{"_id":"HJ2ykGRqYBYiNPYBY","bibbaseid":"mangal-kumar-usingbigdatatoenhancetheboschproductionlineperformanceakagglechallenge-2016","author_short":["Mangal, A.","Kumar, N."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","title":"Using big data to enhance the bosch production line performance: A Kaggle challenge","shorttitle":"Using big data to enhance the bosch production line performance","doi":"10.1109/BigData.2016.7840826","abstract":"This paper describes our approach to the Bosch production line performance challenge run by Kaggle.com. Maximizing the production yield is at the heart of the manufacturing industry. At the Bosch assembly line, data is recorded for products as they progress through each stage. Data science methods are applied to this huge data repository consisting records of tests and measurements made for each component along the assembly line to predict internal failures. We found that it is possible to train a model that predicts which parts are most likely to fail. Thus a smarter failure detection system can be built and the parts tagged likely to fail can be salvaged to decrease operating costs and increase the profit margins.","booktitle":"2016 IEEE International Conference on Big Data (Big Data)","author":[{"propositions":[],"lastnames":["Mangal"],"firstnames":["Ankita"],"suffixes":[]},{"propositions":[],"lastnames":["Kumar"],"firstnames":["Nishant"],"suffixes":[]}],"month":"December","year":"2016","keywords":"Big data, Error analysis, Machine learning algorithms, Manufacturing, Manufacturing automation, Numerical models, Predictive models, Production, data science, failure analysis, predictive models","pages":"2029–2035","bibtex":"@inproceedings{mangal_using_2016,\n\ttitle = {Using big data to enhance the bosch production line performance: {A} {Kaggle} challenge},\n\tshorttitle = {Using big data to enhance the bosch production line performance},\n\tdoi = {10.1109/BigData.2016.7840826},\n\tabstract = {This paper describes our approach to the Bosch production line performance challenge run by Kaggle.com. Maximizing the production yield is at the heart of the manufacturing industry. At the Bosch assembly line, data is recorded for products as they progress through each stage. Data science methods are applied to this huge data repository consisting records of tests and measurements made for each component along the assembly line to predict internal failures. We found that it is possible to train a model that predicts which parts are most likely to fail. Thus a smarter failure detection system can be built and the parts tagged likely to fail can be salvaged to decrease operating costs and increase the profit margins.},\n\tbooktitle = {2016 {IEEE} {International} {Conference} on {Big} {Data} ({Big} {Data})},\n\tauthor = {Mangal, Ankita and Kumar, Nishant},\n\tmonth = dec,\n\tyear = {2016},\n\tkeywords = {Big data, Error analysis, Machine learning algorithms, Manufacturing, Manufacturing automation, Numerical models, Predictive models, Production, data science, failure analysis, predictive models},\n\tpages = {2029--2035},\n}\n\n","author_short":["Mangal, A.","Kumar, N."],"key":"mangal_using_2016","id":"mangal_using_2016","bibbaseid":"mangal-kumar-usingbigdatatoenhancetheboschproductionlineperformanceakagglechallenge-2016","role":"author","urls":{},"keyword":["Big data","Error analysis","Machine learning algorithms","Manufacturing","Manufacturing automation","Numerical models","Predictive models","Production","data science","failure analysis","predictive models"],"metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"https://bibbase.org/zotero/mh_lenguyen","dataSources":["iwKepCrWBps7ojhDx"],"keywords":["big data","error analysis","machine learning algorithms","manufacturing","manufacturing automation","numerical models","predictive models","production","data science","failure analysis","predictive models"],"search_terms":["using","big","data","enhance","bosch","production","line","performance","kaggle","challenge","mangal","kumar"],"title":"Using big data to enhance the bosch production line performance: A Kaggle challenge","year":2016}