An e-commerce full-channel operation management system and method

Abstract

The application provides an e-commerce full-channel operation management system and method, and relates to the technical field of e-commerce management. The e-commerce full-channel operation management system comprises a monitoring acquisition unit, which is used for acquiring a sales emergency index corresponding to a target retail channel of a retail commodity under full-channel visitor monitoring; a sales prediction unit, which is used for performing sales prediction based on the sales emergency index and historical sales data corresponding to the target retail channel to obtain predicted sales; a deployment analysis unit, which is used for performing deployment demand calculation by using current inventory of the target retail channel and the predicted sales to obtain demand delivery quantity; and a personnel matching unit, which is used for performing personnel dimension matching according to the demand delivery quantity, carrier data of the retail commodity and the predicted sales to generate a personnel allocation scheme for operation management. The system and method provided by the application ensure the operation management efficiency of the retail commodity.

Description

Technical Field

[0001] This invention relates to the field of e-commerce management technology, and in particular to an e-commerce omnichannel operation management system and method. Background Technology

[0002] In today's rapidly developing e-commerce industry, consumers' shopping channels are becoming increasingly diversified, ranging from traditional e-commerce platforms to social media, live streaming platforms, and offline stores, making the consumer shopping journey more and more complex. E-commerce channels can include offline stores, live streaming media, and online stores on major e-commerce platforms.

[0003] In the existing e-commerce retail business operation and management process, due to the simultaneous existence of online and offline business, it is not possible to effectively detect the surge in sales signals that occur online and / or offline. As a result, the responsiveness of operational mechanisms such as product inventory, service coordination, and supply and distribution cannot keep pace with the frequently changing customer demands. Consequently, the efficiency of retail product operation and management is greatly reduced, a large number of sales opportunities are missed, product sales drop significantly, and customer churn occurs. Summary of the Invention

[0004] This invention provides an e-commerce omnichannel operation management system and method to solve the technical problem that, due to the simultaneous existence of online and offline businesses, it is difficult to promptly detect surges in sales signals both online and / or offline. This results in the inability of operational mechanisms such as inventory, customer service, and supply and distribution to keep pace with frequently changing customer demands, leading to a significant reduction in the efficiency of retail operations and management, missed sales opportunities, and a substantial decrease in sales volume.

[0005] To achieve the above and other related objectives, this invention provides an e-commerce omnichannel operation and management system, comprising: a monitoring and acquisition unit for acquiring the sales emergency index of retail goods corresponding to the target retail channel under omnichannel visitor monitoring; a sales forecasting unit for forecasting sales based on the sales emergency index and historical sales data corresponding to the target retail channel to obtain forecasted sales; an allocation and analysis unit for calculating allocation demand using the current inventory and forecasted sales of the target retail channel to obtain the required allocation quantity; and a personnel matching unit for matching personnel across various dimensions based on the required allocation quantity, the vehicle data of the retail goods, and the forecasted sales to generate a personnel allocation plan for operation and management.

[0006] In one embodiment of the present invention, the monitoring and acquisition unit includes: a customer flow acquisition subunit, used to acquire real-time visitor traffic under each retail channel; a difference calculation subunit, used to calculate the difference between the real-time visitor traffic corresponding to each retail channel and the corresponding baseline traffic to obtain the visitor excess amount; a range identification subunit, used to identify the corresponding retail channel as the target retail channel when the visitor excess amount is greater than the lower limit of the corresponding retail channel, and obtain the corresponding current excess range according to the visitor excess amount and excess range division table corresponding to the target retail channel, the excess range division table including multiple excess ranges; and an index calculation subunit, used to acquire the sales emergency index corresponding to the target retail channel of the retail product under the omnichannel visitor monitoring according to the current excess range; the calculation formula for the current excess range is: ;in, This indicates the length of the range corresponding to the current exceedance range. This indicates real-time visitor traffic. Indicates the baseline flow rate. This indicates the previous range of exceeding the standard.

[0007] In one embodiment of the present invention, the retail channels include offline store channels, online store channels, and live streaming channels. Real-time visitor traffic includes store traffic corresponding to offline store channels, online store browsing traffic corresponding to online store channels, and live streaming browsing traffic corresponding to live streaming channels. The customer traffic acquisition subunit includes: a first customer traffic acquisition module, used to acquire an image of the retail goods storage area of ​​the store corresponding to the offline store channel, perform facial scanning on the image of the retail goods storage area to obtain a first number of users within a preset time, and calculate the first unit time average number of users within the preset time based on the first number of users and the preset time, as the store traffic under the offline store channel; a second customer traffic acquisition module, used to acquire... The system retrieves browsing records of retail products from online stores within a preset time period. It then filters for duplicate users based on these records to obtain the second number of users within the preset time period. Based on this second number of users and the preset time period, it calculates the average number of users per unit time within the second time period, which is used as the online store browsing traffic for the online store channel. Finally, the system acquires a third user traffic module, which retrieves viewing data of retail products from live streaming channels within a preset time period. It then filters for duplicate users based on these viewing data to obtain the third number of users within the preset time period. Based on this third number of users and the preset time period, it calculates the average number of users per unit time within the third time period, which is used as the live streaming browsing traffic for the live streaming channel.

[0008] In one embodiment of the present invention, the sales forecasting unit includes: a linear forecasting subunit, configured to linearly forecast sales within the forecasting period based on first historical sales data within a historical marketing period prior to the forecasting period, to obtain an initial sales forecast value; a historical detection subunit, configured to detect second historical sales data corresponding to the forecasting period within the historical sales data; a first forecasting subunit, configured to, when second historical sales data is detected, obtain a sales adjustment value based on the second historical sales data, historical sales data prior to the second historical sales data, and the initial sales forecast value; and obtain a first forecasted sales volume based on a sales emergency index, the initial sales forecast value, and the sales adjustment value; a second forecasting subunit, configured to, when second historical sales data is not detected, obtain a second forecasted sales volume based on a sales emergency index and the initial sales forecast value; and a comprehensive output subunit, configured to obtain a forecasted sales volume based on the first forecasted sales volume and the second forecasted sales volume.

[0009] In one embodiment of the present invention, the first prediction subunit includes: a historical prediction module, used to linearly predict sales within a historical period based on historical sales data to obtain an initial value for historical sales prediction; a sales adjustment module, used to adjust the total sales value corresponding to the initial value for sales prediction based on second historical sales data and the initial value for historical sales prediction to obtain a sales adjustment value; an index lookup module, used to look up a table to obtain a daily sales index based on the initial value for sales prediction and the sales adjustment value; a first adjustment calculation module, used to obtain a first sales adjustment amount based on a sales emergency index, a first adjustment coefficient corresponding to the sales emergency index, a daily sales index, and a second adjustment coefficient corresponding to the daily sales index; and a first prediction adjustment module, used to adjust the sales adjustment value using the first sales adjustment amount to obtain a first predicted sales volume; the calculation formula for the first predicted sales volume is: ; This indicates the first predicted sales volume. This indicates the second historical sales data. This represents the initial value of sales forecasts for each year. This represents the initial value of the sales forecast. This indicates the sales emergency index. This represents the daily sales index. This represents the first adjustment coefficient. This represents the second adjustment coefficient.

[0010] In one embodiment of the present invention, the second prediction subunit includes: a second adjustment calculation module, used to obtain a second sales adjustment amount based on a sales emergency index and a first adjustment coefficient corresponding to the sales emergency index; and a second prediction adjustment module, used to adjust the initial value of the sales prediction using the second sales adjustment amount to obtain a second predicted sales amount; the calculation formula for the second predicted sales amount is: ; This indicates the second projected sales volume. This represents the initial value of the sales forecast. This indicates the sales emergency index. This represents the first adjustment coefficient.

[0011] In one embodiment of the present invention, the personnel matching unit includes: a first generation subunit, used to generate sales personnel allocation quantity and distribution personnel allocation quantity based on predicted sales volume; a second generation subunit, used to generate vehicle allocation quantity and distribution personnel allocation quantity based on demand transfer quantity and vehicle data of retail goods; an additional allocation calculation subunit, used to obtain additional personnel data based on sales personnel allocation quantity, distribution personnel allocation quantity, vehicle allocation quantity, distribution personnel allocation quantity and current personnel allocation data of the target retail channel; and a scheme generation subunit, used to generate a personnel allocation scheme based on the additional personnel data for operational management.

[0012] In one embodiment of the present invention, the first generation subunit includes: an extraction module for extracting the sales allocation coefficient corresponding to the retail product; a sales allocation module for generating the sales personnel allocation quantity based on the predicted sales volume and the sales allocation coefficient; and a distribution allocation module for generating the distribution personnel allocation quantity based on the distribution allocation coefficient corresponding to a single retail product.

[0013] In one embodiment of the present invention, the second generation subunit includes: a screening module for screening retail goods for vehicle occupancy in vehicle data to obtain an unoccupied available vehicle threshold; a vehicle allocation module for obtaining a vehicle allocation quantity based on the demand for goods transfer and the maximum capacity corresponding to each vehicle; and a goods transfer allocation module for obtaining a goods transfer personnel allocation quantity based on the vehicle allocation quantity and the personnel maintenance allocation quantity corresponding to each vehicle.

[0014] To achieve the above and other related objectives, the present invention also provides an e-commerce omnichannel operation management method, comprising: acquiring the sales emergency index of retail goods corresponding to the target retail channel under omnichannel visitor monitoring through a monitoring acquisition unit; predicting sales volume based on the sales emergency index and historical sales data corresponding to the target retail channel through a sales forecasting unit to obtain predicted sales volume; calculating allocation demand using the current inventory and predicted sales volume corresponding to the target retail channel through an allocation analysis unit to obtain the required allocation quantity; and matching personnel according to the required allocation quantity, the vehicle data of retail goods, and the predicted sales volume through a personnel matching unit to generate a personnel allocation plan for operation management.

[0015] The beneficial effects of this invention are as follows: The e-commerce omnichannel operation management system and method proposed in this invention obtains the sales emergency index corresponding to the target retail channel. Then, taking into account the sales emergency index that may lead to a surge in sales due to increased visitor traffic, and combining it with the historical sales data of the target retail channel, a comprehensive and accurate prediction of product sales can be achieved. The predicted sales volume is then combined with the current inventory of the target retail channel to calculate the demand for restocking. The demand for restocking, retail product carrier data, and predicted sales are combined and analyzed to achieve personnel allocation across various personnel dimensions, generating a rational and reliable personnel allocation plan. This ensures the timeliness and reliability of goods and personnel allocation and control in e-commerce omnichannel operation management, and guarantees the efficiency of goods supply under the drive of emergency customer traffic. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0017] In the attached diagram: Figure 1 A structural block diagram of the e-commerce omnichannel operation and management system provided in this embodiment of the invention; Figure 2 The diagram shown is a flowchart illustrating an e-commerce omnichannel operation management method according to an embodiment of the present invention.

[0018] The attached figures are labeled as follows: Monitoring and acquisition unit 111; sales forecasting unit 112; allocation and analysis unit 113; personnel matching unit 114. Detailed Implementation

[0019] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0020] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. The drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0021] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0022] Please see Figure 1 This invention provides an e-commerce omnichannel operation management system, comprising: a monitoring and acquisition unit 111, used to acquire the sales emergency index of retail goods corresponding to the target retail channel under omnichannel visitor monitoring; a sales forecasting unit 112, used to forecast sales based on the sales emergency index and historical sales data corresponding to the target retail channel, to obtain the forecasted sales volume; an allocation and analysis unit 113, used to calculate the allocation demand using the current inventory and forecasted sales volume corresponding to the target retail channel, to obtain the required allocation quantity; and a personnel matching unit 114, used to match personnel according to the required allocation quantity, the vehicle data of retail goods, and the forecasted sales volume, to generate a personnel allocation plan for operation management.

[0023] As can be seen from the above, in the process of omnichannel operation and management of e-commerce retail goods, omnichannel visitor monitoring can be used to determine whether factors affecting the predicted sales volume exist. If so, the sales emergency index corresponding to the target retail channel can be obtained through acquisition unit 111. Then, the sales prediction unit 112 takes into account the sales emergency index that may lead to a surge in sales due to increased visitor numbers, and combines it with the historical sales data of the target retail channel to achieve a comprehensive and accurate prediction of product sales. The predicted sales volume is then combined with the current inventory of the target retail channel by allocation and analysis unit 113 to calculate the demand for restocking for the target retail channel. Finally, the personnel matching unit 114 combines the demand for restocking, the carrier data of retail goods, and the predicted sales volume for analysis, realizing the allocation of personnel in various dimensions and generating a rational and reliable personnel allocation plan. This ensures the timeliness and reliability of goods and personnel allocation and control in omnichannel operation and management of e-commerce, and guarantees the efficiency of goods supply under the emergency drive of customer traffic.

[0024] In the e-commerce omnichannel operation management system of the present invention, the monitoring and acquisition unit 111 may further include: a customer flow acquisition subunit, used to acquire real-time visitor traffic under each retail channel; a difference calculation subunit, used to calculate the difference between the real-time visitor traffic corresponding to each retail channel and the corresponding benchmark traffic to obtain the visitor excess amount; a range identification subunit, used to identify the corresponding retail channel as the target retail channel when the visitor excess amount is greater than the lower limit of the corresponding retail channel, and obtain the corresponding current excess range according to the visitor excess amount and excess range division table corresponding to the target retail channel, the excess range division table including multiple excess ranges; and an index calculation subunit, used to acquire the sales emergency index corresponding to the target retail channel of the retail product under omnichannel visitor monitoring according to the current excess range.

[0025] The formula for calculating the current range of exceedance is: ; in, This indicates the length of the range corresponding to the current exceedance range. This indicates real-time visitor traffic. Indicates the baseline flow rate. This indicates the previous range of exceeding the standard.

[0026] In calculating the sales emergency index corresponding to the target retail channel, the real-time visitor traffic monitored for each retail channel can be obtained first through the customer traffic acquisition sub-unit. Then, the real-time visitor traffic corresponding to each retail channel can be calculated through the difference calculation sub-unit. Baseline traffic corresponding to the relevant retail channels Perform interpolation to calculate the excess visitor volume when real-time visitor traffic exceeds the baseline traffic for the corresponding retail channel. Next, the range identification sub-unit can identify the target retail channel when the number of visitors exceeding the lower limit of the corresponding retail channel. Then, the number of visitors exceeding the target retail channel is further divided into its corresponding range using an exceedance range classification table, which is then used as the current exceedance range. Specifically, in determining the current exceedance range, the exceedance ranges in the exceedance range classification table can be arranged in ascending order, and then sequentially stacked. When the stacking reaches the [number of ranges], the [number of ranges] is calculated. When there are multiple exceedance ranges, the total length is obtained based on the length of each exceedance range. This can be achieved by exceeding the visitor limit. With total length Perform difference calculations to obtain the length difference. Then calculate the length difference obtained each time. And the next range of exceeding the standard, that is, the first Exceeding the standard range When comparing sizes, when the length difference... Greater than zero and less than the limit range If so, then the range exceeding the standard can be taken as the current range exceeding the standard. The current excess range is used as the range length to complete the search for the current excess range. Furthermore, after finding the current excess range, the index calculation subunit can use the relationship table established by the empirical values ​​between the excess range and the sales emergency index to find the corresponding sales emergency index, which is the sales emergency index corresponding to the target retail channel.

[0027] Preferably, the retail channels may include offline store channels, online store channels, and live streaming channels, and the real-time visitor traffic may further include store traffic corresponding to offline store channels, online store browsing traffic corresponding to online store channels, and live streaming browsing traffic corresponding to live streaming channels.

[0028] In the monitoring acquisition unit 111, the customer flow acquisition subunit may further include: a first customer flow acquisition module, used to acquire images of the retail goods storage area of ​​the corresponding offline store channel, perform facial scanning on the retail goods storage area images to obtain the first number of users within a preset time period, and calculate the first unit time average number of users within the preset time period based on the first number of users and the preset time period, as the store customer flow under the offline store channel; a second customer flow acquisition module, used to acquire browsing records of the corresponding online store channel retail goods within a preset time period, perform non-duplicate user filtering on the browsing records to obtain the second number of users within the preset time period, and calculate the second unit time average number of users within the preset time period based on the second number of users and the preset time period, as the online store browsing customer flow under the online store channel; and a third customer flow acquisition module, used to acquire viewing data of the corresponding live streaming channel retail goods within a preset time period, perform non-duplicate user filtering on the viewing data to obtain the third number of users within the preset time period, and calculate the third unit time average number of users within the preset time period based on the third number of users and the preset time period, as the live streaming browsing customer flow under the live streaming channel.

[0029] In the process of acquiring real-time visitor traffic across various retail channels, the first-line customer traffic acquisition module can obtain images of the retail merchandise storage areas of corresponding offline stores based on the placement of retail goods. Furthermore, by scanning these images with facial images, if the image matches the same user within a preset time period, the number of users within that time period is counted to determine the first user count within that preset time period. Then, the number of the first user... and preset time Perform a division operation to obtain the average number of users per unit time within the preset time period. This serves as a measure of store traffic within offline retail channels. Furthermore, the second customer traffic acquisition module can obtain browsing records of retail products from online stores within a preset time period. These browsing records can then be filtered for distinct users to determine the number of second users corresponding to each unique user within the preset time period. Then based on the second number of users and preset time The average number of users per second unit of time within the preset time period is obtained by division. Additionally, a third-party user acquisition module can be used to obtain viewing data for retail products on the live streaming channel within a preset time period. By filtering the viewing data for non-duplicate users, the number of non-duplicate third-party users within the preset time period can be determined. Based on the number of third-party users and preset time The average number of users in the third unit of time within the preset time period was calculated. By proactively filtering out duplicate users, the authenticity of the user group appearing within a preset time period can be effectively guaranteed, thereby improving the accuracy of the sales emergency index calculation.

[0030] In the e-commerce omnichannel operation management system of the present invention, the sales forecasting unit 112 may further include: a linear forecasting subunit, used to linearly forecast the sales volume within the forecasting period based on the first historical sales data within the historical marketing period prior to the forecasting period, to obtain an initial value for sales forecast; a historical detection subunit, used to detect the second historical sales data corresponding to the forecasting period in the historical sales data; a first forecasting subunit, used to obtain a sales adjustment value based on the second historical sales data, the historical sales data prior to the second historical sales data, and the initial value for sales forecast when the existence of the second historical sales data is detected; and to obtain a first forecasted sales volume based on the sales emergency index, the initial value for sales forecast, and the sales adjustment value; a second forecasting subunit, used to obtain a second forecasted sales volume based on the sales emergency index and the initial value for sales forecast when the second historical sales data is not detected; and a comprehensive output subunit, used to obtain the forecasted sales volume based on the first forecasted sales volume and the second forecasted sales volume.

[0031] In the process of sales forecasting using the sales emergency index, the linear forecasting subunit first generates a trend chart using the first historical sales data from the historical marketing period preceding the forecast period corresponding to the predicted sales volume. Based on this trend chart, a linear forecast of sales within the forecast period is performed, yielding the initial sales forecast value corresponding to the first historical sales data. Then, the historical detection subunit performs an existence check on the second historical sales data corresponding to the forecast period from the historical sales data of previous years. If the second historical sales data is detected, the first forecasting subunit calculates the sales adjustment value by combining the second historical sales data with the historical sales data preceding it and the initial sales forecast value. Subsequently, the sales emergency index, the initial sales forecast value, and the sales adjustment value are used to further calculate the first predicted sales volume. If the second historical sales data is not detected, the second forecasting subunit directly calculates the corresponding second predicted sales volume using the sales emergency index and the initial sales forecast value. Finally, the comprehensive output subunit outputs either the first predicted sales volume when the second historical sales data is detected, or the second predicted sales volume when the second historical sales data is not detected.

[0032] In the sales forecasting unit 112, the first forecasting subunit may further include: a historical forecasting module, used to linearly forecast sales within a historical period based on historical sales data to obtain an initial value for historical sales forecast; a sales adjustment module, used to adjust the total sales value corresponding to the initial value for sales forecast based on second historical sales data and the initial value for historical sales forecast to obtain a sales adjustment value; an index lookup module, used to look up a table to obtain a daily sales index based on the initial value for sales forecast and the sales adjustment value; a first adjustment calculation module, used to obtain a first sales adjustment amount based on a sales emergency index, a first adjustment coefficient corresponding to the sales emergency index, a daily sales index, and a second adjustment coefficient corresponding to the daily sales index; and a first forecast adjustment module, used to adjust the sales adjustment value through the first sales adjustment amount to obtain a first forecast sales volume.

[0033] The formula for calculating the first predicted sales volume is: ; This indicates the first predicted sales volume. This indicates the second historical sales data. This represents the initial value of sales forecasts for each year. This represents the initial value of the sales forecast. This indicates the sales emergency index. This represents the daily sales index. This represents the first adjustment coefficient. This represents the second adjustment coefficient.

[0034] In the process of calculating the first predicted sales volume when a second historical sales data is detected, a trend chart can be generated using historical sales data from previous years through the historical forecast module. Based on the trend chart, a linear forecast of sales volume within the historical period can be performed, thereby obtaining the initial value of the historical sales volume forecast. For example, linear forecasting can be performed using the least squares method; other linear forecasting methods could also be used. Then, the sales adjustment module uses second-generation historical sales data. Initial values ​​of sales forecasts for previous years Initial value of sales forecast The corresponding total sales value is adjusted to calculate the sales adjustment value. Then, using the index lookup table module, the daily sales index corresponding to the current initial sales forecast value and sales adjustment value is obtained from the correspondence table between the initial sales forecast value, the sales adjustment value, and the daily sales index. Therefore, the sales emergency index can be utilized through the first adjustment calculation module. and daily sales index Combined with the first adjustment coefficient corresponding to the sales emergency index The second adjustment coefficient corresponding to the daily sales index This allows for the calculation of all impacts of the initial and adjusted sales forecast values ​​on the predicted sales volume, thus deriving the first sales adjustment amount. This ensures that the calculation of the first predicted sales volume takes into account multiple factors, guaranteeing the accuracy of the calculation. Furthermore, the first predicted sales volume adjustment module can ultimately adjust the sales volume adjustment value using the calculated first sales volume adjustment amount, i.e., through the calculation formula. To ultimately calculate the corresponding first predicted sales volume. Among them, the sales emergency index and daily sales index All values ​​were pre-calibrated based on experience.

[0035] In addition, in the sales forecasting unit 112, the second forecasting subunit may further include: a second adjustment calculation module, used to obtain a second sales adjustment amount based on the sales emergency index and the first adjustment coefficient corresponding to the sales emergency index; and a second forecast adjustment module, used to adjust the initial value of the sales forecast through the second sales adjustment amount to obtain the second forecast sales.

[0036] The second formula for calculating predicted sales is: ; This indicates the second projected sales volume. This represents the initial value of the sales forecast. This indicates the sales emergency index. This represents the first adjustment coefficient.

[0037] When calculating and determining the second predicted sales volume when no second historical sales data is detected, the sales emergency index can be used first through the second adjustment calculation module. and sales emergency index The corresponding first adjustment coefficient This allows for the calculation of the sales adjustment amount that is only affected by the sales emergency index, in order to obtain the second sales adjustment amount. Then, the second sales volume adjustment can be utilized through the second prediction adjustment module. Directly to the initial value of sales forecast Adjustment is made, that is, through calculation formulas. The second predicted sales volume was calculated. .

[0038] In the e-commerce omnichannel operation management system of the present invention, the personnel matching unit 114 may further include: a first generation subunit, used to generate sales personnel allocation quantity and dispatch personnel allocation quantity based on predicted sales volume; a second generation subunit, used to generate vehicle allocation quantity and dispatch personnel allocation quantity based on demand transfer quantity and vehicle data of retail goods; an additional allocation calculation subunit, used to obtain additional personnel data based on sales personnel allocation quantity, dispatch personnel allocation quantity, vehicle allocation quantity, dispatch personnel allocation quantity and current personnel allocation data of the target retail channel; and a scheme generation subunit, used to generate personnel allocation scheme based on additional personnel data for operation management.

[0039] In the process of classifying personnel during operations management, the first generation subunit can calculate the allocation of sales and distribution personnel using the predicted sales volume of relevant retail goods. Simultaneously, the second generation subunit can calculate the allocation of vehicles and distribution personnel using the demand for retail goods and vehicle data. Then, the additional allocation calculation subunit combines the allocation of sales, distribution, vehicles, and distribution personnel with the current personnel allocation data of the target retail channel to calculate the data for various additional personnel. Finally, the solution generation subunit generates personnel allocation plans based on the additional personnel data, enabling timely responses to surges in retail goods operations management.

[0040] In the personnel matching unit 114, the first generation subunit may further include: an extraction module for extracting the sales allocation coefficient corresponding to the retail product; a sales allocation module for generating the sales personnel allocation quantity based on the predicted sales volume and the sales allocation coefficient; and a distribution allocation module for generating the distribution personnel allocation quantity based on the distribution allocation coefficient corresponding to a single retail product.

[0041] When calculating the allocation of sales personnel and distribution personnel, the sales allocation coefficient corresponding to the relevant retail products can be extracted first through the extraction module. Extract the data, and then use the sales allocation module to utilize the predicted sales volume. and sales allocation coefficient Through calculation formula This module calculates the distribution volume for sales personnel. To further manage the distribution of retail goods, it also allows for the use of a distribution module based on the distribution coefficient corresponding to each individual retail item. and predicted sales Through calculation formula This function calculates the number of personnel to be assigned to distribution. Specifically, it calculates the sales allocation coefficient. The distribution coefficient can be flexibly set according to the unit labor demand corresponding to different retail products. Similarly, the required number of workers per unit can be flexibly set based on the difficulty of packaging and transporting individual retail items.

[0042] In the personnel matching unit 114, the second generation subunit may further include: a screening module, used to screen the vehicle data for vehicle occupancy of retail goods to obtain the threshold of unoccupied available vehicles; a vehicle allocation module, used to obtain the vehicle allocation quantity based on the demand for goods transfer and the maximum load capacity corresponding to each vehicle; and a goods transfer allocation module, used to obtain the goods transfer personnel allocation quantity based on the vehicle allocation quantity and the personnel maintenance allocation quantity corresponding to each vehicle.

[0043] When calculating the allocation of vehicles and personnel for goods transfer, the following steps can be taken: First, the vehicle data corresponding to the retail goods can be queried through the filtering module. Then, the occupied vehicles can be identified and removed from the vehicle data to obtain the threshold of unoccupied available vehicles. The allocation quantity is determined by the required goods transfer quantity and the maximum load capacity of each vehicle in the vehicle allocation module.

[0044] In the specific calculation of vehicle allocation, the current inventory corresponding to the target retail channel can be used first through the allocation analysis unit 113. and predicted sales Calculate the allocation demand to obtain the required amount of goods to be allocated. The number of cargo batches can be determined based on the required cargo volume and the maximum load capacity of each vehicle. Based on the number of cargo batches The corresponding number of cargo batches can be found by referring to the table comparing the number of cargo batches with the baseline vehicle allocation. The corresponding baseline vehicle allocation amount, and when the baseline vehicle allocation amount is less than the available vehicle threshold, the found baseline vehicle allocation amount can be directly used as the vehicle allocation amount. However, when the baseline vehicle allocation is greater than the available vehicle threshold, the available vehicle threshold can be used as the vehicle allocation amount due to insufficient available vehicles. This is to maximize the guarantee of cargo allocation needs.

[0045] In addition, after calculating the vehicle allocation quantity, the personnel maintenance allocation quantity corresponding to each vehicle can be further utilized based on the cargo allocation module. Combined with the corresponding vehicle allocation Through calculation formula To allocate quantities to personnel handling goods. Dynamic calculations are used to ensure the reliability of vehicle and dispatch personnel allocation and to guarantee high efficiency in retail merchandise operation management.

[0046] Finally, in the process of calculating the additional personnel data based on the allocated sales personnel, dispatch personnel, vehicle allocation, and inventory transfer personnel, as well as the current personnel allocation data of the target retail channel, for example, when the current personnel allocation data of the target retail channel shows that the current allocated sales personnel are... The current allocation of dispatch personnel is The current allocation of vehicles is The current allocation quantity for the dispatching personnel is At that time, the sales volume can be allocated to sales personnel. The number of personnel to be dispatched is Vehicle allocation is The allocation of personnel for goods transfer is To enable the addition of sales personnel data Distributing additional personnel data Data on additional personnel deployed to vehicles Data on personnel for inventory transfer and additional staff Perform calculations, that is , , , Additionally, regarding the data on adding personnel to vehicles... When calculating, If the base vehicle allocation is greater than the available vehicle threshold corresponding to the available vehicle threshold, then the base vehicle allocation can be used as the initial allocation. ,use If the calculated personnel addition data for the first vehicle is less than or equal to the available vehicle threshold, then the baseline vehicle allocation can be adjusted as the vehicle allocation amount. Meanwhile, based on the allocation of this vehicle Allocation of goods to dispatch personnel Adjustments are made to be made using the calculation formula. Data on the increase in personnel for goods transfer and allocation Calculations are performed. If the number of additional personnel added to the first vehicle is still greater than the available vehicle threshold, then the available vehicle threshold can be used as the basis for further calculations. ,use The second vehicle's additional personnel data is calculated and used as the current vehicle's additional personnel data. Simultaneously, the allocation of cargo handling personnel is based on the threshold of available vehicles. Continue to implement data on personnel involved in inventory transfer and allocation. The calculation.

[0047] Please see Figure 2 The present invention also provides a method for e-commerce omnichannel operation management, comprising: Step S10: Obtain the sales emergency index of retail products in the target retail channel under omnichannel visitor monitoring through the monitoring acquisition unit 111; Step S20: The sales forecasting unit 112 forecasts the sales volume based on the sales emergency index and the historical sales data corresponding to the target retail channel to obtain the forecasted sales volume. Step S30: The allocation analysis unit 113 calculates the allocation demand using the current inventory and forecasted sales volume of the target retail channel to obtain the required allocation quantity. Step S40: The personnel matching unit 114 matches personnel across various dimensions based on demand, retail product vehicle data, and predicted sales volume to generate a personnel allocation plan for operational management.

[0048] In summary, the e-commerce omnichannel operation management system and method disclosed in this invention acquires the sales emergency index corresponding to the target retail channel through the acquisition unit 111. Then, the sales forecasting unit 112 combines the sales emergency index, which takes into account the potential surge in sales due to increased visitor traffic, with the historical sales data of the target retail channel to achieve a comprehensive and accurate prediction of product sales. The predicted sales volume is then combined with the current inventory of the target retail channel by the allocation and analysis unit 113 to calculate the current demand for inventory transfer for the target retail channel. Finally, the personnel matching unit 114 combines and analyzes the demand for inventory transfer, retail product carrier data, and predicted sales volume to achieve personnel allocation across various dimensions, generating a rational and reliable personnel allocation plan. This ensures the timeliness and reliability of goods and personnel allocation and control in e-commerce omnichannel operation management, guaranteeing efficient goods supply under emergency customer traffic. Therefore, this invention effectively overcomes the various shortcomings of the prior art and has high industrial application value.

[0049] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. An e-commerce omnichannel operation management system, characterized in that, include: The monitoring and acquisition unit is used to acquire the sales emergency index of retail products in the target retail channels under omnichannel visitor monitoring. The sales forecasting unit is used to forecast sales based on the sales emergency index and the historical sales data corresponding to the target retail channel, and obtain the forecasted sales volume. The allocation analysis unit is used to calculate the allocation demand using the current inventory level of the target retail channel and the predicted sales volume, and to obtain the required allocation quantity. as well as The personnel matching unit is used to match personnel across various dimensions based on the demand for goods, the vehicle data of the retail goods, and the predicted sales volume, and generate a personnel allocation plan for operational management.

2. The e-commerce omnichannel operation management system according to claim 1, characterized in that, The monitoring acquisition unit includes: The customer traffic acquisition subunit is used to acquire real-time visitor traffic across various retail channels; The difference calculation subunit is used to calculate the difference between the real-time visitor traffic corresponding to each retail channel and the corresponding benchmark traffic to obtain the visitor excess amount. A range identification subunit is used to, when the number of visitors exceeding the limit is greater than the lower limit of the corresponding retail channel, designate the corresponding retail channel as the target retail channel, and obtain the corresponding current exceeding range based on the number of visitors exceeding the limit and the exceeding range classification table corresponding to the target retail channel. The exceeding range classification table includes multiple exceeding ranges. The index calculation subunit is used to obtain the sales emergency index of the retail product corresponding to the target retail channel under the omnichannel visitor monitoring, based on the current exceedance range. The formula for calculating the current range of exceedance is: ； in, This indicates the length of the range corresponding to the current exceedance range. This indicates real-time visitor traffic. Indicates the baseline flow rate. This indicates the previous range of exceeding the standard.

3. The e-commerce omnichannel operation management system according to claim 2, characterized in that, The retail channels include offline store channels, online store channels, and live streaming channels. The real-time visitor traffic includes the store traffic corresponding to the offline store channels, the online store browsing traffic corresponding to the online store channels, and the live streaming browsing traffic corresponding to the live streaming channels. The passenger flow acquisition subunit includes: The first customer flow acquisition module is used to acquire images of the retail goods storage area of ​​the corresponding store of the offline store channel, perform facial scanning on the images of the retail goods storage area to obtain the first number of users within a preset time period, and calculate the first unit time average number of users within the preset time period based on the first number of users and the preset time period, so as to be the store customer flow under the offline store channel. The second customer flow acquisition module is used to acquire browsing records of retail products corresponding to the online store channel within a preset time period, perform non-duplicate user filtering based on the browsing records to obtain the second number of users within the preset time period, and calculate the second average number of users per unit time within the preset time period based on the second number of users and the preset time period, as the online store browsing customer flow under the online store channel; and The third customer flow acquisition module is used to acquire the viewing data of the retail products corresponding to the live streaming channel within a preset time period, perform non-duplicate user filtering on the viewing data to obtain the number of third users within the preset time period, and calculate the average number of third users per unit time within the preset time period based on the number of third users and the preset time period, so as to be the live streaming browsing customer flow under the live streaming channel.

4. The e-commerce omnichannel operation management system according to claim 1, characterized in that, The sales forecasting unit includes: The linear forecasting subunit is used to perform linear forecasting of sales within the forecasting period based on the first historical sales data within the historical marketing period prior to the forecasting period, so as to obtain the initial value of the sales forecast. The historical detection subunit is used to detect the second historical sales data of the historical sales data corresponding to the prediction period over the same period of the previous year. The first prediction subunit is used to, when the existence of the second historical sales data is detected, obtain a sales adjustment value based on the second historical sales data, historical sales data from previous years prior to the second historical sales data, and the initial sales forecast value; and obtain a first predicted sales volume based on the sales emergency index, the initial sales forecast value, and the sales adjustment value. The second prediction subunit is used to obtain a second predicted sales volume based on the sales emergency index and the initial value of the sales prediction when the second historical sales data is not detected; and The integrated output subunit is used to obtain the predicted sales based on the first predicted sales and the second predicted sales.

5. The e-commerce omnichannel operation management system according to claim 4, characterized in that, The first prediction subunit includes: The historical sales forecast module is used to linearly predict the sales volume within the historical period based on the historical sales data to obtain the initial value of the historical sales forecast. The sales adjustment module is used to adjust the total sales value corresponding to the initial value of the sales forecast based on the second historical sales data and the initial value of the sales forecast for each year, so as to obtain the sales adjustment value. The index lookup module is used to look up the daily sales index based on the initial value of the sales forecast and the sales adjustment value. The first adjustment calculation module is used to obtain a first sales volume adjustment amount based on the sales emergency index, the first adjustment coefficient corresponding to the sales emergency index, the daily sales index, and the second adjustment coefficient corresponding to the daily sales index; and The first prediction adjustment module is used to adjust the sales adjustment value by the first sales adjustment amount to obtain the first predicted sales. The formula for calculating the first predicted sales volume is: ； This indicates the first predicted sales volume. This indicates the second historical sales data. This represents the initial value of sales forecasts for each year. This represents the initial value of the sales forecast. This indicates the sales emergency index. This represents the daily sales index. This represents the first adjustment coefficient. This represents the second adjustment coefficient.

6. The e-commerce omnichannel operation management system according to claim 4, characterized in that, The second prediction subunit includes: The second adjustment calculation module is used to obtain a second sales volume adjustment amount based on the sales emergency index and the first adjustment coefficient corresponding to the sales emergency index; and The second prediction adjustment module is used to adjust the initial value of the sales prediction using the second sales adjustment amount to obtain the second predicted sales. The formula for calculating the second predicted sales volume is: ； This indicates the second projected sales volume. This represents the initial value of the sales forecast. This indicates the sales emergency index. This represents the first adjustment coefficient.

7. The e-commerce omnichannel operation management system according to claim 1, characterized in that, The personnel matching unit includes: The first generation subunit is used to generate the sales personnel allocation quantity and the dispatch personnel allocation quantity based on the predicted sales volume. The second generation subunit is used to generate vehicle allocation quantity and delivery personnel allocation quantity based on the demand for goods and the vehicle data of the retail goods. An additional personnel calculation subunit is used to obtain additional personnel data based on the sales personnel allocation, the dispatch personnel allocation, the vehicle allocation, the goods transfer personnel allocation, and the current personnel allocation data of the target retail channel; and The scheme generation subunit is used to generate a personnel allocation scheme based on the increased personnel data for operational management.

8. The e-commerce omnichannel operation management system according to claim 7, characterized in that, The first generation subunit includes: The extraction module is used to extract the sales allocation coefficient corresponding to the retail product; The sales allocation module is used to generate the sales personnel allocation based on the predicted sales volume and the sales allocation coefficient; and The distribution module is used to generate the number of distribution personnel to be allocated based on the distribution coefficient corresponding to each of the retail products.

9. The e-commerce omnichannel operation management system according to claim 7, characterized in that, The second generation subunit includes: The screening module is used to screen the vehicle data for vehicle occupancy of the retail goods to obtain the threshold of unoccupied available vehicles; The vehicle allocation module is used to obtain the vehicle allocation quantity based on the required cargo quantity and the maximum load capacity of each vehicle; and The cargo allocation module is used to obtain the cargo allocation quantity based on the vehicle allocation quantity and the personnel maintenance allocation quantity corresponding to each vehicle.

10. A method for managing omnichannel e-commerce operations, characterized in that, include: The sales emergency index of retail products in the target retail channels under omnichannel visitor monitoring is obtained through the monitoring acquisition unit; The sales forecasting unit predicts sales based on the sales emergency index and the historical sales data corresponding to the target retail channel, and obtains the predicted sales volume. The allocation analysis unit uses the current inventory level of the target retail channel and the predicted sales volume to calculate the allocation demand and obtain the required allocation quantity. The personnel matching unit matches personnel across various dimensions based on the demand for goods, the vehicle data of the retail goods, and the predicted sales volume, generating a personnel allocation plan for operational management.

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