Analysis of Inventory Level Optimization Using PSONN Model and Fuzzy Logic Approach


  • Yogesh . M.Tech. Scholar, Department of M.E, Sagar Institute of Research and Technology Excellence, Bhopal, India
  • Sudhir Shrivastava Assistant Professor & Dean, Department of M.E, Sagar Institute of Research and Technology Excellence, Bhopal, India



Raw materials, Inventories, Inventory Management, Artificial Intelligence, Fuzzy rules.


Raw materials, intermediate goods and finished goods are termed as inventories while considering it as portion of business’s assets which can be considered as prepared or are prepared for sale. One of the suitable solutions is to design optimal inventory model. Major concern of industry is to design suitable inventory model. Some of the existing inventory management research work are discussed in literature. But this field is still a big area of interest. Many research works used artificial intelligence models for inventory management. One amongst the area for inventory management is worker behavior in a company. So, employees are taken into account to be as an inventory that contributes in growth of an organization. Employee attrition may be a big issue for the organizations specially once trained, technical and key staff leave for a far better chance from the organization. This results in financial loss to replace a trained employee. Therefore, this paper uses the current and past employees’ data to analyze attrition behavior of employees and to provide bonus/promotion to employees having non attrition behavior by using PSONN and fuzzy rules. The result shows that the efficiency of model is improved with respect to existing methods by approximately 2%.


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[1] Alao D. & Adeyemo A. B, Analyzing Employee Attrition Using Decision Tree Algorithm, Computing, Information Systems & Development Informatics, 4(1), 2013, 17-28.
[2] Alduayj, Rajpoot, “Predicting Employee Attrition using Machine Learning”, International Conference on Innovations in Information Technology, IEEE, 2018.
[3] Ford Whitman Harris, “Economic Order Quantity Model”, Institute for Operations Research and the Management Sciences (INFORMS), 24 Dec. 2018.
[4] Pikulkaew Tangtisanon, “Web Service Based Food Additive Inventory Management with Forecasting System”, International Conference on Computer and Communication Systems (ICCCS), 2018.
[5] Hsiao Ching Chen ; Hui Ming Wee ; Yao-Hung Hsieh, “Optimal Supply Chain Inventory Decision Using Artificial Neural Network”, WRI Global Congress on Intelligent Systems, 2009.
[6] Hachicha, W. A simulation meta modelling based neural networks for lot-sizing problem in MTO sector. Journal of Simulation Modelling. 2011;10(4): 191-203. DOI: 10.2507/IJSIMM10(4)3.188
[7] Paul, S. K., Azaeem, A. An artificial neural network model for optimization of finished goods inventory. International Journal of Industrial Engineering Computations. 2011;2(2):431-438. DOI: 10.5267/ j.ijiec.2011.01.005.
[8] Y., & Shrivastava, S., “Analysis of Inventory Level Optimization Using Artificial Intelligence Approach.” IJOSTHE, 7(2), 8, 2019. Retrieved from




How to Cite

., Y., & Shrivastava, S. (2019). Analysis of Inventory Level Optimization Using PSONN Model and Fuzzy Logic Approach. SMART MOVES JOURNAL IJOSCIENCE, 5(10), 1–6.