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To develop a decision support system (DSS) and improved management criteria for operating dispenser‐based single‐piece automatic order picking systems (AOPS) in distribution centers, able to reduce the need for manual decision making based on personal experience or subjective judgement.

Simulation was utilized to analyze the relationships between stochastic demand, setup parameters and performances of an AOPS. A set of rules was then defined to cost‐effectively select the values of setup parameters. A DSS was built incorporating the heuristic rules to dynamically update the equipment setup.

Manual management of an AOPS can be poorly efficient even if largely practiced. Significant economic benefits may result from rule‐based equipment setup instead of the traditional manual decision approach. This was verified resorting to a case study referring to the distribution center of a leading pharmaceuticals distributor in Italy. Major performances improvements resulted regarding manual operation by an experienced logistic manager, including a 40 per cent reduction of the cost per picked order line.

The proposed DSS is able to monitor the system behaviour over a specified time window and automatically set the values of the state variables for the next period. It is able to automatically define the set of items to be allocated on to the machine, to select the number of storage locations allocated to each item and set reorder levels and maximum picking quantities for each item, thus greatly simplifying the task of the logistic manager. Utilization of this DSS enables one to maintain a high level of picking automation efficiency while drastically cutting the required support personnel, thus significantly improving profit margins of high‐volume high‐rotation distribution centers.

The paper addresses, with original methodology, a practically relevant issue which is neglected in the literature. The paper is aimed at distribution centers managers seeking to improve the performances of AOPS and reduce their operating costs.

Prior literature has widely established that the design of storage locations impacts order picking task performance. The purpose of this study is to investigate the performance impact of unit loads, e.g. pallets or rolling cages, utilized by pickers to pack products after picking them from storage locations.

An empirical analysis of archival data on a manual order picking system for deep-freeze products was performed in cooperation with a German brick-and-mortar retailer. The dataset comprises N = 343,259 storage location visits from 17 order pickers. The analysis was also supported by the development and the results of a batch assignment model that takes unit load selection into account.

The analysis reveals that unit load selection affects order picking task performance. Standardized rolling cages can decrease processing time by up to 8.42% compared to standardized isolated rolling boxes used in cold retail supply chains. Potential cost savings originating from optimal batch assignment range from 1.03% to 39.29%, depending on batch characteristics.

This study contributes to the literature on factors impacting order picking task performance, considering the characteristics of unit loads where products are packed on after they have been picked from the storage locations. In addition, it provides potential task performance improvements in cold retail supply chains.

Increasing personnel costs and labour shortages have pushed retailers to give increasing attention to their intralogistics operations. We study hybrid order picking systems, in which humans and robots share work time, workspace and objectives and are in permanent contact. This necessitates a collaboration of humans and their mechanical coworkers (cobots).

Through a longitudinal case study on individual-level technology adaption, we accompanied a pilot testing of an industrial truck that automatically follows order pickers in their travel direction. Grounded on empirical field research and a unique large-scale data set comprising N = 2,086,260 storage location visits, where N = 57,239 storage location visits were performed in a hybrid setting and N = 2,029,021 in a manual setting, we applied a multilevel model to estimate the impact of this cobot settings on task performance.

We show that cobot settings can reduce the time required for picking tasks by as much as 33.57%. Furthermore, practical factors such as product weight, pick density and travel distance mitigate this effect, suggesting that cobots are especially beneficial for short-distance orders.

Given that the literature on hybrid order picking systems has primarily applied simulation approaches, the study is among the first to provide empirical evidence from a real-world setting. The results are discussed from the perspective of Industry 5.0 and can prevent managers from making investment decisions into ineffective robotic technology.

Order picking is one of the most costly logistics processes in warehouses. As a result, the optimization of order picking processes has received an increased attention in recent years. One potential source for improving order picking is the reduction of picker blocking. The purpose of this paper is to investigate picker blocking under different storage assignment and order picker-route combinations and evaluate its effects on the performance of manual order picking processes.

This study develops an agent-based simulation model (ABS) for order picking in a rectangular warehouse. By employing an ABS, we are able to study the behaviour of individual order pickers and their interactions with the environment.

The simulation model determines shortest mean throughput times when the same routing policy is assigned to all order pickers. In addition, it evaluates the efficiency of alternative routing policies–storage assignment combinations.

The paper implies that ABS is well-suited for further investigations in the field of picker blocking, for example, with respect to the individual behaviour of agents.

Based on the results of this paper, warehouse managers can choose an appropriate routing policy that best matches their storage assignment policy and the number of order pickers employed.

This paper is the first to comprehensively study the effects of different combinations of order picker routing and storage assignment policies on the occurrence of picker blocking.

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.

In manual order picking systems, temporary workers are often employed to handle demand peaks. While this increases flexibility, it may hamper productivity, as they are usually unfamiliar with the processes and may have little experience. It is important for managers to understand how quickly inexperienced workers arrive at full productivity and which factors support workers in improving their productivity. This paper aims to investigate how learning improves the performance of order pickers, and how their regulatory focus (RF) and monetary incentives, as management actions, influence learning.

Data was collected in two case studies in controlled field-lab experiments and statistically analysed. This allowed evaluating the validity of hypotheses through an ANOVA, the calculation of correlation coefficients and the application of regression models.

A monetary incentive based on total order picking time and pick errors has a positive influence on order picking time, but not on pick quality. The incentive influences initial productivity, but not the learning rate. A dominant promotion-oriented RF increases the effect of the incentive on initial productivity, but it does not impact worker learning.

This study contributes to behavioral and human-focused order picking management and supports managers in setting up work plans and developing incentive systems for learning and productivity enhancement, considering worker RF.

This work is among the few to empirically investigate the effect of monetary incentives on learning in interaction with RF. It is the first study to investigate these concepts in an order picking scenario.

The purpose of this paper is to study the prevalence of human learning in the order picking process in an experimental study. Further, it aims to compare alternative learning curves from the literature and to assess which learning curves are most suitable to describe learning in order picking.

An experimental study was conducted at a manufacturer of household products. Empirical data was collected in the order picking process, and six learning curves were fitted to the data in a regression analysis.

It is shown that learning occurs in order picking, and that the learning curves of Wright, De Jong and Dar‐El et al. and the three‐parameter hyperbolic model are suitable to approximate the learning effect. The Stanford B model and the time constant model led to unrealistic results.

The results imply that human learning should be considered in planning the order picking process, for example in designing the layout of the warehouse or in setting up work schedules.

The paper is the first to study learning effects in order picking systems, and one of the few papers that use empirical data from an industrial application to study learning effects.

Two researchers suggest a new approach to the most fundamental warehousing operation of all.

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 picker's 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.

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.