Abstract
Logistics cost control is an important means to increase commercial logistics profits and control corporate capital risks. This paper uses BP neural network to build a prediction model to study the analysis of inventory demand and explores the complex relationship between inventory demand and each influencing factor by training the data of inventory demand influencing factors to obtain effective measures for inventory management and control. Moreover, this article chooses the way that BA optimizes the BP neural network to build a predictive model and uses the actual data to conduct a model test to verify the validity of the model. In addition, this article performs performance analysis of the prediction model of this study by setting up experiments. Through comparative experimental research, we can see that the method proposed in this paper has certain effects, can be applied to the actual forecast of logistics inventory, and can provide theoretical references for subsequent related research.
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References
Chan FTS, Chan HK (2017) A simulation approach for managing manufacturing processes and inbound logistics: a case study. Int J Business Syst Res 1(1):115–134
Wang Y, Assogba K, Liu Y et al (2018) Two-echelon location-routing optimization with time windows based on customer clustering. Expert Syst Appl 104:244–260
Yang X, Hao W, Lu Y (2018) Inventory slack routing application in emergency logistics and relief distributions. PLoS ONE 13(6):e0198443
Jing F, Lan Z (2017) Forecast horizon of multi-item dynamic lot size model with perishable inventory. PLoS ONE 12(11):e0187725
Junpeng Y, Chongjian L, Xiao C et al (2017) The application of the integrated medical logistics intelligent integration system in a hospital. China J Pharm Econ 1(4):177–197
Babai MZ, Dallery Y, Boubaker S, Kalai R (2018) A new method to forecast intermittent demand in the presence of inventory obsolescence. Int J Prod Econ 209:30–41
Huang CF (2019) Evaluation of system reliability for a stochastic delivery-flow distribution network with inventory. Ann Oper Res 277(4):1–13
Yu K, Cadeaux J, Song H (2017) Flexibility and quality in logistics and relationships. Ind Market Manag 62(3):211–225
Guo Y (2017) Modeling and simulation of logistics integration of electronic commerce online shopping platform. In: Proceedings of the tenth international conference on management science and engineering management, Springer, Singapore, pp 165–178
Hofmann E (2017) Marco R Industry 4.0 and the current status as well as future prospects on logistics. Comput Ind 89:23–34
Giuffrida M, Mangiaracina R, Perego A et al (2017) Cross border B2C e-commerce to Greater China and the role of logistics: a literature review. Int J Phys Distrib Logist Manag 47(6):772–795
Hui Z, Gengui Z (2017) Spatial agglomeration evolution and influencing factors of logistics enterprises in international inland port—a case study of Yiwu City. Econ Geogr 37(02):98–105
Wang J, Wang JQ, Tian ZP, Zhao DY (2018) A multihesitant fuzzy linguistic multicriteria decision-making approach for logistics outsourcing with incomplete weight information. Int Trans Oper Res 25(3):831–856
Li M (2018) Research on the mechanism and influence factors of urban style building based on cloud computing logistics information. Clust Comput 2:1–8
Vanderroost M, Ragaert P, Verwaeren J et al (2017) The digitization of a food package’s life cycle: existing and emerging computer systems in the pre-logistics phase. Comput Ind 87(C):15–30
Sumets A (2017) Specific aspects of logistics enterprises in the fat-and-oil industry. Agric Resour Econ Int Sci E-J 3((1868-2017-112)):37–44
Santén Vendela (2017) Towards more efficient logistics: increasing load factor in a shipper’s road transport. Int J Logist Manag 28(2):228–250
Wang K, Liang Y, Zhao L (2017) Multi-stage emergency medicine logistics system optimization based on survival probability. Front Eng Manag 4(2):221–228
Perboli G, Rosano M, Saint-Guillain M et al (2018) A simulation-optimization framework for City Logistics. An application on multimodal last-mile delivery. IET Intel Transp Syst 12(4):262–269
Filho JLES, Morais DC (2018) Group decision model based on ordered weighted distance to aid decisions on logistics. Int J Uncertain Fuzziness Knowl Based Syst 26(2):233–254
Hultin, N. (2018). Legacies, logics, logistics: essays in the anthropology of the platform economy by Jane I. Guyer Chicago, University of Chicago Press, 2016. 312 pp. American Anthropologist, 120(3), 620–621
Moghaddam SHA, Mokhtarzade M, Beirami BA (2020) A feature extraction method based on spectral segmentation and integration of hyperspectral images. Int J Appl Earth Observ Geoinf 89:102097
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The study was supported by China National Social Science Foundation Project (19CJY047) and Heilongjiang Philosophy and Social Science Research Planning Project (18JYC259).
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Han, C., Wang, Q. Research on commercial logistics inventory forecasting system based on neural network. Neural Comput & Applic 33, 691–706 (2021). https://doi.org/10.1007/s00521-020-05090-4
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DOI: https://doi.org/10.1007/s00521-020-05090-4