A Machine Learning Approach for Modeling Safety Stock Optimization Equation in the Cosmetics and Beauty Industry

Díaz, David; Marta, Regina; Ortega, G; Ponce, Hiram

doi:10.1007/978-3-030-89817-5_13

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…There have been many researchers who have applied these methods to multi-scenario demand forecasting problems: David Diaz et al used a support vector machine model for inventory demand forecasting in a company in the beauty and cosmetic industry, and used data from a multinational company to demonstrate that their proposed model is sufficiently feasible and can predict reasonable and valuable safety stocks [1] . Jinshang Dai et al used a gray prediction model for a small sample of data with a large amplitude of initial product demand on the market, and improved the gray prediction The method was improved and finally the safety stock was ensured more accurately and effectively in this scenario [2] . S.Fatemeh Faghidian et al hybridized the gray prediction model with other algorithms and proposed a SS model based on the theory, which was validated using data and concluded that the model has a high performance for data with large intermittent demand variation [3] .…”

Section: Related Workmentioning

confidence: 99%

Research on optimization of inventory demand forecast in cloud infrastructure supply chain

Yao,

Xin,

Wan

2024

Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023)

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With the development of computer and cloud computing related technologies, cloud products play a more important role in the economic and cultural activities of enterprises, organizations and individuals. In recent years, numerous cloud service providers have emerged globally to offer cloud products to consumers. Cloud infrastructure is the physical computers that cloud service providers rely on in order to provide cloud products, and cloud infrastructure provides resources such as CPU, GPU, memory, hard disk, and network for cloud products. It can be said that cloud infrastructure is a particularly important aspect of cloud computing. When cloud service providers manage the supply chain of cloud infrastructure, they often have too much or too little inventory, which results in a waste of resources or a failure to meet user demand. Therefore, how to accurately predict the inventory demand of cloud infrastructure supply chain has become a problem that cloud service providers need to solve. In this paper, we propose a GA-LightGBM model for predicting the inventory demand of cloud infrastructure supply chain by comparing multiple models. In this paper, GA-LightGBM is experimentally verified and analyzed with the control model, and it is found that the average RMSE of GA-LightGBM is 358.8792, which is significantly higher than that of each model in the control group; in the process of multiple training and validation, the range of the RMSE and the standard deviation of the RMSE of GA-LightGBM are significantly smaller than that of each model in the control group. It can be seen that the GA-LightGBM model has higher prediction accuracy and stability than the models in the control group. It is recommended that cloud service providers adopt this model to optimize the management of cloud infrastructure supply chain inventory demand forecasting, so as to improve enterprise benefits. The GA-LightGBM model proposed in this paper will also complement research in related areas.

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Section: Related Workmentioning

confidence: 99%