Warehousing business process analysis processing method and system

Abstract

The invention discloses a storage business process analysis processing method and system, and the method comprises the steps: calculating the replenishment number according to scheduling data, obtaining and confirming the replenishment point location information of the next day through a loading terminal, feeding back the replenishment point location information to a tritium cloud-ERP system, triggering an order making process, and generating a specified document according to a preset logic rule; the replenisher and point location level in-transit warehouse and goods channel warehouse data dimensions are introduced, the inventory dynamic state of the goods in a preset link is tracked in real time, and the in-transit inventory of the goods is updated in time; establishing an abnormal scene processing mechanism, recording inventory change and starting a point location level approval flow; an online task handover platform is constructed, so that task information between the tritium cloud-ERP system and the loading terminal is synchronously confirmed in real time to define a commodity responsibility transfer node; and business link data is associated through the unique associated order number, operation historical information is recorded, and full-process traceable management of the storage business is realized. According to the invention, the problems of data non-circulation, low efficiency and poor accuracy in the current warehousing business are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of warehousing, and particularly relates to a method and system for analyzing and processing warehousing business processes. Background Art

[0002] In the field of warehousing management, there are many drawbacks in the traditional process. It seriously relies on manual operations and email communication, resulting in isolated data and forming information islands. Data in different links cannot be effectively circulated and shared, greatly affecting work efficiency. Manual operations are prone to errors, resulting in poor data accuracy. For example, the data of the goods aisle warehouse and the in-machine inventory are often inconsistent, and there is a lack of statistics on abnormal scenarios, making it difficult to effectively analyze the goods damage rate. When handing over goods, due to the lack of a clear definition in the system, it is difficult to clearly divide responsibilities.

[0003] The root causes of these problems lie in high manual dependence, unsmooth data flow, lack of automated and intelligent tools, and imperfect coordination mechanisms. For large retail enterprises, commodities need to go through multiple links from the manufacturer to the consumers, passing through multiple transfer warehouses. Each transfer warehouse is responsible for different regions and finally converges at the store. In this process, the original purchase management system is backward, mostly using manual order placement and manual receipt, with low efficiency and prone to errors. Although the existing system has many data dimensions, it does not use the machine location as the inventory dimension, which is not conducive to precise management. At the same time, there are defects in the system functions, and in actual operation, there is often a chaotic situation where there are two different-dimensional warehouses in the same area, hindering the efficient development of warehousing operations. There is an urgent need for innovative solutions to achieve digital transformation and improve the overall operation level. Summary of the Invention

[0004] To this end, the present invention provides a method and system for analyzing and processing warehousing business processes to solve the problems of unsmooth data flow, low efficiency, and poor accuracy existing in current warehousing operations.

[0005] To achieve the above object, the present invention provides the following technical solution: A method for analyzing and processing warehousing business processes, including:

[0006] Establish a data connection among a visual inventory arrangement system, a Tritium Cloud - ERP system, and a goods replenishment terminal. Calculate the replenishment quantity according to the scheduling data, obtain and confirm the replenishment point information for the next day through the goods replenishment terminal, and feedback the replenishment point information to the Tritium Cloud - ERP system. Trigger the document preparation process according to the preset logical rules to generate a specified document;

[0007] Introduce data dimensions of replenishment workers and in-transit warehouses and goods aisle warehouses at the point level, real-time track the inventory dynamics of goods in preset links, and timely update the in-transit inventory of goods; for abnormal situations such as theft of goods and machine failures, establish an abnormal scenario processing mechanism, record inventory changes, and initiate an approval process at the point level;

[0008] Build an online task handover platform to enable real-time synchronization and confirmation of task information between the Tritium Cloud - ERP system and the goods uploading terminal, so as to define the commodity liability transfer node; associate business process data through a unique associated order number, record the historical information of each operation, and realize traceable management of the entire warehousing business process.

[0009] As an optimal solution for the warehousing business process analysis and processing method, the exponential smoothing method is used to calculate the replenishment quantity according to the scheduling data, and the formula is:

[0010] S t =αY t +(1-α)S t-1

[0011]

[0012] Among them, S t is the smoothed value of the t-th period, S t-1 is the smoothed value of the (t - 1)-th period, Y t is the actual inventory value of the t-th period, α is the smoothing coefficient, 0 < α < 1, is the predicted replenishment quantity of the (t + 1)-th period.

[0013] As an optimal solution for the warehousing business process analysis and processing method, when real-time tracking the inventory dynamics of goods in the preset link, the moving average method is used for preliminary processing of inventory data, and the formula is:

[0014]

[0015] Among them, MA t is the moving average value of the t-th period, Y t-n+1 is the inventory data of the (t - n + 1)-th period, Y t-n+2 is the inventory data of the (t - n + 2)-th period, and n is the number of periods of the moving average.

[0016] As an optimal solution for the warehousing business process analysis and processing method, in the abnormal scenario processing mechanism, the decision tree algorithm is used to judge the abnormal type. Each internal node of the decision tree is a test on an attribute, each branch is a test output, and each leaf node is a category or value. Let X be the feature vector related to inventory, and T be the decision tree model. For the new inventory change situation X, the predicted abnormal type C is obtained through C = T(X).

[0017] As an optimal solution for the warehousing business process analysis and processing method, when building the online task handover platform, the MD5 hash algorithm is used to encrypt the task information, and the formula is:

[0018] H=MD5(M)

[0019] Among them, M is the task information, and H is the output of the MD5 hash algorithm.

[0020] As an optimal solution for the warehousing business process analysis and processing method, when associating business link data through a unique associated order number, the association rule mining algorithm is used to discover the association relationship between business links. Let I = {i1, i2, …, i m} be the set of items, D be the transaction database, each transaction T be a subset of I, and the confidence calculation formula for the association rule X → Y is:

[0021]

[0022] Among them, Support(X) represents the support degree of the item set X in the transaction database D, that is, the proportion of transactions containing X in D; Support(X ∪ Y) represents the support degree of the union of the item sets X and Y in the transaction database D.

[0023] The present invention also provides a warehousing business process analysis and processing system, including:

[0024] A data interaction module, used to establish data connections between the visualization goods arrangement system, the Tritium Cloud - ERP system, and the goods - loading terminal, calculate the replenishment quantity according to the scheduling data, obtain and confirm the replenishment point information for the next day through the goods - loading terminal, and feedback the replenishment point information to the Tritium Cloud - ERP system. According to the preset logic rules, trigger the document - making process and generate specified documents;

[0025] An inventory management and monitoring module, used to introduce data dimensions of in - transit warehouses and goods - lane warehouses at the replenisher and point levels, real - time track the inventory dynamics of goods in preset links, and timely update the in - transit inventory of goods; for abnormal situations such as goods theft and machine failures, establish an abnormal scenario processing mechanism, record inventory changes, and initiate a point - level approval process;

[0026] A task handover and traceability module, used to build an online task handover platform to enable real - time synchronization and confirmation of task information between the Tritium Cloud - ERP system and the goods - loading terminal to define the commodity responsibility transfer node; associate business link data through a unique associated order number, record the historical information of each operation, and realize traceable management of the entire warehousing business process.

[0027] As an optimal solution for the warehousing business process analysis and processing system, in the data interaction module, the exponential smoothing method is used to calculate the replenishment quantity, and the formula is:

[0028] S t = αY t +(1 - α)S t-1

[0029]

[0030] Among them, S t is the smoothed value of the t-th period, and S t-1 is the smoothed value of the (t - 1)-th period, and Y t is the actual inventory value of the t-th period, and α is the smoothing coefficient, where 0 < α < 1, is the replenishment quantity predicted for the (t + 1)-th period.

[0031] As an optimal solution for the warehousing business process analysis and processing system, in the inventory management and monitoring module, the moving average method is used for the preliminary processing of inventory data, and the formula is:

[0032]

[0033] Among them, MA t is the moving average value of the t-th period, Y t-n+1 is the inventory data of the (t - n + 1)-th period, Y t-n+2 is the inventory data of the (t - n + 2)-th period, and n is the number of periods for the moving average;

[0034] In the inventory management and monitoring module, the decision tree algorithm is used to judge the abnormal type for the abnormal scenario processing mechanism. Each internal node of the decision tree is a test on an attribute, each branch is a test output, and each leaf node is a category or value. Let X be the feature vector related to inventory, and T be the decision tree model. For the new inventory change situation X, the predicted abnormal type C is obtained through C = T(X).

[0035] As an optimal solution for the warehousing business process analysis and processing system, in the task handover and traceability module, the MD5 hash algorithm is used to encrypt the task information, and the formula is:

[0036] H = MD5(M)

[0037] Among them, M is the task information, and H is the output of the MD5 hash algorithm;

[0038] In the task handover and traceability module, the association rule mining algorithm is used to discover the association relationships between business processes. Let I = {i1, i2,..., i m} be the set of items, D be the transaction database, and each transaction T be a subset of I. The confidence calculation formula for the association rule X → Y is:

[0039]

[0040] Among them, Support(X) represents the support degree of the item set X in the transaction database D, that is, the proportion of the transactions containing X in D; Support(X ∪ Y) represents the support degree of the union of the item sets X and Y in the transaction database D.

[0041] The beneficial effects of the present invention are as follows:

[0042] First, by establishing data connections among the visual replenishment system, the Tritium Cloud - ERP system, and the merchandising terminal, automatic calculation of replenishment quantities and automation of the order - making process are achieved, significantly reducing manual operation and communication costs and shortening business processing time. It can quickly and accurately predict replenishment quantities. The replenisher confirms the replenishment location information through the merchandising terminal and gives feedback, triggering automatic order - making, avoiding the cumbersome process of manual order - making, and significantly improving the processing efficiency of warehousing operations.

[0043] Second, by introducing data dimensions of in - transit warehouses and aisle warehouses at the replenisher and location levels, the timeliness and accuracy of inventory data are ensured. When abnormal situations such as commodity theft and machine failures occur, the abnormal situation handling mechanism can quickly record inventory changes and initiate an approval process, effectively avoiding inventory data chaos. It enables managers to better grasp the inventory change trend, timely adjust inventory strategies, and reduce inventory costs.

[0044] Third, the constructed online task handover platform realizes real - time synchronization and confirmation of task information between the Tritium Cloud - ERP system and the merchandising terminal, clarifies the nodes of commodity responsibility transfer, and ensures the integrity and security of task information during transmission and storage. At the same time, by associating business link data with a unique associated order number and combining the association rule mining algorithm, it is convenient and fast to trace the business process, improving the reliability and traceability of data management. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings described below are only exemplary, and for those of ordinary skill in the art, without creative efforts, other implementation drawings can be obtained based on the provided drawings.

[0046] The structures, ratios, sizes, etc. depicted in this specification are only used to cooperate with the content disclosed in the specification for those familiar with this technology to understand and read, and are not used to limit the limiting conditions for the implementation of the present invention. Therefore, they do not have a substantial technical meaning. Any modification of the structure, change in the proportional relationship, or adjustment of the size, without affecting the effects that the present invention can produce and the purposes that can be achieved, should still fall within the scope covered by the technical content disclosed in the present invention.

[0047] Figure 1 It is a schematic flow chart of the warehousing operation process analysis and processing method provided by an embodiment of the present invention;

[0048] Figure 2 It is the first part of the application timing diagram of the warehousing operation process analysis and processing method provided by an embodiment of the present invention;

[0049] Figure 3 This is the second part of the application timing diagram for the warehousing business process analysis and processing method provided by the embodiments of the present invention;

[0050] Figure 4 This is the schematic diagram of the architecture of the warehousing business process analysis and processing system provided by the embodiments of the present invention. Specific Embodiments

[0051] The following specific embodiments illustrate the implementation manners of the present invention. Those skilled in the art can easily understand the other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0052] Embodiment 1

[0053] Refer to Figure 1 , Embodiment 1 of the present invention provides a warehousing business process analysis and processing method, including the following steps:

[0054] S1. Establish data connections among the visualization goods arrangement system, the Tritium Cloud - ERP system, and the goods - loading terminal. Calculate the replenishment quantity according to the scheduling data, obtain and confirm the replenishment location information for the next day through the goods - loading terminal, and feedback the replenishment location information to the Tritium Cloud - ERP system. According to the preset logical rules, trigger the order - making process to generate a specified document;

[0055] S2. Introduce data dimensions of replenishment workers and in - transit warehouses and aisle warehouses at the location level, track the inventory dynamics of goods in the preset links in real time, and update the in - transit inventory of goods in a timely manner; for abnormal situations such as goods theft and machine failures, establish an abnormal scenario processing mechanism, record inventory changes, and initiate an approval process at the location level;

[0056] S3. Construct an online task handover platform to enable real - time synchronization and confirmation of task information between the Tritium Cloud - ERP system and the goods - loading terminal to define the node of goods responsibility transfer; associate business link data through a unique associated order number, record the historical information of each operation, and realize traceable management of the entire warehousing business process.

[0057] In this embodiment, in step S1, the calculation of the replenishment quantity according to the scheduling data adopts the exponential smoothing method, and the formula is:

[0058] S t = αY t +(1 - α)S t-1

[0059]

[0060] Among them, S t is the smoothed value of the t-th period, and S t-1 is the smoothed value of the (t - 1)-th period, Y t is the actual inventory value of the t-th period, α is the smoothing coefficient, 0 < α < 1, is the replenishment quantity predicted for the (t + 1)-th period.

[0061] In the warehousing business, under the traditional mode, manual order placement and receipt are not only inefficient but also error-prone. Step S1 of the present invention breaks the data barrier by establishing a data connection among the visual goods arrangement system, the Tritium Cloud - ERP system, and the goods - loading terminal.

[0062] Among them, data is shared in real time among systems, and the scheduling data becomes the key basis for calculating the replenishment quantity. Using the exponential smoothing method to calculate the replenishment quantity fully considers the weights of historical data and current data. For example, for a daily necessity with long - term sales, historical sales data shows that its sales volume is relatively stable, but recently, due to promotional activities, the sales volume has increased. The smoothing coefficient (0 < α < 1) in the exponential smoothing method can flexibly adjust the influence degree of historical data and current data. When α takes a larger value, it focuses more on current data and can quickly respond to changes in sales volume; when α takes a smaller value, it relies more on the stability of historical data. The replenishment quantity calculated in this way is more in line with the actual demand.

[0063] During the implementation process, the replenisher obtains and confirms the replenishment location information for the next day through the goods - loading terminal, and this operation is directly fed back to the Tritium Cloud - ERP system. According to the preset logical rules, the order - making process is automatically triggered to generate specified documents such as the "Picking List" and the "Replenishment List". In the traditional mode, manual order - making is not only error - prone but also has a long order - making cycle, affecting the overall business efficiency. Now, the automated order - making process greatly reduces manual intervention, avoids errors that may occur in manual calculation and manual entry, and improves the accuracy and efficiency of business processing.

[0064] In a possible embodiment, in step S2, when real - time tracking the inventory dynamics of goods in a preset link, the moving average method is used for preliminary processing of inventory data, and the formula is:

[0065]

[0066] Among them, MA t is the moving average value of the t-th period, Y t-n+1 is the inventory data of the (t - n + 1)-th period, Y t-n+2 is the inventory data of the (t - n + 2)-th period, and n is the number of periods for moving average.

[0067] Specifically, the moving average method eliminates random fluctuations in data by calculating the average value of data over a certain period, thus more clearly showing the trend of the data. In inventory management, using the moving average method can smooth the fluctuations of inventory data, enabling inventory managers to more accurately observe the changing trend of inventory. The number of periods n of the moving average determines the degree of smoothing. The larger n is, the more obvious the smoothing effect, but the slower the response to data changes; the smaller n is, the more sensitive the response to data changes, but the relatively weaker the smoothing effect. By calculating the moving average value MA t , more stable reference data can be provided for subsequent inventory management decisions.

[0068] In a possible embodiment, in step S2, in the abnormal scenario processing mechanism, the decision tree algorithm is used to determine the type of abnormality. Each internal node of the decision tree is a test on an attribute, each branch is a test output, and each leaf node is a category or value. Let X be the feature vector related to inventory, and T be the decision tree model. For the new inventory change situation X, the predicted type of abnormality C is obtained through C = T(X).

[0069] Specifically, in the processing of inventory abnormal scenarios, a large amount of inventory data and corresponding types of abnormalities need to be collected first as training data, and then these data are used to construct the decision tree model T. The internal nodes of the decision tree represent inventory-related attributes (such as the amplitude of inventory changes, change time, etc.), each branch represents different test results for this attribute, and the leaf nodes represent different types of abnormalities. When a new inventory change situation X occurs, the decision tree model starts from the root node, conducts tests based on the attribute values of X, traverses downward along the corresponding branches until reaching the leaf node, thereby obtaining the predicted type of abnormality C. This method can automatically determine the type of abnormality according to the characteristics of inventory data, improving the efficiency and accuracy of abnormality handling.

[0070] In a possible embodiment, in step S3, when constructing the online task handover platform, the MD5 hash algorithm is used to encrypt the task information. The formula is:

[0071] H = MD5(M)

[0072] where M is the task information and H is the output of the MD5 hash algorithm.

[0073] Specifically, MD5 is a widely used hashing algorithm that can convert input data M of any length into a hash value H of a fixed length (usually 128 bits). In the task handover platform, using the MD5 algorithm to encrypt task information can ensure the integrity of task information during transmission and storage. The receiving party can recalculate the MD5 hash value of the received task information and compare it with the hash value provided by the sending party. If the two are the same, it indicates that the task information has not been tampered with during transmission.

[0074] In this embodiment, in step S3, when associating business process data through a unique associated order number, an association rule mining algorithm is used to discover the association relationship between business processes. Let I = {i1, i2, …, i m} be the set of items, D be the transaction database, each transaction T be a subset of I, and the confidence calculation formula for the association rule X → Y is:

[0075]

[0076] Among them, Support(X) represents the support degree of the item set X in the transaction database D, that is, the proportion of transactions containing X in D; Support(X ∪ Y) represents the support degree of the union of the item sets X and Y in the transaction database D.

[0077] Specifically, in the warehousing business, the item set I can represent different business processes (such as order generation, replenishment operation, inventory adjustment, etc.), and the transaction database D records the historical data of business operations. The association rule X → Y means that when the business process X occurs, the business process Y also has a certain probability of occurring. The confidence Confidence(X → Y) measures the strength of this association, which represents the proportion of transactions containing the item set Y among the transactions containing the item set X. By calculating the confidence of different association rules, potential association relationships between business processes can be discovered, which helps to optimize business processes, improve work efficiency, and conduct risk early warnings. For example, if a high confidence is found between order generation and replenishment operations, the replenishment process can be automatically triggered when an order is generated, reducing manual intervention.

[0078] See Figure 2 and Figure 3 show the goods sorting process from system processing to manual operation and then to visualization using the method of the present invention:

[0079] In the T0 stage, the system will first generate a task for calculating the replenishment quantity for T + 1. This task will be sent to the user in two ways: "Tritium Cloud - ERP - Outbound T + 1" and "Goods Loading APP T0 + 1", so that the user can timely understand the replenishment requirements.

[0080] After receiving the information, the user needs to check which positions in the daily position schedule recommended by the system require replenishment. For the positions that need replenishment, the user will further refine the modeling to determine the specific replenishment quantity and schedule.

[0081] Next, based on the user's confirmation, the system will calculate the actual quantity of goods to be replenished and send it to the user again through the method of "Tritium Cloud - ERP - Inbound T+1". At the same time, it will also confirm which positions actually need replenishment through the "Corner Recognition" function.

[0082] In the T0+1 stage, the user needs to confirm whether to replenish according to the system's prompt. Once confirmed, the user will generate the final version of the "Picking List" based on the confirmation result. During this process, the system will also automatically generate relevant information such as the total outbound quantity, picking task operations, and warehouse operations.

[0083] In the picking task operation link, the system will generate a list of goods to be picked according to the "Picking List". The picker will pick the goods according to the list and confirm that the quantity of goods is correct through online handover.

[0084] For regularly replenished goods, the replenisher will deliver the goods to the designated location according to the agreed time and method. If a goods withdrawal process is required, the system will generate a "Goods Withdrawal List" and confirm the actual received quantity of goods through online handover. If the actual received quantity is less than the system inventory, the user needs to note the reason and allocate the responsibility of the goods to the warehouse staff.

[0085] Finally, in the warehouse operation link, the system will automatically generate an inbound order, marking the end of the entire replenishment process.

[0086] The present invention will be described below in combination with specific application scenarios:

[0087] Scenario Setting

[0088] A large retail enterprise has warehouses and numerous sales positions in many cities across the country. The types of goods are diverse, covering food, daily necessities, electronic products, etc. Previously, its warehousing operations faced serious efficiency and management problems. For example, the processes of arranging goods, distributing goods, replenishing goods, and returning to the warehouse relied on manual operations and email notifications, resulting in non-interoperable data, frequent inconsistencies between the data of the goods aisle warehouse and the in-machine inventory, a high goods damage rate, and difficulty in clearly defining the responsibilities of goods handover.

[0089] Implementation Process

[0090] System Deployment and Training: When an enterprise introduces this warehousing business process management system, it first organizes relevant personnel such as operations staff, warehouse managers, and replenishers for system operation training. Operations staff learn how to accurately input and update schedule data for positions in the visual goods arrangement system, and at the same time understand the system's data encryption mechanism to ensure the security of data during input and transmission, as well as the scope of access rights for their own accounts to prevent data leakage. Warehouse managers are familiar with the new functional modules of ChuanYun-ERP, including efficiently picking goods according to the picking lists generated by the system, handling inbound and outbound operations, and generating relevant documents, and master the operation specifications for inventory data under different permissions. Replenishers master the use of the goods replenishment APP, such as viewing and confirming replenishment positions, reporting replenishment and withdrawal quantities, etc., and at the same time understand the data encryption and security protection measures on the APP side. During the training process, employee feedback is collected, and operation difficulties and questions are promptly answered and intensively guided to ensure that employees can proficiently use the system.

[0091] Business Process Adjustment: In actual business, operations staff formulate schedule plans for positions in each city in the visual goods arrangement system according to the new process. The system automatically calculates the replenishment quantity according to the preset algorithm and pushes it to the goods replenishment APP of the replenishers. For different types of commodities, the system algorithm comprehensively considers their respective characteristics. For example, for food, in addition to historical sales data, seasonal factors, and current inventory levels, the shelf life of food is also considered. It is calculated that 50 pieces need to be replenished at a certain sales position and the replenisher in charge of this position is notified in a timely manner. For daily necessities, factors such as promotional activities and market demand fluctuations are combined to plan the replenishment quantity for the corresponding positions. For electronic products, the replenishment quantity is determined based on the product's replacement speed and market popularity. Replenishers confirm the positions where replenishment can be made the next day on the APP. If it is found that some positions cannot be replenished due to temporary site restrictions, adjustments can be made within the specified time, and the data is synchronized to ChuanYun-ERP. The warehouse generates the initial and final versions of the "Picking List" and "Replenishment List" accordingly. At the same time, the system has an emergency feedback channel. When emergencies such as natural disasters and supplier delivery delays occur, operations staff can promptly enter relevant information in the system. The system will adjust the replenishment plan according to the preset rules and notify relevant personnel.

[0092] Inventory Management and Monitoring: Warehouse management personnel conduct picking operations based on the "Picking List" and record batch information of goods in Tritium Cloud - ERP. After the replenisher completes replenishment, the actual replenishment quantity is confirmed on the APP, and the system automatically updates the in - transit inventory and the "Replenishment List". For example, during a replenishment process, due to machine jamming and repair of a certain electronic product, 3 items were left on - site. The replenisher records the abnormal situation on the APP, the system starts the abnormal handling process, records the reason for inventory change, and initiates an approval process at the location level. After the business supervisor reviews, the system automatically adjusts the inventory data, counts these 3 items as potential damaged goods, and simultaneously updates the in - transit inventory and relevant document information. In case of force majeure such as natural disasters, the system will automatically freeze relevant inventory operations. After the situation stabilizes, the inventory data is adjusted according to the actual inventory count, and the replenishment plan is re - planned.

[0093] Execution of Goods Withdrawal Process: When goods need to be withdrawn, for example, when a certain daily necessity is approaching its expiration date, the replenisher reports the withdrawal quantity on the APP. In a normal withdrawal scenario, if the inventory of the daily necessity in the goods aisle at a certain location is 20 pieces and the replenisher reports a withdrawal quantity of 20 pieces, the system generates a "Goods Withdrawal Order". After the replenisher brings the goods back to the warehouse, the warehouse management personnel summarize the "Goods Withdrawal Order" in Tritium Cloud - ERP to generate a "Goods Withdrawal and Receipt Order", check the quantity and expiration date, and then perform the warehousing operation. The system synchronously updates the inventory and relevant document status. In an abnormal withdrawal scenario, such as when the machine - counted inventory in the goods aisle decreases due to theft, the system also records the abnormality and processes it according to the established process to ensure the accuracy of inventory data. At the same time, the system classifies and statistically analyzes the reasons for goods withdrawal, providing data support for subsequent optimization of inventory management.

[0094] Implementation Effect

[0095] After a period of operation, the efficiency of the enterprise's warehousing process has been significantly improved. The manual document - making and manual handover links have been greatly reduced, and the error rate has decreased by approximately 80%. The inventory data between the goods aisle warehouse and the in - machine inventory has achieved real - time synchronization and accurate statistics, and the damaged goods rate has decreased by approximately 30%. Through the real - time monitoring system, managers can adjust the warehouse plan in a timely manner, respond to inventory changes in advance, and the overall warehousing operation cost has decreased by approximately 20%. Employees feedback that the new system is easy to operate, the training effect is good, and it can effectively improve work efficiency. At the same time, the system's security protection measures allow employees to operate with confidence, ensuring data accuracy and confidentiality. In the face of emergencies, the system's emergency handling mechanism and flexible adjustment ability ensure the continuity and stability of warehousing operations, effectively improving the enterprise's warehousing management level and economic benefits.

[0096] Example 2

[0097] See Figure 4 , Embodiment 2 of the present invention also provides a warehousing business process analysis and processing system, including:

[0098] The data interaction module 001 is used to establish data connections among the visual merchandising system, the Tritium Cloud - ERP system, and the merchandise uploading terminal, calculate the replenishment quantity according to the scheduling data, obtain and confirm the replenishment location information for the next day through the merchandise uploading terminal, and feedback the replenishment location information to the Tritium Cloud - ERP system. According to the preset logical rules, it triggers the document generation process to generate specified documents.

[0099] The inventory management and monitoring module 002 is used to introduce data dimensions of in - transit warehouses and aisle warehouses at the replenisher and location levels, track the inventory dynamics of goods in the preset links in real - time, and update the in - transit inventory of goods in a timely manner. For abnormal situations such as theft of goods and machine failures, it establishes an abnormal scenario handling mechanism, records inventory changes, and initiates an approval process at the location level.

[0100] The task handover and traceability module 003 is used to build an online task handover platform to enable real - time synchronization and confirmation of task information between the Tritium Cloud - ERP system and the merchandise uploading terminal to define the commodity liability transfer node. It associates business link data through a unique reference number and records the historical information of each operation to achieve traceable management of the entire warehousing business process.

[0101] In a possible embodiment, in the data interaction module 001, the exponential smoothing method is used to calculate the replenishment quantity, and the formula is:

[0102] S t =αY t +(1 - α)S t-1

[0103]

[0104] Where S t is the smoothed value of the t - th period, S t-1 is the smoothed value of the (t - 1) - th period, Y t is the actual inventory value of the t - th period, α is the smoothing coefficient, 0 < α < 1, is the predicted replenishment quantity of the (t + 1) - th period.

[0105] In a possible embodiment, in the inventory management and monitoring module 002, the moving average method is used for the preliminary processing of inventory data, and the formula is:

[0106]

[0107] Where MA t is the moving average value of the t - th period, Y t-n+1 is the inventory data of the (t - n + 1) - th period, Y t-n+2 is the inventory data of the (t - n + 2) - th period, and n is the number of periods for the moving average;

[0108] In the inventory management monitoring module 002, the decision tree algorithm is used to determine the abnormal type for the abnormal scenario processing mechanism. Each internal node of the decision tree is a test on an attribute, each branch is a test output, and each leaf node is a category or value. Let X be the feature vector related to inventory, and T be the decision tree model. For the new inventory change situation X, the predicted abnormal type C is obtained through C = T(X).

[0109] In a possible embodiment, in the task handover traceability module 003, the MD5 hash algorithm is used to encrypt the task information. The formula is:

[0110] H = MD5(M)

[0111] where M is the task information and H is the output of the MD5 hash algorithm;

[0112] In a possible embodiment, in the task handover traceability module 003, the association rule mining algorithm is used to discover the association relationship between business processes. Let I = {i1, i2,..., i m} be the set of items, D be the transaction database, and each transaction T be a subset of I. The confidence calculation formula for the association rule X → Y is:

[0113]

[0114] where Support(X) represents the support degree of the item set X in the transaction database D, that is, the proportion of the transactions containing X in D; Support(X ∪ Y) represents the support degree of the union of the item sets X and Y in the transaction database D.

[0115] It should be noted that the information interaction, execution process, etc. between the above system modules, due to being based on the same concept as the method embodiment in Embodiment 1 of the present application, have the same technical effects as the method embodiment of the present application. The specific content can be seen in the description in the method embodiment shown above in the present application, and will not be elaborated here.

[0116] Embodiment 3

[0117] Embodiment 3 of the present invention provides a non - transitory computer - readable storage medium, in which program code for the warehousing business process analysis and processing method is stored. The program code includes instructions for executing the warehousing business process analysis and processing method in Embodiment 1 or any possible implementation manner thereof.

[0118] A computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that incorporates one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state disk (SSD)), etc.

[0119] Embodiment 4

[0120] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0121] The processor and the memory communicate with each other via a bus; the memory stores program instructions executable by the processor, and the processor can execute the warehousing business process analysis and processing method of Embodiment 1 or any possible implementation manner thereof by invoking the program instructions.

[0122] Specifically, the processor can be implemented by hardware or by software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc.; when implemented by software, the processor can be a general-purpose processor, which is implemented by reading software code stored in the memory. The memory can be integrated in the processor or can exist independently outside the processor.

[0123] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a dedicated computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, a computer, a server, or a data center to another website, a computer, a server, or a data center by wire (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wirelessly (e.g., infrared, wireless, microwave, etc.).

[0124] Obviously, those skilled in the art should understand that the above-mentioned modules or steps of the present invention can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed on a network composed of multiple computing devices. Optionally, they can be implemented by program codes executable by the computing device. Thus, they can be stored in a storage device and executed by the computing device. And in some cases, the steps shown or described can be executed in a sequence different from that here, or they can be separately fabricated into individual integrated circuit modules, or multiple modules or steps among them can be fabricated into a single integrated circuit module for implementation. In this way, the present invention is not limited to any specific combination of hardware and software.

[0125] Although the present invention has been described in detail with general descriptions and specific embodiments above, based on the present invention, some modifications or improvements can be made, which are obvious to those skilled in the art. Therefore, these modifications or improvements made without departing from the spirit of the present invention all fall within the scope of protection required by the present invention.

Claims

1. A method for analyzing and processing a warehousing business process, characterized in that, Including: Establish data connections among the visual inventory arrangement system, the Tritium Cloud - ERP system, and the goods - uploading terminal, calculate the replenishment quantity according to the scheduling data, obtain and confirm the replenishment location information for the next day through the goods - uploading terminal, and feedback the replenishment location information to the Tritium Cloud - ERP system. According to the preset logical rules, trigger the document - making process to generate specified documents; Introduce data dimensions of in - transit warehouses and aisle warehouses at the replenisher and location levels, track the inventory dynamics of goods in preset links in real - time, and update the in - transit inventory of goods in a timely manner; for abnormal situations such as goods theft and machine failures, establish an abnormal - scenario handling mechanism, record inventory changes, and initiate an approval process at the location level; Construct an online task handover platform to enable real - time synchronization and confirmation of task information between the Tritium Cloud - ERP system and the goods - uploading terminal to define the node of goods responsibility transfer; Associate business - link data through a unique associated order number, record the historical information of each operation, and achieve traceable management of the entire warehousing business process.

2. The warehousing business process analysis and processing method according to claim 1, characterized in that The calculation of the replenishment quantity according to the scheduling data uses the exponential smoothing method, and the formula is: S t = αY t + (1 - α)S t-1 Among them, S t is the smoothed value of the t-th period, and S t-1 is the smoothed value of the (t - 1)-th period. Y t is the actual inventory value of the t-th period, α is the smoothing coefficient, where 0 < α < 1, is the replenishment quantity predicted for the (t + 1)-th period.

3. The warehousing business process analysis and processing method according to claim 1, characterized in that When tracking the inventory dynamics of goods in preset links in real - time, the moving average method is used for preliminary processing of inventory data, and the formula is: Among them, MA t is the moving average of the t-th period, Y t-n+1 is the inventory data of the (t - n + 1)-th period, Y t-n+2 is the inventory data of the (t - n + 2)-th period, and n is the number of periods for the moving average.

4. The warehousing business process analysis and processing method according to claim 1, characterized in that In the abnormal - scenario handling mechanism, the decision - tree algorithm is used to judge the abnormal type. Each internal node of the decision tree is a test on an attribute, each branch is a test output, and each leaf node is a category or value. Let X be the feature vector related to inventory, and T be the decision - tree model. For a new inventory change situation X, the predicted abnormal type C is obtained through C = T(X).

5. The warehousing business process analysis and processing method according to claim 1, characterized in that When constructing the online task handover platform, the MD5 hash algorithm is used to encrypt the task information, and the formula is: H = MD5(M) where M is the task information and H is the output of the MD5 hash algorithm.

6. The warehousing business process analysis and processing method according to claim 1, wherein When associating business process data through a unique associated order number, the association rule mining algorithm is used to discover the association relationships between business processes. Let I = {i1, i2, …, i m} be the set of items, D be the transaction database, and each transaction T be a subset of I. The confidence calculation formula for the association rule X → Y is: Among them, Support(X) represents the support degree of the item set X in the transaction database D, that is, the proportion of transactions containing X in D; Support(X∪Y) represents the support degree of the union of the item sets X and Y in the transaction database D.

7. A warehousing business process analysis and processing system, characterized in that, Including: A data - interaction module, which is used to establish data connections among the visual inventory arrangement system, the Tritium Cloud - ERP system, and the goods - uploading terminal, calculate the replenishment quantity according to the scheduling data, obtain and confirm the replenishment location information for the next day through the goods - uploading terminal, and feedback the replenishment location information to the Tritium Cloud - ERP system. According to the preset logical rules, trigger the document - making process to generate specified documents; An inventory - management monitoring module, which is used to introduce data dimensions of in - transit warehouses and aisle warehouses at the replenisher and location levels, track the inventory dynamics of goods in preset links in real - time, and update the in - transit inventory of goods in a timely manner; for abnormal situations such as goods theft and machine failures, establish an abnormal - scenario handling mechanism, record inventory changes, and initiate an approval process at the location level; A task - handover traceability module, which is used to construct an online task handover platform to enable real - time synchronization and confirmation of task information between the Tritium Cloud - ERP system and the goods - uploading terminal to define the node of goods responsibility transfer; Associate business - link data through a unique associated order number, record the historical information of each operation, and achieve traceable management of the entire warehousing business process.

8. The warehousing business process analysis and processing system according to claim 7, wherein In the data interaction module, the exponential smoothing method is used to calculate the replenishment quantity according to the scheduling data, and the formula is: S t = αY t + (1 - α)S t-1 Among them, S t is the smoothed value of the t-th period, S t-1 is the smoothed value of the (t - 1)-th period, Y t is the actual inventory value of the t-th period, α is the smoothing coefficient, 0 < α < 1, is the replenishment quantity predicted for the (t + 1)-th period.

9. The warehousing business process analysis and processing system according to claim 7, wherein In the inventory management monitoring module, the moving average method is used for the preliminary processing of inventory data, and the formula is: Among them, MA t is the moving average value in the t-th period, and Y t-n+1 is the inventory data in the (t - n + 1)-th period, and Y t-n+2 is the inventory data in the (t - n + 2)-th period, where n is the number of periods for the moving average; In the inventory management monitoring module, the decision tree algorithm is used to judge the abnormal type for the abnormal scenario processing mechanism. Each internal node of the decision tree is a test on an attribute, each branch is a test output, and each leaf node is a category or value. Let X be the feature vector related to inventory and T be the decision tree model. For the new inventory change situation X, the predicted abnormal type C is obtained through C = T(X).

10. The warehousing business process analysis and processing system according to claim 6, characterized in that, In the task handover and traceability module, the MD5 hash algorithm is used to encrypt the task information, and the formula is: H = MD5(M) where M is the task information and H is the output of the MD5 hash algorithm; In the task handover traceability module, an association rule mining algorithm is used to discover the association relationships between business processes. Let I = {i1, i2, …, i m} be the set of items, D be the transaction database, and each transaction T be a subset of I. The confidence calculation formula for the association rule X → Y is as follows: where Support(X) represents the support degree of the item set X in the transaction database D, that is, the proportion of the transactions containing X in D; Support(X ∪ Y) represents the support degree of the union of the item sets X and Y in the transaction database D.

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