnover factors affecting the inventory turnover of each category in each store, and is used to finally calculate the inventory turnover plan of each category in each store.

3. The merchandise planning and operation management system according to claim 2, characterized in that, The factor model module (1) further includes: an operation plan (16), which provides a calculation model, and uses the calculation model to generate a planning result, and the planning result includes a satisfaction rate plan result and an inventory turnover plan result, and is used for subsequent prediction of the procurement plan and operation regulation in the process; a satisfaction rate operation sub-plan (14), which is created based on the satisfaction rate factor model (12) according to the business cycle, and the satisfaction rate operation sub-plan (14) in each business cycle calculates the satisfaction rate plan result according to the calculation model provided by the operation plan (16); an inventory turnover operation sub-plan (15), which is created based on the inventory turnover factor model (13) according to the business cycle, and the inventory turnover operation sub-plan (15) in each business cycle calculates the inventory turnover plan result according to the calculation model provided by the operation plan (16).

4. The merchandise planning operation management system according to claim 3, wherein The calculation model includes: a category satisfaction rate calculation model, which is used to calculate the satisfaction rate plan result, and the category satisfaction rate calculation model = ∑ product ability coefficient * product configuration coefficient * (category benchmark satisfaction rate + store level value + regional difference value + weather change coefficient) * marketing / discount value * store exception coefficient; a category inventory turnover plan calculation model, which is used to calculate the inventory turnover plan result, and the category inventory turnover plan calculation model = (category standard inventory turnover * product ability coefficient * product configuration coefficient + store level value + regional difference value) * weather change coefficient * marketing / discount value * store exception coefficient.

5. The merchandise planning operation management system according to claim 3, wherein The product model module (2) includes: Brand Agreement Template (21), which is used to determine the content of the operating brand; Product Line Agreement Template (22), which is used to determine the content of the product line; Category Agreement Template (23), which is used to define categories under different brands and product lines; Size Group Agreement Template (24), which is used to define the sizes corresponding to clothing and the corresponding size widths; Size Group Management Module (25), which is used to uniformly manage size groups and size details.

6. The merchandise planning operation management system according to claim 5, characterized in that The Style Model Module (3) includes: Style Model Management (31), which is used to define the product style fields and content; Style Data Module (32), which is used to collect and manage style data provided by each business department according to multiple data sources. Among them, the multiple data sources include supply market conditions, competitor environment, customer group analysis, fashion trends, store feedback, and sales trends; Style Planning Management (33), which includes Style Planning Rules (331) and a Style Planning Model (332). The Style Planning Rules (331) are used to define the ranking rules, segmentation rules, screening rules, and matching rules for style planning. The Style Planning Model (332) generates style planning model data based on the Style Planning Rules (331) and the style data. Among them, the ranking rules and segmentation rules are used to match the style data, the screening rules are used to filter data that does not meet the conditions, and the matching rules are used to specify the matching order; Planning Model Analysis (34), which is used to optimize and adjust the Style Planning Model (332) and provide basic data support for the Satisfaction Rate Factor Model (12) in the next business cycle.

7. The merchandise planning operation management system according to claim 6, wherein The Style Model Management (31) includes: Quarterly Style Model (311), which defines the style attribute content under different categories and is used to limit the optional values of each style attribute; Style Item Rules (312), which are used to set the definition rules for clothing items; Quarterly Item Planning (313), which defines the item content in different cycles and is used to limit the data that can be collected from the multiple data sources in the Style Data Module (32).

8. The merchandise planning and operation management system according to claim 6, characterized in that, The Style Data Module (32) includes: Style Data Collection (321), which is used to collect the style data provided by the multiple data sources; Style Data Management (322), which is used to uniformly manage the style data provided by the multiple data sources.

9. The merchandise planning operation management system according to claim 6, characterized in that The Planning Model Analysis (34) includes: Brand Agreement Analysis (341), which is used to analyze the difference between the actual proportion of each brand in each store and the interval set in the Brand Agreement Template (21); Product line agreement analysis (342) for analyzing the differences between the actual proportions of each product line in each store and the intervals set in the product line agreement template (22), the actual gross profit margin, and the actual turnover; Category agreement analysis (343) for analyzing the differences between the actual satisfaction rates of each category in each store and the planned satisfaction rates, the gross profit margins, and the turnover situations of the actual categories; Style model analysis (344) for analyzing the turnover, gross profit margin, and sales during a period of each style category product.

10. The merchandise planning and operation management system according to claim 5, wherein, The merchandise operation module (4) includes: Operation rule management (41) for defining operation rules for different business scenarios, where the different business scenarios include new product launches, order chasing, replenishment, transfer, and promotions, and the operation rules are created based on the planning results and the style planning model data; Operation analysis management (42) for analyzing the current merchandise operation status and the store operation status; Operation order management (43) for automatically or manually creating operation orders according to the operation rules, where the operation orders include new product launch orders, order chasing orders, replenishment orders, transfer orders, and promotion orders; Supply chain integration (44) for docking the generated operation order information to the supply chain system; Warehousing and logistics integration (45) for docking the generated operation order information to the warehousing and logistics system.

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