Method, system and medium for evaluating the execution rate of logistics supply lines

Abstract

Disclosed is a method for evaluating the execution rate of a logistics supply line executed by a computer system, wherein the logistics supply line is used to supply materials required for vehicle production, the method comprising: acquiring first data associated with vehicle defects of a vehicle production line; acquiring second data associated with line stoppages of the vehicle production line; acquiring third data associated with the output of the vehicle production line; and calculating the execution rate of the logistics supply line based on the first data, the second data, and the third data.

Description

Technical Field

[0001] The present disclosure relates to the field of vehicle production, and more particularly, to a method, system, and medium for evaluating an execution rate of a logistics supply line for vehicle production. Background Art

[0002] Vehicles are complex industrial products. In order to produce vehicles, large vehicle manufacturers often need to supply thousands or even tens of thousands of materials. During the production process, various materials are provided to the vehicle production department (for example, the final assembly workshop) by the corresponding supply and logistics departments. The process of providing the required materials to the vehicle production department can be collectively referred to as the logistics supply line (Line Delivery). Errors, shortages, and missing materials provided by the logistics supply line may cause defects (cripple) in the vehicle products produced, or even cause the vehicle production line to stop (stoppage). Therefore, there is a need for a method and system that can evaluate and provide feedback on the execution rate (LDP, Line Delivery Performance) of the logistics supply line. Summary of the invention

[0003] The present disclosure provides a method for evaluating the execution rate of a logistics supply line executed by a computer system, wherein the logistics supply line is used to supply materials required for vehicle production, and the method comprises: acquiring first data, wherein the first data is associated with a vehicle defect of a vehicle production line; acquiring second data, wherein the second data is associated with a line stop of the vehicle production line; acquiring third data, wherein the third data is associated with an output of the vehicle production line; and calculating the execution rate of the logistics supply line based on the first data, the second data, and the third data.

[0004] According to one aspect of the present disclosure, a computer system is provided, comprising: one or more processors; and a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by the one or more processors, the one or more processors perform any method described herein.

[0005] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by one or more processors, the one or more processors cause the one or more processors to perform any method described herein.

[0006] According to yet another aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by one or more processors, the one or more processors are caused to perform any method as described herein.

[0007] The method and system disclosed herein can perform real-time and automated evaluation, monitoring and feedback on the execution rate of logistics supply lines, which helps to improve the quality of logistics supply lines, reduce defects in vehicle products and downtime of vehicle production lines. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The accompanying drawings, which constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0009] The present disclosure may be more clearly understood from the following detailed description with reference to the accompanying drawings, in which:

[0010] Figure 1 An example vehicle production environment is shown in which embodiments of the present disclosure can be implemented.

[0011] Figure 2 A flow chart of an exemplary method for evaluating LDP according to an embodiment is shown.

[0012] Figure 3 A data flow diagram for evaluating LDP according to an embodiment is shown.

[0013] Figure 4 A schematic diagram of a computing device according to an embodiment is shown.

[0014] Figure 5 A schematic diagram of a data model for an LDP system according to an embodiment is shown.

[0015] Figure 6 A screenshot of an example user interface for validating defect data is shown.

[0016] Figure 7 A screenshot of an example user interface for confirming downtime is shown.

[0017] Figure 8 A screenshot of an example user interface containing multiple LDP reports is shown.

[0018] Fig. 9 A screenshot of an example user interface for monitoring LDP is shown. DETAILED DESCRIPTION

[0019] The following description is provided to enable those skilled in the art to make and use the embodiments, and is provided in the context of a particular system and its requirements. Various modifications will be apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments and systems without departing from the spirit and scope of the embodiments. Therefore, the embodiments are not limited to the embodiments shown, but are to be given the widest scope consistent with the principles and features disclosed herein.

[0020] Figure 1 An example vehicle production environment 1000 in which embodiments of the present disclosure can be implemented is shown. The vehicle production environment 1000 may include a vehicle production line 1100, a logistics supply system 1200, a plurality of monitoring and management systems 1300, and an LDP system 1400.

[0021] According to an embodiment, the vehicle production line 1100 may be configured to produce vehicles. As an example, the vehicle production line 1100 may be a vehicle assembly production line. The vehicle assembly production line may be configured to assemble various vehicle parts to form a vehicle product. The vehicle parts referred to herein refer to parts that ultimately remain on the vehicle product as part of the vehicle. Examples of vehicle parts may include, but are not limited to, one or more parts for forming a frame, chassis, instrumentation, glass, interior, headlights, fuel tanks, powertrains, seats, bumpers, and the like. The materials involved in vehicle parts may be classified as key materials. The model of each key material is usually specific to the specific vehicle model produced by the vehicle assembly production line, and is therefore difficult to use interchangeably between different vehicle production lines. Therefore, the supply of key materials will significantly affect the normal operation of the vehicle production line 1100.

[0022] In order to maintain the operation of the vehicle production line 1100, the vehicle production line 1100 also needs to consume various production auxiliary materials. Unlike vehicle parts, production auxiliary materials refer to various materials consumed in the vehicle production process that usually do not remain on the vehicle products. Examples of production auxiliary materials may include, for example, general materials such as glue and absorbent paper, labor protection tools such as gloves and helmets, measuring tools such as electric meters and calipers, mechanical tools such as wrenches and pliers, and so on. The models of production auxiliary materials are usually not specific to vehicle models and can be used interchangeably between different vehicle production lines. Although production auxiliary materials are not key materials that constitute vehicle products, shortages of production auxiliary materials will also bring risks to the normal operation of the vehicle production line 1100 to a certain extent.

[0023] According to an embodiment, the vehicle production line 1100 may also optionally include a detection link and a rework link. The detection link may detect one or more defects existing in a produced (e.g., assembled) vehicle product. The rework link may repair the detected one or more defects. For example, the rework link may add one or more parts omitted in the assembly link to the vehicle. The rework time spent in the rework link may be used as a measure of the degree of defect of the vehicle product.

[0024] According to an embodiment, the logistics supply line system 1200 may be responsible for supplying various materials required by the vehicle production line 1100, including both vehicle parts and production auxiliary materials. The logistics supply line system 1200 may include facilities and equipment for purchasing, warehousing, transporting, and distributing various materials. The logistics supply line system 1200 may include multiple subsystems for vehicle parts and production auxiliary materials, respectively. The logistics supply line system 1200 may be operated by the logistics and supply department of the vehicle manufacturing plant. In order to maintain the normal operation of the vehicle production line 1100, the logistics supply line system 1200 should promptly distribute various materials of sufficient quantity and correct model to the corresponding vehicle production line 1100. If the materials provided by the logistics supply line system 1200 are wrong, insufficient or missing, it may cause the vehicle production line 1100 to produce a vehicle product with one or more defects. For example, if the logistics supply line system 1200 fails to supply a certain vehicle part in time and causes the vehicle part to be missing at the vehicle production line 1100, it may cause one or more vehicle products assembled by the vehicle production line 1100 to be unable to be equipped with the vehicle part. Errors, insufficiencies, or lack of materials may also cause the vehicle production line 1100 to stop. For example, the vehicle production line 1100 may have to wait for the supply of such parts to resume before continuing production. Alternatively, in order to repair a vehicle product that is missing the vehicle part, the vehicle part may need to be reassembled in the reprocessing stage. If the reprocessing stage takes too long, it may also cause congestion in the vehicle production line 1100 and force it to stop. Defects in vehicle products and / or the reprocessing time of vehicle products can be used as indicators to measure the performance of the logistics supply line system 1200.

[0025] According to an embodiment, a plurality of monitoring and management systems 1300 may be associated with a vehicle production line 1100. The plurality of monitoring and management systems 1300 may include a detection system 1300-1 for detecting defects of vehicle products. The detection system 1300-1 may be configured to collect data related to defects of vehicle products. These data may include the type, location, quantity of defects and possible causes of the defects. As an example, the detection system 1300-1 may detect the lack of a certain vehicle part on a vehicle product. Additionally, the detection system 1300-1 may determine whether the lack of a certain vehicle part is caused by the deficiency or lack of the corresponding vehicle part, or whether it is caused by an error in the corresponding operating link. Additionally or alternatively, the data collected by the detection system 1300-1 may also include reprocessing data associated with repairing defects of vehicle products. The reprocessing data may include the location, type, and reprocessing time spent on the reprocessed defects, and the like. An example of the detection system 1300-1 is the quality control system (IPSQ) system of BMW Brilliance.

[0026] According to an embodiment, the plurality of monitoring and management systems 1300 may further include a production monitoring system 1300-2 for counting the production of vehicle products. The production monitoring system 1300-2 may be configured to collect production data of vehicle products associated with the vehicle production line 1100. The production data may indicate the actual production of the vehicle production line 1100 within a specified time period (e.g., per hour, per production shift). An example of the production monitoring system 1300-2 is the production execution control system (IPSL) system of BMW Brilliance.

[0027] According to an embodiment, the plurality of monitoring and management systems 1300 may further include an additional management system 1300-N. The additional management system 1300-N may include a plurality of subsystems. For example, the additional management system 1300-N may include or communicate with a control subsystem of the vehicle production line 1100. The control subsystem may be located, for example, in a control room and may record an operation log of the vehicle production line 1100. The downtime of the vehicle production line 1100 may be derived based on the operation log. The additional management system 1300-N may also be associated with an e-mail address of a control personnel in the control room, so that one or more indicators of the vehicle production line 1100, such as an objective equipment operating rate (OEE) and a target output, etc., may be received from the e-mail address of the control personnel. The target equipment operating rate is one of the most basic planning parameters of the production line operating rate, which may indicate the expected ratio of the actual operating time of the vehicle production line 1100 to the duration of the production shift. The target output may indicate the expected output of the vehicle production line 1100 within a specified time period.

[0028] According to an embodiment, the vehicle production environment 1000 may include an LDP system 1400. The LDP system 1400 may be configured to perform the method described herein related to evaluating the execution rate of the logistics supply line. The LDP system 1400 may be communicatively coupled to each of the multiple monitoring and management systems 1300 (1300-1, 1300-2, 1300-N) and receive one or more data from the multiple monitoring and management systems 1300. The LDP system 1400 may evaluate the LDP of the vehicle production line 1100 based on the received data. The LDP system 1400 may also be communicatively coupled to the logistics supply line system 1200 and adjust the operation of the logistics supply line system 1200 based on the obtained LDP. Additionally, the LDP system 1400 may also generate an LDP report based on the obtained LDP and send it to management and decision-making departments (not shown). The LDP system 1400 may be configured to perform the steps or a portion thereof of the method described in more detail below.

[0029] According to an embodiment, the LDP system 1400 may be implemented as a computing device. Figure 4 An embodiment of a computing device is described that can be used to implement the LDP system 1400. The computing device can perform steps of the methods described herein or portions thereof by executing a computer program stored on a non-transitory computer-readable storage medium by one or more processors.

[0030] It should be noted that Figure 1 The vehicle production environment 1000 is merely illustrative. In other embodiments, the vehicle production environment 1000 may include more or fewer components. It is understood that the various components of the vehicle production environment 1000 may be located in the same place or distributed in different places. For example, the LDP system 1400 may not be located near the vehicle production line 1100, but may receive and send data remotely via a communication link. Figure 1 The various communication links shown may be various wired or wireless communication links.

[0031] Figure 2 A flow chart of an exemplary method 2000 for evaluating LDP according to an embodiment is shown. Figure 1 Method 2000 is described with reference to the components of the example vehicle production environment 1000. Method 2000 may be performed by Figure 1 For example, the method 2000 may be performed in response to one or more processors of a computing device executing a computer program stored on a non-transitory computer-readable storage medium.

[0032] According to an embodiment, the method 2000 may be used to evaluate the implementation rate (LDP) of a logistics supply line, wherein the logistics supply line is used to supply materials required for vehicle production. The method 2000 may include steps 2100 to 2400.

[0033] In step 2100, the LDP system 1400 may acquire first data. The first data may be associated with a vehicle defect of a vehicle production line. The first data may include data describing the type, location, quantity, and possible cause of the defect. The first data may include reprocessing data associated with repairing a defect of a vehicle product, such as the location, type, and reprocessing time taken to repair the defect of the reprocessed defect, etc. The first data may be provided by a detection system 1300-1 for detecting defects of a vehicle product.

[0034] In step 2200, the LDP system 1400 may acquire second data. The second data may be associated with a line stop of a vehicle production line. The second data may include line stop data. The line stop data may indicate the line stop time and the line stop reason of the vehicle production line 1100. The line stop time may include one or more of the line stop start time, the line stop end time, and the line stop duration. The line stop reason may describe one or more factors causing the line stop, such as indicating whether the line stop is caused by material supply reasons, and what material caused the line stop. The second data may be provided by an additional management system 1300-N.

[0035] In step 2300, the LDP system 1400 may acquire third data. The third data may be associated with the output of the vehicle production line. The third data may indicate the output of the vehicle production line 1100 in each specified time period. According to an embodiment, the third data may be provided by the production monitoring system 1300-2.

[0036] In step 2400 , the LDP system 1400 may calculate an LDP based on the first data, the second data, and the third data.

[0037] As mentioned above, the materials required for vehicle production can be classified into key categories representing vehicle parts and non-key categories representing production auxiliary materials. Preferably, LDPs can be calculated for materials of key categories and non-key categories respectively. Accordingly, the calculated LDPs can include a first LDP associated with vehicle parts and a second LDP associated with production auxiliary materials.

[0038] According to an embodiment, the first LDP and the second LDP may be calculated as:

[0039] LDP_M=(Cr+St_M)*α / (V+St_M+St_T) (1)

[0040] LDP_T=(Cr+St_T)*α / (V+St_M+St_T) (2)

[0041] Wherein, LDP_M represents the first LDP, LDP_T represents the second LDP; Cr represents the first data associated with the vehicle defect of the vehicle production line; St_M represents the first line stop data related to the vehicle parts in the second data; St_T represents the second line stop data related to the production auxiliary materials in the second data; V represents the output of the vehicle production line, that is, the third data. α represents the proportional factor, which can normalize the calculation result. α can take any suitable value. For example, an exemplary value of α can be 1000000ppm (parts per million). The calculated LDP combines the defect data, output data and line stop data of the vehicle product and provides a quantitative standard for evaluating the performance of the logistics supply line.

[0042] Combine the following Figure 3 The data flow associated with evaluating LDP is further described. Figure 3 A data flow graph 3000 for evaluating an LDP according to an embodiment is shown.

[0043] like Figure 3 As shown, the inspection system 1300 - 1 may provide the defect data as the first data to the LDP system 1400 . Specifically, the inspection system 1300 - 1 may provide the LDP system 1400 with rework data, and the rework data may include the rework time of the vehicle production line 1100 .

[0044] According to a preferred embodiment, before the LDP system 1400 uses the reprocessed data from the detection system 1300-1 to calculate the LDP, the reprocessed data may be preprocessed 3100. The preprocessing 3100 is shown in a dashed box because it is optional.

[0045] For example, preprocessing 3100 may include selecting data related to errors, deficiencies, or missing of materials from the reprocessing data, and filtering out data unrelated to errors, deficiencies, and missing of materials. For example, when the reprocessing process is caused only by the operator's error, the corresponding reprocessing data entry may be filtered out. When the reprocessing process is caused by the material supply line system 1200 not providing materials in a timely and accurate manner, the corresponding reprocessing data entry may be retained. Additionally, preprocessing 3100 may also include marking the selected data as being associated with vehicle parts or as being associated with production auxiliary materials. The selected data may be marked by labeling the data, thereby classifying the data. This classification is conducive to calculating the first LDP associated with vehicle parts and the second LDP associated with production auxiliary materials, respectively. Further, preprocessing 3100 may also include associating the selected data with the corresponding supply and logistics department 1200, so that each reprocessing data entry may be associated with the logistics and supply department responsible for the corresponding material. In addition, the number of vehicle products associated with each reprocessing data entry in the vehicle product may also be identified.

[0046] According to a preferred embodiment, the preprocessing 3100 may also include confirming the selected reprocessing data. For example, the data after one or more of the above preprocessing may be sent to the supply and logistics department. Then, the confirmed data may be received from the supply and logistics department and provided to the LDP system 1400 as confirmed defect data. The confirmed defect data may include a signature or other identification information of the supply and logistics department (or its personnel) that performs the confirmation, thereby allowing the responsibility for the reliability of the data to be traced. Figure 6 A screenshot of an example user interface 6000 for confirming defect data is shown. The user interface 6000 lists defect data entries associated with one or more defects, and each defect data entry may include fields associated with a defect location 6100, a defect type 6200, a defect area 6300, and a reprocessing time 6400. The supply and logistics department may confirm whether the defect data is accurate. The confirmed defect data may be sent to the LDP system 1400 for calculating the LDP.

[0047] Return to Figure 3 , Figure 3 Also shown is a process in which the LDP system 1400 obtains line stop data (an example of the second data). Acquiring the line stop data may include obtaining the planned output and hourly output of the vehicle production line, obtaining the line stop time of the vehicle production line, and obtaining the target equipment operation rate. Acquiring the second data may also include calculating the second data based on the planned output, hourly output, line stop time, and target equipment operation rate. As an example, the second data may be calculated as:

[0048] St=(PV / Hpur)*OEE / β*Dt (3)

[0049] Among them, PV represents the planned output, Hpur represents the output per hour, OEE represents the target equipment operating rate, Dt represents the downtime, and β represents the time coefficient.

[0050] According to an embodiment, the various parameters in equation (3) can be obtained from multiple data sources (e.g., various subsystems of the additional management system 1300-N). For example, the planned production PV and the hourly production Hpur can be obtained from the production monitoring system 1300-2. Alternatively, the data can be obtained from the planned data directory, which can be automatically generated by the system or partially manually entered. The downtime Dt can be obtained from the control subsystem that records the operation log of the vehicle production line 1100. The target equipment operation rate OEE can be obtained from the e-mail of the control personnel in the control room.

[0051] According to a preferred embodiment, obtaining the line stop data may further include confirming the line stop time 3200. The confirmation process 3200 is shown in a dotted box because it is optional. The confirmation process 3200 may include sending data related to the line stop time to the supply and logistics department, and receiving confirmation from the supply and logistics department. Similar to the confirmation of defect data, the confirmed line stop data may contain the signature or other identification information of the supply and logistics department (or its personnel) that performs the confirmation, thereby allowing the responsibility for the reliability of the data to be traced. Figure 7 A screenshot of an example user interface 7000 for confirming downtime is shown.

[0052] According to a preferred embodiment, obtaining the line stop data may further include preprocessing the line stop data 3300. The preprocessing 3300 is shown in a dotted box because it is optional. The preprocessing 3300 may include determining first line stop data related to vehicle parts in the line stop data, and determining second line stop data related to production auxiliary materials. The determined data may be marked by labeling the data, thereby classifying the data. This classification is conducive to respectively calculating the first LDP associated with the vehicle part and the second LDP associated with the production auxiliary material.

[0053] Return to Figure 3 , Figure 3 The process of obtaining the production data (an example of the third data) by the LDP system 1400 is also shown. As shown in the figure, the LDP system 1400 can obtain the production data from the production monitoring system 1300-2.

[0054] After obtaining defect data, line stop data, and production data, the LDP system 1400 can calculate the LDP based on the obtained data. For example, the LDP system can calculate the first LDP associated with the vehicle parts and the second LDP associated with the production auxiliary materials according to the aforementioned equations (1) and (2), respectively. In addition, the LDP system can calculate the comprehensive LDP based on the first LDP and the second LDP. Calculating the comprehensive LDP may include weighted averaging the first LDP and the second LDP. Preferably, since the first LDP involves vehicle parts that are more important for maintaining the normal operation of the vehicle production line 1100, the first LDP may have a greater weight than the second LDP.

[0055] According to an embodiment, the LDP system 1400 may generate an LDP report based on the obtained LDP. The LDP report may be provided to the management and decision-making department. The management and decision-making department may adjust one or more parameters of the vehicle production line 1100 based on the LDP report, such as target production, target equipment operation rate, etc. Figure 8 A screenshot of an example user interface 8000 including multiple LDP reports is shown.

[0056] According to an embodiment, the LDP system 1400 may also provide feedback to the logistics supply line system 1200 based on the obtained LDP. For example, the LDP system 1400 may compare the obtained LDP with a predetermined threshold condition. In response to the obtained LDP not satisfying the predetermined threshold condition, the LDP system 1400 may send an alarm to the logistics supply line system 1200. The sent alarm may contain information about the material category associated with the LDP. Fig. 9 A screenshot of an example user interface 9000 for monitoring LDP is shown. LDP values ​​in different states can be marked with different colors. For example, LDP values ​​that meet the threshold condition can be marked with green to indicate a normal state. LDP values ​​close to the threshold condition can be marked with yellow to indicate a critical state. LDP values ​​that do not meet the threshold condition can be marked with red to indicate an alarm state. Preferably, the LDP system 1400 can also analyze the LDP values ​​that do not meet the threshold condition, and determine which data among the first data, the second data, or the third data causes the LDP value to fail to meet the threshold condition, and give a corresponding indication in the alarm. For example, the LDP system 1400 can identify that the main reason for the LDP value not meeting the threshold condition is that the value of the first data (defective data) is too large. In this case, the LDP system 1400 can analyze the first data to identify the type and location of the associated defect that causes the value of the first data to be too large, and the type of material that causes the defect. The LDP system 1400 may include the above information in an alarm and provide it to the logistics supply line system 1200 so that the logistics supply line system 1200 can make adjustments for the specific type of defect / material.

[0057] Figure 4 A schematic diagram of a computing device 4000 according to an embodiment is shown. The computing device 4000 may be used to implement the LDP system 1400 of the present disclosure. The computing device 4000 may be implemented as a server, a computer, a tablet computer, a smart phone, a wearable device, etc. The computing device 4000 may include a communication module 4100, a storage module 4200, and a processing circuit 4300.

[0058] According to an embodiment, the communication module 4100 can perform communication between the computing device 4000 and other devices. According to an embodiment, the communication module 4100 can be applicable to different wired or wireless communication protocols, including but not limited to various Ethernet communications, optical fiber communications, cellular communications, Bluetooth, Wi-Fi, etc. According to an embodiment, the communication module 4100 can be implemented as communication interface components such as antenna devices, radio frequency circuits, and part of the baseband processing circuit. The communication module 4100 is drawn with a dotted line because it can also be located in the processing circuit 4300 or outside the computing device 4000.

[0059] According to an embodiment, the storage module 4200 may store information generated by the processing circuit 4300, information received from other devices through the communication module 4100, programs, machine codes and data for the operation of the computing device 4000, etc. The storage module 4200 is drawn with a dotted line because it may also be located within the processing circuit 4300 or outside the computing device 4000. The storage module 4200 may be a volatile memory and / or a non-volatile memory. For example, the storage module 4200 may include, but is not limited to, a random access memory (RAM), a dynamic random access memory (DRAM), a static random access memory (SRAM), a read-only memory (ROM), and a flash memory.

[0060] According to an embodiment, the processing circuit 4300 may be used to implement various functions of the computing device 4000. According to an embodiment, the computing device 4000 may be implemented as the LDP system 1400. Accordingly, the processing circuit 4300 may perform the corresponding methods and functions described herein. The processing circuit 4300 may be implemented as one or more processors, integrated circuits, field programmable logic arrays, and the like. The processing circuit 4300 may execute computer programs or instructions to perform one or more methods and functions described in the present disclosure.

[0061] Figure 5A schematic diagram of a data model 5000 for an LDP system according to an embodiment is shown. The data model 5000 may include a data lake 5100 and an LDP database 5200 that are communicatively coupled to each other. The data lake 5100 may be configured to store various data related to the vehicle production line 1100. The LDP database 5200 may be configured to store various data related to the LDP. For example, the production data of the vehicle production line 1100 may be initially sent to the data lake 5100 by the IPSL system, and then forwarded to the LDP database 5200 by the data lake 5100. In contrast, the reprocessing data may be sent directly to the LDP database 5200. The LDP database 5200 may be considered as a database dedicated to the LDP, which may be connected to the LDP interaction interface 5300. The LDP interaction interface 5300 may provide user interaction with the LDP database 5200. For example, the LDP interaction interface 5300 may allow the user to perform the aforementioned Figure 3 The one or more confirmation processes described (e.g., confirmation process 3200) allow receiving instructions and statements for calculating LDP from users, allow generating LDP reports based on the obtained LDP, and / or provide the generated LDP reports to management and decision-making departments, etc. In addition, the LDP interaction interface 5300 also allows receiving data pool synchronization instructions, which enable the LDP database 5200 to synchronize the data therein to the data pool 5100, so that the data is stored in the data pool 5100 as data assets. The data pool 5100 can be connected to the data multiplexing interface 5400, so that the data assets synchronized to the data pool 5100 can be reused by one or more other systems. The LDP database 5200 and the data therein itself may not be directly interfaced with the other one or more systems, thereby maintaining the security and reliability of the data in the LDP database 5200.

[0062] The method and system disclosed in the present invention provide an automated evaluation method for the quality of the logistics supply line, which can evaluate, monitor, feedback and adjust the LDP in real time, thereby improving the quality of the logistics supply line, reducing defects in vehicle products and downtime of vehicle production lines. In addition, the obtained data can be stored in the data pool as a data asset, so that it can be reused by other departments and use cases, which can reduce the workload of collecting data and improve the efficiency of using data.

[0063] The present disclosure may be implemented as a system, method, and / or computer program product. The computer program product may include (one or more) computer-readable storage media having computer-readable program instructions thereon for causing a processor to execute aspects of the present disclosure.

[0064] Computer readable storage medium can be a tangible device that can keep and store instructions for being used by instruction execution device.Computer readable storage medium can be, for example, (but not limited to) electronic storage device, magnetic storage device, optical storage device, electromagnetic storage device, semiconductor storage device or any suitable combination of the above.The non-exhaustive list of more specific examples of computer readable storage medium includes the following: portable computer disk, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanical encoding device (such as punch card or convex structure in groove with instruction stored thereon) and any suitable combination of the above.Computer readable storage medium as used herein is not interpreted as instantaneous signal itself, such as radio wave or other free propagating electromagnetic wave, electromagnetic wave propagated by waveguide or other transmission medium (for example, light pulse by optical fiber cable) or electrical signal transmitted by wire.

[0065] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network (e.g., the Internet, a local area network, a wide area network, and / or a wireless network). The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards these computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0066] The computer-readable program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​(such as Smalltalk, C++, etc.) and conventional procedural programming languages ​​(such as "C" programming languages ​​or similar programming languages). The computer-readable program instructions may be executed completely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network (including a local area network (LAN) or a wide area network (WAN)), or may be connected to an external computer (such as using an Internet service provider via the Internet). In some embodiments, an electronic circuit may be customized by utilizing the state information of a computer-readable program instruction, including, for example, a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), and the electronic circuit may execute a computer-readable program instruction to perform aspects of the present disclosure.

[0067] Aspects of the present disclosure are described herein with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments. It will be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0068] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / behaviors specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these computer-readable program instructions can guide the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium in which the instructions are stored includes a manufactured product, which includes instructions for implementing aspects of the functions / behaviors specified in one or more boxes in the flowchart and / or block diagram.

[0069] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operating steps are performed on the computer, other programmable apparatus, or other device to produce a computer-implemented process, so that the instructions executed on the computer, other programmable apparatus, or other device implement the functions / behaviors specified in one or more boxes in the flowchart and / or block diagram.

[0070] The flow chart and block diagram in the figure show the possible architecture, function and operation of the system, method and computer program product according to each embodiment of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a part of a module, segment or instruction, and a part of the module, segment or instruction contains one or more executable instructions for realizing the specified (one or more) logical functions. In some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the figure. For example, depending on the functions involved, two continuous boxes can actually be executed substantially in parallel, or these boxes can sometimes be executed in the opposite order. It will also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a system based on special hardware that performs a specified function or behavior or performs a combination of special hardware and computer instructions.

[0071] It should also be understood by those skilled in the art that the various operations illustrated in order in the embodiments of the present disclosure are not necessarily performed in the illustrated order. Those skilled in the art may adjust the order of the operations as needed. Those skilled in the art may also add more operations or omit some of the operations as needed.

[0072] The description of various embodiments of the present disclosure has been given for the purpose of illustration, but is not intended to be exhaustive or limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein are selected to best explain the principles of the embodiments, practical applications, or improvements to technologies found in the marketplace, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for evaluating the execution rate of a logistics supply line executed by a computer system, wherein the logistics supply line is used to supply materials required for vehicle production, the method comprising: acquiring first data associated with a vehicle defect of a vehicle production line; acquiring second data, the second data being associated with a line stop of a vehicle production line; acquiring third data, wherein the third data is associated with an output of a vehicle production line; as well as Calculate the execution rate of the logistics supply line based on the first data, the second data and the third data; The materials required for vehicle production are classified into key categories representing vehicle parts and non-key categories representing production auxiliary materials, and the execution rate of the logistics supply line includes at least a first execution rate associated with vehicle parts and a second execution rate associated with production auxiliary materials. The first execution rate is calculated as: LDP_M=(Cr+St_M)*α / (V+St_M+St_T); Wherein, the second execution rate is calculated as: LDP_T=(Cr+St_T)*α / (V+St_M+St_T); Among them, LDP_M represents the first execution rate, LDP_T represents the second execution rate, Cr represents the first data associated with vehicle defects in the vehicle production line, St_M represents the first line stop data related to vehicle parts in the second data, St_T represents the second line stop data related to production auxiliary materials in the second data, V represents the output of the vehicle production line, and α represents the proportional factor.

2. The method of claim 1, wherein: The first data is reprocessing data associated with vehicle defects of a vehicle production line, and the method includes: before calculating the execution rate of the logistics supply line, preprocessing the reprocessing data, the preprocessing including: selecting data related to errors, deficiencies and missing of materials from said reprocessing data; marking the selected data as being associated with a vehicle part or associated with a production auxiliary material; and Associate the selected data with the corresponding supply and logistics departments.

3. The method of claim 2, wherein: The pre-processing further comprises: Send the processed data to the supply and logistics department for confirmation; and The confirmed data is received from the supply and logistics department as the confirmed first data.

4. The method of claim 1, wherein: Acquiring the second data includes: Get the planned output and hourly output of the vehicle production line; Obtain the downtime of the vehicle production line; Obtaining target equipment availability; and The second data is calculated based on the planned output, hourly output, line downtime, and target equipment operating rate.

5. The method of claim 4, wherein: The second data is calculated as: St=(PV / Hpur)*OEE / β*Dt Among them, PV represents the planned output, Hpur represents the output per hour, OEE represents the target equipment operating rate, Dt represents the downtime, and β represents the time coefficient.

6. The method of claim 4, further comprising: Send downtime-related data to supply and logistics departments; as well as Receive confirmation from Supply and Logistics.

7. The method of claim 4, wherein: The method comprises: before calculating the execution rate of the logistics supply line, processing the second data, wherein the processing comprises: determining first line stop data related to vehicle parts in the second data; and Determine that the second data is second line stop data related to production of auxiliary materials.

8. The method of claim 1, further comprising: Generate a report on the execution rate of the logistics supply line.

9. The method of claim 1, further comprising: In response to the execution rate of the logistics supply line not satisfying a threshold condition, an alarm is sent to the supply and logistics departments of the corresponding materials.

10. A computing device comprising: one or more processors; as well as A non-transitory computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by the one or more processors, the one or more processors execute the method according to any one of claims 1 to 9.

11. A non-transitory computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by one or more processors, the one or more processors are caused to perform the method according to any one of claims 1 to 9.

12. A computer program product, comprising a computer program, which, when executed by one or more processors, causes the one or more processors to perform the method according to any one of claims 1 to 9.

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