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A CNN鈥揕STM Hybrid Deep Learning Model for Detergent Products Demand Forecasting: A Case Study
<!DOCTYPE html> <!--[if IE 8]> <html class="ie ie8"> <![endif]--> <!--[if IE 9]> <html class="ie ie9"> <![endif]--> <!--[if gt IE 9]><!--> <html> <!--<![endif]--> <head> <meta charset="utf-8" /> <title>A CNN鈥揕STM Hybrid Deep Learning Model for Detergent Products Demand Forecasting: A Case Study</title> <!-- favicon --> <link rel="shortcut icon" type="image/ico" href="./data/ijsom/coversheet/favicon.ico" /> <!-- mobile settings --> <meta name="viewport" content="width=device-width, maximum-scale=1, initial-scale=1, user-scalable=0" /> <!--[if IE]><meta http-equiv='X-UA-Compatible' content='IE=edge,chrome=1'><![endif]--> <!-- user defined metatags --> <meta name="keywords" content="Forecasting Demand,Detergent Products,Hybrid Neural Networks Based-Model,Convolution Neural Networks,Long Short-Term Memory" /> <meta name="description" content="An accurate forecast of current and future demand is an essential initial step for almost all the facets of supply chain optimization, including inventory strategy, production scheduling, distribution management, and marketing policies. Simply put, a more accurate demand prediction can lead to a more optimized supply chain process, allowing for better inventory control and higher customer satisfaction. Classical demand predictions are based principally on qualitative approaches relying on data from experts' opinions; quantitative forecasts based on historical data through statistical and artificial neural network models or a mix of qualitative and quantitative techniques that is also widely used and has shown good performances. Detergents and cleaning products demand is extremely volatile and has undergone substantial change, especially during the COVID-19 health crisis. In this paper, we present a hybrid Neural Network approach for accurate demand forecasts of the detergent manufacturing industry. It mainly consists of the combination of Long Short-Term Memory (LSTM) with Convolution Neural Network (CNN) based approaches. We performed a series of experiments on real data sets and assessed the performance of the proposed CNN鈥揕STM hybrid model. Numerical results showed that the combination of LSTM layers with complementary CNN layers provides more accurate results than other state-of-the-art forecasting models." /> <meta name="title" content="A CNN鈥揕STM Hybrid Deep Learning Model for Detergent Products Demand Forecasting: A Case Study" /> <meta name="googlebot" content="NOODP" /> <meta name="citation_title" content="A CNN鈥揕STM Hybrid Deep Learning Model for Detergent Products Demand Forecasting: A Case Study" /> <meta name="citation_author" content="Ghazouani, Imen" /> <meta name="citation_author_institution" content="Industrial Engineering Department, National Engineering, School of Tunis, University of Tunis El Manar, Tunis, Tunis, Tunisia" /> <meta name="citation_author" content="Masmoudi, Imen" /> <meta name="citation_author_institution" content="Industrial Engineering Department, National Engineering, School of Tunis, University of Tunis El Manar, Tunis, Tunis, Tunisia" /> <meta name="citation_author" content="Mejri, Imen" /> <meta name="citation_author_institution" content="LR-OASIS, National Engineering School of Tunis University of Tunis El Manar, Tunis, Tunisia" /> <meta name="citation_author" content="LAYEB, SAFA BHAR" /> <meta name="citation_author_institution" content="Bp 37, Le Belvedere 1002 Tunis, Tunisia" /> <meta name="citation_abstract" content="An accurate forecast of current and future demand is an essential initial step for almost all the facets of supply chain optimization, including inventory strategy, production scheduling, distribution management, and marketing policies. Simply put, a more accurate demand prediction can lead to a more optimized supply chain process, allowing for better inventory control and higher customer satisfaction. Classical demand predictions are based principally on qualitative approaches relying on data from experts' opinions; quantitative forecasts based on historical data through statistical and artificial neural network models or a mix of qualitative and quantitative techniques that is also widely used and has shown good performances. Detergents and cleaning products demand is extremely volatile and has undergone substantial change, especially during the COVID-19 health crisis. In this paper, we present a hybrid Neural Network approach for accurate demand forecasts of the detergent manufacturing industry. It mainly consists of the combination of Long Short-Term Memory (LSTM) with Convolution Neural Network (CNN) based approaches. We performed a series of experiments on real data sets and assessed the performance of the proposed CNN鈥揕STM hybrid model. Numerical results showed that the combination of LSTM layers with complementary CNN layers provides more accurate results than other state-of-the-art forecasting models." /> <meta name="citation_id" content="2943" /> <meta name="citation_publication_date" content="2024/11/01" /> <meta name="citation_date" content="2024-11-01" /> <meta name="citation_journal_title" content="International Journal of Supply and Operations Management" /> <meta name="citation_issn" content="23831359" /> <meta name="citation_volume" content="11" /> <meta name="citation_issue" content="4" /> <meta name="citation_firstpage" content="417" /> <meta name="citation_lastpage" content="429" /> <meta name="citation_publisher" content="Kharazmi University" /> <meta name="citation_doi" content="10.22034/ijsom.2024.109931.2752" /> <meta name="DC.Identifier" content="10.22034/ijsom.2024.109931.2752" /> <meta name="citation_abstract_html_url" content="http://www.ijsom.com/article_2943.html" /> <meta name="citation_pdf_url" content="http://www.ijsom.com/article_2943_569071884d949f211e15073fea345613.pdf" /> <meta name="DC.Title" content="A CNN鈥揕STM Hybrid Deep Learning Model for Detergent Products Demand Forecasting: A Case Study" /> <meta name="DC.Source" content="International Journal of Supply and Operations Management" /> <meta name="DC.Date" content="01/11/2024" /> <meta name="DC.Date.issued" content="2024-11-01" /> <meta name="DC.Format" content="application/pdf" /> <meta name="DC.Contributor" content="Ghazouani, Imen" /> <meta name="DC.Contributor" content="Masmoudi, Imen" /> <meta name="DC.Contributor" content="Mejri, Imen" /> <meta name="DC.Contributor" content="LAYEB, SAFA BHAR" /> <meta name="og:title" content="A CNN鈥揕STM Hybrid Deep Learning Model for Detergent Products Demand Forecasting: A Case Study" /> <meta name="og:description" content="An accurate forecast of current and future demand is an essential initial step for almost all the facets of supply chain optimization, including inventory strategy, production scheduling, distribution management, and marketing policies. Simply put, a more accurate demand prediction can lead to a more optimized supply chain process, allowing for better inventory control and higher customer satisfaction. Classical demand predictions are based principally on qualitative approaches relying on data from experts' opinions; quantitative forecasts based on historical data through statistical and artificial neural network models or a mix of qualitative and quantitative techniques that is also widely used and has shown good performances. Detergents and cleaning products demand is extremely volatile and has undergone substantial change, especially during the COVID-19 health crisis. In this paper, we present a hybrid Neural Network approach for accurate demand forecasts of the detergent manufacturing industry. It mainly consists of the combination of Long Short-Term Memory (LSTM) with Convolution Neural Network (CNN) based approaches. We performed a series of experiments on real data sets and assessed the performance of the proposed CNN鈥揕STM hybrid model. Numerical results showed that the combination of LSTM layers with complementary CNN layers provides more accurate results than other state-of-the-art forecasting models." /> <meta name="og:url" content="http://www.ijsom.com/article_2943.html" /> <!-- WEB FONTS : use %7C instead of | (pipe) --> <link href="./themes/base/front/assets/css/social-icon-font.css" rel="stylesheet" type="text/css" /> <!-- CORE CSS --> <link href="./themes/base/front/assets/plugins/bootstrap/css/bootstrap.min.css?v=0.02" rel="stylesheet" type="text/css" /> <link href="./themes/old/front/assets/css/header.css?v=0.05" rel="stylesheet" type="text/css" /> <link href="./themes/old/front/assets/css/footer.css" rel="stylesheet" type="text/css" /> <link href="./inc/css/essentials.css?v=0.2" rel="stylesheet" type="text/css" /> <link href="./inc/css/cookieconsent.min.css" rel="stylesheet" type="text/css" /> <link href="./inc/css/print.css" rel="stylesheet" type="text/css" media="print"/> <!-- RTL CSS --> <link 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<h1 class="margin-bottom-20 size-18 ltr"><span class="article_title bold"> A CNN鈥揕STM Hybrid Deep Learning Model for Detergent Products Demand Forecasting: A Case Study</span></h1> <div> <div class="margin-bottom-3"> </div> <p class="margin-bottom-3">Document Type : TORS 2022</p> <p class="padding-0" style="margin:12px -2px 0 -2px"><strong>Authors</strong></p> <ul class="list-inline list-inline-seprator margin-bottom-6 ltr"> <li class="padding-3"> <a href="./?_action=article&au=130150&_au=Imen++Ghazouani">Imen Ghazouani</a> <sup><a href="#aff1" >1</a></sup> </li> <li class="padding-3"> <a href="./?_action=article&au=130151&_au=Imen++Masmoudi">Imen Masmoudi</a> <sup><a href="#aff1" >1</a></sup> </li> <li class="padding-3"> <a href="./?_action=article&au=130152&_au=Imen++Mejri">Imen Mejri</a> <sup><a href="#aff2" >2</a></sup> </li> <li class="padding-3"> <a href="./?_action=article&au=129928&_au=SAFA+BHAR+LAYEB">SAFA BHAR LAYEB</a> <sup><a href="mailto:safa.layeb@enit.utm.tn" data-toggle="tooltip" data-placement="bottom" title="Email to Corresponding Author"><i class="fa fa-envelope-o" ></i></a></sup> <sup class="ltr"><a class=" text-green" href="https://www.orcid.org/0000-0003-2536-7872" data-toggle="tooltip" data-placement="bottom" data-html="true" title="ORCID: 0000-0003-2536-7872" target="_blank"><i class="ai ai-orcid size-13" ></i></a></sup> <sup><a href="#aff3" >3</a></sup> </li> </ul> <p class="margin-bottom-3 ltr" id="aff1"> <sup>1</sup> Industrial Engineering Department, National Engineering, School of Tunis, University of Tunis El Manar, Tunis, Tunis, Tunisia </p> <p class="margin-bottom-3 ltr" id="aff2"> <sup>2</sup> LR-OASIS, National Engineering School of Tunis University of Tunis El Manar, Tunis, Tunisia </p> <p class="margin-bottom-3 ltr" id="aff3"> <sup>3</sup> Bp 37, Le Belvedere 1002 Tunis, Tunisia </p> <div class="margin-bottom-3 ltr" id="ar_doi" title="DOI"><i class="ai ai-doi size-25 text-orange"></i> <span dir="ltr"><a href="https://dx.doi.org/10.22034/ijsom.2024.109931.2752">10.22034/ijsom.2024.109931.2752</a></span></div> <p style="margin:12px -2px 0 -2px"><strong>Abstract</strong></p> <div class="padding_abstract justify ltr">An accurate forecast of current and future demand is an essential initial step for almost all the facets of supply chain optimization, including inventory strategy, production scheduling, distribution management, and marketing policies. Simply put, a more accurate demand prediction can lead to a more optimized supply chain process, allowing for better inventory control and higher customer satisfaction. Classical demand predictions are based principally on qualitative approaches relying on data from experts' opinions; quantitative forecasts based on historical data through statistical and artificial neural network models or a mix of qualitative and quantitative techniques that is also widely used and has shown good performances. Detergents and cleaning products demand is extremely volatile and has undergone substantial change, especially during the COVID-19 health crisis. In this paper, we present a hybrid Neural Network approach for accurate demand forecasts of the detergent manufacturing industry. It mainly consists of the combination of Long Short-Term Memory (LSTM) with Convolution Neural Network (CNN) based approaches. We performed a series of experiments on real data sets and assessed the performance of the proposed CNN–LSTM hybrid model. Numerical results showed that the combination of LSTM layers with complementary CNN layers provides more accurate results than other state-of-the-art forecasting models.</div> <p class="padding-0" style="margin:12px -2px 0 -2px"><strong>Keywords</strong></p> <ul class="block list-inline list-inline-seprator margin-bottom-6 ltr"> <li class="padding-3"> <a class="tag_a" href="./?_action=article&kw=106540&_kw=Forecasting+Demand" >Forecasting Demand</a> </li> <li class="padding-3"> <a class="tag_a" href="./?_action=article&kw=106541&_kw=Detergent+Products" >Detergent Products</a> </li> <li class="padding-3"> <a class="tag_a" href="./?_action=article&kw=106542&_kw=Hybrid+Neural+Networks+Based-Model" >Hybrid Neural Networks Based-Model</a> </li> <li class="padding-3"> <a class="tag_a" href="./?_action=article&kw=106543&_kw=Convolution+Neural+Networks" >Convolution Neural Networks</a> </li> <li class="padding-3"> <a class="tag_a" href="./?_action=article&kw=106544&_kw=Long+Short-Term+Memory" >Long Short-Term Memory</a> </li> </ul> </div> <hr> <div class="page_break"></div> <div class="panel"> <div class="panel-heading card-header"> <h4 class="panel-title "> <a data-toggle="collapse" data-parent="#accordions" href="#collapsesRef"><i class="fa fa-plus"></i> References</a> </h4> </div> <div id="collapsesRef" class="panel-collapse collapse"> <div class="panel-body justify"> <div class="padding-3 margin-top-3 ltr justify">Abbasimehr, H., Shabani, M., & Yousefi, M. (2020). An optimized model using LSTM network for demand forecasting. <em>Computers & industrial engineering, 143</em>, 106435. </div> <div class="padding-3 margin-top-3 ltr justify">Armstrong, J. S. (Ed.). (2001). <em>Principles of forecasting: a handbook for researchers and practitioners</em> (Vol. 30). Springer Science & Business Media. </div> <div class="padding-3 margin-top-3 ltr justify">Au, K. F., Choi, T. M., & Yu, Y. (2008). Fashion retail forecasting by evolutionary neural networks. <em>International Journal of Production Economics, 114</em>(2), 615-630. </div> <div class="padding-3 margin-top-3 ltr justify">Bandara, K., Shi, P., Bergmeir, C., Hewamalage, H., Tran, Q., & Seaman, B. (2019, December). Sales demand forecast in e-commerce using a long short-term memory neural network methodology. In International Conference on Neural Information Processing (pp. 462-474). Springer, Cham. </div> <div class="padding-3 margin-top-3 ltr justify">Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). <em>Time series analysis: forecasting and control</em>. John Wiley & Sons. </div> <div class="padding-3 margin-top-3 ltr justify">Brownlee, J. (2017). <em>How to Tune LSTM Hyperparameters with Keras for Time Series Forecasting</em>. </div> <div class="padding-3 margin-top-3 ltr justify">Chang, P. C., Wang, Y. W., & Tsai, C. Y. (2005). Evolving neural network for printed circuit board sales forecasting. <em>Expert Systems with Applications, 29</em>(1), 83-92. </div> <div class="padding-3 margin-top-3 ltr justify">Chang, P. C., Wang, Y. W., & Liu, C. H. (2007). The development of a weighted evolving fuzzy neural network for PCB sales forecasting. <em>Expert Systems with Applications, 32</em>(1), 86-96. </div> <div class="padding-3 margin-top-3 ltr justify">De Gooijer, J. G., & Hyndman, R. J. (2006). 25 years of time series forecasting. <em>International journal of forecasting, 22</em>(3), 443-473. </div> <div class="padding-3 margin-top-3 ltr justify">Fattah, J., Ezzine, L., Aman, Z., El Moussami, H., & Lachhab, A. (2018). Forecasting of demand using ARIMA model<em>. International Journal of Engineering Business Management, 10</em>, 1847979018808673. </div> <div class="padding-3 margin-top-3 ltr justify">Goodwin, P., Ord, J. K., Öller, L. E., Sniezek, J. A., & Leonard, M. (2002). <em>Principles of Forecasting: A Handbook for Researchers and Practitioners: J. Scott Armstrong (Ed.)</em>, (2001), Boston: Kluwer Academic Publishers. </div> <div class="padding-3 margin-top-3 ltr justify">Hansen, J. V., & Nelson, R. D. (1997). Neural networks and traditional time series methods: a synergistic combination in state economic forecasts. <em>IEEE transactions on Neural Networks, 8</em>(4), 863-873. </div> <div class="padding-3 margin-top-3 ltr justify">Husna, A., Amin, S. H., & Shah, B. (2021). Demand forecasting in supply chain management using different deep learning methods. In Demand forecasting and order planning in supply chains and humanitarian logistics (pp. 140-170). IGI Global. </div> <div class="padding-3 margin-top-3 ltr justify">Hyndman, R. J., & Athanasopoulos, G. (2018). <em>Forecasting: principles and practice</em>. OTexts. </div> <div class="padding-3 margin-top-3 ltr justify">Jiang, F., Yang, X., & Li, S. (2018). Comparison of forecasting India’s energy demand using an MGM, ARIMA model, MGM- ARIMA model, and BP neural network model. <em>Sustainability, 10</em>(7), 2225. </div> <div class="padding-3 margin-top-3 ltr justify">J Rao, R. D., & Parikh, J. K. (1996). Forecast and analysis of demand for petroleum products in India. <em>Energy policy, 24</em>(6), 583-592. </div> <div class="padding-3 margin-top-3 ltr justify">Khashei, M., & Bijari, M. (2011). A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. <em>Applied Soft Computing, 11</em>(2), 2664-2675. </div> <div class="padding-3 margin-top-3 ltr justify">Kim, M., Choi, W., Jeon, Y., & Liu, L. (2019). A hybrid neural network model for power demand forecasting. <em>Energies, 12</em>(5), 931. </div> <div class="padding-3 margin-top-3 ltr justify">Law, R., Li, G., Fong, D. K. C., & Han, X. (2019). Tourism demand forecasting: A deep learning approach. <em>Annals of Tourism Research, 75</em>, 410-423. </div> <div class="padding-3 margin-top-3 ltr justify">Luo, C. S., Zhou, L. Y., & Wei, Q. F. (2013). <em>Application of SARIMA model in cucumber price forecast</em>. In Applied Mechanics and Materials (Vol. 373, pp. 1686-1690). Trans Tech Publications Ltd. </div> <div class="padding-3 margin-top-3 ltr justify">Mbarek, H. B., Layeb, S. B., Aissaoui, N. O., Jaoua, A., & Hadj-Alouane, A. B (2022, September). Predicting Patient Arrival Rates in a Multi-Specialty Outpatient Department. Proceedings of the 3rd Asia Pacific International Conference on Industrial Engineering and Operations Management, Johor Bahru, Malaysia. </div> <div class="padding-3 margin-top-3 ltr justify">McKinney, W., Perktold, J., & Seabold, S. (2011). <em>Time series analysis in Python with statsmodels</em>. Jarrodmillman Com, 96-102. </div> <div class="padding-3 margin-top-3 ltr justify">Mejri, I., Bouzid, A., Bacha, S., & Layeb, S. B. (2021, September). Forecasting Demand Using ARIMA Model and LSTM Neural Network: a Case of Detergent Manufacturing Industry. In 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) (pp. 346-353). IEEE. </div> <div class="padding-3 margin-top-3 ltr justify">Ouerghi, A., Hasni, M., Jaidi, Z., & Layeb, S. B. (2019). A new combined linear-artificial neural network-based model for accurate inflation forecasting in Tunisia. <em>International Journal of Decision Sciences, Risk and Management, 8</em>(4), 220-233. </div> <div class="padding-3 margin-top-3 ltr justify">Ramanathan, U., & Muyldermans, L. (2010). Identifying demand factors for promotional planning and forecasting: A case of a soft drink company in the UK. <em>International journal of production economics, 128</em>(2), 538-545. </div> <div class="padding-3 margin-top-3 ltr justify">Remus, W., & O’Connor, M. (2001). <em>Neural networks for time-series forecasting. In Principles of forecasting</em> (pp. 245-256). Springer, Boston, MA. </div> <div class="padding-3 margin-top-3 ltr justify">Rosenberg, R. D. (1982). Forecasting derived product demand in commercial construction. <em>Industrial Marketing Management, 11</em>(1), 39-46. </div> <div class="padding-3 margin-top-3 ltr justify">Sen, P., Roy, M., & Pal, P. (2016). Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization. <em>Energy, 116</em>, 1031- 1038. </div> <div class="padding-3 margin-top-3 ltr justify">Weng, Y., Wang, X., Hua, J., Wang, H., Kang, M., & Wang, F. Y. (2019). Forecasting horticultural products price using ARIMA model and neural network based on a large-scale data set collected by web crawler. <em>IEEE Transactions on Computational Social Systems, 6</em>(3), 547-553. </div> <div class="padding-3 margin-top-3 ltr justify">Yu, Y., Choi, T. M., & Hui, C. L. (2011). An intelligent fast sales forecasting model for fashion products. <em>Expert Systems with Applications, 38</em>(6), 7373-7379. </div> <div class="padding-3 margin-top-3 ltr justify">Yildiz, H., DuHadway, S., Narasimhan, R., & Narayanan, S. (2016). Production planning using evolving demand forecasts in the automotive industry. <em>IEEE Transactions on Engineering Management, 63</em>(3), 296-304.</div> </div> </div> </div> </div> </div> <!-- /CENTER --> <!-- LEFT --> <div class="col-lg-3 col-md-3 col-sm-4"> <div class="panel panel-default my_panel-default margin-bottom-10"> <div class="panel-body ar_info_pnl" id="ar_info_pnl_cover"> <div id="pnl_cover"> <div class="row" > <div class="col-xs-6 col-md-6 nomargin-bottom"> <a href="javascript:loadModal('International Journal of Supply and Operations Management', './data/ijsom/coversheet/cover_en.jpg')"> <img src="data/ijsom/coversheet/cover_en.jpg" alt="International Journal of Supply and Operations Management" style="width: 100%;"> </a> </div> <div class="col-xs-6 col-md-6 nomargin-bottom"> <h6><a href="./issue_542_546.html">Volume 11, Issue 4</a><br/>November 2024<div id="sp_ar_pages">Pages <span dir="ltr">417-429</span></div></h6> </div> </div> </div> </div> </div> <!-- Download 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Masmoudi, I., Mejri, I., & LAYEB, S. (2024). A CNN鈥揕STM Hybrid Deep Learning Model for Detergent Products Demand Forecasting: A Case Study. <em>International Journal of Supply and Operations Management</em>, 11(4), 417-429. doi: 10.22034/ijsom.2024.109931.2752</p> </div> </div> </div> </div> <div id="cite-mla" class="modal fade" tabindex="-1" role="dialog" aria-labelledby="myModalLabel" aria-hidden="true"> <div class="modal-dialog"> <div class="modal-content"> <!-- Modal Header --> <div class="modal-header"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> <h4 class="modal-title" id="myModalLabel">MLA</h4> </div> <!-- Modal Body --> <div class="modal-body"> <p>Imen Ghazouani; Imen Masmoudi; Imen Mejri; SAFA BHAR LAYEB. "A CNN鈥揕STM Hybrid Deep Learning Model for Detergent Products Demand Forecasting: A Case Study". <em>International Journal of Supply and Operations Management</em>, 11, 4, 2024, 417-429. doi: 10.22034/ijsom.2024.109931.2752</p> </div> </div> </div> </div> <div id="cite-harvard" class="modal fade" tabindex="-1" role="dialog" aria-labelledby="myModalLabel" aria-hidden="true"> <div class="modal-dialog"> <div class="modal-content"> <!-- Modal Header --> <div class="modal-header"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> <h4 class="modal-title" id="myModalLabel">HARVARD</h4> </div> <!-- Modal Body --> <div class="modal-body"> <p>Ghazouani, I., Masmoudi, I., Mejri, I., LAYEB, S. (2024). 'A CNN鈥揕STM Hybrid Deep Learning Model for Detergent Products Demand Forecasting: A Case Study', <em>International Journal of Supply and Operations Management</em>, 11(4), pp. 417-429. doi: 10.22034/ijsom.2024.109931.2752</p> </div> </div> </div> </div> <div id="cite-vancouver" class="modal fade" tabindex="-1" role="dialog" aria-labelledby="myModalLabel" aria-hidden="true"> <div class="modal-dialog"> <div class="modal-content"> <!-- Modal Header --> <div class="modal-header"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> <h4 class="modal-title" id="myModalLabel">VANCOUVER</h4> </div> <!-- Modal Body --> <div class="modal-body"> <p>Ghazouani, I., Masmoudi, I., Mejri, I., LAYEB, S. A CNN鈥揕STM Hybrid Deep Learning Model for Detergent Products Demand Forecasting: A Case Study. <em>International Journal of Supply and Operations Management</em>, 2024; 11(4): 417-429. doi: 10.22034/ijsom.2024.109931.2752</p> </div> </div> </div> </div> </div> </div> <!-- /MAIN CONTENT --> <!-- Subscribe --> <section class="alternate padding-xxs"> </section> <!-- /Subscribe --> <!-- FOOTER --> <div class="container"> <footer id="footer"> <div class="scrollup" id="scroll" href="#"><span></span></div> <div class="row"> <div class="col-md-2"> <!-- Links --> <h4 class="">Explore Journal</h4> <ul class="footer-links list-unstyled"> <li id="fli_home"><a href="./">Home</a></li> <li id="fli_about"><a href="./journal/about">About Journal</a></li> <li id="fli_Edb"><a href="./journal/editorial.board">Editorial Board</a></li> <li id="fli_submit"><a href="./author">Submit Manuscript</a></li> <li id="fli_contactus"><a href="./journal/contact.us">Contact Us</a></li> <li id="fli_glossary"><a href="./journal/glossary">Glossary</a></li> <li id="fli_order_hrdj"><a href="./journal/subscription.form">Hard Copy Subscription</a></li> <li id="fli_sitemap"><a href="./sitemap.xml?usr">Sitemap</a></li> </ul> <!-- /Links --> </div> <div class="col-md-3"> <!-- Latest News --> <h4 class="">Latest News</h4> <ul class="footer-posts list-unstyled"> <li> <a href="./news?newsCode=173">SD of ISC: Sustainable Development of Intelligent Supply Chains based on Trends and Future Directions: Application of Novel Solution Techniques</a> <small class="ltr">2023-03-05</small> </li> </ul> <!-- /Latest News --> </div> <div class="col-md-3"> <!-- Footer Note --> <div><p><a title="Linkedin" href="http://www.linkedin.com/company/ijsom?trk=eml-cp_mktg-btn-welcome-20120607%2F"><img src="images/linkedin.jpg" alt="linkedin" /></a></p></div> <!-- /Footer Note --> </div> <div class="col-md-4"> <!-- Newsletter Form --> <h4 class="">Newsletter Subscription</h4> <p>Subscribe to the journal newsletter and receive the latest news and updates</p> <form class="validate" action="" method="post" data-success="Subscription saved successfully." data-toastr-position="bottom-right"> <input type="hidden" name="_token" value="26a49232e71786a2265fcfcebaf2983876e99cd7c9a7eed4"/> <div class="input-group"> <span class="input-group-addon"><i class="fa fa-envelope"></i></span> <input type="email" id="email" name="email" required="required" class="form-control required sbs_email" placeholder="Enter your Email" oninvalid="this.setCustomValidity('Enter a valid email address.')" oninput="this.setCustomValidity('')"> <span class="input-group-btn"> <button class="btn btn-primary mybtn" type="submit">Subscribe</button> </span> </div> </form> <!-- /Newsletter Form --> <!-- Social Icons --> <div class="margin-top-20"> <a class="noborder" href="" target="_blank" class="social-icon social-icon-border social-facebook pull-left block" data-toggle="tooltip" data-placement="top" title="Facebook"> <i class="fa fa-facebook-square" aria-hidden="true"></i> </a> <a class="noborder" href="" target="_blank" class="social-icon social-icon-border social-facebook pull-left block" data-toggle="tooltip" data-placement="top" title="Twitter"> <i class="fa fa-twitter-square" aria-hidden="true"></i> </a> <a class="noborder" href="" target="_blank" class="social-icon social-icon-border social-facebook pull-left block" data-toggle="tooltip" data-placement="top" title="Linkedin"> <i class="fa fa-linkedin-square" aria-hidden="true"></i> </a> <a class="noborder" href="./ju.rss" class="social-icon social-icon-border social-rss pull-left block" data-toggle="tooltip" data-placement="top" title="Rss"><i class="fa fa-rss-square" aria-hidden="true"></i></a> </div> </div> </div> <div class="copyright" style="position: relative"> <ul class="nomargin list-inline mobile-block"> <li>© Journal Management System. <span id='sp_crt'>Powered by <a target='_blank' href='https://www.sinaweb.net/'>Sinaweb</a></span></li> </ul> </div> </footer> </div> <!-- /FOOTER --> </div> <!-- /wrapper --> <!-- SCROLL TO TOP --> <a href="#" id="toTop_old"></a> <!-- PRELOADER --> <div id="preloader"> <div class="inner"> <span class="loader"></span> </div> </div><!-- /PRELOADER --> <!-- JAVASCRIPT FILES --> <script type="text/javascript">var plugin_path = './themes/base/front/assets/plugins/';</script> <script type="text/javascript" src="./themes/base/front/assets/js/scripts.js?v=0.02"></script> <!-- user defined scripts--> <!-- Extra Script Scripts --> <script type="text/javascript"> $('ul.nav li.dropdown').hover(function() { if (window.matchMedia('(max-width: 767px)').matches) return; 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