ERP inventory scheduling optimization method and system for multi-warehouse collaboration

Abstract

This invention relates to the field of computer data processing technology for warehouse management and inventory scheduling, and provides an ERP inventory scheduling optimization method and system for multi-warehouse collaboration. The method includes: when a specified pattern is determined to exist, predicting the goods reconfirmation index for each warehouse under a target order based on the changing trend; determining a reference goods reconfirmation index based on samples from each warehouse when no goods maintenance is required; generating a correction factor based on the difference between the predicted goods reconfirmation index for each warehouse and the reference goods reconfirmation index; and correcting the suitability value of each warehouse based on the correction factor. This invention enables scheduling decisions to more realistically reflect the actual outbound performance of this type of goods under long-term operating conditions, reduces the rework risk caused by reconfirmation, and improves the accuracy and stability of multi-warehouse collaborative scheduling.

Description

Technical Field

[0001] This invention belongs to the field of computer data processing technology for warehouse management and inventory scheduling, and particularly relates to an ERP inventory scheduling optimization method and system for multi-warehouse collaboration. Background Technology

[0002] As businesses grow and supply chains become more complex, warehouse management systems based on ERP platforms are widely used for inventory scheduling and order fulfillment in multi-warehouse collaborative scenarios. In existing technologies, when selecting a warehouse for a target order, the ERP system typically generates adaptability values ​​for multiple warehouses to characterize their overall suitability for executing the order. These adaptability values ​​are generally calculated based on factors such as inventory availability, logistical conditions between the warehouse and the delivery location, outbound load, and basic scheduling constraints, allowing for comparison among multiple warehouses and the selection of the target warehouse.

[0003] The above methods are well-suited for handling regular goods with frequent turnover and stable status. However, for specific types of goods stored for extended periods with long order intervals and requiring maintenance in the later stages of storage, certain limitations have gradually become apparent in practical applications. Due to differences in maintenance personnel allocation, equipment conditions, and the frequency of maintenance procedures across different warehouses, these goods often experience varying degrees of reconfirmation during the outbound management phase. This may necessitate additional status verification, inventory checks, or re-picking operations. These reconfirmation processes typically occur after the warehouse has completed picking, increasing the probability of rework in the outbound process and significantly reducing overall outbound efficiency.

[0004] However, existing technologies, when generating adaptability values, typically only focus on inventory and logistics information directly available at the time of order placement, without considering potential reconfirmation during the outbound management phase. This is primarily because reconfirmation is inherently delayed and relies on manual judgment, making it difficult to predict at the initial stage of a target order. Consequently, differences in execution across different warehouses during the subsequent outbound phase cannot be reflected in scheduling decisions in advance. Especially when multiple warehouses have similar outbound and inventory conditions, existing scheduling methods struggle to differentiate the impact of warehouse maintenance differences on outbound execution capabilities. This may lead to the selection of warehouses with lower efficiency during the subsequent reconfirmation phase to execute orders, impacting overall warehouse management effectiveness.

[0005] Therefore, how to reasonably characterize the implicit differences in cargo maintenance and outbound verification among different warehouses during multi-warehouse collaborative scheduling, without changing the existing ERP scheduling system, and reflect these differences in warehouse adaptability assessment, has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0006] The purpose of this invention is to provide an ERP inventory scheduling optimization method and system for multi-warehouse collaboration, aiming to solve the problems mentioned in the background art.

[0007] This invention is implemented as follows: an ERP inventory scheduling optimization method for multi-warehouse collaboration, the method comprising: When setting adaptability values ​​for multiple warehouses for a target order, if it is found that the outbound and inventory conditions of different warehouses are consistent under the target order, but the goods maintenance resources invested by different warehouses in the goods maintenance process are different, the historical scheduling database is retrieved, and historical scheduling samples are selected for each warehouse. The order requirements conditions corresponding to each sample are consistent with the target order. Analyze the samples from each warehouse to obtain the cargo reconfirmation index during the outbound management phase, and construct the trend of the cargo reconfirmation index over time. Determine whether the trend of each warehouse has a specified pattern: all show an upward change, and the growth rate of the cargo reconfirmation index increases when the investment in cargo maintenance resources decreases. When the specified pattern is determined to exist, the goods reconfirmation index of each warehouse under the target order is predicted based on the trend of change, and a reference goods reconfirmation index is determined based on the sample of each warehouse when no goods maintenance is required. A correction factor is generated based on the difference between the predicted goods reconfirmation index of each warehouse and the reference goods reconfirmation index, and the adaptability value of each warehouse is corrected based on the correction factor.

[0008] As a further limitation of the technical solution of the present invention, the consistent outbound and inventory conditions of different warehouses under the target order means that the initial storage conditions of the goods corresponding to the target order are consistent for each warehouse, and that the differences in inventory availability, outbound feasibility and basic scheduling constraints of the target goods available for outbound are within a preset tolerance range, provided that the requirements of the target order are met.

[0009] As a further limitation of the technical solution of the embodiment of the present invention, the goods corresponding to the target order are goods that are stored for a long time and have a long interval between order requirements. The goods have an initial storage stage in the warehousing process where no goods maintenance is required when the storage time has not reached the preset maintenance threshold, and a subsequent storage stage where goods maintenance is required after the storage time has reached the preset maintenance threshold. The calculation process of the cargo maintenance resources includes: obtaining the set of maintenance procedures corresponding to the cargo, obtaining the occupancy information of maintenance personnel, maintenance equipment or maintenance workstations required for each maintenance procedure, and quantifying the cargo maintenance resources based on the number of times the maintenance procedures are executed and the amount of maintenance personnel, maintenance equipment or maintenance workstations invested.

[0010] As a further limitation of the technical solution of the present invention, the order requirements corresponding to each sample being consistent with the target order means that each historical scheduling sample is the same as the target order in terms of goods type, required quantity and outbound timeliness requirements, or that the differences in their corresponding parameters are within a preset tolerance range under the premise of meeting the requirements of the target order.

[0011] As a further limitation of the technical solution of the present invention, the calculation process of the goods reconfirmation index includes: obtaining the record information of manual review, status confirmation or inventory verification operations triggered by the target goods in the outbound management stage in the historical scheduling sample, and quantitatively calculating the goods reconfirmation index based on the frequency of the review or confirmation operations and the number of goods batches or units involved in the record information.

[0012] As a further limitation of the technical solution of this embodiment of the invention, when it is determined that the specified pattern exists, the steps of predicting the goods reconfirmation index of each warehouse under the target order based on the changing trend, determining the reference goods reconfirmation index based on the sample of each warehouse when no goods maintenance is required, generating a correction factor based on the difference between the predicted goods reconfirmation index of each warehouse and the reference goods reconfirmation index, and correcting the adaptability value of each warehouse based on the correction factor include: Under the premise that the specified mode exists, obtain the trend of the goods reconfirmation index over time in the historical scheduling samples corresponding to each warehouse, and map the current time point corresponding to the target order to the trend of the change, so as to predict the goods reconfirmation index corresponding to the target order in each warehouse. Extract historical scheduling samples from each warehouse that do not require cargo maintenance, and calculate reference cargo reconfirmation indicators based on the cargo reconfirmation indicators corresponding to the samples; Based on the difference between the predicted goods reconfirmation index of each warehouse and the corresponding reference goods reconfirmation index, a correction factor is generated, and the adaptability value generated by each warehouse for the target order is corrected based on the correction factor to obtain the optimized adaptability value. Based on the optimized adaptability value, the scheduling warehouse for executing the target order is determined.

[0013] An ERP inventory scheduling optimization system for multi-warehouse collaboration, the system comprising: The sample screening module is used to retrieve historical scheduling databases when setting adaptability values ​​for multiple warehouses for a target order. If it is found that the outbound and inventory conditions of different warehouses are consistent under the target order, but the cargo maintenance resources invested by different warehouses in the cargo maintenance process are different, the module will select historical scheduling samples for each warehouse. The order requirements of each sample are consistent with the target order. The trend analysis module is used to analyze samples from each warehouse, obtain the cargo reconfirmation index statistically recorded during the outbound management phase, construct the trend of cargo reconfirmation index over time, and determine whether the trend of each warehouse has a specified pattern: all show an upward change, and the growth rate of cargo reconfirmation index increases when cargo maintenance resource input decreases. The adaptability correction module is used to predict the goods reconfirmation index of each warehouse under the target order based on the changing trend when the specified pattern is determined, and to determine the reference goods reconfirmation index based on the sample of each warehouse when no goods maintenance is required. The module generates a correction factor based on the difference between the predicted goods reconfirmation index of each warehouse and the reference goods reconfirmation index, and corrects the adaptability value of each warehouse based on the correction factor.

[0014] As a further limitation of the technical solution of the present invention, the consistent outbound and inventory conditions of different warehouses under the target order means that the initial storage conditions of the goods corresponding to the target order are consistent for each warehouse, and that the differences in inventory availability, outbound feasibility and basic scheduling constraints of the target goods available for outbound are within a preset tolerance range, provided that the requirements of the target order are met.

[0015] As a further limitation of the technical solution of the embodiment of the present invention, the goods corresponding to the target order are goods that are stored for a long time and have a long interval between order requirements. The goods have an initial storage stage in the warehousing process where no goods maintenance is required when the storage time has not reached the preset maintenance threshold, and a subsequent storage stage where goods maintenance is required after the storage time has reached the preset maintenance threshold. The calculation process of the cargo maintenance resources includes: obtaining the set of maintenance procedures corresponding to the cargo, obtaining the occupancy information of maintenance personnel, maintenance equipment or maintenance workstations required for each maintenance procedure, and quantifying the cargo maintenance resources based on the number of times the maintenance procedures are executed and the amount of maintenance personnel, maintenance equipment or maintenance workstations invested.

[0016] As a further limitation of the technical solution of the present invention, the order requirements corresponding to each sample being consistent with the target order means that each historical scheduling sample is the same as the target order in terms of goods type, required quantity and outbound timeliness requirements, or that the differences in their corresponding parameters are within a preset tolerance range under the premise of meeting the requirements of the target order.

[0017] Compared with the prior art, the present invention has the following beneficial effects: This invention proposes an inventory scheduling optimization method for multi-warehouse collaboration for a specific type of goods that requires long-term storage, has long order demand intervals, and needs maintenance in the later stages of storage. Due to differences in maintenance resources across different warehouses, this type of goods is prone to varying degrees of reconfirmation during the outbound management phase, an impact that existing technologies typically do not consider when generating warehouse suitability values.

[0018] This invention, under the premise of consistent outbound and inventory conditions across different warehouses, quantifies cargo reconfirmation indicators through historical scheduling samples and identifies specified patterns of their evolution over time. It then predicts the impact of reconfirmation at the current time point of the target order and adjusts the fit value in the form of a correction factor. This allows scheduling decisions to more accurately reflect the actual outbound performance of this type of cargo under long-term operating conditions, reduces the risk of rework caused by reconfirmation, and improves the accuracy and stability of multi-warehouse collaborative scheduling. Attached Figure Description

[0019] Figure 1 A flowchart of the method provided in the embodiments of the present invention; Figure 2 This is a flowchart illustrating the correction of the initial adaptability values ​​of each warehouse in the method provided in this embodiment of the invention. Figure 3 The application architecture diagram of the system provided in the embodiments of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0021] Figure 1 A flowchart of the method provided by an embodiment of the present invention is shown.

[0022] Specifically, an ERP inventory scheduling optimization method for multi-warehouse collaboration includes the following steps: Step S100: When setting adaptability values ​​for multiple warehouses for a target order, if it is found that the outbound and inventory conditions of different warehouses are consistent under the target order, and the goods maintenance resources invested by different warehouses in the goods maintenance process are different, the historical scheduling database is retrieved, and historical scheduling samples are selected for each warehouse. The order requirements corresponding to each sample are consistent with the target order.

[0023] The consistent outbound and inventory conditions of different warehouses under the target order means that each warehouse has the same initial storage conditions for the goods corresponding to the target order, and that the differences in inventory availability, outbound feasibility and basic scheduling constraints of the target goods available for outbound are within a preset tolerance range, provided that the requirements of the target order are met.

[0024] The goods corresponding to the target order are goods that are stored for a long time and have long order demand intervals. The goods have an initial storage stage in the warehousing process where no goods maintenance is required when the storage time has not reached the preset maintenance threshold, and a subsequent storage stage where goods maintenance is required after the storage time has reached the preset maintenance threshold.

[0025] The calculation process of the cargo maintenance resources includes: obtaining the set of maintenance procedures corresponding to the cargo, obtaining the occupancy information of maintenance personnel, maintenance equipment or maintenance workstations required for each maintenance procedure, and quantifying the cargo maintenance resources based on the number of times the maintenance procedures are executed and the amount of maintenance personnel, maintenance equipment or maintenance workstations invested.

[0026] The requirement that the order conditions corresponding to each sample are consistent with the target order means that each historical scheduling sample is the same as the target order in terms of goods type, required quantity and outbound timeliness, or that the corresponding parameter differences are within the preset tolerance range under the premise of meeting the requirements of the target order.

[0027] In this embodiment of the invention, the adaptability value is an evaluation parameter used to characterize the overall adaptability of different warehouses when executing a target order. It is used to compare and select multiple warehouses in a multi-warehouse collaboration scenario. The adaptability value can be generated based on an existing ERP platform and is typically calculated by the inventory management module, logistics management module, or scheduling management module. In the prior art, the adaptability value is usually determined based on factors such as the inventory availability of the target goods in each warehouse, the logistics distance or transportation cost between the warehouse and the target order's delivery address, the current outbound load of the warehouse, and basic scheduling constraints. This approach can reflect the warehouse's execution capabilities at the inventory and logistics levels to a certain extent and is therefore widely used in existing ERP systems.

[0028] However, during the research process, the inventors discovered that existing technologies, when generating adaptability values, typically do not consider the verification process required for goods during the actual outbound management phase. This verification refers to the process where, after the warehouse has completed the picking operation of the target goods according to the scheduling results, before formal outbound processing, the goods to be outbound need to undergo status confirmation, specification confirmation, or compliance confirmation manually or by equipment to determine whether the picked goods truly meet the requirements of the target order. When the verification results do not meet the requirements, it is often necessary to re-inspect, re-pick, or replace the completed picking results, thus interrupting the outbound process. Since this verification usually occurs after large-scale picking is completed, the failure of verification for individual goods can trigger rework of the entire outbound process, significantly reducing outbound efficiency and adversely affecting the overall operational evaluation of the warehouse.

[0029] Furthermore, existing technologies generally consider the aforementioned review process to be highly subjective, relying primarily on the experience and judgment of manual pickers or ad-hoc operations. Therefore, it is difficult to accurately predict this at the initial stage of a target order, and consequently, this review process is not included in the adaptation value generation process. However, those skilled in the art have discovered that for goods types with long-term storage and long order demand intervals, this review process is not entirely random, but rather intrinsically related to the maintenance stages the goods undergo during warehousing and the level of maintenance resources invested.

[0030] Specifically, for goods requiring long-term storage and with long order intervals, such as spare parts for industrial equipment, long-term stockpiled key components, or functional materials requiring periodic maintenance, the goods are typically in the initial storage stage before the storage duration reaches a preset maintenance threshold. During this stage, the physical and functional states of the goods remain relatively stable, and the probability of goods reconfirmation failure before outbound is low. However, once the storage duration reaches or exceeds the preset maintenance threshold, the goods enter the subsequent storage stage. At this point, the availability of the goods depends more on the maintenance resources invested by the warehouse. Due to differences in maintenance personnel, equipment, and workstations among different warehouses, there can be differences in the degree of recovery, integrity of identification, or verifiability of goods. When maintenance resources are insufficient, it is easier to trigger status confirmation or inventory verification operations during the outbound management stage, thereby increasing the likelihood of goods reconfirmation.

[0031] Based on the above understanding, the core research point of this invention is that existing technologies do not identify the correlation between cargo maintenance resources and outbound verification status, nor do they incorporate this correlation into the prediction of the verification status of the target order at the current time point, resulting in existing adaptability values ​​being insufficiently objective in evaluating the actual execution capability of a warehouse. This invention identifies and utilizes this correlation to correct existing adaptability values, thereby improving the accuracy of multi-warehouse collaborative scheduling.

[0032] In step S100, the present invention first sets "identifying that different warehouses have consistent outbound and inventory conditions under the target order" as a prerequisite. The purpose is to eliminate the impact of explicit differences in initial inventory status and basic outbound capacity among different warehouses. By ensuring that different warehouses have consistent initial storage conditions for the goods corresponding to the target order, and that, under the premise of meeting the target order requirements, the differences in inventory availability, outbound feasibility, and basic scheduling constraints are within a preset tolerance range, the differences between different warehouses can be limited to primarily stemming from differences in warehouse operation and maintenance methods. This allows subsequent analysis to focus on the impact caused by differences in goods maintenance resources.

[0033] The reason why different warehouses invest different resources in cargo maintenance is mainly due to differences in equipment configuration, personnel structure, maintenance procedures, and management strategies. For example, different warehouses may allocate different numbers of maintenance workstations, use different frequencies of maintenance procedures, or have differences in the ratio of maintenance personnel to maintenance equipment for the same type of goods. By obtaining a set of maintenance procedures and combining the number of times the maintenance procedures are executed with information on the occupancy of maintenance personnel, equipment, or workstations, cargo maintenance resources can be quantitatively calculated. This allows for a unified representation of the maintenance resource investment in different warehouses. This calculation method has the advantages of strong objectivity and ease of implementation in existing ERP or warehouse management systems. Furthermore, in other implementations, cargo maintenance resources can also be determined through maintenance man-hours, maintenance frequency, or maintenance resource occupancy rates.

[0034] In this step, the present invention also filters historical scheduling samples from the historical scheduling database for each warehouse. The significance of this sample selection lies in the fact that historical samples can reflect the actual outbound performance of goods under different maintenance resource input conditions, providing basic data for subsequent reconfirmation indicator analysis. To ensure comparability between different samples, the present invention limits the order requirements for each sample to be consistent with the target order, that is, the same in terms of goods type, required quantity, and outbound timeliness requirements, or the differences in their corresponding parameters are within a preset tolerance range. This method ensures that the samples are on the same basis in key conditions such as demand scale and timeliness requirements, thereby avoiding interference with the analysis results of goods reconfirmation indicators due to differences in the orders themselves.

[0035] The historical scheduling database originates from the existing ERP platform's inventory management, outbound management, or scheduling management modules. It is used for the unified storage and management of scheduling and outbound status during the historical order execution process. The historical scheduling database includes at least the following data types: order requirement data corresponding to historical orders, representing information such as goods type, required quantity, and outbound timeliness requirements; warehouse execution data associated with historical orders, representing the outbound and inventory conditions of different warehouses when executing historical orders; maintenance data related to the goods maintenance process, representing the input of maintenance procedures, personnel, equipment, or workstations during warehousing; and outbound record data related to the outbound management stage, representing operation records such as manual review, status confirmation, or inventory verification triggered during the outbound process of historical orders.

[0036] Furthermore, the ERP inventory scheduling optimization method for multi-warehouse collaboration also includes the following steps: Step S200: Analyze the samples of each warehouse, obtain the cargo reconfirmation index statistically recorded during the outbound management stage, and construct the trend of cargo reconfirmation index over time. Determine whether the trend of each warehouse has a specified pattern: all show an upward change, and the growth rate of cargo reconfirmation index increases when cargo maintenance resource input decreases.

[0037] The calculation process of the cargo reconfirmation index includes: obtaining the record information of manual review, status confirmation or inventory verification operations triggered by the target cargo in the outbound management stage in the historical scheduling sample, and quantitatively calculating the cargo reconfirmation index based on the frequency of the review or confirmation operations and the number of cargo batches or units involved in the record information.

[0038] In this embodiment of the invention, step S200 is a process of quantitative analysis of the outbound verification stage involved in the sample, based on the historical scheduling sample screening and comparability limitation completed in step S100. Its purpose is to transform the verification situation, which is originally difficult to consider directly in the suitability value setting stage, into statistically comparable goods verification indicators. It should be noted that the quantification method itself is an existing technical means, and the data involved can be directly obtained from existing ERP platforms. This invention does not create a new verification mechanism, but rather mines the regularity of changes in existing data over time and maintenance resources.

[0039] Specifically, when analyzing historical scheduling samples for each warehouse, outbound record information related to historical orders is extracted from the outbound management module of the ERP platform. This outbound record information includes, but is not limited to, manual review operations, status confirmation operations, or inventory verification operations triggered during the outbound management phase. By statistically analyzing the occurrence of these operations in each historical scheduling sample, the frequency of review or confirmation operations and the corresponding number of goods batches or goods units can be obtained. Based on the above statistical results, the goods reconfirmation index is quantitatively calculated. For example, in multiple historical scheduling samples of the same warehouse, if a certain type of goods requires repeated status confirmation or re-verification before outbound, the corresponding goods reconfirmation index value is high; conversely, if goods generally do not require review operations during the outbound process, the corresponding goods reconfirmation index value is low.

[0040] After calculating the goods verification index, the goods verification index for the same warehouse at different times is organized in chronological order to construct a trend of goods verification index changes over time. By analyzing the changing trends for different warehouses, it is determined whether a specified pattern exists, i.e., the goods verification index for all warehouses generally increases, and the increase in goods verification index increases when the investment in goods maintenance resources decreases. The purpose of identifying this specified pattern is to objectively reflect that the differences in the investment in goods maintenance resources among different warehouses as described in step S100 do indeed affect the goods verification situation in the outbound management stage, and this effect is more obvious when the investment in goods maintenance resources is low. This reveals that insufficient goods maintenance resources amplify the subsequent changes in verification situation over time, providing a basis for predicting the goods verification index of target orders based on the changing trend.

[0041] The statement that "the growth rate of the goods reconfirmation index increases when the investment in goods maintenance resources decreases" specifically refers to the fact that, when analyzing the same type of goods stored for a long time with long order demand intervals, warehouses with lower investment in maintenance resources show a faster increase in the goods reconfirmation index over time after entering the subsequent storage stage compared to warehouses with sufficient investment in maintenance personnel, equipment, or workstations. For example, in some warehouses, due to low frequency of maintenance procedures or insufficient occupancy of maintenance workstations, goods are more likely to have unclear labels or uncertain status in the later stages of storage, thus requiring frequent manual verification or inventory checks during the outbound management stage, resulting in a significant increase in the goods reconfirmation index in a short period of time. In contrast, in warehouses with sufficient investment in maintenance resources, the increase in the reconfirmation index is relatively gradual even after the goods enter the subsequent storage stage.

[0042] It should be noted that, in addition to the calculation method described above based on the frequency of review or confirmation operations and the quantity of goods involved, other implementations may also employ different methods to calculate the goods reconfirmation index. For example, the goods reconfirmation index may be characterized based on the proportion of orders with review operations in the outbound management phase, the proportion of goods units triggering review to the total outbound goods units, or the proportion of review operations in the outbound process. None of these different calculation methods affect the technical concept of this invention: identifying a specified pattern by analyzing the trend of goods reconfirmation index changes over time.

[0043] Furthermore, the ERP inventory scheduling optimization method for multi-warehouse collaboration also includes the following steps: Step S300: When it is determined that the specified pattern exists, predict the goods reconfirmation index of each warehouse under the target order based on the trend of change, and determine the reference goods reconfirmation index based on the sample of each warehouse when no goods maintenance is required. Generate a correction factor based on the difference between the predicted goods reconfirmation index of each warehouse and the reference goods reconfirmation index, and correct the adaptability value of each warehouse based on the correction factor.

[0044] Specifically, Figure 2 A flowchart is shown showing the correction of the initial fit values ​​for each warehouse.

[0045] Specifically, when the specified pattern is determined to exist, the following steps are taken: First, based on the changing trend, the goods reconfirmation index for each warehouse under the target order is predicted. Then, a reference goods reconfirmation index is determined based on samples from each warehouse when no goods maintenance is required. A correction factor is generated based on the difference between the predicted goods reconfirmation index for each warehouse and the reference goods reconfirmation index. Finally, the suitability value for each warehouse is corrected based on the correction factor. Step S301: Under the premise that the specified mode exists, obtain the changing trend of the goods reconfirmation index over time in the historical scheduling samples corresponding to each warehouse, and map the current time point corresponding to the target order to the changing trend, so as to predict the goods reconfirmation index corresponding to the target order in each warehouse. Step S302: Extract historical scheduling samples from each warehouse that do not require goods maintenance, and calculate reference goods reconfirmation index based on the goods reconfirmation index corresponding to the samples; Step S303: Generate a correction factor based on the difference between the predicted goods reconfirmation index of each warehouse and the corresponding reference goods reconfirmation index, and correct the adaptability value generated by each warehouse for the target order based on the correction factor to obtain the optimized adaptability value. Step S304: Based on the optimized adaptability value, determine the scheduling warehouse for executing the target order.

[0046] In this embodiment of the invention, step S300 is a core step that, given that step S200 has identified the changes in the goods reconfirmation indicators corresponding to each warehouse over time and the existence of the specified pattern, predicts the possible reconfirmation situation of the target order at the current time point and incorporates the prediction result into the adaptability value correction process. Through this step, the impact of reconfirmation, which would normally only appear in the outbound management stage, can be reflected in advance in the warehouse selection decision corresponding to the target order, thereby improving the objectivity and accuracy of multi-warehouse collaborative scheduling.

[0047] Specifically, in step S301, assuming the existence of the specified pattern, the trend of the goods reconfirmation index over time in the historical scheduling samples corresponding to each warehouse is first obtained. This trend can be obtained based on the goods reconfirmation index time series constructed in step S200, and characterized using storage duration or order occurrence time as the time dimension. Based on this, the current time point corresponding to the target order is mapped to the trend, thereby predicting the goods reconfirmation index corresponding to the target order in each warehouse. This prediction process can be implemented using existing time series analysis or trend extrapolation methods, such as linear extrapolation based on historical trends or piecewise trend fitting. These techniques are existing technologies, and this invention does not limit the specific prediction algorithm but rather introduces the prediction results into the subsequent adaptation value correction process.

[0048] In step S302, historical scheduling samples that do not require cargo maintenance are extracted from the historical scheduling samples corresponding to each warehouse, and a reference cargo reconfirmation index is calculated based on the cargo reconfirmation index corresponding to the samples. Samples that do not require cargo maintenance are used as a reference because the cargo corresponding to these samples is in the initial storage stage, and its reconfirmation status is less affected by differences in cargo maintenance resources, reflecting the basic reconfirmation level under ideal maintenance conditions or before the maintenance impact manifests. By statistically analyzing the cargo reconfirmation index of these samples, such as calculating the average or representative value, a reference cargo reconfirmation index for comparison can be obtained, thus providing a unified benchmark for subsequently measuring the degree of deterioration of the reconfirmation status of different warehouses at the current point in time.

[0049] In step S303, a correction factor is generated based on the difference between the predicted goods reconfirmation index of each warehouse and the corresponding reference goods reconfirmation index. The suitability value generated by each warehouse for the target order is then corrected based on this correction factor. Specifically, the increase percentage of the predicted goods reconfirmation index of each warehouse compared to the reference goods reconfirmation index can be calculated first, for example, using the ratio of the difference between the two to the reference goods reconfirmation index as the increase percentage. This increase percentage is then combined with a preset correction magnitude coefficient to generate a correction factor for correcting the suitability value. In one example, if the predicted goods reconfirmation index of a warehouse is 6% higher than the reference goods reconfirmation index, and the preset correction magnitude coefficient is 0.5, the corresponding correction factor can be 3%. This correction factor is then applied as a reduction percentage to the suitability value of that warehouse, causing the suitability value to decrease by 3%. To avoid over-correction of the suitability value, a lower limit control can be set for the correction result, for example, limiting the corrected suitability value to no less than a preset minimum value, thereby ensuring the stability of the scheduling decision. Through this method, the reconfirmation risk can be introduced into the suitability value correction process in a controllable manner.

[0050] In step S304, based on the optimized adaptability value, the scheduling warehouse for executing the target order is determined. Specifically, during multi-warehouse collaborative scheduling, the optimized adaptability values ​​of each warehouse can be compared, and the warehouse with the highest adaptability value can be selected as the scheduling warehouse for the target order. Alternatively, under the premise of satisfying a preset scheduling strategy, a selection can be made from warehouses with relatively good adaptability values. By introducing the modified adaptability value, the potential impact of reconfirmation during the subsequent outbound management stage can be avoided when relying solely on similar inventory and logistics conditions, thereby reducing the risk of rework during the outbound process and improving overall scheduling efficiency.

[0051] In summary, this invention effectively addresses the core problem of existing technologies that fail to consider the impact of the outbound verification stage when generating adaptation values. This is achieved by defining comparable premises and identifying differences in goods maintenance resources in step S100, quantifying and analyzing the changing patterns of goods reconfirmation indicators over time in step S200, and predicting and correcting the reconfirmation status of target orders based on these trends in step S300. This invention can more realistically reflect the warehouse's execution capabilities under long-term operating conditions in multi-warehouse collaborative scheduling, reducing the decline in outbound efficiency caused by frequent reconfirmation triggers. It has good engineering feasibility and application prospects, and is suitable for various inventory scheduling and warehouse management scenarios based on ERP platforms.

[0052] This invention does not remodel the scheduling algorithm itself, but rather corrects the scheduling adaptability by introducing the amplification effect of reconfirmation caused by warehouse maintenance differences, based on existing scheduling results. Specifically, this invention does not change the calculation logic used to generate the initial adaptability value in the existing ERP platform, nor does it replace existing evaluation factors such as inventory, logistics, or basic scheduling constraints. Instead, given that these existing evaluation results have already been generated, it further identifies and quantifies the reconfirmation impact that may occur in the subsequent outbound management stage, and feeds this impact back into the adaptability value in the form of a correction factor. In this way, this invention can maintain the stability of the existing scheduling system while introducing a characterization of implicit execution differences during long-term operation, making scheduling decisions closer to the actual performance of the warehouse in real outbound scenarios.

[0053] Furthermore, Figure 3 An application architecture diagram of the system provided in an embodiment of the present invention is shown.

[0054] In another preferred embodiment of the present invention, an ERP inventory scheduling optimization system for multi-warehouse collaboration includes: The sample screening module 100 is used to retrieve the historical scheduling database when setting adaptability values ​​for multiple warehouses for a target order. If it is found that the outbound and inventory conditions of different warehouses are consistent under the target order, but the cargo maintenance resources invested by different warehouses in the cargo maintenance process are different, the module will select historical scheduling samples for each warehouse. The order requirements of each sample are consistent with the target order.

[0055] The consistent outbound and inventory conditions of different warehouses under the target order means that each warehouse has the same initial storage conditions for the goods corresponding to the target order, and that the differences in inventory availability, outbound feasibility and basic scheduling constraints of the target goods available for outbound are within a preset tolerance range, provided that the requirements of the target order are met.

[0056] The goods corresponding to the target order are goods that require long-term storage and have long order demand intervals. During the warehousing process, the goods have an initial storage phase in which no maintenance is required when the storage time has not reached a preset maintenance threshold, and a subsequent storage phase in which maintenance is required after the storage time has reached the preset maintenance threshold. The calculation process of the goods maintenance resources includes: obtaining the set of maintenance procedures corresponding to the goods, and obtaining the occupancy information of maintenance personnel, maintenance equipment or maintenance workstations required for each maintenance procedure, and quantitatively calculating the goods maintenance resources based on the number of times the maintenance procedures are executed and the amount of maintenance personnel, maintenance equipment or maintenance workstations invested.

[0057] The requirement that the order conditions corresponding to each sample are consistent with the target order means that each historical scheduling sample is the same as the target order in terms of goods type, required quantity and outbound timeliness, or that the corresponding parameter differences are within the preset tolerance range under the premise of meeting the requirements of the target order.

[0058] Furthermore, the ERP inventory scheduling optimization system for multi-warehouse collaboration also includes: The trend analysis module 200 is used to analyze samples from each warehouse, obtain the cargo reconfirmation index statistically recorded during the outbound management phase, construct the trend of cargo reconfirmation index over time, and determine whether the trend of each warehouse has a specified pattern: all show an upward change, and the growth rate of cargo reconfirmation index increases when cargo maintenance resource input decreases.

[0059] Furthermore, the ERP inventory scheduling optimization system for multi-warehouse collaboration also includes: The adaptability correction module 300 is used to predict the goods reconfirmation index of each warehouse under the target order based on the changing trend when the specified pattern is determined, and to determine the reference goods reconfirmation index based on the sample of each warehouse when no goods maintenance is required. It generates a correction factor based on the difference between the predicted goods reconfirmation index of each warehouse and the reference goods reconfirmation index, and corrects the adaptability value of each warehouse based on the correction factor.

[0060] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0061] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0062] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0063] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0064] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An ERP inventory scheduling optimization method for multi-warehouse collaboration, characterized in that, The method includes: When setting adaptability values ​​for multiple warehouses for a target order, if it is found that the outbound and inventory conditions of different warehouses are consistent under the target order, but the goods maintenance resources invested by different warehouses in the goods maintenance process are different, the historical scheduling database is retrieved, and historical scheduling samples are selected for each warehouse. The order requirements conditions corresponding to each sample are consistent with the target order. Analyze the samples from each warehouse to obtain the cargo reconfirmation index during the outbound management phase, and construct the trend of the cargo reconfirmation index over time. Determine whether the trend of each warehouse has a specified pattern: all show an upward change, and the growth rate of the cargo reconfirmation index increases when the investment in cargo maintenance resources decreases. When the specified pattern is determined to exist, the goods reconfirmation index of each warehouse under the target order is predicted based on the trend of change, and a reference goods reconfirmation index is determined based on the sample of each warehouse when no goods maintenance is required. A correction factor is generated based on the difference between the predicted goods reconfirmation index of each warehouse and the reference goods reconfirmation index, and the adaptability value of each warehouse is corrected based on the correction factor.

2. The ERP inventory scheduling optimization method for multi-warehouse collaboration according to claim 1, characterized in that, The consistent outbound and inventory conditions of different warehouses under the target order means that each warehouse has the same initial storage conditions for the goods corresponding to the target order, and that the differences in inventory availability, outbound feasibility and basic scheduling constraints of the target goods available for outbound are within a preset tolerance range, provided that the requirements of the target order are met.

3. The ERP inventory scheduling optimization method for multi-warehouse collaboration according to claim 1, characterized in that, The goods corresponding to the target order are goods that are stored for a long time and have long order demand intervals. The goods have an initial storage stage in the warehousing process where no goods maintenance is required when the storage time has not reached the preset maintenance threshold, and a subsequent storage stage where goods maintenance is required after the storage time has reached the preset maintenance threshold. The calculation process of the cargo maintenance resources includes: obtaining the set of maintenance procedures corresponding to the cargo, obtaining the occupancy information of maintenance personnel, maintenance equipment or maintenance workstations required for each maintenance procedure, and quantifying the cargo maintenance resources based on the number of times the maintenance procedures are executed and the amount of maintenance personnel, maintenance equipment or maintenance workstations invested.

4. The ERP inventory scheduling optimization method for multi-warehouse collaboration according to claim 1, characterized in that, The requirement that the order conditions corresponding to each sample are consistent with the target order means that each historical scheduling sample is the same as the target order in terms of goods type, required quantity and outbound timeliness, or that the corresponding parameter differences are within the preset tolerance range under the premise of meeting the requirements of the target order.

5. The ERP inventory scheduling optimization method for multi-warehouse collaboration according to claim 1, characterized in that, The calculation process of the cargo reconfirmation index includes: obtaining the record information of manual review, status confirmation or inventory verification operations triggered by the target cargo in the outbound management stage in the historical scheduling sample, and quantitatively calculating the cargo reconfirmation index based on the frequency of the review or confirmation operations and the number of cargo batches or units involved in the record information.

6. The ERP inventory scheduling optimization method for multi-warehouse collaboration according to claim 1, characterized in that, When the specified pattern is determined to exist, the steps of predicting the goods reconfirmation index for each warehouse under the target order based on the changing trend, determining the reference goods reconfirmation index based on the sample of each warehouse when no goods maintenance is required, generating a correction factor based on the difference between the predicted goods reconfirmation index of each warehouse and the reference goods reconfirmation index, and correcting the adaptability value of each warehouse based on the correction factor include: Under the premise that the specified mode exists, obtain the trend of the goods reconfirmation index over time in the historical scheduling samples corresponding to each warehouse, and map the current time point corresponding to the target order to the trend of the change, so as to predict the goods reconfirmation index corresponding to the target order in each warehouse. Extract historical scheduling samples from each warehouse that do not require cargo maintenance, and calculate reference cargo reconfirmation indicators based on the cargo reconfirmation indicators corresponding to the samples; Based on the difference between the predicted goods reconfirmation index of each warehouse and the corresponding reference goods reconfirmation index, a correction factor is generated, and the adaptability value generated by each warehouse for the target order is corrected based on the correction factor to obtain the optimized adaptability value. Based on the optimized adaptability value, the scheduling warehouse for executing the target order is determined.

7. An ERP inventory scheduling optimization system for multi-warehouse collaboration, characterized in that, The system includes: The sample screening module is used to retrieve historical scheduling databases when setting adaptability values ​​for multiple warehouses for a target order. If it is found that the outbound and inventory conditions of different warehouses are consistent under the target order, but the cargo maintenance resources invested by different warehouses in the cargo maintenance process are different, the module will select historical scheduling samples for each warehouse. The order requirements of each sample are consistent with the target order. The trend analysis module is used to analyze samples from each warehouse, obtain the cargo reconfirmation index statistically recorded during the outbound management phase, construct the trend of cargo reconfirmation index over time, and determine whether the trend of each warehouse has a specified pattern: all show an upward change, and the growth rate of cargo reconfirmation index increases when cargo maintenance resource input decreases. The adaptability correction module is used to predict the goods reconfirmation index of each warehouse under the target order based on the changing trend when the specified pattern is determined, and to determine the reference goods reconfirmation index based on the sample of each warehouse when no goods maintenance is required. The module generates a correction factor based on the difference between the predicted goods reconfirmation index of each warehouse and the reference goods reconfirmation index, and corrects the adaptability value of each warehouse based on the correction factor.

8. The ERP inventory scheduling optimization system for multi-warehouse collaboration according to claim 7, characterized in that, The consistent outbound and inventory conditions of different warehouses under the target order means that each warehouse has the same initial storage conditions for the goods corresponding to the target order, and that the differences in inventory availability, outbound feasibility and basic scheduling constraints of the target goods available for outbound are within a preset tolerance range, provided that the requirements of the target order are met.

9. The ERP inventory scheduling optimization system for multi-warehouse collaboration according to claim 7, characterized in that, The goods corresponding to the target order are goods that are stored for a long time and have long order demand intervals. The goods have an initial storage stage in the warehousing process where no goods maintenance is required when the storage time has not reached the preset maintenance threshold, and a subsequent storage stage where goods maintenance is required after the storage time has reached the preset maintenance threshold. The calculation process of the cargo maintenance resources includes: obtaining the set of maintenance procedures corresponding to the cargo, obtaining the occupancy information of maintenance personnel, maintenance equipment or maintenance workstations required for each maintenance procedure, and quantifying the cargo maintenance resources based on the number of times the maintenance procedures are executed and the amount of maintenance personnel, maintenance equipment or maintenance workstations invested.

10. The ERP inventory scheduling optimization system for multi-warehouse collaboration according to claim 7, characterized in that, The requirement that the order conditions corresponding to each sample are consistent with the target order means that each historical scheduling sample is the same as the target order in terms of goods type, required quantity and outbound timeliness, or that the corresponding parameter differences are within the preset tolerance range under the premise of meeting the requirements of the target order.

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