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Improving order-picking efficiency via storage assignments strategies

Tsige, M.T. (2013) Improving order-picking efficiency via storage assignments strategies.

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Abstract:Order picking at a fictitious warehouse is the field of the research. Which is the most labor intensive and costly activity of most warehouse, approximately 55% of the total warehouse operating expenses are related to order picking operations. Companies nowadays want to reduce supply chain costs and improve productivity within their warehouse; consequently the order picking process needs to be efficient. In order to increase the order picking efficiency different decisions at different levels can be taken. This research focuses on storage assignment strategies to increase the order picking efficiency. Storage assignment strategy is a set of rules which can be used to assign stock keeping units (SKUs) to storage locations. The research is executed in the following way. We first perform a literature study regarding the general order picking process and storage assignment strategies which have a considerable impact on order picking efficiency. Following the literature review four storage assignment strategies are selected and implemented. These are random, cube-per-order index (COI) based on popularity (i.e., the number of orders that require a particular SKU), interaction frequency heuristic of order oriented slotting policy (IFH-OOS), and class-based (ABC-storage) policies. All but IFH-OOS are frequently used in a warehouse. To briefly explain the basic idea behind the above mentioned storage policies: In a random storage policy, each SKU is randomly assigned to an empty location in a warehouse. Where each empty location has the same probability of being selected for storage. In class-based storage policy, the most frequently requested item is assigned to the closest location to the Input and Output point (I/O-point). Items are first categorized into three classes – A, B, and C. Each class is then assigned to a dedicated area of the warehouse based on the number of transactions it generates. Generally, Class A items are closest to the I/O-point and Class C the farthest. Storage of items belonging to a class within the designated area is done randomly. Under COI based on popularity storage policy, the basic idea is to store SKUs with the highest popularity closest to the I/O-point. The IFH-OOS policy on the other hand allocates pairs of SKUs appearing in multiple orders in adjacent locations. In addition, frequently requested pairs of SKUs with high interaction frequency are stored close to the I/O-point. Next, we made assumptions regarding the warehouse layout, the number of SKUs that the warehouse stocks, and the set of orders to be picked. Finally, we develop a Monte Carlo simulation model using Visual Basic for Applications (VBA) in order to identify the most efficient storage assignment strategy. Storage assignment strategies were not the only experimental factors that we vary, but also the percentage of SKUs that appear in an order set. Our first experiment is conducted, when the order sets randomly generated from a normal distribution. In which all SKUs have equal probability of appearing in an order set. In the second experiment, we control the order generation in such a way that approximately 20% of SKUs to appear in 80% of the order set. With the intention that, the average interaction frequency between SKUs will have a relatively large value and favor the interaction frequency based storage assignment strategies.
Item Type:Essay (Master)
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:85 business administration, organizational science
Programme:Industrial Engineering and Management MSc (60029)
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