Exploring an Interactive Value-Adding Data-Driven Model of Consumer Electronics Supply Chain Based on Least Squares Support Vector Machine

Wan, Xiao-le; Zhang, Zhen; Rong, Ximin; Meng, Qingchun

doi:10.1155/2016/3717650

Cited by 10 publications

(6 citation statements)

References 30 publications

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“…Nourikhah and Akbari (2016) used Bayesian data analysis with a generalized linear model (GLM) to estimate the overall satisfaction of the users in the form of the posterior distribution of opinions [48]. Wan et al (2016) introduced least squares support vector machine (LS-SVM) innovatively into the study on consumer electronics supply chains [23]. These studies took consumers as a whole, then from the perspective of the supply chain or enterprises, analyzing consumers' interaction impact on supply chain or their features.…”

Section: Research Methods Of Consumer Online Communitymentioning

confidence: 99%

“…Nevertheless, with the rapid network development, the "Internet +" technologies that combine information technologies arouse the significant impacts of consumers on the market [22,23]. More and more consumers are active 2 Complexity in expressing their experiences of some bought products or giving purchase suggestions to others [24,25] in the consumer community formed by different media, including cell phone, PC, or PAD [26].…”

Section: Introductionmentioning

confidence: 99%

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Properties Exploring and Information Mining in Consumer Community Network: A Case of Huawei Pollen Club

et al. 2018

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Substantial changes took place in the role of consumers in the supply chain with the development of practices. They became creators from consumers of product values. More and more consumers express their consumption experiences by posting in network community. Consumer community network is an important place for feedback of product experiences and facilitating product innovation in future. Manufacturers can promote improvement and innovation of products by exploring effective information on the consumer community network, thus improving the experience level of consumers. Therefore, how to explore information in topics (posts) and their relationships becomes very important. Is it possible to describe the structure of consumer community network by complex network and explore information about products and consumers? There is important and positive significance to study the collaborative innovation in the supply chain in which consumers participate. In this paper, the consumer community network was constructed by Boolean retrieve programming and discussed in the methodology and empirical way based on the community data of Huawei P10/P10 Plus. In methodology, interaction difference and uniformity within consumer community were explored by the density of isolated nodes and generalized variance of degree of network. In empirical studies, community network users were divided into ordinary user group, intermediary user group, and enterprise user group according to empirical data, and corresponding interaction networks were constructed. A contrastive analysis on the interaction of these three groups was carried out by combining the existing properties and innovative properties. Topics in each network were put in the order according to significance. Research conclusions have important significance to enrich the network analysis methods, explore the effective information in consumer community network, facilitate product improvement and innovations, and improve the experience level of consumers.

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Section: Research Methods Of Consumer Online Communitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Properties Exploring and Information Mining in Consumer Community Network: A Case of Huawei Pollen Club

et al. 2018

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“…Hsu developed an integrated conceptual model and explored the effects of brand-evangelism-related behavioural decisions of enterprises on VC members [20]. Wan et al used a least squares support vector machine to study consumer electronics supply chains [21]. However, all of the above studies were mainly analyses of the influencing factors but did not consider the differences among VCs.…”

Section: Literature Reviewmentioning

confidence: 99%

The Spread of Information in Virtual Communities

Zhang

Meng

et al. 2020

Complexity

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With the growth of online commerce, companies have created virtual communities (VCs) where users can create posts and reply to posts about the company’s products. VCs can be represented as networks, with users as nodes and relationships between users as edges. Information propagates through edges. In VC studies, it is important to know how the number of topics concerning the product grows over time and what network features make a user more influential than others in the information-spreading process. The existing literature has not provided a quantitative method with which to determine key points during the topic emergence process. Also, few researchers have considered the link between multilayer physical features and the nodes’ spreading influence. In this paper, we present two new ideas to enrich network theory as applied to VCs: a novel application of an adjusted coefficient of determination to topic growth and an adjustment to the Jaccard coefficient to measure the connection between two users. A two-layer network model was first used to study the spread of topics through a VC. A random forest method was then applied to rank various factors that might determine an individual user’s importance in topic spreading through a VC. Our research provides insightful ways for enterprises to mine information from VCs.

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“…Yet, [2] argues that there have been few large scale comparison studies of machine learning models for regression or time series aimed at forecasting problems. For the electronics manufacturing industry, [30] introduced SVM regression to the supply chain of various producers. They concluded that it yields good results compared to other forecasting methods.…”

Section: Literature Reviewmentioning

confidence: 99%

Demand Forecasting Techniques for Build-to-Order Lean Manufacturing Supply Chains

Rivera-Castro

Nazarov

Xiang

et al. 2019

Advances in Neural Networks – ISNN 2019

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Build-to-order (BTO) supply chains have become commonplace in industries such as electronics, automotive and fashion. They enable building products based on individual requirements with a short lead time and minimum inventory and production costs. Due to their nature, they differ significantly from traditional supply chains. However, there have not been studies dedicated to demand forecasting methods for this type of setting. This work makes two contributions. First, it presents a new and unique data set from a manufacturer in the BTO sector. Second, it proposes a novel data transformation technique for demand forecasting of BTO products. Results from thirteen forecasting methods show that the approach compares well to the state-of-the-art while being easy to implement and to explain to decision-makers.

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Exploring an Interactive Value-Adding Data-Driven Model of Consumer Electronics Supply Chain Based on Least Squares Support Vector Machine

Cited by 10 publications

References 30 publications

Properties Exploring and Information Mining in Consumer Community Network: A Case of Huawei Pollen Club

Properties Exploring and Information Mining in Consumer Community Network: A Case of Huawei Pollen Club

The Spread of Information in Virtual Communities

Demand Forecasting Techniques for Build-to-Order Lean Manufacturing Supply Chains

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