@article{(Open Science Index):https://publications.waset.org/pdf/10013352,
	  title     = {Artificial Neural Network Approach for Inventory Management Problem},
	  author    = {Govind Shay Sharma and  Randhir Singh Baghel},
	  country	= {},
	  institution	= {},
	  abstract     = {The stock management of raw materials and finished goods is a significant issue for industries in fulfilling customer demand. Optimization of inventory strategies is crucial to enhancing customer service, reducing lead times and costs, and meeting market demand. This paper suggests finding an approach to predict the optimum stock level by utilizing past stocks and forecasting the required quantities. In this paper, we utilized Artificial Neural Network (ANN) to determine the optimal value. The objective of this paper is to discuss the optimized ANN that can find the best solution for the inventory model. In the context of the paper, we mentioned that the k-means algorithm is employed to create homogeneous groups of items. These groups likely exhibit similar characteristics or attributes that make them suitable for being managed using uniform inventory control policies. The paper proposes a method that uses the neural fit algorithm to control the cost of inventory.},
	    journal   = {International Journal of Mathematical and Computational Sciences},
	  volume    = {17},
	  number    = {11},
	  year      = {2023},
	  pages     = {154 - 158},
	  ee        = {https://publications.waset.org/pdf/10013352},
	  url   	= {https://publications.waset.org/vol/203},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 203, 2023},
	}