CNN model optimization and intelligent balance model for material demand forecast

Tang, Zheng; Ge, Yuemeng

doi:10.1007/s13198-021-01157-0

Cited by 3 publications

(5 citation statements)

References 23 publications

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“…DL algorithms by having high levels of abstraction are expected to improve the accuracy of the prediction process (Mocanu et al 2016). The forecasting problem in the collected material refers to source forecasting (Charmchi et al 2021), demand forecasting (Nikolopoulos et al 2021;Chien et al 2020;Koç and Türkoğlu 2021;Bousqaoui et al 2021;Mocanu et al 2016;Kilimci et al 2019;Punia et al 2020;Tang and Ge 2021), sales forecasting (Weng et al 2019a;Liu et al 2020;Piccialli et al 2021), price forecasting (Weng et al 2019a, b;Guo 2020), performance forecasting (Shankar et al 2020), or a combination of these problems named as hybrid forecasting (Khan et al 2020;Wu et al 2021).…”

Section: Forecastingmentioning

confidence: 99%

“…In production and manufacturing, row materials' demand plan usually deviates from the actual requirement and the plans should be revised several times (Pechmann and Zarte 2017). Having an accurate material demand forecast can reduce purchasing and production costs of supply chains (Tang and Ge 2021). The authors Tang and Ge (2021) proposed a forecasting model that uses sales demand and previous material demand time series data as input and determines material demand value as output for consumer products.…”

Section: Fig 7 Share Of Each Category Of D1mentioning

confidence: 99%

“…Having an accurate material demand forecast can reduce purchasing and production costs of supply chains (Tang and Ge 2021). The authors Tang and Ge (2021) proposed a forecasting model that uses sales demand and previous material demand time series data as input and determines material demand value as output for consumer products. Chien et al (2020) developed a novel framework based on deep reinforcement learning (DRL) to select the optimal demand forecasting model among a series of various forecasting models for each item distributed by an electronics distributor considering the demand pattern of the items.…”

Section: Fig 7 Share Of Each Category Of D1mentioning

confidence: 99%

“…Then employed the Vector autoregressive (VAR) model to determine the appropriate lay order of the outputs of CNN and historical data, and finally used several prediction methods including MLR, BPNN, SVM, RNN, and LSTM to forecast price, production, consumption, and inventory of oil. Tang and Ge (2021) combined CNN and LSTM to design a material forecast model analyzing three independent variables including sales demand forecast, transit warehouse inventory, and material features. Piccialli et al (2021) proposed a hybrid framework consisting of LSTM-Autoencoder, CNN, and several machine learning algorithms to forecast the respiratory diseases bookings.…”

Section: Deep Reinforcement Learning (Drl) and Its Applications In Th...mentioning

confidence: 99%

“…it is evident that using RNN in the forecasting problem has the maximum number of papers (13 papers), followed by using CNN for the quality management (7 papers), and CNN for the forecasting (5 papers), respectively. In terms of studied industries, we can see that in the consumer products industry, forecasting (demand forecasting in Tang and Ge (2021), Nikolopoulos et al (2021), Bousqaoui et al (2021), andPunia et al (2020), and sales forecasting in Weng et al (2019a), Liu et al (2020) is the most frequent problem solved with RNN and its variants. The quality management in the food supply chain using CNN is the second most frequent combination, followed by forecasting in the consumer products supply chain applying CNN.…”

Section: Applications Of Hybrid Architectures In the Scmmentioning

confidence: 99%

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Applications of deep learning into supply chain management: a systematic literature review and a framework for future research

Shavaki

Ghahnavieh

2022

Artif Intell Rev

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In today’s complex and ever-changing world, Supply Chain Management (SCM) is increasingly becoming a cornerstone to any company to reckon with in this global era for all industries. The rapidly growing interest in the application of Deep Learning (a class of machine learning algorithms) in SCM, has urged the need for an up-to-date systematic review on the research development. The main purpose of this study is to provide a comprehensive vision by reviewing a set of 43 papers about applications of Deep Learning (DL) methods to the SCM, as well as the trends, perspectives, and potential research gaps. This review uses content analysis to answer three research questions namely: 1- What SCM problems have been solved by the use of DL techniques? 2- What DL algorithms have been used to solve these problems? 3- What alternative algorithms have been used to tackle the same problems? And do DL outperform these methods and through which evaluation metrics? This review also responds to this call by developing a conceptual framework in a value-adding perspective that provides a full picture of areas on where and how DL can be applied within the SCM context. This makes it easier to identify potential applications to corporations, in addition to potential future research areas to science. It might also provide businesses a competitive advantage over their competitors by allowing them to add value to their data by analyzing it quickly and precisely.

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Section: Forecastingmentioning

confidence: 99%

Section: Fig 7 Share Of Each Category Of D1mentioning

confidence: 99%

Section: Fig 7 Share Of Each Category Of D1mentioning

confidence: 99%

Section: Deep Reinforcement Learning (Drl) and Its Applications In Th...mentioning

confidence: 99%

Section: Applications Of Hybrid Architectures In the Scmmentioning

confidence: 99%

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Applications of deep learning into supply chain management: a systematic literature review and a framework for future research

Shavaki

Ghahnavieh

2022

Artif Intell Rev

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Impact of industrial robots on environmental pollution: evidence from China

Liu

2023

Sci Rep

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The application of industrial robots is considered a significant factor affecting environmental pollution. Selecting industrial wastewater discharge, industrial SO2 emissions and industrial soot emissions as the evaluation indicators of environmental pollution, this paper uses the panel data model and mediation effect model to empirically examine the impact of industrial robots on environmental pollution and its mechanisms. The conclusions are as follows: (1) Industrial robots can significantly reduce environmental pollution. (2) Industrial robots can reduce environmental pollution by improving the level of green technology innovation and optimizing the structure of employment skills. (3) With the increase in emissions of industrial wastewater, industrial SO2, and industrial dust, the impacts generated by industrial robots are exhibiting trends of a “W” shape, gradual intensification, and progressive weakening. (4) Regarding regional heterogeneity, industrial robots in the eastern region have the greatest negative impact on environmental pollution, followed by the central region, and the western region has the least negative impact on environmental pollution. Regarding time heterogeneity, the emission reduction effect of industrial robots after 2013 is greater than that before 2013. Based on the above conclusions, this paper suggests that the Chinese government and enterprises should increase investment in the robot industry. Using industrial robots to drive innovation in green technology and optimize employment skill structures, reducing environmental pollution.

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Optimizing Supply Chain Management Through BO-CNN-LSTM for Demand Forecasting and Inventory Management

Liu,

Vakharia

2024

Journal of Organizational and End User Computing

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This project addresses demand forecasting and inventory optimization in supply chain management. Traditional methods have limitations with complex demand patterns and large-scale data. Deep learning techniques are employed to enhance accuracy and efficiency. The project utilizes BO-CNN-LSTM, leveraging Bayesian optimization for hyperparameter tuning, Convolutional Neural Networks (CNNs) for spatiotemporal feature extraction, and Long Short-Term Memory Networks (LSTMs) for modeling sequential data. Experimental results validate the effectiveness of the approach, outperforming traditional methods. Practical implementation in supply chain management improves operational efficiency and cost control.

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CNN model optimization and intelligent balance model for material demand forecast

Cited by 3 publications

References 23 publications

Applications of deep learning into supply chain management: a systematic literature review and a framework for future research

Applications of deep learning into supply chain management: a systematic literature review and a framework for future research

Impact of industrial robots on environmental pollution: evidence from China

Optimizing Supply Chain Management Through BO-CNN-LSTM for Demand Forecasting and Inventory Management

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