Supply chain optimization methods, systems and media based on customer analysis and market trends

Abstract

The present invention discloses a supply chain optimization method, system and medium based on customer analysis and market trends; it relates to the technical field of supply chain optimization; this solution makes methodological improvements based on the traditional warehouse stocking method based on demand forecasts and historical data, on the one hand, a first inventory strategy is obtained by performing infant stage matching calculations based on customer information and target products, and the impact of changes in infant age groups on product demand is considered to achieve accurate demand calculation for products; on the other hand, the inventory strategy is corrected by considering the impact of seasonal factors and timeliness factors on product demand, and the inventory strategy is optimized for products whose demand is affected by seasons or timeliness, so as to avoid inventory backlogs and expiration problems caused by excessive purchases, balance supply and demand, and reduce costs and losses.

Description

Technical Field

[0001] The present invention relates to the technical field of supply chain optimization, and in particular to a supply chain optimization method, system and medium based on customer analysis and market trends. Background Art

[0002] With the development of the digital economy and the improvement of people's living standards, consumer demand is gradually developing towards personalized and diversified needs. In order to better seize market resources, the marketing of maternal and infant products is constantly transforming and upgrading under the empowerment of the digital economy. With the development of vertical maternal and infant product chain operation routes, the supply chain capabilities of maternal and infant product chain stores are becoming increasingly prominent in operations.

[0003] With the expansion of maternal and infant product chain stores, new supply chain problems are also becoming increasingly apparent. Since maternal and infant products include many types and their product attributes vary greatly, the factors affecting the demand for products with different attributes are different. The main factors affecting the demand are product timeliness, infant growth stage and season. For example, baby food, milk powder, complementary food and other products have a short shelf life. The demand for clothing and warmth products changes with the seasons. The demand for infant milk powder changes with the age of the infant. In addition, the shelf life of infant milk powder is very short, so the demand is small in a short period of time. The general supply warehouse needs to have accurate inventory management methods, otherwise it is easy to have problems such as product backlog and expiration or product out of stock. The traditional warehouse stocking method based on demand forecasts and historical data has inaccurate product demand forecasts when applied to the maternal and infant product supply chain, resulting in inventory backlogs, expiration or out of stock in the general supply warehouse, and the inability to supply goods to the chain stores in a timely manner. Summary of the invention

[0004] The technical problem to be solved by the present invention is: since the demand for maternal and infant products is affected by the stage of infants, the season or the timeliness of the products, the traditional warehouse stocking method based on demand forecasts and historical data, when applied to the maternal and infant products supply chain, has inaccurate product demand forecasts, resulting in inventory backlogs, expiration or out-of-stock in the main supply warehouse and inability to supply goods to various chain stores in a timely manner; the purpose of the present invention is to provide a supply chain optimization method, system and medium based on customer analysis and market trends. On the one hand, a first inventory strategy is obtained by calculating the infant stage matching between customer information and target products, considering the impact of changes in the infant age group on product demand, and realizing accurate demand calculation for products; on the other hand, the inventory strategy is corrected by considering the impact of market trends, seasonal factors and timeliness factors on product demand, and the inventory strategy is optimized for products whose demand is affected by seasons or timeliness, avoiding inventory backlogs and expiration problems caused by excessive procurement, balancing supply and demand, and reducing costs and losses.

[0005] The present invention is achieved through the following technical solutions:

[0006] This solution provides supply chain optimization methods based on customer analysis and market trends, including:

[0007] Obtain customer information and target products in the target supply area;

[0008] Inputting customer information and target products into a first demand model to determine a first inventory strategy for the target products; the first demand model performs infant stage matching calculations based on the customer information and the target products;

[0009] Input the target product into the second demand model to calculate the second inventory strategy of the target product; the second demand model analyzes the market preference of the target product based on big data, and determines the second inventory strategy of the target product according to the market preference;

[0010] After modifying the first inventory strategy by taking into account seasonal factors and product timeliness factors, the second inventory strategy is combined to obtain the final inventory strategy for the target product;

[0011] Solve the final inventory policy to obtain an optimal solution, and replenish the inventory of the target product according to the optimal solution.

[0012] Working principle of this solution: Since the demand for maternal and infant products is affected by the stage of infants, the season or the timeliness of products, the traditional warehouse stocking method based on demand forecasts and historical data has inaccurate product demand forecasts when applied to the maternal and infant products supply chain, resulting in inventory backlogs, expiration or out-of-stock in the supply warehouse, and the inability to supply goods to various chain stores in a timely manner. This solution is based on the traditional warehouse stocking method based on demand forecasts and historical data, and makes methodological improvements. On the one hand, the first inventory strategy is obtained by matching customer information with the target product according to the stage of infants, considering the impact of changes in infant age groups on product demand, and realizing accurate demand calculation for products; on the other hand, the inventory strategy is corrected by considering the impact of seasonal factors and timeliness factors on product demand, and the inventory strategy is optimized for products whose demand is affected by seasons or timeliness, avoiding inventory backlogs and expiration problems caused by excessive purchases, balancing supply and demand, and reducing costs and losses.

[0013] This solution proposes a new technical concept for the warehouse strategy of target products under the operation model of maternal and infant product chain physical stores combined with online sales: based on the direct customer data already mastered by the operator, combined with market preference data, the warehousing strategy of the target products is jointly determined to ensure the accuracy of the warehouse strategy; at the same time, the stage influence, seasonal influence and timeliness influence of maternal and infant products are comprehensively considered to adjust the warehousing strategy; to avoid backlogs and expiration, or supply interruptions in the maternal and infant product supply chain.

[0014] The target supply area in this plan refers to the supply area of ​​the current warehouse, which includes multiple maternal and child chain physical stores, and the target product refers to any product supplied by the current warehouse to the chain physical stores; in addition, the market trend described in this plan refers to the preference trend for different brands of target products in the entire maternal and child market.

[0015] A further optimization scheme is that the method for obtaining customer information in the target supply area includes:

[0016] Obtaining basic member information of all chain stores in the target supply area; the members include stable members and potential members; the potential members include customers who have ordered products in the chain stores; the basic information includes personal information and ordering information;

[0017] Based on the basic information, calculate the infant stage of each member at the current time:

[0018] For members with personal information and ordering information, the current infant stage of the member is calculated based on the personal information;

[0019] For members who only have ordering information, the stage to which the ordered product belongs is used as the infant stage to which the member currently belongs.

[0020] Stable members have a higher degree of demand and stability for products than potential members; chain stores include physical stores and online sales methods that match physical stores, and stable members include: members who have registered in physical stores, members who have registered in online sales methods, and customers who are continuously followed up by shopping guides in physical stores or online sales methods and have stable demand. For example, if customer A is not a registered member, but customer A regularly orders products through shopping guides in physical stores / online sales methods, or regularly orders products directly online through online sales methods; then customer A's personal information can be obtained from the shopping guide's communication records, or by analyzing the ordered products.

[0021] The potential members also include customers who have performed multiple operations on products online; for example, if customer B browses product 1 online multiple times and adds product 1 to the shopping cart, then customer B has a certain degree of demand.

[0022] A further optimization scheme is that the method of inputting customer information and target products into a first demand model to determine a first inventory strategy for the target products includes:

[0023] Get the infant stage i to which the target product belongs, and count the total number of members N matching the infant stage i at the current time;

[0024] Count the intended brands of the target product for each member in the infant stage i, and calculate the member's intention degree for each intended brand based on the intention perception model;

[0025] Select the brands with the highest intention of each member as the target brands of each member;

[0026] The first inventory strategy of the target product is calculated based on the target brand of each member and the total number of members N.

[0027] A further optimization scheme is that the intention perception model includes:

[0028] Member A's intention for B's target product for:

[0029] ;

[0030] Wherein, C1 represents the total number of consultations by member a on the target product of intended brand b; C2 = 0 or 1; when C2 = 0, it means that the total browsing time of member a on the target product of brand b is less than the first time threshold T; when C2 = 1, it means that the total browsing time of member a on the target product of brand b is greater than the first time threshold T, or member a has added the target product of intended brand b to the shopping cart; K represents the number of orders made by member a on the target product regarding intended brand b; m represents the number of positive comments made by member a on the target product; N represents the number of times member a switches from other brands to intended brand b in the historical orders of the target product; M represents the time from the beginning of browsing to the blocking of the push information of intended brand b target product by member a; k represents the intention coefficient, which is 1~1.2.

[0031] A further optimization scheme is that the first inventory strategy for the target product is calculated based on the target brand of each member and the total number of members N, including the method:

[0032] Count the quantity of each target brand as the minimum inventory quantity of each target brand;

[0033] Set the total inventory threshold of the target product based on the target product and the total number of members N;

[0034] Within the total inventory threshold and under the condition that each target brand has a minimum inventory quantity, allocate the optimal inventory quantity for each target brand:

[0035] ;

[0036] Among them, a represents the optimal inventory quantity of target brand A, and a1 represents the minimum inventory quantity of target brand A; b represents the optimal inventory quantity of target brand B, and b1 represents the minimum inventory quantity of target brand B; c represents the optimal inventory quantity of target brand C, and c1 represents the minimum inventory quantity of target brand C; f represents the optimal inventory quantity of target brand F, and f1 represents the minimum inventory quantity of target brand F; Q represents the total inventory threshold of the target product; s represents the influencing factor of the total inventory threshold of the target product, which is related to the attributes of the target product.

[0037] A further optimization scheme is that the target product is input into the second demand model to calculate the second inventory strategy, including the method:

[0038] Obtain all brands of target products based on big data and analyze the market preference of each brand;

[0039] Arrange the brands in descending order of market preference to obtain the brand market preference sequence;

[0040] According to the arrangement relationship of the brand market preference sequence, each target brand is arranged to obtain the target brand market preference sequence;

[0041] As an optimization solution, each target brand is found from the brand market preference sequence, and the target brand market preference sequence is arranged according to the arrangement relationship of the target brand in the brand market preference sequence.

[0042] The relationship between the minimum inventory quantities of each target brand is determined according to the arrangement relationship of the target brand market preference sequence: the higher the target brand is, the larger the minimum inventory quantity of the target brand is.

[0043] A further optimization scheme is to obtain all brands of target products based on big data and analyze the market preference of each brand, including the following methods:

[0044] Obtain online evaluation texts of all brands of the target product, and extract evaluation indicators of the target product from the online evaluation texts;

[0045] Taking the evaluation index as the influencing factor, sales volume, total number of collections, click-through conversion rate and repurchase recommendation index as variables, the entropy weight of each variable is calculated based on the entropy weight method;

[0046] The market preference of each brand is obtained by performing weighted summation calculation based on the entropy weight of each variable.

[0047] A further optimization scheme is that after the first inventory strategy is modified by considering seasonal factors and product timeliness factors, the second inventory strategy is combined to obtain the final inventory strategy for the target product, including the method:

[0048] Modified first inventory strategy:

[0049] Determine whether the sales volume of the target product is related to the season; if so, configure the first coefficient in combination with the current season to linearly increase or linearly decrease the total inventory threshold; otherwise, do not adjust the total inventory threshold of the target product;

[0050] Determine whether the target product has a shelf life D. If so, when the shelf life D> the validity period threshold De, set the second coefficient to linearly reduce the total inventory threshold of the target product; when the shelf life D≤ the validity period threshold De, set the third coefficient to linearly reduce the total inventory threshold of the target product; otherwise, do not adjust the total inventory threshold of the target product; wherein the third coefficient is greater than the second coefficient;

[0051] The first inventory policy and the second inventory policy are combined to obtain a final inventory policy for the target product.

[0052] This solution also provides a supply chain optimization system based on customer risk and market trend, which is used to implement the above-mentioned supply chain optimization method based on customer risk and market trend; the system includes:

[0053] The acquisition module is used to obtain customer information and target products in the target supply area;

[0054] A first calculation module is used to input customer information and target products into a first demand model to determine a first inventory strategy for the target products; the first demand model performs infant stage matching calculation based on the customer information and the target products;

[0055] A second calculation module is used to input the target product into a second demand model to calculate a second inventory strategy for the target product; the second demand model analyzes the market preference of the target product based on big data, and determines the second inventory strategy for the target product according to the market preference;

[0056] A correction module, configured to correct the first inventory strategy by taking into account seasonal factors and product timeliness factors, and then combine the second inventory strategy to obtain a final inventory strategy for the target product;

[0057] The solution module is used to solve the final inventory strategy to obtain an optimal solution, and replenish the inventory of the target product according to the optimal solution.

[0058] The present solution also provides a computer-readable medium having a computer program stored thereon, and the computer program is executed by a processor to implement the supply chain optimization method based on customer risks and market trends as described above.

[0059] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0060] 1. The present invention provides a supply chain optimization method, system and medium based on customer analysis and market trends. This solution improves the method on the basis of the traditional warehouse stocking method based on demand forecasts and historical data. On the one hand, the first inventory strategy is obtained by performing infant stage matching calculations based on customer information and target products, and the impact of changes in infant age groups on product demand is considered to achieve accurate demand calculation for products. On the other hand, the inventory strategy is corrected by considering the impact of seasonal factors and timeliness factors on product demand, and the inventory strategy is optimized for products whose demand is affected by seasons or timeliness, so as to avoid inventory backlogs and expiration problems caused by excessive purchases, balance supply and demand, and reduce costs and losses.

[0061] 2. The present invention provides a supply chain optimization method, system and medium based on customer analysis and market trends; this solution proposes a new technical concept for the warehouse strategy of target products in the operation mode of maternal and infant product chain physical stores combined with online sales: based on the direct customer data already mastered by the operator, combined with market preference data to jointly determine the warehousing strategy of the target product to ensure the accuracy of the warehouse strategy; at the same time, comprehensively consider the stage influence, seasonal influence and timeliness influence of maternal and infant products to adjust the warehousing strategy; avoid backlogs and expiration, or supply interruptions in the maternal and infant product supply chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without creative work. In the drawings:

[0063] Figure 1 A flowchart of the supply chain optimization method based on customer analysis and market trends;

[0064] Figure 2 A flowchart of a method for determining a first inventory strategy;

[0065] Figure 3 A flow chart of a method for determining a second inventory strategy;

[0066] Figure 4 Schematic diagram of the supply chain optimization system based on customer analysis and market trends. DETAILED DESCRIPTION

[0067] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0068] Since the demand for maternal and infant products is affected by the stage of infants, the season or the timeliness of the product, the traditional warehouse stocking method based on demand forecast and historical data is applied to the maternal and infant product supply chain. There is an inaccurate product demand forecast, which leads to the problem of inventory backlog, expiration or out of stock in the supply warehouse and inability to supply goods to each chain store in a timely manner. In view of this, this solution provides the following embodiments to solve the above technical problems.

[0069] Embodiment 1: This embodiment provides a supply chain optimization method based on customer analysis and market trends, such as Figure 1 As shown, including:

[0070] Step 1: Obtain customer information and target products in the target supply area; In the specific implementation process, this step includes:

[0071] S11, obtaining basic information of members of all chain stores in the target supply area; the members include stable members and potential members; the potential members include customers who have ordered products in the chain stores; the basic information includes personal information and ordering information;

[0072] In the specific implementation process, stable members have higher demand and stability for goods than potential members; chain stores include physical stores and online sales methods that match physical stores, and stable members include: members who have registered in physical stores, members who have registered in online sales methods, and customers who are continuously followed up by shopping guides in physical stores or online sales methods and have stable demand. For example, if customer A is not a registered member, but customer A regularly orders products through shopping guides in physical stores / online sales methods, or regularly orders products directly through online sales methods; then customer A's personal information can be obtained from the shopping guide communication records, or through analysis of ordered products.

[0073] In the above steps, potential members also include customers who have made multiple operations on products through online sales; for example, if customer B browses product 1 online multiple times and adds product 1 to the shopping cart, then customer B has a certain degree of demand.

[0074] S12, calculating the infant stage of each member at the current time according to the basic information:

[0075] For members with personal information and ordering information, the current infant stage of the member is calculated based on the personal information;

[0076] For members who only have ordering information, the stage to which the ordered product belongs is used as the infant stage to which the member currently belongs.

[0077] Generally, maternal and infant products are infant-specific, and the differences between them are quite large. The early stage of pregnancy is 1-3 months of pregnancy, and basic vitamins and minerals need to be supplemented. The mid-term pregnancy is 4-6 months of pregnancy, and you need to wear comfortable maternity clothes and underwear to adapt to the gradual increase in the size of the abdomen. To ensure sleep, you may need to use a special maternity pillow to support the abdomen and waist and improve sleep quality. The late stage of pregnancy is 7-9 months of pregnancy, and you need to prepare delivery-related supplies, such as maternity pads, chest protection shields, massage oils, soothing bath products, etc. The newborn stage is 0-2 months after birth, and you need to use particularly soft and non-irritating baby skin care products, such as baby shower gel, moisturizer, and use baby bottles and milk powder, and consider the safety and easy cleaning of the bottles. The early stage of infants is 2-6 months after the birth of newborns, and you need to introduce complementary foods, supplies such as baby complementary food machines, baby tableware, etc., as well as toys that stimulate sensory development, such as colorful toys and musical toys. The middle stage of infancy is from 6 to 12 months after birth, when crawling mats and walkers are needed; when babies start to feed themselves, leak-proof cups and easy-to-grip tableware need to be prepared. The late stage of infancy is from 1 to 2 years old, when toys that can promote cognitive and language development are needed, such as puzzles and building blocks. The current stage of customers can be easily inferred based on the stages of maternal and infant products.

[0078] Step 2: Input the customer information and the target product into the first demand model to determine the first inventory strategy for the target product; the first demand model performs infant stage matching calculation based on the customer information and the target product; Figure 2 As shown, step 2 specifically includes the following sub-steps:

[0079] S21, obtaining the infant stage i to which the target product belongs, and counting the total number N of members matching the infant stage i at the current time;

[0080] S22, counting the intended brands of the target product for each member in the infant stage i, and calculating the intention degree of each member for each intended brand according to the intention perception model;

[0081] In the specific implementation process, the chain store's business system will have customer tracking data recorded in customer tracking records (such as communication records between business personnel and customers, customer questionnaire contents, customer online activity tracks, etc.), and the members' intended brands for target products can be counted based on the business system; at the same time, data such as target product brands that customers have consulted, target product brands that customers have ordered, and target product brands that customers have participated in promotional activities in the intention perception model can also be obtained based on customer tracking data.

[0082] The intention perception model includes:

[0083] Member A's intention for B's target product for:

[0084] ;

[0085] Among them, C1 represents the total number of consultations of member a on the target product of b intended brand; C2 = 0 or 1; when C2 = 0, it means that the total browsing time of member a on the target product of b intended brand is less than the first time threshold T; when C2 = 1, it means that the total browsing time of member a on the target product of b intended brand is greater than the first time threshold T, or member a has added the target product of b intended brand to the shopping cart; K represents the number of orders of the target product by member a for b intended brand; m represents the number of favorable comments of member a on the target product; N represents the number of times member a switches from other brands to b intended brand in the historical orders of the target product; M represents the time from the beginning of browsing to the blocking of the push information of b intended brand target product by member a; k represents the intention coefficient, which is 1~1.2;

[0086] S23, selecting the brand with the highest intention of each member as the target brand of each member;

[0087] S24, calculating the first inventory strategy of the target product based on the target brand of each member and the total number of members N. The specific implementation process of step S24 includes:

[0088] S241, counting the quantity of each target brand as the minimum inventory quantity of each target brand;

[0089] S242, setting a total inventory threshold of the target product according to the target product and the total number of members N;

[0090] S243, within the total inventory threshold and under the condition that each target brand has a minimum inventory quantity, allocate the optimal inventory quantity of each target brand. The first inventory strategy is expressed by the formula:

[0091] ;

[0092] Among them, a represents the optimal inventory quantity of target brand A, and a1 represents the minimum inventory quantity of target brand A; b represents the optimal inventory quantity of target brand B, and b1 represents the minimum inventory quantity of target brand B; c represents the optimal inventory quantity of target brand C, and c1 represents the minimum inventory quantity of target brand C; f represents the optimal inventory quantity of target brand F, and f1 represents the minimum inventory quantity of target brand F; Q represents the total inventory threshold; s represents the influencing factor of the total inventory threshold, which is related to the attributes of the target product.

[0093] Step 3: Input the target product into the second demand model to calculate the second inventory strategy of the target product; the second demand model analyzes the market preference of the target product based on big data, and determines the second inventory strategy of the target product according to the market preference; Figure 3 As shown, step three specifically includes the following sub-steps:

[0094] S31, based on big data, obtain all brands of target products and analyze the market preference of each brand; when this step is implemented specifically, the method includes:

[0095] S311, obtain online evaluation texts of all brands of the target product, and extract evaluation indicators of the target product from the online evaluation texts; specifically, crawl the online review texts of the target product, divide the online evaluation texts into words with independent meanings, remove special symbols, stop words, etc.; then calculate the importance of each word through a word frequency statistics algorithm (TF-IDF algorithm), and use words that meet the importance standards as evaluation indicators.

[0096] S312, taking the evaluation index as the influencing factor, sales volume, total number of collections, click-through conversion rate and repurchase recommendation index as variables, the entropy weight of each variable is calculated based on the entropy weight method; the entropy weight method is an objective weighting method, which uses information entropy to calculate the entropy weight of each indicator according to the degree of data dispersion of each indicator.

[0097] S313, performing weighted sum calculation according to the entropy weight of each variable to obtain the market preference of each brand.

[0098] S32, arrange the brands in descending order of market preference to obtain the brand market preference sequence;

[0099] S33, according to the arrangement relationship of the brand market preference sequence, each target brand is arranged to obtain the target brand market preference sequence; specifically, each target brand is found from the brand market preference sequence, and according to the arrangement relationship of the target brand in the brand market preference sequence, the target brand market preference sequence is arranged.

[0100] S34, determining the relationship between the minimum inventory quantities of each target brand according to the arrangement relationship of the target brand market preference sequence: the higher the target brand is, the larger the minimum inventory quantity of the target brand is.

[0101] In the specific implementation process, for example, the brand market preference sequence is {Brand 1, Brand 2, Brand 3, Brand 4, Brand 5, Brand 6, Brand 7, Brand 8...}; the target brands include: Brand 1, Brand 5, Brand 7 and Brand 8; then according to the arrangement relationship of the brand market preference sequence, the minimum inventory quantity of Brand 1 > the minimum inventory quantity of Brand 5 > the minimum inventory quantity of Brand 7 > the minimum inventory quantity of Brand 8.

[0102] Step 4: After modifying the first inventory strategy by considering seasonal factors and product timeliness factors, the second inventory strategy is combined to obtain the final inventory strategy for the target product; Step 4 specifically includes the following sub-steps:

[0103] S41, modify the first inventory strategy:

[0104] Determine whether the sales volume of the target product is related to the season; if so, configure the first coefficient in combination with the current season to linearly increase or linearly decrease the total inventory threshold; otherwise, do not adjust the total inventory threshold; Determine whether the sales volume of the target product is related to the season, and fit the correlation relationship between the sales volume of the target product and the seasonal changes, and make a judgment based on the correlation relationship;

[0105] Determine whether the target product has a shelf life D. If so, when the shelf life D> the validity period threshold De, set the second coefficient to linearly reduce the total inventory threshold; when the shelf life D≤ the validity period threshold De, set the third coefficient to linearly reduce the total inventory threshold; otherwise, do not adjust the total inventory threshold; wherein the third coefficient is greater than the second coefficient;

[0106] S42, combining the first inventory strategy and the second inventory strategy to obtain a final inventory strategy for the target product.

[0107] Finally, the final inventory strategy is expressed by the formula:

[0108] ;

[0109] Wherein, k1 represents the first coefficient; k2 represents the second coefficient; k3 represents the third coefficient; when s=k1, it indicates that the target product is related to the season; when s=k2, it indicates that the target product has a shelf life D, and the shelf life D>the validity threshold De; when s=k3, it indicates that the target product has a shelf life D, and the shelf life D≤the validity threshold De; when s=k1k3, it indicates that the target product is related to the season and has a shelf life D, and the shelf life D≤the validity threshold De; when s=k1k2, it indicates that the target product is related to the season and has a shelf life D, and the shelf life D>the validity threshold De.

[0110] Step 5: Solve the final inventory strategy to obtain the optimal solution, and replenish the inventory of the target product according to the optimal solution.

[0111] Specifically, the optimal inventory quantity (a, b, c, ..., f) of each target brand can be calculated according to a conventional iterative solution algorithm (such as the Jacobi iteration method or the Gauss-Seidel iteration method and other commonly used iterative solution algorithms), and the inventory of each target brand can be replenished according to the optimal inventory quantity.

[0112] This embodiment makes methodological improvements based on the traditional warehouse stocking method based on demand forecasts and historical data. On the one hand, the first inventory strategy is obtained by performing infant stage matching calculations based on customer information and target products, and the impact of changes in infant age groups on product demand is considered to achieve accurate demand calculation for products. On the other hand, the inventory strategy is revised by considering the impact of seasonal factors and timeliness factors on product demand. The inventory strategy is optimized for products whose demand is affected by seasons or timeliness, to avoid inventory backlogs and expiration problems caused by excessive procurement, balance supply and demand, and reduce costs and losses.

[0113] The target supply area in this embodiment refers to the supply area of ​​the current warehouse, which includes multiple maternal and child chain physical stores, and the target product refers to any product supplied by the current warehouse to the chain physical stores; in addition, the market trend described in this solution refers to the preference trend for different brands of target products in the entire maternal and child market.

[0114] Embodiment 2: This embodiment provides a supply chain optimization system based on customer risk and market trends, such as Figure 4 As shown, the system is used to implement the above-mentioned supply chain optimization method based on customer risks and market trends; the system includes:

[0115] The acquisition module is used to obtain customer information and target products in the target supply area;

[0116] A first calculation module is used to input customer information and target products into a first demand model to determine a first inventory strategy for the target products; the first demand model performs infant stage matching calculation based on the customer information and the target products;

[0117] A second calculation module is used to input the target product into a second demand model to calculate a second inventory strategy for the target product; the second demand model analyzes the market preference of the target product based on big data, and determines the second inventory strategy for the target product according to the market preference;

[0118] A correction module, configured to correct the first inventory strategy by taking into account seasonal factors and product timeliness factors, and then combine the second inventory strategy to obtain a final inventory strategy for the target product;

[0119] The solution module is used to solve the final inventory strategy to obtain an optimal solution, and replenish the inventory of the target product according to the optimal solution.

[0120] Embodiment 3: This embodiment provides a computer-readable medium on which a computer program is stored. The computer program is executed by a processor to implement the supply chain optimization method based on customer risk and market trend as described above, which specifically includes the following steps:

[0121] Step 1: Obtain customer information and target products in the target supply area;

[0122] Step 2: Inputting the customer information and the target product into the first demand model to determine the first inventory strategy for the target product; the first demand model performs infant stage matching calculation based on the customer information and the target product;

[0123] Step 3: Input the target product into the second demand model to calculate the second inventory strategy of the target product; the second demand model analyzes the market preference of the target product based on big data, and determines the second inventory strategy of the target product according to the market preference;

[0124] Step 4: After modifying the first inventory strategy by considering seasonal factors and product timeliness factors, the second inventory strategy is combined to obtain the final inventory strategy for the target product;

[0125] Step 5: Solve the final inventory strategy to obtain the optimal solution, and replenish the inventory of the target product according to the optimal solution.

[0126] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. Supply chain optimization method based on customer analysis and market trends, characterized by: include: Obtain customer information and target products in the target supply area; Inputting customer information and target products into a first demand model to determine a first inventory strategy for the target products; The first demand model performs infant stage matching calculation based on customer information and target products; The method of inputting customer information and target products into a first demand model to determine a first inventory strategy for the target products includes: Get the infant stage i to which the target product belongs, and count the total number of members N matching the infant stage i at the current time; Count the intended brands of the target product for each member in the infant stage i, and calculate the member's intention degree for each intended brand based on the intention perception model; Select the brands with the highest intention of each member as the target brands of each member; Calculate the first inventory strategy of the target product based on the target brand of each member and the total number of members N; specifically include Process: Count the quantity of each target brand as the minimum inventory quantity of each target brand; Set the total inventory threshold of the target product based on the target product and the total number of members N; Allocate the optimal inventory quantity for each target brand within the total inventory threshold and under the condition that each target brand has a minimum inventory quantity; The intention perception model includes: Member A's intention for B's target product for: ； Among them, C1 represents the total number of consultations of member a on the target product of b intended brand; C2 = 0 or 1; when C2 = 0, it means that the total browsing time of member a on the target product of b intended brand is less than the first time threshold T; when C2 = 1, it means that the total browsing time of member a on the target product of b intended brand is greater than the first time threshold T, or member a has added the target product of b intended brand to the shopping cart; K represents the number of orders of the target product by member a for b intended brand; m represents the number of positive comments of member a on the target product; M represents the time from the beginning of browsing to the blocking of the push information of b intended brand target product by member a; k represents the intention coefficient, which ranges from 1 to 1.2; Input the target product into the second demand model to calculate the second inventory strategy of the target product; the second demand model analyzes the market preference of the target product based on big data, and determines the second inventory strategy of the target product according to the market preference; The step of inputting the target product into the second demand model to calculate the second inventory strategy for the target product includes: Based on big data, all brands of target products are obtained, and the market preference of each brand is analyzed; the specific method includes: obtaining online evaluation texts of all brands of target products, and extracting evaluation indicators of target products from online evaluation texts; taking evaluation indicators as influencing factors, sales volume, total number of collections, click-through conversion rate and repurchase recommendation index as variables, and calculating the entropy weight of each variable based on the entropy weight method; performing weighted summation calculation based on the entropy weight of each variable to obtain the market preference of each brand; Arrange the brands in descending order of market preference to obtain the brand market preference sequence; According to the arrangement relationship of the brand market preference sequence, each target brand is arranged to obtain the target brand market preference sequence; The relationship between the minimum inventory quantities of each target brand is determined based on the ranking relationship of the target brand market preference sequence: the higher the target brand is, the larger the minimum inventory quantity of the target brand is; After modifying the first inventory strategy by taking into account seasonal factors and product timeliness factors, the second inventory strategy is combined to obtain the final inventory strategy for the target product; Solve the final inventory policy to obtain an optimal solution, and replenish the inventory of the target product according to the optimal solution.

2. The supply chain optimization method based on customer analysis and market trends according to claim 1, characterized in that: The method for acquiring customer information in the target supply area includes: Obtaining basic member information of all chain stores in the target supply area; the members include stable members and potential members; the potential members include customers who have ordered products in the chain stores; the basic information includes personal information and ordering information; Based on the basic information, calculate the infant stage of each member at the current time: For members with personal information and ordering information, the current infant stage of the member is calculated based on the personal information; For members who only have ordering information, the stage to which the ordered product belongs is used as the infant stage to which the member currently belongs.

3. The supply chain optimization method based on customer analysis and market trends according to claim 1, characterized in that: After the first inventory strategy is modified by considering seasonal factors and product timeliness factors, the second inventory strategy is combined to obtain the final inventory strategy of the target product, including the following method: Modified first inventory strategy: Determine whether the sales volume of the target product is related to the season; if so, configure the first coefficient in combination with the current season to linearly increase or linearly decrease the total inventory threshold; otherwise, do not adjust the total inventory threshold; Determine whether the target product has a shelf life D. If so, when the shelf life D> the validity period threshold De, set the second coefficient to linearly reduce the total inventory threshold; when the shelf life D≤ the validity period threshold De, set the third coefficient to linearly reduce the total inventory threshold; otherwise, do not adjust the total inventory threshold; Where the third coefficient is greater than the second coefficient; The first inventory policy and the second inventory policy are combined to obtain a final inventory policy for the target product.

4. Supply chain optimization system based on customer analysis and market trends, characterized by: A method for optimizing a supply chain based on customer analysis and market trends according to any one of claims 1 to 3; the system comprising: The acquisition module is used to obtain customer information and target products in the target supply area; A first calculation module is used to input customer information and target products into a first demand model to determine a first inventory strategy for the target products; the first demand model performs infant stage matching calculation based on the customer information and the target products; A second calculation module is used to input the target product into a second demand model to calculate a second inventory strategy for the target product; the second demand model analyzes the market preference of the target product based on big data, and determines the second inventory strategy for the target product according to the market preference; A correction module, configured to correct the first inventory strategy by taking into account seasonal factors and product timeliness factors, and then combine the second inventory strategy to obtain a final inventory strategy for the target product; The solution module is used to solve the final inventory strategy to obtain an optimal solution, and replenish the inventory of the target product according to the optimal solution.

5. A computer readable medium having a computer program stored thereon, characterized in that: The computer program is executed by a processor to implement the supply chain optimization method based on customer analysis and market trends as described in any one of claims 1 to 3.

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