Product life cycle based demand forecasting by using artificial bee colony algorithm optimized two-stage polynomial fitting

Liu, Yue; Ju, Wangwei; Zhao, Jing; Gao, Junjun; Jiazhou, Zheng; Jiang, Aiping

doi:10.3233/jifs-169014

Cited by 14 publications

(5 citation statements)

References 20 publications

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“…Demand forecasting is one of the most essential components of supply chain management, directly influencing a company's overall performance and competitiveness [10]. It is an important task for retailers as it is required for various operational decisions [11].…”

Section: Research Background and Literature Reviewmentioning

confidence: 99%

Improved Demand Forecasting of a Retail Store Using a Hybrid Machine Learning Model

Taparia,

Mishra,

Gupta

et al. 2023

JGEU

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Accurate demand forecasting is a competitive advantage for all supply chain components, including retailers. Approaches like naïve, moving average, weighted average, and exponential smoothing are commonly used to forecast demand. However, these simple approaches may lead to higher inventory and lost sales costs when the trend in demand is non-linear. Additionally, price strongly influences demand, and we can’t neglect the impact of price on demand. Similarly, the demand for a stock keeping unit (SKU) depends on the price of the competitor for the same SKU and the price of the competitive SKU. We thus propose a demand prediction model that considers historical demand data and the SKU price to forecast the demand. Our approach uses different machine-learning regressor algorithms and identifies the best machine-learning algorithm for the SKU with the lowest forecasting error. We further extend the forecasting model by training a hybrid model from the best two regression algorithms individually for each SKU. Forecasting error minimisation is the driving criterion for our literature. We evaluated the approach on 1000 SKUs, and the result showed that the Random Forest is the best-performing regressor algorithm with the lowest mean absolute percentage error (MAPE) of 8%. Furthermore, the hybrid model resulted in a lower inventory and lost sales cost with a MAPE of 7.74%. Overall, our proposed hybrid demand forecasting model can help retailers make informed decisions about inventory management, leading to improved operational efficiency and profitability.

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Section: Research Background and Literature Reviewmentioning

confidence: 99%

Improved Demand Forecasting of a Retail Store Using a Hybrid Machine Learning Model

Taparia,

Mishra,

Gupta

et al. 2023

JGEU

View full text Add to dashboard Cite

show abstract

“…For example, Hu [45] proposed a grey prediction model based on a genetic algorithm to further improve the prediction accuracy of the G(1,1) model. Meanwhile, Liu et al [46] used artificial bee colonies, which optimizes the fitting of polynomial parameters in the life-cycle demand forecast model to achieve high-precision forecasting effects. Adamowski [47] and Wang [48] have used neural network algorithms to build forecasting models.…”

Section: E-commerce Enterprise Demand Forecastingmentioning

confidence: 99%

Demand Forecasting of E-Commerce Enterprises Based on Horizontal Federated Learning from the Perspective of Sustainable Development

Cui

Yang

et al. 2021

Sustainability

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Public health emergencies have brought great challenges to the stability of the e-commerce supply chain. Demand forecasting is a key driver for the sound development of e-commerce enterprises. To prevent the potential privacy leakage of e-commerce enterprises in the process of demand forecasting using multi-party data, and to improve the accuracy of demand forecasting models, we propose an e-commerce enterprise demand forecasting method based on Horizontal Federated Learning and ConvLSTM, from the perspective of sustainable development. First, in view of the shortcomings of traditional RNN and LSTM demand forecasting models, which cannot handle multi-dimensional time-series problems, we propose a demand forecasting model based on ConvLSTM. Secondly, to address the problem that data cannot be directly shared and exchanged between e-commerce enterprises of the same type, the goal of demand information sharing modeling is realized indirectly through Horizontal Federated Learning. Experimental results on a large number of real data sets show that, compared with benchmark experiments, our proposed method can improve the accuracy of e-commerce enterprise demand forecasting models while avoiding privacy data leakage, and the bullwhip effect value is closer to 1. Therefore, we effectively alleviate the bullwhip effect of the entire supply chain system in demand forecasting, and promote the sustainable development of e-commerce companies.

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“…The prediction results are analyzed and evaluated by error index. Common indicators for evaluating demand forecasting methods include mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE) and correlation coefficient square R 2 [16]. In this paper, RMSE, MAPE and R 2 are selected as the criteria to test the accuracy of the VMD-SVM bullwhip effect weakening model.…”

Section: Bullwhip Effect Weakening Model Based On Vmd-svm Algorithmmentioning

confidence: 99%

“…By comparing ARIMA model, it is proved that the model can effectively improve the prediction accuracy. Liu Y et al [16] used the artificial bee colony algorithm (ABC) to optimize the fitting of polynomial parameters in the demand forecasting model of product life cycle (PLC), and realized the goal of precise ordering and reducing safety stock. Sadeghi et al [17] used the genetic algorithm (GA) embedded with boundary operator in the meta heuristic algorithm of particle swarm optimization (PSO) as a local searcher to explore the approximate optimal solution of the constrained inventory problem.…”

Section: Introductionmentioning

confidence: 99%

A Bullwhip Effect Weakening Approach Based on VMD-SVM Algorithm under the Background of Intelligent Manufacturing

et al. 2021

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In view of the current situation that the maturity of enterprise intelligent manufacturing capability is generally low and the information asymmetry in the upstream and downstream of the supply chain is high, taking any supply and demand link in the supply chain as an example, a group of initial demand signals that change nonlinearly over time are divided into intrinsic mode functions and noise residuals with different data characteristics by means of the variational modal decomposition (VMD) algorithm. On the basis of signal denoising and reconstruction, the support vector machine (SVM) algorithm is used to make regression prediction of the reconstructed signal with each intrinsic mode function as sample attribute. Compared with the regression prediction results of the original demand signal, it is verified that the VMD-SVM bullwhip effect weakening model can effectively filter the demand noise generated by each link in the supply chain and improve the accuracy of demand information transmission. It has a certain reference value to the weakening of the bullwhip effect and the improvement of supply chain synergy efficiency.

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Product life cycle based demand forecasting by using artificial bee colony algorithm optimized two-stage polynomial fitting

Cited by 14 publications

References 20 publications

Improved Demand Forecasting of a Retail Store Using a Hybrid Machine Learning Model

Improved Demand Forecasting of a Retail Store Using a Hybrid Machine Learning Model

Demand Forecasting of E-Commerce Enterprises Based on Horizontal Federated Learning from the Perspective of Sustainable Development

A Bullwhip Effect Weakening Approach Based on VMD-SVM Algorithm under the Background of Intelligent Manufacturing

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