Cognitive computing for big data systems over internet of things for enterprise information systems

Sangaiah, Arun Kumar; Chaudhary, Ankit; Tsai, Chun-Wei; Wang, Jin; Mercaldo, Francesco

doi:10.1080/17517575.2020.1814422

Cited by 11 publications

(13 citation statements)

References 13 publications

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“…Cognitive computing is also highly relevant for enterprise information systems, in particular in big data analytics contexts (Khanra et al, 2020;Sangaiah et al, 2020;Dai et al, 2020).…”

Section: Logic-based Methodsmentioning

confidence: 99%

AI-enabled Enterprise Information Systems for Manufacturing

Zdravković

Panetto

Weichhart

2021

Enterprise Information Systems

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Research work presented in this paper considers the contemporary Enterprise Information Systems (EIS) functional architecture and carries out the literature review of AI applications integrated in Customer Relationship Management, Supply Chain Management, Inventory and logistics, Production Planning and Scheduling, Finance and accounting, Product Lifecycle Management and Human Resources, with special attention to the manufacturing enterprises. As a review outcome, the enhanced capabilities are identified in each of those processes and proposed as AI services. AI-enablement is related to the capabilities of the enterprise to implement improved decision making or automation by using (a) Machine Learning (ML) models or (b) logicbased systems. It is a process of the enterprise transformation leading to the convergence of the 4 major disruptive technologies, namely Industrial Internet of Things (or Cyber-Physical Systems), Agent-based Distributed Systems, Cloud Computing and Artificial Intelligence (non-symbolic AI like Machine Learning, Neural Networks; symbolic AI like Reasoning and Logic/Model-based AI).

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“…Cognitive computing is also highly relevant for enterprise information systems, in particular in big data analytics contexts (Khanra et al, 2020;Sangaiah et al, 2020;Dai et al, 2020).…”

Section: Logic-based Methodsmentioning

confidence: 99%

AI-enabled Enterprise Information Systems for Manufacturing

Zdravković

Panetto

Weichhart

2021

Enterprise Information Systems

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“…This effort encourages small-scale, supply networks of regional socioeconomic actors to use community sources based on local area needs, resulting in ecological advantages. This leads to far more renewable ways of greener manufacturing (Sangaiah et al, 2020). Nevertheless, there are variations about the degree of amenities considered to include dispersed manufacturing (Groggert, 2017).…”

Section: Production Processmentioning

confidence: 99%

“…Wiech (2022) determined methods which separate world class companies from conventional production groups -complete quality, provider connection, worker participation, lean activities, prosperous upkeep, as well as technologies for organizations were created. Sangaiah et al (2020) employee involvement, producing vision and strategy, innovation.…”

Section: Top Class Manufacturingmentioning

confidence: 99%

“…What can make a production business world class will be the measurement and training, which led to great performance (Sangaiah et al, 2020). Wang et al (2021a) have reported that good management determination, client connection, provider relationship, job power managing, job perceptions, item design and style procedure, statistical management as well as responses, along with process flow management are several methods which describe the regular functionality of producing businesses.…”

Section: Top Class Manufacturingmentioning

confidence: 99%

“…The use of BDA may also lead to information bottlenecks, which affect efficiency. Sangaiah et al (2020) have argued that provide chain experts are inundated with information, encouraging fresh means of considering the way information is generated, structured, and examined. The amount, velocity, and variety of information, therefore, provide impetus towards the businesses to follow, as well as ideal details analytical features (e.g., information science, big data and predictive analytics) to enhance today's source chain procedures and their performance.…”

Section: Big Data In Manufacturingmentioning

confidence: 99%

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Impact of big data analytics on distributed manufacturing: Does big data help?

Sazu¹,

Jahan²

2022

J Process Man & New Technol

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Big data (BD) analytics has brought progressive improvement in the business environment. It provides businesses with optimized production, personalization and improvement in the way production is dispersed. Nevertheless, conflicts arise in the use of these methods in certain industries, like retail items, which usually basis on large-scale production and prolonged supply chain. The study develops a theoretical structure to investigate if big data coupled with different production solutions can provide for a dispersed production system. Through investigation of twenty-one buyer products business instances applying secondary and main data, the study investigated changing production processes, the inherent catalyst, the function of analytics, and its effect on distributed production. The study discovers several uses of distributed manufacturing principles to evaluate the current production processes worked for larger customer product solutions by using analytics and industry analysis. The evaluation's suggested structure mentioned in this research has a deeper impact on planning, comprehension relationships, among factors of data analytics and distributed production.

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Research on mixed decision implications based on formal concept analysis

Ren

Zhai

2023

International Journal of Cognitive Computing in Engineering

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Cognitive computing for big data systems over internet of things for enterprise information systems

Cited by 11 publications

References 13 publications

AI-enabled Enterprise Information Systems for Manufacturing

AI-enabled Enterprise Information Systems for Manufacturing

Impact of big data analytics on distributed manufacturing: Does big data help?

Research on mixed decision implications based on formal concept analysis

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