Supply chain monitoring method and device, supply chain system, equipment and medium

Abstract

The invention relates to a supply chain monitoring method and device, a supply chain system, equipment and a medium. The method comprises the following steps: acquiring supply chain monitoring data related to a business scene from a demand plan management device, a contract management device, a purchase management device, a quality control management device, a distribution management device and a reverse logistics device, the supply chain monitoring data comprises at least one of demand data, purchase data, contract data, supply data, quality control data, distribution data and reverse material data; performing dynamic analysis processing on the supply chain monitoring data to obtain statistical state data and prediction state data corresponding to the service scene; and performing risk assessment on the statistical state data and the prediction state data by using a dynamic threshold value, and determining a risk mechanism corresponding to the service scene, the dynamic threshold value being threshold value data generated according to the statistical state data corresponding to the service scene in a historical period. By adopting the method, the monitoring efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the field of supply chain technology, and in particular to a supply chain monitoring method, apparatus, supply chain system, equipment, and medium. Background Art

[0002] As a complex system connecting various links such as raw material supply, production and manufacturing, and circulation and sales, the supply chain is characterized by large amounts of data and strong dynamics.

[0003] In traditional supply chain monitoring methods, since different links in the supply chain usually operate independently, decentralized data processing devices are used to collect and process the data generated by each link separately, and offline batch processing mode is relied upon to perform statistical analysis and risk monitoring on the collected data.

[0004] However, supply chain monitoring methods in traditional technologies have problems such as data dispersion and delayed response, resulting in low efficiency of supply chain monitoring. Summary of the Invention

[0005] Based on this, it is necessary to provide a supply chain monitoring method, device, supply chain system, equipment and medium that can improve monitoring efficiency in response to the above technical problems.

[0006] In a first aspect, the present application provides a supply chain monitoring method, the method comprising:

[0007] Acquiring supply chain monitoring data related to the business scenario from a demand planning management device, a contract management device, a procurement management device, a quality control management device, a distribution management device, and a reverse logistics device, where the supply chain monitoring data includes at least one of demand data, procurement data, contract data, supply data, quality control data, distribution data, and reverse material data;

[0008] Dynamically analyze and process supply chain monitoring data to obtain statistical status data and forecast status data corresponding to business scenarios;

[0009] Use dynamic thresholds to conduct risk assessments on statistical status data and predicted status data to determine the risk mechanism corresponding to the business scenario. The dynamic threshold is the threshold data generated based on the statistical status data corresponding to the business scenario in the historical period.

[0010] In one embodiment, the method further comprises at least one of the following:

[0011] Perform statistical processing on demand data according to demand items to obtain demand statistical data, and generate a demand data dashboard based on the demand statistical data;

[0012] Determine the contract subject data from the contract data based on the preset evaluation subject, perform cumulative calculations on the contract subject data to obtain contract statistical data corresponding to each evaluation subject, and generate a contract data dashboard based on the contract statistical data;

[0013] According to the categories of deployable resources, the distribution data is statistically processed to obtain distribution statistics, and a distribution data dashboard is generated based on the distribution statistics;

[0014] Based on the reverse material data, determine the material scrap disposal status, and generate a reverse data dashboard based on the material scrap disposal status.

[0015] In one embodiment, the method further comprises:

[0016] receiving display operation instructions;

[0017] Based on the display operation instruction, the data display page corresponding to the display operation instruction is determined. The data display page includes at least one of a business scenario dashboard, a demand data dashboard, a procurement data dashboard, a contract data dashboard, a supply data dashboard, a distribution data dashboard and a reverse data dashboard. The business scenario dashboard includes statistical status data and forecast status data corresponding to the business scenario.

[0018] In one embodiment, the forecast status data includes: a procurement completion forecast value;

[0019] Dynamically analyze and process supply chain monitoring data to obtain forecast status data corresponding to business scenarios, including:

[0020] Perform segmented statistical processing on procurement data and supply data to obtain the procurement completion rate corresponding to each historical moment;

[0021] Generate a procurement time series based on the completion rate of each procurement;

[0022] Based on the preset procurement forecast model, the procurement completion rates in the procurement time series are processed with autoregression and error correction to obtain intermediate forecast values.

[0023] The intermediate prediction value is added to the procurement time series, and the process of obtaining the intermediate prediction value is repeated until the preset iteration condition is reached. The intermediate prediction value is output as the procurement completion prediction value corresponding to the target time.

[0024] In one embodiment, the method further comprises:

[0025] receiving data calibration instructions;

[0026] Based on the data calibration instruction, the procurement forecast model is updated using procurement data, supply data and procurement time series to obtain an updated procurement forecast model;

[0027] The accuracy of the procurement forecast model and the updated procurement forecast model is compared, and the model with higher accuracy is used as the model for generating the procurement completion forecast value at the next target moment.

[0028] In one embodiment, the business scenario includes a material supply scenario, and the method further includes:

[0029] Determine the material supply reference value based on the statistical status data corresponding to the material supply scenario in the historical period;

[0030] Determine the floating deviation value based on the scenario label corresponding to the material supply scenario;

[0031] Based on the material supply reference value and the floating deviation value, a dynamic threshold value is obtained.

[0032] In a second aspect, the present application also provides a supply chain system, including:

[0033] Demand planning management device, contract management device, procurement management device, quality control management device, distribution management device and reverse logistics device;

[0034] A supply chain monitoring device is used to obtain supply chain monitoring data related to business scenarios from a demand planning management device, a contract management device, a procurement management device, a quality control management device, a distribution management device, and a reverse logistics device, where the supply chain monitoring data includes at least one of demand data, procurement data, contract data, supply data, quality control data, distribution data, and reverse material data; dynamically analyze and process the supply chain monitoring data to obtain statistical status data and forecast status data corresponding to the business scenarios; use dynamic thresholds to perform risk assessment on the statistical status data and forecast status data to determine the risk mechanism corresponding to the business scenarios, where the dynamic thresholds are threshold data generated based on the statistical status data corresponding to the business scenarios within a historical period.

[0035] In a third aspect, the present application further provides a supply chain monitoring device, comprising:

[0036] a data acquisition module, configured to acquire supply chain monitoring data related to the business scenario from the demand planning management device, the contract management device, the procurement management device, the quality control management device, the distribution management device, and the reverse logistics device, wherein the supply chain monitoring data includes at least one of demand data, procurement data, contract data, supply data, quality control data, distribution data, and reverse material data;

[0037] Dynamic analysis module, used to dynamically analyze and process supply chain monitoring data to obtain statistical status data and forecast status data corresponding to business scenarios;

[0038] The risk assessment module is used to perform risk assessment on statistical status data and predictive status data using dynamic thresholds to determine the risk mechanism corresponding to the business scenario. The dynamic threshold is the threshold data generated based on the statistical status data corresponding to the business scenario in the historical period.

[0039] In a fourth aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program and the processor executes the supply chain monitoring method of the first aspect.

[0040] In a fifth aspect, the present application also provides a computer-readable storage medium having a computer program stored thereon, and the computer program is executed by a processor as in the supply chain monitoring method of the first aspect.

[0041] The above-mentioned supply chain monitoring methods, devices, supply chain systems, equipment and media, on the one hand, provide a supply chain monitoring method that obtains original supply chain monitoring data from each management device to realize full-chain data monitoring of the supply chain; dynamically analyzes and processes the supply chain monitoring data based on specific business scenarios, and combines the data generated by each management device for analysis, avoiding the problem of data islands in the existing technology and improving the correlation between data; uses the statistical status data corresponding to the business scenarios in the historical period to generate dynamic thresholds to perform risk assessment on the statistical status data and the predicted status data to realize closed-loop management, which can effectively improve the flexibility of the supply chain monitoring process to deal with business scenarios, improve the accuracy and timeliness of risk assessment, and thus effectively improve monitoring efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 A diagram illustrating an application environment of a supply chain monitoring method in one embodiment;

[0044] Figure 2 1 is a flow chart of a supply chain monitoring method according to an embodiment;

[0045] Figure 3 Schematic diagram of a flow chart of steps for obtaining predicted status data in one embodiment;

[0046] Figure 4 A flowchart illustrating steps for updating a procurement forecast model in one embodiment;

[0047] Figure 5A schematic flow chart of a supply chain monitoring method according to another embodiment;

[0048] Figure 6 is a structural block diagram of a supply chain monitoring device in one embodiment;

[0049] Figure 7 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0051] It should be noted that the terms "first," "second," etc. used in this application may be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "including," "having," and any variations thereof used in this application are intended to cover non-exclusive inclusions. The term "plurality" used in this application refers to two or more.

[0052] The supply chain monitoring method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, the terminal 102 communicates with the server 104 via a network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104 or placed on the cloud or other network servers.

[0053] Terminal 102 may be, but is not limited to, a demand planning management device, contract management device, procurement management device, quality control management device, distribution management device, or reverse logistics device within a supply chain system. Server 104 may be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. Server 104 obtains supply chain monitoring data related to business scenarios from terminal 102; dynamically analyzes and processes the supply chain monitoring data to obtain statistical status data and predicted status data corresponding to the business scenarios; and uses dynamic thresholds to perform risk assessments on the statistical status data and predicted status data to determine the risk mechanism corresponding to the business scenarios.

[0054] In an exemplary embodiment, Figure 2 As shown, a supply chain monitoring method is provided, which is applied to Figure 1 The server 104 in the example is used as an example to illustrate the process, including the following steps 202 to 206.

[0055] At step 202, the supply chain monitoring data involved in the business scenario is obtained from the demand planning management device, the contract management device, the procurement management device, the quality control management device, the distribution management device, and the reverse logistics device.

[0056] The supply chain monitoring data includes at least one of demand data, procurement data, contract data, supply data, quality control data, distribution data, and reverse logistics data.

[0057] In some embodiments, the process of obtaining the supply chain monitoring data can include: based on the API interface reserved by each management device, calling the API interface according to a time period, or based on an event trigger to call the API interface to obtain the supply chain monitoring data; or, through the database of each management device, directly batch exporting the database data as the supply chain monitoring data.

[0058] Specifically, the demand data is obtained from the demand planning management device, the contract data is obtained from the contract management device, the procurement data is obtained from the procurement management device, the supply data and the quality control data are obtained from the quality control management device, the distribution data is obtained from the distribution management device, and the reverse logistics data is obtained from the reverse logistics device.

[0059] At step 204, the supply chain monitoring data is dynamically analyzed and processed to obtain statistical state data and prediction state data corresponding to the business scenario.

[0060] The supply chain monitoring data in one business scenario can involve one or more management devices, and the dynamic analysis and processing includes: continuously, periodically, or in real time, statistically analyzing the supply chain monitoring data involved in the business scenario to obtain the statistical state data to present the current state of each item of data in the business scenario; and further includes predicting future trends based on existing monitoring data with the help of a prediction model.

[0061] For example, the statistical state data can include the procurement completion rate, the contract signing rate, the supply on-time rate, the quality control pass rate, and the distribution on-time rate at the current time or in a historical time interval; and the prediction state data can include the procurement completion rate, the contract signing rate, the supply on-time rate, the quality control pass rate, and the distribution on-time rate in the future time or in a future time interval.

[0062] For example, the quality control data includes the number of qualified materials and the total number of quality inspections, and the quality control pass rate is calculated based on the number of qualified materials and the total number of quality inspections:

[0063] Quality control pass rate = (number of qualified materials / total number of quality inspections) x 100%.

[0064] In some embodiments, the supply chain monitoring data can be predicted based on a time series model such as an ARIMA model or a Prophet model to obtain the prediction state data.

[0065] In a possible implementation, the method further includes: preprocessing the supply chain monitoring data to obtain structured monitoring data, and dynamically analyzing and processing the structured monitoring data to obtain statistical status data and predicted status data corresponding to the business scenario.

[0066] Among them, the preprocessing process includes: unified naming, deduplication processing and completion processing. Unified naming refers to determining the data naming prefix according to the field of the supply chain monitoring data according to the naming mapping table, and automatically converting the fields of the supply chain monitoring data to achieve standardized naming. For example, the contract data in the supply chain monitoring data is named with the prefix "contract_id". After unified naming, if data with the same name appears, deduplication processing is performed based on the hash value calculated for the duplicate records, and the data record with the latest timestamp is retained. After unified naming, if missing numerical data is found, completion processing is performed based on the existing data according to the preset interpolation method or based on the default value to complete the missing data.

[0067] In this embodiment, the supply chain monitoring data is pre-processed before dynamic analysis and processing to generate structured monitoring data, thereby solving the problem of data dispersion and non-uniform format of the supply chain monitoring data directly obtained from various management devices.

[0068] Step 206 : Perform risk assessment on the statistical status data and the predicted status data using dynamic thresholds to determine the risk mechanism corresponding to the business scenario.

[0069] The dynamic threshold is the threshold data generated based on the statistical status data corresponding to the business scenario in the historical period.

[0070] Among them, the risk mechanism includes risk levels and early warning measures; the risk assessment process may include: judging the risk level of statistical status data and predictive status data in business scenarios based on dynamic thresholds, and determining the early warning measures corresponding to the risk level.

[0071] Exemplarily, the risk levels include three levels. For level one risk, a low-risk warning is triggered, and the warning measures include marking prompt charts and prompt icons on the data display page; for level two risk, a medium-risk warning is triggered, and the warning measures include generating a data summary based on statistical status data and predicted status data that exceed dynamic thresholds, and pushing it to departments related to the business scenario via email; for level three risk, a high-risk warning is triggered, and the warning measures include notifying the person in charge of the business scenario via SMS or voice, and generating an emergency processing work order.

[0072] In the above-mentioned supply chain monitoring method, the original supply chain monitoring data is obtained from each management device to realize full-chain data monitoring of the supply chain; the supply chain monitoring data is dynamically analyzed and processed based on specific business scenarios, and the data generated by each management device are combined for analysis to avoid the problem of data islands in the existing technology and improve the correlation between data; the statistical status data corresponding to the business scenarios in the historical period are used to generate dynamic thresholds to perform risk assessment on the statistical status data and the predicted status data to realize closed-loop management, which can effectively improve the flexibility of the supply chain monitoring process to deal with business scenarios, improve the accuracy and timeliness of risk assessment, and thus effectively improve the monitoring efficiency.

[0073] In an exemplary embodiment, based on Figure 2 In the embodiment shown, the supply chain monitoring method provided further includes at least one of the following:

[0074] 1. Perform statistical processing on demand data according to demand items to obtain demand statistical data, and generate a demand data dashboard based on the demand statistical data.

[0075] Among them, demand data may include demand items such as demand forecast batch, material name, order making date, unit name, expected declaration amount, and actual declaration amount; demand statistical data may include the number of demand items, the expected demand declaration amount for this year, the actual declaration amount, and the arrival amount.

[0076] For example, in the process of statistically processing the annual demand declaration situation, the demand statistical data obtained includes a monthly or quarterly summary of the actual declared amount and the arrival amount and a visual chart formed therefrom.

[0077] 2. Based on the preset evaluation targets, determine the contract subject data from the contract data, perform cumulative calculations on the contract subject data to obtain the contract statistical data corresponding to each evaluation target, and generate a contract data dashboard based on the contract statistical data.

[0078] Evaluation criteria may include the number of contracts, the contract amount, the type of materials involved, the contract category number, the contract signing time, and the contract signing timeliness rate. For example, for the contract quantity evaluation criterion, a contract data analysis model can be used to accumulate the number of contracts within the evaluation interval to obtain the cumulative contract volume. The evaluation interval can be a calendar year, and the corresponding contract statistics are the annual number of contracts. For the contract signing timeliness rate evaluation criterion, a contract signing time analysis model can be constructed based on the contract data, including the issuance time of the winning notice, the contract signing completion time, and the supplier name, to calculate the contract signing timeliness rate.

[0079] 3. According to the adjustable resource category, the distribution data is processed to obtain distribution statistical data, and a distribution data board is generated according to the distribution statistical data.

[0080] The adjustable resource category can include money, time, warehouse, and vehicle. For example, a corresponding resource allocation model can be called according to the adjustable resource category to monitor the distribution of the adjustable distribution resource, and the cumulative distribution, the annual distribution, the number of warehouses, or the number of vehicles is counted.

[0081] 4. According to the reverse material data, the material scrapping disposal situation is determined, and a reverse data board is generated according to the material scrapping disposal situation.

[0082] The reverse material data can include the name of the scrapped material, the storage time of the scrapped material, the disposal time of the scrapped material, and the disposal situation of the scrapped material based on the category of the scrapped material or the set storage time interval.

[0083] 5. The procurement data is classified and statistically processed according to the procurement object to obtain procurement statistical data, and a procurement data board is generated according to the procurement statistical data.

[0084] The category of the procurement object can include material, expert, and bid evaluation room. For the material category, the number of procurement projects in the current year can be obtained based on the procurement data such as demand unit, number of demand items, and number of supplied items. For the expert category, the annual expert data can be obtained based on the number of experts. For the bid evaluation room category, the use of the bid evaluation room in the current year can be counted based on the number of bid evaluation rooms and the construction of bid evaluation room data, and the use rate of the bid evaluation room can be obtained.

[0085] 6. The supply data and the quality control data are respectively processed to obtain supply statistical data and quality control statistical data, and a supply data board is generated according to the supply statistical data and the quality control statistical data.

[0086] The supply data can include the number of projects in supply, the amount of money in supply, the supply data analysis report constructed by the number of arrivals, and the supplier name, the number of suppliers, and the number of new suppliers in the current year. Based on the supply data analysis report, the annual supply on-time rate, the annual cumulative supply, the growth of suppliers, and the cumulative supply amount are obtained. The quality control data can include the cumulative quality control amount, the cumulative quality control amount, the annual quality control amount, the annual quality control amount, the monthly quality control amount, the monthly quality control amount, and the quality control defect data. According to the quality control data analysis model, the cumulative quality control amount and the annual quality control amount are obtained.

[0087] In the embodiments of the present application, the supply chain monitoring data of each management device is respectively analyzed and processed to obtain the data statistics corresponding to each management device, and the data board corresponding to each management device is generated to realize the data visualization of each management device and improve the comprehensiveness of the supply chain monitoring method.

[0088] In a possible implementation, the method further includes: receiving a display operation instruction; determining a data display page corresponding to the display operation instruction based on the display operation instruction, the data display page including at least one of a business scenario board, a demand data board, a procurement data board, a contract data board, a supply data board, a distribution data board, and a reverse data board, and the business scenario board including statistical state data and predicted state data corresponding to a business scenario.

[0089] In a possible implementation, the method further includes: analyzing the display operation instruction to obtain a display permission; and displaying the data boards within the display permission range on the data display page.

[0090] For example, for a display operation instruction sent by a warehouse staff, the distribution data board can be displayed on the data display page according to the display permission of the warehouse staff, and the data boards outside the display permission such as the contract data board are hidden. For the distribution data board displayed on the data display page, the distribution statistical data obtained by processing the distribution data resource is loaded from the data storage device. When the data is not ready, a loading animation is displayed in the distribution data board, and a background asynchronous calculation task is started to speed up the loading speed.

[0091] For example, for a data board with a high display frequency, the data to be displayed in the data board can be stored in a cache to reduce the computing power and data storage device pressure.

[0092] In a possible implementation, the method further includes: receiving an adjustment operation instruction, the adjustment operation instruction can include an operation instruction generated by a drag operation or a click operation performed by an operator; and based on the adjustment operation instruction, performing a shift, expansion or thumbnail operation on the data board on the data display page or the data displayed in the data board. For example, based on the adjustment operation instruction, the position of each data board can be adjusted, the secondary index data in the data board can be hidden, or the chart in the data board can be closed. In this embodiment, the data display page can be adjusted in time based on the adjustment operation instruction and the operation of the operator, and the data display page can be automatically configured based on the selection preference of the operator when displayed next time.

[0093] In an embodiment of the present application, by combining and arranging the data dashboards corresponding to each management device, it is possible to adapt to the monitoring needs of different operators, improve the intuitiveness of the data display page, and improve the loading performance of the data display page, thereby improving the flexibility and scenario adaptability of the supply chain monitoring method.

[0094] In an exemplary embodiment, the forecast status data in the provided method includes: a procurement completion forecast value.

[0095] based on Figure 2 The embodiment shown, as Figure 3 As shown in the figure, the process of dynamically analyzing and processing supply chain monitoring data to obtain forecast status data corresponding to the business scenario includes:

[0096] Step 302: Perform segmented statistical processing on the purchase data and supply data to obtain the purchase completion rate corresponding to each historical moment.

[0097] The time periods may be divided into monthly or quarterly periods, and the purchase data and supply data in each time period may be calculated to obtain the purchase completion rate corresponding to the historical moment at the end of each time period.

[0098] Step 304: Generate a purchase time series based on the completion rate of each purchase.

[0099] Among them, each purchase completion rate in the purchase time series is arranged according to time, and the last element in the purchase time series is the purchase completion rate corresponding to the latest historical moment.

[0100] Step 306 : Based on the preset procurement forecast model, autoregressive processing and error correction processing are performed on each procurement completion rate in the procurement time series to obtain an intermediate forecast value.

[0101] Among them, the autoregressive processing is to multiply each purchase completion rate in the purchase time series by the autoregressive coefficient in the purchase forecast model to obtain the forecast item; the error correction processing is to use the past forecast error multiplied by the moving average component coefficient in the purchase forecast model to obtain the correction item; the forecast item, the correction item and the offset item in the purchase forecast model are summed to obtain the intermediate forecast value, that is, the purchase completion forecast value one time period away from the latest historical moment.

[0102] Step 308: Add the intermediate prediction value to the procurement time series, repeat the process of obtaining the intermediate prediction value until the preset iteration condition is met, and output the intermediate prediction value as the procurement completion prediction value corresponding to the target time.

[0103] The iteration condition can be the number of forecast periods corresponding to the target time, where each forecast period is a time period. When repeatedly executing to obtain intermediate forecast values, the previous period's procurement completion forecast value is incorporated into the forecast process. The previous period's forecast error can be replaced with an estimated value or historical average error to obtain the procurement completion forecast value corresponding to the target time.

[0104] In an embodiment of the present application, dynamic analysis of at least one supply chain monitoring data is performed based on a time series, which can accurately obtain predicted status data corresponding to a future target moment, thereby improving the dynamic analysis effect of the business scenario.

[0105] In an exemplary embodiment, based on Figure 3 The embodiment shown, as Figure 4 As shown, the provided method also includes:

[0106] Step 402: Receive a data calibration instruction.

[0107] The data calibration instruction may be manually issued by an operator, generated periodically, or triggered when the error of the procurement forecast model exceeds a threshold.

[0108] Step 404 : Based on the data calibration instruction, the procurement forecast model is updated using the procurement data, supply data, and procurement time series to obtain an updated procurement forecast model.

[0109] Among them, the process of updating the procurement forecast model includes: re-estimating the autoregressive coefficient, moving average component coefficient and offset term based on procurement data, supply data and procurement time series to form an updated procurement forecast model.

[0110] Step 406 , compare the accuracy of the procurement forecast model and the updated procurement forecast model, and use the model with higher accuracy as the model for generating the procurement completion prediction value at the next target moment.

[0111] Among them, the mean absolute error, root mean square error or mean absolute percentage error can be used as evaluation indicators. A test set can be divided from the procurement data, supply data and procurement time series, and the prediction accuracy of the two models can be calculated respectively to determine the model that generates the procurement completion prediction value at the next target moment.

[0112] In some embodiments, a minimum threshold for accuracy improvement can be set, such as replacing the procurement forecast model with the updated procurement forecast model only when the accuracy of the updated procurement forecast model improves by more than 5%, to prevent frequent model switching.

[0113] In the supply chain monitoring method provided in this application, the forecast status data also includes the contract signing forecast value, the supply timeliness forecast value, the quality control qualified forecast value and the delivery timeliness forecast value. The method for obtaining each forecast status data refers to the above-mentioned method for obtaining the procurement completion forecast value, which will not be repeated here.

[0114] In an embodiment of the present application, after receiving the data calibration instruction, the prediction model is retrained based on the latest acquired supply chain monitoring data, a model with higher accuracy is selected to generate the prediction value for the next target moment, and the prediction model is dynamically updated to enhance the adaptability of the prediction model to the latest data and prevent model aging, thereby ensuring the validity and accuracy of the prediction status data corresponding to the business scenario.

[0115] In an exemplary embodiment, based on Figure 2 In the embodiment shown, the business scenario includes a material supply scenario, and the method also includes: determining a material supply reference value based on statistical status data corresponding to the material supply scenario in a historical period; determining a floating deviation value based on a scenario label corresponding to the material supply scenario; and obtaining a dynamic threshold value based on the material supply reference value and the floating deviation value.

[0116] For example, for the material supply scenario, the average of the material supply quantity in the historical period is used as the statistical status data to determine the material supply reference value, and the material supply reference value is determined to be 10%. If there is a scenario label of "promotion period" or "out of stock period", a floating deviation value of 3% is set, and the final dynamic threshold is calculated to be 10%±3%.

[0117] In the method provided in this application, business scenarios also include bulk procurement scenarios, contract fulfillment scenarios, and joint delivery scenarios. The process of determining the dynamic threshold in each business scenario refers to the process of obtaining the dynamic threshold in the above-mentioned material supply scenario, and will not be repeated here.

[0118] In one possible embodiment, the provided method also includes using a preset standard range to perform standard judgment processing on the statistical status data and the predicted status data. If the statistical status data and the predicted status data are both within the set standard range, the static thresholds corresponding to the statistical status data and the predicted status data are used to perform risk assessment respectively; if the statistical status data and the predicted status data are not within the standard range, the dynamic thresholds are used to perform risk assessment on the statistical status data and the predicted status data.

[0119] In an embodiment of the present application, a dynamic threshold is determined by combining the statistical status data within a historical period and the scenario label corresponding to the business scenario. The fluctuation range of the threshold can be flexibly adjusted in combination with historical conditions and current business scenarios, which helps to distinguish normal fluctuations in business scenarios from real risks, thereby improving the accuracy of risk assessment and the flexibility of supply chain monitoring to respond to different business scenarios.

[0120] In an exemplary embodiment, Figure 5 As shown, a supply chain monitoring method is provided, comprising:

[0121] Step 501: Obtain supply chain monitoring data related to the business scenario from a demand planning management device, a contract management device, a procurement management device, a quality control management device, a distribution management device, and a reverse logistics device.

[0122] The supply chain monitoring data includes at least one of demand data, procurement data, contract data, supply data, quality control data, distribution data and reverse material data.

[0123] Step 502 : Dynamically analyze and process the supply chain monitoring data to obtain statistical status data and forecast status data corresponding to the business scenario.

[0124] Among them, the prediction status data includes: procurement completion prediction value; step 502 further includes: performing segmented statistical processing on procurement data and supply data to obtain the procurement completion rate corresponding to each historical moment; generating a procurement time series according to each procurement completion rate; based on a preset procurement prediction model, performing autoregressive processing and error correction processing on each procurement completion rate in the procurement time series to obtain an intermediate prediction value; adding the intermediate prediction value to the procurement time series, repeating the process of obtaining the intermediate prediction value until the preset iteration condition is reached, and outputting the intermediate prediction value as the procurement completion prediction value corresponding to the target moment.

[0125] Step 503 , using dynamic thresholds to perform risk assessment on the statistical status data and the predicted status data, and determining the risk mechanism corresponding to the business scenario. The dynamic threshold is threshold data generated based on the statistical status data corresponding to the business scenario in the historical period.

[0126] Among them, the business scenario includes a material supply scenario, and the method also includes: determining a material supply reference value based on statistical status data corresponding to the material supply scenario in a historical period; determining a floating deviation value based on a scenario label corresponding to the material supply scenario; and obtaining a dynamic threshold based on the material supply reference value and the floating deviation value.

[0127] Step 504: Receive a display operation instruction.

[0128] Step 505: Based on the display operation instruction, determine the data display page corresponding to the display operation instruction.

[0129] Among them, the data display page includes at least one of a business scenario dashboard, a demand data dashboard, a procurement data dashboard, a contract data dashboard, a supply data dashboard, a distribution data dashboard and a reverse data dashboard. The business scenario dashboard includes statistical status data and forecast status data corresponding to the business scenario.

[0130] The method further includes at least one of the following:

[0131] Perform statistical processing on demand data according to demand items to obtain demand statistical data, and generate a demand data dashboard based on the demand statistical data;

[0132] Determine the contract subject data from the contract data based on the preset evaluation subject, perform cumulative calculations on the contract subject data to obtain contract statistical data corresponding to each evaluation subject, and generate a contract data dashboard based on the contract statistical data;

[0133] According to the categories of deployable resources, the distribution data is statistically processed to obtain distribution statistics, and a distribution data dashboard is generated based on the distribution statistics;

[0134] Based on the reverse material data, determine the material scrap disposal status, and generate a reverse data dashboard based on the material scrap disposal status.

[0135] Step 506: Receive a data calibration instruction.

[0136] Step 507 : Based on the data calibration instruction, the procurement forecast model is updated using the procurement data, supply data, and procurement time series to obtain an updated procurement forecast model.

[0137] Step 508 : Compare the accuracy of the procurement forecast model and the updated procurement forecast model, and use the model with higher accuracy as the model for generating the procurement completion prediction value at the next target moment.

[0138] It should be understood that, although the various steps in the flowcharts involved in the above embodiments are displayed in sequence according to the instructions of the arrows, these steps are not necessarily performed in sequence in the order indicated by the arrows. Unless clearly stated herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of the steps or stages in other steps or other steps. It is understandable that the various steps in different embodiments can be freely combined as needed, and the various non-contradictory schemes formed by the combination all fall within the scope of protection of this application.

[0139] In an exemplary embodiment, a supply chain system is provided, comprising a demand planning management device, a contract management device, a procurement management device, a quality control management device, a distribution management device, and a reverse logistics device;

[0140] The supply chain monitoring device is used to acquire supply chain monitoring data related to a business scenario from a demand plan management device, a contract management device, a procurement management device, a quality control management device, a distribution management device, and a reverse logistics device, the supply chain monitoring data including at least one of demand data, procurement data, contract data, supply data, quality control data, distribution data, and reverse logistics data; to perform dynamic analysis and processing on the supply chain monitoring data to obtain statistical state data and predicted state data corresponding to the business scenario; and to perform risk assessment on the statistical state data and the predicted state data by using a dynamic threshold value to determine a risk mechanism corresponding to the business scenario, the dynamic threshold value being threshold data generated according to the statistical state data corresponding to the business scenario in a historical period.

[0141] Based on the same inventive concept, the embodiments of the present application also provide a supply chain monitoring device for implementing the above-mentioned supply chain monitoring method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above-mentioned method, and therefore the specific limitations in one or more supply chain monitoring device embodiments provided below can be referred to the limitations of the supply chain monitoring method in the above, which will not be described here again.

[0142] In one exemplary embodiment, as shown in Figure 6 a supply chain monitoring device is provided, which includes a data acquisition module 602, a dynamic analysis module 604, and a risk assessment module 606, wherein:

[0143] The data acquisition module 602 is configured to acquire supply chain monitoring data related to a business scenario from a demand plan management device, a contract management device, a procurement management device, a quality control management device, a distribution management device, and a reverse logistics device, the supply chain monitoring data including at least one of demand data, procurement data, contract data, supply data, quality control data, distribution data, and reverse logistics data.

[0144] The dynamic analysis module 604 is configured to perform dynamic analysis and processing on the supply chain monitoring data to obtain statistical state data and predicted state data corresponding to the business scenario.

[0145] The risk assessment module 606 is configured to perform risk assessment on the statistical state data and the predicted state data by using a dynamic threshold value to determine a risk mechanism corresponding to the business scenario, the dynamic threshold value being threshold data generated according to the statistical state data corresponding to the business scenario in a historical period.

[0146] In one embodiment, the device also includes a dashboard generation module for performing at least one of the following: performing statistical processing on demand data according to demand items to obtain demand statistical data, and generating a demand data dashboard based on the demand statistical data; determining contract subject data from contract data based on preset evaluation subjects, performing cumulative operation processing on the contract subject data to obtain contract statistical data corresponding to each evaluation subject, and generating a contract data dashboard based on the contract statistical data; performing resource statistical processing on distribution data according to the category of deployable resources to obtain distribution statistical data, and generating a distribution data dashboard based on the distribution statistical data; determining the material scrap disposal status based on reverse material data, and generating a reverse data dashboard based on the material scrap disposal status.

[0147] In one embodiment, the device also includes: a data display module for receiving display operation instructions; based on the display operation instructions, determining the data display page corresponding to the display operation instruction, the data display page includes at least one of a business scenario dashboard, a demand data dashboard, a procurement data dashboard, a contract data dashboard, a supply data dashboard, a distribution data dashboard and a reverse data dashboard, and the business scenario dashboard includes statistical status data and forecast status data corresponding to the business scenario.

[0148] In one embodiment, the forecast status data includes: a procurement completion forecast value; a dynamic analysis module 604 is further used to perform segmented statistical processing on procurement data and supply data to obtain a procurement completion rate corresponding to each historical moment; a procurement time series is generated according to each procurement completion rate; based on a preset procurement forecast model, each procurement completion rate in the procurement time series is subjected to autoregressive processing and error correction processing to obtain an intermediate forecast value; the intermediate forecast value is added to the procurement time series, and the process of obtaining the intermediate forecast value is repeated until a preset iteration condition is reached, and the intermediate forecast value is output as the procurement completion forecast value corresponding to the target moment.

[0149] In one embodiment, the dynamic analysis module 604 is further used to receive data calibration instructions; based on the data calibration instructions, the procurement forecast model is updated using procurement data, supply data and procurement time series to obtain an updated procurement forecast model; the procurement forecast model and the updated procurement forecast model are compared in terms of accuracy, and the model with higher accuracy is used as the model for generating the procurement completion prediction value at the next target moment.

[0150] In one embodiment, the risk assessment module 606 is also used to determine the material supply reference value based on the statistical status data corresponding to the material supply scenario in the historical period; determine the floating deviation value based on the scenario label corresponding to the material supply scenario; and obtain a dynamic threshold value based on the material supply reference value and the floating deviation value.

[0151] Each module in the aforementioned supply chain monitoring device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor within a computer device in the form of hardware, or can be stored in a computer device memory in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0152] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 7 As shown. The computer device includes a processor, a memory, an input / output interface (I / O), and a communication interface. The processor, memory, and I / O interface are connected via a system bus, and the communication interface is connected to the system bus via the I / O interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store supply chain monitoring data such as demand data, procurement data, contract data, supply data, quality control data, distribution data, and reverse material data. The I / O interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, a supply chain monitoring method is implemented.

[0153] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0154] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0155] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0156] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0157] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0158] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0159] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0160] The above embodiments merely illustrate several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A supply chain monitoring method, characterized in that: The method comprises: Acquiring supply chain monitoring data related to the business scenario from a demand planning management device, a contract management device, a procurement management device, a quality control management device, a distribution management device, and a reverse logistics device, wherein the supply chain monitoring data includes at least one of demand data, procurement data, contract data, supply data, quality control data, distribution data, and reverse material data; Dynamically analyzing and processing the supply chain monitoring data to obtain statistical status data and forecast status data corresponding to the business scenario; The statistical status data and the predicted status data are risk assessed using a dynamic threshold to determine a risk mechanism corresponding to the business scenario. The dynamic threshold is threshold data generated based on the statistical status data corresponding to the business scenario in a historical period.

2. The method according to claim 1, characterized in that The method further comprises at least one of the following: Performing statistical processing on the demand data according to the demand items to obtain demand statistical data, and generating a demand data dashboard based on the demand statistical data; Determining contract subject data from the contract data based on preset evaluation subjects, performing cumulative calculation processing on the contract subject data to obtain contract statistical data corresponding to each evaluation subject, and generating a contract data dashboard based on the contract statistical data; Perform resource statistics processing on the delivery data according to the types of deployable resources to obtain delivery statistical data, and generate a delivery data dashboard based on the delivery statistical data; According to the reverse material data, the material scrapping disposal situation is determined, and a reverse data dashboard is generated according to the material scrapping disposal situation.

3. The method according to claim 2, characterized in that The method further comprises: receiving display operation instructions; Based on the display operation instruction, a data display page corresponding to the display operation instruction is determined, wherein the data display page includes at least one of a business scenario dashboard, the demand data dashboard, the procurement data dashboard, the contract data dashboard, the supply data dashboard, the distribution data dashboard and the reverse data dashboard, and the business scenario dashboard includes statistical status data and forecast status data corresponding to the business scenario.

4. The method according to claim 1, wherein The forecast status data includes: procurement completion forecast value; Dynamically analyzing and processing the supply chain monitoring data to obtain forecast status data corresponding to the business scenario, including: Performing segmented statistical processing on the purchase data and the supply data to obtain the purchase completion rate corresponding to each historical moment; generating a procurement time series according to each of the procurement completion rates; Based on a preset procurement forecasting model, autoregressive processing and error correction processing are performed on each procurement completion rate in the procurement time series to obtain an intermediate forecast value; The intermediate prediction value is added to the procurement time series, and the process of obtaining the intermediate prediction value is repeated until a preset iteration condition is reached, and the intermediate prediction value is output as the procurement completion prediction value corresponding to the target time.

5. The method according to claim 4, characterized in that The method further comprises: receiving data calibration instructions; Based on the data calibration instruction, the procurement forecast model is updated using the procurement data, the supply data, and the procurement time series to obtain an updated procurement forecast model; The procurement forecast model and the updated procurement forecast model are compared in terms of accuracy, and the model with higher accuracy is used as the model for generating the procurement completion forecast value at the next target moment.

6. The method according to claim 1, characterized in that The business scenario includes a material supply scenario, and the method further includes: Determine the material supply reference value based on the statistical status data corresponding to the material supply scenario in the historical period; Determine a floating deviation value according to a scenario label corresponding to the material supply scenario; The dynamic threshold is obtained based on the material supply reference value and the floating deviation value.

7. A supply chain system, characterized in that: The supply chain system includes: Demand planning management device, contract management device, procurement management device, quality control management device, distribution management device and reverse logistics device; A supply chain monitoring device is used to obtain supply chain monitoring data related to business scenarios from a demand planning management device, a contract management device, a procurement management device, a quality control management device, a distribution management device, and a reverse logistics device, wherein the supply chain monitoring data includes at least one of demand data, procurement data, contract data, supply data, quality control data, distribution data, and reverse material data; dynamically analyze and process the supply chain monitoring data to obtain statistical status data and forecast status data corresponding to the business scenario; and use a dynamic threshold value to perform a risk assessment on the statistical status data and the forecast status data to determine the risk mechanism corresponding to the business scenario, wherein the dynamic threshold value is threshold data generated based on the statistical status data corresponding to the business scenario within a historical period.

8. A supply chain monitoring device, characterized in that: The device comprises: a data acquisition module, configured to acquire supply chain monitoring data related to the business scenario from the demand planning management device, the contract management device, the procurement management device, the quality control management device, the distribution management device, and the reverse logistics device, wherein the supply chain monitoring data includes at least one of demand data, procurement data, contract data, supply data, quality control data, distribution data, and reverse material data; A dynamic analysis module, configured to dynamically analyze and process the supply chain monitoring data to obtain statistical status data and forecast status data corresponding to the business scenario; The risk assessment module is used to use dynamic thresholds to perform risk assessment on the statistical status data and the predicted status data to determine the risk mechanism corresponding to the business scenario. The dynamic threshold is threshold data generated based on the statistical status data corresponding to the business scenario in a historical period.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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