Prediction of consumer purchase behaviour using Bayesian network: an operational improvement and new results based on RFID data

Zuo, Yi

doi:10.1504/ijkesdp.2016.075976

Cited by 8 publications

(4 citation statements)

References 26 publications

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“…In addition, consumer behaviour prediction plays an important role in manufacturing business stage, e.g., to improve the consumers' purchase decision-makeing predictions. In [70], a Bayesian network based approach was proposed to predict the customer purchase behaviour. In particular, the analysis is based on massive RFID data, which was collected through RFID tags (4) Prescriptive analytics Prescriptive analytics extends the results of descriptive, diagnostic and predictive analytics to make right decisions in order to achieve predicted outcomes (i.e., what should we do to achieve the goal?).…”

Section: A Data Acquisitionmentioning

confidence: 99%

Section: A Data Acquisitionmentioning

confidence: 99%

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Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies

Dai

Wang

et al. 2019

Enterprise Information Systems

246

137

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Section: A Data Acquisitionmentioning

confidence: 99%

Section: A Data Acquisitionmentioning

confidence: 99%

Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies

Dai

Wang

et al. 2019

Enterprise Information Systems

246

137

View full text Add to dashboard Cite

show abstract

“…1b, industrial practitioners and researchers have begun their implementation. For example, the work [44] presents a Bayesian networkbased approach to analyze the consumers' purchase behaviour via analyzing RFID data, which is collected from RFID-tags attached to in-store shopping carts. Moreover, a deep learning method is adopted to identify possible machine faults through analyzing mechanic data collected from the real industrial environments such as induction motors, gearboxes, and bearings [45].…”

Section: Ai-driven Customized Manufacturingmentioning

confidence: 99%

Artificial-Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and Challenges

et al. 2021

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The traditional production paradigm of large batch production does not offer flexibility towards satisfying the requirements of individual customers. A new generation of smart factories is expected to support new multi-variety and small-batch customized production modes. For that, Artificial Intelligence (AI) is enabling higher value-added manufacturing by accelerating the integration of manufacturing and information communication technologies, including computing, communication, and control. The characteristics of a customized smart factory are to include self-perception, operations optimization, dynamic reconfiguration, and intelligent decision-making. The AI technologies will allow manufacturing systems to perceive the environment, adapt to the external needs, and extract the process knowledge, including business models, such as intelligent production, networked collaboration, and extended service models.This paper focuses on the implementation of AI in customized manufacturing (CM). The architecture of an AI-driven customized smart factory is presented. Details of intelligent manufacturing devices, intelligent information interaction, and construction of a flexible manufacturing line are showcased. The state-of-the-art AI technologies of potential use in CM, i.e., machine learning, multi-agent systems, Internet of Things, big data, and cloud-edge computing are surveyed. The AI-enabled technologies in a customized smart factory are validated with a case study of customized packaging. The experimental results have demonstrated that the AI-assisted CM offers the possibility of higher production flexibility and efficiency. Challenges and solutions related to AI in CM are also discussed.

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“…Consumer behaviour prediction plays an important role in many business application. In [218], a Bayesian network based approach was proposed to predict the customer purchase behaviour; the analysis is based on massive RFID data collected through RFID tags attached at customers. In [68], a novel method based on deep convolutional neural networks (CNN) was proposed to identify electricity theft (i.e., a malicious consumer behaviour) in smart grids.…”

Section: ) Internet Of Thingsmentioning

confidence: 99%

Big Data Analytics for Large-scale Wireless Networks

et al. 2019

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The wide proliferation of various wireless communication systems and wireless devices has led to the arrival of big data era in large scale wireless networks. Big data of large scale wireless networks has the key features of wide variety, high volume, real-time velocity and huge value leading to the unique research challenges that are different from existing computing systems. In this paper, we present a survey of the state-of-art big data analytics (BDA) approaches for large scale wireless networks. In particular, we categorize the life cycle of BDA into four consecutive stages: Data Acquisition, Data Preprocessing, Data Storage and Data Analytics. We then present a detailed survey of the technical solutions to the challenges in BDA for large scale wireless networks according to each stage in the life cycle of BDA. Moreover, we discuss the open research issues and outline the future directions in this promising area.

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Prediction of consumer purchase behaviour using Bayesian network: an operational improvement and new results based on RFID data

Cited by 8 publications

References 26 publications

Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies

Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies

Artificial-Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and Challenges

Big Data Analytics for Large-scale Wireless Networks

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