Predicting Material Requirements in the Automotive Industry Using Data Mining

Advanced capabilities in artificial intelligence pave the way for improving the prediction of material requirements in automotive industry applications. Due to uncertainty of demand, it is essential to understand how historical data on customer orders can effectively be used to predict the quantities of parts with long lead times. For determining the accuracy of these predications, we propose a novel data mining technique. Our experimental evaluation uses a unique, real-world data set. Throughout the experiments, the proposed technique achieves high accuracy of up to 98%. Our research contributes to the emerging field of data-driven decision support in the automotive industry.

[1]  Jörg Leukel,et al.  Individualization of Goods and Services: Towards a Logistics Knowledge Infrastructure for Agile Supply Chains , 2011, AAAI Spring Symposium: AI for Business Agility.

[2]  Walter Brenner,et al.  How AUDI AG Established Big Data Analytics in Its Digital Transformation , 2017, MIS Q. Executive.

[3]  Philip M. Wolfe,et al.  Building an active material requirements planning system , 2000 .

[4]  Qiang Tu,et al.  Measuring Modularity-Based Manufacturing Practices and Their Impact on Mass Customization Capability: A Customer-Driven Perspective , 2004, Decis. Sci..

[5]  Yingjie Tian,et al.  A Comprehensive Survey of Clustering Algorithms , 2015, Annals of Data Science.

[6]  Jay Lee,et al.  Recent advances and trends in predictive manufacturing systems in big data environment , 2013 .

[7]  Ching-Lai Hwang,et al.  Part-period balancing with uncertainty: a fuzzy sets theory approach , 1990 .

[8]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[9]  Richard Curran,et al.  A Predictive Method for the Estimation of Material Demand for Aircraft Non-Routine Maintenance , 2013, ISPE CE.

[10]  H.L. Lee,et al.  Aligning Supply Chain Strategies with Product Uncertainties , 2002, IEEE Engineering Management Review.

[11]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[12]  Karl Kurbel,et al.  MRP: Material Requirements Planning , 2013 .

[13]  Gerard Salton,et al.  A vector space model for automatic indexing , 1975, CACM.

[14]  Stefan Kirn,et al.  A Formalization of Multiagent Organizations in Business Information Systems , 2016, BIS.

[15]  Jörg Leukel,et al.  Online Media Sentiment: Understanding Machine Learning-based Classifiers , 2016, ECIS.

[16]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[17]  Angelo Oreste Andrisano,et al.  A review on artificial intelligence applications to the optimal design of dedicated and reconfigurable manufacturing systems , 2014, The International Journal of Advanced Manufacturing Technology.

[18]  Hansjörg Fromm,et al.  A Similarity-Based Approach for the All-Time Demand Prediction of New Automotive Spare Parts , 2018, HICSS.

[19]  Costas D. Maranas,et al.  Managing demand uncertainty in supply chain planning , 2003, Comput. Chem. Eng..

[20]  Kalle Lyytinen,et al.  Organizing for Innovation in the Digitized World , 2012, Organ. Sci..

[21]  Andrew McCallum,et al.  A comparison of event models for naive bayes text classification , 1998, AAAI 1998.

[22]  Bernhard Mitschang,et al.  A Hybrid Approach to Implement Data Driven Optimization into Production Environments , 2018, BIS.

[23]  Herbert Meyr,et al.  Supply chain planning in the German automotive industry , 2004 .

[24]  Akira Takeishi,et al.  Modularization in the Auto Industry: Interlinked Multiple Hierarchies of Product, Production, and Supplier Systems , 2001 .