- CONCEPTUALIZING PREDICTIVE METHODOLOGY IN AUTOMOTIVE INDUSTRY: IMPLICATION ON BUSINESS. - Due to an on-going increase in competition level of automotive industry in Vietnam and the Vietnamese ambition to localize the automotive industry, quality issues of assemblies have become a vital concern. - It is challenging to accurately identify and model the quality issues of assemblies in an assembly line. - So, this study intends to develop a conceptualization for businesses, using data mining analysis and regression method, to build a predictive model for engine assembly failures.. - Subsequently, data mining with clustering methods and generalized linear regression model is conducted to analyse the current pattern of the dataset and introduce a potential modelling system for the future behaviour of quality issues. - To validate the model quality, the model is tested with both training and testing data. - These datasets are generated from Ford assembly line in a 6-month period focusing mostly on failure occurrence.. - The predictive power of the proposed model is supported in both data sets.. - Keywords: Automotive industry, data mining, predictive model, quality issues. - Automotive industry is a fast-growing, potential sector and one of the most important contributors to Vietnamese national economic growth. - However, automotive industry in Vietnam has mainly relied on knock-down kit production in a self-assembly structure i.e. - One of the main reasons is that the quality of domestically manufactured assemblies has always been questioned. - Moreover, due to the 0% tax rate for imported whole cars validated since 2018 of Vietnamese government and the mindset “foreign products equal to great quality” of Vietnamese people, it has become more challenging for domestic car manufacturers in Vietnam to compete in terms of quality-cost-delivery approach. - This leads to a hindrance towards the ambition/strategy to localize automotive industry of Vietnam as a country in general and Vietnamese organisations in particular. - In order to overcome the difficulties and obtain a better quality, understanding and modelling quality issues are significantly vital to Vietnamese automotive industry. - This paper aims to study and conceptualize the methodology to determine an appropriate model for the quality issues of engine assemblies. - The research dataset is collected from Ford Motor Company in the UK, which is famous for its quality control system producing well-known high-quality models over time periods e.g. - especially, the manufacturer in the UK is the biggest technical centre consisting of an end-to-end process i.e. - By understanding how a leading automotive organisation in a developed country was beneficial from the methodology as the most optimised example, the method might be generalized and applied to many aspects in Vietnamese automotive industry ranged from solely assemblies-produced to wholly car-manufactured business.. - What is the pattern and behaviour of the dataset?. - To establish a modelling method for engine assembly quality issues from the dataset:. - What is the proper method to model the pattern of the dataset?. - To present the method, the research focuses on the quality issues of one particular part i.e. - engine assemblies for manufacturing diesel and patrol engines, off which a fully-assembled engine is built up from various assemblies in the assembly line. - According to Law (2007), in manufacturing systems, quality issues are the major cause of variability, largely influence overall system and should be modelled accurately. - Literature to date has mostly focused on modelling failure time, the time between failures and the duration between failures of the entire population of an engine over time (Proschan, 1963. - Moreover, the importance of practical modelling failures for real-life situations in the automotive industry has just been realised in a few decades. - Using a regression formula and data mining to model, the background of formulas and statistical techniques of this paper is outstanding references on failures-modelling methods in manufacturing system, such as Law (2007). - According to the manufacturing-based approach, quality can be defined as the conformance of products to a specific design or specification (Crosby, 1979 and Gilmore, 1974). - as products should be manufactured properly in the first trial. - So, quality issues happen when products are associated with scraps or reworks, indicating existing flaws within the first operation, which lead to increased production cost. - In the automotive industry, an assembly failure is identified when the testing machine indicates that the engine possesses a problematic feature, for example, the design of the engine assembly is not followed original specification. - This paper considers the role of features and failure rate per time unit in modelling the variability of engine assembly failures.. - Generally, the variability of quality issues has been debated throughout time.. - Many researchers have claimed the existing relationship between engine assembly failure rates and time factor, namely the classical Bathtub curve – figure 1 (Amstadter (1977). - Since engine assemblies may consist of both electronic and mechanical components, there is a possibility that the variability of engine assembly failures will be either time dependent or independent. - Modelling quality issues can be differently established depending on business requirements of suppliers and manufacturers, two main sides of the automotive industry. - Hanifin and Liberty (1976) found out that Machining-A GPSS-V Simulation technique could enhance the quality control process of material handling and production in the USA for both stakeholders. - Lu (2009) suggested a new classification method, Arrow, and applied finite mixture distributions to the current simulation system in Ford Motor Company UK to model the breakdowns duration of machines in assembly lines. - As for suppliers, Kulkarni and Gohil (2012) applied Soft system methodology (SSM) to improve the assembly line in the Sweden automotive industry.. - Understanding data behaviour is vital to the success of one company but raw data is difficult to understand in its initial appearance. - Fortunately, data patterns can be exploited by processing data with Big Data analytics. - Accordingly, the behaviour of failures can be identified and there is a chance to predict the re-occurrence (Baesens, 2014). - A normal process of data mining tends to start with Descriptive analytics, which enables analysts dig deeply into a massive amount of data for a condensed and more focused information. - Thereupon, necessary business and decision patterns can be extracted and organisations can make the right decisions. - Each member in the group possesses a high degree of similarity within its group (MacQueen, 1967). - There is a vast collection of clustering algorithms, leading to the difficulty of choosing a proper algorithm for certain circumstances (Dubes and Jain, 1976). - They proposed a set of eligible criteria, considering the situation, the data structure and the sensitivity of the technique to variability. - In comparison to other data mining methods, clustering method can reduce the negative effect of noise data by separating them into an outlier group (Zhang et al., 1997. - Moreover, the classification function of clustering can be used to simplify a large set of data when finding out distributions for variables in a simulation model (Lu, 2009). - Thereafter, predictive analytic can be carried out in a better way with regression model to identify the failure frequency. - As a predictive analytics technique, regression is introduced to predict the future behaviour of the data using linear functions, especially when the target variable is numerical. - their influence/interaction on the variable, the analysts could foretell the behaviour of relevant activities in the forthcoming time. - At the moment, there are no practices on data mining and predictive model in Vietnam, especially on the engine assembly quality issues in the assembly line. - There are only studies about the performance and development of the industry (Tran and Ngo, 2014. - Hypothesis 1: The frequency of failures can be predicted by the existence of stations, models, operations and features to which they belong to.. - The study rationale is originated from all of these papers: figure out which parameters are important to model the failures of machines and/or engines in the assembly line.. - With regard to the methodology, the design of descriptive analytics is a revised version of three studies: Lu (2009). - This paper uses data mining in both data analysis and modelling establishment.. - Clustering technique is used to identify the significant characteristics of the quality issue data, which are the main causes for failures in engine assemblies. - The 6-month dataset which was collected from Ford Motor Company by direct visits to the factory site, includes of 84 stations and 27 engine models with 23441 failures recorded in the assembly line. - Each entry of engine assembly data includes an ID series number, type of model, station, operation, operation count, failure date, failure time and features. - To define, station represents the location where the product is manufactured, operation represents the function of the station at a particular time, operation count represents the times the engine is being reworked, and feature represents a brief description of the design reasons for failures (each is given a specific letter-numeric mixed code).. - Clustering methods. - Training data (January to the third week of June) is used for data analysis and pattern- finding process, whereas 1110 data entries of the final week’s data i.e. - testing data is used to test the model validity. - For instances, Model has 27 kinds, it would be number 1 for model 4R8Q-6009-AA, 2 for model 5U3Q-6006-AA and 27 for the final model in the list, Tension_Bolt. - To find out the current pattern of the dataset and identify the importance level of each independent variables, descriptive analytics Two-step clustering is conducted.. - Then, k-mean algorithm is used to examine the accuracy of the grouping process via verifying the number of groups.. - K means clustering: According to MacQueen (1967), each k-means cluster is represented by the centre of the cluster. - Systematic component: Any set of 𝑋 = (𝑋1, 𝑋2, 𝑋3, 𝑋4)are explanatory variables and together their linear combination contribute to the linear predictor:. - 𝜇 depends on the linear predictor. - It is for transforming the expectation of the response variable, 𝜇 = 𝐸(𝑌), to the linear predictor.. - To test the validity of the model, test of error is run to compare the correlation between the actual data and the predicted data. - A graphical comparison between actual and predicted value of failure count is also conducted to provide a richer view on the model quality. - Failure time 24/24 hour every day in a week: 1 to 7 = Sunday to Saturday Regarding to the model development, descriptive analytics Clustering methods indicate that the failure behaviour is mainly controlled by four factors and time is actually not significant when building the model, as shown in table 1 with the significance level of each factor.. - Especially, a similar trend between actual and predicted values is shown in the testing dataset (figure 6), leading to a more confirmative support for the predictive power of the model.. - Results have claimed that data mining techniques are useful in studying behaviour and developing a predictive model for the behaviour of assembly failure issues in the automotive industry, as in the sample case of Ford assembly line.. - Notably, the significance of time is not zero (0.2) as shown in Table 1, indicating a possibility that time may have predictive power in other scenarios. - In this study, the data mining clustering methods indicate that time is not so important when it stands alone. - Also, there are not much applications of Big Data analytics in the automotive sector for quality control. - Hence, this study is worthwhile as a reference to the area of modelling quality issues for Vietnamese automotive company with a highlight in data mining. - By obtaining an adequate modelling system, companies have a great chance to learn about failures, avoid their adverse impacts and produce qualified assemblies in the future. - Consequently, labour allocation, goods manufacturing standards, operation and quality control, production cost and customer demand can be efficiently managed. - To conclude, this study has set a foundation for Big Data analytics and can act as the first stepping stone for employing data mining techniques and regression model to quality check in the automotive industry. - Chiu, T., Fang, D., Chen, J., Wang, Y., &. - In Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining, 263-268. - Chwif, L., Banks, J., &. - Dubes, R., &. - Performance of the Vietnamese Automobile Industry: A Measurement using DEA. - Hillmann, M., Stühler, S., Schloske, A., Geisinger, D., &. - Development of the Vietnamese Automotive Industry and EDI Infrastructure. - Kulkarni, K., &. - Assembly line improvement within the automotive industry: Application of soft systems methodology to manufacturing. - Modelling breakdown durations in simulation models of engine assembly lines, Doctoral dissertation, University of Southampton. - In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability. - McCullagh, P., &. - Mechanical Reliability in the Process Industries.. - Zhang, T., Ramakrishnan, R., &. - Data Mining and Knowledge Discovery
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