Automobile and part quality analysis and evaluation method and storage medium

Abstract

The invention discloses an automobile and part quality analysis and evaluation method based on data statistics. The method comprises the following steps: acquiring automobile warranty data in an automobile warranty period; calculating quality evaluation data under different vehicle types and different parts based on the collected vehicle maintenance data; and based on the quality evaluation data, the quality is judged according to vehicle types and parts. The method has the advantages that instructions of the vehicle and / or parts can be monitored and evaluated through data statistics, the production process and quality monitoring are effectively guided, and the effectiveness and timeliness of automobile quality monitoring are improved.

Description

Technical Field

[0001] This invention relates to the field of digital quality control in the automotive industry, and in particular to a method for analyzing and evaluating the quality of automobiles and their components based on statistical data. Background Technology

[0002] For automobile manufacturers, vehicle quality is of paramount importance. Monitoring vehicle quality to evaluate the entire vehicle or individual components allows for effective quality control, enabling the monitoring of operational safety, strengthening operational data analysis and mining, and establishing a mechanism for identifying vehicles with potential problems. This, in turn, allows for proactive and preventative measures to be taken, creating a closed-loop system to address vehicle quality issues in advance.

[0003] However, current technologies only address shortcomings and can only respond to quality issues after they occur. When a large number of quality problems erupt before remediation efforts are initiated, it becomes impossible to effectively respond to faults or quickly adjust production based on them. Consequently, production costs and quality monitoring cannot be effectively and timely achieved.

[0004] Statistical analysis of after-sales claim data for products within the warranty period is one of the most important ways to conduct after-sales quality investigations. It can not only comprehensively reflect the overall situation of product after-sales quality from a macro perspective, but also provide decision-making references for senior management to formulate quality strategies through comparative analysis of industry competitors. Therefore, data collection from after-sales data can be used to analyze and evaluate quality, providing basic data for subsequent production improvements. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a data-based statistical method for analyzing and evaluating the quality of automobiles and components. This method can monitor and evaluate vehicle and / or component instructions through data statistics, effectively guide production processes and quality monitoring, and improve the effectiveness and timeliness of automobile quality monitoring.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: a method for analyzing and evaluating the quality of automobiles and parts based on data statistics, which involves collecting automobile warranty data during the warranty period; calculating quality assessment data by vehicle model and by part based on the collected automobile maintenance data; and judging the quality by vehicle model and by part based on the quality assessment data.

[0007] The system provides guidance and alerts for automobile production based on quality assessment data.

[0008] The system determines the quality status and issues corresponding alarms based on the quality assessment data and the set quality assessment thresholds; it also determines the quality trend based on the monthly change curve of the quality assessment data and issues corresponding alarms based on the quality trend.

[0009] The quality assessment data includes the failure rate per thousand vehicles (IPTV) and the actual number of months the vehicle was put into use (MIS); the quality of vehicles or components is assessed based on the IPTV data and the corresponding MIS data.

[0010] IPTV data is calculated based on vehicle warranty data within the vehicle's warranty period. The quality of IPTV data is then judged based on the IPTV data and set thresholds, and corresponding reminders are given.

[0011] Collect information on vehicles under warranty, and statistically analyze the total number of claims, the total number of sample vehicles, and the age of each vehicle within the required warranty period for each vehicle model or component. Based on the collected data, calculate IPTV data.

[0012] Calculate the IPTV values ​​under different MISs based on the MIS data; calculate the incremental ratio of the failure rate per thousand vehicles with a set time as an independent incremental unit, take the vehicle sample size as the actual number of vehicles sold, calculate the failure rate per thousand vehicles separately using the linear method for each unit, and then calculate the IPTV values ​​under all MISs by summing them up.

[0013] The system calculates the confidence level to determine whether the corresponding IPTV value under the MIS needs to be calculated. When the confidence level is lower than the set threshold, the data is not statistically calculated. Only when the confidence level is higher than the set threshold is the IPTV value under the current MIS calculated.

[0014] The calculation of confidence level includes:

[0015] Confidence level = α * tanh x * mis's weight;

[0016] Where α is the confidence level parameter; tanh is the hyperbolic tangent function; and x is the initial parameter for each mis.

[0017] The initial parameter x for each MIS is equal to the sales data / production data, and the value of x ranges from [0,1).

[0018] The weight of mis = (lg 10*(production mis)+1).

[0019] A computer storage medium storing a computer program, the computer program being executed by a processor to implement the method.

[0020] The advantages of this invention are: it allows for the monitoring and evaluation of vehicle and / or component instructions through data statistics, effectively guiding production processes and quality monitoring, and improving the effectiveness and timeliness of automotive quality monitoring. Data collection from after-sales data is used to analyze and evaluate quality, providing foundational data for subsequent production improvements. Attached Figure Description

[0021] The following is a brief explanation of the contents of each of the accompanying drawings and the markings in the drawings:

[0022] Figure 1 This is a flowchart of the quality assessment method of the present invention. Detailed Implementation

[0023] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and the description of the preferred embodiments.

[0024] This invention relates to the field of digital quality management in the automotive industry. Through statistical algorithms, it accurately predicts the failure rates of complete vehicles, KD (knock-down kits), and spare parts at various after-sales stages, with a particular focus on real-time predictions of the failure rate at the end of the month, providing forward-looking guidance for after-sales service and spare parts management. An innovative MIS (Manufacturing Information System) point calculation and IPTV (Per Thousand Vehicles) assessment model is developed to achieve comprehensive quality evaluation of different vehicle models, series, parts, manufacturers, and geographical regions, ensuring the comprehensiveness and objectivity of the assessment system. This method can statistically calculate the failure rate and cumulative failure rate of a specific fault, or even a specific vehicle model, within a certain statistical period based on collected fault information. The severity of the fault's impact on product quality can be determined from the cumulative failure rate, providing quality analysts with an important basis for decision-making.

[0025] like Figure 1 As shown, a data-based statistical method for analyzing and evaluating the quality of automobiles and auto parts involves collecting vehicle warranty data during the warranty period; calculating quality assessment data for different vehicle models and auto parts based on the collected vehicle maintenance data; and judging the quality based on the quality assessment data for different vehicle models and auto parts.

[0026] The system provides guidance and alerts for automobile production based on quality assessment data. Specifically, it includes: judging the quality status and issuing corresponding alerts based on the quality assessment data and the set quality assessment thresholds; and judging the quality trend based on the monthly change curve of the quality assessment data and issuing corresponding alerts based on the quality trend.

[0027] Quality assessment data refers to the calculation of MIS data and corresponding IPTV data for vehicles or parts based on warranty data within the warranty period. Based on the IPTV data under different MIS data, it is used to determine whether there are quality problems in the current vehicle or parts and to guide subsequent production and problem investigation.

[0028] MIS: Month in Service (actual month of use after deployment);

[0029] IPTV: Incidents Per Thousand Vehicles;

[0030] Quality assessment data includes IPTV (Per Thousand Vehicles Failure Rate) and MIS (Manufacturing Information System) data for the actual months of use. Vehicle or component quality is evaluated based on IPTV data and corresponding MIS data. Multiple dimensions are used to calculate IPTV data corresponding to different dimensions of MIS. The resulting IPTV data can guide production and determine production quality, providing foundational data for subsequent production and quality improvement.

[0031] Since different Management Information Systems (MIS) represent different warranty periods, vehicle maintenance data within different MIS periods can be used to obtain vehicle warranty data at both the overall vehicle and individual component levels. Then, vehicle IPTV data for different dimensions and different MIS periods can be calculated. Based on the IPTV data and set thresholds, the quality is assessed, and corresponding alerts are issued. For example, if IPTV exceeds the threshold, an alarm is triggered to alert the user to the quality anomaly.

[0032] In this embodiment, the specific calculation method for IPTV includes: collecting vehicle information that is under warranty during the warranty period, counting all claims, the total number of sample vehicles, and the age of each vehicle within the required warranty period for different vehicle models or parts, and calculating IPTV data based on the collected data of all claims, the total number of sample vehicles, and the age of each vehicle within the required warranty period.

[0033] When calculating IPTV values ​​under different MIS, the calculation of IPTV under the later MIS depends on the accumulation of the previous IPTV. That is, the IPTV values ​​under different MIS are calculated separately according to the MIS data. The incremental ratio of the failure rate per thousand vehicles is calculated with a set time as an independent incremental unit. The sample number of vehicles is taken as the actual number of vehicles sold. The failure rate per thousand vehicles is calculated separately for each unit using the linear method. Then, the IPTV values ​​under all MIS are calculated by accumulation.

[0034] In a preferred embodiment of this application, a confidence level is set. IPTV values ​​are only calculated when the MIS confidence level is greater than a threshold. The calculation of the confidence level determines whether the corresponding IPTV value under the MIS needs to be calculated. When the confidence level is lower than the set threshold, the data is not statistically calculated. IPTV values ​​under the current MIS are only calculated when the confidence level is greater than the set threshold.

[0035] The calculation of confidence level includes: Confidence level = α * tanh x * mis's weight;

[0036] Where α is the confidence level parameter; tanh is the hyperbolic tangent function; and x is the initial parameter for each mis.

[0037] The initial parameter x for each MIS is equal to the sales data / production data, and the value of x ranges from [0,1).

[0038] The weight of mis = (lg 10*(production mis)+1).

[0039] This embodiment also provides a computer storage medium storing a computer program. When the computer program is run by a processor, it implements the automobile and component quality analysis and evaluation method described in the above embodiment, thereby enabling the tracking and quality evaluation of automobiles and their components.

[0040] In this embodiment, warranty data within the warranty period is collected through a big data platform and fed into a data lake. The warranty period and vehicle manufacturing date are supplemented based on the dimensional data in the data lake, completing data cleaning and supplementation. Then, IPTV-MIS data (i.e., IPTV data under different MIS dimensions) is calculated.

[0041] Numerical statistic: Total number of claims within the required after-sales period * 1000 * Required after-sales period;

[0042] Denominator statistic: Total number of sample vehicles * ∑(Age of each vehicle within the required sales period) / Total number of vehicles

[0043] IPTV-MIS Statistics:

[0044] IPTV numerator / IPTV denominator = (Total number of claims within the required after-sales period * Required after-sales period) / ∑(Age of each vehicle within the required after-sales period) * 1000.

[0045] MIS weighted display:

[0046] The incremental ratio of the failure rate per thousand vehicles is calculated using 30 days as an independent incremental unit. The sample size of vehicles is taken as the actual number of vehicles sold. The failure rate per thousand vehicles is calculated separately for each unit using the linear method and then summed up.

[0047] For example: 2MIS = 1MIS + (Total number of claims within the required after-sales period * Required after-sales period) / ∑ (Age of each vehicle within the required after-sales period) * 1000. (Here, 2MIS should be the IPTV value under 2MIS)

[0048] The dimensional statistics in this solution are based on dimensions such as vehicle model, vehicle series, part number, engine number, part manufacturer claim type, and claim status, which are associated with VIN numbers to perform different calculations and provide alarm reminders.

[0049] When calculating IPTV, it is necessary to determine the confidence level and decide whether to continue calculating the IPTV value based on whether the confidence level is met.

[0050] Confidence level calculation formula:

[0051] 1. The weight of MIS = (lg 10 * ((number of days between new car purchase and repair / (365 / 12)) + 0.99) - 1)

[0052] 2. The initial parameter x for each MIS is equal to the sales data / production data, and the value of x ranges from [0,1).

[0053] 3. Substitute the activation function tanh for judgment.

[0054] 4. Confidence level = α * tanh x * mis weights

[0055] Report display: The data is correlated with the dimensional information and the weighted MIS to obtain the IPTV value of different MIS for each dimension, and the IPTV is used to represent the change in quality.

[0056] This methodology not only comprehensively captures the overall situation of product after-sales quality from a macro perspective, but also, through in-depth comparative analysis, focuses on the industry competitive landscape, providing senior management with a clear quality strategy blueprint. Data interpretation and analysis from this perspective provide strong support for decision-making, helping to formulate forward-looking quality strategies and ensuring the company's continued success in the fierce market competition. Simultaneously, this methodology delves into the micro level, conducting meticulous investigations and analyses of specific failure issues, ensuring that the quality of every link and every component is strictly monitored. Through this process, various responsible departments and regions within the company can obtain timely and accurate information, thereby taking effective preventative or corrective measures to eliminate potential quality hazards. This not only significantly reduces after-sales warranty claims costs caused by quality issues throughout the product lifecycle, but also effectively reduces customer dissatisfaction and complaints, continuously improving customer satisfaction and brand reputation.

[0057] Furthermore, this method has accumulated valuable experience for Chery Automobile in developing future new models and improving the quality of its component suppliers during the handling of specific quality issues. By collecting, statistically analyzing, and calculating fault information, it is possible to accurately grasp the fault rate and cumulative fault rate of specific faults or models within a specific period, providing quality analysts with a basis for decision-making and ensuring that every improvement measure is targeted and effective in enhancing product quality and market competitiveness.

[0058] Of course, this method also has a tracking function, which can monitor the effectiveness of implemented improvement measures in real time based on the statistical results of QMIS, and evaluate market feedback to ensure the effectiveness and sustainability of the improvement measures. This closed-loop management mechanism makes quality improvement no longer a one-off action, but a continuous optimization process throughout the product lifecycle, ensuring that Chery Automobile always maintains industry-leading quality standards and customer satisfaction.

[0059] In summary, this total quality management system, through a dual-track strategy of macro and micro levels, has achieved comprehensive control and continuous optimization of product after-sales quality, and has built a complete quality improvement path for Chery Automobile, from problem identification and decision-making to effect evaluation, laying a solid foundation for the company's future development and market competitiveness.

[0060] MIS calculation: Divide the time interval from the date of new vehicle purchase to the date of repair by the number of days in each month. The value of MIS is obtained by rounding down after obtaining the number of months of use.

[0061] The calculation formula is as follows:

[0062] Months of use = (Number of days between new car purchase and repair) / (365 / 12)) + 0.99

[0063] Calculation rules:

[0064] Less than 0.99 months = 0MIS

[0065] 0.99 to 1.99 months = 1 MIS

[0066] 1.99 to 2.99 months = 2 MIS

[0067] 2.99 to 3.99 months = 3MIS

[0068] 3.99 to 4.99 months = 4 MIS

[0069] 4.99 to 5.99 months = 5 MIS

[0070] 5.99 to 6.99 months = 6 MIS

[0071] 6.99 to 7.99 months = 7 MIS

[0072] 7.99 to 8.99 months = 8 MIS

[0073] 8.99 to 9.99 months = 9MIS

[0074] 9.99 to 10.99 months = 10 MIS

[0075] 10.99 to 11.99 months = 11 MIS

[0076] 11.99 to 12.99 months = 12 MIS

[0077] Linear methods: methods for studying linear interactions between variables. A linear interaction refers to a relationship where the variables change proportionally when they interact. Mathematically, this relationship is characterized by linear equations; hence, the method for studying this relationship is called the linear method.

[0078] Window method: a problem-solving method for solving mathematical problems. In this paper, the result set of the linear method is divided into 30 days as a window, and each MIS value is stored as a unit as a snapshot.

[0079] After the warranty data collected during the warranty period is entered into the lake, the data is cleaned according to the threshold settings.

[0080] Formula: Sales date n Production date o

[0081] product_mis = (no) / (365 / 12)

[0082] The weight of mis = (lg 10 * (production mis) + 1)

[0083] The initial parameter x for each MIS is equal to the sales data / production data, and the value of x ranges from [0,1).

[0084] 3. Substitute the hyperbola tangent function tanh into the equation to make a judgment.

[0085] 4. Confidence parameter α = 2

[0086] Confidence level = α * tanh x * mis weight

[0087] Threshold setting:

[0088] The threshold can be customized for data cleaning; a normal setting is 0.5.

[0089] When the threshold is set to 0.5, the first 6 mis data values ​​are not counted. When the threshold is greater than 0.5, mis calculation can be performed.

[0090] After years of application, it has been found that while the linear method is most accurate in estimating the early linear portion of typical product failure rate curves, the error increases with the length of the sales period. Therefore, this research significantly improves this method by establishing a unit method. This method divides the required analysis period into 30-day units (buckets), and each unit calculates parameters such as the failure rate per thousand vehicles independently using the linear method, then sums them up. This effectively solves the calculation error problem of the nonlinear portion. Specifically:

[0091] (1) The sample size of vehicles is the actual number that has been sold;

[0092] (2) Calculate the incremental ratio of the failure rate per thousand vehicles with 30 days as an independent incremental unit;

[0093] (3) The failure rate of each unit is calculated separately using the linear method and then summed.

[0094] Assuming the required after-sales period is 90 days:

[0095] The after-sales service is divided into 30-day periods as a unit.

[0096] Total number of all claims or total number of all expenses within a unit every 30 days;

[0097] Calculate the adjusted number of vehicles sold within each bucket (AVS):

[0098] The number of vehicle samples in each unit needs to be adjusted by dividing the usage time of each vehicle in that unit by 30.

[0099] Vehicles whose after-sales period exceeds this unit are counted as 1 vehicle, i.e., AVS = 30 / 30 = 1;

[0100] Vehicles with a warranty period less than the full term of this unit will have their warranty period calculated proportionally. For example, if the warranty period is 15 days or more, the AVS will be calculated as 15 / 30 = 0.5.

[0101] Unit method algorithm formula

[0102]

[0103] Adjusted sample size of sold vehicles = ∑(Number of days since vehicle sale within the unit / 30)

[0104] Calculation formula details: Report date m, Sales date n, Production date o, Repair date psale_mis = ceil((pn) / (365 / 12))

[0105] report_mis=ceil((mn) / (365 / 12))

[0106] By using the above method to statistically analyze IPTV corresponding to different MIS under different dimensions, basic data can be provided for subsequent production and quality monitoring. The business scenarios are as follows:

[0107] Business Scenario 1

[0108] 1. The IPTV value for the current production month can be determined based on iptv_mis. When IPTV > 1000, it is necessary to focus on developing corresponding solutions to reduce the failure rate per thousand times.

[0109] 2. For vehicles with IPTV > 1000, you can find detailed data on their malfunctions and the IPTV value of each component.

[0110] When the IPTV of each component exceeds 200, the supplier of that component needs to be inspected and evaluated. The video recordings of components produced in months with IPTV exceeding 200 should be analyzed to improve the quality standards of that component.

[0111] Business Scenario 2

[0112] Based on the cumulative IPTV-MIS data (distinguished by production month), the curve trend can be analyzed and predicted for the vehicle manufacturing quality in a given production month.

[0113] 1. When the curve is relatively flat, it indicates that the situation is basically stable after the vehicles are sold.

[0114] 2. When the curve has a higher slope, it indicates that the vehicle failure rate of the current MIS is higher. At this time, the business department will focus on the MIS value with a higher slope and analyze why the IPTV of the current MIS will increase sharply.

[0115] Business Scenario 3: Evaluating Suppliers

[0116] We will rank suppliers based on the IPTV of the entire vehicle and the IPTV of each component, and strengthen supervision and control over component suppliers with higher IPTV values. We will ensure that the IPTV value of all components is below 200.

[0117] Obviously, the specific implementation of this invention is not limited to the above-described methods. Any non-substantial improvements made using the inventive concept and technical solution of this invention are within the protection scope of this invention.

Claims

1. A method for quality analysis and evaluation of automobiles and auto parts, characterized in that: Collect vehicle warranty data during the vehicle warranty period; calculate quality assessment data by vehicle model and component based on the collected vehicle maintenance data; and make quality judgments by vehicle model and component based on the quality assessment data.

2. The method for quality analysis and evaluation of automobiles and auto parts as described in claim 1, characterized in that: The system provides guidance and alerts for automobile production based on quality assessment data.

3. The method for quality analysis and evaluation of automobiles and auto parts as described in claim 2, characterized in that: The system determines the quality status and issues corresponding alarms based on the quality assessment data and the set quality assessment thresholds; it also determines the quality trend based on the monthly change curve of the quality assessment data and issues corresponding alarms based on the quality trend.

4. A method for quality analysis and evaluation of automobiles and auto parts as described in any one of claims 1-3, characterized in that: The quality assessment data includes the failure rate per thousand vehicles (IPTV) and the actual number of months the vehicle was put into use (MIS); the quality of vehicles or components is assessed based on the IPTV data and the corresponding MIS data.

5. The method for quality analysis and evaluation of automobiles and components as described in claim 4, characterized in that: IPTV data is calculated based on vehicle warranty data within the vehicle's warranty period. The quality of IPTV data is then judged based on the IPTV data and set thresholds, and corresponding reminders are given.

6. The method for quality analysis and evaluation of automobiles and auto parts as described in claim 5, characterized in that: Collect information on vehicles under warranty, and statistically analyze the total number of claims, the total number of sample vehicles, and the age of each vehicle within the required warranty period for each vehicle model or component. Based on the collected data, calculate IPTV data.

7. A method for quality analysis and evaluation of automobiles and auto parts as described in any one of claims 1-6, characterized in that: Calculate the IPTV values ​​under different MISs based on the MIS data; calculate the incremental ratio of the failure rate per thousand vehicles with a set time as an independent incremental unit, take the vehicle sample size as the actual number of vehicles sold, calculate the failure rate per thousand vehicles separately using the linear method for each unit, and then calculate the IPTV values ​​under all MISs by summing them up.

8. The method for quality analysis and evaluation of automobiles and components as described in claim 7, characterized in that: The system calculates the confidence level to determine whether the corresponding IPTV value under the MIS needs to be calculated. When the confidence level is lower than the set threshold, the data is not statistically calculated. Only when the confidence level is higher than the set threshold is the IPTV value under the current MIS calculated.

9. The method for quality analysis and evaluation of automobiles and auto parts as described in claim 8, characterized in that: The calculation of confidence level includes: Confidence level = α * tanh x * mis's weight; Where α is the confidence level parameter; tanh is the hyperbolic tangent function; and x is the initial parameter for each mis. The initial parameter x for each MIS is equal to the sales data / production data, and the value of x ranges from [0,1). The weight of mis = (lg 10*(production mis)+1).

10. A computer storage medium, characterized in that: The computer storage medium stores a computer program, which is executed by a processor to implement the method as described in any one of claims 1-9.

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