Smart Advisor: An Intelligent Inventory Prediction Based On Regression Model

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Ömer Küçük
https://orcid.org/0000-0003-3180-5250
Farzad Kiani
https://orcid.org/0000-0002-0354-9344

Abstract

Today one of the biggest expense items of the enterprises is raw material and stock amounts. Therefore, proper inventory management is very important for the profitability of the enterprises. Products that are not purchased on time cause interruptions in production and products left over because the expiration date has passed will also cause losses for businesses. Therefore, proper inventory management is critical for profit / loss situations of businesses. In this paper we presented a model to predict the demand of certain stock items by using a regression model. Our model can analysis and computer the prediction results on a given dataset. We evaluate our model on sample dataset and provide the analysis as well calculations over the existing inventory. Accurate analysis of stock consumption enables accurate estimation of the amount of stock to be consumed in the future. Accurate forecasting of stock consumption helps to take corrective steps in decision making. That is, it only allows you to buy in sufficient quantity when necessary. These stages are critical for economic stock management. For this reason, robust and adaptable approaches that can provide models ensure that stock consumption can be managed properly. It is difficult to find previously written sources on estimating the direction of stock movements. One of the most important reasons for this is the lack of incentive to make such studies in the academic literature. As a result, articles written about the subject and the work done have been limited, the results have not reached the reproducible level.

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How to Cite
Küçük, Ömer, & Kiani, F. (2018). Smart Advisor: An Intelligent Inventory Prediction Based On Regression Model. International Journal of Machine Learning and Networked Collaborative Engineering, 2(03), 86-94. Retrieved from http://mlnce.net/index.php/Home/article/view/41
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