Applying machine learning (ML) in the supply chain: predict the quality defect percentage in delivery from supplier
DOI:
https://doi.org/10.20397/2177-6652/2024.v24i1.2564Palavras-chave:
Machine learning, quality, defects, suppliersResumo
Study Objective: The objective of this article was to describe the process of developing and implementing an ML model in the supply chain to predict the percentage of defective parts prior to supplier delivery.
Methodology/Approach: The methodology used was based on an action research project using a case study approach that described the steps divided into five phases: acquiring data, preparing data, analyzing data, communicating results, and applying results. The machine learning model applied the supplier's performance data related to the supply chain between the years 2021 and 2022.
Originality/Relevance: Despite the growing interest in ML techniques by many companies, challenges remain in known situations and potential applications in building explainable business and decision models. Thus, there is little empirical evidence of the relationship between effective implementation of machine learning (ML) techniques and their real effect on supply chain performance.
Main Results: The results show that, by employing the proposed method, inspection volumes can be reduced by more than 30%, and therefore, economic advantages can be generated by reducing inspections on material receipt.
Theoretical/Methodological Contributions: The main contribution was to demonstrate how the application of ML models can have a positive impact on supplier management process performance. Additionally, the article also describes how to use ML algorithms without the need to write code. Thus, the article can be a possible reference for organizations wishing to use similar ML approaches in their supply chains and improve the quality levels of their suppliers' performance.
Referências
Aguiar, M. C. (2014). Análise de causa raiz: levantamento dos métodos e exemplificação. (Master's thesis). Pontifícia Universidade Católica do Rio de Janeiro, Departamento de Engenharia Industrial.
Bai, Y., Sun, Z., Deng, J., Li, L., Long, J., & Li, C. (2018). Manufacturing quality prediction using intelligent learning approaches: a comparative study. Sustainability, 10(1), 85.
Berendt, B., Bringmann, B., Fromont, E., Garriga, G., Miettinen, P., Tatti, N., & Tresp, V. (2016). Machine learning and knowledge discovery in databases. In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases.
Boon-itt, S. (2010). An empirical model of the relationship between manufacturing capabilities: Evidence from the Thai automotive industry. NIDA Development Journal, 59(2), 19–45.
Bustillo, A., Urbikain, G., Perez, J. M., Pereira, O. M., & de Lacalle, L. N. L. (2018). Smart optimization of a friction-drilling process based on boosting ensembles. Journal of Manufacturing Systems, 48, 108-121.
Cheng, Y.-J., Chen, M.-H., Cheng, F.-C., Cheng, Y.-C., Lin, Y.-S., & Yang, C.-J. (2018). Developing a decision support system (DSS) for a dental manufacturing production line based on data mining. Applied System Innovation, 1(2), 17.
Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, e623.
Das, B., Pal, S., & Bag, S. (2017). Torque based defect detection and weld quality modelling in friction stir welding process. Journal of Manufacturing Processes, 27, 8-17.
Deng, L., & Li, X. (2013). Machine learning paradigms for speech recognition: An overview. IEEE Transactions on Audio, Speech, and Language Processing, 21, 1060-1089.
Di Bucchianico, A. (2008). Coefficient of determination (R2). In Encyclopedia of Statistics in Quality and Reliability (pp. 291-295). John Wiley & Sons Ltd.
Dogan, A., & Birant, D. (2020). Machine learning and data mining in manufacturing. Expert Systems with Applications, 114060.
Freund, Y., & Schapire, R.E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119-139.
Friedman, J.H. (2002). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4), 367-378.
Gandhi, K., Schmidt, B., & Ng, A.H. (2018). Towards data mining based decision support in manufacturing maintenance. Procedia CIRP, 72, 261-265.
Gashi, M., & Thalmann, S. (2020). Taking complexity into account: A structured literature review on multi-component systems in the context of predictive maintenance. Information Systems, Springer International Publishing, 31-44.
Görz, G., Schneeberger, J., & Schmid, U. (2013). Handbuch der Künstlichen Intelligenz. [e-book]. München: Oldenbourg.
Harding, J.A., Shahbaz, M., Srinivas, & A. Kusiak. (2006). Data mining in manufacturing: A review. Journal of Manufacturing Science and Engineering, 128, 969.
Hecker, D., Döbel, I., Petersen, U., Rauschert, A., Schmitz, V., & Voss, A. (2017). Zukunftsmarkt künstliche Intelligenz. Potenziale und Anwendungen. Fraunhofer.
Hirsch, V., Reimann, P., & Mitschang, B. (2019). Data-driven fault diagnosis in end-of-line testing of complex products. 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA), IEEE, 492-503.
Hu, L., Zhou, M., Xiang, F., & Feng, Q. (2018). Modeling and recognition of steel.
Huang, Y.-J., Pan, C.-L., Lin, S.-C., & Guo, M.-H. (2018). Machine-learning approach in detection and classification for defects in TSV-based 3-D IC. IEEE Transactions on Components, Packaging and Manufacturing Technology, 8(4), 699-706.
Imoto, K., Nakai, T., Ike, T., Haruki, K., & Sato, Y. (2018). A CNN-based transfer learning method for defect classification in semiconductor manufacturing. International Symposium on Semiconductor Manufacturing (ISSM), IEEE, 1-3.
International Organization for Standardization. (2015). Quality management systems - Requirements (ISO standard no. 9001:2015). Retrieved from https://www.iso.org/obp/ui/#iso:std:iso:9001:ed-5:v1:en
Jiang, C., Zhang, H., Ren, Y., Han, Z., Chen, K.-C., & Hanzo, L. (2017). Machine learning paradigms for next-generation wireless networks. IEEE Wireless Communications, 24, 98-105.
Kelley, K., & Lai, K. (2011). Accuracy in parameter estimation for the root mean square error of approximation: Sample size planning for narrow confidence intervals. Multivariate Behavioral Research, 46(1), 1-32.
Koufteros, X. A., Vonderembse, M. A., & Doll, W. J. (2002). Examine the competitive capabilities of manufacturing firms. Structural Equation Modelling, 9(2), 256-282.
Kovacic, M., & Sarler, B. (2009). Application of the genetic programming for increasing the soft annealing productivity in steel industry. Materials and Manufacturing Processes, 24, 369-374.
Kujawińska, A., Rogalewicz, M., Muchowski, M., & Stańkowska, M. (2018). Application of cluster analysis in making decision about purchase of additional materials for welding process. Smart Technology, Springer, 10-20.
Kusiak, A. (2006). Data mining: manufacturing and service applications. International Journal of Production Research, 44(19), 4175-4191.
Lee, J., Noh, S.D., Kim, H.-J., & Kan, Y.-S. (2018). Implementation of cyber-physical production systems for quality prediction and operation control in metal casting. Sensors, 18(5), 1428.
Lee, K. B., Cheon, S., & Kim, C. O. (2017). A convolutional neural network for fault classification and diagnosis in semiconductor manufacturing processes. IEEE Transactions on Semiconductor Manufacturing, 30(2), 135-142.
Liaw, A., Wiener, M., et al. (2002). Classification and regression by randomforest. R News, 2(3), 18-22.
Lim, H.K., Kim, Y., & Kim, M.-K. (2017). Failure prediction using sequential pattern mining in the wire bonding process. IEEE Transactions on Semiconductor Manufacturing, 30(3), 285-292.
Lingitz, L., Gallina, V., Ansari, F., Gyulai, D., Pfeiffer, A., Sihn, W., & Monostori, L. (2018). Lead time prediction using machine learning algorithms: A case study by a semiconductor manufacturer. Procedia CIRP, 72, 1051-1056.
McGrath, R. G. (2013). Transient advantage. Harvard Business Review, 91(6), 62-70.
Mohammed, M., Bashier, E. B. M., & Khan, M. B. (2017). Machine learning: Algorithms and applications. CRC Press, Boca Raton.
Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2012). Foundations of machine learning. Cambridge, MA: The MIT Press. Retrieved from https://pdfs.semanticscholar.org/e923/9469aba4bccf3e36d1c27894721e8dbefc44.pdf.
Naik, A., & Samant, L. (2016). Correlation review of classification algorithm using data mining tool: WEKA, Rapidminer, Tanagra, Orange and Knime. Procedia Computer Science, 85, 662-668.
Priore, P., Ponte, B., Puente, J., & Gómez, A. (2018). Learning-based scheduling of flexible manufacturing systems using ensemble methods. Computers & Industrial Engineering, 126, 282-291.
Ren, L., Sun, Y., Cui, J., & Zhang, L. (2018). Bearing remaining useful life prediction based on deep autoencoder and deep neural networks. Journal of Manufacturing Systems, 48, 71-77.
Ren, L., Sun, Y., Cui, J., & Zhang, L. (2018). Bearing remaining useful life prediction based on deep autoencoder and deep neural networks. Journal of Manufacturing Systems, 48, 71-77.
Robinson, C. J., & Malhotra, M. K. (2005). Defining the concept of supply chain quality management and its relevance to academic and industrial practice. International Journal of Production Economics, 96(3), 315-337.
Robinson, C. J., & Malhotra, M. K. (2005). Defining the concept of supply chain quality management and its relevance to academic and industrial practice. International Journal of Production Economics, 96(3), 315–337.
Sammut, C., & Webb, G. I. (2010). Mean absolute error. In Encyclopedia of Machine Learning (pp. 652).
Sammut, C., & Webb, G. I. (2010). Mean squared error. In Encyclopedia of Machine Learning (p. 653).
Schmidt, B., & Wang, L. (2015). Predictive maintenance: literature review and future trends. In The International Conference on Flexible Automation and Intelligent Manufacturing (FAIM), 23–26 June 2015, vol. 1, University of Wolverhampton, UK, The Choir Press, pp. 232-239.
Shao, S.-Y., Sun, W.-J., Yan, R.-Q., Wang, P., & Gao, R. X. (2017). A deep learning approach for fault diagnosis of induction motors in manufacturing. Chinese Journal of Mechanical Engineering, 30(6), 1347-1356.
Slack, N., Chambers, S., & Johnston, R. (2002). Administração da Produção (2nd ed.). São Paulo: Atlas.
Syafrudin, M., Alfian, G., Fitriyani, N. L., & Rhee, J. (2018). Performance analysis of iot-based sensor, big data processing, and machine learning model for real-time monitoring system in automotive manufacturing. Sensors, 18(9), 2946.
Syed-Yusof, S. M., Sun, W.-J., Yan, R.-Q., Wang, P., & Gao, R. X. (2017). A deep learning approach for fault diagnosis of induction motors in manufacturing. Chinese Journal of Mechanical Engineering, 30(6), 1347-1356.
Thalmann, S., Gursch, H., Suschnigg, J., Gashi, M., Ennsbrunner, H., Fuchs, A. K., ... & Kappl, G. (2019). Cognitive decision support for industrial product life cycles: A position paper. In COGNITIVE 2019: The Eleventh International Conference on Advanced Cognitive Technologies and Applications (pp. 3-9).
Thalmann, S., Gursch, H., Suschnigg, J., Gashi, M., Ennsbrunner, H., Fuchs, A. K., Schreck, T., Mutlu, B., Mangler, J., Kappl, G., et al. (2019). Cognitive decision support for industrial product life cycles: a position paper. COGNITIVE 2019: The Eleventh International Conference on Advanced Cognitive Technologies and Applications, 3-9.
Wang, K.-S. (2013). Towards zero-defect manufacturing (zdm)-a data mining approach. Advances in Manufacturing, 1(1), 62-74.
Zhang, X., Kano, M., Tani, M., Mori, J., Ise, J., & Harada, K. (2020). Prediction and causal analysis of defects in steel products: handling nonnegative and highly overdispersed count data. Control Engineering Practice, 95, 104258.
Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019). Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 115, 213-237.
Zidek, K., Maxim, V., Pitel, J., & Hosovsky, A. (2016). Embedded vision equipment of industrial robot for inline detection of product errors by clustering-classification algorithms. International Journal of Advanced Robotic Systems, 13(5).
Downloads
Publicado
Como Citar
Edição
Seção
Licença
Copyright (c) 2024 Revista Gestão & Tecnologia
Este trabalho está licenciado sob uma licença Creative Commons Attribution-NonCommercial 4.0 International License.
Os direitos, inclusive os de tradução, são reservados. É permitido citar parte de artigos sem autorização prévia desde que seja identificada a fonte. A reprodução total de artigos é proibida. Em caso de dúvidas, consulte o Editor.
