An innovative demand forecasting approach for the server industry

Tsao, Yu‐Chung; Chiu, Shih-Hao; Lu, Jye‐Chyi; Vu, Thuy-Linh

doi:10.1016/j.technovation.2021.102371

Cited by 12 publications

(11 citation statements)

References 25 publications

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“…2) Group 2: Made up of three articles from the retail sector that propose the use of simple machine learning methods in conjunction with clustering methods as a way to extract useful patterns for forecasting. First, it processes the time series with RF, and then models the errors produced by it with a multiple linear regression (MLR) using Internet search intensity indices as independent variables [17]. The second document [18] uses k-means to separate the data into different clusters, to then identify which "Suport Vector Regression" (SVR) or "Extreme Learning Machine" (ELM) method is the best predictor for each cluster.…”

Section: F Automatic Grouping Of Articlesmentioning

confidence: 99%

“…Nine documents also raise the difficulties caused by the fact that the factors that affect demand are very diverse and numerous, such as calendar factors, climatic factors, economic factors, market factors, etc. Which makes the construction of adequate models extremely challenging [12], [13], [17], [29], [30], [26], [34], [37], [41].…”

Section: Numerous Casual Factorsmentioning

confidence: 99%

“…Simple Machine Learning Methods Within this group are the traditional machine learning methods multiple linear regression (MLR) and support vector machine (SVM). Twelve documents propose the use of these methods, however they are proposed in combination with other more complex methods [12], [15], [18]- [21], [29], [30], [35], [40] or with classical statistical methods [17].…”

Section: Keyword Inputmentioning

confidence: 99%

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A Systematic Review of the Literature on the Use of Artificial Intelligence in Forecasting the Demand for Products and Services in Various Sectors

Villar,

Lengua

2024

IJACSA

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This systematic review, carried out under the PRISMA methodology, aims to identify the recently proposed artificial intelligence models for demand forecasting, distinguishing the problems they try to overcome, recognizing the artificial intelligence methods used, detailing the performance metrics used, recognizing the performance achieved by these models and identifying what is new in them. Studies in the manufacturing, retail trade, tourism and electric energy sectors were considered in order to facilitate the transfer of knowledge from different sectors. 33 articles were analyzed, with the main results being that the proposed models are generally ensembles of various artificial intelligence methods; that the complexity of data and its scarcity are the main problems addressed; that combinations of simple machine learning, "bagging", "boosting" and deep neural networks, are the most used methods; that the performance of the proposed models surpasses the classic statistical methods and other reference models; and that, finally, the proposed novelties cover aspects such as the type of data used, the pattern extraction techniques used, the assembly forms of the applied models and the use of algorithms for automating the adjustment of the models. Finally, a forecast model is proposed that includes the most innovative aspects found in this research.

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Section: F Automatic Grouping Of Articlesmentioning

confidence: 99%

Section: Numerous Casual Factorsmentioning

confidence: 99%

Section: Keyword Inputmentioning

confidence: 99%

See 1 more Smart Citation

A Systematic Review of the Literature on the Use of Artificial Intelligence in Forecasting the Demand for Products and Services in Various Sectors

Villar,

Lengua

2024

IJACSA

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“…The models that can be used for this purpose are linear regression, generalized additive models for the location, the scale and the shape, and quantile regression. Beside the mentioned models, there are also other demand forecasting models that can be used, such as optimization methods (Petrovic, Xie, Burnham & Petrovic, 2008;Mimovic, 2012), machine learning (Tsao, Chen, Chiu, Lu & Vu, 2021), and so forth. The following part is a presentation of the papers describing the models proposed for demand forecasting.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing production and sale based on mathematical statistics and the genetic algorithm

Nestić¹,

Aleksić²,

Gil³

et al. 2022

Ekon horizonti

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Enhancing production and sale has a very significant effect on the competitive advantage of any production enterprise. In practice, especially in companies with highly diversified production, products have a different impact on generating revenue. Therefore, operational management pay attention to the products of the utmost importance. The Pareto analysis is the most broadly used product classification method. It can be said that the results obtained by this analysis are still very burdened by decisionmakers' subjective attitudes. This paper proposes a model for selecting products with the biggest impact on generating revenue in an exact way. In the model's first stage, whether there is a linear relationship between volume demand and a discounted amount is analyzed applying mathematical statistics methods. In the second stage, the Genetic Algorithm (GA) method is proposed so as to obtain a near-optimal set of the most important products. The proposed model is shown to be a useful and effective assessment tool for sales and operational management in a production enterprise.

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“…In supply chains, social media is an essential instrument in the context of Supply Chain 4.0 (Makris et al 2019 ), which is utilized in different functional areas, such as procurement and supply management (e.g., Wankmüller and Reiner 2021 ; Wu et al 2021 ; Agnihotri et al 2021 ), manufacturing (plan/design and resource management) (e.g., Sharifi and Shokouhyar 2021 ; Giannakis et al 2020 ), demand and inventory management (e.g., Beheshti-Kashi and Thoben 2016 ; Iftikhar and Khan 2020 ; Tsao et al 2021 ), logistics management (e.g., Suma et al 2017 ; Orji et al 2019 ), supply chain relationship management—including supplier, producer, customer (e.g., Cho et al 2020 ; Bashir et al 2021 ; Jalal et al 2021 ). In general, the research topic of social media applications in the supply chain is emerging and will become dominant in the future.…”

Section: Introductionmentioning

confidence: 99%

Application of social media in supply chain 4.0 practices: a bibliometric analysis and research trends

et al. 2023

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In recent years, although scholars and practitioners in the supply chain (SC) field have become increasingly aware of the importance of social media for multipurpose applications, the subject remains unclarified about its functions and outcomes, especially in the 4.0 context. This review paper aims to visualize the big picture and structure of knowledge related to the application of social media in managing supply chains by adopting a bibliometric analysis on a sample of 354 research articles retrieved from the Web of Science during the period between 2008 and 2022 with two widely used bibliometric techniques: co-citation and co-word analysis. We discovered five major clusters of how social media is used in the supply chain context, including (1) in enhancing supply chain sustainability and business transformation, (2) social media analytics as an effective technique to extract business value, (3) in facilitating communication and coordination in the supply chain network, (4) in promoting social interaction and customer relationship management, and (5) in the development of supply chain relationship management. These will enable practitioners to adopt early changes in their SC, allowing them to become competitive and gain market leadership. Supply chain managers can connect with other SC stakeholders and share information through social media to improve operations, establish relationships, reduce risks, and altogether reach for sustainability. Potential research ideas are also offered for future investigation.

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An innovative demand forecasting approach for the server industry

Cited by 12 publications

References 25 publications

A Systematic Review of the Literature on the Use of Artificial Intelligence in Forecasting the Demand for Products and Services in Various Sectors

A Systematic Review of the Literature on the Use of Artificial Intelligence in Forecasting the Demand for Products and Services in Various Sectors

Enhancing production and sale based on mathematical statistics and the genetic algorithm

Application of social media in supply chain 4.0 practices: a bibliometric analysis and research trends

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