• RAVINDRA PANDHARINATH AMBHORE
• RAJESH B. BUKTAR

Warehouse management plays important role from receiving raw material to the shipping of final product and in automobile warehouse this is a critical task as so many parts are involved in it. Because of which Proper warehouse management adds efficiency and quality control to process by movement of goods at highest speed. Ordering stock out items plays an important role in warehouse management of automobile companies as most of cost and time is involved in ordering a stock out products. Currently different methods like ABS,VED, Economical order quantity etc used for warehouse management. Out of all these Economical order quantity is widely used for ordering stock out product. The problem with this method is that it will order each product separately based on Economical order quantity. Suppose if there is some pattern in stock out of two or more product then they can be ordered simultaneously this will reduce transportation cost and ordering cost. This article is about identifying this pattern from stock out data set and for this Apriori algorithm which is one type of Machine learning algorithm is used. Currently Apriori algorithm is used in market basket analysis for identifying customer behavior by identifying pattern from data of purchased products by using Association rule. In this article Apriori algorithm is used to identify relationship between stock out items of warehouse of an automobile company so that they can be ordered simultaneously to reduce cost, save the time and avoid shortage of Goods in automobile warehouse.

Warehouse management, Machine learning, Association rule, Apriori algorithm.

"WAREHOUSE MANAGEMENT USING MACHINE LEARNING ALGORITHM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 9, page no.e748-e754, September 2021, Available :http://www.jetir.org/papers/JETIR2109491.pdf

"WAREHOUSE MANAGEMENT USING MACHINE LEARNING ALGORITHM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 9, page no. ppe748-e754, September 2021, Available at : http://www.jetir.org/papers/JETIR2109491.pdf