Using Time Series Models in Product Based Order Forecasting

Fatih Yücalar

doi:10.38088/jise.1422178

Research Article

Using Time Series Models in Product Based Order Forecasting

Year 2024, Volume: 8 Issue: 1, 36 - 52, 07.06.2024

Fatih Yücalar

https://doi.org/10.38088/jise.1422178

Abstract

Production systems play a vital role in maximizing consumer satisfaction by efficiently transforming inputs such as labor, raw materials, and capital into products or services aligned with consumer demands. An order-based production takes place in poultry meat and meat products production facilities, which face various difficulties in meeting changing customer demands and managing the supply of raw materials. To optimize production and increase customer loyalty, these facilities use strategic scheduling, considering their daily production capacity and fluctuating customer orders. In this study, estimating which customer and product type the future order quantities will come from for the relevant facilities, increasing customer satisfaction by facilitating order processes and minimizing storage costs are discussed. With this study, the number of orders was estimated, and it was aimed to meet the orders in the most accurate way. In the estimations, the order data of a poultry meat and meat products production facility between 2013 and 2021 were used. Since the order figures will change every year in cases such as the customer working with the facility, growing, or shrinking, better results have been tried to be obtained with the arrangements made on the data set used and three different data sets have been obtained. Estimation processes were performed for these three data sets using LSTM and Prophet algorithms. While the RMSE value was 7.07 in the LSTM model in experimental studies, this value was obtained as 10.96 for Prophet. In the results obtained, it was observed that the arrangements made on the data set positively affected the accuracy of the estimations and the LSTM algorithm produced better results than the Prophet algorithm.

Keywords

Production optimization , Poultry production system , Time Series , LSTM , Prophet

References

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[7] Lakshmanan, B., Vivek Raja, P.S.N., Kalathiappan, V. (2020). Sales Demand Forecasting Using LSTM Network. In: Dash, S., Lakshmi, C., Das, S., Panigrahi, B. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, Springer, Singapore, vol. 1056, pp. 125–132.
[8] Torres, J. F., Martínez-Álvarez, F., Troncoso, A. (2022). A Deep LSTM Network For The Spanish Electricity Consumption Forecasting. Neural Computing and Applications, 34: 10533–10545.
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[12] Bailey, M. A., Hess, J. B., Krehling, J. T., Macklin, K. S. (2021). Broiler performance and litter ammonia levels as affected by sulfur added to the bird’s diet. Journal of Applied Poultry Research, 30(2).
[13] Solano-Blanco, Alfaima L., González, Jaime E. & Medaglia, Andrés L., (2023). Production planning decisions in the broiler chicken supply chain with growth uncertainty. Operations Research Perspectives, Elsevier, vol. 10(C).
[14] Bai, L., Cui, L., Zhang, Z., Xu, L., Wang, Y., Hancock, E. R. (2023). Entropic Dynamic Time Warping Kernels for Co-Evolving Financial Time Series Analysis. IEEE Transactions on Neural Networks and Learning Systems, 34(3): 1808 - 1822.
[15] Zhang, L., Wang, R., Li, Z., Li, J., Ge, Y., Wa, S., Huang, S., Lv, C. (2023) Time-Series Neural Network: A High-Accuracy Time-Series Forecasting Method Based on Kernel Filter and Time Attention. Information, 14, 500.
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[17] Sinnaiah, T., Adam, S. and Mahadi, B. (2023). A strategic management process: the role of decision-making style and organizational performance. Journal of Work-Applied Management, 15(1): 37-50.
[18] Lindemann, B., Müller, T., Vietz, H., Jazdi, N., Weyrich, M. (2021). A survey on long short-term memory networks for time series prediction. Procedia CIRP, 99: 650-655.
[19] Yucalar, Fatih. (2023). Developing an Advanced Software Requirements Classification Model Using BERT: An Empirical Evaluation Study on Newly Generated Turkish Data. Applied Sciences, 13(20), 11127.
[20] Zaini, N., Ean, L. W., Ahmed, A.N., Malek, M. A., Chow, M. F. (2022). PM2.5 forecasting for an urban area based on deep learning and decomposition method. Scientific Reports, 12(1), 17565.
[21] Yong, Y., Xiaosheng, S., Changhua, Hu., Jianxun Z. (2019). A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures. Neural Computation, 31(7):1235-1270.
[22] Karasulu, B., Yücalar, F., Borandag, E. (2022). A hybrid approach based on deep learning for gender recognition using human ear images. Journal of the Faculty of Engineering and Architecture of Gazi University, 37(3): 1579-1594.
[23] Dobilas, S. (2023). LSTM Recurrent Neural Networks — How to Teach a Network to Remember the Past. https://towardsdatascience.com/lstm-recurrent-neural-networks-how-to-teach-a-network-to-remember-the-past-55e54c2ff22e (Accessed: January 16, 2024).
[24] Srivastava, P. (2023). Essentials of Deep Learning: Introduction to Long Short-Term Memory, Analytics Vidhya. https://www.analyticsvidhya.com/blog/2017/12/fundamentals-of-deep-learning-introduction-to-lstm/ (Accessed: January 17, 2024).
[25] Meng, J., Yang, X., Yang, C., Liu, Y. (2021). Comparative Analysis of Prophet and LSTM Model. Journal of Physics: Conference Series, 1910(1): 12-59.
[26] Datapred. (2018). A better Facebook Prophet. https://www.datapred.com/blog/a-better-facebook-prophet (Accessed: January 18, 2024).
[27] Hyndman, R. J. (2014). Measuring Forecast Accuracy. In: Business Forecasting: Practical Problems and Solutions. John Wiley & Sons, Hoboken, 177-183.
[28] Aytaç, U. C., Kucukyilmaz, T., Tarakcıoğlu, G. S. (2022). Comparison of Time Series Models for Predicting Online Gaming Company Revenue, Journal of Statistics and Applied Sciences, 6: 25-36.
[29] Wang, Q., Peng, R. Q., Wang, J. Q., Li, Z., Qu, H. B. (2020). NEWLSTM: An Optimized Long Short-Term Memory Language Model for Sequence Prediction, IEEE Access, 8: 65395-65401.

Year 2024, Volume: 8 Issue: 1, 36 - 52, 07.06.2024

Fatih Yücalar

https://doi.org/10.38088/jise.1422178

Abstract

References

[1] Ediz, Ç., Turan, A. H. (2020). Information Technology Applications in Multivariate Production Planning Decision. International Journal of Economics and Administrative Studies, Prof. Dr. Talha Ustasüleyman Special Issue, 19-30.
[2] Zhang, Y., Jia, Z., Dai, Y. (2018). Real-Time Performance Analysis of Industrial Serial Production Systems with Flexible Manufacturing. 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems (SCIS) and 19th International Symposium on Advanced Intelligent Systems (ISIS), Toyama, Japan, pp. 360-365.
[3] Kozaklı, Ö., Mert, M., Fırat, M. Z. (2021). Türkiye etlik piliç üretiminin zaman serisi yöntemi ile modellenmesi. Ege Üniversitesi Ziraat Fakültesi Dergisi, 58(4), 557-567.
[4] Holimchayachotikul, P., Murino, T., Payongyam, P., Sopadang, A., Savino, M., Elpidio, R. (2010). Application of Artificial Neural Network for Demand Forecasting in Supply Chain of Thai Frozen Chicken Products Export Industry. 12th The International Conference on Harbor, Maritime & Multimodal Logistics Modelling and Simulation. Morocco.
[5] Taylor, S. J., Letham, B. (2017). Prophet: Forecasting at Scale. PeerJ Preprints, 5:e3190v2.
[6] Çabuk, M., Yücalar, F., Toçoğlu, M. A. (2023). Automated Analysis of E-Commerce Product Reviews with Machine Learning. European Journal of Science and Technology, 52: 110-121.
[7] Lakshmanan, B., Vivek Raja, P.S.N., Kalathiappan, V. (2020). Sales Demand Forecasting Using LSTM Network. In: Dash, S., Lakshmi, C., Das, S., Panigrahi, B. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, Springer, Singapore, vol. 1056, pp. 125–132.
[8] Torres, J. F., Martínez-Álvarez, F., Troncoso, A. (2022). A Deep LSTM Network For The Spanish Electricity Consumption Forecasting. Neural Computing and Applications, 34: 10533–10545.
[9] Chandriah, K. K., Naraganahalli , R. V. (2021). RNN / LSTM with modified Adam optimizer in deep learning approach for automobile spare parts demand forecasting. Multimedia Tools and Applications, 80(17): 26145-26159.
[10] Çevik, Z. (2015). TMS 41 Çerçevesinde Kanatlı Kümes Hayvanlarının Değerleme ve Raporlanması, Doktora Tezi. Sakarya Üniversitesi, Sosyal Bilimler Enstitüsü, Muhasebe ve Finansman Anabilim Dalı, 210s, Sakarya.
[11] Çelik, S., Özmelioğlu, K., Karaali, A., Özdemir, V. (2014). Etlik Piliç Yetiştiriciliği. https://www.tarimorman.gov.tr/HAYGEM/Belgeler/Hayvanc%C4%B1l%C4%B1k/Kanatl%C4%B1%20Yeti%C5%9Ftiricili%C4%9Fi/Etlik%20Pili%C3%A7%20Yetistiriciligi.pdf (Accessed: January 10, 2024).
[12] Bailey, M. A., Hess, J. B., Krehling, J. T., Macklin, K. S. (2021). Broiler performance and litter ammonia levels as affected by sulfur added to the bird’s diet. Journal of Applied Poultry Research, 30(2).
[13] Solano-Blanco, Alfaima L., González, Jaime E. & Medaglia, Andrés L., (2023). Production planning decisions in the broiler chicken supply chain with growth uncertainty. Operations Research Perspectives, Elsevier, vol. 10(C).
[14] Bai, L., Cui, L., Zhang, Z., Xu, L., Wang, Y., Hancock, E. R. (2023). Entropic Dynamic Time Warping Kernels for Co-Evolving Financial Time Series Analysis. IEEE Transactions on Neural Networks and Learning Systems, 34(3): 1808 - 1822.
[15] Zhang, L., Wang, R., Li, Z., Li, J., Ge, Y., Wa, S., Huang, S., Lv, C. (2023) Time-Series Neural Network: A High-Accuracy Time-Series Forecasting Method Based on Kernel Filter and Time Attention. Information, 14, 500.
[16] Kotu, V., Deshpande, B. (2019). “Chapter 1 – Introduction”, Data Science: Concepts and Practice, Second Edition, Morgan Kaufmann, pp. 1-18.
[17] Sinnaiah, T., Adam, S. and Mahadi, B. (2023). A strategic management process: the role of decision-making style and organizational performance. Journal of Work-Applied Management, 15(1): 37-50.
[18] Lindemann, B., Müller, T., Vietz, H., Jazdi, N., Weyrich, M. (2021). A survey on long short-term memory networks for time series prediction. Procedia CIRP, 99: 650-655.
[19] Yucalar, Fatih. (2023). Developing an Advanced Software Requirements Classification Model Using BERT: An Empirical Evaluation Study on Newly Generated Turkish Data. Applied Sciences, 13(20), 11127.
[20] Zaini, N., Ean, L. W., Ahmed, A.N., Malek, M. A., Chow, M. F. (2022). PM2.5 forecasting for an urban area based on deep learning and decomposition method. Scientific Reports, 12(1), 17565.
[21] Yong, Y., Xiaosheng, S., Changhua, Hu., Jianxun Z. (2019). A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures. Neural Computation, 31(7):1235-1270.
[22] Karasulu, B., Yücalar, F., Borandag, E. (2022). A hybrid approach based on deep learning for gender recognition using human ear images. Journal of the Faculty of Engineering and Architecture of Gazi University, 37(3): 1579-1594.
[23] Dobilas, S. (2023). LSTM Recurrent Neural Networks — How to Teach a Network to Remember the Past. https://towardsdatascience.com/lstm-recurrent-neural-networks-how-to-teach-a-network-to-remember-the-past-55e54c2ff22e (Accessed: January 16, 2024).
[24] Srivastava, P. (2023). Essentials of Deep Learning: Introduction to Long Short-Term Memory, Analytics Vidhya. https://www.analyticsvidhya.com/blog/2017/12/fundamentals-of-deep-learning-introduction-to-lstm/ (Accessed: January 17, 2024).
[25] Meng, J., Yang, X., Yang, C., Liu, Y. (2021). Comparative Analysis of Prophet and LSTM Model. Journal of Physics: Conference Series, 1910(1): 12-59.
[26] Datapred. (2018). A better Facebook Prophet. https://www.datapred.com/blog/a-better-facebook-prophet (Accessed: January 18, 2024).
[27] Hyndman, R. J. (2014). Measuring Forecast Accuracy. In: Business Forecasting: Practical Problems and Solutions. John Wiley & Sons, Hoboken, 177-183.
[28] Aytaç, U. C., Kucukyilmaz, T., Tarakcıoğlu, G. S. (2022). Comparison of Time Series Models for Predicting Online Gaming Company Revenue, Journal of Statistics and Applied Sciences, 6: 25-36.
[29] Wang, Q., Peng, R. Q., Wang, J. Q., Li, Z., Qu, H. B. (2020). NEWLSTM: An Optimized Long Short-Term Memory Language Model for Sequence Prediction, IEEE Access, 8: 65395-65401.

There are 29 citations in total.

Details

Primary Language	English
Subjects	Deep Learning, Data Engineering and Data Science
Journal Section	Research Articles
Authors	Fatih Yücalar 0000-0002-1006-2227
Early Pub Date	April 19, 2024
Publication Date	June 7, 2024
Submission Date	January 18, 2024
Acceptance Date	February 24, 2024
Published in Issue	Year 2024 Volume: 8 Issue: 1

Cite

APA	Yücalar, F. (2024). Using Time Series Models in Product Based Order Forecasting. Journal of Innovative Science and Engineering, 8(1), 36-52. https://doi.org/10.38088/jise.1422178
AMA	Yücalar F. Using Time Series Models in Product Based Order Forecasting. JISE. June 2024;8(1):36-52. doi:10.38088/jise.1422178
Chicago	Yücalar, Fatih. “Using Time Series Models in Product Based Order Forecasting”. Journal of Innovative Science and Engineering 8, no. 1 (June 2024): 36-52. https://doi.org/10.38088/jise.1422178.
EndNote	Yücalar F (June 1, 2024) Using Time Series Models in Product Based Order Forecasting. Journal of Innovative Science and Engineering 8 1 36–52.
IEEE	F. Yücalar, “Using Time Series Models in Product Based Order Forecasting”, JISE, vol. 8, no. 1, pp. 36–52, 2024, doi: 10.38088/jise.1422178.
ISNAD	Yücalar, Fatih. “Using Time Series Models in Product Based Order Forecasting”. Journal of Innovative Science and Engineering 8/1 (June2024), 36-52. https://doi.org/10.38088/jise.1422178.
JAMA	Yücalar F. Using Time Series Models in Product Based Order Forecasting. JISE. 2024;8:36–52.
MLA	Yücalar, Fatih. “Using Time Series Models in Product Based Order Forecasting”. Journal of Innovative Science and Engineering, vol. 8, no. 1, 2024, pp. 36-52, doi:10.38088/jise.1422178.
Vancouver	Yücalar F. Using Time Series Models in Product Based Order Forecasting. JISE. 2024;8(1):36-52.

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