Engineering change management method, device and equipment

Abstract

The present application discloses an engineering change management method, apparatus, and equipment, which relate to the field of engineering change technology, including: using an application programming interface to establish a communication connection between a product lifecycle management system and multiple business systems to form a global data collection network; collecting multi-dimensional engineering change data related to the current engineering change from each business system through the application programming interface; classifying and integrating the multi-dimensional engineering change data according to multiple engineering change cost elements, and predicting the engineering change cost based on the various types of change cost data obtained; predicting the change forecast revenue generated by the engineering change based on the multi-dimensional engineering change data; predicting the return on investment based on the change forecast revenue and engineering change cost, and comparing the return on investment with the size of a preset decision threshold to obtain an engineering change decision result. The present application can realize the interconnection between different business systems and automated engineering change decision-making, and improve the decision-making efficiency and accuracy.

Description

Technical Field

[0001] The present application relates to the technical field of engineering change, and in particular to an engineering change management method, device and equipment. Background Art

[0002] As the core component of server hardware systems, PCBA (Printed Circuit Board Assembly) must continuously adapt to technological advancements such as memory upgrades, interface upgrades, and innovative liquid cooling architectures. Furthermore, it faces diverse challenges such as domestic chip substitution and in-depth customer customization. Furthermore, various engineering changes, such as hardware design defect fixes, firmware updates, technology iterations and performance upgrades, supply chain adjustments, compliance and security requirements, production and process optimization, and customer customization requirements, may arise due to agile project development and delivery requirements, new technology demands, production optimization, or changes in the external environment.

[0003] Currently, managing PCBA engineering changes typically requires making engineering change decisions based on change cost data. However, this data is dispersed across multiple business systems (such as R&D, production, procurement, planning, and sales), lacking unified data standards and real-time synchronization mechanisms. This prevents interconnection between these systems, leading to data incompatibility between different business systems and, consequently, automated engineering change decision-making. Furthermore, PCBA engineering changes involve multiple processes, including design, materials, production, and testing. Traditional engineering change decision-making methods rely on manual experience, resulting in low efficiency and the potential for missing hidden costs. Furthermore, current engineering change decision-making methods lack dynamic, quantitative analysis of the impact of changes, making it difficult to balance conflicts among multiple objectives, such as cost, delivery time, and quality. When conflicting evaluation conclusions arise, decisions are often made based on the subjective judgment of expert teams, lacking quantitative decision-making data to support them, leading to a high risk of decision-making errors. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide an engineering change management method, device, and equipment that can achieve interconnection between different business systems, allowing data between different business systems to be transmitted to each other, thereby providing a data foundation for automated engineering change decision-making, and realizing automated engineering change decision-making, saving labor costs and time costs, improving decision-making efficiency and accuracy, and at the same time reducing the risk of decision-making errors. The specific solution is as follows:

[0005] In a first aspect, the present application discloses an engineering change management method, which is applied to a product lifecycle management system, comprising:

[0006] Use the pre-set application programming interface to establish communication connections between the product lifecycle management system and multiple business systems to form a global data collection network that communicates between different systems;

[0007] When the product lifecycle management system receives a printed circuit board assembly engineering change request, it collects data related to the current engineering change from multiple business systems through the application programming interface to obtain multi-dimensional engineering change data. It then categorizes and integrates the multi-dimensional engineering change data according to multiple preset engineering change cost elements to obtain multiple types of change cost data.

[0008] Input each type of change cost data into the corresponding engineering change cost prediction model to predict the engineering change cost corresponding to the corresponding engineering change cost element, obtain the current engineering change cost, and create an engineering change cost matrix based on each type of change cost data and the corresponding current engineering change cost;

[0009] Input multi-dimensional engineering change data into a pre-created engineering change benefit model to predict the positive change benefits generated by the current engineering change, obtain the predicted change benefits, and create a change benefit matrix based on the predicted change benefits;

[0010] Based on the change benefit matrix and the engineering change cost matrix, the return on investment of the current engineering change is predicted, and the return on investment is compared with the preset decision threshold to obtain the decision result of whether to execute the current engineering change.

[0011] In a second aspect, the present application discloses an engineering change management device, which is applied to a product lifecycle management system, comprising:

[0012] A network construction module is used to establish communication connections between the product lifecycle management system and multiple business systems using a preset application programming interface to form a global data collection network that allows intercommunication between different systems;

[0013] The data collection module is used to collect data related to the current engineering change from multiple business systems through a preset application programming interface when the product lifecycle management system receives a printed circuit board assembly engineering change request, thereby obtaining multi-dimensional engineering change data;

[0014] The classification and integration module is used to classify and integrate multi-dimensional engineering change data according to multiple preset engineering change cost elements to obtain multiple types of change cost data;

[0015] The first prediction module is used to input various types of change cost data into the corresponding engineering change cost prediction model to predict the engineering change cost corresponding to the corresponding engineering change cost element to obtain the current engineering change cost;

[0016] The first creation module is used to create an engineering change cost matrix based on various change cost data and corresponding current engineering change costs;

[0017] The second prediction module is used to input the multi-dimensional engineering change data into a pre-created engineering change benefit model to predict the positive change benefit generated by the current engineering change and obtain the change prediction benefit;

[0018] The second creation module is used to create a change benefit matrix based on the change predicted benefit;

[0019] The engineering change decision module is used to predict the return on investment of the current engineering change based on the change benefit matrix and the engineering change cost matrix, and compare the return on investment with the preset decision threshold to obtain the decision result on whether to execute the current engineering change.

[0020] In a third aspect, the present application discloses an electronic device comprising a processor and a memory; wherein the processor implements the aforementioned engineering change management method when executing a computer program stored in the memory.

[0021] As can be seen, the present application first uses a preset application programming interface to establish a communication connection between the product lifecycle management system and multiple business systems to form a global data collection network that communicates between different systems. When the product lifecycle management system receives a printed circuit board assembly engineering change request, it collects data related to the current engineering change from multiple business systems through the application programming interface to obtain multi-dimensional engineering change data. The multi-dimensional engineering change data is then classified and integrated according to multiple preset engineering change cost elements to obtain multiple types of change cost data. Each type of change cost data is input into a corresponding engineering change cost prediction model to predict the engineering change cost corresponding to the corresponding engineering change cost element to obtain the current engineering change cost. An engineering change cost matrix is created based on the various change cost data and the corresponding current engineering change cost. The multi-dimensional engineering change data is then input into a pre-created engineering change benefit model to predict the positive change benefit generated by the current engineering change to obtain a change prediction benefit. A change benefit matrix is created based on the change prediction benefit. Finally, the return on investment of the current engineering change is predicted based on the change benefit matrix and the engineering change cost matrix. The return on investment of the current engineering change is compared with the preset decision threshold to obtain a decision result on whether to execute the current engineering change.

[0022] The beneficial effects of the present application are as follows: when managing engineering changes, the present application first uses a preset application programming interface to establish a communication connection between the product lifecycle management system and multiple business systems to form a global data collection network that is interoperable between different systems, thereby realizing the interconnection between different business systems and enabling data between different business systems to be transmitted to each other, thereby providing a data basis for automated engineering change decision-making. In addition, the present application specifically makes change decisions based on engineering change data of multiple dimensions collected from multiple business systems. Since the decision data considered is more comprehensive, the accuracy of engineering change decisions can be improved. In addition, the present application creates an engineering change cost matrix (including engineering change costs of multiple engineering change cost elements) and a change benefit matrix in a matrix manner, and predicts the return on investment of the current engineering change based on the data in the two matrices, and then obtains a decision result based on the relationship between the return on investment and the decision threshold. Through the above method, automated engineering change decision-making is realized, which saves labor costs and time costs compared to traditional manual methods, thereby improving the efficiency of engineering change decisions and reducing the risk of decision-making errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without any creative work.

[0024] Figure 1 A flow chart of an engineering change management method disclosed in this application;

[0025] Figure 2 A schematic diagram of a specific knowledge graph disclosed in this application;

[0026] Figure 3 A flowchart of a specific engineering change management method disclosed in this application;

[0027] Figure 4 This is a schematic structural diagram of an engineering change management device disclosed in this application;

[0028] Figure 5 This is a structural diagram of an electronic device disclosed in this application. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0030] The present application discloses an engineering change management method applied to a product life cycle management system. Figure 1 As shown, the method includes:

[0031] Step S11: Using a preset application programming interface to establish a communication connection between the product lifecycle management system and multiple business systems, so as to form a global data collection network that is interoperable between different systems.

[0032] In this embodiment, based on Industrial Internet of Things (IIoT) technology and utilizing a pre-defined application programming interface (API), a communication connection is first established between a product lifecycle management (PLM) system and multiple business systems, thereby forming a global data collection network that enables interconnection and communication between the PLM system and various business systems. The multiple business systems can be any of a number of business systems in R&D, production, procurement, planning, sales, and other links. This allows the PLM system to collect data related to current engineering changes from various business systems, such as the Engineering Change Notification (ECN) system, the Manufacturing Execution System (MES), the Supplier Relationship Management (SRM), the Enterprise Resource Planning (ERP), and the Quality Management System (QMS), thereby generating multi-dimensional engineering change data.

[0033] Step S12: When a printed circuit board assembly engineering change request is received, data related to the current engineering change is collected from multiple business systems through an application programming interface to obtain multi-dimensional engineering change data.

[0034] It should be pointed out that the engineering change decision-making scheme proposed in this application is specifically applied to the product lifecycle management system. When the system receives a printed circuit board assembly (PCBA) engineering change request, it first collects data related to the current engineering change from multiple business systems and local systems (i.e., the product lifecycle management system) through the application programming interface to obtain multi-dimensional engineering change data.

[0035] Specifically, the multi-dimensional engineering change data may include any of engineering change approval-related data, supplier relationship data, product and raw material inventory data, and manufacturing process data. It is understandable that engineering changes may be caused by a variety of factors, such as design adjustments, changes in construction schedules, changes in cost budgets, or updates to risk assessment results. Making engineering change decisions based on a single data source will lead to a higher risk of decision-making errors. The global data collection network constructed by this application based on industrial Internet of Things technology can collect multi-dimensional engineering change data from multiple business systems and use it as the basic data for engineering change decisions. Since the decision-making data considered is more comprehensive, the accuracy of engineering change decisions can be improved, thereby reducing the risk of decision-making errors.

[0036] In one specific embodiment, the product lifecycle management system can first obtain ECN documents from the engineering change notification system and then automatically process them to extract key information for subsequent decision-making and analysis. For example, it can parse and collect data related to the engineering change in the ECN document, including information such as the change object, change type, change list, and change impact. Regarding data collection from the enterprise resource planning system, a created API can be used to collect data related to the current engineering change from the enterprise resource planning system, such as real-time inventory materials, in-transit / in-process materials, and work-in-progress quantities. Data collected from the manufacturing execution system can specifically include complete machine and PCBA manufacturing process information, such as the production process and process of the changed object, as well as the tooling and fixtures involved in each process, and labor hours, to calculate the post-change production cost and change impact. Supplier relationship data collected from the supplier relationship management system specifically refers to material procurement information related to the engineering change, such as the purchase cost of each material and the procurement cycle. This information is used to calculate the direct costs of the material change, such as inventory scrapping costs and purchase price difference costs. This information can be collected through the API interface, or through direct database connection or file transfer.

[0037] Specifically, RFID (Radio Frequency Identification) counters, barcode scanners, measuring sensors and other equipment can be deployed in advance on the production line to collect real-time information such as the number of component replacements and tooling replacements involved in the change. When an engineering change decision needs to be made, data such as downtime, rework time, and capacity loss caused by production plan changes can be obtained from the MES system through the OPC (OLE for Process Control, a standardized communication protocol) interface protocol. Real-time inventory data, in-transit / work-in-progress materials, and work-in-progress quantities can also be collected from the ERP system through the API interface created on the ERP system.

[0038] Step S13: Classify and integrate the multi-dimensional engineering change data according to a plurality of preset engineering change cost elements to obtain multiple categories of change cost data.

[0039] In this embodiment, after collecting multi-dimensional engineering change data related to the current engineering change from multiple business systems, the multi-dimensional engineering change data can be further classified and integrated according to multiple preset engineering change cost elements to obtain multiple categories of change cost data; wherein, the multiple engineering change cost elements include but are not limited to direct cost elements, indirect cost elements and risk cost elements, which correspond to direct costs (such as material, labor and other costs), indirect costs (such as management, delay loss and other costs) and risk costs respectively.

[0040] Alternatively, edge computing can be used to locally aggregate sensor data from multi-dimensional engineering change data through industrial gateways, and perform pre-processing operations on the sensor data, such as classification, cleaning, and de-duplication. By collecting and integrating engineering change data from multiple business systems, a multi-dimensional cost view can be constructed, forming a comprehensive foundation for change impact chain analysis. Product lifecycle management systems can also connect to edge computers to retrieve data related to current engineering changes.

[0041] Step S14: Input each type of change cost data into the corresponding engineering change cost prediction model to predict the engineering change cost corresponding to the corresponding engineering change cost element, obtain the current engineering change cost, and create an engineering change cost matrix based on each type of change cost data and the corresponding current engineering change cost.

[0042] In this embodiment, various types of change cost data can be input into a pre-created engineering change cost prediction model, so as to predict the engineering change costs corresponding to the corresponding engineering change cost elements based on the various types of change cost data, obtain the current engineering change cost, and then create an engineering change cost matrix based on the various types of change cost data and the corresponding current engineering change cost.

[0043] In a specific implementation, each type of change cost data is input into the corresponding engineering change cost prediction model to predict the engineering change cost corresponding to the corresponding engineering change cost element to obtain the current engineering change cost. Specifically, it may include: inputting the change cost data corresponding to the direct cost element into the trained engineering change direct cost prediction model to predict the direct cost caused by the current engineering change based on the change cost data corresponding to the direct cost element to obtain the current direct change cost; the engineering change direct cost prediction model is a model obtained by training the first cost prediction model created based on artificial intelligence using historical multi-dimensional engineering change direct cost data; inputting the change cost data corresponding to the indirect cost element into the trained engineering change indirect cost prediction model to obtain the current direct change cost. In the cost prediction model, the indirect costs resulting from the current engineering change are predicted based on the change cost data corresponding to the indirect cost elements, thereby obtaining the current indirect change cost. The engineering change indirect cost prediction model is a model obtained by training a second cost prediction model created based on artificial intelligence using historical multi-dimensional engineering change indirect cost data. The change cost data corresponding to the risk cost elements are input into the trained engineering change risk cost prediction model, and the risk costs resulting from the current engineering change are predicted based on the change cost data corresponding to the risk cost elements, thereby obtaining the current change risk cost. The engineering change risk cost prediction model is a model obtained by training a third cost prediction model created based on artificial intelligence using historical multi-dimensional engineering change risk cost data. In this embodiment, multiple cost prediction models can be pre-created based on different artificial intelligence models and trained to obtain models for predicting change costs for change cost data corresponding to different engineering change cost elements. The number of cost prediction models is the same as the number of engineering change cost elements, and artificial intelligence models include but are not limited to RNN (recurrent neural network) models based on attention mechanisms, CNN (convolutional neural network) models, multimodal large models, and the like. When the engineering change cost elements include direct cost elements, indirect cost elements and risk cost elements, the corresponding engineering change cost prediction models are respectively the engineering change direct cost prediction model, the engineering change indirect cost prediction model and the engineering change risk cost prediction model. The three models can adopt the same network structure or different network structures, and can be selected according to actual application requirements.

[0044] Among them, the direct cost prediction model for engineering changes is used to predict the direct costs caused by engineering changes, such as material change costs (such as material price differences) and tooling input costs. The material change costs require the collection of material PN (Product Number) information (from the MES system), material price information, and material quantity information; the indirect cost prediction model for engineering changes is used to predict the indirect costs caused by engineering changes, such as production capacity loss costs and quality re-inspection costs; the risk cost prediction model for engineering changes is used to predict the risk costs caused by engineering changes, such as supply interruption risk costs and customer claim risk costs.

[0045] In addition, new training set data can be collected according to a preset collection cycle to continuously optimize and update each model. By using multiple cost prediction models pre-created based on artificial intelligence to predict the engineering change costs corresponding to each engineering change cost element, automated engineering change decision-making can be achieved. Compared with traditional manual methods, this saves manpower and time costs, thereby improving the efficiency of engineering change decision-making.

[0046] In this embodiment, the project change cost matrix is created based on various types of change cost data and the corresponding current project change costs. Specifically, it may include: establishing associations between various types of change cost data and the corresponding current project change costs through the knowledge graph; and creating a project change cost matrix containing the change cost data and the current project change costs based on the associations. In this embodiment, see Figure 2 As shown, the knowledge graph can be used to establish associations between various types of change cost data and the corresponding current engineering change costs. Based on these associations, an engineering change cost matrix containing the change cost data and the current engineering change costs can then be created. Specifically, the reasoning process for the paths in the knowledge graph can be: if "Change A → results in → material change ↑," and "Material change ↑ → triggers → direct cost ↑," then "Change A → direct impact → direct cost ↑" can be inferred. Furthermore, the knowledge graph can be used to associate the collected engineering change data with the final predicted engineering decision results.

[0047] Step S15: Input the multi-dimensional engineering change data into a pre-created engineering change benefit model to predict the positive change benefit generated by the current engineering change, obtain the change prediction benefit, and create a change benefit matrix based on the change prediction benefit.

[0048] It is understandable that in addition to change costs, engineering changes will also generate positive change benefits. Therefore, after creating an engineering change cost matrix based on various change cost data and the corresponding current engineering change costs, the multi-dimensional engineering change data can be further input into the pre-created engineering change benefit model to predict the positive change benefits generated by the current engineering change (such as cost savings, risk reduction, quality improvement, etc. brought about by shortened construction period), obtain the change forecast benefits, and then create a change benefit matrix based on the obtained change forecast benefits.

[0049] Specifically, the predicted benefits of changes include the predicted benefits of demand-related changes and the predicted benefits of vulnerability-solving changes; among them, the engineering change benefit model is a mathematical model used to calculate the positive change benefits generated by engineering changes; the calculation formula of the mathematical model corresponding to the predicted benefits of demand-related changes is the product of the expected order growth number and the order profit; the calculation formula of the mathematical model corresponding to the predicted benefits of vulnerability-solving changes is the product of the probability of failure, maintenance cost and maintenance quantity.

[0050] Specifically, the predicted revenue from demand changes refers to the revenue generated by changes based on new demands from the market, client, and project segments. The specific calculation formula is:

[0051] ;

[0052] The predicted revenue from bug-solving changes refers to the revenue from changes that resolve design bugs on the R&D side and customer complaints on the marketing side. The specific calculation formula is:

[0053] ;

[0054] Where, represents the probability of occurrence of failure mode i (which can be derived based on testing, production, and market failure rate assessments), Indicates the cost of a single repair (including labor, spare parts, logistics, etc.), Indicates the number of products currently under warranty in the market (such as the number of repairable products).

[0055] Furthermore, a change benefit matrix is created based on the predicted change benefits, as shown in Table 1:

[0056] Table 1 Change benefit matrix

[0057]

[0058] Step S16: Predict the return on investment of the current engineering change based on the change benefit matrix and the engineering change cost matrix, and compare the return on investment with a preset decision threshold to obtain a decision result on whether to execute the current engineering change.

[0059] In this embodiment, the return on investment (ROI) of the current engineering change can be predicted based on the data in the change benefit matrix and the engineering change cost matrix, and then the return on investment is compared with the preset decision threshold to obtain a comparison result, and then it is determined whether to execute the current engineering change based on the comparison result.

[0060] Specifically, based on the change benefit matrix and the engineering change cost matrix, the return on investment of the current engineering change is predicted, and the return on investment is compared with the preset decision threshold to obtain the decision result of whether to execute the current engineering change, which may include: calculating the sum of the current direct change cost, the current indirect change cost and the current change risk cost in the engineering change cost matrix to obtain the total change cost; calculating the sum of the predicted benefits of demand-type changes and the predicted benefits of vulnerability-solving changes in the change benefit matrix to obtain the total predicted benefits of the engineering change; calculating the ratio of the total predicted benefits of the engineering change to the total change cost to obtain the return on investment of the current engineering change, and judging whether the return on investment is greater than the first decision threshold; if the return on investment is greater than the first decision threshold, executing the current engineering change; if the return on investment is not greater than the first decision threshold, judging whether the return on investment is less than the second decision threshold; the second decision threshold is less than the first decision threshold; if the return on investment is less than the second decision threshold, not executing the current engineering change. In this embodiment, the sum of all change costs in the engineering change cost matrix (including the sum of direct change costs, current indirect change costs, and current change risk costs) is calculated to obtain the total change cost TCC. Then, the sum of all change forecast benefits in the change benefit matrix is calculated, such as the forecast benefit of demand-related changes. and predicting benefits of vulnerability-solving changes The total project change forecast benefit is obtained by summing the total project change benefit. Next, the return on investment (ROI) of the current project change is calculated. The specific calculation formula is: The calculation is then saved to the change request form as the basis for engineering change decisions. Furthermore, the system determines whether the return on investment (ROI) (ROI) exceeds a first decision threshold. If so (for example, ROI > 1.2), executing the current engineering change will yield a high return, and the engineering change can be directly executed. If the ROI is not greater than the first decision threshold, the system further determines whether it is less than a second decision threshold. If the ROI is less than the second decision threshold, for example, ROI < 0.7, executing the current engineering change will yield a low return, and the engineering change request is directly rejected. By calculating the ROI of the current engineering change and using a preset threshold to determine whether to execute the current engineering change, quantitative engineering change decisions can be made by considering both investment and return. Compared to traditional manual methods, this approach saves labor and time costs, does not rely on manual experience, and is less likely to miss hidden costs. This improves the accuracy and efficiency of engineering change decisions, while also balancing conflicts among multiple objectives such as cost, delivery time, and quality.

[0061] In a specific implementation, the return on investment (ROI) can be calculated in the following manner, specifically including: first calculating the product of the direct change cost and the direct weighted addition coefficient, the product of the indirect change cost and the indirect weighted addition coefficient, and the product of the change risk cost and the risk weighted addition coefficient in the engineering change cost matrix, respectively, to obtain the corresponding direct change product, indirect change product, and change risk product, and then after multiplying the direct change product, the indirect change product, and the change risk product, the total change cost is obtained, and then the sum of the predicted benefits of demand-related changes and the predicted benefits of vulnerability-solving changes in the change benefit matrix is calculated to obtain the total engineering change predicted benefits, and then the ratio of the total engineering change predicted benefits to the total change cost is calculated to obtain the return on investment (ROI) of the current engineering change. The calculation formula for the total change cost TCC can be expressed as:

[0062] ;

[0063] Where, Indicates the weighted addition coefficient corresponding to each change cost.

[0064] Calculating the return on investment (ROI) by multiplying different change costs by the corresponding weighted addition coefficient can meet more diverse application needs and more accurately derive a return on investment (ROI) that is consistent with reality, so as to make more accurate change decisions, thereby further improving the accuracy of engineering change decisions.

[0065] In another embodiment, it may also include: if the return on investment is between the second decision threshold and the first decision threshold, sending an upgrade review request for the current engineering change to the change control committee terminal; after receiving the engineering change supplementary data for the total engineering change predicted benefits and / or total change costs sent by the change control committee terminal, recalculating the return on investment of the current engineering change based on the engineering change supplementary data to obtain the investment return ratio; judging whether the investment return ratio is greater than the first decision threshold, and if the investment return ratio is greater than the first decision threshold, executing the current engineering change. In this embodiment, if the return on investment (ROI) is between the second decision threshold and the first decision threshold, such as 0.7 ≤ ROI ≤ 1.2, an upgrade review request for the current engineering change can be sent to a Change Control Board (CCB) terminal for manual approval by the CCB. The CCB can supplement some data related to the current engineering change (such as data on the total engineering change forecast revenue and / or total change cost) by adding comments or overwriting suggestions on the CCB terminal, and then send the supplemented data to the product lifecycle management system. After receiving the supplemented data sent by the CCB terminal, the product lifecycle management system can recalculate the return on investment of the current engineering change based on the supplemented data and make a new decision, namely, determine whether the new ROI is greater than the first decision threshold. If so, the current engineering change is executed. In addition, the CCB can directly provide an opinion on whether to directly execute or reject the current engineering change, and make a corresponding decision directly based on the opinion provided by the CCB.

[0066] It should be pointed out that before the current engineering change is officially implemented, the return on investment (ROI) will be monitored in real time. If a deviation is found, an early warning will be triggered to notify the CCB terminal to make timely adjustments and make new change decisions.

[0067] Furthermore, intelligent decision proposals can be generated based on the total project change forecast benefits, total change costs, and decision results. This triggers a feedback process for change execution or optimization, thus achieving closed-loop management from data perception to strategy optimization. Visualization tools can also be used to visualize the change benefit matrix, project change cost matrix, and intelligent decision proposals.

[0068] It can be seen that when managing engineering changes, the embodiment of the present application first uses a preset application programming interface to establish a communication connection between the product lifecycle management system and multiple business systems to form a global data collection network that communicates between different systems, thereby realizing the interconnection between different business systems and enabling data between different business systems to be transmitted to each other, thereby providing a data foundation for automated engineering change decision-making. In addition, the embodiment of the present application makes change decisions based on engineering change data of multiple dimensions collected from multiple business systems. Since the decision data considered is more comprehensive, the accuracy of engineering change decisions can be improved. In addition, the embodiment of the present application creates an engineering change cost matrix (including engineering change costs of multiple engineering change cost elements) and a change benefit matrix in a matrix manner, and predicts the return on investment of the current engineering change based on the data in the two matrices, and then obtains a decision result based on the relationship between the return on investment and the decision threshold. Through the above method, automated engineering change decision-making is achieved, which saves labor costs and time costs compared to traditional manual methods, thereby improving the efficiency of engineering change decisions and reducing the risk of decision-making errors.

[0069] The present application embodiment discloses a specific engineering change management method, see Figure 3 As shown, the method includes:

[0070] Step S21: Using a preset application programming interface to establish a communication connection between the product lifecycle management system and multiple business systems, so as to form a global data collection network that is interoperable between different systems.

[0071] Step S22: When a printed circuit board assembly engineering change request is received, data related to the current engineering change is collected from multiple business systems through an application programming interface to obtain multi-dimensional engineering change data.

[0072] Step S23: Classify and integrate the multi-dimensional engineering change data according to a plurality of preset engineering change cost elements to obtain multiple types of change cost data; the plurality of engineering change cost elements include direct cost elements, indirect cost elements and risk cost elements.

[0073] Step S24: Input the change cost data corresponding to the direct cost elements into the pre-created engineering change direct cost prediction model to predict the direct cost caused by the current engineering change based on the change cost data corresponding to the direct cost elements, and obtain the current direct change cost; the engineering change direct cost prediction model is a mathematical model for calculating the direct cost of engineering changes; the current direct change cost includes material change cost, changed inventory scrap cost, tooling change cost and material obsolete scrap cost.

[0074] In this embodiment, a mathematical model for calculating the direct cost of engineering changes can be used to calculate the direct change costs caused by the current engineering change. The direct change costs refer to the change costs of new direct material demand, scrapping, tooling and fixture procurement, repair, and scrapping caused by the engineering change, such as PCBA material change costs, PCBA change inventory scrapping costs, PCBA tooling and fixture change costs, and material obsolete scrapping costs.

[0075] Among them, PCBA material change cost (such as material price difference cost) is used to evaluate the impact of subsequent PCBA material procurement costs caused by engineering changes. The specific calculation formula is:

[0076] ;

[0077] Where, Indicates the new unit price (tax included) of item i in the ECN change list. Indicates the old unit price (tax included) of item i in the ECN change list. It represents the total estimated purchase amount of item i during the remaining product life cycle.

[0078] For example, see Table 2, which shows a specific material price difference cost calculation process:

[0079] Table 2 Material price difference cost matrix

[0080]

[0081] A negative value indicates cost savings, and a positive value indicates cost increases.

[0082] Specifically, the PCBA change inventory scrapping cost (such as the discount loss rate of PCBA inventory) is used to evaluate the cost loss of PCBA scrapping caused by engineering changes. The specific calculation formula is:

[0083] ;

[0084] Where, Indicates the number of PCBA stocks and work-in-progress that need to be scrapped (including all complete machine factories and board factories). Indicates the average purchase cost of the corresponding PCBA, Indicates the scrap recycling value coefficient of the PCBA (between 0 and 1, the default value for PCBA semi-finished products is 0.3-0.6).

[0085] For example, when the PCB layout changes and 100 old-version PCBAs need to be eliminated, the scrapping cost is: 100 × ¥800 × 0.4 = ¥32,000.

[0086] Specifically, the PCBA tooling change cost is used to evaluate the procurement cost of tooling changes caused by engineering changes. The specific calculation formula is:

[0087] ;

[0088] Where, Represents the purchase cost of new fixtures and / or test racks, Indicates the net loss of the old fixture due to early scrapping. It represents the cost of renovating reusable equipment and / or the salvage value of old tooling.

[0089] For example, when a high-speed backplane connector replacement requires a fixture update, the specific tooling change cost is: ¥500,000 (new purchase) + ¥80,000 (old damage) - ¥120,000 (renovation) = ¥460,000.

[0090] Specifically, the cost of obsolete and scrapped materials is used to assess the cost of obsolete and / or scrapped electronic material inventory caused by engineering changes. The specific calculation formula is:

[0091] ;

[0092] Where, represents the quantity of slow-moving materials of category k, represents the historical average purchase price of the kth type of slow-moving materials, Represents the residual value of the kth category of obsolete materials (such as supplier recycling, internal transfer, second-hand market price, etc.).

[0093] For example, when a schematic change causes 10,000 resistors to be scrapped, the cost of obsolete materials is: 10,000 × (¥0.8 - ¥0.05) = ¥7500.

[0094] Step S25: Input the change cost data corresponding to the indirect cost elements into the pre-created engineering change indirect cost prediction model to predict the indirect costs caused by the current engineering change based on the change cost data corresponding to the indirect cost elements, and obtain the current indirect change cost; the engineering change indirect cost prediction model is a mathematical model used to calculate the indirect cost of engineering changes; the current indirect change cost includes inventory rework cost and whole machine rework cost.

[0095] In this embodiment, the indirect change costs caused by the current engineering change can be calculated using a mathematical model for calculating the indirect costs of engineering changes (i.e., an engineering change indirect cost prediction model); wherein the indirect change costs specifically include the costs of whole-machine rework caused by the change and the manpower loss and material loss caused by PCBA rework, such as inventory rework costs and whole-machine rework costs.

[0096] Specifically, the whole machine rework cost is used to evaluate the whole machine rework cost loss caused by engineering changes. The specific calculation formula is:

[0097] ;

[0098] Where, Indicates the standard working hours for rework of the whole machine of category A (need to distinguish between disassembly, assembly, debugging and other processes), Indicates the comprehensive labor rate (such as the rate of operators + technical support personnel), Indicates the number of complete machines that need to be reworked. Represents the cost of quality inspection after rework (including costs of functional testing, stability testing, aging testing, etc.).

[0099] For example, if a PCBA firmware upgrade requires rework for 20 units in stock and online, the rework cost is (2*¥500+¥100)*20=¥22,000.

[0100] Specifically, the inventory rework cost (i.e., the rework cost of inventory PCBAs) is used to evaluate the PCBA rework cost loss caused by engineering changes. The specific calculation formula is:

[0101] ;

[0102] Where, Represents the time required for reworking a single board (including desoldering, cleaning, mounting, testing, etc.). Indicates the composite labor rate of the SMT (Surface Mount Technology) production line (including technicians + engineers), Represents the cost of consumables such as solder paste, desoldering ribbon, and cleaning agents.

[0103] Step S26: Input the change cost data corresponding to the risk cost elements into a pre-created engineering change risk cost prediction model to predict the risk cost caused by the current engineering change based on the change cost data corresponding to the risk cost elements, and obtain the current change risk cost; the engineering change risk cost prediction model is a mathematical model used to calculate the engineering change risk cost; the current change risk cost includes the supply interruption risk cost and the customer claim risk cost.

[0104] In this embodiment, a mathematical model for calculating engineering change risk costs (i.e., an engineering change risk cost prediction model) can be used to calculate the change risk costs resulting from the current engineering change, including the supply disruption risk cost and the customer claim risk cost. The change risk cost is used to assess the risk costs of supply disruption, customer claims, and other risks associated with the engineering change. Specifically, the change risk cost can be calculated based on factors such as supply risk and customer complaint risk.

[0105] Step S27: Create an engineering change cost matrix based on various change cost data and the corresponding current engineering change costs, and input the multi-dimensional engineering change data into a pre-created engineering change benefit model to predict the positive change benefits generated by the current engineering change, obtain the change prediction benefits, and create a change benefit matrix based on the change prediction benefits.

[0106] In this embodiment, an engineering change cost matrix may be created based on the change cost data and the corresponding current engineering change cost, as shown in Table 3:

[0107] Table 3 Engineering change cost matrix

[0108]

[0109] As can be seen from Table 3, in addition to the cost type, cost elements and engineering change cost values, the created engineering change cost matrix also records other information, such as calculation method, data source, impact scope and risk weight level.

[0110] Step S28: Predict the return on investment of the current engineering change based on the change benefit matrix and the engineering change cost matrix, and compare the return on investment with the preset decision threshold to obtain a decision result on whether to execute the current engineering change.

[0111] For more specific processing procedures of the above steps S21 to S23 and S28, reference may be made to the corresponding contents disclosed in the aforementioned embodiments, which will not be repeated here.

[0112] It can be seen that the embodiment of the present application collects engineering change cost data of multiple dimensions from multiple business systems, such as change approval-related data, real-time quotations for supply chain procurement, product and raw material inventory data, and production process data. Since the collected data is more comprehensive, the accuracy of engineering change cost prediction is improved. In addition, the present application predicts the positive change benefits generated by the engineering change, and calculates the return on investment of the engineering change based on the predicted change benefits and the total change cost, and decides whether to execute the engineering change cost based on the relationship between the return on investment and the preset threshold. The above method realizes automated engineering change cost decision-making, which saves labor costs and improves decision-making efficiency compared to traditional manual methods, thereby improving change approval efficiency, shortening the change approval cycle, and reducing the incidence of overspending events.

[0113] In one specific implementation, when receiving an engineering change request (ECR) to replace the CPU (Central Processing Unit) in a 5288 server model from a Xeon Silver to an EPYC to meet subsequent orders from customer A in market, the ECR can be parsed to obtain key change parameters, such as material specifications, process adjustments (mold modifications), a change list (involving the CPU and related components), and rework of inventory PCBA orders. It should be noted that an ECR can be initiated by the R&D department by submitting a change request through the system and filling out a structured form.

[0114] Next, we analyzed and categorized the extracted key change parameters to determine direct costs (such as the price difference of the CPU and related materials, and the cost of reproducing process tooling), indirect costs (such as the cost of reworking and retesting 200 PCBAs), and risk costs (e.g., the PCBA rework is not expected to cause delivery delays and is risk-free). Furthermore, we conducted a revenue forecast: the CPU replacement is expected to increase new orders by 2,000 units. Next, based on this data, we created an engineering change cost matrix and a change benefit matrix, as shown in Tables 4 and 5:

[0115] Table 4 Engineering change cost matrix

[0116]

[0117] Table 5 Change benefit matrix

[0118]

[0119] Next, the return on investment (ROI) is calculated based on the information in the engineering change cost matrix and the change benefit matrix, that is, ROI = 2000 / 2250 ≈ 0.89. The system then determines whether the current return on investment (ROI) = 0.89 is greater than the decision threshold of 1.2. Since 0.89 is between 0.7 and 1.2, the "upgrade CCB review" process is triggered, which automatically sends an approval request to the CCB terminal for intervention through the change control committee. For example, the marketing department can supplement the data, stating that "competitors have released similar technologies, and if the change is not made, 1,000 orders will be lost." After supplementing the data, the actual ROI value is calculated again, and the new ROI value is obtained: R demand = 3000 → ROI = 1.33 (>1.2). At this time, the decision result indicates that the change can be executed immediately.

[0120] In addition, if data anomalies are discovered before the change is executed, for example, during implementation, trade relations are affected, supplier inventory is limited, and material procurement costs increase by 20% (TCC increases to 26.5 million), the real-time calculation shows that the return on investment (ROI) = 1.13 < 1.2 (originally 1.33). At this time, an early warning can be triggered and the CCB terminal can be notified so that the change control committee can make auxiliary decisions, such as deciding to introduce new suppliers through strategic procurement to control procurement costs.

[0121] Correspondingly, the embodiment of the present application also discloses an engineering change management device, which is applied to a product life cycle management system, see Figure 4 As shown, the device includes:

[0122] A network construction module 11 is used to establish a communication connection between the product lifecycle management system and multiple business systems using a preset application programming interface to form a global data collection network that is interoperable between different systems;

[0123] The data collection module 12 is used to collect data related to the current engineering change from multiple business systems through the application programming interface when the product lifecycle management system receives a printed circuit board assembly engineering change request, thereby obtaining multi-dimensional engineering change data;

[0124] The classification and integration module 13 is used to classify and integrate the multi-dimensional engineering change data according to a plurality of preset engineering change cost elements to obtain multiple types of change cost data;

[0125] The first prediction module 14 is used to input various types of change cost data into corresponding engineering change cost prediction models to predict the engineering change costs corresponding to corresponding engineering change cost elements to obtain the current engineering change costs;

[0126] A first creation module 15 is configured to create an engineering change cost matrix based on various types of change cost data and corresponding current engineering change costs;

[0127] The second prediction module 16 is used to input the multi-dimensional engineering change data into a pre-created engineering change benefit model to predict the positive change benefit generated by the current engineering change and obtain the change prediction benefit;

[0128] The second creation module 17 is used to create a change benefit matrix based on the change prediction benefit;

[0129] The engineering change decision module 18 is used to predict the return on investment of the current engineering change based on the change benefit matrix and the engineering change cost matrix, and compare the return on investment with the preset decision threshold to obtain a decision result on whether to execute the current engineering change.

[0130] Among them, the specific work processes of the above modules can refer to the corresponding contents disclosed in the aforementioned embodiments, which will not be repeated here.

[0131] When managing engineering changes, the embodiment of the present application first uses a preset application programming interface to establish a communication connection between the product lifecycle management system and multiple business systems to form a global data collection network that communicates between different systems. This achieves interconnection between different business systems, allowing data between different business systems to be transmitted to each other, thereby providing a data foundation for automated engineering change decision-making. In addition, the embodiment of the present application makes change decisions based on engineering change data of multiple dimensions collected from multiple business systems. Since the decision data considered is more comprehensive, the accuracy of engineering change decisions can be improved. In addition, the embodiment of the present application creates an engineering change cost matrix (including engineering change costs of multiple engineering change cost elements) and a change benefit matrix in a matrix manner, and predicts the return on investment of the current engineering change based on the data in the two matrices. The decision result is then obtained based on the relationship between the return on investment and the decision threshold. Through the above method, automated engineering change decision-making is achieved. Compared with traditional manual methods, it saves labor costs and time costs, thereby improving the efficiency of engineering change decisions and reducing the risk of decision errors.

[0132] In some specific embodiments, the plurality of engineering change cost elements include direct cost elements, indirect cost elements, and risk cost elements;

[0133] Accordingly, the first prediction module 14 may specifically include:

[0134] A first prediction unit is configured to input the change cost data corresponding to the direct cost elements into a trained engineering change direct cost prediction model to predict the direct cost caused by the current engineering change based on the change cost data corresponding to the direct cost elements, thereby obtaining the current direct change cost; the engineering change direct cost prediction model is a model obtained by training a first cost prediction model created based on artificial intelligence using historical multi-dimensional engineering change direct cost data;

[0135] The second prediction unit is configured to input the change cost data corresponding to the indirect cost elements into the trained engineering change indirect cost prediction model to predict the indirect costs caused by the current engineering change based on the change cost data corresponding to the indirect cost elements, thereby obtaining the current indirect change cost; the engineering change indirect cost prediction model is a model obtained by training the second cost prediction model created based on artificial intelligence using historical multi-dimensional engineering change indirect cost data;

[0136] The third prediction unit is used to input the change cost data corresponding to the risk cost elements into the trained engineering change risk cost prediction model, so as to predict the risk cost caused by the current engineering change based on the change cost data corresponding to the risk cost elements, and obtain the current change risk cost; the engineering change risk cost prediction model is a model obtained by training the third cost prediction model created based on artificial intelligence using historical multi-dimensional engineering change risk cost data.

[0137] In some specific embodiments, the first prediction module 14 may specifically include:

[0138] a fourth prediction unit, configured to input the change cost data corresponding to the direct cost elements into a pre-created engineering change direct cost prediction model, to predict the direct costs caused by the current engineering change based on the change cost data corresponding to the direct cost elements, thereby obtaining the current direct change cost; the engineering change direct cost prediction model is a mathematical model for calculating the direct cost of the engineering change; the current direct change cost includes material change cost, changed inventory scrap cost, tooling and fixture change cost, and obsolete material scrap cost;

[0139] a fifth prediction unit, configured to input the change cost data corresponding to the indirect cost elements into a pre-created engineering change indirect cost prediction model, to predict the indirect costs caused by the current engineering change based on the change cost data corresponding to the indirect cost elements, thereby obtaining a current indirect change cost; the engineering change indirect cost prediction model is a mathematical model for calculating the indirect costs of engineering changes; the current indirect change costs include inventory rework costs and complete machine rework costs;

[0140] The sixth prediction unit is used to input the change cost data corresponding to the risk cost elements into a pre-created engineering change risk cost prediction model to predict the risk cost caused by the current engineering change based on the change cost data corresponding to the risk cost elements, and obtain the current change risk cost; the engineering change risk cost prediction model is a mathematical model for calculating the engineering change risk cost; the current change risk cost includes the supply interruption risk cost and the customer claim risk cost.

[0141] In some specific embodiments, the second prediction module 16 may specifically include:

[0142] The change benefit prediction unit is used to input multi-dimensional engineering change data into a pre-created engineering change benefit model to predict the positive change benefits generated by the current engineering change, and obtain the predicted benefits of demand-related changes and vulnerability-solving changes;

[0143] Among them, the engineering change benefit model is a mathematical model used to calculate the positive change benefits generated by engineering changes; the calculation formula of the mathematical model corresponding to the predicted benefits of demand-type changes is the product of the expected order growth number and the order profit; the calculation formula of the mathematical model corresponding to the predicted benefits of vulnerability-solving changes is the product of the probability of failure, maintenance cost and the number of repairs.

[0144] In some specific embodiments, the first creation module 15 may specifically include:

[0145] A relationship creation unit is used to establish the relationship between various types of change cost data and the corresponding current project change costs through the knowledge graph;

[0146] The matrix creation unit is used to create an engineering change cost matrix containing change cost data and current engineering change cost based on the association relationship.

[0147] In some specific embodiments, the engineering change decision module 18 may specifically include:

[0148] The first calculation unit is used to calculate the sum of the current direct change cost, the current indirect change cost and the current change risk cost in the engineering change cost matrix to obtain the total change cost;

[0149] The second calculation unit is used to calculate the sum of the predicted benefits of demand-related changes and the predicted benefits of vulnerability resolution changes in the change benefit matrix to obtain the total predicted benefits of project changes;

[0150] The third calculation unit is used to calculate the ratio of the total project change predicted benefits to the total change costs to obtain the return on investment of the current project change;

[0151] a first judging unit, configured to judge whether the rate of return on investment is greater than a first decision threshold;

[0152] a first execution unit, configured to execute the current engineering change if the return on investment is greater than a first decision threshold;

[0153] a second judgment unit, configured to judge whether the investment return rate is less than a second decision threshold if the investment return rate is not greater than the first decision threshold; the second decision threshold is less than the first decision threshold;

[0154] The control unit is configured to not execute the current engineering change if the return on investment is less than a second decision threshold.

[0155] In some specific embodiments, the engineering change management device may further include:

[0156] a sending unit, configured to send an upgrade review request for the current engineering change to a change control committee terminal if the return on investment is between the second decision threshold and the first decision threshold;

[0157] a fourth calculation unit configured to, upon receiving engineering change supplementary data regarding the total engineering change predicted benefit and / or total change cost sent by the change control committee terminal, recalculate the return on investment of the current engineering change based on the engineering change supplementary data to obtain an investment return ratio;

[0158] a third judgment unit, configured to judge whether the investment return ratio is greater than a first decision threshold;

[0159] The second execution unit is configured to execute the current engineering change if the investment return ratio is greater than the first decision threshold.

[0160] In some specific embodiments, the plurality of business systems include any of an engineering change notification system, a manufacturing execution system, a supplier relationship management system, an enterprise resource planning system, and a quality management system;

[0161] Accordingly, the multi-dimensional engineering change data includes any of engineering change approval-related data, supplier relationship data, product and raw material inventory data, and production and manufacturing process data.

[0162] Furthermore, the embodiment of the present application also discloses an electronic device, Figure 5 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content in the diagram should not be considered as any limitation to the scope of application of the present application.

[0163] Figure 5 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of the present application. The electronic device 20 may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps of the engineering change management method disclosed in any of the aforementioned embodiments. Furthermore, the electronic device 20 in this embodiment may be a computer.

[0164] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device. The communication protocol it follows is any communication protocol that can be applied to the technical solution of this application and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world. Its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0165] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or CD, etc. The resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0166] The operating system 221 is used to manage and control the hardware devices on the electronic device 20 and the computer program 222, and can be Windows Server, NetWare, Unix, Linux, etc. In addition to including computer programs that can be used to implement the engineering change management method performed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 can further include computer programs that can be used to perform other specific tasks.

[0167] Furthermore, this application discloses a computer-readable storage medium for storing a computer program; wherein, when executed by a processor, the computer program implements the aforementioned engineering change management method. The specific steps of this method can be referred to the corresponding contents disclosed in the aforementioned embodiments and will not be repeated here.

[0168] Furthermore, an embodiment of the present application also discloses a computer program product, including a computer program / instruction, which implements the steps of the engineering change management method disclosed above when the computer program / instruction is executed by a processor.

[0169] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. Reference can be made to the descriptions of the identical or similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and the relevant parts can be referred to the descriptions of the methods.

[0170] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0171] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0172] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0173] The above is a detailed introduction to the engineering change management method, device and equipment provided by the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core ideas. At the same time, for general technical personnel in this field, according to the ideas of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for engineering change management, characterized in that: include: Use the pre-set application programming interface to establish communication connections between the product lifecycle management system and multiple business systems to form a global data collection network that communicates between different systems; When the product lifecycle management system receives a printed circuit board assembly engineering change request, data related to the current engineering change is collected from the multiple business systems through the application programming interface to obtain multi-dimensional engineering change data; the multiple engineering change cost elements include direct cost elements, indirect cost elements, and risk cost elements; Classifying and integrating the multi-dimensional engineering change data according to a plurality of preset engineering change cost elements to obtain multiple categories of change cost data; Inputting each type of change cost data into a corresponding engineering change cost prediction model to predict the engineering change cost corresponding to the corresponding engineering change cost element to obtain the current engineering change cost, and creating an engineering change cost matrix based on each type of change cost data and the corresponding current engineering change cost; Inputting the multi-dimensional engineering change data into a pre-created engineering change benefit model to predict the positive change benefit generated by the current engineering change, obtaining a change prediction benefit, and creating a change benefit matrix based on the change prediction benefit; Predicting a return on investment of the current engineering change based on the change benefit matrix and the engineering change cost matrix, and comparing the return on investment with a preset decision threshold to obtain a decision result on whether to execute the current engineering change; Inputting the multi-dimensional engineering change data into a pre-created engineering change benefit model to predict the positive change benefit generated by the current engineering change to obtain predicted change benefits includes: inputting the multi-dimensional engineering change data into a pre-created engineering change benefit model to predict the positive change benefit generated by the current engineering change to obtain predicted change benefits for demand-related changes and predicted changes for vulnerability resolution; Among them, the engineering change benefit model is a mathematical model used to calculate the positive change benefits generated by engineering changes; the calculation formula of the mathematical model corresponding to the demand-type change prediction benefit is the product of the expected order growth number and the order profit; the calculation formula of the mathematical model corresponding to the vulnerability-solving change prediction benefit is the product of the probability of failure, maintenance cost and maintenance quantity.

2. The engineering change management method according to claim 1, characterized in that: Inputting each type of change cost data into the corresponding engineering change cost prediction model to predict the engineering change cost corresponding to the corresponding engineering change cost element to obtain the current engineering change cost includes: Inputting the change cost data corresponding to the direct cost elements into a trained engineering change direct cost prediction model to predict the direct cost caused by the current engineering change based on the change cost data corresponding to the direct cost elements, thereby obtaining the current direct change cost; the engineering change direct cost prediction model is a model obtained by training a first cost prediction model created based on artificial intelligence using historical multi-dimensional engineering change direct cost data; Inputting the change cost data corresponding to the indirect cost elements into a trained engineering change indirect cost prediction model to predict the indirect costs caused by the current engineering change based on the change cost data corresponding to the indirect cost elements, thereby obtaining the current indirect change cost; the engineering change indirect cost prediction model is a model obtained by training a second cost prediction model created based on artificial intelligence using historical multi-dimensional engineering change indirect cost data; The change cost data corresponding to the risk cost element is input into the trained engineering change risk cost prediction model to predict the risk cost caused by the current engineering change based on the change cost data corresponding to the risk cost element to obtain the current change risk cost; the engineering change risk cost prediction model is a model obtained by training a third cost prediction model created based on artificial intelligence using historical multi-dimensional engineering change risk cost data.

3. The engineering change management method according to claim 2, characterized in that: Inputting each type of change cost data into the corresponding engineering change cost prediction model to predict the engineering change cost corresponding to the corresponding engineering change cost element to obtain the current engineering change cost includes: Inputting the change cost data corresponding to the direct cost elements into a pre-created engineering change direct cost forecasting model to forecast the direct costs caused by the current engineering change based on the change cost data corresponding to the direct cost elements, thereby obtaining the current direct change cost; the engineering change direct cost forecasting model is a mathematical model for calculating the direct cost of engineering changes; the current direct change cost includes material change costs, changed inventory scrap costs, tooling and fixture change costs, and obsolete material scrap costs; Inputting the change cost data corresponding to the indirect cost elements into a pre-created engineering change indirect cost forecasting model to forecast the indirect costs caused by the current engineering change based on the change cost data corresponding to the indirect cost elements, thereby obtaining a current indirect change cost; the engineering change indirect cost forecasting model is a mathematical model for calculating the indirect costs of engineering changes; the current indirect change costs include inventory rework costs and complete machine rework costs; The change cost data corresponding to the risk cost elements are input into a pre-created engineering change risk cost prediction model to predict the risk cost caused by the current engineering change based on the change cost data corresponding to the risk cost elements, so as to obtain the current change risk cost; the engineering change risk cost prediction model is a mathematical model for calculating the engineering change risk cost; the current change risk cost includes the supply interruption risk cost and the customer claim risk cost.

4. The engineering change management method according to claim 1, characterized in that: The step of creating an engineering change cost matrix based on various types of change cost data and the corresponding current engineering change costs includes: Establishing associations between various types of change cost data and corresponding current project change costs through a knowledge graph; An engineering change cost matrix including the change cost data and the current engineering change cost is created based on the association relationship.

5. The engineering change management method according to claim 1, characterized in that: The step of predicting the return on investment of the current engineering change based on the change benefit matrix and the engineering change cost matrix, and comparing the return on investment with a preset decision threshold to obtain a decision result on whether to execute the current engineering change includes: Calculating the sum of the current direct change cost, the current indirect change cost, and the current change risk cost in the engineering change cost matrix to obtain a total change cost; Calculating the sum of the predicted benefits of the demand-related changes and the predicted benefits of the vulnerability resolution changes in the change benefit matrix to obtain the total predicted benefits of the project changes; Calculating a ratio of the total project change predicted benefit to the total change cost to obtain a return on investment of the current project change, and determining whether the return on investment is greater than a first decision threshold; If the return on investment is greater than the first decision threshold, executing the current engineering change; If the investment return rate is not greater than the first decision threshold, determining whether the investment return rate is less than a second decision threshold; the second decision threshold is less than the first decision threshold; If the return on investment is less than the second decision threshold, the current engineering change is not performed.

6. The engineering change management method according to claim 5, characterized in that: Also includes: If the return on investment is between the second decision threshold and the first decision threshold, sending an upgrade review request for the current engineering change to a change control committee terminal; After receiving engineering change supplementary data for the total engineering change predicted benefit and / or the total change cost sent by the change control committee terminal, recalculating the return on investment of the current engineering change based on the engineering change supplementary data to obtain an investment return ratio; It is determined whether the investment return ratio is greater than the first decision threshold; if the investment return ratio is greater than the first decision threshold, the current project change is executed.

7. The engineering change management method according to any one of claims 1 to 6, characterized in that: The plurality of business systems include any of an engineering change notification system, a manufacturing execution system, a supplier relationship management system, an enterprise resource planning system, and a quality management system; Accordingly, the multi-dimensional engineering change data includes any of engineering change approval related data, supplier relationship data, product and raw material inventory data, and production and manufacturing process data.

8. An engineering change management device, characterized in that: include: A network construction module is used to establish communication connections between the product lifecycle management system and multiple business systems using a preset application programming interface to form a global data collection network that allows intercommunication between different systems; a data acquisition module configured to, when the product lifecycle management system receives a printed circuit board assembly engineering change request, collect data related to the current engineering change from the multiple business systems through the application programming interface to obtain multi-dimensional engineering change data; A classification and integration module, configured to classify and integrate the multi-dimensional engineering change data according to a plurality of preset engineering change cost elements to obtain multiple types of change cost data; The multiple engineering change cost elements include direct cost elements, indirect cost elements and risk cost elements; A first prediction module is used to input each type of change cost data into a corresponding engineering change cost prediction model to predict the engineering change cost corresponding to the corresponding engineering change cost element to obtain the current engineering change cost; A first creation module is configured to create an engineering change cost matrix based on various types of change cost data and corresponding current engineering change costs; A second prediction module is configured to input the multi-dimensional engineering change data into a pre-created engineering change benefit model to predict the positive change benefit generated by the current engineering change to obtain a change prediction benefit; A second creation module is used to create a change benefit matrix based on the change predicted benefit; an engineering change decision module, configured to predict a rate of return on investment of the current engineering change based on the change benefit matrix and the engineering change cost matrix, and compare the rate of return on investment with a preset decision threshold to determine whether to execute the current engineering change; The second prediction module is specifically configured to input the multi-dimensional engineering change data into a pre-created engineering change benefit model to predict the positive change benefits generated by the current engineering change, thereby obtaining the predicted benefits of demand-related changes and the predicted benefits of vulnerability-solving changes; The engineering change benefit model is a mathematical model used to calculate the positive change benefits generated by engineering changes; The calculation formula of the mathematical model corresponding to the predicted benefits of demand-related changes is the product of the expected order growth quantity and the order profit; the calculation formula of the mathematical model corresponding to the predicted benefits of vulnerability-solving changes is the product of the probability of failure, maintenance cost and the number of repairs.

9. An electronic device, characterized in that: The method comprises a processor and a memory; wherein, when the processor executes the computer program stored in the memory, the engineering change management method according to any one of claims 1 to 7 is implemented.

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