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In the mid‐1970s Mothercare decided to establish a new distribution centre and mail order warehouse; a number of alternative order picking systems were considered for the company's world‐wide mail order operation. This article examines the various alternatives and describes the system which was finally selected — a carousel storage and selection arrangement. Current performance statistics of the system are provided which are related to Mothercare's sales volumes.

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.

Demand forecast methodologies have been studied extensively to improve operations in e-commerce. However, every forecast inevitably contains errors, and this may result in a disproportionate impact on operations, particularly in the dynamic nature of fulfilling orders in e-commerce. This paper aims to quantify the impact that forecast error in order demand has on order picking, the most costly and complex operations in e-order fulfilment, in order to enhance the application of the demand forecast in an e-fulfilment centre.

The paper presents a Gaussian regression based mathematical method that translates the error of forecast accuracy in order demand to the performance fluctuations in e-order fulfilment. In addition, the impact under distinct order picking methodologies, namely order batching and wave picking. As described.

A structured model is developed to evaluate the impact of demand forecast error in order picking performance. The findings in terms of global results and local distribution have important implications for organizational decision-making in both long-term strategic planning and short-term daily workforce planning.

Earlier research examined demand forecasting methodologies in warehouse operations. And order picking and examining the impact of error in demand forecasting on order picking operations has been identified as a research gap. This paper contributes to closing this research gap by presenting a mathematical model that quantifies impact of demand forecast error into fluctuations in order picking performance.

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.

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.

Vertical lift module (VLM) is a parts-to-picker system for order picking of small products, which are stored into two columns of trays served by a lifting crane. A dual-bay VLM order picking (dual-bay VLM-OP) system is a particular solution where the operator works in parallel with the crane, allowing higher throughput performance. The purpose of this paper is to define models for different operating configurations able to improve the total throughput of the dual-bay VLM-OP system.

Analytical models are developed to estimate the throughput of a dual-bay VLM-OP. A deep evaluation has been carried out, considering different storage assignment policies and the sequencing retrieval of trays.

A more accurate estimation of the throughput is demonstrated, compared to the application of previous models. Some use guidelines for practitioners and academics are derived from the analysis based on real data.

Differing from previous contributions, these models include the acceleration/deceleration of the crane and the probability of storage and retrieve of each single tray. This permits to apply these models to different storage assignment policies and to suggest when these policies can be profitably applied. They can also model the sequencing retrieval of trays.

This paper aims to explore methods of defining ejecting zones (EZs) used in automatic picking systems (APSs), particularly in A-frame APSs. An A-frame APS automatically ejects products onto a conveyor, which then brings the products to their destination. EZs are moving zones on a conveyor, and each EZ corresponds to one picking order. Products are ejected as a zone passes channels in which the products are stored.

First, three EZ types are defined, and their operations are explained. Second, picking orders are analyzed and categorized by considering the structure and the picking mechanism of an A-frame APS. In addition, picking-order instances reflecting actual data are randomly generated according to each category. Finally, the performance of the EZs is evaluated using the picking-order instances and computer simulations.

The results from the computer simulations suggest the EZ types suitable for use with various picking order types considering order fulfilment speed and energy usage.

In this paper, the authors only adopt a triangular distribution which is considered most practical distribution in the industry.

It is believed that these results can provide managers and operators with useful guides to facilitate the effective operation of an A-frame APS. The provided ideas have been implemented at the pharmaceutical warehouse of the largest logistics company in Korea.

The result shows that the proposed idea could save energy consumption and the APS have potential to save labor involvement in picking.

It is essential to define the EZs when operating an A-frame APS efficiently, but there is almost no research in this area. This paper focuses on defining EZs, as well as methods to utilize these zones.

This paper identifies, configures and analyses a solution aimed at increasing the efficiency of in-store picking for e-grocers and combining the traditional store-based option with a warehouse-based logic (creating a back area dedicated to the most required online items).

The adopted methodology is a multi-method approach combining analytical modelling and interviews with practitioners. Interviews were performed with managers, whose collaboration allowed the development and application of an empirically-grounded model, aimed to estimate the performances of the proposed picking solution in its different configurations. Various scenarios are modelled and different policies are evaluated.

The proposed solution entails time benefits compared to traditional store-based picking for three main reasons: lower travel time (due to the absence of offline customers), lower retrieval time (tied to the more efficient product allocation in the back) and lower time to manage stock-outs (since there are no missing items in the back). Considering the batching policies, order picking is always outperformed by batch and zone picking, as they allow for the reduction of the average travelled distance per order. Conversely, zone picking is more efficient than batch picking when demand volumes are high.

From an academic perspective, this work proposes a picking solution that combines the store-based and warehouse-based logics (traditionally seen as opposite/alternative choices). From a managerial perspective, it may support the definition of the picking process for traditional grocers that are offering – or aim to offer – e-commerce services to their customers.

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.

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