Sketching a methodology for efficient Supply Chain Management agents enhanced through Data Mining

Symeonidis, Andreas L.; Nikolaidou, Vivia; Mitkas, Pericles A.

doi:10.1504/ijiids.2008.017244

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…Discovering knowledge through web mining can help multinational firms such as Amazon.com and e-Bay identify future customer bases, develop pricing strategies, evaluate trading partners, and increase revenue. For example, Symeonidis et al (2008) utilised data mining techniques to evaluate the performances of intelligent trading agents and then maximise revenue potential in e-synchronised SC environments including electronic bidding.…”

Section: E-synchronised Scmmentioning

confidence: 99%

Artificial intelligence in supply chain management: theory and applications

Min

2009

International Journal of Logistics Research and Applications

308

123

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Artificial intelligence (AI) was introduced to develop and create "thinking machines" that are capable of mimicking, learning, and replacing human intelligence. Since the late 1970s, AI has shown great promise in improving human decision-making processes and the subsequent productivity in various business endeavors due to its ability to recognise business patterns, learn business phenomena, seek information, and analyse data intelligently. Despite its widespread acceptance as a decision-aid tool, AI has seen limited application in supply chain management (SCM). To fully exploit the potential benefits of AI for SCM, this paper explores various sub-fields of AI that are most suitable for solving practical problems relevant to SCM. In so doing, this paper reviews the past record of success in AI applications to SCM and identifies the most fruitful areas of SCM in which to apply AI.

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Section: E-synchronised Scmmentioning

confidence: 99%