A STUDY OF IMPARTIAL ELEMENTS OF SCM USING ARTIFICIAL INTELLIGENCE
Koli Neha Rajendra
Department of Management, OPJS University, Churu, Rajasthan
Prof. (Dr.) Prakash Divakaran
Department of Management, OPJS University, Churu, Rajasthan
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An early goal of AI researchers was to create "thinking machines" that could mimic and ultimately replace human intelligence. Artificial intelligence (AI) has come a long way since the late 1970s, when it was first introduced, and now shows great potential in increasing human decision-making processes and, by extension, productivity across a wide range of business endeavors thanks to its ability to detect business patterns, comprehend business phenomena, seek information, and intelligently evaluate data. Despite its widespread use as a helpful tool for making judgment calls, artificial intelligence (AI) has not yet been widely implemented in SCM. In order to fully benefit from AI in this field, we explore the several AI subfields that are most suited to addressing practical difficulties in SCM. In this paper, we achieve precisely that by analyzing the past successes of AI in supply chain management to predict its future use.
Keywords:
artificial intelligence; supply chain management; knowledge management
References
- Allen, M.K., 1986. The development of an artificial intelligence system for inventory management using multiple experts. Unpublished PhD dissertation. Columbus, Ohio: The Ohio State University.
- Allen, M.K. and Helferich, O.K., 1990. Putting expert systems to work in logistics. Oak Brook, IL: Council of Logistics Management.
- Amer, Y., et al., 2008. Optimizing order fulfillment using design for six sigma and fuzzy logic. International Journal of Management Science and Engineering Management, 3 (2), 83–99.
- Antony, J., et al., 2006. Design of synchronized supply chain: a genetic algorithm based six sigma constrained approach. International Journal of Systems and Management, 2 (2), 120–141.
- Awad, E.M., 1996. Building expert systems: principles, procedures, and applications. St. Paul, MN: West Publishing Company. Baily, P., et al., 2005. Purchasing principles and management. 9th ed. Essex, England: Pearson Education Limited.
- Balamurugan, K., Selladurai, V., and Ilamathi, B., 2006. Solving unequal area facility layout problems using genetic algorithm. International Journal of Logistics Systems and Management, 2 (3), 281–301.
- Balan, S., Vrat, P., and Kumar, P., 2007. Reducing the Bullwhip effect in a supply chain with fuzzy logic approach. International Journal of Integrated Supply Management, 3 (3), 261–282.
- Baxter, N., Collings, D., and Adjali, I., 2003. Agent-based modeling – intelligent customer relationship management. BT Technology Journal, 21 (2), 126–132.
- Bielli, M., Caramia, M., and Carotenuto, P., 2002. Genetic algorithms in bus network optimization. Transportation Research Part C, 10, 19–34.
- Bortfeldt, A. and Gehring, H., 2001. A hybrid genetic algorithm for the container loading problem. European Journal of Operational Research, 131, 143–161.
