Inventory risk assessment method and device and electronic equipment
By acquiring and analyzing the maximum inventory level, current inventory level, sales data, and preset replenishment quantity of target products, and using a sales forecasting model to predict inventory risk, the problem of inaccurate inventory risk assessment in existing technologies is solved, and more efficient risk identification and assessment is achieved.
Patent Information
- Application Number
- CN202610166277.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-02-05
- Publication Date
- 2026-05-15
AI Technical Summary
Existing technologies are less accurate and less flexible in assessing inventory risk, relying solely on fixed inventory levels or changes in inventory levels over a period of time for notification, resulting in inaccurate assessments.
By obtaining the target product's maximum inventory level, current inventory level, sales data in the first time period, and the preset replenishment quantity in the second time period, the sales forecasting model is used to predict the second sales data. Combining the current inventory level, the preset replenishment quantity, and the maximum inventory level, the probability that the target product's inventory level will be 0 or exceed the maximum inventory level in the second time period is calculated, and the inventory risk is comprehensively assessed.
It improves the accuracy of inventory risk assessment, enabling earlier identification of stockout or warehouse overload risks, and provides more accurate inventory risk scores through multi-dimensional analysis.
Smart Images
Figure CN122048028A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of data analysis technology, specifically relating to an inventory risk assessment method, apparatus, and electronic equipment. Background Technology
[0002] Online sales with inventory may face the risk of stockouts or warehouse overload. Currently, notifications are only issued based on whether the inventory is less than a fixed quantity or exceeds a fixed quantity for a period of time. This is not flexible enough, and the predictive dimensions are too limited, resulting in low accuracy in assessing inventory risks. Summary of the Invention
[0003] The purpose of this application is to provide an inventory risk assessment method, apparatus, and electronic device that can solve the problem of low accuracy in assessing inventory risk using existing methods.
[0004] In a first aspect, embodiments of this application provide an inventory risk assessment method, the method comprising: The maximum inventory of the target product, the current inventory at the current moment, the first sales data in the first time period, and the preset replenishment quantity in the second time period are obtained. The current moment is the moment when the maximum inventory is obtained, the first time period is the time period before the current moment, and the second time period is the time period after the current moment. Based on the first sales data, predict the second sales data of the target product during the second time period; Based on the current inventory level, the second sales data, and the preset replenishment quantity, a first parameter is determined. The first parameter is used to indicate the probability that the target product will be in a first risk state during the second time period. The first risk state is the state where the inventory level of the target product is 0. Based on the current inventory level, the second sales data, the preset replenishment quantity, and the maximum inventory level, a second parameter is determined. The second parameter is used to indicate the probability that the target product will experience a second risk state during the second time period. The second risk state is the state in which the inventory level of the target product is greater than the maximum inventory level. Based on the first parameter and the second parameter, the inventory risk score of the target product during the second time period is obtained.
[0005] Secondly, embodiments of this application provide an inventory risk assessment device, the device comprising: The first acquisition module is used to acquire the maximum inventory of the target product, the current inventory at the current moment, the first sales data in the first time period, and the preset replenishment quantity in the second time period. The current moment is the moment when the maximum inventory is acquired, the first time period is the time period before the current moment, and the second time period is the time period after the current moment. The first prediction module is used to predict the second sales data of the target product in the second time period based on the first sales data; The first determining module determines a first parameter based on the current inventory level, the second sales data, and the preset replenishment level. The first parameter is used to indicate the probability that the target product will be in a first risk state during the second time period. The first risk state is the state where the inventory level of the target product is 0. The second determining module is used to determine a second parameter based on the current inventory level, the second sales data, the preset replenishment quantity, and the maximum inventory level. The second parameter is used to indicate the probability that the target product will be in a second risk state during the second time period. The second risk state is the state in which the inventory level of the target product is greater than the maximum inventory level. The third determining module is used to obtain the inventory risk score of the target product in the second time period based on the first parameter and the second parameter.
[0006] Thirdly, embodiments of this application provide an electronic device, which includes a processor and a memory. The memory stores programs or instructions that can run on the processor, and when the programs or instructions are executed by the processor, they implement the steps of the inventory risk assessment method as described in the first aspect.
[0007] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the inventory risk assessment method as described in the first aspect.
[0008] Fifthly, a computer program product is provided, including computer instructions that, when executed by a processor, implement the steps of the inventory risk assessment method as described in the first aspect.
[0009] In this embodiment, the maximum inventory level of the target product, the current inventory level at the current moment, the first sales data within a first time period, and the preset replenishment quantity within a second time period are obtained. The current moment is the moment when the maximum inventory level is obtained, the first time period is the time period before the current moment, and the second time period is the time period after the current moment. Based on the first sales data, the second sales data of the target product within the second time period is predicted. Based on the current inventory level, the second sales data, and the preset replenishment quantity, a first parameter is determined. The first parameter indicates the probability that the target product will experience a first risk state within the second time period, where the first risk state is a state where the inventory level of the target product is 0. Based on the current inventory level, the second sales data, the preset replenishment quantity, and the maximum inventory level, a second parameter is determined. The second parameter indicates the probability that the target product will experience a second risk state within the second time period, where the second risk state is a state where the inventory level of the target product is greater than the maximum inventory level. Based on the first parameter and the second parameter, an inventory risk score for the target product within the second time period is obtained. The method in this application analyzes the probability of the target product having an inventory level greater than the maximum inventory level in the second time period and the probability of the target product having an inventory level of 0, based on parameters from multiple dimensions such as the maximum inventory level, the current inventory level at the current moment, the first sales data in the first time period, and the preset replenishment level in the second time period. This results in an inventory risk score. Compared with the single prediction dimension of the prior art, the method in this application embodiment has higher accuracy in assessing inventory risk. Attached Figure Description
[0010] Figure 1 One of the flowcharts illustrating the inventory risk assessment method provided in this application embodiment; Figure 2 A second schematic flowchart illustrating the inventory risk assessment method provided in this application embodiment; Figure 3 This is a schematic diagram of the structure of the inventory risk assessment device provided in the embodiments of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation
[0011] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.
[0012] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.
[0013] The inventory risk assessment method provided in this application will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.
[0014] like Figure 1 As shown, the inventory risk assessment method of this application embodiment includes the following steps: Step 101: Obtain the maximum inventory of the target product, the current inventory at the current moment, the first sales data in the first time period, and the preset replenishment quantity in the second time period. The current moment is the moment when the maximum inventory is obtained, the first time period is the time period before the current moment, and the second time period is the time period after the current moment.
[0015] The maximum inventory level refers to the maximum quantity of the target product that can be stored in the target warehousing system. In essence, the maximum inventory level is determined based on the volume of the target product and the storage space of the warehousing system. For example, if a warehouse has a shelf with 10 storage locations, and each location can hold a maximum of one box of the target product, then the maximum inventory level of the target product in this warehouse is 10 boxes.
[0016] The current inventory level at the current moment and the first sales volume within the first time period can be determined by collecting the sales records of the target product within the first time period. The timestamp of the sales record can be accurate to the hour; the inventory unit (SKU) used in the sales record is the internationally recognized Global Trade Item Number-12 (GTIN-12) system, which is usually composed of letters, numbers, or symbols to identify the specific attributes of the product (such as color, size, packaging, etc.); the sales data included in the sales record can be obtained by capturing the event stream of the transaction system.
[0017] The preset replenishment quantity can be determined based on the procurement information of the target product. It refers to the quantity of goods for which replenishment / procurement has been initiated but have not yet arrived in the warehouse, and can be aggregated according to the expected arrival date. For example, if the target product is bottled water, and a total of 100 bottles were procured, with a second time period of three days, and the procured bottled water is sent to the warehouse during the second time period, the preset replenishment quantity could be: .
[0018] Step 102: Based on the first sales data, predict the second sales data of the target product in the second time period.
[0019] The second sales data can be considered to be the same as the first sales data. Taking mineral water as an example, the first sales data in the first time period is as follows: ; The predicted second sales data for the second time period is the same as the first sales data. The second sales data is as follows: .
[0020] The manually labeled adjustment factor can be obtained, and the product of the adjustment factor and the first sales data is used as the second sales data. For example, if the target product is mineral water, and the sales volume is predicted to increase in the second time period, the adjustment factor is 2, and the first sales data in the first time period is: ; The predicted second sales data for the second time period is the same as the first sales data. The second sales data is as follows: .
[0021] Step 103: Based on the current inventory level, the second sales data, and the preset replenishment level, determine a first parameter. The first parameter is used to indicate the probability that the target product will experience a first risk state during the second time period. The first risk state is the state where the inventory level of the target product is 0.
[0022] In this step, based on the current inventory level and the preset replenishment quantity, the theoretical quantity of the target product stored in the warehousing system during the second time period can be determined. The second sales data can then determine the quantity of the target product to be removed from the warehousing system during the second time period. For example, if the target product is bottled water, the current inventory is 100 bottles, and the preset replenishment quantity is: ; Without considering the sales of the target product, the theoretical quantity of the target product stored in the warehousing system during the second time period is: ; The second sales figure is: ; Based on the above data, the actual quantity of the target goods in the warehousing system during the second time period can be calculated as follows: ; This allows us to calculate the probability that the actual quantity of the target product in the warehousing system is 0. In the example above, the probability of the actual quantity of the target product being 0 is 1 / 3, and the first parameter is 1 / 3. It should be noted that an actual quantity of the target product in the warehousing system of 0 indicates a shortage of the target product, which is the first risk state.
[0023] Step 104: Based on the current inventory level, the second sales data, the preset replenishment quantity, the maximum inventory level, and the maximum inventory time, determine the second parameter. The second parameter is used to indicate the probability that the target product will experience a second risk state during the second time period. The second risk state is the state in which the inventory level of the target product is greater than the maximum inventory level.
[0024] In this step, based on the current inventory level, the second sales data, and the preset replenishment quantity, the probability that the quantity of the target product in the warehousing system exceeds the maximum inventory level of the target product in the warehousing system during the second time period is calculated. For example, in the above example, the actual quantity of the target product in the warehousing system during the second time period is: ; With a maximum inventory of 75 bottles, the probability that the quantity of the target product in the warehousing system exceeds the maximum inventory level for that product is 1 / 3, and the second parameter is also 1 / 3. It should be noted that if the actual quantity of the target product in the warehousing system exceeds the maximum inventory level, it indicates a warehouse overflow situation for the target product, placing it in the second risk state.
[0025] Step 105: Based on the first parameter and the second parameter, obtain the inventory risk score of the target product during the second time period.
[0026] By combining the first and second parameters, an inventory risk score can be derived, indicating the probability of the target product experiencing a first or second risk state within a second time period. For example, the inventory risk score is obtained by adding the first and second parameters together.
[0027] In the method of this application embodiment, based on parameters of multiple dimensions such as maximum inventory, current inventory at the current moment, first sales data in the first time period, and preset replenishment quantity in the second time period, the probability of the target product having an inventory greater than the maximum inventory in the second time period and / or the probability of the target product having an inventory of 0 is analyzed to obtain an inventory risk score. Compared with the single prediction dimension of the prior art, the method of this application embodiment has higher accuracy in inventory risk assessment.
[0028] Optionally, step 102, based on the first sales data, predicts the second sales data of the target product within the second time period, including: The first sales data is input into the sales prediction model to obtain the second sales data output by the sales prediction model. The sales forecasting model is a model obtained by training a preset model, and the training process of the preset model includes: Obtain a training dataset and the preset model. The training dataset includes multiple training data, each of which includes historical sales data of the product and future sales data corresponding to the historical sales data. The time period corresponding to the historical sales data is before the time period corresponding to the future sales data. The preset model is a recurrent neural network. The historical sales data in the training dataset is used as the input data of the preset model, and the future sales data corresponding to the historical sales data is used as the label to train the preset model to obtain the sales prediction data.
[0029] In this embodiment, the second sales data is predicted using a sales prediction model. The sales prediction model can be a Long Short-Term Memory (LSTM) network, including an input layer, a hidden layer, and an output layer. The input layer receives the sales data; the hidden layer includes multiple LSTM units to encode the sales data and capture patterns; the output layer processes the output from the hidden layer to achieve the final prediction. For example, the input layer includes 30 time steps (corresponding to 30 days of historical sales data); the hidden layer includes 64 LSTM units with dropout=0.2 (setting 20% of the LSTM unit outputs to 0 to prevent overfitting); the output layer outputs sales data for the next Rt days (the second sales data). .
[0030] In addition, for training data, Box-Cox can be used to eliminate heteroscedasticity in sales data, and MinMaxScaler can be used to normalize the data to the [0,1] interval before inputting it into the model for training.
[0031] In this embodiment, a network model is used to predict the second sales data, which is more efficient than manual calculation.
[0032] Optionally, step 103, based on the current inventory level, the second sales data, and the preset replenishment quantity, determines the first parameter, including: The sum of the current inventory and the preset replenishment quantity is determined as the second parameter; The ratio of the second sales data to the second parameter is determined as the third parameter; The third parameter is used as the base and the sales parameter is used as the exponent to calculate the exponential function value, which is then determined as the fourth parameter. The sales parameter is an integer and its value ranges from 0 to N-1, with N being the same as the second parameter. The value of the exponential function calculated using the natural base as the base and the opposite of the third parameter as the exponent is determined as the fifth parameter; The product of the fourth parameter and the fifth parameter is determined as the sixth parameter; The ratio of the factorial of the sixth parameter and the sales parameter is determined as the seventh parameter, which is used to indicate the probability that the inventory of the target product can meet the sales demand of the second sales data during the second time period. Based on the seventh parameter, the first parameter is determined, and the sum of the first parameter and the seventh parameter is 1.
[0033] In this embodiment, the first parameter is determined as follows: ; in, For the first parameter, The current inventory level, For the preset replenishment quantity, The sales parameter represents the total sales volume of the target product during the second time period, with a value range of (0-). ), This is the second sales data. For the second parameter, As the third parameter, The fourth parameter, The fifth parameter, The sixth parameter, This is the seventh parameter.
[0034] The seventh parameter uses the Poisson probability formula. This indicates the total sales volume of the target product during the second time period. The inventory of the target product is less than the inventory of the target product. The probability of ), can be understood as follows: the sum of "the probability that the total sales volume of the target product in the second time period is less than the inventory of the target product" and "the probability that the total sales volume of the target product in the second time period is greater than or equal to the inventory of the target product" is 1. , obtained This means "the probability that the total sales volume of the target product is greater than or equal to the inventory of the target product in the second time period", which is the probability that the inventory of the target product is 0.
[0035] The method described in this embodiment for calculating the probability of the occurrence of the first risk state can improve the accuracy of the calculation results.
[0036] Optionally, step 104 involves determining a second parameter based on the current inventory level, the second sales data, the preset replenishment quantity, and the maximum inventory level, including: The sum of the current inventory and the preset replenishment quantity is determined as the second parameter; The difference between the second parameter and the second sales data is determined as the eighth parameter; Based on the maximum inventory time of the target product, a ninth parameter is determined, wherein the ninth parameter is a first preset value when the maximum inventory time is less than a preset threshold, and the ninth parameter is a second preset value when the maximum inventory time is greater than or equal to the preset threshold, wherein the first preset value is greater than the second preset value. The second parameter is determined based on the maximum inventory level, the eighth parameter, and the ninth parameter.
[0037] The maximum inventory time can be understood as an inherent parameter of the target product, which indicates the maximum duration for which a target product can be stored in the warehousing system. When the storage time of the target product in the warehousing system exceeds the maximum inventory time, the target product needs to be removed from the warehousing system.
[0038] The following explanation is provided regarding maximum inventory time: For target products such as food, medicine, and cosmetics, the maximum storage time is the shelf life of the target product. If the storage time of the target product exceeds the shelf life, the target product will spoil or its safety will decrease, and it will no longer be able to be sold, so there is no need to continue storing it. For target products such as clothing and toys whose sales are related to their flowability, the maximum inventory time is also related to the popularity of the target product. For example, if a winter garment is difficult to sell in the summer, continuing to store it will affect the inventory of summer garments, and thus affect the sales of summer garments. Therefore, the maximum inventory time of winter garments can be set in advance, and they can be cleared out in time after the time expires.
[0039] The second parameter can be determined as follows: , , ; in, The current inventory level, For the preset replenishment quantity, This is the second sales data. The maximum inventory time, The maximum inventory level, For the second parameter, For the second parameter, The eighth parameter represents the remaining inventory quantity. The ninth parameter... The risk correction parameter is determined based on the value of the maximum inventory time; a longer maximum inventory time indicates lower risk, while a shorter maximum inventory time indicates higher risk.
[0040] The method described in this embodiment for calculating the probability of the occurrence of the second risk state can improve the accuracy of the calculation results.
[0041] Optionally, obtaining the inventory risk score of the target product during the second time period based on the first parameter and the second parameter includes: ; ; in, For the first parameter, For the second parameter, Score the inventory risk. As the first preset weight, This is the second preset weight. The specific values of the first and second preset weights can be adjusted according to the implementation of the method to improve the accuracy of inventory risk scoring.
[0042] Optionally, after step 102, the method further includes: Obtain the purchase cost and holding cost of the target product, wherein the purchase cost indicates the cost paid to purchase the target product, and the holding cost indicates the cost incurred in holding the target product; The product of the second sales data and the procurement cost is determined as the tenth parameter; The ratio of the tenth parameter to the holding cost is determined as the eleventh parameter; The product of the eleventh parameter and the preset order quantity coefficient is determined as the order quantity of the target product during the second time period.
[0043] The order quantity can be determined in the following ways: ; in, S represents the purchase cost, and H represents the holding cost. This is the tenth parameter. The eleventh parameter, This is a preset order quantity coefficient. Using the above method, the order quantity that minimizes the total cost when replenishing stock during the second time period can be calculated.
[0044] Optionally, after step 105, the method further includes: If the inventory risk score is greater than a preset value, an early warning message is generated and the preset replenishment quantity is adjusted.
[0045] If the inventory risk score is greater than the preset value, it indicates that the target product will be in the first or second risk state in the second time period. An early warning message will be generated to notify relevant personnel to analyze the specific risk situation. If there is a shortage, the preset replenishment quantity will be increased; if there is a warehouse overload, the preset replenishment quantity will be reduced, thereby reducing the probability of shortages or warehouse overload.
[0046] Optionally, after step 104, the method further includes: Obtain the preset discount rate, discount rate adjustment parameters, and second risk reference value; The difference between the second parameter and the second risk reference value is determined as the twelfth parameter; The product of the discount rate adjustment parameter and the twelfth parameter is determined as the thirteenth parameter; The sum of the thirteenth parameter and the preset discount rate is determined as the target discount rate; The price of the target product is adjusted based on the target discount rate.
[0047] In this embodiment, the sales discount rate can be determined as follows: ; in, For a preset discount rate, for example, take , Adjust the parameters for the discount rate, for example, set it to 5%. As a second risk reference value, for example, 0.7, is ( ) is the twelfth parameter, in When the value is 1, The thirteenth parameter, in When taking a non-1 value, This is the thirteenth parameter.
[0048] Adjusting the sales discount rate during the second time period using the methods described above is beneficial for increasing the sales volume of the target product.
[0049] Optionally, after step 102, the method further includes: Obtain the actual sales data for the second time period, and adjust the number of days in the second time period based on the actual sales data and the second sales data.
[0050] The adjusted number of days in the second time period can be: ; ; in, This indicates the number of days in the second time period before the adjustment. The adjusted second time period, This represents actual sales data. This indicates the second sales figure.
[0051] By adjusting the number of days in the second time period using the above method, we can ensure that the second sales data remains highly accurate within the corresponding number of days.
[0052] Combination Figure 2 The inventory risk assessment method of this application embodiment will be described in general as follows: When maintenance personnel access the warehouse monitoring system, the data acquisition module collects the maximum inventory level, maximum inventory time, current inventory level, and preset replenishment quantity of the target product stored in the inventory database, and the second sales data for the second time period calculated from the first sales data within the first time period from the sales database. The data acquisition module sends the current inventory level, second sales data, and preset replenishment quantity to the stockout scoring module; it also sends the current inventory level, second sales data, preset replenishment quantity, maximum inventory level, and maximum inventory time to the overstock scoring module. The stockout scoring module calculates a first parameter based on the current inventory level, second sales data, and preset replenishment quantity. The overstock scoring module calculates a second parameter based on the current inventory level, second sales data, preset replenishment quantity, maximum inventory level, and maximum inventory time. The risk fusion module determines the inventory risk score based on the first and second parameters, and the early warning module issues warning information and operational suggestions (such as adjusting the preset replenishment quantity) based on the inventory risk score.
[0053] like Figure 3As shown in the illustration, this application also provides an inventory risk assessment device 300, which includes: The first acquisition module 301 is used to acquire the maximum inventory of the target product, the current inventory at the current moment, the first sales data in the first time period, and the preset replenishment quantity in the second time period. The current moment is the moment when the maximum inventory is acquired, the first time period is the time period before the current moment, and the second time period is the time period after the current moment. The first prediction module 302 is used to predict the second sales data of the target product in the second time period based on the first sales data; The first determining module 303 is used to determine a first parameter based on the current inventory, the second sales data and the preset replenishment quantity. The first parameter is used to indicate the probability that the target product will be in a first risk state during the second time period. The first risk state is the state where the inventory of the target product is 0. The second determining module 304 is used to determine a second parameter based on the current inventory level, the second sales data, the preset replenishment level, and the maximum inventory level. The second parameter is used to indicate the probability that the target product will be in a second risk state during the second time period. The second risk state is the state in which the inventory level of the target product is greater than the maximum inventory level. The third determining module 305 is used to obtain the inventory risk score of the target product in the second time period based on the first parameter and the second parameter.
[0054] Optionally, the first prediction module 302 is also used for: The first sales data is input into the sales prediction model to obtain the second sales data output by the sales prediction model. The sales forecasting model is a model obtained by training a preset model, and the training process of the preset model includes: Obtain a training dataset and the preset model. The training dataset includes multiple training data, each of which includes historical sales data of the product and future sales data corresponding to the historical sales data. The time period corresponding to the historical sales data is before the time period corresponding to the future sales data. The preset model is a recurrent neural network. The historical sales data in the training dataset is used as the input data of the preset model, and the future sales data corresponding to the historical sales data is used as the label to train the preset model to obtain the sales prediction data.
[0055] Optionally, the first determining module 303 is further configured to: The sum of the current inventory and the preset replenishment quantity is determined as the second parameter; The ratio of the second sales data to the second parameter is determined as the third parameter; The third parameter is used as the base and the sales parameter is used as the exponent to calculate the exponential function value, which is then determined as the fourth parameter. The sales parameter is an integer and its value ranges from 0 to N-1, with N being the same as the second parameter. The value of the exponential function calculated using the natural base as the base and the opposite of the third parameter as the exponent is determined as the fifth parameter; The product of the fourth parameter and the fifth parameter is determined as the sixth parameter; The ratio of the factorial of the sixth parameter and the sales parameter is determined as the seventh parameter, which is used to indicate the probability that the inventory of the target product can meet the sales demand of the second sales data during the second time period. Based on the seventh parameter, the first parameter is determined, and the sum of the first parameter and the seventh parameter is 1.
[0056] Optionally, the first parameter is: , ； in, The current inventory level, For the preset replenishment quantity, This refers to the number of times the first risk state occurs within the second time period. This is the second sales data.
[0057] Optionally, the second determining module 304 is further configured to: Obtain the purchase cost and holding cost of the target product, wherein the purchase cost indicates the cost paid to purchase the target product, and the holding cost indicates the cost incurred in holding the target product; The product of the second sales data and the procurement cost is determined as the tenth parameter; The ratio of the tenth parameter to the holding cost is determined as the eleventh parameter; The product of the eleventh parameter and the preset order quantity coefficient is determined as the order quantity of the target product during the second time period.
[0058] Optionally, the second parameter is: , , ; in, The current inventory level, For the preset replenishment quantity, This is the second sales data. The maximum inventory time, This refers to the maximum inventory level.
[0059] Optionally, the inventory risk score is: ; ; in, For the first parameter, For the second parameter, Score the inventory risk. As the first preset weight, This is the second preset weight.
[0060] Optionally, the device 300 is also used for: Obtain the purchase cost and holding cost of the target product, wherein the purchase cost indicates the cost paid to purchase the target product, and the holding cost indicates the cost incurred in holding the target product; The product of the second sales data and the procurement cost is determined as the tenth parameter; The ratio of the tenth parameter to the holding cost is determined as the eleventh parameter; The product of the eleventh parameter and the preset order quantity coefficient is determined as the order quantity of the target product during the second time period.
[0061] Optionally, the device 300 is also used for: If the inventory risk score is greater than a preset value, an early warning message is generated and the preset replenishment quantity is adjusted.
[0062] Optionally, the device 300 is also used for: Obtain the preset discount rate, discount rate adjustment parameters, and second risk reference value; The difference between the second parameter and the second risk reference value is determined as the twelfth parameter; The product of the discount rate adjustment parameter and the twelfth parameter is determined as the thirteenth parameter; The sum of the thirteenth parameter and the preset discount rate is determined as the target discount rate; The price of the target product is adjusted based on the target discount rate.
[0063] It should be noted that the inventory risk assessment device 300 provided in this application embodiment can achieve the following: Figure 1The entire technical process of the inventory risk assessment method shown in the embodiment, and achieving the same technical effect, will not be repeated here to avoid duplication.
[0064] The inventory risk assessment device in this application embodiment can be an electronic device or a component of an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal or other devices besides a terminal. For example, the electronic device can be a mobile phone, tablet computer, laptop computer, PDA, in-vehicle electronic device, mobile internet device (MID), augmented reality (AR) / virtual reality (VR) device, robot, wearable device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc. Non-mobile electronic devices can also be servers, network attached storage (NAS), personal computers (PCs), televisions (TVs), ATMs, or self-service machines, etc. This application embodiment does not specifically limit the specific devices.
[0065] Optionally, such as Figure 4 As shown, this application embodiment also provides an electronic device 400, including a processor 401 and a memory 402. The memory 402 stores a program or instructions that can run on the processor 401. When the program or instructions are executed by the processor 401, they implement the various steps of the above-described inventory risk assessment method embodiment and can achieve the same technical effect. To avoid repetition, they will not be described again here.
[0066] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.
[0067] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described inventory risk assessment method embodiments and achieve the same technical effect. To avoid repetition, they will not be described again here.
[0068] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.
[0069] This application also provides a computer program product, including computer instructions, which, when executed by a processor, implement the above-described... Figure 1 The various processes of the inventory risk assessment method embodiment shown can achieve the same technical effect, and will not be described again here to avoid repetition.
[0070] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.
[0071] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.
[0072] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
Claims
1. An inventory risk assessment method, characterized in that, The method includes: The maximum inventory of the target product, the current inventory at the current moment, the first sales data in the first time period, and the preset replenishment quantity in the second time period are obtained. The current moment is the moment when the maximum inventory is obtained, the first time period is the time period before the current moment, and the second time period is the time period after the current moment. Based on the first sales data, predict the second sales data of the target product during the second time period; Based on the current inventory level, the second sales data, and the preset replenishment quantity, a first parameter is determined. The first parameter is used to indicate the probability that the target product will be in a first risk state during the second time period. The first risk state is the state where the inventory level of the target product is 0. Based on the current inventory level, the second sales data, the preset replenishment quantity, and the maximum inventory level, a second parameter is determined. The second parameter is used to indicate the probability that the target product will experience a second risk state during the second time period. The second risk state is the state in which the inventory level of the target product is greater than the maximum inventory level. Based on the first parameter and the second parameter, the inventory risk score of the target product during the second time period is obtained.
2. The method as described in claim 1, characterized in that, The step of determining the first parameter based on the current inventory level, the second sales data, and the preset replenishment quantity includes: The sum of the current inventory and the preset replenishment quantity is determined as the second parameter; The ratio of the second sales data to the second parameter is determined as the third parameter; The third parameter is used as the base and the sales parameter is used as the exponent to calculate the exponential function value, which is then determined as the fourth parameter. The sales parameter is an integer and its value ranges from 0 to N-1, with N being the same as the second parameter. The value of the exponential function calculated using the natural base as the base and the opposite of the third parameter as the exponent is determined as the fifth parameter; The product of the fourth parameter and the fifth parameter is determined as the sixth parameter; The ratio of the factorial of the sixth parameter and the sales parameter is determined as the seventh parameter, which is used to indicate the probability that the inventory of the target product can meet the sales demand of the second sales data during the second time period. Based on the seventh parameter, the first parameter is determined, and the sum of the first parameter and the seventh parameter is 1.
3. The method as described in claim 1, characterized in that, The determination of the second parameter based on the current inventory level, the second sales data, the preset replenishment quantity, and the maximum inventory level includes: The sum of the current inventory and the preset replenishment quantity is determined as the second parameter; The difference between the second parameter and the second sales data is determined as the eighth parameter; Based on the maximum inventory time of the target product, a ninth parameter is determined, wherein the ninth parameter is a first preset value when the maximum inventory time is less than a preset threshold, and the ninth parameter is a second preset value when the maximum inventory time is greater than or equal to the preset threshold, wherein the first preset value is greater than the second preset value. The second parameter is determined based on the maximum inventory level, the eighth parameter, and the ninth parameter.
4. The method according to any one of claims 1 to 3, characterized in that, After predicting the second sales data of the target product within the second time period based on the first sales data, the method further includes: Obtain the purchase cost and holding cost of the target product, wherein the purchase cost indicates the cost paid to purchase the target product, and the holding cost indicates the cost incurred in holding the target product; The product of the second sales data and the procurement cost is determined as the tenth parameter; The ratio of the tenth parameter to the holding cost is determined as the eleventh parameter; The product of the eleventh parameter and the preset order quantity coefficient is determined as the order quantity of the target product during the second time period.
5. The method according to any one of claims 1 to 3, characterized in that, After predicting the second sales data of the target product within the second time period based on the first sales data, the method further includes: If the inventory risk score is greater than a preset value, an early warning message is generated and the preset replenishment quantity is adjusted.
6. The method according to any one of claims 1 to 3, characterized in that, After determining the second parameter based on the current inventory level, the second sales data, the preset replenishment quantity, and the maximum inventory level, the method further includes: Obtain the preset discount rate, discount rate adjustment parameters, and second risk reference value; The difference between the second parameter and the second risk reference value is determined as the twelfth parameter; The product of the discount rate adjustment parameter and the twelfth parameter is determined as the thirteenth parameter; The sum of the thirteenth parameter and the preset discount rate is determined as the target discount rate; The price of the target product is adjusted based on the target discount rate.
7. An inventory risk assessment device, characterized in that, The device includes: The first acquisition module is used to acquire the maximum inventory of the target product, the current inventory at the current moment, the first sales data in the first time period, and the preset replenishment quantity in the second time period. The current moment is the moment when the maximum inventory is acquired, the first time period is the time period before the current moment, and the second time period is the time period after the current moment. The first prediction module is used to predict the second sales data of the target product in the second time period based on the first sales data; The first determining module determines a first parameter based on the current inventory level, the second sales data, and the preset replenishment level. The first parameter is used to indicate the probability that the target product will be in a first risk state during the second time period. The first risk state is the state where the inventory level of the target product is 0. The second determining module is used to determine a second parameter based on the current inventory level, the second sales data, the preset replenishment quantity, and the maximum inventory level. The second parameter is used to indicate the probability that the target product will be in a second risk state during the second time period. The second risk state is the state in which the inventory level of the target product is greater than the maximum inventory level. The third determining module is used to obtain the inventory risk score of the target product in the second time period based on the first parameter and the second parameter.
8. An electronic device, characterized in that, The electronic device includes a processor and a memory, the memory storing programs or instructions that can run on the processor, the programs or instructions being executed by the processor to implement the steps of the inventory risk assessment method as described in any one of claims 1 to 6.
9. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the inventory risk assessment method as described in any one of claims 1 to 6.
10. A computer program product, characterized in that, It includes computer instructions that, when executed by a processor, implement the steps of the inventory risk assessment method as described in any one of claims 1 to 6.