APPLYING A HYBRID MACHINE LEARNING APPROACH TO THE PRODUCTION FLOOR: A CASE OF PAPER COUNTING SYSTEM IN PRINTING INDUSTRY
Keywords:Recurrent Neural Network, Forecasting, Printing Industry, Estimation
To satisfy the requirement of quality control in printing and packaging industry, a sheet counting apparatus is developed, which adopts a hybrid machine learning approach and is able to provide a real-time and noncontact measurement of its quantity. With a brief introduction of the system architecture, our main work focuses on the forecasting approach using neural network in different environment. The basic principle is to identify each paper profile from various attributes from the company’s Enterprise Resource Planning (ERP) system together with measured heights of the pallet and provide workers an estimated sheet paper count. According to experiments and tests in real production lines, our hybrid approach can reach a very high measuring accuracy for printing papers or cards with any thickness.
The Thai Printing Association. The rise of Print Media 2021. Thai Magazine [internet] 2021 May 14 [cited 2022 Jul 1];130. Available from: https://www.thaiprint.org/2021/05/vol130/industrial130-01/ (in Thai)
GNEWS. The export revenue from paper packaging. DITP [Internet]. 2021 [cited 2022 Jul 1]. Available from: https://gnews.apps.go.th/news?news=32790 (in Thai)
Micol Policarpo L, da Silveira DE, da Rosa Righi R, Antunes Stoffel R, da Costa CA, Victória Barbosa JL, et al. Machine learning through the lens of e-commerce initiatives: An up-to-date systematic literature review. Computer Science Review 2021;41:100414.
Stevenson WJ, Sum CC. Operations management. 2nd ed. Singapore: McGraw-Hill Education (Asia); 2014.
Hegde S. Six Inventory Control Techniques for the Printing Industry [Internet]. 2020 [cited 2022 Aug 1]. Available from: https://publication.sipmm.edu.sg/six-inventory-control-techniques-printing-industry/
Chokpaiboon K. The Application of Lean Manufacturing: A Case Study of Printing Process [thesis]. Bangkok: Srinakharinwirot University; 2012. (in Thai)
Lipiak J, Salwin M. The improvement of sustainability with reference to the printing industry–case study. In: Hamrol A, Grabowska M, Maletic D, Woll R, editors. Advances in Manufacturing II Volume 3 - Quality Engineering and Management. Springer, Cham; 2019. p. 254–66.
Chen T, Wang Y, Xiao C. An apparatus and method for real-time stacked sheets counting with line-scan cameras. IEEE Transactions on Instrumentation and Measurement 2015;64(7):1876–84.
Pham D, Ha M, San C, Xiao C. Accurate stacked-sheet counting method based on deep learning. Journal of the Optical Society of America A 2020;37(7):1206-18.
Hämäläinen E, Tapaninen U. Accuracy of forecasting in a Nordic paper mill’s supply chain: A case study. Norsk Geografisk Tidsskrift - Norwegian Journal of Geography 2011;65(2):104–13.
Tavakkoli A, Hemmasi AH, Talaeipour M, Bazyar B, Tajdini A. Forecasting of printing and writing paper consumption in Iran using artificial neural network and classical methods. Iranian journal of Wood and Paper Science Research 2015;30(4): 632-51.
Pumkasron P, Uraichot P. The study of forecasting models and appropriate inventory management case study: carton packaging. Thai Industrial Engineering Network Journal. 2015;1(1):14-22. (in Thai)
Huang SJ, Chiu NH, Chen LW. Integration of the grey relational analysis with genetic algorithm for software effort estimation. European Journal of Operational Research 2008;188(3):898-909.
Kuflik T, Boger Z, Shoval P. Filtering search results using an optimal set of terms identified by an artificial neural network. Information Processing and Management 2006;42(2):469-83.
Zhu D, Premkumar G, Zhang X, Chu CH. Data mining for network intrusion detection: a comparison of alternative methods. Decision Sciences [Internet]. 2001 [cited 2022 Jul 1];32(4):635-60. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/j.1540-5915.2001.tb00975.x
Kim YS, Street WN, Russell GJ, Menczer F. Customer targeting: a neural network approach guided by genetic algorithms. Management Science [Internet]. 2005 [cited 2022 Jul 1];
(2): 264-76 Available from: https://pubsonline.informs.org/doi/abs/10.1287/mnsc.1040.0296
Thieme RJ, Song M, Calantone RJ. Artificial Neural Network Decision Support Systems for New Product Development Project Selection. Journal of Marketing Research [Internet]. 2018 [cited 2022 Jul 1];37(4):499-507. Available from: https://journals.sagepub.com/doi/abs/10.1509/jmkr.37.4.499.18790
Al-Ahmari AMA. Prediction and optimisation models for turning operations. International Journal of Production Research [Internet]. 2008 [cited 2022 Jul 1];46(15):4061-81. Available from: https://www.tandfonline.com/doi/abs/10.1080/00207540601113265
Bhattacharyya P, Sengupta D, Mukhopadhyay S, Chattopadhyay AB. On-line tool condition monitoring in face milling using current and power signals. International Journal of Production Research [Internet]. 2008 [cited 2022 Jul 1];46(4):1187-201. Available from: https://www.tandfonline.com/doi/abs/10.1080/00207540600940288
Das P, Datta S. Exploring the non-linearity in empirical modelling of a steel system using statistical and neural network models. International Journal of Production Research [Internet]. 2007 [cited 2022 Jul 1];45(3):699-717 [cited 2022 Jul 1]. Available from: https://www.tandfonline.com/doi/abs/10.1080/00207540600792465
Wang KJ, Chen JC, Lin YS. A hybrid knowledge discovery model using decision tree and neural network for selecting dispatching rules of a semiconductor final testing factory. Production Planning and Control [Internet]. 2005 [cited 2022 Jul 1];16(7):665–80. Available from: https://www.tandfonline.com/doi/abs/10.1080/09537280500213757
Werbos PJ. Backpropagation through time: what it does and how to do it. Proceedings of the IEEE [Internet] 1990 [cited 2022 Jul 1];78(10):1550-60. Available from: http://ieeexplore.ieee.org/document/58337/?reload=true
Sagheer A, Kotb M. Time series forecasting of petroleum production using deep LSTM recurrent networks. Neurocomputing [Internet] 2019 [cited 2022 Jul 1];323:203-13. Available from: https://doi.org/10.1016/j.neucom.2018.09.082
Kantasa-ard A, Nouiri M, Bekrar A, Ait el cadi A, Sallez Y. Machine Learning in forecasting in the Physical Internet: a case study of agricultural products in Thailand. International Journal of Production Research 2021;59(24):7491–515.
Zhang GP, Qi M. Neural network forecasting for seasonal and trend time series. European Journal of Operational Research 2005;160(2):501-14.
Cadavid JP, Lamouri S, Grabot B, Fortin A. Machine learning in production planning and control: A review of empirical literature. IFAC-PapersOnLine. 2019;52(13):385-90.
Navya N. Forecasting of futures trading volume of selected agricultural commodities using neural networks [thesis]. Bangalore, India: University of Agricultural Sciences GKVK; 2011.
Benkachcha S, Benhra J, El Hassani H. Seasonal time series forecasting models based on artificial neural network. International Journal of Computer Applications [Internet] 2015 [cited 2022 Aug 1];116(20):9-14. Available from: http://research.ijcaonline.org/volume116/number20/pxc3902805.pdf
Greff K, Srivastava RK, Koutnik J, Steunebrink BR, Schmidhuber J. LSTM: A Search Space Odyssey. IEEE Transactions on Neural Networks and Learning Systems 2017;28(10):2222-32.
Pham VC, Pham NC. Developing the hybrid forecasting model on the short-term. ICIC Express Letters 2020;14(10):1017-24.
Carbonneau R, Laframboise K, Vahidov R. Application of machine learning techniques for supply chain demand forecasting. European Journal of Operational Research 2008;184(3):1140-54.
The MathWorks . Using MATLAB version 6. USA: The MathWorks; 2000.
Hyndman RJ, Koehler AB. Another look at measures of forecast accuracy. International Journal of Forecasting [Internet]. 2006 [cited 2022 Aug 1];22(4):679-88. Available from: https://www.sciencedirect.com/science/article/pii/S0169207006000239
Acar Y, Gardner ES. Forecasting method selection in a global supply chain. International Journal of Forecasting 2012;28(4):842-8.
Cao J, Li Z, Li J. Financial time series forecasting model based on CEEMDAN and LSTM. Physica A: Statistical Mechanics and its Applications [Internet]. 2019 [cited 2022 Aug 1];519:127–39. Available from: https://doi.org/10.1016/j.physa.2018.11.061
Chaierk R, Chokagorn P, Chantophas W. Variable Selection in Multiple Linear Regression Analysis [Internet]. 2017 [cited 2022 Sep 30]. Available from: http://sc2.kku.ac.th/stat/statweb/images/Eventpic/60/Seminar/01_11_.pdf (in Thai).
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