Charles G. Petersen

An evaluation of order picking routeing policies

Order picking, the assembly of a customer’s order from items in storage, is an essential link in the supply chain and is the major cost component of warehousing. The critical issue is to simultaneously reduce the cost and increase the speed of the order picking activity. The main objectives are to: evaluate various routeing policies in a random storage environment; evaluate the impact of warehouse shape and pick‐up/drop‐off location; and examine the interaction of the routeing policies, warehouse shape, and pick‐up/drop‐off location under different pick list sizes. The experimental results clearly indicate that the optimal routeing procedure generates significantly shorter routes than heuristic methods. The composite and largest gap routeing policies are, however, significantly better than simpler heuristic procedures. Further testing, in addition, indicates that the shape of the warehouse and the location of the pick‐up/drop‐off point can affect the picking efficiency.

Improving order‐picking performance through the implementation of class‐based storage

Class‐based storage (CBS) partitions stock‐keeping units (SKUs) into storage classes by demand and randomly assigns storage locations within each storage class area. This study compares the performance implications of CBS to both random and volume‐based storage (VBS) for a manual order picking warehouse. In addition, this study considers the effect of the number of storage classes, the partition of storage classes, and the storage implementation strategy applied in the warehouse. The simulation results show that CBS provides savings in picker travel over random storage and offers performance that approaches VBS. Other operational issues having an impact on warehouse performance are examined. The results offer managers insight for improving distribution center operations.

The impact of routing and storage policies on warehouse efficiency

Order picking, the activity by which a number of goods are retrieved from a warehousing system to satisfy a number of customer orders, is an essential link in the supply chain and is the major cost component of warehousing. The critical issue is to simultaneously reduce the cost and increase the speed of the order picking activity. The main objectives of this paper are: evaluate various routing heuristics and an optimal routine in a volume‐based and random storage environment; compare the performance of volume‐based storage to random storage; and examine the impact of travel speed and picking rates on routing and storage policy performance. The experimental results show the solution gap between routing heuristics and optimal routing is highly dependent on the travel speed and picking rate, the storage policy, and the size of the pick list. In addition, volume‐based storage produced significant savings over random storage, but again these savings are dependent on the travel speed and picking rate.

Considerations in order picking zone configuration

In today’s competitive global economy, the focus is on faster delivery of small more frequent orders of inventory at a lower total cost. This often precludes the use of full pallet picking in warehouses so firms commonly use manual picking of cases and broken‐cases. Many firms increase the efficiency of their warehouses by using zone picking. Zone picking requires that a worker only pick those stock‐keeping units (SKUs) stored within their picking zone. In this paper we examine the configuration or shape of these picking zones by simulating a bin‐shelving warehouse to measure picker travel where SKUs are assigned storage locations either using random or volume‐based storage. The results show that the size or storage capacity of the zone, the number of items on the pick list, and the storage policy have a significant effect on picking zone configuration. In addition, we found that the absence of a back cross aisle also affected picking zone configuration. These results offer solutions to managers looking to implement improvements in distribution center operations.

Improving order picking performance utilizing slotting and golden zone storage

PurposeWith the current interest in all aspects of supply chain management, the demands on warehousing have changed significantly within the past few years. In an attempt to meet this challenge, warehouses have become more concerned with proper slotting and storage techniques. This paper seeks to evaluate slotting measures and storage assignment strategies in a simulated manual bin‐shelving (low level picker‐to‐part) warehouse in terms of travel distance and the fulfillment time to complete an order.Design/methodology/approachThe approach utilises Monte Carlo simulation of a manual bin‐shelving pick area.FindingsThe results illustrate that popularity, turnover, and cube‐per‐order index (COI) performed best among slotting measures. Several new storage assignment strategies utilizing the concept of “golden zone” picking, which slots high demand stock‐keeping units (SKUs) at the height between the pickers waist and shoulders, were introduced. Results from the simulation study show that the golden zone storage assignment strategies generated significant savings in order fulfillment time compared to storage policies that ignore the golden zone concept.Originality/valueProvides an evaluation of slotting measures and storage assignment strategies that generated significant savings in order fulfillment time.

Journal ranking analyses of operations management research

Purpose – Several published studies have ranked journals based on perceived quality according to operations management (OM) researchers. The purpose of this paper is to examine the ranking of journals for OM research using meta analysis.Design/methodology/approach – The study begins by using a meta‐analysis approach to combine results of five recent OM journal ranking studies. A new citation analysis using OM research articles published in International Journal of Operations & Production Management, Journal of Operations Management, and Production and Operations Management between 1999 and 2005 is presented.Findings – Results of the meta‐analysis and the citation analysis have many similarities, but there are some striking differences suggesting the evolution of OM research away from operations research and engineering. The results also illustrate the diversity of OM research ranging from analytical modeling to empirical studies influenced by other business disciplines and the behavioral sciences.Original...