Warehouse Order Research Articles

A Systematic Literature Review: Design and Control of Warehouse Order Picking in Development of E-commerce Transaction

Order picking is the process of retrieving items from storage to meet a specific customer order, whichis known to be the most labor-intensive and costly function among all the warehouse functions. Thisfunction is also important in that it has a critical impact on downstream customer service especiallyin facing the current covid-19 pandemic. This research aims to provide information, description andmore comprehensively identifies the design and control of warehouse order picking by usingempirical literature review. In this research, the stages are Identification of potential study, StudySelection based on criteria, quality assessment, data extraction, and data analysis. Based on theliterature review systematically compiled it is obtained 37 main articles that correspond to the purposeof research. From these 37 articles can be identified further research on design and control ofwarehouse order picking that provides increased transactions in e-commerce.

Optimization of warehouse picking to maximize the picked orders considering practical aspects

Majority of previous studies on optimization of warehouse order picking lack real-world application and overlook complexities such as varying items’ weight, workload balance, and heterogeneous pickers. To address these issues, in this study, mathematical programming models are developed based on two approaches to assign orders to pickers and determine pickers’ routes in a North American consumer goods warehouse. Both approaches maximize the number of picked orders. However, in the simultaneous modelling approach, decisions are made in one model, while in the sequential modelling approach, first routing decisions then order assignment decisions are made. The sequential modelling approach compared with the simultaneous one yields better results in a shorter time. For the considered warehouse, the results suggest that the warehouse may not be able to handle more than 700 orders per shift without expanding its workforce. Furthermore, adding one picker increases the number of picked orders by 2%. Additionally, balancing pickers' workload is more effective when there are fewer than 600 orders available for picking per shift, because with a higher number of orders, pickers remain busy throughout the entire shift. The proposed mathematical modelling approaches enable warehouse managers and staff to process more orders with available resources (equipment, time, staff) and make better decisions using available data.

Optimising Warehouse Order Picking Through An Integrated Harmony Search and Simulated Annealing Algorithm: Application For A Food Packaging Manufacturer

Optimising Warehouse Order Picking Through An Integrated Harmony Search and Simulated Annealing Algorithm: Application For A Food Packaging Manufacturer

Exploring the impact of augmented reality smart glasses on worker well-being in warehouse order picking

This paper examines the use of Augmented Reality Smart Glasses (ARSGs) in order-picking tasks in warehouses and explores their impact on workers' well-being. While previous research has primarily focused on the performance advantages of ARSGs, this study provides a new perspective by investigating the human factor and perceptions of worker well-being in two empirical studies. The results corroborate previous findings that ARSGs enhance order-picking efficiency by improving workers' speed and reducing error rates. Additionally, the results reveal that using ARSGs directly increases perceptions of well-being compared to traditional support tools. However, contrary to expectations, ARSG use does not generally lead to higher job satisfaction or perceived productivity enhancements for all workers. Technology savviness is identified as a crucial individual factor that shapes these effects, with tech-savvy workers benefiting more from ARSG use. Overall, this research underscores the importance of tailoring ARSG implementation to the specific needs and characteristics of the workforce and highlights the need for further investigation into the beneficiaries of ARSG technology.

Effect of logistics management practices on marketing performance of small and medium scale enetrprises in Calabar, Cross River

The study examined the effect of logistics management practices in Calabar. The specific objectives were to investigate the effect of warehouse management, inventory, transportation and order processing on marketing performance of SMSEs in Calabar, Cross River State. The research design used for this study is the survey method. Primary data were collected from small and medium scale enterprises (SMSEs) in Calabar using questionnaire. The data were analysed using inferential and descriptive statistical tools. Based on the results, it was revealed that warehouse management, inventory management, transportation management and order processing have a significant effect on marketing performance of SMSEs in Calabar. On the basis of the findings, it was concluded that logistics and supply chain managers should encourage effective and efficient logistics management practices in their organizations to enhance their marketing performance.

Ergonomic Evaluation of Human–Robot Collaborative Order Picking: A Combined Laboratory and Simulation Study

Thanks to rapid technological developments in robotics, various automation technologies are being applied in warehouses today. Order picking, as a key process in warehouse operations, has drawn attention in academia and practice for decades. In addition to many studies dedicated to manual and fully automated order picking, efforts have also been made to study semi-automated warehouses in which humans and robots collaborate. However, these studies mostly focused on system efficiency and ignored ergonomic aspects. Order picking was confirmed as a labor-intensive process in an environment in which workers are at a high risk of developing health problems. Therefore, this study addresses the investigation of physical human working conditions in both manual and robot-assisted order picking systems via real-life laboratory experiments and simulation modeling. We used a motion capture system to assess human working postures when working with and without robot assistance. In addition, we estimated the daily workload by applying the energy expenditure concept. Using simulation experiments, we were able to extend the results to various practical scenarios with different design variables, for example warehouse layouts, order sizes, and human-robot team configuration. Our preliminary results reveal that human-robot collaboration can reduce human workload. Posture evaluation also shows a slight improvement.

The Variance Learning Curve in Retail Order Picking

The notion that prior experience can improve future performance has reached general acceptance in the operations and supply chain management literature. However, prior experience may not only have a positive impact on average performance but in addition also on the variance of performance. We empirically examine this issue by considering the performance of 19 temporary workers in retail order picking new to this task, performing 22,603 storage location visits and use a two-stage estimation method of a heteroskedastic learning curve model. In the first stage, we estimate the impact of prior experience on task performance time. For the second stage, we extract the error terms from the first stage model and use it as the dependent variable in the second stage. Through this, we can estimate the impact of prior experience on performance variability. Our first stage model suggests that prior experience can improve average task performance on a system level for all new temporary order pickers observed. However, on an individual level, these learning curves are heterogeneous, with varying intercepts, slopes, and functional forms. Going beyond the impact of experience on average performance, our second stage model finds that one week of experience working in the examined warehouse can reduce performance variability by 26%, but at diminishing increments. These findings suggest that prior experience can mitigate variability in task performance time, which indicates that the recently discussed variance learning curve exists for order picking tasks. Our results allow managers to make informed decisions on how to benefit from the positive effects of prior experience on average performance and performance variability when employing a temporary workforce for the example case of retail warehouse order picking.

Optimising Warehouse Order Picking: Real Case Application in the Shoe Manufacturing Industry

Optimising Warehouse Order Picking: Real Case Application in the Shoe Manufacturing Industry

Enhancing E-Commerce Warehouse Order Fulfillment Through Predictive Order Reservation Using Machine Learning

Enhancing E-Commerce Warehouse Order Fulfillment Through Predictive Order Reservation Using Machine Learning

Application of supportive and substitutive technologies in manual warehouse order picking: a content analysis

Order picking in warehouses is a labour- and time-intensive logistical process that significantly impacts the efficiency of supply chains. Although technical progress facilitates the automation of specific order picking tasks, human workers remain the primary actors of order picking. Owing to high operating costs associated with manual order picking, its design and management have been increasingly researched for decades. Because manual order picking systems are socio-technical systems, human factors and workers’ interaction with technology are essential for operational success. As innovative technologies become increasingly utilised, such as augmented reality or exoskeletons, warehouse managers need to consider the effects of supportive and substitutive technologies on operational outcomes. However, the potentials and obstacles of using technologies in manual order picking require further investigations. Therefore, this study analyses literature content on supportive and substitutive technologies in manual warehouse order picking and investigates the existing state of research in this field. Text mining is employed to enhance the insights regarding the content analysis. Additionally, future research opportunities on the integration of supportive and substitutive technologies are proposed for manual order picking improvement and development of sustainable and human-centered logistics systems, according to the Industry 5.0 vision.

A DMAIC approach for warehouse storage and order picking process improvement

A DMAIC approach for warehouse storage and order picking process improvement

Warehouse Order Picking Differences in Bulgarian Enterprises

Warehousing is an important logistics activity related to the management of material flows in the supply chain. To ensure this movement in the warehouses, it is necessary to perform a number of integrated warehouse processes. The order picking process is particularly the most significant, reflecting the whole logistics system. The purpose of the study is to analyze warehousing practices in this field and to define main conclusions. The work presents the results of a survey conducted among Bulgarian trade and manufacturing enterprises, and some of the order picking practices are studied. Based on the data, a number of conclusions are drawn about the practices, especially regarding the application of information systems and technologies. The main focus of the work is the differences between the trade and manufacturing enterprises, as well as their sizes. The main conclusions are related to the correct understanding of the importance of order picking processes and, at the same time, to the insufficient utilization of their potential.

An Innovative Layout Design and Storage Assignment Method for Manual Order Picking with Respect to Ergonomic Criteria

Background: This paper aims to improve the overall performance of manual warehouse Order Picking (OP) processes by proposing an innovative method for designing a picking area layout, and introducing a storage assignment strategy with respect to ergonomics and workers’ physical fatigue. Methods: The proposed method categorizes the available picking slots based on size and ABC analysis. Ιt takes into consideration a set of ergonomic constraints pertinent to the rack heights and travel distance restrictions for each slot type, leading to the assignment of a location to each slot type based on its individual characteristics. In doing so, the proposed method introduces an innovative ‘flame-shape’ aisle layout. Finally, the products are assigned to their optimal locations, targeting OP time minimization, balanced workload allocation, and ergonomics optimization through a ranking system measuring the ‘difficulty’ of retrieving the products based on their weight, popularity, and slot location. Results: The proposed method led to a productivity rise of 14.9% along with a significant decrease of the ‘difficulty’ index, by 31%. Conclusions: The results prove that a prominent performance improvement can be achieved when both travel distance and manual workload minimization are targeted for determining the picking area layout and storage design.

Research on Logistic Warehouse Scheduling Management With IoT and Human-Machine Interface

The automated deployment of the internet of things (IoT) and the human-machine interface provides the best advancement for dispersed warehouse scheduling management (WSM). In this paper, superior data systematic move toward warehouse scheduling management (WSM) has been suggested using the computational method to allow smart logistics. Furthermore, this paper introduces the human-machine interface framework (HMI) using IoT for collaborative warehouse order fulfillment. It consists of a layer of physical equipment, an ambient middleware network, a framework of multi-agents, and source planning. This approach is chosen to enhance the reaction capabilities of decentralized warehouse scheduling management in a dynamic environment. The simulation outcome has been performed, and the suggested method realizes a high product delivery ratio (96.5%), operational cost (94.9%), demand prediction ratio (96.5%), accuracy ratio (98.4%), and performance ratio (97.2%).

What should be lean buffer threshold for the forward-reserve warehouse?

PurposeThe assorted piece-wise retail orders in a cosmetics warehouse are fulfilled through a separate fast-picking area called Forward Buffer (FB). This study determines “just-right” size of FB to ensure desired Customer Service Level (CSL) at least storage wastages. It also investigates the impact of FB capacity and demand variations on FB leanness.Design/methodology/approachA Value Stream Mapping (VSM) tool is applied to analyse the warehouse activities and mathematical model is implemented in MATLAB to quantify the leanness at desired CSL. A comprehensive framework is developed to determine lean FB buffer size for a Retail Distribution Centre (RDC) of a cosmetics industry.FindingsThe CSL increases monotonically; however, the results concerning spent efforts towards CSL improvement gets diminished with raised demand variances. The desired CSL can be achieved at least FB capacity and fewer Storage Waste (SW) as it shifts towards more lean system regime. It is not possible to improve Value Added (VA) time beyond certain constraints and therefore, it is recommended to reduce Non-Value Added (NVA) order processing activities to improve leanness.Research limitations/implicationsThis study determines “just-right” capacity and investigates the impact of buffer and demand variations on leanness. It helps managers to analyse warehouse processes and design customized distribution policies in food, beverage and retail grocery warehouse.Practical implicationsProposed buffering model offers customized strategies beyond pre-set CSL by varying it dynamically to reduce wastages. The mathematical model deriving lean sizing and mitigation guidelines are constructive development for managers.Originality/valueThis research provides an inventive approach of VSM model and Mathematical algorithm endorsing lean thinking to design effective buffering policies in a forward warehouse.

Using smart lighting systems to reduce energy costs in warehouses: A simulation study

ABSTRACT Despite the various technical solutions for making lighting ‘smart,’ today’s lighting systems are often kept simple, and they are frequently not adjusted to user behaviours. This is especially the case for production and logistics facilities such as warehouses, where large areas have to be illuminated, and where lighting is often fully turned on while the warehouse operates. This paper presents a simulation model developed to evaluate the cost benefits potentially resulting from using smart lighting systems in warehouses. The simulation model allows for varying warehouse design and order picking process parameters. In addition, three different operating strategies for lighting systems representing different types of smart lighting technologies are implemented and compared to a conventional lighting system. A structured simulation study provides insights into how smart lighting systems interact with system design and process parameters, and how both collectively influence warehouse operating costs. The results of the simulation model and data obtained from a practical case indicate that smart lighting systems have great potential for reducing the energy consumption in warehouses relative to conventional lighting, and that, in addition to savings in cost, they can contribute to improving the environmental footprints of warehouses.

Order batching and picker scheduling in warehouse order picking

This article focuses on the integration of order batching and picker scheduling decisions while taking into account two objectives that have been considered in the literature, namely the minimization of both total travel time to collect all items and makespan of the pickers. This integrated problem not only occurs naturally in wave picking systems in which the latest picking time of orders becomes the key performance metric, but also arises when there is a limit on the picker operating time. We present models that result from combining these objectives and analyze their relationship through bounds. We propose a column generation-based exact algorithm for the integrated problem. The novelty of the proposed approach lies in the ability of efficiently solving the integrated order batching and picker scheduling problem to optimality by designing a column generation subproblem based on the set of batches, which makes it a challenging optimization problem due to its size. We alleviate this difficulty by reformulating this subproblem, which allows efficient implicit enumeration of its variables. We have also devised a Variable Neighborhood Search algorithm used as a subprocedure within the proposed exact solution algorithm. Finally, we conduct experiments on randomly generated instances and show that the proposed algorithms are capable of solving instances with up to 100 orders.

Effective Designing of Order Picking Systems Using Dynamic Simulation

In this article, we describe the use of dynamic simulation when designing an effective system for order picking within a distribution warehouse. The simulation model was created in the Witness software environment for discrete dynamic simulation and is a modification of a general simulation model of material flows in supplier systems. Using the example of a batch system for picking orders in a drugstore goods warehouse, we discuss the possibilities of using a general simulation model of material flows as an effective framework for the development of system support for warehouse processes using WMS. The simulation model is based on the possibility of dividing any material flow in the supply system into a finite number of movements with the possibility of using one of the sources and fulfilment of certain conditions. In order to achieve the required optimisation of the order picking system, which depends, in particular, on the unknown duration of goods collection at the picking location, and on the duration of goods sorting in consolidation, the “what-if” analysis has been used as a tool to measure the impact of uncertainty of one or more variables entering the model on the uncertainty of output variables. The study showed that minimisation of the number of physical elements in the model leads to a significantly higher speed of its operation. By means of dynamic simulation, it is possible to test a large number of variants of the picking system layout in a relatively short time and minimise the risk of erroneous decisions associated with the implementation of a suitable WMS.

Sensitivity Analysis Modeling for the Evaluation of the Order Picking Process Performance

Order picking is the process of recovering goods from the picking locations to fulfil a specific customer order, which is known to be the most laborious and expensive function of all the warehousing functions. However, the effectiveness of the preparation process depends primarily on managers decisions, which are often based on intuition and experience. Due to the increasing complexity of the processes, resulting mainly from the volatility of demand and the multitude and uncertain nature of operating parameters, these decisions are still far from providing an optimal mode of operation. In this paper, an approach of dynamic modeling and control of the manual order picking process is proposed in order to carry out a sensitivity analysis. First, the model of the order picking process with its performance indicators based on dynamic modeling will be presented by detailing the different components of the system. Then the process of sensitivity analysis based on Monte Carlo simulation that will be run on the Simulink software will be outlined in order to locate the parameters most influencing the model’s outputs. The proposed approach aims to determine the reactivity of the operating processes allowing compliance with defined operational performance, which, for example, helps warehouse mangers making optimal tactical decisions in terms of means and resources to implement in a warehouse order picking activity.

A method for efficiently routing order pickers in the leaf warehouse

Warehouse order picking is a critical operation for every supply chain because of its direct influence on customer satisfaction and the high time investment that is usually required for completing it. Hence, improving order picking efficiency can enhance both customer satisfaction and warehouse throughput. The most critical step in order picking is the travelling along the aisles of the warehouse, which accounts for a major share of the total warehouse operating cost. To minimize this cost for the special case of a warehouse with a leaf layout, the paper at hand proposes an efficient optimal order picker routing policy based on an Eulerian graph and a dynamic programming procedure. It further develops four simple routing heuristics, referred to as leaf S-shape, leaf return, leaf midpoint, and leaf largest gap, for guiding the order picker through the leaf warehouse. The performance of the routing heuristics is compared to the exact algorithm proposed in this paper in a systematic numerical experiment. Finally, we present some managerial insights for managing order picking in the leaf warehouse.