Order delivery ability analysis method and device

Abstract

The invention provides an order delivery capability analysis method and device, and the method comprises the steps: configuring reference delivery efficiency for evaluating the fluctuation range of delivery efficiency, and enabling the reference delivery efficiency to be the average delivery time consumption of a unit product obtained based on the statistics of historical order delivery data, the delivery process parameters of the orders are monitored, after the orders are delivered, the fluctuation amplitude of the actual delivery efficiency of the orders is calculated, and the fluctuation amplitude is the variation amplitude of the actual delivery efficiency of each order relative to the reference delivery efficiency; and performing correlation analysis on the delivery process parameters and the fluctuation amplitude of the actual delivery efficiency, and identifying a delivery weak link according to a correlation analysis result, so that the dynamic change condition of the order delivery capability of the manufacturing enterprise can be reflected, and the weak link in the order delivery process can be accurately identified.

Description

Technical Field

[0001] This invention relates to the field of industrial manufacturing technology, and in particular to a method and apparatus for analyzing order delivery capability. Background Technology

[0002] Order delivery capability refers to a manufacturing company's ability to fulfill customer orders according to the time, quantity, and quality requirements stipulated in the contract. It is a crucial indicator reflecting a manufacturing company's market competitiveness. Because numerous internal and external factors influence a manufacturing company's order delivery capability, and these factors are often dynamic, the company's order delivery capability is not static but dynamically changes with these factors. Factors affecting order delivery capability include production efficiency and supply chain factors. Production efficiency factors include changes such as reduced human and equipment resources, and increased defect and rework rates. Supply chain factors include changes such as decreased material inventory levels and longer material procurement cycles. Currently, common order delivery capability analysis methods evaluate it based on the delivery dates of completed orders, using the ratio of orders delivered before the promised delivery date to the total number of orders. This evaluation method only reflects a past state of the manufacturing company's order delivery capability. Since order delivery capability is dynamic, this method does not reflect the true state of the manufacturing company and has no reference value for subsequent order production management. Summary of the Invention

[0003] Based on the above-mentioned problems, this invention proposes an order delivery capability analysis method and apparatus that can reflect the dynamic changes in the order delivery capability of manufacturing enterprises and accurately identify the weak links in the order delivery process.

[0004] In view of this, a first aspect of the present invention proposes an order delivery capability analysis method, comprising:

[0005] Configure a reference delivery efficiency for evaluating the fluctuation range of delivery efficiency, wherein the reference delivery efficiency is the average delivery time per unit product obtained based on historical order delivery data statistics;

[0006] During the order delivery process, monitor the order delivery parameters;

[0007] After an order is delivered, the fluctuation range of the actual delivery efficiency of the order is calculated, where the fluctuation range is the change of the actual delivery efficiency of each order relative to the reference delivery efficiency.

[0008] Perform a correlation analysis on the fluctuation range of the delivery process parameters and the actual delivery efficiency;

[0009] Weaknesses in delivery were identified based on the results of correlation analysis.

[0010] Furthermore, the steps for configuring a reference delivery efficiency for evaluating the fluctuation range of delivery efficiency specifically include:

[0011] Extract valid delivery order data from historical order delivery data. The valid delivery order data consists of historical orders that were not delayed in the production plan or execution process due to unplanned delay factors during the completion of order delivery.

[0012] Construct a valid delivery order dataset based on the aforementioned valid delivery order data;

[0013] The reference delivery efficiency is obtained by statistically analyzing the average delivery time per unit product for each order in the valid delivery order dataset.

[0014] Furthermore, the step of statistically analyzing the average delivery time per unit product for each order in the valid delivery order dataset to obtain the reference delivery efficiency specifically includes:

[0015] Obtain the basic information of each valid delivery order in the valid delivery order dataset. The basic information includes the order generation time ti_order, the order delivery time ti_delivery, and the number of products delivered Qi, where i is a positive integer between 1 and n_valid, and n_valid is the number of valid delivery orders in the valid delivery order dataset.

[0016] The delivery cycle Ti of the valid delivery order is calculated based on the order generation time ti_order and the order delivery time ti_delivery;

[0017] Calculate the reference delivery efficiency R_ref=[∑(Qi / Ti)] / n_valid.

[0018] Furthermore, the specific steps for monitoring order delivery process parameters include:

[0019] Establish synchronous communication connections with the business management systems of each process stage in the order delivery process;

[0020] The process parameters of each order in the corresponding business management system are periodically synchronized from the business management system. The process parameters include the time consumption data of each sub-stage of the order in the business management system.

[0021] Furthermore, the steps for calculating the fluctuation range of the actual delivery efficiency of an order specifically include:

[0022] Obtain basic order information, including order generation time t_order, order delivery time t_delivery, and product delivery quantity Q;

[0023] The delivery cycle T of an order is calculated based on the order generation time t_order and the order delivery time t_delivery.

[0024] Calculate the actual delivery efficiency of the order: R = Q / T;

[0025] Calculate the fluctuation range of the actual delivery efficiency relative to the reference delivery efficiency, ΔR = R - R_ref.

[0026] Furthermore, the step of performing correlation analysis on the fluctuation range of the delivery process parameters and the actual delivery efficiency specifically includes:

[0027] Configure the horizontal comparison period for the delivery process parameters;

[0028] Orders delivered within a horizontal comparison period are identified as target orders;

[0029] Obtain the delivery process parameters of the target order, and perform a horizontal comparison of the delivery process parameters of the target order;

[0030] The correlation between the delivery process parameters and the fluctuation range of the actual delivery efficiency is determined based on the results of the horizontal comparison.

[0031] Furthermore, the step of performing a horizontal comparison of the delivery process parameters of the target order specifically includes:

[0032] Let Pjk represent the k-th delivery process parameter of the j-th target order;

[0033] Let k iterate from 1 to n_para to perform the following processing for each delivery process parameter:

[0034] If the number of the traversed delivery process parameter is denoted as k0, then there are a total of n_order delivery process parameters Pjk0 with the number k0 for n_order target orders;

[0035] The first numerical sequence is obtained by arranging the n_order delivery process parameters Pjk0 according to their numerical values;

[0036] Based on the order of the first numerical sequence, the fluctuation range of the actual delivery efficiency of the target order is arranged to generate a second numerical sequence;

[0037] The first numerical sequence is normalized and then fitted to a first straight line, and the second numerical sequence is normalized and then fitted to a second straight line.

[0038] Calculate the first slope spk0 of the first line and the second slope srk0 of the second line.

[0039] Furthermore, the step of determining the correlation between the delivery process parameters and the fluctuation range of the actual delivery efficiency based on the results of the horizontal comparison specifically includes:

[0040] Get the pre-configured average distance threshold;

[0041] Calculate the average distance of each normalized numerical element in the second numerical sequence of parameters for the k-th delivery process relative to the second straight line;

[0042] Determine whether the average distance is greater than the average distance threshold;

[0043] When the average distance is greater than the average distance threshold, it is determined that there is no correlation between the k-th delivery process parameter of the target order and the fluctuation range of the actual delivery efficiency.

[0044] Furthermore, after determining whether the average distance is greater than the average distance threshold, the method further includes: (The first slope of the k-th delivery process parameter is denoted as spk, and the second slope of the k-th delivery process parameter is denoted as srk.)

[0045] When the average distance is less than or equal to the average distance threshold, a pre-configured first slope difference threshold is obtained;

[0046] Determine whether both the first slope spk and the second slope srk are greater than 0;

[0047] When both the first slope spk and the second slope srk are greater than 0, calculate the difference between the first slope spk and the second slope srk;

[0048] Determine whether the difference is less than the first slope difference threshold;

[0049] When the difference is less than the first slope difference threshold, the k-th delivery process parameter is determined to be a positive correlation parameter of the fluctuation range of the actual delivery efficiency.

[0050] A second aspect of the present invention provides an order delivery capability analysis apparatus, comprising a memory and a processor, wherein the processor executes a computer program stored in the memory to implement the order delivery capability analysis method provided by any of the first aspects of the present invention.

[0051] This invention proposes an order delivery capability analysis method and apparatus. By configuring a reference delivery efficiency for evaluating the fluctuation range of delivery efficiency, the reference delivery efficiency is the average delivery time per unit product obtained based on historical order delivery data. During the order delivery process, delivery process parameters are monitored. After the order is completed, the fluctuation range of the actual delivery efficiency is calculated. The fluctuation range is the change in the actual delivery efficiency of each order relative to the reference delivery efficiency. Correlation analysis is performed on the delivery process parameters and the fluctuation range of the actual delivery efficiency. Based on the correlation analysis results, weak links in delivery are identified. This method can reflect the dynamic changes in the order delivery capability of manufacturing enterprises and accurately identify weak links in the order delivery process. Attached Figure Description

[0052] Figure 1 This is a flowchart of an order delivery capability analysis method provided in one embodiment of the present invention. Detailed Implementation

[0053] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0054] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0055] In the description of this invention, the term "multiple" refers to two or more. Unless otherwise explicitly defined, the terms "upper," "lower," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. The terms "connect," "install," "fix," etc., should be interpreted broadly. For example, "connect" can be a fixed connection, a detachable connection, or an integral connection; it can be a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "multiple" means two or more.

[0056] In the description of this specification, the terms "one embodiment," "some implementations," "specific embodiment," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0057] The following description, with reference to the accompanying drawings, illustrates a method and apparatus for analyzing order delivery capabilities according to some embodiments of the present invention.

[0058] like Figure 1 As shown, the first aspect of the present invention proposes an order delivery capability analysis method, comprising:

[0059] Configure a reference delivery efficiency for evaluating the fluctuation range of delivery efficiency, wherein the reference delivery efficiency is the average delivery time per unit product obtained based on historical order delivery data statistics;

[0060] During the order delivery process, monitor the order delivery parameters;

[0061] After an order is delivered, the fluctuation range of the actual delivery efficiency of the order is calculated, where the fluctuation range is the change of the actual delivery efficiency of each order relative to the reference delivery efficiency.

[0062] Perform a correlation analysis on the fluctuation range of the delivery process parameters and the actual delivery efficiency;

[0063] Weaknesses in delivery were identified based on the results of correlation analysis.

[0064] The reference delivery efficiency is a reference indicator reflecting the timeliness of order delivery. In some embodiments of the present invention, the reference delivery efficiency can be an empirical data point manually configured by the user based on the actual implementation of each process step in the order delivery process during actual production and operation. For most manufacturing enterprises, corresponding time limits are set for each process step and its sub-steps in the order delivery process. The execution time requirements of some process steps are related to the order delivery quantity, such as material procurement and transportation, or production execution; while other process steps are not related to the order delivery quantity, such as the formulation or approval of production plans. The reference delivery efficiency calculated using the maximum time limit requirements of each process step in the order delivery process is usually lower than the actual delivery efficiency of most orders.

[0065] The historical order delivery data is a collection of historical orders that have already been delivered.

[0066] The delivery process parameters reflect the timeliness of each sub-step in the order delivery process, including the time consumption data of each sub-step within the overall order delivery process. Changes in the values ​​of the delivery process parameters at each stage of the order delivery process can affect the actual delivery efficiency of the order. In practice, relevant managers may use management or scheduling methods to control the impact of each delivery process parameter on order delivery efficiency; therefore, order delivery efficiency is also an important indicator reflecting the management level of a manufacturing enterprise.

[0067] In other embodiments of the present invention, the step of configuring a reference delivery efficiency for evaluating the fluctuation range of delivery efficiency specifically includes:

[0068] Extract valid delivery order data from historical order delivery data. The valid delivery order data consists of historical orders that were not delayed in the production plan or execution process due to unplanned delay factors during the completion of order delivery.

[0069] Construct a valid delivery order dataset based on the aforementioned valid delivery order data;

[0070] The reference delivery efficiency is obtained by statistically analyzing the average delivery time per unit product for each order in the valid delivery order dataset.

[0071] Unplanned delay factors refer to unexpected events that occur during order delivery, including but not limited to material delivery delays, emergency order insertions, equipment malfunctions, accidental mold breakage, or personnel shortages due to unplanned operator absences. Unlike expected situations such as normal mold wear and tear or material transportation time, these unplanned delay factors, due to their unpredictability and suddenness, prevent timely adjustments during order delivery, leading to delays in corresponding process steps or ultimately, postponements of the order delivery time.

[0072] The effective delivery order refers to a historical order in which no unplanned delays occurred during the order delivery process, and each process step was completed within the planned time limit during the entire order delivery execution process.

[0073] Furthermore, the step of statistically analyzing the average delivery time per unit product for each order in the valid delivery order dataset to obtain the reference delivery efficiency specifically includes:

[0074] Obtain the basic information of each valid delivery order in the valid delivery order dataset. The basic information includes the order generation time ti_order, the order delivery time ti_delivery, and the number of products delivered Qi, where i is a positive integer between 1 and n_valid, and n_valid is the number of valid delivery orders in the valid delivery order dataset.

[0075] The delivery cycle Ti of the valid delivery order is calculated based on the order generation time ti_order and the order delivery time ti_delivery;

[0076] Calculate the reference delivery efficiency R_ref=[∑(Qi / Ti)] / n_valid.

[0077] The order generation time refers to the time when the corresponding production order document is generated in the order management system after the customer completes the order placement. The order delivery time refers to the time when the product is finally delivered to the customer after production and transportation are completed. In actual implementation, the order delivery time is usually defined as the time when relevant personnel upload the customer's signed receipt document to the system after confirming order delivery.

[0078] The product delivery quantity refers to the target production quantity of the products stipulated in the order contract, which is also the final quantity of products delivered to the customer. When the two are inconsistent, the final quantity of products delivered to the customer shall be referred to as the delivery quantity.

[0079] The order delivery cycle is the time taken from order generation to final delivery. That is, for the i-th valid delivery order in the valid delivery order dataset, its order delivery cycle Ti is the time interval between its order generation time ti_order and order delivery time ti_delivery, and the order delivery cycle Ti = ti_delivery - ti_order.

[0080] In the step of calculating the reference delivery efficiency R_ref in the above implementation, ∑(Qi / Ti) refers to the accumulation of Qi / Ti from 1 to n_valid.

[0081] Furthermore, the specific steps for monitoring order delivery process parameters include:

[0082] Establish synchronous communication connections with the business management systems of each process stage in the order delivery process;

[0083] The process parameters of each order in the corresponding business management system are periodically synchronized from the business management system. The process parameters include the time consumption data of each sub-stage of the order in the business management system.

[0084] As an example of the present invention, the order delivery process may include the generation of production orders, the formulation of production plans, the procurement, delivery and warehousing of production materials, the execution of the production process, the quality inspection of finished products, inventory management, and transportation. The business management system refers to the management system corresponding to the business modules of each process environment. The entire order delivery process involves data interaction between multiple business management systems, including CRM (Customer Relationship Management), OMS (Order Management System), SRM (Supplier Relationship Management), SCM (Supply Chain Management), WMS (Warehouse Management System), MES (Manufacturing Execution System), QMS (Quality Management System), and TMS (Transportation Management System).

[0085] It should be understood that the various business management systems listed above are merely examples. The order delivery process described may not necessarily involve all of the above business management systems. In practical applications, there are some management systems on the market that integrate the functions of several of the above business management systems for specific fields. For example, some ERP (Enterprise Resource Planning) systems integrate all or part of the functions of customer relationship management, order management, supplier relationship management, supply chain management, warehouse management, manufacturing execution, quality management, and transportation management.

[0086] In the order delivery process, each step has several sub-processes. For example, the procurement of production materials may include the creation and approval of material lists, as well as the procurement, transportation, and warehousing of materials. In the technical solution of this invention, each step of the sub-process is referred to as a sub-step.

[0087] Furthermore, the steps for calculating the fluctuation range of the actual delivery efficiency of an order specifically include:

[0088] Obtain basic order information, including order generation time t_order, order delivery time t_delivery, and product delivery quantity Q;

[0089] The delivery cycle T of an order is calculated based on the order generation time t_order and the order delivery time t_delivery.

[0090] Calculate the actual delivery efficiency of the order: R = Q / T;

[0091] Calculate the fluctuation range of the actual delivery efficiency relative to the reference delivery efficiency, ΔR = R - R_ref.

[0092] Similarly, in the technical solution of the above embodiments, the actual delivery efficiency of an order is calculated from the order's delivery quantity and delivery cycle. The difference between the actual delivery efficiency of each order and the reference delivery efficiency is determined as the fluctuation range of the actual delivery efficiency of the corresponding order.

[0093] Furthermore, the step of performing correlation analysis on the fluctuation range of the delivery process parameters and the actual delivery efficiency specifically includes:

[0094] Configure the horizontal comparison period for the delivery process parameters;

[0095] Orders delivered within a horizontal comparison period are identified as target orders;

[0096] Obtain the delivery process parameters of the target order, and perform a horizontal comparison of the delivery process parameters of the target order;

[0097] The correlation between the delivery process parameters and the fluctuation range of the actual delivery efficiency is determined based on the results of the horizontal comparison.

[0098] The horizontal comparison period is configured as a delivery cycle length that includes at least a number of orders. For example, at least five or more orders can be delivered within the horizontal comparison period. Depending on the actual implementation needs, the horizontal comparison period can be configured as a monthly or quarterly unit. For example, the horizontal comparison period can be configured as half a month, a month, a quarter, or two quarters.

[0099] In the step of identifying target orders that are delivered within a horizontal comparison period, the target orders are those whose delivery dates fall within the same horizontal comparison period. In this embodiment, it is not required that the order creation date or the start date of production of the target orders fall within the same horizontal comparison period.

[0100] Furthermore, the step of performing a horizontal comparison of the delivery process parameters of the target order specifically includes:

[0101] Let Pjk represent the k-th delivery process parameter of the j-th target order;

[0102] Let k iterate from 1 to n_para to perform the following processing for each delivery process parameter:

[0103] If the number of the traversed delivery process parameter is denoted as k0, then there are a total of n_order delivery process parameters Pjk0 with the number k0 for n_order target orders;

[0104] The first numerical sequence is obtained by arranging the n_order delivery process parameters Pjk0 according to their numerical values;

[0105] Based on the order of the first numerical sequence, the fluctuation range of the actual delivery efficiency of the target order is arranged to generate a second numerical sequence;

[0106] The first numerical sequence is normalized and then fitted to a first straight line, and the second numerical sequence is normalized and then fitted to a second straight line.

[0107] Calculate the first slope spk0 of the first line and the second slope srk0 of the second line.

[0108] In the technical solution of the above implementation, j is a positive integer between 1 and n_order, k is a positive integer between 1 and n_para, n_order is the number of target orders in the horizontal comparison period, and n_para is the number of delivery process parameters of the order.

[0109] The number of numerical elements in the first numerical sequence is the same as the number of target orders, meaning that the j-th delivery process parameter Pjk0 in the first numerical sequence is the k0-th delivery process parameter for the j-th target order. Similarly, the number of numerical elements in the second numerical sequence is the same as the number of target orders, meaning that the j-th numerical element in the second numerical sequence represents the fluctuation range of the actual delivery efficiency of the j-th target order. Furthermore, the second numerical sequence is associated with the first numerical sequence based on the order; that is, the first numerical element in the second numerical sequence represents the fluctuation range of the actual delivery efficiency of the target order corresponding to the first numerical element in the first numerical sequence; the second numerical element in the second numerical sequence represents the fluctuation range of the actual delivery efficiency of the target order corresponding to the second numerical element in the first numerical sequence, and so on.

[0110] In the steps of fitting the first numerical sequence to a first straight line after normalization and fitting the second numerical sequence to a second straight line after normalization, the normalized numerical elements in the first and second numerical sequences are respectively used as Y-axis data, and arranged in an n_order integer order from 1 to n_order to form X-axis data, thereby fitting the first and second straight lines in the XY orthogonal coordinate system.

[0111] Furthermore, the step of determining the correlation between the delivery process parameters and the fluctuation range of the actual delivery efficiency based on the results of the horizontal comparison specifically includes:

[0112] Get the pre-configured average distance threshold;

[0113] Calculate the average distance of each normalized numerical element in the second numerical sequence of parameters for the k-th delivery process relative to the second straight line;

[0114] Determine whether the average distance is greater than the average distance threshold;

[0115] When the average distance is greater than the average distance threshold, it is determined that there is no correlation between the k-th delivery process parameter of the target order and the fluctuation range of the actual delivery efficiency.

[0116] Furthermore, in the step of calculating the average distance of each normalized numerical element in the second numerical sequence relative to the second straight line, the vertical distance of each normalized numerical element in the second numerical sequence projected onto the second straight line is determined as the distance of each normalized numerical element in the second numerical sequence relative to the second straight line, and the average distance is calculated based on this.

[0117] Furthermore, after determining whether the average distance is greater than the average distance threshold, the method further includes: (The first slope of the k-th delivery process parameter is denoted as spk, and the second slope of the k-th delivery process parameter is denoted as srk.)

[0118] When the average distance is less than or equal to the average distance threshold, a pre-configured first slope difference threshold is obtained;

[0119] Determine whether both the first slope spk and the second slope srk are greater than 0;

[0120] When both the first slope spk and the second slope srk are greater than 0, calculate the difference between the first slope spk and the second slope srk;

[0121] Determine whether the difference is less than the first slope difference threshold;

[0122] When the difference is less than the first slope difference threshold, the k-th delivery process parameter is determined to be a positive correlation parameter of the fluctuation range of the actual delivery efficiency.

[0123] Furthermore, the steps for identifying delivery weaknesses based on the correlation analysis results specifically include:

[0124] Obtain the first numerical sequence and the second numerical sequence after normalization of the positive correlation parameters;

[0125] Calculate the similarity between the first numerical sequence and the second numerical sequence;

[0126] The sub-link corresponding to the positively correlated parameter with the highest similarity between the first numerical sequence and the second numerical sequence is identified as the weak link in delivery.

[0127] The purpose of calculating the similarity between the first and second numerical sequences is to determine the similarity in the magnitude of their changes. The simplest way is to compare their standard deviations or variances; the closer their standard deviations or variances are, the higher their similarity.

[0128] Alternatively, the cosine similarity between the two numerical sequences can be used as the similarity between the first and second numerical sequences. This involves vectorizing the two numerical sequences using the same method and then calculating the cosine similarity between the two vectors. Using cosine similarity as the similarity between the first and second numerical sequences more accurately reflects the similarity of their variation amplitudes, thus enabling more accurate identification of the delivery weaknesses.

[0129] A second aspect of the present invention provides an order delivery capability analysis apparatus, comprising a memory and a processor, wherein the processor executes a computer program stored in the memory to implement the order delivery capability analysis method provided by any of the first aspects of the present invention.

[0130] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0131] As described above, these embodiments of the present invention do not exhaustively cover all details, nor do they limit the invention to the specific embodiments described. Clearly, many modifications and variations can be made based on the above description. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to effectively utilize the invention and its modifications. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for analyzing order delivery capacity, characterized in that, include: Configure a reference delivery efficiency for evaluating the fluctuation range of delivery efficiency, wherein the reference delivery efficiency is the average delivery time per unit product obtained based on historical order delivery data statistics; During the order delivery process, monitor the order delivery parameters; After an order is delivered, the fluctuation range of the actual delivery efficiency of the order is calculated, where the fluctuation range is the change of the actual delivery efficiency of each order relative to the reference delivery efficiency. Perform a correlation analysis on the fluctuation range of the delivery process parameters and the actual delivery efficiency; Weaknesses in delivery were identified based on the results of correlation analysis.

2. The order delivery capability analysis method according to claim 1, characterized in that, The steps for configuring a reference delivery efficiency for evaluating the fluctuation range of delivery efficiency specifically include: Extract valid delivery order data from historical order delivery data. The valid delivery order data consists of historical orders that were not delayed in the production plan or execution process due to unplanned delay factors during the completion of order delivery. Construct a valid delivery order dataset based on the aforementioned valid delivery order data; The reference delivery efficiency is obtained by statistically analyzing the average delivery time per unit product for each order in the valid delivery order dataset.

3. The order delivery capability analysis method according to claim 2, characterized in that, The specific steps for obtaining the reference delivery efficiency by statistically analyzing the average delivery time per unit product for each order in the valid delivery order dataset include: Obtain the basic information of each valid delivery order in the valid delivery order dataset. The basic information includes the order generation time ti_order, the order delivery time ti_delivery, and the number of products delivered Qi, where i is a positive integer between 1 and n_valid, and n_valid is the number of valid delivery orders in the valid delivery order dataset. The delivery cycle Ti of the valid delivery order is calculated based on the order generation time ti_order and the order delivery time ti_delivery; Calculate the reference delivery efficiency R_ref=[∑(Qi / Ti)] / n_valid.

4. The order delivery capability analysis method according to claim 1, characterized in that, The specific steps for monitoring order delivery process parameters include: Establish synchronous communication connections with the business management systems of each process stage in the order delivery process; The process parameters of each order in the corresponding business management system are periodically synchronized from the business management system. The process parameters include the time consumption data of each sub-stage of the order in the business management system.

5. The order delivery capability analysis method according to claim 1, characterized in that, The specific steps for calculating the fluctuation range of actual order delivery efficiency include: Obtain basic order information, including order generation time t_order, order delivery time t_delivery, and product delivery quantity Q; The delivery cycle T of an order is calculated based on the order generation time t_order and the order delivery time t_delivery. Calculate the actual delivery efficiency of the order: R = Q / T; Calculate the fluctuation range of the actual delivery efficiency relative to the reference delivery efficiency, ΔR = R - R_ref.

6. The order delivery capability analysis method according to claim 1, characterized in that, The steps for performing correlation analysis on the fluctuation range of the delivery process parameters and the actual delivery efficiency specifically include: Configure the horizontal comparison period for the delivery process parameters; Orders delivered within a horizontal comparison period are identified as target orders; Obtain the delivery process parameters of the target order, and perform a horizontal comparison of the delivery process parameters of the target order; The correlation between the delivery process parameters and the fluctuation range of the actual delivery efficiency is determined based on the results of the horizontal comparison.

7. The order delivery capability analysis method according to claim 6, characterized in that, The steps for performing a horizontal comparison of the delivery process parameters of the target order specifically include: Let Pjk represent the k-th delivery process parameter of the j-th target order; Let k iterate from 1 to n_para to perform the following processing for each delivery process parameter: If the number of the traversed delivery process parameter is denoted as k0, then there are a total of n_order delivery process parameters Pjk0 with the number k0 for n_order target orders; The first numerical sequence is obtained by arranging the n_order delivery process parameters Pjk0 according to their numerical values; Based on the order of the first numerical sequence, the fluctuation range of the actual delivery efficiency of the target order is arranged to generate a second numerical sequence; The first numerical sequence is normalized and then fitted to a first straight line, and the second numerical sequence is normalized and then fitted to a second straight line. Calculate the first slope spk0 of the first line and the second slope srk0 of the second line.

8. The order delivery capability analysis method according to claim 7, characterized in that, The steps for determining the correlation between the delivery process parameters and the fluctuation range of the actual delivery efficiency based on the results of horizontal comparison specifically include: Get the pre-configured average distance threshold; Calculate the average distance of each normalized numerical element in the second numerical sequence of parameters for the k-th delivery process relative to the second straight line; Determine whether the average distance is greater than the average distance threshold; When the average distance is greater than the average distance threshold, it is determined that there is no correlation between the k-th delivery process parameter of the target order and the fluctuation range of the actual delivery efficiency.

9. The order delivery capability analysis method according to claim 8, characterized in that, The first slope of the k-th delivery process parameter is denoted as spk, and the second slope of the k-th delivery process parameter is denoted as srk. After the step of determining whether the average distance is greater than the average distance threshold, the method further includes: When the average distance is less than or equal to the average distance threshold, a pre-configured first slope difference threshold is obtained; Determine whether both the first slope spk and the second slope srk are greater than 0; When both the first slope spk and the second slope srk are greater than 0, calculate the difference between the first slope spk and the second slope srk; Determine whether the difference is less than the first slope difference threshold; When the difference is less than the first slope difference threshold, the k-th delivery process parameter is determined to be a positive correlation parameter of the fluctuation range of the actual delivery efficiency.

10. An order delivery capability analysis device, characterized in that, It includes a memory and a processor, the processor executing a computer program stored in the memory to implement the order delivery capability analysis method as described in any one of claims 1-9.

Images