A Hybrid Forecasting Framework with Neural Network and Time-Series Method for Intermittent Demand in Semiconductor Supply Chain

Fu, Wenhan; Chien, Chen–Fu; Lin, Zih-Hao

doi:10.1007/978-3-319-99707-0_9

Cited by 18 publications

(11 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the results showed that the multivariate models reduced inventory costs, with the MLP model outperforming the others regarding overstock and average fill rate. (Fu, Chien & Lin, 2018). Aburto and Weber (2007) also proposed a hybrid model for forecasting demand for supermarket products in Chile using the residuals of an ARIMAX model and seasonality as the input to an MLP network (SARIMAX-MLP).…”

Section: Literature Reviewmentioning

confidence: 99%

Demand forecasting using a hybrid model based on artificial neural networks: A study case on electrical products

Quiñones

Ovalle

Alfonso-Morales

2023

JIEM

View full text Add to dashboard Cite

Purpose: This work aims to evaluate demand forecasting models to determine if using exogenous factors and machine learning techniques helps improve performance compared to univariate statistical models, allowing manufacturing companies to manage demand better.Design/methodology/approach: We implemented a multivariate Auto-Regressive Moving Average with eXogenous input (ARMAX) statistical model and a Neural Network-ARMAX (NN-ARMAX) hybrid model for forecasting. Later, we compared both to a standard univariate statistical model to forecast the demand for electrical products in a Colombian manufacturing company.Findings: The outcomes demonstrated that the NN-ARMAX model outperformed the other two. Indeed, demand management improved with the reduction of overstock and out-of-stock products.Research limitations/implications: The findings and conclusions in this work are limited to Colombian manufacturing companies that sell electrical products to the construction industry. Moreover, the experts from the company that provided us with the data also selected the external factors based on their own experiences, i.e., we might have disregarded potential factors.Practical implications: This work suggests that a model using neural networks and including exogenous variables can improve demand forecasting accuracy, promoting this approach in manufacturing companies dealing with demand planning issues.Originality/value: The findings in this work demonstrate the convenience of using the proposed hybrid model to improve demand forecasting accuracy and thus provide a reliable basis for its implementation in supply chain planning for the electrical/construction sector in Colombian manufacturing companies.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Demand forecasting using a hybrid model based on artificial neural networks: A study case on electrical products

Quiñones

Ovalle

Alfonso-Morales

2023

JIEM

View full text Add to dashboard Cite

show abstract

“…At this point, the first part was for aggregation while the other parts were for decomposition. Fu et al [25] used a hybrid of a recurrent neural network and Syntetos-Boylan approximation for semiconductor product demand forecasting. The proposed method can handle the intermittent demand occurrence and the deficient downstream information in the supply chain.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In the literature, SO methods (e.g., [32][33][34][35]) and AI based methods (e.g., [24,25]) are widely used to solve the supply chain problems. However, there is still lack of studies that show the integration of the SO method and AI based methods to solve the IRP.…”

Section: Lead Time Based Analysismentioning

confidence: 99%

A Novel Hybrid Artificial Intelligence Based Methodology for the Inventory Routing Problem

et al. 2019

View full text Add to dashboard Cite

In this paper, a new hybrid method including simulation optimization and artificial intelligence based simulation is created to solve the inventory routing problem (IRP) in which three different routing strategies are evaluated for uneven demand patterns including intermittent, erratic, and lumpy demand. The proposed method includes two phases. In the first phase, a nondominated sorting genetic algorithm II based simulation is employed to perform a multi-objective search for the IRP where the objectives of the method are total supply chain cost minimization and average service level maximization. In the second phase, artificial neural network based simulation is used to adjust the reorder point and order-up-to-level by forecasting the customer demand at each replenishment time. The results of the study demonstrated that the average service level is at least 98.54% in the supply chain. From this, it can be concluded that the proposed method can provide a tremendous opportunity to improve the average service level under uncertain environments. In addition, it is determined that different routing strategies can be selected for different demand patterns according to the considered performance measures.

show abstract

“…Ying and Hanbin [26] studied demand forecasting based on ANN and developed a threelayer ANN model for forecasting market demand [27]. Several examples of ANNs-based applications: financial failure [28], wind speed [29], foreign exchange rates [30], intraday electricity demand [31], and ATM cash demand [32], orders treatment center [33], semiconductor supply chain [34], heat demand forecasting at city level [35].…”

Section: Introductionmentioning

confidence: 99%

Order Demand Forecast Using a Combined Approach of Stepwise Linear Regression Coefficients and Artificial Neural Network

Gündoğdu

2022

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

View full text Add to dashboard Cite

Abstract Nowadays, businesses' forecasts to meet the demands have become more critical. This study aimed to predict the fifteen-day order demand for an order fulfillment center using a Multilayer Perceptron Neural Network (MLPNN). The dataset used in the study was created from a real database of a large Brazilian logistics company and thirteen variables. Linear Regression Coefficients (LRC) were used as a feature selection method to reduce estimation errors. The study showed that among the variables, order type_A (A5), order type_B (A6), and order type_C (A7) had the most significant impact on total order forecasting. The effect of A6 was found to be greater than the effect of A7 and A5. The performance of the proposed model was evaluated using the mean absolute percent error (MAPE). LRC-MLPNN provided a MAPE of 2.97%. The results showed that better forecasting performance was obtained by selecting the independent variables to be used as input to the forecasting model with LRC. The proposed model can also be applied to different estimation problems.

show abstract

A Hybrid Forecasting Framework with Neural Network and Time-Series Method for Intermittent Demand in Semiconductor Supply Chain

Cited by 18 publications

References 16 publications

Demand forecasting using a hybrid model based on artificial neural networks: A study case on electrical products

Demand forecasting using a hybrid model based on artificial neural networks: A study case on electrical products

A Novel Hybrid Artificial Intelligence Based Methodology for the Inventory Routing Problem

Order Demand Forecast Using a Combined Approach of Stepwise Linear Regression Coefficients and Artificial Neural Network

Contact Info

Product

Resources

About