Engineering change management method, device and equipment

Abstract

The invention discloses an engineering change management method, device and equipment, and relates to the technical field of engineering change, and the method comprises the steps: building communication connection between a product life cycle management system and a plurality of business systems through an application programming interface, so as to form a global data collection network; collecting multi-dimensional engineering change data related to the current engineering change from each business system through an application programming interface; classifying and integrating the multi-dimensional engineering change data according to the plurality of engineering change cost elements, and predicting the engineering change cost based on the obtained various change cost data; predicting a change prediction income generated by the engineering change based on the multi-dimensional engineering change data; and predicting a return on investment based on the change prediction income and the engineering change cost, and comparing the return on investment with a preset decision threshold to obtain an engineering change decision result. According to the invention, interconnection between different service systems and automatic engineering change decision making can be realized, and decision making efficiency and accuracy are improved.

Description

Technical Field

[0001] This application relates to the technical field of engineering change, and particularly relates to an engineering change management method, device and equipment. Background Art

[0002] As the core carrier of the server hardware system, PCBA (Printed Circuit Board Assembly) needs to continuously cope with technological leaps such as memory upgrade, interface replacement, and liquid cooling heat dissipation architecture innovation. At the same time, it also faces diversified challenges such as the replacement of domestic chips and in-depth customer customization. In addition, various engineering changes will also occur due to the needs of agile development and delivery of projects, new technology requirements, production optimization, or external environmental changes, such as hardware design defect repair, firmware update, technology iteration and performance upgrade, supply chain adjustment, compliance and safety requirements, production and process optimization, and customer customization requirements.

[0003] Currently, when managing PCBA engineering changes, it is usually necessary to make engineering change decisions based on change cost data. However, the change cost data is scattered in multiple business systems (such as business systems in R & D, production, procurement, planning, sales, etc.), lacking a unified data standard and real-time synchronization mechanism, making it impossible for multiple business systems to be interconnected, resulting in the inability to interoperate data between different business systems, and thus unable to achieve automated engineering change decisions. Moreover, PCBA engineering changes involve multiple links such as design, materials, production, and testing. Traditional engineering change decision-making methods rely on manual experience, with low efficiency and prone to missing hidden costs. In addition, the current engineering change decision-making methods lack dynamic quantitative analysis of the impact of changes, making it difficult to balance the conflicts between multiple objectives such as cost, delivery time, and quality. And when there are conflicts in the evaluation conclusions, decisions are often made based on the subjective judgment of the expert team, lacking quantitative decision-making data as support, resulting in a relatively 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, which can achieve the interconnection between different business systems, enable the data between different business systems to be transmitted to each other, thereby providing a data basis for automated engineering change decisions, and realizing automated engineering change decisions, saving labor costs and time costs, improving the efficiency and accuracy of decision-making, and at the same time reducing the risk of decision-making errors. The specific solutions are as follows: In a first aspect, this application discloses an engineering change management method, which is applied to a product life cycle management system and includes: Establish a communication connection between the product life cycle management system and multiple business systems by using a preset application programming interface to form a global data acquisition network that interoperates between different systems; When the product life cycle management system receives a printed circuit board assembly engineering change request, it collects the data related to the current engineering change from multiple business systems through the application programming interface to obtain multi-dimensional engineering change data; classifies and integrates the multi-dimensional engineering change data according to a plurality of preset engineering change cost elements to obtain multiple types of change cost data; Respectively 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; Input the multi-dimensional engineering change data into the 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; 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 of whether to execute the current engineering change.

[0005] In a second aspect, the present application discloses an engineering change management device applied to a product life cycle management system, including: A network construction module for establishing a communication connection between the product life cycle management system and multiple business systems by using a preset application programming interface to form a global data collection network for intercommunication between different systems; A data collection module for collecting the data related to the current engineering change from multiple business systems through a preset application programming interface when the product life cycle management system receives a printed circuit board assembly engineering change request to obtain multi-dimensional engineering change data; A classification and integration module for classifying and integrating the multi-dimensional engineering change data according to a plurality of preset engineering change cost elements to obtain multiple types of change cost data; A first prediction module for respectively 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 and obtain the current engineering change cost; A first creation module for creating an engineering change cost matrix based on each type of change cost data and the corresponding current engineering change cost; A second prediction module for inputting the multi-dimensional engineering change data into the pre-created engineering change benefit model to predict the positive change benefit generated by the current engineering change and obtain the change prediction benefit; A second creation module for creating a change benefit matrix based on the change prediction benefit; An engineering change decision-making module, which 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, compare the return on investment with a preset decision threshold, and obtain a decision result on whether to execute the current engineering change.

[0006] In a third aspect, the present application discloses an electronic device, including a processor and a memory; wherein, when the processor executes a computer program stored in the memory, the foregoing engineering change management method is implemented.

[0007] It can be seen that the present application first uses a preset application programming interface to establish a communication connection between the product life cycle management system and multiple business systems to form a global data acquisition network for intercommunication between different systems. When the product life cycle 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. Then, the multi-dimensional engineering change data is classified and integrated according to a preset plurality of engineering change cost elements to obtain multiple types of change cost data, and each type of change cost data is respectively input 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. Then, the multi-dimensional engineering change data is input 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. Finally, the return on investment of the current engineering change is predicted based on the change benefit matrix and the engineering change cost matrix, and the return on investment is compared with a preset decision threshold to obtain a decision result on whether to execute the current engineering change.

[0008] The beneficial effects of this application are as follows: When managing engineering changes, this application first uses a preset application programming interface to establish a communication connection between the product life cycle management system and multiple business systems, so as to form a global data acquisition network for interconnection between different systems, realizing the interconnection between different business systems, enabling the data between different business systems to be transmitted to each other, thereby providing a data basis for automated engineering change decision-making. Moreover, this application specifically makes change decisions based on engineering change data from multiple dimensions collected in multiple business systems. Since the decision-making data considered is more comprehensive, the accuracy of engineering change decisions can be improved. In addition, this application creates an engineering change cost matrix (including the engineering change cost with multiple engineering change cost elements) and a change benefit matrix in the form of a matrix, predicts the return on investment of the current engineering change based on the data in the two matrices, and then obtains the decision result based on the size relationship between the return on investment and the decision threshold. Through the above method, automated engineering change decision-making is realized, saving labor costs and time costs compared with the traditional manual method, thus improving the efficiency of engineering change decisions and reducing the risk of decision-making errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings described below are only the embodiments of this application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on the provided drawings.

[0010] Figure 1 It is a flowchart of an engineering change management method disclosed in this application; Figure 2 It is a schematic diagram of a specific knowledge graph disclosed in this application; Figure 3 It is a flowchart of a specific engineering change management method disclosed in this application; Figure 4 It is a schematic diagram of the structure of an engineering change management device disclosed in this application; Figure 5 It is a structural diagram of an electronic device disclosed in this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0011] The following will clearly and completely describe the technical solutions in the embodiments of this application with reference to the drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of this application.

[0012] An embodiment of this application discloses an engineering change management method, which is applied to a product lifecycle management system. Refer to Figure 1 as shown, this method includes: Step S11: Use 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 can communicate between different systems.

[0013] In this embodiment, first, based on the Industrial Internet of Things (IIoT) technology and using a preset application programming interface (API, Application Programming Interface), a communication connection is established between the product lifecycle management (PLM, Product Lifecycle Management) system and multiple business systems, thereby forming a global data collection network in which the product lifecycle management and each business system can communicate with each other. Among them, multiple business systems can be any multiple business systems in the links of research and development, production, procurement, planning, sales, etc., so that the product lifecycle management system can obtain multi-dimensional engineering change data by collecting data related to the current engineering change from different business systems, such as the Engineering Change Notice (ECN), Manufacturing Execution System (MES), Supplier Relationship Management System (SRM), Enterprise Resource Planning System (ERP), and Quality Management System (QMS).

[0014] Step S12: When a printed circuit board assembly engineering change request is received, collect data related to the current engineering change from multiple business systems through the application programming interface to obtain multi-dimensional engineering change data.

[0015] It should be noted that the engineering change decision-making scheme proposed in this application is specifically applied to the product lifecycle management system. When this system receives a printed circuit board assembly (i.e., PCBA) engineering change request, it first collects data related to the current engineering change from multiple business systems and the local system (i.e., the product lifecycle management system) through the application programming interface to obtain multi-dimensional engineering change data.

[0016] Specifically, the multi-dimensional engineering change data may specifically include any combination of engineering change approval-related data, supplier relationship data, product and raw material inventory data, and production manufacturing process data. It can be understood that engineering changes may be triggered by various factors, such as design adjustments, construction schedule changes, cost budget fluctuations, or updated risk assessment results. Making engineering change decisions based on a single data source will result in a relatively high risk of decision-making errors. However, the global data acquisition network constructed by the present 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.

[0017] In a specific implementation manner, the product life cycle management system can first obtain the ECN document from the engineering change notice system and then automatically process the ECN document to extract key information for subsequent decision-making analysis. For example, it can parse and collect the engineering change-related data in the ECN document, including information such as the change object, change type, change list, and change impact scope. For the data collection in the enterprise resource planning system, relevant data related to the current engineering change can be collected from the enterprise resource planning system through the created API, such as real-time inventory materials, in-transit / in-process materials, and the quantity of work-in-progress. The data collected from the manufacturing execution system can specifically include the manufacturing process information of the whole machine and PCBA, such as the production process, processes, and the tooling fixtures and man-hours information involved in each process of the change object, which is used to calculate the production cost after the change and the change impact. The supplier relationship data collected from the supplier relationship management system specifically refers to the material procurement information related to the engineering change, such as the unit procurement cost of materials, procurement cycle, etc., which is used to calculate the direct costs brought about by the material change, such as inventory scrapping costs, procurement price difference costs, etc. These information can be collected through the API interface. In addition, it can also be obtained through direct database connection or file transfer.

[0018] Specifically, devices such as RFID (Radio Frequency Identification) counters, barcode scanners, and measurement sensors can be pre-deployed on the production line to collect information such as the quantity of component replacements and the quantity of tooling fixture replacements involved in the change in real time. When engineering change decisions need to be made, data such as downtime, rework time, and production 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. Data such as real-time inventory materials, in-transit / in-process materials, and the quantity of work-in-progress can also be collected from the ERP system through the API interface created on the ERP system.

[0019] Step S13: 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.

[0020] In this embodiment, after collecting the multi-dimensional engineering change data related to the current engineering change from multiple business systems, further, the multi-dimensional engineering change data can be classified and integrated according to a plurality of preset engineering change cost elements to obtain multiple types of change cost data; among them, the plurality of engineering change cost elements include, but are not limited to, direct cost elements, indirect cost elements, and risk cost elements, corresponding to direct costs (such as material, labor, etc. costs), indirect costs (such as management, delay losses, etc. costs), and risk costs respectively.

[0021] In addition, the edge computing method can be adopted to locally aggregate the sensor data in the multi-dimensional engineering change data through an industrial gateway, and perform preprocessing operations on the sensor data, such as classification, cleaning, and impurity removal. Through the collection and integration of the engineering change data of multiple business systems, a multi-dimensional cost view can be constructed, thereby forming a basis for the analysis of the complete change impact chain. And the product life cycle management system can be linked to the edge computer to read the data related to the current engineering change from the edge computer.

[0022] Step S14: Input each type of change cost data into the corresponding engineering change cost prediction model respectively 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.

[0023] In this embodiment, each type of change cost data can be input into the pre-created engineering change cost prediction model, so as to predict the engineering change cost corresponding to the corresponding engineering change cost element based on each type of change cost data, obtain the current engineering change cost, and then create an engineering change cost matrix based on each type of change cost data and the corresponding current engineering change cost.

[0024] In a specific embodiment, various types of change cost data are respectively input into corresponding engineering change cost prediction models to predict the engineering change costs corresponding to the corresponding engineering change cost elements, and the current engineering change cost is obtained. 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, and obtaining 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 predict the indirect cost caused by the current engineering change based on the change cost data corresponding to the indirect cost element, and 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; inputting the change cost data corresponding to the risk cost element 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, and obtaining 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. In this embodiment, multiple cost prediction models can be created in advance based on different artificial intelligence models and the models can be trained to obtain models respectively used for predicting the change costs of the change cost data corresponding to different engineering change cost elements; among them, the number of cost prediction models is the same as the number of engineering change cost elements, and the artificial intelligence models include but are not limited to RNN (Recurrent Neural Network) models based on the attention mechanism, CNN (Convolutional Neural Network) models, multi-modal large models, etc. 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, which can be selected according to actual application requirements.

[0025] Among them, the direct cost prediction model for engineering change is used to predict the direct costs caused by engineering changes, such as material change costs (such as price differences due to material changes), tooling investment costs, etc. For material change costs, information on material PN (Product Number, from the MES system), material price information, and material quantity information need to be collected; the indirect cost prediction model for engineering change is used to predict the indirect costs caused by engineering changes, such as production capacity loss costs, quality re-inspection costs, etc.; the risk cost prediction model for engineering change is used to predict the risk costs caused by engineering changes, such as supply interruption risk costs, customer claim risk costs, etc.

[0026] In addition, new training set data can be collected according to a preset collection period to continuously optimize and update each model. By predicting the engineering change costs corresponding to each engineering change cost element through multiple cost prediction models created in advance based on artificial intelligence, automated engineering change decisions can be achieved, saving labor costs and time costs compared with traditional manual methods, thus improving the efficiency of engineering change decisions.

[0027] In this embodiment, an engineering change cost matrix is created based on various types of change cost data and the corresponding current engineering change costs. Specifically, it can include: establishing an association relationship between various types of change cost data and the corresponding current engineering change costs through a knowledge graph; creating an engineering change cost matrix containing change cost data and current engineering change costs based on the association relationship. In this embodiment, as shown in Figure 2 it is possible to establish an association relationship between various types of change cost data and the corresponding current engineering change costs through a knowledge graph, and then create an engineering change cost matrix containing change cost data and current engineering change costs based on the association relationship. Specifically, the reasoning process of the path in the knowledge graph can be: if "Change A → causes → Material replacement ↑", and "Material replacement ↑ → triggers → Direct cost ↑", then it can be inferred that "Change A → directly affects → Direct cost ↑". In addition, the collected engineering change data can be associated with the final predicted engineering decision result through the knowledge graph.

[0028] Step S15: Input the multi-dimensional engineering change data into the 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.

[0029] It is understandable that in addition to the change cost, 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 a 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 periods), obtain the predicted change benefits, and then create a change benefit matrix based on the obtained predicted change benefits.

[0030] Specifically, the predicted change benefits include predicted change benefits for requirement changes and predicted change benefits for bug-fixing 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 for the mathematical model corresponding to the predicted change benefits for requirement changes is the product of the predicted increase in order quantity and the order profit; the calculation formula for the mathematical model corresponding to the predicted change benefits for bug-fixing changes is the product of the probability of failure occurrence, the repair cost, and the number of repairs.

[0031] Specifically, the predicted change benefits for requirement changes refer to the change benefits generated according to new requirements from the market side, the client side, and the project side. The specific calculation formula is: ; The predicted change benefits for bug-fixing changes refer to the change benefits generated by fixing design bugs at the R & D side, customer complaints at the market side, etc. The specific calculation formula is: ; In the formula, represents the probability of occurrence of failure mode i (which can be obtained based on the evaluation of test, production, and market failure rates), represents the cost of a single repair (including costs such as labor, spare parts, and logistics), represents the number of products currently under warranty in the market (such as the number of repairable products).

[0032] Furthermore, create a change benefit matrix based on the predicted change benefits. See Table 1 below: Table 1 Change Benefit Matrix

[0033] Step S16: Predict the return on investment of the current engineering change based on the change benefit matrix and the engineering change cost matrix, compare the return on investment with the preset decision threshold, and obtain the decision result of whether to execute the current engineering change.

[0034] In this embodiment, the return on investment (i.e., ROI, Return on Investment) 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 size of the return on investment and the preset decision threshold are compared to obtain a comparison result, and whether to execute the current engineering change is determined according to the comparison result.

[0035] Specifically, 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 size of the return on investment and the preset decision threshold to obtain a decision result on whether to execute the current engineering change 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 change and the predicted benefits of bug-fixing change 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 determining whether the return on investment is greater than the first decision threshold; if the return on investment is greater than the first decision threshold, execute the current engineering change; if the return on investment is not greater than the first decision threshold, determine 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, do not execute the current engineering change. In this embodiment, first calculate the sum of all change costs in the engineering change cost matrix (including the sum of the direct change cost, the current indirect change cost, and the current change risk cost) to obtain the total change cost TCC, and then calculate the sum of all predicted change benefits in the change benefit matrix, such as calculating the predicted benefits of demand change and the predicted benefits of bug-fixing change to obtain the total predicted benefits of the engineering change; then, calculate the return on investment ROI of the current engineering change. The specific calculation formula is: , and save it to the change application form as the basis for engineering change decision-making. Further, determine whether the return on investment ROI is greater than the first decision threshold. If it is greater (for example, ROI > 1.2), it indicates that a high return will be generated after executing the current engineering change. At this time, the current engineering change can be directly executed. If the return on investment ROI is not greater than the first decision threshold, then further determine whether the return on investment is less than the second decision threshold. If the return on investment ROI is less than the second decision threshold, for example, ROI < 0.7, it indicates that a low return will be generated after executing the current engineering change. At this time, directly reject the execution request of the current engineering change, that is, do not execute the current engineering change. By calculating the return on investment of the current engineering change and the preset threshold to decide whether to execute the current engineering change, since both the investment and return factors are considered, quantitative engineering change decision-making can be achieved. Compared with the traditional manual method, it saves labor costs and time costs, does not rely on manual experience, and is not likely to miss hidden costs, thus improving the accuracy and efficiency of engineering change decision-making, and at the same time can balance the conflicts among multiple objectives such as cost, delivery time, and quality.

[0036] In a specific implementation manner, the following method can be used to calculate the return on investment ROI, which specifically includes: first, calculate the product of the direct change cost and the direct weight addition coefficient, the product of the indirect change cost and the indirect weight addition coefficient, and the product of the change risk cost and the risk weight addition coefficient in the engineering change cost matrix respectively to obtain the corresponding direct change product, indirect change product, and change risk product. Then, after adding the direct change product, indirect change product, and change risk product, the total change cost is obtained. Next, calculate the sum of the demand change prediction benefit and the bug-fixing change prediction benefit in the change benefit matrix to obtain the total engineering change prediction benefit. Then, calculate the ratio of the total engineering change prediction benefit to the total change cost to obtain the return on investment ROI of the current engineering change. Among them, the calculation formula for the total change cost TCC can be expressed as: ; In the formula, represents the weight addition coefficient corresponding to each change cost.

[0037] By calculating the product of different change costs and the corresponding weight addition coefficients to calculate the return on investment ROI, it can meet more diverse application requirements, more accurately obtain the ROI that conforms to the actual situation, and make a more accurate change decision, thus further improving the accuracy of engineering change decision-making.

[0038] In another embodiment, it may further 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 Board (CCB) terminal; when receiving the engineering change supplementary data for the total predicted engineering change benefit and / or total change cost sent by the CCB terminal, recalculating the return on investment of the current engineering change based on the engineering change supplementary data to obtain the investment benefit ratio; determining whether the investment benefit ratio is greater than the first decision threshold, and if the investment benefit 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 may be sent to the Change Control Board (CCB) terminal for manual approval by the change control board. The change control board may supplement some data related to the current engineering change (such as data for the total predicted engineering change benefit and / or total change cost) on the CCB terminal by adding notes or overriding suggestions, and then send the supplemented data to the product lifecycle management system; when the product lifecycle management system receives the supplemented data sent by the CCB terminal, it may recalculate the return on investment of the current engineering change based on the supplemented data and make a new decision, that is, determine whether the new ROI is greater than the first decision threshold, and if it is greater, execute the current engineering change. In addition, the change control board may also directly give opinions on directly executing the current engineering change or rejecting the current engineering change, and make corresponding decisions directly according to the opinions given by the change control board.

[0039] It should be noted that before officially executing the current engineering change, the situation of the return on investment (ROI) will be monitored in real time, and when a deviation is found, an alarm will be triggered to notify the CCB terminal to adjust in time and make a new change decision.

[0040] In addition, an intelligent decision recommendation report may be generated based on the total predicted engineering change benefit, total change cost, and decision result, and a feedback process for change execution or optimization may be triggered, so as to realize closed-loop management from data perception to strategy optimization. At the same time, a visual display of the change benefit matrix, engineering change cost matrix, intelligent decision recommendation report, etc. may also be performed through a visualization tool.

[0041] It can be seen that when managing engineering changes in the embodiments of the present application, a communication connection between the product life cycle management system and multiple business systems is first established by using a preset application programming interface to form a global data acquisition network for intercommunication between different systems, realizing the interconnection between different business systems, enabling the data between different business systems to be transmitted to each other, thus providing a data basis for automated engineering change decision-making. Moreover, the embodiments of the present application make change decisions based on the engineering change data in multiple dimensions collected from multiple business systems. Since the decision-making data considered is more comprehensive, the accuracy of engineering change decisions can be improved. In addition, the embodiments of the present application create an engineering change cost matrix (engineering change cost including multiple engineering change cost elements) and a change benefit matrix in a matrix manner, predict the return on investment of the current engineering change according to the data in the two matrices, and then obtain the decision result based on the magnitude relationship between the return on investment and the decision threshold. Through the above method, automated engineering change decision-making is realized, saving labor costs and time costs compared with the traditional manual method, thus improving the efficiency of engineering change decisions and reducing the risk of decision-making errors.

[0042] The embodiments of the present application disclose a specific engineering change management method. See Figure 3 As shown, the method includes: Step S21: Establish a communication connection between the product life cycle management system and multiple business systems by using a preset application programming interface to form a global data acquisition network for intercommunication between different systems.

[0043] Step S22: When a printed circuit board assembly engineering change request is received, collect the data related to the current engineering change from multiple business systems through the application programming interface to obtain multi-dimensional engineering change data.

[0044] Step S23: Classify and integrate the multi-dimensional engineering change data according to a preset plurality of engineering change cost elements to obtain multiple categories of change cost data; the plurality of engineering change cost elements include direct cost elements, indirect cost elements, and risk cost elements.

[0045] Step S24: Input the change cost data corresponding to the direct cost element into a 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 element, and obtain the current direct change cost; the engineering change direct cost prediction model is a mathematical model used for calculating the engineering change direct cost; the current direct change cost includes material change cost, change inventory scrap cost, tooling fixture change cost, and material stagnation scrap cost.

[0046] In this embodiment, a mathematical model for calculating the direct cost of engineering change can be used to calculate the direct change cost caused by the current engineering change. The direct change cost refers to the change cost of newly added, scrapped, purchased, repaired, and scrapped direct material requirements due to engineering change, such as PCBA material change cost, PCBA change inventory scrap cost, PCBA tooling fixture change cost, and material stagnation and scrap cost, etc.

[0047] Among them, the PCBA material change cost (such as material price difference cost) is used to evaluate the impact of the subsequent PCBA material procurement cost caused by engineering change. The specific calculation formula is: ; In the formula, represents the new unit price (including tax) of the i-th material in the ECN change list, represents the old unit price (including tax) of the i-th material in the ECN change list, represents the estimated total procurement volume of the i-th material during the remaining product life cycle.

[0048] For example, as shown in Table 2, Table 2 shows a specific process for calculating the material price difference cost: Table 2 Material Price Difference Cost Matrix

[0049] Among them, negative values indicate cost savings, and positive values indicate cost increases.

[0050] Specifically, the PCBA change inventory scrap cost (such as the discount loss rate of PCBA inventory) is used to evaluate the cost loss of PCBA scrap caused by engineering change. The specific calculation formula is: ; In the formula, represents the quantity of PCBA inventory and work-in-progress that needs to be scrapped due to the change (including all whole-machine factories and board factories), represents the average procurement cost of the corresponding PCBA, represents the PCBA scrap recovery value coefficient (ranging from 0 to 1, and the PCBA semi-finished product can default to 0.3 - 0.6).

[0051] For example, when the PCB layout (layout) changes and 100 pieces of the old version of PCBA need to be phased out, the scrap cost is: 100 × ¥800 × 0.4 = ¥32000.

[0052] Specifically, the PCBA tooling fixture change cost is used to evaluate the procurement cost of tooling fixture change caused by engineering change. The specific calculation formula is: ; In the formula, represents the procurement cost of newly added fixtures and / or test racks, represents the net loss of the early scrapping of old fixtures, represents the transformation cost of reusable equipment and / or the recovery salvage value of old tooling.

[0053] For example, when the replacement of high-speed backplane connectors leads to the update of fixtures, the specific change cost of tooling fixtures is: ¥500,000 (new purchase) + ¥80,000 (old damage) - ¥120,000 (transformation) = ¥460,000.

[0054] Specifically, the material stagnation and scrapping cost is used to evaluate the cost of electronic material inventory stagnation and / or scrapping caused by engineering changes. The specific calculation formula is: ; In the formula, represents the quantity of the k-th type of stagnant material, represents the historical average purchase price of the k-th type of stagnant material, represents the material salvage value of the k-th type of stagnant material (such as supplier recovery, internal transfer, second-hand market price, etc.).

[0055] For example, when the schematic change causes 10,000 resistors to be scrapped, the material stagnation and scrapping cost is: 10,000 × (¥0.8 - ¥0.05) = ¥7,500.

[0056] Step S25: 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 cost 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 for calculating the engineering change indirect cost; the current indirect change cost includes inventory rework cost and whole machine rework cost.

[0057] In this embodiment, the indirect change cost caused by the current engineering change can be calculated through a mathematical model for calculating the engineering change indirect cost (i.e., the engineering change indirect cost prediction model); among them, the indirect change cost specifically includes the whole machine rework cost caused by the change and the costs such as labor loss and material loss caused by the PCBA rework behavior, such as inventory rework cost and whole machine rework cost.

[0058] 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: ; In the formula, It represents the standard rework man-hours for the whole machine of type a (the processes such as disassembly, assembly, and debugging need to be distinguished). It represents the comprehensive labor rate (such as the rates of operators + technical support personnel). It represents the quantity of whole machines that need to be reworked. It represents the quality inspection cost after rework (including costs such as functional testing, stability testing, and aging test).

[0059] For example, for a PCBA firmware upgrade, a total of 20 in-stock and online whole machines need to be reworked, then the rework cost is: (2 * ¥500 + ¥100) * 20 = ¥22000.

[0060] Specifically, the in-stock rework cost (i.e., the rework cost of in-stock PCBA) is used to evaluate the PCBA rework cost loss caused by engineering changes. The specific calculation formula is: ; In the formula, It represents the single-board rework man-hours (including man-hours such as desoldering, cleaning, component mounting, and inspection). It represents the composite labor rate of the SMT (Surface Mount Technology) production line (including technicians + engineers). It represents the costs of consumables such as solder paste, solder wick, and cleaning agent.

[0061] Step S26: Input the change cost data corresponding to the risk cost elements into the 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 for calculating the engineering change risk cost; the current change risk cost includes the supply interruption risk cost and the customer claim risk cost.

[0062] In this embodiment, the change risk cost caused by the current engineering change, including the supply interruption risk cost and the customer claim risk cost, can be calculated through the mathematical model for calculating the engineering change risk cost (i.e., the engineering change risk cost prediction model). Among them, the change risk cost is used to evaluate the risk costs such as supply interruption and customer claims brought by engineering changes, and can be specifically evaluated and calculated according to supply risks, customer complaint risks, etc.

[0063] Step S27: Create an engineering change cost matrix based on various types of change cost data and the corresponding current engineering change cost, and input the multi-dimensional engineering change data into the 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.

[0064] In this embodiment, an engineering change cost matrix can be created based on the change cost data and the corresponding current engineering change cost. See Table 3 as follows: Table 3 Engineering Change Cost Matrix

[0065] As can be seen from Table 3, in the created engineering change cost matrix, in addition to the cost type, cost elements, and engineering change cost values, other information is also recorded, such as calculation methods, data sources, influence scopes, and risk weight levels, etc.

[0066] Step S28: Predict the return on investment of the current engineering change based on the change benefit matrix and the engineering change cost matrix, compare the return on investment with the preset decision threshold, and obtain the decision result on whether to execute the current engineering change.

[0067] Among them, for the more specific processing procedures of the above steps S21 to S23 and S28, reference can be made to the corresponding content disclosed in the foregoing embodiments, and details will not be elaborated here.

[0068] It can be seen that the embodiments of the present application collect engineering change cost data from multiple dimensions in multiple business systems, such as data related to change approvals, real-time quotes for supply chain purchases, product and raw material inventory data, and production process data, etc. 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 engineering changes, calculates the return on investment of engineering changes based on the predicted change benefits and the total change costs, and makes a decision on whether to execute engineering changes based on the relationship between the return on investment and the preset threshold. Through the above method, automated engineering change cost decision-making is achieved. Compared with the traditional manual method, it saves labor costs, improves the decision-making efficiency, thereby enhancing the change approval efficiency, shortening the change approval cycle, and reducing the incidence of overspending events.

[0069] In a specific implementation manner, when receiving the following engineering change request: replacing the CPU (Central Processing Unit) of the 5288 model server from Xeon Silver to EPYC to meet the subsequent market demand of Customer A, the engineering change request can be parsed first to obtain key change parameters, such as material specifications, process adjustments (die modification), change lists (involving CPUs and associated components), and inventory PCBA order rework. It should be noted that the engineering change request can be submitted by the R & D department through the system by filling out a structured form.

[0070] Next, analyze, classify, and integrate the extracted key change parameters to obtain direct costs (such as the price difference of CPUs and related materials, the cost of reproducing process tooling), indirect costs (such as the cost of reworking and retesting 200 pcs of PCBA), and risk costs (such as the rework of PCBA is not expected to cause delivery delays, so there is no risk); further, conduct revenue forecasting: it is expected that 2,000 new orders will be added after replacing the CPU. Then, based on the above data, create an engineering change cost matrix and a change revenue matrix, as shown in Tables 4 and 5: Table 4 Engineering Change Cost Matrix

[0071] Table 5 Change Revenue Matrix

[0072] Next, calculate the return on investment ROI based on the information in the engineering change cost matrix and the change revenue matrix, that is, ROI = 2000 / 2250 ≈ 0.89, and determine 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, that is, an approval request is automatically sent to the CCB terminal for intervention by the change control board. For example, through the marketing department to supplement data, stating that "competitive products have released similar technologies, and 1,000 orders will be lost if there is no change". After supplementing the data, calculate the actual ROI value again to obtain the new ROI value: R demand = 3000 → ROI = 1.33 (> 1.2). At this time, the decision result indicates that the change can be executed immediately.

[0073] In addition, if data anomalies are found before the change is executed, for example, during implementation, due to the impact of trade relations, the supplier's inventory is limited, and the material procurement cost has increased by 20% (TCC has increased to 26.5 million), then the calculated return on investment ROI = 1.13 < 1.2 (original 1.33). At this time, an early warning can be triggered and the CCB terminal can be notified for auxiliary decision-making by the change control board, such as deciding to introduce the notice to the war procurement to develop new suppliers to control the procurement cost.

[0074] Correspondingly, the embodiment of the present application also discloses an engineering change management device, which is applied to the product life cycle management system. See Figure 4 as shown, the device includes: A network construction module 11, configured to establish a communication connection between the product life cycle management system and multiple business systems by using a preset application programming interface to form a global data acquisition network for intercommunication between different systems; The data acquisition module 12 is configured to collect data related to the current engineering change from multiple business systems through an application programming interface when the product life cycle management system receives a printed circuit board assembly engineering change request, so as to obtain multi-dimensional engineering change data; The classification and integration module 13 is 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 first prediction module 14 is configured to input each type of change cost data into the corresponding engineering change cost prediction model respectively to predict the engineering change cost corresponding to the corresponding engineering change cost element, so as to obtain the current engineering change cost; The first creation module 15 is configured to create an engineering change cost matrix based on each type of change cost data and the corresponding current engineering change cost; The second prediction module 16 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, so as to obtain the change predicted benefit; The second creation module 17 is configured to create a change benefit matrix based on the change predicted benefit; The engineering change decision module 18 is configured 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 a preset decision threshold to obtain a decision result on whether to execute the current engineering change.

[0075] Among them, the specific working processes of the above-mentioned respective modules may refer to the corresponding content disclosed in the foregoing embodiments, and will not be elaborated herein.

[0076] When managing an engineering change in an embodiment of the present application, a communication connection between a product life cycle management system and multiple business systems is first established by using a preset application programming interface to form a global data acquisition network that interoperates between different systems, realizing the interconnection between different business systems, enabling data between different business systems to be transmitted to each other, thereby providing a data basis for automated engineering change decision-making. Moreover, the embodiment of the present application makes a change decision based on multi-dimensional engineering change data collected from multiple business systems. Since the decision-making data considered is more comprehensive, the accuracy of the engineering change decision can be improved. In addition, the embodiment of the present application creates an engineering change cost matrix (including the engineering change cost of multiple engineering change cost elements) and a change benefit matrix in a matrix manner, predicts the return on investment of the current engineering change according to the data in the two matrices, and then obtains a decision result based on the magnitude relationship between the return on investment and the decision threshold. Through the above method, automated engineering change decision-making is realized, saving labor costs and time costs compared with the traditional manual method, thereby improving the efficiency of the engineering change decision-making and reducing the risk of decision-making errors.

[0077] In some specific embodiments, the multiple engineering change cost elements include direct cost elements, indirect cost elements, and risk cost elements; Correspondingly, the first prediction module 14 may specifically include: A first prediction unit, configured to input the change cost data corresponding to the direct cost element into the trained engineering change direct cost prediction model, so as to predict the direct cost caused by the current engineering change based on the change cost data corresponding to the direct cost element, and 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; A second prediction unit, configured to input the change cost data corresponding to the indirect cost element into the trained engineering change indirect cost prediction model, so as to predict the indirect cost caused by the current engineering change based on the change cost data corresponding to the indirect cost element, and obtain 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; A third prediction unit, configured to input the change cost data corresponding to the risk cost element 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 element, 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.

[0078] In some specific embodiments, the first prediction module 14 may specifically include: A fourth prediction unit, configured to input the change cost data corresponding to the direct cost element into the pre-created engineering change direct cost prediction model, so as to predict the direct cost caused by the current engineering change based on the change cost data corresponding to the direct cost element, and obtain the current direct change cost; the engineering change direct cost prediction model is a mathematical model for calculating the engineering change direct cost; the current direct change cost includes material change cost, change inventory scrap cost, tooling fixture change cost, and material stagnation scrap cost; A fifth prediction unit, configured to input the change cost data corresponding to the indirect cost element into the pre-created engineering change indirect cost prediction model, so as to predict the indirect cost caused by the current engineering change based on the change cost data corresponding to the indirect cost element, and obtain the current indirect change cost; the engineering change indirect cost prediction model is a mathematical model for calculating the engineering change indirect cost; the current indirect change cost includes inventory rework cost and whole machine rework cost; A sixth prediction unit is configured to input the change cost data corresponding to the risk cost element into a pre-created 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 element, 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.

[0079] In some specific embodiments, the second prediction module 16 may specifically include: A change benefit prediction unit is configured to input multi-dimensional engineering change data into a pre-created engineering change benefit model, so as to predict the positive change benefit generated by the current engineering change, and obtain the demand-based change prediction benefit and the vulnerability-fixing change prediction benefit; Wherein, the engineering change benefit model is a mathematical model for calculating the positive change benefit generated by the engineering change; the calculation formula of the mathematical model corresponding to the demand-based change prediction benefit is the product of the expected order growth quantity and the order profit; the calculation formula of the mathematical model corresponding to the vulnerability-fixing change prediction benefit is the product of the failure occurrence probability, the repair cost, and the repair quantity.

[0080] In some specific embodiments, the first creation module 15 may specifically include: A relationship creation unit is configured to establish an association relationship between various types of change cost data and the corresponding current engineering change cost through a knowledge graph; A matrix creation unit is configured to create an engineering change cost matrix including the change cost data and the current engineering change cost based on the association relationship.

[0081] In some specific embodiments, the engineering change decision module 18 may specifically include: A first calculation unit is configured 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; A second calculation unit is configured to calculate the sum of the demand-based change prediction benefit and the vulnerability-fixing change prediction benefit in the change benefit matrix to obtain the total engineering change prediction benefit; A third calculation unit is configured to calculate the ratio of the total engineering change prediction benefit to the total change cost to obtain the return on investment of the current engineering change; A first judgment unit is configured to judge whether the return on investment is greater than a first decision threshold; A first execution unit is configured to execute the current engineering change if the return on investment is greater than the first decision threshold; A second judgment unit, configured to judge whether the return on investment is less than a second decision threshold if the return on investment is not greater than a first decision threshold; the second decision threshold is less than the first decision threshold. A control unit, configured to not execute the current engineering change if the return on investment is less than the second decision threshold.

[0082] In some specific embodiments, the engineering change management device may further include: A sending unit, configured to send an upgrade review request for the current engineering change to the change control board terminal if the return on investment is between the second decision threshold and the first decision threshold; A fourth calculation unit, configured to recalculate the return on investment of the current engineering change based on the engineering change supplementary data to obtain an investment return ratio when receiving the engineering change supplementary data for the total predicted benefits of the engineering change and / or the total change cost sent by the change control board terminal; A third judgment unit, configured to judge whether the investment return ratio is greater than the first decision threshold; A second execution unit, configured to execute the current engineering change if the investment return ratio is greater than the first decision threshold.

[0083] In some specific embodiments, the multiple business systems include any combination 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; Correspondingly, the multi-dimensional engineering change data includes any combination of engineering change approval-related data, supplier relationship data, product and raw material inventory data, and production manufacturing process data.

[0084] Furthermore, an embodiment of the present application also discloses an electronic device, Figure 5 It is a structural diagram of an electronic device 20 shown according to an exemplary embodiment, and the content in the figure should not be regarded as any limitation on the scope of use of the present application.

[0085] Figure 5 It is a schematic structural diagram of an electronic device 20 provided by an embodiment of the present application. The electronic device 20 may specifically 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. Among them, the memory 22 is used to store a computer program, and the computer program is loaded and executed by the processor 21 to implement the relevant steps in the engineering change management method disclosed in any of the foregoing embodiments. In addition, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0086] In this embodiment, the power supply 23 is used to provide operating voltages 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 external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and specific limitations are not imposed on it here; the input / output interface 25 is used to obtain external input data or output data to the outside, and the specific interface type can be selected according to specific application requirements, and no specific limitations are made here.

[0087] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, a random access memory, a magnetic disk, an optical disc, etc., and 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.

[0088] Among them, the operating system 221 is used to manage and control each hardware device on the electronic device 20 and the computer program 222, and it can be Windows Server, Netware, Unix, Linux, etc. In addition to the computer program capable of implementing the engineering change management method executed by the electronic device 20 disclosed in any of the foregoing embodiments, the computer program 222 can further include computer programs capable of performing other specific tasks.

[0089] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the engineering change management method disclosed above is implemented. For the specific steps of this method, reference can be made to the corresponding content disclosed in the foregoing embodiments, and details are not described herein again.

[0090] Furthermore, the embodiment of this application also discloses a computer program product, including a computer program / instructions, and when the computer program / instructions are executed by a processor, the steps of the engineering change management method disclosed above are implemented.

[0091] In this specification, the various embodiments are described in a progressive manner. Each embodiment focuses on the differences from other embodiments, and the same or similar parts among the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0092] Those skilled in the art may further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0093] The steps of the methods or algorithms described in combination with the embodiments disclosed herein can be directly implemented by hardware, software modules executed by a processor, or a combination of the two. The software modules can be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium well-known in the technical field.

[0094] Finally, it should also be noted that in this document, relational terms such as "first" and "second" are only used 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 term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0095] The above has provided a detailed introduction to an engineering change management method, apparatus and device provided by this application. Specific examples are used in this document to elaborate on the principles and implementation manners of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to this application.

Claims

1. An engineering change management method, characterized in that, Including: Establish a communication connection between the product lifecycle management system and multiple business systems by using a preset application programming interface to form a global data collection network for intercommunication between different systems; When the product lifecycle management system receives a printed circuit board assembly engineering change request, collect the data related to the current engineering change from the multiple business systems through the application programming interface to obtain multi-dimensional engineering change data; Classify and integrate the multi-dimensional engineering change data according to a preset plurality of engineering change cost elements to obtain multiple types of change cost data; Input each type of the change cost data into the corresponding engineering change cost prediction model respectively 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 the change cost data and the corresponding current engineering change cost; 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; 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 size of the return on investment and a preset decision threshold to obtain a decision result on whether to execute the current engineering change.

2. The engineering change management method according to claim 1, wherein, The plurality of engineering change cost elements include direct cost elements, indirect cost elements, and risk cost elements; Correspondingly, the inputting each type of the change cost data into the corresponding engineering change cost prediction model respectively to predict the engineering change cost corresponding to the corresponding engineering change cost element, obtain the current engineering change cost, includes: Input 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, and obtain 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 by using historical multi-dimensional engineering change direct cost data; Input the change cost data corresponding to the indirect cost element into the trained engineering change indirect cost prediction model to predict the indirect cost caused by the current engineering change based on the change cost data corresponding to the indirect cost element, and obtain 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 by using historical multi-dimensional engineering change indirect cost data; Input the change cost data corresponding to the risk cost element 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, and 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, wherein, The step of separately inputting the change cost data of each type into the corresponding engineering change cost prediction model to predict the engineering change cost corresponding to the corresponding engineering change cost element and obtain the current engineering change cost includes: Input the change cost data corresponding to the direct cost element 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 element, and obtain the current direct change cost; the engineering change direct cost prediction model is a mathematical model used for calculating the direct cost of engineering change; the current direct change cost includes material change cost, change inventory scrap cost, tooling and fixture change cost, and material stagnation and scrap cost; Input the change cost data corresponding to the indirect cost element into the pre-created engineering change indirect cost prediction model to predict the indirect cost caused by the current engineering change based on the change cost data corresponding to the indirect cost element, and obtain the current indirect change cost; the engineering change indirect cost prediction model is a mathematical model used for calculating the indirect cost of engineering change; the current indirect change cost includes inventory rework cost and whole machine rework cost; Input the change cost data corresponding to the risk cost element into the 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 element, and obtain the current change risk cost; the engineering change risk cost prediction model is a mathematical model used for calculating the risk cost of engineering change; the current change risk cost includes supply interruption risk cost and customer claim risk cost.

4. The engineering change management method according to claim 3, characterized in that The step of inputting the multi-dimensional engineering change data into the pre-created engineering change benefit model to predict the positive change benefit generated by the current engineering change and obtain the change prediction benefit includes: Input the multi-dimensional engineering change data into the pre-created engineering change benefit model to predict the positive change benefit generated by the current engineering change, and obtain the demand type change prediction benefit and the bug fix type change prediction benefit; Among them, the engineering change benefit model is a mathematical model used for calculating the positive change benefit generated by engineering change; the calculation formula of the mathematical model corresponding to the demand type change prediction benefit is the product of the expected order growth quantity and the order profit; the calculation formula of the mathematical model corresponding to the bug fix type change prediction benefit is the product of the failure occurrence probability, the repair cost, and the repair quantity.

5. The engineering change management method according to claim 4, wherein, Creating an engineering change cost matrix based on various types of the change cost data and the corresponding current engineering change cost, including: Establishing an association relationship between various types of the change cost data and the corresponding current engineering change cost through a knowledge graph; Creating an engineering change cost matrix including the change cost data and the current engineering change cost based on the association relationship.

6. The engineering change management method according to claim 4, wherein 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 size of the return on investment and a preset decision threshold to obtain a decision result on whether to execute the current engineering change, including: 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 benefit of requirement type changes and the predicted benefit of bug fixing type changes in the change benefit matrix to obtain the total predicted benefit of the engineering change; Calculating the ratio of the total predicted benefit of the engineering change to the total change cost to obtain the return on investment of the current engineering 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, execute the current engineering change; If the return on investment is not greater than the first decision threshold, determine whether the return on investment 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, do not execute the current engineering change.

7. The engineering change management method according to claim 6, wherein Further including: If the return on investment is between the second decision threshold and the first decision threshold, send an upgrade review request for the current engineering change to the change control board terminal; When receiving the engineering change supplementary data for the total predicted benefit of the engineering change and / or the total change cost sent by the change control board terminal, recalculate the return on investment of the current engineering change based on the engineering change supplementary data to obtain an investment benefit ratio; Determine whether the investment benefit ratio is greater than the first decision threshold. If the investment benefit ratio is greater than the first decision threshold, execute the current engineering change.

8. The engineering change management method according to any one of claims 1 to 7, characterized in that, The multiple business systems include any combination 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; Correspondingly, the multi-dimensional engineering change data includes any combination of engineering change approval related data, supplier relationship data, product and raw material inventory data, and production manufacturing process data.

9. An engineering change management device, characterized in that, Including: A network construction module, configured to establish a communication connection between a product life cycle management system and multiple business systems by using a preset application programming interface to form a global data acquisition network for interconnection between different systems; A data acquisition module, configured to 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 when the product life cycle management system receives a printed circuit board assembly engineering change request. 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, so as to obtain multiple types of change cost data; A first prediction module, configured to respectively input each type of the 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, so as to obtain the current engineering change cost; A first creation module, configured to create an engineering change cost matrix based on each type of the change cost data and the corresponding current engineering change cost; A second prediction module, 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, so as to obtain a change prediction benefit; A second creation module, configured to create a change benefit matrix based on the change prediction benefit; An engineering change decision module, configured 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 a preset decision threshold to obtain a decision result on whether to execute the current engineering change.

10. An electronic device, characterized in that, It includes 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 8 is implemented.

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