In this study, the authors have investigated warehouse processes to identify critical ones that are wasteful. The aim of this research study was to improve the efficiency of warehouse processes by reducing travel time and cost in replenishment and order picking. To achieve this objective, the authors have proposed a mathematical model and discrete event simulation study. For the simulation model, the Dijkstra algorithm has been selected to schedule forklifts driving and picking vehicles routes in internal transport. According to the extensive simulation analysis approximately 67 % of waste could be reduced in warehouses. Of course, this number depends significantly on a warehouse layout, operations and material handling equipment used in warehouses.
The paper focuses on identifying the influence of warehouse size to effectiveness of products storage policy. In the paper different storage policy was analysed. To evaluate the effectiveness of selected orderpicking warehouses the dedicated software for simulation of orders picking process was used. The effectiveness of product allocation planning was evaluated by the total time that is needed for the order-picking process. For the route planning it was assumed that the nearest product was appointed as next to pick. In our research, a discrete event simulation analysis by using special software called PickupSimulo was performed. In this research different warehouse sizes were investigated. The layout ranged from 2.188 m 2 to 22.021 m 2. Based on the simulation study it is possible to support warehouse managers for choosing the best products storage policy. This will reduce retrieval time and improve the effectiveness of order picking.
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.
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