A Supply Chain Collaborative Forecasting Method and System

Abstract

The present invention relates to the technical field of supply chain collaborative forecasting, and particularly to a supply chain collaborative forecasting method and system. The method includes the steps of: obtaining the degree of supply chain data fluctuation at the collection moment by collecting the differences between the supply chain data of each dimension at the collection moment and the historical supply chain data; obtaining the transportation end positions corresponding to the outbound data in the supply chain data, and obtaining the credibility of the change of the supply chain data at the collection moment according to the Euclidean distance between the transportation end points of the outbound data at the collection moment; calculating the importance at the collection moment; during the process of obtaining the predicted value of the supply chain data at the next moment by using the EWMA algorithm, correcting the weights of each collection moment by using the importance of each collection moment to obtain the supply chain collaborative forecasting result at the next moment, effectively improving the accuracy of supply chain collaborative forecasting.

Description

Technical Field

[0001] The present invention relates to the technical field of supply chain collaborative forecasting, and particularly to a supply chain collaborative forecasting method and system. Background Art

[0002] The inbound volume, outbound volume, and inventory volume of a supply chain warehouse can intuitively reflect the real-time status of inventory. By monitoring and analyzing inventory data, the quantity, location, and status of inventory can be grasped in real time. Each department can intuitively understand the work progress and requirements of other departments, thereby breaking departmental barriers, ensuring the transparency of the supply chain, and enhancing the collaborative effect among departments.

[0003] In order to monitor the supply chain collaboration effect, a patent application document with the publication number CN119250740A in the prior art discloses a hierarchical collaborative control and warning method, device, equipment, and medium based on supply chain integration. This application monitors and collects inventory data and contract performance data of the supply chain in real time; obtains the procurement data of the supply chain, combines the inventory data and demand data, and establishes a data analysis model based on machine learning to generate a warning evaluation coefficient; compares and analyzes the warning evaluation coefficient with a pre-set warning evaluation threshold, determines the warning level, issues a warning prompt, and performs maintenance according to the warning prompt.

[0004] Obviously, the above solution mainly identifies anomalies based on the ex-post analysis of data such as inventory, delivery, and procurement, lacks the direct forecasting ability for sudden demand changes such as seasonality, and cannot effectively avoid potential anomalies. Moreover, the above solution establishes a data analysis model based on machine learning to generate a warning evaluation coefficient, but does not mention the training data and algorithm details of the model. If the training data cannot cover various situations of sudden demand changes such as seasonality, the model may not be able to accurately identify relevant anomalies, and the accuracy is relatively low.

[0005] Therefore, it is urgent to solve the problem of how to accurately predict the supply chain collaboration level at future moments in the presence of sudden demand changes such as seasonality in product supply. Summary of the Invention

[0006] In order to solve the technical problem of how to accurately predict the supply chain collaboration level at future moments in the presence of sudden demand changes such as seasonality in product supply, the present invention provides a supply chain collaborative forecasting method and system.

[0007] In the first aspect, the present invention provides a supply chain collaborative forecasting method, adopting the following technical solution:

[0008] A supply chain collaborative forecasting method includes the steps:

[0009] Obtain the supply chain data of each dimension of the product at each collection moment; obtain the degree of supply chain data fluctuation at this collection moment through the difference between the supply chain data of each dimension at the collection moment and the historical supply chain data; obtain the transportation end position corresponding to the outbound data in the supply chain data, and record the Euclidean distance between the transportation ends of two outbound data in the historical supply chain data at any collection moment as a position index at this collection moment. According to the difference between each position index at the collection moment and the mean value of the position index, obtain the credibility of the supply chain data change at this collection moment; calculate the importance degree at the collection moment, and the importance degree is positively correlated with the degree of supply chain data fluctuation and the credibility of supply chain data change at this collection moment; in the process of obtaining the predicted value of the supply chain data at the next moment by the EWMA algorithm, use the importance degree of each collection moment to correct the weight of each collection moment to obtain the supply chain collaborative prediction result at the next moment.

[0010] The present invention can predict various factors including seasonal or sudden demands by predicting the collaborative degree of the supply chain, so as to effectively identify potential abnormal situations of the collaborative degree and make timely handling. In this process, the present invention takes into account that when the EWMA algorithm obtains the predicted value of the supply chain at the next moment, setting the corresponding weights according to the chronological order of the collection times of the historical collection moments may ignore the influence of seasonal changes of some collection moments; based on this, the present invention sets weights for each collection moment by quantifying the possibility of seasonal changes for each collection moment, so that the supply chain collaborative prediction result at the next moment can continue the seasonal characteristics, thereby accurately obtaining the evaluation of the predicted value of the supply chain data at the next moment. On this basis, the present invention also takes into account that the characteristics of occasional orders are relatively close to the seasonal characteristics and will affect the prediction accuracy at the next moment; based on this, the present invention corrects the possibility of seasonal changes for each collection moment by obtaining the distribution of the outbound endpoints of the product, which can effectively distinguish the data fluctuations driven by real market demands from the fluctuations caused by occasional factors, accurately obtain the corrected weight values of each collection moment, and thus effectively improve the accuracy of the supply chain collaborative prediction result at the next moment.

[0011] According to a supply chain collaborative prediction method provided by the present invention, the obtaining the supply chain data of each dimension of the product at each collection moment includes: obtaining the inbound quantity, outbound quantity and inventory quantity of the warehouse, and the outbound data of the warehouse each time at each collection moment, and preprocessing to obtain the supply chain data of each dimension.

[0012] The present invention takes into account that there may be problems such as inconsistent dimensions and missing data among the supply chain data of different dimensions. Therefore, the data quality is improved through preprocessing to prepare for supply chain collaborative prediction.

[0013] A supply chain collaborative forecasting method provided by the present invention, obtaining the degree of fluctuation of supply chain data at the collection moment, includes: presetting the length of historical supply chain data at each collection moment; recording the average value of the absolute values of the differences between the supply chain data of each dimension at the collection moment and the historical supply chain data as the fluctuation index of this dimension; and normalizing the cumulative sum of the fluctuation indexes of all dimensions at this collection moment to obtain the degree of fluctuation of supply chain data at this collection moment.

[0014] By analyzing the fluctuation of supply chain data in the short term, the present invention can accurately obtain the degree of fluctuation of supply chain data at the collection moment. The higher the degree of fluctuation of supply chain data at the collection moment, the more significantly the supply chain is affected by external factors, the higher the mutability of product flow in the warehouse, and the higher the possibility of seasonal changes.

[0015] A supply chain collaborative forecasting method provided by the present invention takes the longitude and latitude of the transportation destination corresponding to the outbound data as the location of this transportation destination.

[0016] A supply chain collaborative forecasting method provided by the present invention, the credibility of the change of supply chain data at the collection moment satisfies the relational expression:

[0017] ;

[0018] is the credibility of the change of supply chain data at the collection moment, is the number of outbound data in the historical supply chain data at the collection moment, is the Euclidean distance between the th and the th transportation destinations of outbound data in the historical supply chain data at the collection moment, is the average value of the position indexes in the historical supply chain data at the collection moment, is the exponential function with base e.

[0019] The present invention provides an accurate calculation formula for the credibility of the change of supply chain data at the collection moment. By analyzing the Euclidean distance between outbound destination positions, the distribution of outbound data can be accurately obtained. Thus, the authenticity of the supply chain data fluctuation can be verified according to the closeness between the distribution of outbound data and the actual market sales network layout, and the degree of change of supply chain data can be accurately obtained.

[0020] A supply chain collaborative forecasting method provided by the present invention, the calculating the importance degree of the acquisition moment includes: multiplying the fluctuation degree of the supply chain data at each acquisition moment by the credibility of the change of the supply chain data to obtain the degree of data change at this moment; normalizing the ratio of the degree of data change at the acquisition moment to the degree of data change at the previous acquisition moment to obtain the importance degree of this acquisition moment.

[0021] A supply chain collaborative forecasting method provided by the present invention, in the process of obtaining the predicted value of the supply chain data at the next moment by the EWMA algorithm, the weights of each acquisition moment are corrected by using the importance degree of each acquisition moment, including: multiplying the weight of each acquisition moment by the importance degree as the weight index of this acquisition moment; taking the ratio of the weight index of the acquisition moment to the sum value of the weight indexes of the historical supply chain data acquisition moments as the corrected weight value of this acquisition moment.

[0022] A supply chain collaborative forecasting method provided by the present invention, the weights of each acquisition moment are corrected by using the importance degree of each acquisition moment to obtain the supply chain collaborative forecasting result at the next moment, including: performing weighted average on the supply chain data of each dimension corresponding to the historical supply chain data at this acquisition moment by using the corrected weight value of the acquisition moment to obtain the predicted value of the supply chain data of each dimension at the next moment; obtaining the supply chain collaborative forecasting result at the next moment according to the predicted values of the supply chain data of each dimension at the next moment.

[0023] A supply chain collaborative forecasting method provided by the present invention, the obtaining the supply chain collaborative forecasting result at the next moment includes: after obtaining the sum value of the predicted value of the inbound quantity and the predicted value of the inventory quantity at the next moment, taking the absolute value obtained by subtracting the predicted value of the outbound quantity from the sum value as the supply chain collaborative forecasting result at the next moment.

[0024] The present invention takes into account that when the degree of collaboration is relatively high, the difference between the sum value of the inbound quantity and the inventory quantity of the supply chain and the outbound quantity is relatively small. Therefore, according to the difference between the sum value of the predicted value of the inbound quantity and the predicted value of the inventory quantity at the next moment and the predicted value of the outbound quantity, the supply chain collaborative forecasting result at the next moment can be accurately obtained.

[0025] In the second aspect, the present invention provides a supply chain collaborative forecasting system, adopting the following technical solution:

[0026] A supply chain collaborative forecasting system includes: a processor and a memory, and the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned supply chain collaborative forecasting method is implemented.

[0027] By adopting the above technical solution, a computer program is generated from the above supply chain collaborative prediction method and stored in a memory to be loaded and executed by a processor, so as to manufacture a terminal device according to the memory and the processor, which is convenient to use.

[0028] The present invention has the following technical effects:

[0029] Based on the above technical solution, for a supply chain collaborative prediction method and system provided by the present invention, when obtaining the supply chain collaborative prediction result at the next moment, by predicting the collaborative degree of the supply chain, sudden demands such as seasonal demands can be predicted, so as to effectively identify potential abnormal collaborative degrees. In this process, the present invention sets weights for each acquisition moment by quantifying the possibility that each acquisition moment may be a seasonal change, so that the supply chain collaborative prediction result at the next moment can continue the seasonal characteristics, thereby accurately obtaining the supply chain data prediction value at the next moment to evaluate the supply chain collaborative prediction result. On this basis, the present invention also corrects the possibility of seasonal change at the acquisition moment by obtaining the distribution of the product outbound endpoints, which can effectively distinguish the data fluctuations driven by real market demands from the fluctuations caused by accidental factors, accurately obtain the corrected weight values of each acquisition moment, and thus effectively improve the accuracy of the supply chain collaborative prediction result at the next moment. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a flowchart in a supply chain collaborative prediction method provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments.

[0032] In order to solve the technical problem of how to accurately predict the supply chain collaborative degree at a future moment in the presence of sudden demand changes such as seasonality in product supply, an embodiment of the present invention discloses a supply chain collaborative prediction method. This method analyzes the changes in supply chain data in the product supply chain to obtain its fluctuation degree, and determines its weight by calculating the credibility of its fluctuation degree after correction, which can effectively reduce the impact of sudden demand changes such as seasonality on the supply chain data prediction result, improve the accuracy of supply chain collaborative prediction, and facilitate enterprises to coordinate in a timely manner.

[0033] Specifically, please refer to Figure 1 as shown in Figure 1 It is a flowchart in a supply chain collaborative prediction method provided by an embodiment of the present invention. The method specifically includes the following steps:

[0034] S1: Obtain the supply chain data of each dimension of the product at each collection moment.

[0035] Among them, the dimensions of the product supply chain data may include the inbound quantity, outbound quantity, and inventory quantity of the warehouse.

[0036] It should be noted that the inbound quantity, outbound quantity, and inventory quantity in the product supply chain data can intuitively reflect the changes in the product supply and demand relationship. Before supply chain collaborative forecasting, all supply chain data can be entered into the ERP system separately, and databases of different types of supply chain data can be established in the ERP system. After entering the supply chain data of the product, the ERP system will associate and store the ID of the product with the supply chain data of the product.

[0037] For the sake of easy understanding, in the embodiments of the present invention, the supply chain collaboration degree of any product in a warehouse is predicted, but it does not mean that the embodiments of the present invention are only limited to this.

[0038] Specifically, when obtaining the supply chain data of each dimension of the product, the warehouse terminal can periodically access the enterprise's ERP system through methods such as SQL query and API connection, and send an authentication information request to the inventory query API of the ERP system at each collection moment. The request should at least include the ID authentication, access token, access target, etc. that are uniquely corresponding to each type of product. After receiving the authentication information request, the ERP system verifies the authentication information, and after successful verification, returns the corresponding supply chain data according to the access target, that is, the inbound quantity, outbound quantity, and inventory quantity of the product in the warehouse.

[0039] Among them, the period for obtaining the supply chain data can be set to once per hour; the period can be specifically set according to actual needs, and the embodiments of the present invention do not limit this too much here.

[0040] Exemplarily, in the embodiments of the present invention, obtaining the supply chain data of each dimension of the product at each collection moment includes: obtaining the inbound quantity, outbound quantity, and inventory quantity of the warehouse at each collection moment, and obtaining the supply chain data of each dimension after preprocessing.

[0041] Among them, the preprocessing can be interpolation of missing data, data standardization processing to eliminate the dimension, etc., which can be specifically set according to actual needs.

[0042] It can be understood that the inbound quantity, outbound quantity, and inventory quantity of the warehouse are the dimensions of the supply chain data. Obtaining the supply chain data of each dimension of the product is obtaining the inbound quantity, outbound quantity, and inventory quantity of the product. The inbound quantity, outbound quantity, and inventory quantity obtained at each collection moment are the supply chain data at that collection moment.

[0043] After obtaining the supply chain data of the product at the current warehouse terminal, the supply chain collaboration state of the products in the current warehouse can be predicted by analyzing the supply chain data of the product.

[0044] The exponentially weighted moving average method (hereinafter referred to as EWMA) is a time series prediction method. This algorithm performs weighted averaging by assigning different weights to historical data to achieve prediction, and has high real-time performance and adaptability. Therefore, in the embodiments of the present invention, EWMA is used to predict the degree of supply chain collaboration.

[0045] S2: Obtain the degree of supply chain data fluctuation at the collection moment by calculating the difference between the supply chain data of each dimension at the collection moment and the historical supply chain data.

[0046] It should be noted that during the normal product supply process of an enterprise, the degree of collaboration in the product flow in the warehouse is usually relatively high, and all links of the supply chain can operate efficiently and stably, that is, the data of each dimension are in a relatively stable state during local time periods. When there are sudden product demands such as seasonal demands, the data of each dimension will show obvious fluctuations in the short term. For example, during the peak season of seasonal demand (such as the peak season for heating equipment demand in winter), the outbound volume will increase sharply. At the same time, in order to meet the demand, the inbound volume and inventory will also change significantly, and the stable state will be changed, thus affecting the degree of supply chain collaboration.

[0047] Based on this, before predicting the supply chain prediction value of the next moment at the current collection moment through the EWMA algorithm in the embodiments of the present invention, it is possible to first obtain the data fluctuation situation at each collection moment, obtain the possibility of being affected by external factors such as seasonal changes at each collection moment, and thus obtain the weight of each collection moment when calculating the supply chain prediction value of the next moment based on the data fluctuation situation at the collection moment.

[0048] It can be understood that the demand prediction of the supply chain has requirements for agility and needs to adjust the replenishment strategy in real time according to the collaborative prediction result. Therefore, the embodiments of the present invention adopt short-term fluctuation analysis, which can capture the real market demand signal more accurately and quickly.

[0049] Exemplarily, in the embodiments of the present invention, obtaining the degree of supply chain data fluctuation at the collection moment includes: presetting the length of the historical supply chain data at each collection moment; recording the average value of the absolute values of the differences between the supply chain data of each dimension at the collection moment and the historical supply chain data as the fluctuation index of this dimension; and performing normalization processing on the cumulative sum of the fluctuation indexes of all dimensions at the collection moment to obtain the degree of supply chain data fluctuation at the collection moment.

[0050] Among them, the length of the historical supply chain data at each collection moment is the number of historical collection moments at each collection moment; the length of the historical supply chain data at each collection moment can be set to 10, and can be specifically set according to actual needs.

[0051] For example, when obtaining the historical supply chain data at the current collection moment, the supply chain data at the current collection moment can be used as the first historical collection moment at the current collection moment, and other collection moments can be obtained according to the interval from the current collection moment, so as to obtain the historical collection moments at the current moment composed of the current collection moment and other collection moments, and the supply chain data corresponding to the historical collection moments is used as the historical supply chain data at the current collection moment.

[0052] For the convenience of understanding, an embodiment of the present invention provides a relational expression for calculating the fluctuation degree of the supply chain data at the collection moment:

[0053] ;

[0054] is the fluctuation degree of the supply chain data at the collection moment, is the number of dimensions of the supply chain data, is the number of historical supply chain data at the collection moment, is the data value of the th dimension at the is the data value of the th dimension at the is the standard normalization function, is the absolute value symbol.

[0055] In the above formula, the fluctuation degree of the supply chain data at the collection moment is the fluctuation degree of the historical supply chain data at this collection moment. The greater the fluctuation degree of the historical supply chain data at the collection moment, the lower the operating stability in the historical period of this collection moment, and the more unstable the changes of the data in each dimension.

[0056] represents the fluctuation index of the th dimension at the collection moment. The larger this value, the greater the change in the data of the th dimension in the short-term historical collection period corresponding to the historical supply chain data at the collection moment relative to the

[0057] Based on the above steps, the degree of supply chain data fluctuation at each collection moment can be obtained. The degree of supply chain data fluctuation at each collection moment can reflect the possibility of being impacted by external factors such as seasonality at each collection moment. External factors such as seasonality may continue for a relatively long period in the future. Therefore, when predicting the collaboration degree in the future period based on the EWMA algorithm, corresponding weights need to be set according to the possibility of being impacted by external factors such as seasonality at each collection moment, so as to accurately obtain the collaborative prediction result for the future period.

[0058] However, in addition to seasonal demand, the production activities of enterprises may also cause large fluctuations in supply chain data in the short term. For example, a company purchases a large number of orders for company activities, or small merchants stockpile goods, etc. Obviously, this situation is not sustainable in the future period. Therefore, setting weights for each collection moment only based on the fluctuation of historical short-term data may cause the final collaborative prediction result to tend to accidental events that are not sustainable, reducing the accuracy of supply chain collaborative prediction.

[0059] Based on this, the embodiments of the present invention further verify the authenticity of the degree of supply chain data fluctuation at the collection moment by continuing to execute the following steps.

[0060] S3: Obtain the transportation end positions corresponding to the outbound data in the supply chain data, and obtain the credibility of the change in the supply chain data at this collection moment through the Euclidean distance between the transportation end points of the outbound data in the historical supply chain data at the collection moment.

[0061] It should be noted that the outbound end point in the outbound data of each product can reflect the distribution of product demand. In the case of a large degree of fluctuation, if the distribution of the outbound end point positions is relatively dispersed, it indicates that the greater the possibility that the degree of supply chain data fluctuation at the collection moment is due to real market demand, and the product demands in multiple regions increase; on the contrary, if the distribution of the outbound end point positions is relatively concentrated, it indicates that the greater the possibility that the degree of supply chain data fluctuation at the collection moment is due to local stockpiling or large orders.

[0062] Based on this, the embodiments of the present invention can verify the degree of supply chain data fluctuation at each collection moment through the outbound end point positions of the product outbound data, so as to accurately obtain the credibility of the degree of seasonal change at each collection moment.

[0063] Exemplarily, in the embodiments of the present invention, the outbound data when the product is shipped each time can be obtained through the ERP system. Among them, the outbound data at least includes the ID of the product and the outbound transportation end point when placing an order.

[0064] Among them, the longitude and latitude of the transportation end point corresponding to the outbound data can be used as the transportation end point position of the outbound data.

[0065] It can be understood that the longitude and latitude of the transportation destination are dimensionless data, and the outbound data is directly related to the user's order. Therefore, when analyzing the outbound data, the number of outbound data in the historical supply chain data at the current collection moment is the number of outbound data in the historical period corresponding to the historical supply chain data at the current collection moment, and each outbound data corresponds to an outbound transportation destination.

[0066] Exemplarily, in the embodiments of the present invention, when calculating the credibility of the change in the supply chain data at the collection moment, the transportation destination position corresponding to the outbound data in the supply chain data can be obtained, and the Euclidean distance between the transportation destinations of two outbound data in the historical supply chain data at any collection moment is recorded as a position index at this collection moment. Based on the difference between each position index at the collection moment and the mean value of the position index, the credibility of the change in the supply chain data at this collection moment is obtained.

[0067] It can be understood that the Euclidean distance between the transportation destinations of every two outbound data is one of the position indexes at this collection moment, and finally the mean value of the position index can be obtained based on multiple position indexes at this collection moment. When analyzing the Euclidean distance between the transportation destinations of the outbound data, only the Euclidean distance between the transportation destination of each outbound data and the transportation destinations of other outbound data can be calculated.

[0068] For the sake of easy understanding, the embodiments of the present invention provide a relational expression for the credibility of the change in the supply chain data at the collection moment, which is specifically as follows:

[0069] ;

[0070] is the credibility of the change in the supply chain data at the collection moment, is the number of outbound data in the historical supply chain data at the collection moment, is the Euclidean distance between the transportation destinations of the th and th outbound data in the historical supply chain data at the collection moment, is the mean value of the position index in the historical supply chain data at the collection moment, is the exponential function with e as the base.

[0071] In the above formula, represents one of the position indexes at the collection moment.

[0072] represents the difference between one of the position indexes at the collection moment and the mean value of the position index. is the The standard deviation of the Euclidean distance between the transportation destinations of the outbound data in the historical supply chain data at the collection time. The larger this value is, it indicates that the In the historical collection period corresponding to the historical supply chain data at the collection time, the position distribution of the transportation destinations of the outbound data is more dispersed, that is, the transportation destinations are widely distributed in different regions. Therefore, the The higher the credibility that the change in the supply chain data at the collection time is driven by real market demand, and the lower the possibility caused by accidental factors.

[0073] After obtaining the credibility of the change in the supply chain data at each collection time based on the above steps, the fluctuation degree at the collection time can be corrected according to the credibility of the change in the supply chain data at each collection time, so as to accurately obtain the possibility that the fluctuation at each collection time is a seasonal change. The higher the possibility that the fluctuation at the collection time is a seasonal change, the higher the corresponding importance.

[0074] S4: Calculate the importance of the collection time; use the importance of each collection time to correct the weight of each collection time to obtain the corrected weight value of each collection time.

[0075] Among them, the importance is positively correlated with the fluctuation degree of the supply chain data at this collection time and the credibility of the change in the supply chain data.

[0076] It should be noted that there are accidental factors such as merchants' stockpiling and large one-time orders in the supply chain, which cause the data fluctuation degree not to truly reflect the state of the supply chain. If the prediction model biases towards this kind of change, it will lead to the prediction result deviating from the actual situation. Based on this, the embodiment of the present invention can use the credibility of the change in the supply chain data to correct the degree of data change, and can filter out the unreliable fluctuations caused by accidental factors.

[0077] Exemplarily, in the embodiment of the present invention, calculating the importance of the collection time includes: recording the product of the fluctuation degree of the supply chain data at each collection time and the credibility of the change in the supply chain data as the degree of data change at this time; normalizing the ratio of the degree of data change at the collection time to the degree of data change at its previous collection time to obtain the importance of this collection time.

[0078] Among them, the larger the ratio of the degree of data change at the current collection time to the degree of data change at its previous collection time, it indicates that the degree of change at the current collection time is greater than that at its previous collection time, the change in the supply chain data at the current collection time is more significant, there are greater changes in market demand or supply chain links, and the corresponding importance is also higher.

[0079] It can be understood that the higher the importance of the current collection moment, the greater the possibility that the supply chain demand at the current collection moment will continue to appear at the next moment in the future of the current collection moment, and the higher the importance in the supply chain collaborative prediction at the next moment. Based on this, the corrected weight value of each collection moment can be accurately obtained, so that the predicted value at the next moment can be accurately obtained based on the corrected weight values of each collection moment in the EWMA algorithm.

[0080] Exemplarily, in the embodiment of the present invention, the weights of each collection moment are corrected by using the importance of each collection moment, including: taking the product of the weight of each collection moment and the importance as the weight index of this collection moment; taking the ratio of the weight index of the collection moment to the sum of the weight indexes of its historical supply chain data collection moments as the corrected weight value of this collection moment.

[0081] Among them, the weight of each collection moment is a preset value in the EWMA algorithm. Generally, the weight of the collection moment closer to the current collection moment is larger, and it can be specifically set according to actual needs. The embodiment of the present invention does not limit it too much here.

[0082] S5: In the process of the EWMA algorithm obtaining the predicted value of the supply chain data at the next moment of the collection moment, the corrected weight values of each collection moment are used to obtain the supply chain collaborative prediction result at the next moment.

[0083] Exemplarily, in the embodiment of the present invention, using the corrected weight values of each collection moment to obtain the supply chain collaborative prediction result at the next moment includes: using the corrected weight value of the collection moment to perform weighted averaging on the supply chain data of each dimension corresponding to the historical supply chain data of this collection moment to obtain the predicted value of the supply chain data of each dimension at the next moment; obtaining the supply chain collaborative prediction result at the next moment according to the predicted values of the supply chain data of each dimension at the next moment.

[0084] Specifically, when obtaining the predicted value of the supply chain data of any dimension at the next moment of the current collection moment, the corrected weight value of the collection moment corresponding to the historical supply chain data of the current collection moment and the data of this dimension can be used in the EWMA algorithm to calculate and obtain the predicted value of the supply chain data of this dimension at the next moment.

[0085] Illustrate the calculation of the predicted value of the inbound quantity at the next moment of the current collection moment: When the current collection moment is the 4th collection moment and calculating the predicted value of the inbound quantity at the 5th collection moment, the inbound quantity at the 1st collection moment in the historical supply chain data at the current collection moment can be used as the smoothed value at the 1st collection moment. Respectively obtain the product of the corrected weight value at the 2nd collection moment and the inbound quantity, and the product of the corrected weight value at the 1st collection moment and the smoothed value. The sum of the two products is used as the smoothed value at the 2nd collection moment; and so on. Finally, obtain the product of the corrected weight value at the 4th collection moment and the inbound quantity, and the product of the corrected weight value at the 3rd collection moment and the smoothed value. The sum of the two products is used as the smoothed value at the 4th collection moment, and the smoothed value at the 4th collection moment is the predicted value of the inbound quantity at the 5th collection moment.

[0086] After obtaining the predicted values of the supply chain data for each dimension at the next moment based on the above steps, the supply chain collaborative prediction result at the next moment can be obtained according to the predicted values of the supply chain data for each dimension.

[0087] It should be noted that the dynamic balance of the product inventory, inbound quantity and outbound quantity in the warehouse can measure the supply chain collaborative effect. When the collaborative degree of the product flow process is relatively high, the sum of the inventory quantity and the inbound quantity is relatively close to the outbound quantity. At this time, the products in the current warehouse will neither cause supply interruption due to inventory shortage nor occupy a large amount of funds and warehouse space due to excessive inventory backlog, thus increasing costs in vain.

[0088] Exemplarily, in the embodiment of the present invention, obtaining the supply chain collaborative prediction result at the next moment includes: after obtaining the sum of the predicted value of the inbound quantity and the predicted value of the inventory quantity at the next moment, using the absolute value obtained by subtracting the predicted value of the outbound quantity from the sum as the supply chain collaborative prediction result at the next moment.

[0089] It can be understood that if the supply chain collaborative prediction result at the next moment is too large, it indicates that there may be inventory shortage or excessive backlog of products in the current warehouse. At this time, a prompt needs to be sent in time for the staff to handle.

[0090] Exemplarily, in the embodiment of the present invention, after obtaining the supply chain collaborative prediction result at the next moment, it is also possible to: after the supply chain collaborative prediction result at the next moment is greater than the preset maximum allowable threshold, send out a prompt.

[0091] Among them, the maximum allowable threshold can be set to 100; the maximum allowable threshold can be specifically set according to actual needs, and the embodiment of the present invention does not limit it too much here.

[0092] It can be seen that in the embodiment of the present invention, when obtaining the supply chain collaborative prediction result, the supply chain data of each dimension of the product can be obtained at each acquisition moment; the degree of supply chain data fluctuation at this acquisition moment can be obtained through the difference between the supply chain data of each dimension at the acquisition moment and the historical supply chain data; the transportation end position corresponding to the outbound data in the supply chain data is obtained, and the Euclidean distance between the transportation ends of two outbound data in the historical supply chain data at any acquisition moment is recorded as a position index at this acquisition moment. According to the difference between each position index at the acquisition moment and the mean value of the position index, the credibility of the change in the supply chain data at this acquisition moment is obtained; the importance at the acquisition moment is calculated, and the importance is positively correlated with the degree of supply chain data fluctuation and the credibility of the change in the supply chain data at this acquisition moment; in the process of using the EWMA algorithm to obtain the predicted value of the supply chain data at the next moment of the acquisition moment, the importance of each acquisition moment is used to correct the weight of each acquisition moment to obtain the supply chain collaborative prediction result at the next moment, effectively improving the accuracy of the supply chain collaborative prediction.

[0093] The embodiment of the present invention also discloses a supply chain collaborative prediction system, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, a supply chain collaborative prediction method provided by the present invention is implemented.

[0094] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface. Their settings and functions are known in the art, so they will not be described in detail here.

[0095] In the present invention, the aforementioned memory can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, device, or device.

[0096] The above are all preferred embodiments of the present invention. The protection scope of the present invention is not limited by this. Therefore, all equivalent changes made according to the structure, shape, and principle of the present invention should be covered within the protection scope of the present invention.

Claims

1. A supply chain collaborative forecasting method, characterized in that, Including: Obtaining supply chain data of each dimension of the product at each collection moment; Obtaining the degree of supply chain data fluctuation at the collection moment through the differences between the supply chain data of each dimension at the collection moment and the historical supply chain data; Obtain the transportation end position corresponding to the outbound data in the supply chain data. Denote the Euclidean distance between the transportation ends of two outbound data in the historical supply chain data at any collection moment as a position index at this collection moment. According to the difference between each position index at the collection moment and the mean value of the position index, obtain the credibility of the change in the supply chain data at this collection moment, satisfying the relational expression: ; is the credibility of the change in supply chain data at the collection time, is the number of outbound data in the historical supply chain data at the collection time, is the Euclidean distance between the th and the th outbound data transport destinations in the historical supply chain data at the collection time, is the mean value of the position indicators in the historical supply chain data at the collection time, is the exponential function with base e; Calculating the importance at the collection moment, where the importance is positively correlated with the degree of supply chain data fluctuation at the collection moment and the credibility of the change in supply chain data, including: recording the product of the degree of supply chain data fluctuation at each collection moment and the credibility of the change in supply chain data as the degree of data change at that moment; normalizing the ratio of the degree of data change at the collection moment to the degree of data change at the previous collection moment to obtain the importance at the collection moment; During the process of obtaining the predicted value of the supply chain data at the next moment by the EWMA algorithm, using the importance at each collection moment to correct the weights at each collection moment to obtain the supply chain collaborative prediction result at the next moment, including: taking the product of the weight at each collection moment and the importance as the weight index at that collection moment; taking the ratio of the weight index at the collection moment to the sum of the weight indexes at the historical supply chain data collection moments of that collection moment as the corrected weight value at that collection moment.

2. The supply chain collaborative prediction method according to claim 1, characterized in that The obtaining of the supply chain data of each dimension of the product at each collection moment includes: Obtaining the inbound quantity, outbound quantity, and inventory quantity of the warehouse, as well as the outbound data of the warehouse each time, at each collection moment, and obtaining the supply chain data of each dimension after preprocessing.

3. A supply chain collaborative forecasting method according to claim 1, characterized in that, The obtaining of the degree of supply chain data fluctuation at the collection moment includes: Presetting the length of the historical supply chain data at each collection moment; recording the average absolute value of the differences between the supply chain data of each dimension at the collection moment and the historical supply chain data as the fluctuation index of that dimension; normalizing the cumulative sum of the fluctuation indexes of all dimensions at the collection moment to obtain the degree of supply chain data fluctuation at the collection moment.

4. A supply chain collaborative forecasting method according to claim 1, characterized in that Taking the longitude and latitude of the transportation destination corresponding to the outbound data as the location of the transportation destination.

5. A supply chain collaborative forecasting method according to claim 1, characterized in that The using of the importance at each collection moment to correct the weights at each collection moment to obtain the supply chain collaborative prediction result at the next moment includes: Performing weighted averaging on the supply chain data of each dimension corresponding to the historical supply chain data at the collection moment using the corrected weight value at the collection moment to obtain the predicted value of the supply chain data of each dimension at the next moment; Obtaining the supply chain collaborative prediction result at the next moment according to the predicted values of the supply chain data of each dimension at the next moment.

6. A supply chain collaborative forecasting method according to claim 2, characterized in that, The obtaining of the supply chain collaborative prediction result at the next moment includes: After obtaining the sum value of the predicted inbound quantity and the predicted inventory quantity at the next moment, taking the absolute value of the difference between the sum value and the predicted outbound quantity as the supply chain collaborative prediction result at the next moment.

7. A supply chain collaborative forecasting system, characterized in that, Including: A processor and a memory, where the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a supply chain collaborative prediction method according to any one of claims 1-6 is implemented.

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