Intelligent machine agent architecture for adaptive control optimization of manufacturing processes

Kruger, Grant H.; Shih, Albert J.; Hattingh, D.G.; Niekerk, T.I. van

doi:10.1016/j.aei.2011.08.003

Cited by 23 publications

(5 citation statements)

References 46 publications

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“…Some authors even argue that AI can "enable organizations to divide and allocate tasks as well as to integrate efforts in novel ways" [27, p. 67]. AI enables the integration of business systems with business or manufacturing operations that reduces process variation and improves global optimization [28].…”

Section: Ai Use As a Dynamic Information Processing Capabilitymentioning

confidence: 99%

Artificial Intelligence, Firm Resilience to Supply Chain Disruptions, and Firm Performance

Sullivan¹,

Wamba²

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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In today's uncertain and disruptive environment, every firm in the supply chain is susceptible to disruptions that may require high levels of firm resilience. We argue that recent advances in artificial intelligence (AI) may help. This paper expands our understanding of the role of AI in shaping firm resilience to supply chain disruptions and, in turn, enhancing firm performance. In doing so, we conceptualize AI use as a dynamic information processing capability-consisting of three dimensions: coordinating/integration, learning, and strategic competitive response capability-as an antecedent of firm resilience to supply chain disruptions, and firm resilience as a mediation factor that links AI use and firm performance. By analyzing the data gathered using a two-stage survey from 107 companies in Europe, we found AI use has a direct impact on firm resilience, and firm resilience fully mediates the relationship between AI use and firm performance. The findings of this study contribute to IT and supply chain literature.

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Section: Ai Use As a Dynamic Information Processing Capabilitymentioning

confidence: 99%

Artificial Intelligence, Firm Resilience to Supply Chain Disruptions, and Firm Performance

Sullivan¹,

Wamba²

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…An intelligent architecture of interactive data mining method can be used as a powerful tool for emulating cognitive process of human analysts (Shu, 2007). Also, it is interesting to incorporate methodological commonalities in intelligent production research for adaptive control optimization of production processes (Kruger, Shih, Hattingh, & van Niekerk, 2011). In fact, expert knowledge and data mining discovered knowledge can cooperate and complement each other in the investigation, analysis and further problem solving activities of complex situations (KamsuFoguem et al, 2012).…”

Section: Resultsmentioning

confidence: 99%

Mining association rules for the quality improvement of the production process

Kamsu-Foguem

Rigal

Mauget

2013

Expert Systems with Applications

132

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To cite this version:Bernard Kamsu-Foguem, Fabien Rigal, Félix Mauget. Mining association rules for the quality improvement of the production process. Expert Systems with Applications, Elsevier, 2013, vol. 40, pp. 1034-1045 b s t r a c tAcademics and practitioners have a common interest in the continuing development of methods and computer applications that support or perform knowledge-intensive engineering tasks. Operations management dysfunctions and lost production time are problems of enormous magnitude that impact the performance and quality of industrial systems as well as their cost of production. Association rule mining is a data mining technique used to find out useful and invaluable information from huge databases. This work develops a better conceptual base for improving the application of association rule mining methods to extract knowledge on operations and information management. The emphasis of the paper is on the improvement of the operations processes. The application example details an industrial experiment in which association rule mining is used to analyze the manufacturing process of a fully integrated provider of drilling products. The study reports some new interesting results with data mining and knowledge discovery techniques applied to a drill production process. Experiment's results on real-life data sets show that the proposed approach is useful in finding effective knowledge associated to dysfunctions causes.

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“…It was demonstrated also in [24] where the processes of learning and rational decision making are integrated through neural networks and genetic algorithms to offer a manufacturing control solution or in [25] where the solution could come from the enhancement of the reasoning mechanisms at individual and collective level and from a reconsideration of the goal achievement representation at all the levels of the manufacturing process.…”

Section: Agent-based Implementation For Hmesmentioning

confidence: 98%

Intelligent open architecture solutions for holonic manufacturing execution systems: Domain « key technologies for manufacturing» perspective

Varvara

2014

2014 IEEE International Conference on Automation, Quality and Testing, Robotics

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During the last decade, the Collaborative Manufacturing research domain has been associated with innovative solutions to challenges of modern manufacturing, namely dynamic changing of product catalog, distribution of resources, unexpected disturbances or heterogeneous manufacturing environments. They refer to interdisciplinary results covering all the production perspectives from organizational level -decentralization and autonomy, structural level -reconfigurable systems, and communication levelinteroperability. This paper makes an assessment of the most interesting achievements on the collaborative manufacturing, based on the IMS2020 project roadmap key research areas, presented on three emerging approaches: holonic organization for mechatronic control; agent-based implementation for distributed intelligent control; service-oriented communication for coordination, cooperation and collaboration. Some details about the role of intelligent products/resources are explicitly presented. Finally, some conclusions on the state of art in the domain are inferred and some future research challenges are identified.

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Intelligent machine agent architecture for adaptive control optimization of manufacturing processes

Cited by 23 publications

References 46 publications

Artificial Intelligence, Firm Resilience to Supply Chain Disruptions, and Firm Performance

Artificial Intelligence, Firm Resilience to Supply Chain Disruptions, and Firm Performance

Mining association rules for the quality improvement of the production process

Intelligent open architecture solutions for holonic manufacturing execution systems: Domain « key technologies for manufacturing» perspective

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Intelligent machine agent architecture for adaptive control optimization of manufacturing processes

Cited by 23 publications

References 46 publications

Artificial Intelligence, Firm Resilience to Supply Chain Disruptions, and Firm Performance

Artificial Intelligence, Firm Resilience to Supply Chain Disruptions, and Firm Performance

Mining association rules for the quality improvement of the production process

Intelligent open architecture solutions for holonic manufacturing execution systems: Domain &#x00AB; key technologies for manufacturing&#x00BB; perspective

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Intelligent open architecture solutions for holonic manufacturing execution systems: Domain « key technologies for manufacturing» perspective