Predicting Future Demand Analysis in the Logistics Sector Using Machine Learning Methods
DOI:
https://doi.org/10.58190/imiens.2023.70Keywords:
Data analysis and prediction, Forecasting future needs, Logistics sector, Machine learning, Time series forecastingAbstract
In this study, the potential of machine learning methods for analyzing future needs in the logistics sector was investigated. The research is conducted using the MATLAB platform. Numeric pallet demand data obtained from a logistics company are employed to train MLP, LSTM, and CNN models. Data security and confidentiality take priority during the data collection process. This dataset, comprising a total of 3,062 daily records, serves as the primary data source for the study. In the data preprocessing phase, missing or erroneous data is rectified, and outliers are detected and corrected. The models are tested to predict pallet quantities over periods of 25 days and 4 weeks. The results are evaluated by comparing the model predictions with actual data. Model performances are assessed using metrics such as MSE, RMSE, NRMSE, MAE, ESD, and RC. The outcomes of the last 25 days demonstrate that the LSTM model exhibits the lowest MSE (6,410.5571) and RMSE (80.0660) values. For the MLP model, the MSE value is calculated as 20,536.5564, and the RMSE value is 143.3058. Performance evaluations for the CNN model yield an MSE of 8,492.4297 and an RMSE of 92.1544. Furthermore, it is observed that the MLP model provides the best results for the 4-week forecasts. The results of this study indicate the success of the models used for predicting pallet transportation quantities in the logistics sector. In addition to this study, a contribution is made toward enabling logistics companies to make more informed and strategic decisions.
Downloads
References
Gutelius, B. and N. Theodore, The future of warehouse work: Technological change in the US logistics industry. 2019.
Gubán, M. and G. Kovács, Industry 4.0 Conceptions. Acta Technica Corviniensis-Bulletin of Engineering, 2017. 10(1).
Karagulle, A.O., Green business for sustainable development and competitiveness: an overview of Turkish logistics industry. Procedia-Social and Behavioral Sciences, 2012. 41: p. 456-460.
Stough, R.R., New technologies in logistics management, in Handbook of Logistics and Supply-Chain Management. 2017, Emerald Group Publishing Limited.
Zhao, J. and F. Xie, Cognitive and artificial intelligence system for logistics industry. International Journal of Innovative Computing and Applications, 2020. 11(2-3): p. 84-88.
Tsolaki, K., et al., Utilizing machine learning on freight transportation and logistics applications: A review. ICT Express, 2022.
Han, Z., et al., A review of deep learning models for time series prediction. IEEE Sensors Journal, 2019. 21(6): p. 7833-7848.
Dingli, A. and K.S. Fournier, Financial time series forecasting-a deep learning approach. International Journal of Machine Learning and Computing, 2017. 7(5): p. 118-122.
Goyal, A., et al. A solution to forecast demand using long short-term memory recurrent neural networks for time series forecasting. in Proceedings of the Midwest Decision Sciences Institute Conference, Indianapolis, IN, USA. 2018.
Lolli, F., et al., Machine learning for multi-criteria inventory classification applied to intermittent demand. Production Planning & Control, 2019. 30(1): p. 76-89.
Van Gils, T., et al., The use of time series forecasting in zone order picking systems to predict order pickers’ workload. International Journal of Production Research, 2017. 55(21): p. 6380-6393.
Chan, H.K., S. Xu, and X. Qi, A comparison of time series methods for forecasting container throughput. International journal of logistics research and applications, 2019. 22(3): p. 294-303.
Talupula, A., Demand forecasting of outbound logistics using machine learning. 2019.
Sohrabpour, V., et al., Export sales forecasting using artificial intelligence. Technological Forecasting and Social Change, 2021. 163: p. 120480.
Deng, C. and Y. Liu, A Deep Learning-Based Inventory Management and Demand Prediction Optimization Method for Anomaly Detection. Wireless Communications and Mobile Computing, 2021. 2021: p. 1-14.
Ribeiro, A.M.N., et al., Short-and very short-term firm-level load forecasting for warehouses: a comparison of machine learning and deep learning models. Energies, 2022. 15(3): p. 750.
Tang, Y.-M., et al., Forecasting economic recession through share price in the logistics industry with artificial intelligence (AI). Computation, 2020. 8(3): p. 70.
Hochreiter, S. and J. Schmidhuber, Long short-term memory. Neural computation, 1997. 9(8): p. 1735-1780.
Widiasari, I.R. and L.E. Nugroho. Deep learning multilayer perceptron (MLP) for flood prediction model using wireless sensor network based hydrology time series data mining. in 2017 International Conference on Innovative and Creative Information Technology (ICITech). 2017. IEEE.
Noriega, L., Multilayer perceptron tutorial. School of Computing. Staffordshire University, 2005. 4: p. 5.
Bishop, C.M., Neural networks for pattern recognition. 1995: Oxford university press.
Cinar, I. and M. Koklu, Classification of rice varieties using artificial intelligence methods. International Journal of Intelligent Systems and Applications in Engineering, 2019. 7(3): p. 188-194.
Desai, M. and M. Shah, An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN). Clinical eHealth, 2021. 4: p. 1-11.
Ahmed, N.K., et al., An empirical comparison of machine learning models for time series forecasting. Econometric reviews, 2010. 29(5-6): p. 594-621.
Koklu, M. and K. Sabanci, Estimation of credit card customers payment status by using kNN and MLP. International Journal of Intelligent Systems and Applications in Engineering, 2016. 4(Special Issue-1): p. 249-251.
Chauhan, R., K.K. Ghanshala, and R. Joshi. Convolutional neural network (CNN) for image detection and recognition. in 2018 first international conference on secure cyber computing and communication (ICSCCC). 2018. IEEE.
Singh, D., et al., Classification and analysis of pistachio species with pre-trained deep learning models. Electronics, 2022. 11(7): p. 981.
Dogan, M., et al., Dry bean cultivars classification using deep cnn features and salp swarm algorithm based extreme learning machine. Computers and Electronics in Agriculture, 2023. 204: p. 107575.
Kursun, R., et al. Flower Recognition System with Optimized Features for Deep Features. in 2022 11th Mediterranean Conference on Embedded Computing (MECO). 2022. IEEE.
Taspinar, Y.S., et al., Computer vision classification of dry beans (Phaseolus vulgaris L.) based on deep transfer learning techniques. European Food Research and Technology, 2022. 248(11): p. 2707-2725.
Koklu, M., et al., A CNN-SVM study based on selected deep features for grapevine leaves classification. Measurement, 2022. 188: p. 110425.
Alzubaidi, L., et al., Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of big Data, 2021. 8: p. 1-74.
LeCun, Y., Y. Bengio, and G. Hinton, Deep learning. nature, 2015. 521(7553): p. 436-444.
Kishore, B., et al., Computer-aided multiclass classification of corn from corn images integrating deep feature extraction. Computational Intelligence and Neuroscience, 2022. 2022.
Koklu, M., I. Cinar, and Y.S. Taspinar, Classification of rice varieties with deep learning methods. Computers and electronics in agriculture, 2021. 187: p. 106285.
Butuner, R., et al., Classification of deep image features of lentil varieties with machine learning techniques. European Food Research and Technology, 2023. 249(5): p. 1303-1316.
Cinar, I. and M. Koklu, Identification of rice varieties using machine learning algorithms. Journal of Agricultural Sciences, 2022: p. 9-9.
Yasin, E.T., R. Kursun, and M. Koklu, Deep Learning-Based Classification of Black Gram Plant Leaf Diseases: A Comparative Study.
Mascarenhas, S. and M. Agarwal. A comparison between VGG16, VGG19 and ResNet50 architecture frameworks for Image Classification. in 2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON). 2021. IEEE.
He, K., et al. Deep residual learning for image recognition. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
Tian, X. and C. Chen. Modulation pattern recognition based on Resnet50 neural network. in 2019 IEEE 2nd International Conference on Information Communication and Signal Processing (ICICSP). 2019. IEEE.
Gencturk, B., et al., Detection of hazelnut varieties and development of mobile application with CNN data fusion feature reduction-based models. European Food Research and Technology, 2023: p. 1-14.
Qi, J., et al., On mean absolute error for deep neural network based vector-to-vector regression. IEEE Signal Processing Letters, 2020. 27: p. 1485-1489.
Botchkarev, A., Performance metrics (error measures) in machine learning regression, forecasting and prognostics: Properties and typology. arXiv preprint arXiv:1809.03006, 2018.
Chicco, D., M.J. Warrens, and G. Jurman, The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 2021. 7: p. e623.
Wang, W. and Y. Lu. Analysis of the mean absolute error (MAE) and the root mean square error (RMSE) in assessing rounding model. in IOP conference series: materials science and engineering. 2018. IOP Publishing.
Chai, T. and R.R. Draxler, Root mean square error (RMSE) or mean absolute error (MAE). Geoscientific model development discussions, 2014. 7(1): p. 1525-1534.
Bishop, C.M. and N.M. Nasrabadi, Pattern recognition and machine learning. Vol. 4. 2006: Springer.
Géron, A., Hands-on machine learning with scikit-learn and tensorflow: Concepts. Tools, and Techniques to build intelligent systems, 2017.
Willmott, C.J. and K. Matsuura, Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate research, 2005. 30(1): p. 79-82.
Frías-Paredes, L., et al., Dynamic mean absolute error as new measure for assessing forecasting errors. Energy conversion and management, 2018. 162: p. 176-188.
Lee, D.K., J. In, and S. Lee, Standard deviation and standard error of the mean. Korean journal of anesthesiology, 2015. 68(3): p. 220-223.
Gao, J., Machine learning applications for data center optimization. 2014.
Mousavi, S.M. and G.C. Beroza, A machine‐learning approach for earthquake magnitude estimation. Geophysical Research Letters, 2020. 47(1): p. e2019GL085976.
Kendall, M.G., Rank correlation methods. 1948.
Zar, J.H., Spearman rank correlation. Encyclopedia of biostatistics, 2005. 7.
Kendall, M.G., A new measure of rank correlation. Biometrika, 1938. 30(1/2): p. 81-93.
Shi, H., M. Drton, and F. Han, On the power of Chatterjee’s rank correlation. Biometrika, 2022. 109(2): p. 317-333.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Intelligent Methods In Engineering Sciences
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
