Digital supply chain collaborative optimization logistics management platform

Abstract

The invention belongs to the technical field of supply chain collaborative logistics management, and particularly discloses a digital supply chain collaborative optimization logistics management platform, which focuses on a supply chain formed by a plurality of retailers, distributors and manufacturers, and the distributors carry out sales rising trend analysis on the managed retailers. When the oneness rises, the transverse inventory allocation between the retailers is triggered, and when the global association rises, the retailers requiring replenishment are screened out to send replenishment requests to the manufacturers, so that differential replenishment decisions of the distributors cooperating with the retailers and the manufacturers are implemented, inventory overstock caused by wrong replenishment is avoided to the maximum extent, and the replenishment efficiency is improved. And when global associated sales rises, a real-time scheduling table is obtained by docking a production system of a manufacturer, and replenishment time is cooperatively determined by using an available capacity capture mode, so that efficient cooperation of the distributor and the manufacturer is realized, the replenishment time is ensured to be consistent with a production plan, scheduling conflicts are avoided, and replenishment is ensured to be completed on time.

Description

Technical Field

[0001] The present invention belongs to the technical field of supply chain collaborative logistics management, and in particular to supply chain collaborative logistics replenishment management technology, and specifically discloses a digital supply chain collaborative optimization logistics management platform. Background Art

[0002] With the in-depth development of specialized division of labor, the modern product sales model has evolved from manufacturers directly facing consumers to a supply chain system where manufacturers focus on production and manufacturing, distributors are responsible for warehousing and distribution in the middle, and retailers directly serve end consumers. In this structure, in order to improve the overall operating efficiency of the supply chain, it is necessary to rely on efficient logistics management to achieve precise control of inventory, thereby effectively reducing inventory backlogs and out-of-stock phenomena. Among them, accurate replenishment operations are the core link to ensure efficient logistics management.

[0003] There are many technical solutions for replenishment management in the prior art, but these solutions are still insufficient in some aspects. The following are two specific examples and their limitations: Example 1: China Invention Patent Publication No. CN117745184A proposes a supply chain order information management method and system, which obtains the product sales speed and sales surplus from the distributor information, calculates the accurate remaining sales time, and formulates a replenishment plan that meets the distributor's needs. The system reminds distributors to replenish according to the remaining sales time, helping them grasp the appropriate replenishment time, thereby improving replenishment efficiency.

[0004] The above solution determines the replenishment plan based only on the sales data of a single node in the supply chain, ignoring the global and local differences in fluctuations in product sales demand. When the increase in product sales demand is local (i.e., only individual distributors experience sales growth), if replenishment is still carried out according to the regular replenishment plan, it may lead to unnecessary inventory backlogs, increase holding costs, and may cause waste of resources.

[0005] Example 2: China Invention Patent Publication No. CN114581154A proposes a replenishment quantity prediction method, device, storage medium and electronic device for the supply chain. The relationship between the feature vector and the replenishment quantity is established by training a preset model, and the prediction model is used to perform end-to-end replenishment quantity prediction based on the current inventory situation, and assist in inventory management decision-making. The replenishment decision day refers to the date on which the replenishment quantity of goods is determined according to the replenishment strategy. It is usually set periodically and includes information such as the date of the replenishment decision day, replenishment duration, product category, inventory and demand, etc., in order to ensure the service level while achieving the lowest inventory cost.

[0006] The above scheme mainly focuses on the product replenishment time and periodic replenishment decisions when determining the replenishment decision date, but it is not connected with the manufacturer's production capacity data. This may cause the replenishment time to be out of line with the production plan, resulting in production scheduling conflicts, inability to fully utilize manufacturing resources, and prone to production delays.

[0007] It can be seen that the above two existing technologies have deficiencies in the decision-making of whether replenishment is needed and the replenishment time in the replenishment operation. The main reason is the lack of integration of comprehensive data between retailers, distributors and manufacturers, which leads to information isolation and reduces the collaborative efficiency of the supply chain, thus affecting the accuracy and timeliness of replenishment decisions to a certain extent. Summary of the invention

[0008] In view of this, the present invention aims to propose a digital supply chain collaborative optimization logistics management platform, which optimizes replenishment decisions by focusing on the supply chain composed of retailers, distributors, and manufacturers to integrate sales data, inventory data, and production capacity data.

[0009] The purpose of the present invention can be achieved through the following technical solutions: A digital supply chain collaborative optimization logistics management platform, including: a multi-source retail data upload module: retailers upload product sales data and inventory data to distributors in real time through interface sharing.

[0010] Sales trend diagnosis module: Distributors identify the stage increases in product sales data of multiple retailers and determine whether the upward trend is a single increase or a globally correlated increase.

[0011] Differentiated replenishment decision module: triggers horizontal inventory transfer between retailers when the singleness increases, and screens retailers that need replenishment when the global correlation increases.

[0012] Demand replenishment collaborative determination module: For demand replenishment retailers, the total demand replenishment quantity is evaluated, and after connecting to the manufacturer's production system to obtain the real-time production schedule, the appropriate replenishment time is collaboratively determined using the available capacity capture method.

[0013] Automatic replenishment execution module: automatically generates replenishment orders based on appropriate replenishment time and total required replenishment quantity, and pushes replenishment instructions to manufacturers.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. The present invention enables distributors to analyze sales growth trends of several retailers under their management, triggering horizontal inventory transfers between retailers when a single increase occurs, and screening out retailers in need of replenishment to send replenishment requests to manufacturers when a global correlation increase occurs, thereby implementing differentiated replenishment decisions by distributors in collaboration with retailers and manufacturers, minimizing inventory backlogs caused by incorrect replenishment, and reducing operating costs while enhancing supply chain flexibility.

[0015] 2. When global related sales increase, the present invention obtains real-time production schedules by connecting to the manufacturer's production system, and collaboratively determines appropriate replenishment time using available capacity capture. This method achieves efficient collaboration between distributors and manufacturers, ensures that replenishment time is aligned with production plans, avoids production schedule conflicts, ensures that replenishment is completed on time, and maximizes the use of manufacturing resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0017] Figure 1 This is a schematic diagram of the relationship between the modules of the platform of the present invention.

[0018] Figure 2 This is a schematic diagram of the supply chain structure in the present invention.

[0019] Figure 3 The present invention is a flowchart of the steps for determining whether the rising trend is a single rise or a globally correlated rise. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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 are within the scope of protection of the present invention.

[0021] See also Figure 2 As shown, the present invention focuses on a supply chain consisting of manufacturers, distributors, and several retailers. In this supply chain, manufacturers are responsible for the design, development, and production of products. They use raw materials, parts, and other resources and transform these resources into finished products through a series of process flows. Their role in the supply chain is to provide products for the entire supply chain and is the starting point of the supply chain.

[0022] Distributors are responsible for the intermediate warehousing and distribution of products, ensuring that products can flow smoothly from manufacturers to retailers. They usually have multiple warehouses and logistics centers located in different geographical locations. Their role in the supply chain is to connect manufacturers and retailers, play a buffering and regulating role, and ensure the smooth operation of the supply chain.

[0023] Retailers are directly facing consumers and are responsible for product display, sales and service. They deliver products to end users through physical stores, online stores or other sales channels. Their role in the supply chain is as follows: As the last link in the supply chain, retailers are directly facing consumers and can quickly perceive changes in market demand and adjust sales strategies in a timely manner.

[0024] The relationship between the above roles is as follows: Manufacturers and distributors: Manufacturers provide distributors with product information, technical documents and marketing support; distributors provide manufacturers with feedback on market demand and inventory status.

[0025] Distributors place orders with manufacturers based on market demand, and manufacturers arrange production and delivery based on the orders.

[0026] Distributors and retailers: Distributors perform inventory management and replenishment operations based on retailers’ needs to ensure that retailers have sufficient inventory supply.

[0027] Distributors are responsible for delivering products from warehouses to retailers’ stores or warehouses, ensuring that products reach sales terminals in a timely manner.

[0028] Retailers and consumers: Retailers sell products to consumers through various channels (such as physical stores and online stores) and provide corresponding pre-sales, in-sales and after-sales services.

[0029] Based on the supply chain constructed as above, the present invention proposes a digital supply chain collaborative optimization logistics management platform, including a multi-source retail data uploading module, a sales trend diagnosis module, a differentiated replenishment decision module, a demand replenishment collaborative determination module and an automatic replenishment execution module.

[0030] See also Figure 1 As shown, the relationship between the modules included in the above platform is that the multi-source retail data upload module serves as the data input of the sales trend diagnosis module, outputs the sales rising trend diagnosis result as the input of the differentiated replenishment decision module, outputs the targeted replenishment strategy as the input of the demand replenishment collaborative determination module, outputs the demand replenishment information when the global correlation increases as the input of the automatic replenishment execution module, outputs the replenishment order and sends the replenishment instruction to the manufacturer, so as to ensure the automation and efficiency of the replenishment process.

[0031] The multi-source retail data uploading module is used by retailers to upload product sales data and inventory data to distributors in real time through interface sharing.

[0032] In a specific embodiment of the present invention, the implementation process of the multi-source retail data upload module is as follows: the distributor builds a retail database and logically divides the retail database according to the number of retailers, generating an independent blockchain sub-library for each retailer.

[0033] Through the data interface, each blockchain sub-library is connected to the sales accounting terminal and inventory accounting terminal of the corresponding retailer to form a distributed data sharing network.

[0034] It is important to understand that in a supply chain consisting of manufacturers, distributors, and several retailers, one distributor usually needs to manage multiple retailers. In order to accurately understand product sales, the traditional method requires retailers to periodically collect and report sales data to distributors. However, this method has the following problems: Lag: The data reporting cycle is long, resulting in untimely information updates. Data authenticity issues: Manually collected and reported data may have errors or risks of being tampered with. Data leakage risk: Data may face security threats during transmission, leading to sensitive information leakage.

[0035] In view of the above problems, the present invention establishes an independent blockchain sub-library for each retailer to ensure the data independence and privacy protection of each retailer. These blockchain sub-libraries not only facilitate the unified management and coordination of distributors, but also avoid the problem of data mixing or authority conflict. By establishing a shared connection between the blockchain sub-library and the retailer's sales accounting terminal and inventory accounting terminal, all sales and inventory data can be synchronized to the blockchain sub-library in real time, and the complete transaction history can be recorded. On the one hand, this greatly reduces the lag of data reporting and ensures the timeliness and accuracy of information. On the other hand, it makes the information flow between distributors and retailers smoother and reduces the communication cost. The tamper-proof characteristics of blockchain technology ensure the authenticity and integrity of each sales data and inventory data, and prevent malicious tampering or data loss. In addition, due to the distributed ledger characteristics of blockchain, the data on all nodes remain consistent, further enhancing the credibility of the data.

[0036] The sales trend diagnosis module is used by distributors to identify stage increases in product sales data of multiple retailers, and to determine whether the increase trend is a single increase or a global related increase.

[0037] In the specific implementation of the above module, the distributor identifies the stage increase of product sales data of multiple retailers by referring to the following process: define a fixed time window and automatically intercept the product sales data uploaded within this time window.

[0038] The fixed time windows mentioned above can be flexibly set according to business needs. Common settings include but are not limited to: one week (for example, Monday to Sunday), one month (for example, the 1st to the end of each month), and one quarter (for example, the first to the last day of each quarter).

[0039] By setting a fixed time window, the following goals can be achieved: Phased data analysis: Divide sales data into fixed time periods to facilitate phased sales trend analysis.

[0040] Unified data benchmark: Ensure that data from different retailers are compared and analyzed on the same time basis to improve data consistency and comparability.

[0041] Automated processing: The system can automatically capture and process sales data within a fixed time window, reducing manual intervention and improving data processing efficiency.

[0042] It should be added that the product sales data uploaded by retailers to distributors is presented in the form of sales records, which contain key information such as sales date and sales volume.

[0043] The sales volume information is extracted from the product sales data, and the data in the time window is divided into multiple time intervals according to preset rules, and then the corresponding sales volume of each retailer in each time interval is counted.

[0044] For example, when the time window is one week, the time interval may be every day.

[0045] When the time window is one month, the time interval can be weekly.

[0046] When the time window is a quarter, the time interval can be every two weeks.

[0047] By dividing the fixed time window into finer time periods, changes in sales trends can be identified more accurately.

[0048] In the specific implementation of the above solution, a specific method for counting the corresponding sales volume of each retailer in each time interval is to accumulate the sales volume of each sales record of each retailer in each time interval to obtain the sales volume in the time interval.

[0049] It should be emphasized that the present invention studies similar products from different retailers.

[0050] Compare the sales volume of each retailer in the current time interval with the sales volume in the previous time interval to calculate the sales growth rate. The specific sales growth rate calculation formula is: , where represents the sales growth rate, , They represent the sales volume in the current time interval and the sales volume in the previous time interval respectively.

[0051] The sales growth rate of each retailer in each time interval is compared with the pre-set threshold. If the sales growth rate of a retailer in a certain time interval reaches or exceeds the set threshold, then starting from this time interval, the proportion of the duration of its sales growth in the entire time window is counted and compared with the configured benchmark ratio. If this ratio reaches or exceeds the configured benchmark ratio, it is determined that the retailer's product sales have entered a stage of rising state. At the same time, the retailer is marked as a growing retailer, and the time interval when the sales growth rate reaches the set threshold is marked as a growth time interval.

[0052] In the implementation of the above solution, setting a sales growth rate threshold is a criterion for judging whether sales growth is significant. Common thresholds can refer to the following ranges: Low threshold: 5%, applicable to products with relatively stable market demand. Medium threshold: 15%, applicable to the general consumer goods market. High threshold: 30%, applicable to fast-growing products.

[0053] The specific threshold to be selected depends on the specific business needs and market environment.

[0054] The configuration benchmark ratio is used to measure whether the sales growth duration is significant enough in the entire time window. Similarly, the common benchmark ratios can refer to the following ranges: Low benchmark ratio: 20%, suitable for markets with large short-term fluctuations. Medium benchmark ratio: 40%, suitable for general market conditions. High benchmark ratio: 60%, suitable for markets that require a longer period of time to verify the growth trend.

[0055] The specific benchmark ratio chosen also depends on specific business needs and market environment.

[0056] In the above operation example, assume that the time window we set is one month, divided into one time interval per week (i.e. 4 weeks), and select the following parameters: threshold: 15%, baseline ratio: 40%.

[0057] Specifically, the sales volume data of a retailer in the time period under the time window is shown in Table 1.

[0058] Table 1: Sales volume data for time intervals under the time window

[0059] In Table 1 above, since there is no sales volume data for the first week from the previous week, there is no sales growth rate for the first week. The sales growth rates for the third and fourth weeks both reach the set threshold of 15%, and the proportion of time intervals that continuously reach the set threshold in the time window is 50%, which meets the benchmark ratio of 40%. Therefore, the retailer is a growth retailer, and the third and fourth weeks are marked as growth time intervals.

[0060] The above operation uses scientific time window division, sales growth rate calculation, threshold judgment and other methods, combined with double verification of sales growth rate and duration ratio, to more accurately identify the true phased upward trend rather than accidental sales fluctuations. This method effectively filters out noise data, reduces the misjudgment rate, and provides a reliable basis for subsequent replenishment decisions.

[0061] In further specific implementation of the above modules, see Figure 3 As shown, the process of determining whether the rising trend is a single rise or a globally correlated rise is as follows: (1) Count the number of retailers identified as growing retailers in the stage markings. If there is only one growing retailer, directly determine that its rising trend is a single rise. If there are multiple growing retailers, execute (2)-(4).

[0062] (2) Conduct a synchronization analysis on the growth time periods of all growing retailers, summarize the number of retailers that have grown in the same time period, and calculate their proportion to the total number of growing retailers. This proportion is defined as the same-growth ratio.

[0063] (3) Based on the sales volume data within the intercepted time window, a correlation analysis is performed on the time series sales volume of all growing retailers, the correlation coefficient between each pair of retailers is calculated, and the average value is taken as the overall correlation indicator.

[0064] The correlation analysis mentioned above can use the Pearson correlation coefficient or the Spearman rank correlation coefficient. The value range of the correlation coefficient is between -1 and 1, and the closer its absolute value is to 1, the higher the degree of correlation. Since the present invention focuses on the correlation of sales growth, the closer the correlation coefficient is to 1, the more consistent the sales growth pattern between retailers is, and there is a stronger synergy effect.

[0065] (4) The calculated same-increase ratio and correlation coefficient are compared with the critical thresholds preset by the system. If both the same-increase ratio and the correlation coefficient reach or exceed the corresponding critical thresholds, the upward trend is judged to be a globally correlated increase; otherwise, the upward trend is judged to be a single increase.

[0066] In the above operation example, the critical threshold of the same increase ratio may be 60%, and the critical threshold of the correlation coefficient may be 0.7.

[0067] The above method comprehensively analyzes the sales growth trend from multiple perspectives, including the number of growing retailers, the synchronization of growth time intervals, and the correlation of sales volume, forming a complete logical framework. Through the dual verification of the same-growth ratio and the correlation coefficient, it effectively distinguishes between single growth (local phenomenon) and global correlation growth (overall trend), avoiding misjudgment. This multi-dimensional analysis ensures the comprehensiveness and accuracy of the judgment results.

[0068] The differentiated replenishment decision module triggers the horizontal transfer of inventory between retailers when the singularity increases, and selects the retailers who need replenishment when the global correlation increases.

[0069] It should be noted that when retailers experience sales increases, they usually request replenishment from distributors. Traditionally, distributors will directly issue replenishment orders to manufacturers without considering whether the sales increase is a single local increase or a global correlation increase. Sales increases at some retailers may be affected by specific factors (such as promotions, seasonal demand, or local market changes), which may lead to short-term sales peaks. In this case, traditional replenishment strategies may over-replenish, exceeding actual market demand, resulting in inventory backlogs.

[0070] Based on the above situation, by horizontally transferring inventory between retailers when there is a single increase, and only asking manufacturers for replenishment when there is a global correlation increase, we can achieve refined replenishment and minimize the occurrence of inventory backlogs.

[0071] In the optimization implementation described above, before triggering the horizontal transfer of inventory between retailers when the singularity increases, it is necessary to determine whether to trigger the transfer. The specific implementation is as follows: when the singularity increases, the single-growth retailer is screened out, and its current inventory and existing sales speed are obtained, thereby predicting how long the retailer's current inventory can support.

[0072] The above-mentioned current sales speed refers to the rate at which a retailer sells a product within a specific time window. For example, the sales volume of each time interval within the time window can be calculated by a moving average method to obtain the daily sales volume as the current sales speed.

[0073] The above prediction of how long a retailer's current inventory can last is obtained by dividing the current inventory level by the current sales velocity.

[0074] Compare the predicted current inventory support time with the supply chain response cycle. If the current inventory support time is lower than the supply chain response cycle, it is determined that horizontal inventory transfer needs to be triggered. Otherwise, horizontal inventory transfer does not need to be triggered.

[0075] It is important to know that the supply chain response cycle refers to the sum of the historical replenishment cycle from distributors to retailers and the delivery cycle from manufacturers to distributors for similar products.

[0076] The replenishment cycle from distributor to retailer mentioned above refers to the time required from the retailer issuing a replenishment order to the distributor to the distributor delivering the goods to the retailer.

[0077] The manufacturer-to-distributor lead time is the time it takes from when a distributor places a purchase order to when the manufacturer delivers the goods to the distributor.

[0078] It is important to understand that when comparing the predicted current inventory support time with the supply chain response cycle, if the current inventory support time is lower than the supply chain response cycle, it means that the inventory may be exhausted before the new goods arrive, resulting in out-of-stock situations. Especially in the case of a single increase, it indicates that the sales growth of other retailers is relatively sluggish. At this time, by triggering a horizontal transfer, the inventory can be transferred from these retailers with sluggish sales to the single growth retailers, replenishing their supply in a timely manner and avoiding the risk of out-of-stock situations. At the same time, this horizontal transfer helps to consume the inventory of retailers with sluggish sales and prevent them from having inventory backlogs, thereby achieving the rational use of inventory resources within the entire supply chain network.

[0079] After determining that horizontal inventory transfer needs to be triggered, the specific transfer process is as follows: Screen potential transfer parties from retailers other than single-growth retailers. The screening criteria include: a) The current inventory can support time longer than the supply chain response cycle.

[0080] b) The transportation distance to a single growth retailer meets the preset range.

[0081] The current inventory support time of the potential transferor and the transportation distance between the potential transferor and the single growth retailer are used in the formula Quantify the replenishment advantage, including Indicates The quantitative value of the potential transferor's replenishment advantage, Indicates the potential transfer party number. , The scoring function that represents the duration of the current inventory. represents the scoring function of the transport distance, , They represent the weight factors of the current inventory support time and transportation distance, respectively, and .

[0082] It should be noted that in the above replenishment advantage quantification formula, since the current inventory support time has a positive impact on the replenishment advantage, it is expressed by addition, and the transportation distance has a negative impact on the replenishment advantage, so it is expressed by subtraction.

[0083] In the example representation of the formula ,in Indicates How long can the current inventory of the potential transferor support? Indicates the maximum inventory support time among all potential transfer parties.

[0084] ,in Indicates The transportation distance between the potential transferor and the single growth retailer, Indicates the maximum transportation distance among all potential transfer parties.

[0085] The above example uses normalization in the scoring function for determining the current inventory support time and transportation distance, which enables uniform comparison of data of different dimensions.

[0086] In a further example, the weight factors of the current inventory support time and transportation distance can be adjusted according to actual needs. For example, if more emphasis is placed on inventory adequacy, then If we pay more attention to transportation efficiency, then .

[0087] In the above data example of replenishment advantage quantification, it is assumed that there are two potential transfer parties A and B. The specific replenishment advantage quantification data are shown in Table 2.

[0088] Table 2: Quantitative data on replenishment advantages of potential transferors

[0089] When the weight factors of the current inventory support time and transportation distance are 0.7 and 0.3, , It can be seen that potential transferor B has a higher replenishment advantage, and potential transferor B is preferably selected to transfer inventory to a single growth retailer.

[0090] According to the quantitative value of replenishment advantage, potential transfer parties are sorted from large to small according to replenishment advantage, and the sorting result is used as the priority order for horizontal inventory transfer between retailers.

[0091] The present invention can transfer inventory from potential transfer retailers to single growth retailers within the same supply chain network by triggering horizontal transfer in the case of single growth, which reflects the collaborative management of distributors and several retailers. This method can more effectively utilize existing inventory resources, optimize inventory distribution, and greatly reduce problems caused by local excess or insufficient inventory.

[0092] In a further optimized implementation of the above description, when the global correlation increases, the retailers in need of replenishment are screened according to the following process: a set of retailers participating in the global correlation increase trend is extracted from all retailers, and for each retailer in the above set, the current inventory support time is predicted based on its current inventory level and existing sales speed.

[0093] Compare the current inventory support time of each retailer with the supply chain response cycle, screen out retailers whose current inventory support time is lower than the supply chain response cycle, and mark them as demand replenishment retailers.

[0094] The demand replenishment collaborative determination module evaluates the total demand replenishment quantity for the demand replenishment retailer, and after connecting to the manufacturer's production system to obtain the real-time production schedule, collaboratively determines the appropriate replenishment time based on the available capacity capture method.

[0095] In a preferred implementation of the above solution, the total demand replenishment quantity for the demand replenishment retailers is evaluated as follows: based on the current sales rate and supply chain response cycle of each demand replenishment retailer, the sales volume within the supply chain response cycle is estimated. Exemplarily, the current sales rate and the supply chain response cycle can be multiplied to obtain the estimated sales volume.

[0096] Defining the demand replenishment model , in the model represents the demand replenishment quantity, represents the estimated sales volume of the retailer demanding replenishment up to the supply chain response cycle, represents the sales potential adjustment factor, represents the current inventory of the retailer that needs to replenish, It represents the set safety stock amount. Specifically, the sales potential adjustment factor is composed of the sales cycle stability factor and the sales peak proximity factor.

[0097] It should be added that the safety stock mentioned in the demand replenishment model is the additional inventory reserved to cope with uncertainties such as demand fluctuations and supply chain disruptions. It ensures that a certain service level can be maintained in emergencies to avoid the risk of out-of-stock. When calculating the demand replenishment, it is necessary not only to consider meeting the expected sales demand, but also to consider dealing with potential risks and uncertainties. Therefore, the safety stock is usually added to the calculation results to ensure that there is enough inventory to cope with possible fluctuations.

[0098] The sales cycle stability factor is calculated by the following steps: Step 1: Calculate the monthly average sales volume of the demand replenishment retailer's multi-year monthly sales data.

[0099] Step 2: Calculate the percentage of months in which monthly sales volume does not reach the monthly average sales volume.

[0100] Step 3: Subtract the percentage of that month from the value 1.

[0101] This factor reflects the stability and consistency of a retailer's sales data. Specifically: A high stability factor means that sales in most months are close to or exceed the monthly average sales, indicating that sales are relatively stable and consistent throughout the year.

[0102] A low stability factor means that the sales volume in more months is lower than the monthly average sales volume, indicating that sales fluctuate greatly and lack stability.

[0103] The sales peak proximity factor is obtained through the following steps: S1: Analyze the monthly sales data of demand replenishment retailers for multiple years and identify the sales peak month (i.e., the month with the highest sales volume).

[0104] S2: Determine the current month and calculate the time difference (in months) between the current month and the most recent sales peak month.

[0105] S3: Use the value 1 minus the ratio of the difference period to the longest difference period (considering that there are 12 months in a year, the longest difference period is 11 months) to obtain the sales peak proximity factor.

[0106] This factor reflects how close the current month is to the most recent sales peak. Specifically, a high proximity factor (close to 1) means that the current month is close to the sales peak month, indicating that market demand is at its peak.

[0107] A low proximity factor (close to 0) means that the current month is far away from the peak sales month, indicating that the market demand is in a low or stable period.

[0108] By combining the sales cycle stability factor and the sales peak proximity factor to form a sales potential adjustment factor, the demand replenishment quantity can be evaluated more accurately. This factor can comprehensively consider the stability of sales data and the fluctuation of current market demand, and provide a comprehensive evaluation indicator to dynamically reflect the sales potential of the product. Applying it to the demand replenishment model can make compensatory adjustments based on the static estimated sales volume obtained based on the existing sales rate and supply chain response cycle, so that the final estimated sales volume is closer to the actual situation, thereby improving the accuracy of the forecast.

[0109] In the specific combination calculation example, by calculating the average of the sales cycle stability factor and the sales peak proximity factor, a sales potential adjustment factor with a value range of 0 to 1 can be obtained. This method ensures that when the sales potential adjustment factor is close to 1, the adjusted estimated sales volume can be close to the original estimated sales volume, thereby ensuring the prudence of the forecast and minimizing over-replenishment.

[0110] In the data example described above, assuming that the sales cycle stability factor of a demand replenishment retailer is 0.70, the sales peak approach factor is: 0.7273, and the calculated sales potential adjustment factor is 0.714.

[0111] Assuming the estimated sales volume is 1,000 units, the sales potential adjustment factor is used to make a compensating adjustment and the adjusted estimated sales volume is 714 units. In this case, the adjusted estimated sales volume is lower than the original estimated sales volume, but still remains at a high level, reflecting that the market demand is relatively strong but inventory still needs to be carefully controlled.

[0112] Substitute the estimated sales volume and current inventory of each demand replenishment retailer into the demand replenishment model, calculate the demand replenishment quantity of each demand replenishment retailer, and add them up to get the total demand replenishment quantity.

[0113] In a further preferred implementation, the following process is used to collaboratively determine the appropriate replenishment time based on the available capacity capture method: the inventory support duration of each retailer requiring replenishment is calculated based on the current inventory level and existing sales velocity, and the inventory support deadline is determined based on the current time.

[0114] Find the earliest inventory support cut-off date among all retailers who need to replenish stocks, and move the minimum inventory support cut-off date forward by one supply chain response cycle to obtain the initial replenishment time.

[0115] Identify whether there is available capacity at the initial replenishment time in the manufacturer's production schedule. If there is available capacity, determine that date as the appropriate replenishment time. Otherwise, identify the date with the most recent available capacity in the schedule before that date in reverse order and use it as the appropriate replenishment time.

[0116] It should be understood that the available capacity mentioned above refers to the production lines and related equipment that the manufacturer has available for production, and these production lines are not occupied by other orders or tasks.

[0117] The above method can effectively combine the manufacturer's production schedule to collaboratively determine the appropriate replenishment time. This method ensures that the replenishment plan is seamlessly connected with the production plan, avoids supply interruptions caused by production delays, ensures supply continuity, and fully utilizes the manufacturer's production capacity.

[0118] The automatic replenishment execution module automatically generates a replenishment order according to the appropriate replenishment time and the total required replenishment quantity, and pushes a replenishment instruction to the manufacturer.

[0119] The above automatic replenishment can automatically trigger the replenishment process when the preset conditions are met, avoiding replenishment delays caused by manual operation delays, which is conducive to shortening the response time.

[0120] When determining to replenish the manufacturer, the present invention obtains the real-time production schedule by connecting to the manufacturer's production system, and collaboratively determines the appropriate replenishment time using the available capacity capture method. This method achieves efficient collaboration between distributors and manufacturers, ensures that the replenishment time is aligned with the production plan, avoids production scheduling conflicts, ensures that replenishment is completed on time, and maximizes the use of manufacturing resources.

[0121] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A digital supply chain collaborative optimization logistics management platform, characterized by ,include: Multi-source retail data upload module: retailers upload product sales data and inventory data to distributors in real time through interface sharing; Sales trend diagnosis module: Distributors identify the phased increase in product sales data of multiple retailers and determine whether the upward trend is a single increase or a global related increase; Differentiated replenishment decision module: triggers horizontal transfer of inventory between retailers when the singleness increases, and selects retailers in need of replenishment when the global correlation increases; Demand replenishment collaborative determination module: evaluates the total demand replenishment volume for demand replenishment retailers, connects to the manufacturer's production system to obtain the real-time production schedule, and then uses the available capacity capture method to collaboratively determine the appropriate replenishment time; Automatic replenishment execution module: automatically generates replenishment orders based on appropriate replenishment time and total required replenishment quantity, and pushes replenishment instructions to manufacturers.

2. A digital supply chain collaborative optimization logistics management platform as claimed in claim 1, characterized in that: The specific implementation process of the multi-source retail data upload module is as follows: Distributors build a retail database and logically divide the retail database according to the number of retailers, generating an independent blockchain sub-database for each retailer; Through the data interface, each blockchain sub-library is connected to the sales accounting terminal and inventory accounting terminal of the corresponding retailer to form a distributed data sharing network.

3. A digital supply chain collaborative optimization logistics management platform as claimed in claim 1, characterized in that: The process of identifying the stage increase of product sales data of multiple retailers by the distributor is as follows: Determine a fixed time window to automatically capture product sales data uploaded within this time window; Extract sales volume information from product sales data, and divide the data in the time window into multiple time intervals according to preset rules, and then count the corresponding sales volume of each retailer in each time interval; Compare each retailer's sales volume in the current time period with the sales volume in the previous time period to calculate the sales growth rate; The sales growth rate of each retailer in each time interval is compared with the pre-set threshold. If the sales growth rate of a retailer in a certain time interval reaches or exceeds the set threshold, then starting from this time interval, the proportion of the duration of its sales growth in the entire time window is counted and compared with the configured benchmark ratio. If this ratio reaches or exceeds the configured benchmark ratio, it is determined that the retailer's product sales have entered a stage of rising state. At the same time, the retailer is marked as a growing retailer, and the time interval when the sales growth rate reaches the set threshold is marked as a growth time interval.

4. A digital supply chain collaborative optimization logistics management platform as claimed in claim 3, characterized in that: The process of determining whether the rising trend is a single rise or a global related rise is as follows: (1) Count the number of growing retailers identified in the phased marking. If there is only one growing retailer, its rising trend is directly determined to be a single rising trend. If there are multiple growing retailers, execute (2) to (4); (2) Conduct a synchronization analysis on the growth time intervals of all growing retailers, summarize the number of retailers that have grown in the same time interval, and calculate their proportion to the total number of growing retailers, defining this proportion as the same-growth ratio; (3) Based on the sales data within the intercepted time window, the time series sales of all growing retailers are analyzed for correlation, the correlation coefficient between each pair of retailers is calculated, and the average value is taken as the overall correlation index; (4) The calculated same-increase ratio and correlation coefficient are compared with the critical thresholds preset by the system. If both the same-increase ratio and the correlation coefficient reach or exceed the corresponding critical thresholds, the upward trend is judged to be a globally correlated increase; otherwise, the upward trend is judged to be a single increase.

5. A digital supply chain collaborative optimization logistics management platform as claimed in claim 4, characterized in that: Before triggering the horizontal transfer of inventory between retailers when the unit price increases, it is necessary to determine whether to trigger the transfer. The specific implementation is as follows: When the single growth rate increases, single growth retailers are selected and their current inventory and current sales speed are obtained to predict how long the current inventory of the retailer can support; Compare the predicted current inventory support time with the supply chain response cycle. If the current inventory support time is lower than the supply chain response cycle, it is determined that horizontal inventory transfer needs to be triggered. Otherwise, horizontal inventory transfer does not need to be triggered.

6. A digital supply chain collaborative optimization logistics management platform as claimed in claim 4, characterized in that: The process of triggering horizontal transfer of inventory between retailers when the unit price rises is as follows: Screen potential transferors from retailers other than the single growth retailer, with the following screening criteria: a) The current inventory support time is longer than the supply chain response cycle; b) The transportation distance to the single growth retailer meets the preset range; The current inventory support time of the potential transferor and the transportation distance between the potential transferor and the single growth retailer are used in the formula Quantify the replenishment advantage, including Indicates The quantitative value of the potential transferor's replenishment advantage, Indicates the potential transfer party number. , The scoring function that represents the duration of the current inventory. represents the scoring function of the transport distance, , They represent the weight factors of the current inventory support time and transportation distance respectively; According to the quantitative value of replenishment advantage, potential transfer parties are sorted from large to small according to replenishment advantage, and the sorting result is used as the priority order for horizontal inventory transfer between retailers.

7. A digital supply chain collaborative optimization logistics management platform as claimed in claim 5, characterized in that: The supply chain response cycle refers to the sum of the historical replenishment cycle from distributors to retailers and the delivery cycle from manufacturers to distributors of similar products.

8. A digital supply chain collaborative optimization logistics management platform as claimed in claim 4, characterized in that: The process of selecting retailers who need to replenish when the global correlation increases is as follows: Extracting a set of retailers that participate in the global correlation rising trend from all retailers, and predicting the current inventory support time for each retailer in the above set based on its current inventory and current sales velocity; Compare the current inventory support time of each retailer with the supply chain response cycle, screen out retailers whose current inventory support time is lower than the supply chain response cycle, and mark them as demand replenishment retailers.

9. A digital supply chain collaborative optimization logistics management platform as claimed in claim 1, characterized in that: The evaluation of the total demand replenishment quantity for the demand replenishment retailer is implemented as follows: Estimate the sales volume within the supply chain response cycle of each demand replenishment retailer based on its current sales rate and supply chain response cycle; Defining the demand replenishment model , in the model represents the demand replenishment quantity, represents the estimated sales volume of the retailer demanding replenishment up to the supply chain response cycle, represents the sales potential adjustment factor, represents the current inventory of the retailer that needs to replenish, Indicates the set safety stock amount. Specifically, the sales potential adjustment factor is composed of the sales cycle stability factor and the sales peak proximity factor. Substitute the estimated sales volume and current inventory of each demand replenishment retailer into the demand replenishment model, calculate the demand replenishment quantity of each demand replenishment retailer, and add them up to get the total demand replenishment quantity.

10. A digital supply chain collaborative optimization logistics management platform as claimed in claim 8, characterized in that: The process of collaboratively determining the appropriate replenishment time by using the available capacity capture method is as follows: Calculate the current inventory support duration of each retailer that needs to replenish its stock based on its current inventory volume and existing sales velocity, and determine the inventory support end date; Find the earliest inventory support cut-off date among all retailers who need to replenish stocks, and move the minimum inventory support cut-off date forward by one supply chain response cycle to obtain the initial replenishment time; Identify whether there is available capacity at the initial replenishment time in the manufacturer's production schedule. If there is available capacity, determine that date as the appropriate replenishment time. Otherwise, identify the date with the most recent available capacity in the schedule before that date in reverse order and use it as the appropriate replenishment time.

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