Matrix intelligent delivery and replenishment method and system

Abstract

The invention relates to the technical field of workflow data processing, in particular to a matrix intelligent delivery and replenishment method and system. The matrix intelligent delivery and replenishment method comprises the following steps: collecting store levels, sales data and existing inventory information, measuring and calculating the level of each store, and setting a store level replenishment coefficient; calculating the replenishment amount of each store according to the store level replenishment coefficient in combination with the basic replenishment amount, the existing inventory adjustment and the safe inventory threshold; checking the replenishment plan to ensure that the replenishment plan conforms to the overall inventory management and sales strategy, and performing adjustment according to needs; and the replenishment plan is executed, and inventory and sales conditions are continuously monitored, so that a replenishment strategy can be adjusted in time. According to the matrix intelligent delivery and replenishment method and system, the stores are guided to perform reasonable replenishment, so that the sales enthusiasm of each store is improved, the stores with poor sales indexes can be ensured to sell normally, the efficiency and accuracy of inventory management are improved, and positive promotion of sales is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of system integration, and in particular to a matrix intelligent delivery and replenishment method and system. Background Art

[0002] Tailoring replenishment strategies for each sales unit (store) has always been a major pain point for major retail industries, and even major chain brands. Currently, there are two mainstream solutions:

[0003] One type is differentiated replenishment based on sales data. According to the past sales data of each sales unit, replenishment is carried out for the sales unit based on the basic logic of replenishing as much as sold.

[0004] Although differentiated replenishment has increased the sales enthusiasm of stores, it is not friendly to stores with poor sales data. In the long run, stores will gradually gather in places with large traffic, while it will be difficult to expand their business territory in the western region with a smaller population and lower purchasing power.

[0005] The other type is homogeneous replenishment, which is to allocate the same supply to all sales units to reflect the fairness issue. In contrast to differentiated replenishment, homogeneous replenishment will greatly reduce the sales enthusiasm of stores.

[0006] Because the above two replenishment logics are too extreme, when differentiated replenishment based on sales data is required and the replenishment quantity of each sales unit needs to be relatively balanced, the two current strategies become inadequate.

[0007] In view of the deficiencies in the prior art, the present invention proposes a matrix intelligent delivery and replenishment method and system. Summary of the invention

[0008] In order to make up for the defects of the prior art, the present invention provides a simple and efficient matrix intelligent delivery and replenishment method and system.

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

[0010] A matrix intelligent delivery and replenishment method, characterized in that it includes the following steps:

[0011] Step S1: Data collection

[0012] Collect store level, sales data and on-hand inventory information from store management systems, sales records and inventory management systems;

[0013] Step S2: Level calculation

[0014] Calculate the level of each store based on the collected sales data and set the store level replenishment coefficient;

[0015] Step S3: Calculate replenishment quantity

[0016] Calculate the replenishment quantity for each store based on the store-level replenishment factor, combined with the base replenishment quantity, existing inventory adjustment, and safety stock threshold;

[0017] Step S4: Review and Adjustment

[0018] Review replenishment plans to ensure compliance with overall inventory management and sales strategies, and make adjustments as needed;

[0019] Step S5: Execution and monitoring

[0020] Execute replenishment plans and continuously monitor inventory and sales to adjust replenishment strategies in a timely manner.

[0021] In step S1, an e-commerce platform is selected by user-defined, and the delivery data, reservation data, sales data and inventory data of the e-commerce platform are collected to obtain the delivery quantity, order quantity, reservation quantity and reservation winning quantity of each store and region in different time periods.

[0022] In step S2, the method for calculating the level of each store includes the following steps:

[0023] Step S2.1: Calculate the reservation popularity lr of store n n , the formula is as follows:

[0024]

[0025] Among them, l n is the number of reservations for store n, p n is the number of placements in store n;

[0026] Step S2.2: Calculate the transaction rate dr of store n n , the formula is as follows:

[0027]

[0028] Among them, n is the sales quantity of store n, w n is the number of winning tickets for store n;

[0029] Step S2.3: Use the reservation popularity and transaction rate of each store to form a training sample X = {x (1) ,x (2) ,……x (n)}, where x (n) = {lr n ,dr n};

[0030] Select k cluster centers from the training sample X, where k is the total number of store levels, and record the selected cluster center as μ 1 ,μ 2 ,……μ k , calculate the level c of store n (n) , that is, the category with the closest Euclidean distance from store n to the cluster center. The formula is as follows:

[0031] c (n) =arg j min∥x (n) -μ j ∥ 2

[0032] Step S2.4, the following formula continuously updates the cluster center point:

[0033]

[0034] Among them, m is the number of stores at the gathering point;

[0035] Step S2.5, continuously repeating steps S2.1 to S2.4 until the distortion function converges, and clustering the stores into different levels;

[0036] The distortion function formula is as follows:

[0037]

[0038] A rectangular coordinate system is established with the reservation popularity and the transaction rate as coordinate axes respectively; wherein μ is the two-dimensional coordinate of the clustering point of the level to which store n belongs in the rectangular coordinate system.

[0039] In step S3, the replenishment quantity Q of store n is calculated. n , the calculation formula is as follows:

[0040]

[0041] Among them, S n is the monthly sales volume of store n, S avg is the average historical monthly sales of stores of the same level, G n is the store-level replenishment coefficient of store n, calculated as follows:

[0042]

[0043] Among them, G min is the lowest level store level replenishment coefficient, set it to 1; μ n is the coordinate of the cluster center point of the corresponding level of store n; μ min is the coordinate of the lowest level cluster center point;

[0044] Target monthly sales S under target inventory turnover rate target , the calculation formula is as follows:

[0045]

[0046] Where T is a replenishment cycle (e.g. one month);

[0047] The safety stock factor is customized based on uncertain factors (such as seasonal demand changes, supply chain disruptions, etc.), and its value is between 1.1-1.5.

[0048] A matrix intelligent delivery and replenishment system, comprising:

[0049] The data collection module is responsible for collecting store levels, sales data and existing inventory information from the store management system, sales records and inventory management system;

[0050] The level calculation module is responsible for calculating the level of each store based on the collected sales data and setting the store level replenishment coefficient;

[0051] The replenishment quantity calculation module is responsible for calculating the replenishment quantity for each store based on the store-level replenishment coefficient, combined with the basic replenishment quantity, existing inventory adjustment and safety stock threshold;

[0052] The review and adjustment module is responsible for reviewing the replenishment plan to ensure that it complies with the overall inventory management and sales strategy, and making adjustments as needed;

[0053] The execution and monitoring module is responsible for executing the replenishment plan and continuously monitoring inventory and sales in order to adjust the replenishment strategy in a timely manner.

[0054] The data collection module customarily selects an e-commerce platform, collects the delivery data, reservation data, sales data and inventory data of the e-commerce platform, and obtains the delivery quantity, order quantity, reservation quantity and reservation winning quantity of each store and region in different time periods.

[0055] In the grade calculation module, the calculation method of the grade of each store includes the following steps:

[0056] Step S2.1: Calculate the reservation popularity lr of store n n , the formula is as follows:

[0057]

[0058] Among them, l n is the number of reservations for store n, p n is the number of placements in store n;

[0059] Step S2.2: Calculate the transaction rate dr of store nn , the formula is as follows:

[0060]

[0061] Among them, n is the sales quantity of store n, w n is the number of winning tickets for store n;

[0062] Step S2.3: Use the reservation popularity and transaction rate of each store to form a training sample X = {x (1) ,x (2) ,……x (n)}, where x (n) = {lr n ,dr n};

[0063] Select k cluster centers from the training sample X, where k is the total number of store levels, and record the selected cluster center as μ 1 ,μ 2 ,……μ k , calculate the level c of store n (n) , that is, the category with the closest Euclidean distance from store n to the cluster center. The formula is as follows:

[0064] c (n) =arg j min∥x (n) -μ j ∥ 2

[0065] Step S2.4, the following formula continuously updates the cluster center point:

[0066]

[0067] Among them, m is the number of stores at the gathering point;

[0068] Step S2.5, continuously repeating steps S2.1 to S2.4 until the distortion function converges, and clustering the stores into different levels;

[0069] The distortion function formula is as follows:

[0070]

[0071] A rectangular coordinate system is established with the reservation popularity and the transaction rate as coordinate axes respectively; wherein μ is the two-dimensional coordinate of the clustering point of the level to which store n belongs in the rectangular coordinate system.

[0072] In the replenishment quantity calculation module, the replenishment quantity Q of store n is calculated. n , the calculation formula is as follows:

[0073]

[0074] Among them, S n is the monthly sales volume of store n, S avg is the average historical monthly sales of stores of the same level, G n is the store-level replenishment coefficient of store n, calculated as follows:

[0075]

[0076] Among them, G min is the lowest level store level replenishment coefficient, set it to 1; μ n is the coordinate of the cluster center point of the corresponding level of store n; μ min is the coordinate of the lowest level cluster center point;

[0077] Target monthly sales S under target inventory turnover rate target , the calculation formula is as follows:

[0078]

[0079] Where T is a replenishment cycle (e.g. one month);

[0080] The safety stock factor is customized based on uncertain factors (such as seasonal demand changes, supply chain disruptions, etc.), and its value is between 1.1-1.5.

[0081] A mobile terminal system integration device based on cross-platform style adaptation technology is characterized by: comprising a memory and a processor; the memory is used to store a computer program, and the processor is used to implement the method steps described above when executing the computer program.

[0082] A readable storage medium, characterized in that: a computer program is stored on the readable storage medium, and when the computer program is executed by a processor, the method steps described above are implemented.

[0083] The beneficial effects of the present invention are as follows: the matrixed intelligent replenishment method and system can differentiate the calculation of store replenishment quantities while ensuring the basic replenishment quantity, and guide stores to make reasonable replenishment, which not only improves the sales enthusiasm of each store, but also ensures the normal sales of stores with poor sales indicators, improves the efficiency and accuracy of inventory management, and helps to promote sales in a positive way. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0085] Attached Figure 1 This is a schematic diagram of the matrix intelligent delivery and replenishment method of the present invention. DETAILED DESCRIPTION

[0086] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0087] The matrix intelligent delivery and replenishment method comprises the following steps:

[0088] Step S1: Data collection

[0089] Collect store level, sales data and on-hand inventory information from store management systems, sales records and inventory management systems;

[0090] Step S2: Level calculation

[0091] Calculate the level of each store based on the collected sales data and set the store level replenishment coefficient;

[0092] Step S3: Calculate replenishment quantity

[0093] Calculate the replenishment quantity for each store based on the store-level replenishment factor, combined with the base replenishment quantity, existing inventory adjustment, and safety stock threshold;

[0094] Step S4: Review and Adjustment

[0095] Review replenishment plans to ensure compliance with overall inventory management and sales strategies, and make adjustments as needed;

[0096] Step S5: Execution and monitoring

[0097] Execute replenishment plans and continuously monitor inventory and sales to adjust replenishment strategies in a timely manner.

[0098] In step S1, an e-commerce platform is selected by user-defined, and the delivery data, reservation data, sales data and inventory data of the e-commerce platform are collected to obtain the delivery quantity, order quantity, reservation quantity and reservation winning quantity of each store and region in different time periods.

[0099] In step S2, the method for calculating the level of each store includes the following steps:

[0100] Step S2.1: Calculate the reservation popularity lr of store n n , the formula is as follows:

[0101]

[0102] Among them, l n is the number of reservations for store n, p n is the number of placements in store n;

[0103] Step S2.2: Calculate the transaction rate dr of store n n , the formula is as follows:

[0104]

[0105] Among them, n is the sales quantity of store n, w n is the number of winning tickets for store n;

[0106] Step S2.3: Use the reservation popularity and transaction rate of each store to form a training sample X = {x (1) ,x (2) ,……x (n)}, where x (n) = {lr n ,dr n};

[0107] Select k cluster centers from the training sample X, where k is the total number of store levels, and record the selected cluster center as μ 1 ,μ 2 ,……μ k , calculate the level c of store n (n) , that is, the category with the closest Euclidean distance from store n to the cluster center. The formula is as follows:

[0108] c (n) =arg j min∥x (n) -μ j ∥ 2

[0109] Step S2.4, the following formula continuously updates the cluster center point:

[0110]

[0111] Among them, m is the number of stores at the gathering point;

[0112] Step S2.5, continuously repeating steps S2.1 to S2.4 until the distortion function converges, and clustering the stores into different levels;

[0113] The distortion function formula is as follows:

[0114]

[0115] A rectangular coordinate system is established with the reservation popularity and the transaction rate as coordinate axes respectively; wherein μ is the two-dimensional coordinate of the clustering point of the level to which store n belongs in the rectangular coordinate system.

[0116] In step S3, the replenishment quantity Q of store n is calculated. n , the calculation formula is as follows:

[0117]

[0118] Among them, S n is the monthly sales volume of store n, S avg is the average historical monthly sales of stores of the same level, G n is the store-level replenishment coefficient of store n, calculated as follows:

[0119]

[0120] Among them, G min is the lowest level store level replenishment coefficient, set it to 1; μ n is the coordinate of the cluster center point of the corresponding level of store n; μ min is the coordinate of the lowest level cluster center point;

[0121] Target monthly sales S under target inventory turnover rate target , the calculation formula is as follows:

[0122]

[0123] Where T is a replenishment cycle (e.g. one month);

[0124] The safety stock factor is customized based on uncertain factors (such as seasonal demand changes, supply chain disruptions, etc.), and its value is between 1.1-1.5.

[0125] The matrix intelligent delivery and replenishment system includes:

[0126] The data collection module is responsible for collecting store levels, sales data and existing inventory information from the store management system, sales records and inventory management system;

[0127] The level calculation module is responsible for calculating the level of each store based on the collected sales data and setting the store level replenishment coefficient;

[0128] The replenishment quantity calculation module is responsible for calculating the replenishment quantity for each store based on the store-level replenishment coefficient, combined with the basic replenishment quantity, existing inventory adjustment and safety stock threshold;

[0129] The review and adjustment module is responsible for reviewing the replenishment plan to ensure that it complies with the overall inventory management and sales strategy, and making adjustments as needed;

[0130] The execution and monitoring module is responsible for executing the replenishment plan and continuously monitoring inventory and sales in order to adjust the replenishment strategy in a timely manner.

[0131] The data collection module customarily selects an e-commerce platform, collects the delivery data, reservation data, sales data and inventory data of the e-commerce platform, and obtains the delivery quantity, order quantity, reservation quantity and reservation winning quantity of each store and region in different time periods.

[0132] In the grade calculation module, the calculation method of the grade of each store includes the following steps:

[0133] Step S2.1: Calculate the reservation popularity lr of store n n , the formula is as follows:

[0134]

[0135] Among them, l n is the number of reservations for store n, p n is the number of placements in store n;

[0136] Step S2.2: Calculate the transaction rate dr of store n n , the formula is as follows:

[0137]

[0138] Among them, n is the sales quantity of store n, w n is the number of winning tickets for store n;

[0139] Step S2.3: Use the reservation popularity and transaction rate of each store to form a training sample X = {x (1) ,x (2) ,……x (n)}, where x (n) = {lr n ,dr n};

[0140] Select k cluster centers from the training sample X, where k is the total number of store levels, and record the selected cluster center as μ 1 ,μ 2 ,……μ k , calculate the level c of store n (n) , that is, the category with the closest Euclidean distance from store n to the cluster center. The formula is as follows:

[0141] c (n) =arg j min∥x (n) -μ j ∥ 2

[0142] Step S2.4, the following formula continuously updates the cluster center point:

[0143]

[0144] Among them, m is the number of stores at the gathering point;

[0145] Step S2.5, continuously repeating steps S2.1 to S2.4 until the distortion function converges, and clustering the stores into different levels;

[0146] The distortion function formula is as follows:

[0147]

[0148] A rectangular coordinate system is established with the reservation popularity and the transaction rate as coordinate axes respectively; wherein μ is the two-dimensional coordinate of the clustering point of the level to which store n belongs in the rectangular coordinate system.

[0149] In the replenishment quantity calculation module, the replenishment quantity Q of store n is calculated. n , the calculation formula is as follows:

[0150]

[0151] Among them, S n is the monthly sales volume of store n, S avg is the average historical monthly sales of stores of the same level, G n is the store-level replenishment coefficient of store n, calculated as follows:

[0152]

[0153] Among them, G min is the lowest level store level replenishment coefficient, set it to 1; μ n is the coordinate of the cluster center point of the corresponding level of store n; μ min is the coordinate of the lowest level cluster center point;

[0154] Target monthly sales S under target inventory turnover rate target , the calculation formula is as follows:

[0155]

[0156] Where T is a replenishment cycle (e.g. one month);

[0157] The safety stock factor is customized based on uncertain factors (such as seasonal demand changes, supply chain disruptions, etc.), and its value is between 1.1-1.5.

[0158] The mobile terminal system integration device based on the cross-platform style adaptation technology includes a memory and a processor; the memory is used to store a computer program, and the processor is used to implement the method steps described above when executing the computer program.

[0159] The readable storage medium stores a computer program, and when the computer program is executed by a processor, the method steps described above are implemented.

[0160] In summary, the matrix-based intelligent replenishment method and system extracts sales data such as the winning rate, average daily shipment quantity and number of appointments based on the sales data of each sales unit for analysis, and divides each sales unit into several levels according to the score of the indicators, and then allocates goods according to the level of each level. The replenishment quantity of sales units of the same level is equal.

[0161] Compared with the other two replenishment modes, the matrix intelligent replenishment method and system can ensure that the replenishment volume of each store is not too different while differentiated replenishment is carried out according to sales data, thereby encouraging stores to improve sales data and strive for a larger replenishment volume, effectively improving the sales enthusiasm of stores; at the same time, it can avoid stores with poor sales data from having no goods to sell. There are some stores in retail enterprises whose existence is not simply for profit, but to expand their business territory. For such stores, the matrix intelligent replenishment method and system demonstrates its superiority.

[0162] The embodiment described above is only one specific implementation of the present invention. Common changes and substitutions made by those skilled in the art within the scope of the technical solution of the present invention should be included in the protection scope of the present invention.

Claims

1. A matrix intelligent delivery and replenishment method, characterized by: The following steps are involved: Step S1: Data collection Collect store level, sales data and on-hand inventory information from store management systems, sales records and inventory management systems; Step S2: Level calculation Calculate the level of each store based on the collected sales data and set the store level replenishment coefficient; Step S3: Calculate replenishment quantity Calculate the replenishment quantity for each store based on the store-level replenishment factor, combined with the base replenishment quantity, existing inventory adjustment, and safety stock threshold; Step S4: Review and Adjustment Review replenishment plans to ensure compliance with overall inventory management and sales strategies, and make adjustments as needed; Step S5: Execution and monitoring Execute replenishment plans and continuously monitor inventory and sales to adjust replenishment strategies in a timely manner.

2. The matrix intelligent delivery and replenishment method according to claim 1 is characterized by: In step S1, an e-commerce platform is selected by user-defined, and the delivery data, reservation data, sales data and inventory data of the e-commerce platform are collected to obtain the delivery quantity, order quantity, reservation quantity and reservation winning quantity of each store and region in different time periods.

3. The matrix intelligent delivery and replenishment method according to claim 1 is characterized by: In step S2, the method for calculating the level of each store includes the following steps: Step S2.1: Calculate the reservation popularity lr of store n n , the formula is as follows: Among them, l n is the number of reservations for store n, p n is the number of placements in store n; Step S2.2: Calculate the transaction rate dr of store n n , the formula is as follows: Among them, n is the sales quantity of store n, w n is the number of winning tickets for store n; Step S2.3: Use the reservation popularity and transaction rate of each store to form a training sample X = {x (1) ,x (2) ,……x (n) }, where x (n) = {lr n ,dr n }; Select k cluster centers from the training sample X, where k is the total number of store levels, and record the selected cluster centers as μ1, μ2, …, μ k , calculate the level c of store n (n) , that is, the category with the closest Euclidean distance from store n to the cluster center. The formula is as follows: c (n) =arg j min∥x (n) -m j ∥ 2 Step S2.4, the following formula continuously updates the cluster center point: Among them, m is the number of stores at the gathering point; Step S2.5, continuously repeating steps S2.1 to S2.4 until the distortion function converges, and clustering the stores into different levels; The distortion function formula is as follows: A rectangular coordinate system is established with the reservation popularity and the transaction rate as coordinate axes respectively; wherein μ is the two-dimensional coordinate of the clustering point of the level to which store n belongs in the rectangular coordinate system.

4. The matrix intelligent delivery and replenishment method according to claim 3 is characterized by: In step S3, the replenishment quantity Q of store n is calculated. n , the calculation formula is as follows: Among them, S n is the monthly sales volume of store n, S avg is the average historical monthly sales of stores of the same level, G n is the store-level replenishment coefficient of store n, calculated as follows: Among them, G min is the lowest level store level replenishment coefficient, set it to 1; μ n is the coordinate of the cluster center point of the corresponding level of store n; μ min is the coordinate of the lowest level cluster center point; Target monthly sales S under target inventory turnover rate target , the calculation formula is as follows: Where T is the replenishment cycle; The safety stock factor is between 1.1 and 1.

5.

5. A matrix intelligent delivery and replenishment system, characterized by: include: The data collection module is responsible for collecting store levels, sales data and existing inventory information from the store management system, sales records and inventory management system; The level calculation module is responsible for calculating the level of each store based on the collected sales data and setting the store level replenishment coefficient; The replenishment quantity calculation module is responsible for calculating the replenishment quantity for each store based on the store-level replenishment coefficient, combined with the basic replenishment quantity, existing inventory adjustment and safety stock threshold; The review and adjustment module is responsible for reviewing the replenishment plan to ensure that it complies with the overall inventory management and sales strategy, and making adjustments as needed; The execution and monitoring module is responsible for executing the replenishment plan and continuously monitoring inventory and sales in order to adjust the replenishment strategy in a timely manner.

6. The matrix intelligent delivery and replenishment system according to claim 5 is characterized by: The data collection module customarily selects an e-commerce platform, collects the delivery data, reservation data, sales data and inventory data of the e-commerce platform, and obtains the delivery quantity, order quantity, reservation quantity and reservation winning quantity of each store and region in different time periods.

7. The matrix intelligent delivery and replenishment system according to claim 5 is characterized by: In the grade calculation module, the calculation method of the grade of each store includes the following steps: Step S2.1: Calculate the reservation popularity lr of store n n , the formula is as follows: Among them, l n is the number of reservations for store n, p n is the number of placements in store n; Step S2.2: Calculate the transaction rate dr of store n n , the formula is as follows: Among them, n is the sales quantity of store n, w n is the number of winning tickets for store n; Step S2.3: Use the reservation popularity and transaction rate of each store to form a training sample X = {x (1) ,x (2) ,……x (n) }, where x (n) = {lr n ,dr n }; Select k cluster centers from the training sample X, where k is the total number of store levels, and record the selected cluster centers as μ1, μ2, …, μ k , calculate the level c of store n (n) , that is, the category with the closest Euclidean distance from store n to the cluster center. The formula is as follows: c (n) =arg j min∥x (n) -m j ∥ 2 Step S2.4, the following formula continuously updates the cluster center point: Among them, m is the number of stores at the gathering point; Step S2.5, continuously repeating steps S2.1 to S2.4 until the distortion function converges, and clustering the stores into different levels; The distortion function formula is as follows: A rectangular coordinate system is established with the reservation popularity and the transaction rate as coordinate axes respectively; wherein μ is the two-dimensional coordinate of the clustering point of the level to which store n belongs in the rectangular coordinate system.

8. The matrix intelligent delivery and replenishment system according to claim 7 is characterized by: In the replenishment quantity calculation module, the replenishment quantity Q of store n is calculated. n , the calculation formula is as follows: Among them, S n is the monthly sales volume of store n, S avg is the average historical monthly sales of stores of the same level, G n is the store-level replenishment coefficient of store n, calculated as follows: Among them, G min is the lowest level store level replenishment coefficient, set it to 1; μ n is the coordinate of the cluster center point of the corresponding level of store n; μ min is the coordinate of the lowest level cluster center point; Target monthly sales S under target inventory turnover rate target , the calculation formula is as follows: Where T is the replenishment cycle; The safety stock factor is between 1.1 and 1.

5.

9. A mobile terminal system integration device based on cross-platform style adaptation technology, characterized in that: The method comprises a memory and a processor; the memory is used to store a computer program, and the processor is used to implement the method according to any one of claims 1 to 4 when executing the computer program.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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