Improve the orders picking in e-commerce by using WMS data and BigData analysis

Abstract

The primary purpose of the research is the improvement of the orders picking process without additional investments for the software, employees, tool and inventories. For problem-solving, the data about picking is exported and preprocessed from WMS. The BigData analysis and product clustering in Tableau software is delivered using the data, where the Product Allocation Problem (PAP) is solved. Picking time for reference scenario and new analysed one is calculated and compared. The presented research proves that standard data collected by WMS could be used for solving PAP for the reduction of total picking time. The method delivered by authors could be in a typical warehouse, where forklifts and employees do the order picking process. The plan after an upgrade could be used for automatic picking, and implemented WMS. For BigData analysis, Tableau is connected to WMS database. Such solution could be used for everyday analysis and planning the allocation of products. The presented method is easy to use; there is no need to invest in expensive software and automation of the picking process to achieve the high performance of the orders picking process. However, its application allows the increase of efficiency rates. Storekeepers can select more products at the same time. The presented research is original because of using simple methods and analysis of specific data, which until now are only used to calculate employee performance indicators.

Keywords

• product allocation problem (pap)
• bigdata analysis
• tableau analysis
• clustering
• the effectiveness of orders picking
• e-commerce
• warehouse logistics