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The just-in-time concept, mass customization, omnichannel distribution, and the rising global population have all fueled the logistics sector. Consequently, using automation inside the warehouses to make them more dynamic and sustainable for the future is one of the crucial components to adapt to this quick shift. Giants in the industry and technology are becoming more interested in the "smart warehouse" system, built with innovative warehousing technologies, as an achievable solution for the development of warehouses in the future. To contextualize the past and provide light on prospective future directions, a study of current articles in the literature is important. This study evaluates works published in the previous 32 years related to flexible automation in warehouses to create a framework that future academics might use to guide them in establishing an original conceptual model that might be implemented at warehouses. One hundred eleven selected, examined, and categorized publications were published between 1990 and 2022 to establish a significant foundation for earlier research. Results indicated that combining automated machinery, collecting data technologies, and management systems are essential to creating a flexible automated warehouse. Finally, based on the examined literature, a flexible automated warehouse framework is suggested to direct future research in this field. [ABSTRACT FROM AUTHOR]

In warehouses with vast quantities of heavy goods, heavy-duty forklift Automated Guided Vehicles (AGVs) play a key role in facilitating efficient warehouse automation. Due to their large load capacity and high inertia, heavy-duty forklift AGVs struggle to automatically navigate optimized routes. Additionally, rapid acceleration and deceleration can pose safety hazards. This paper proposes a velocity-adaptive model predictive control (MPC)-based path tracking method for heavy-duty forklift AGVs. The movement of heavy-duty forklift-type AGVs is categorized into straight-line and curve-turning motions, corresponding to the straight and curved sections of the reference path, respectively. These sections are segmented based on their curvature. The best driving speeds for straight and curved sections were 1.5 m/s and 0.3 m/s, respectively, while the optimal acceleration rates were 0.2 m/s2 for acceleration and −0.2 m/s2 for deceleration in straight paths and 0.3 m/s2 for acceleration with −0.15 m/s2 for deceleration in curves. Moreover, preferred sampling times, prediction domain, and control domain were determined through simulations at various speeds. Four path tracking methods, including pure tracking, Linear Quadratic Regulator (LQR), MPC, and the velocity-adaptive MPC, were simulated and evaluated under straight-line, turning, and complex double lane change conditions. Field experiments conducted in a warehouse environment demonstrated the effectiveness of the proposed path tracking method. Findings have implications for advancing path tracking control in narrow aisles. [ABSTRACT FROM AUTHOR]

Recent dramatic increase in e-commerce has also increased the adoption of automation technologies in warehouses. Autonomous mobile robots (AMRs) are from those technologies widely utilized in warehouse operations. It is important to design the operation of those robotic systems in such a way that, they meet the current and future system requirements correctly. In this paper, we study flexible travel of AMRs in warehouses by developing smart deadlock and collision prevention algorithms on agent-based modelling. By that, AMR agents can interact with each other and environment, so that they can make smart decisions maximizing their goals. We compare the performance of the developed flexible travel system with non-flexible designs where there is a single AMR dedicated to a specific zone so that no deadlock or collision possibility takes place. The results show that AMRs may provide up to 39% improvement in the flexible system compared to its non-flexible design. [ABSTRACT FROM AUTHOR]

In this work, we present a novel design for an embedded Radio Frequency (RF) antenna array that can distinguish various materials by analyzing changes in Received Signal Strength Indicator (RSSI) values. The use of a low-cost and small-form-factor microcontroller by Espressif makes this design both cost-effective and suitable for integration into various applications, differentiating it from previous studies. To enhance the material classification performance, a combination of Kalman filter and Support Vector Machine is proposed which does not require a large amount of training data for model optimization. Results demonstrate that the proposed machine learning model is able to perform material classification within a 2 m range, with an average accuracy of over 96%. Such a system is well-suited for intelligent mobile robotic applications particularly in warehouse automation or smart manufacturing lines due to its ability for proximal remote sensing, real-time monitoring, and multimodal sensing. [ABSTRACT FROM AUTHOR]

Problem definition: This paper studies the single-warehouse assortment selection problem that aims to minimize the order fulfillment cost under the cardinality constraint. We propose two fulfillment-related cost functions corresponding to spillover fulfillment and order splitting. This problem includes the fill rate maximization problem as a special case. We show that although the objective function is submodular for a broad class of cost functions, the fill rate maximization problem with the largest order size being two is NP-hard. Methodology/results: To make the problem tractable to solve, we formulate the general warehouse assortment problem under the two types of cost functions as mixed integer linear programs (MILPs). We also provide a dynamic programming algorithm to solve the problem in polynomial time if orders are nonoverlapping. Furthermore, we propose a simple heuristic called the marginal choice indexing (MCI) policy that allows the warehouse to store the most popular products. This policy is easy to compute, and hence, it is scalable to large-size problems. Although the performance of MCI can be arbitrarily bad in some extreme scenarios, we find a general condition under which it is optimal. This condition is satisfied by many multi-purchase choice models. Managerial implications: Through extensive numerical experiments on a real-world data set from RiRiShun Logistics, we find that the MCI policy is surprisingly near optimal in all the settings we tested. Simply applying the MCI policy, the fill rate is estimated to improve by 9.18% on average compared with the current practice for the local transfer centers on the training data set. More surprisingly, the MCI policy outperforms the MILP optimal solution in 14 of 25 cases on the test data set, illustrating its robustness against demand fluctuations. History: This paper has been accepted as part of the 2021 M&SOM Data-Driven Research Challenge. Funding: This work was supported by the Singapore Ministry of Education (MoE) Tier 1 [Grant 23-0619-P0001]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.0428. [ABSTRACT FROM AUTHOR]

Direct Air Capture (DAC) offers a promising pathway for combating climate change by removing carbon dioxide (CO2) directly from the atmosphere. Here, we discuss Heirloom's approach to DAC, which uses naturally occurring minerals, namely, calcium carbonate (CaCO3), in a cyclic process that leverages warehouse automation systems previously developed for large warehouses. The integration of DAC with warehouse automation systems unlocks a degree of manufacturability, scalability, operational efficiency, and financial viability. For successful scaling, DAC technologies and project developers must think through key scalability constraints, including modularity, constructability, supply chains, and leveraging existing infrastructure. [ABSTRACT FROM AUTHOR]

Robotics and automation have developed as key technologies for supply chain management as a result of the increased demand for quicker and more effective supply chains. Robotics and automation improve supply chain management by lowering long-haul expenses, boosting work and usage strength, reducing errors, declining repetitive stock checks, updating orchestrating, taking care of times, and assembling induction to the problematic and hazardous places. Robotics aids in design, creation, etc. Automation helps to do tasks that are often done by people through the use of self-operating physical machines, computer software, and other technology. Despite being widely accepted as a tool to aid in decision-making, supply chain management (SCM) has very seldom used AI and ML. This article investigates several AI and ML sub-fields that are best suited for resolving real-world SCM-related issues in order to fully realize the potential benefits of AI and Ml for SCM. In doing so, this article examines the track record of successful AI and ML applications to supply chain management and highlights the most fruitful SCM domains to apply AI and ML. And also find out the how robotics and automations helps in warehouse management. The most recent developments in robotics and automation for supply chain management are thoroughly reviewed in this paper. We first give a general overview of the difficulties that supply chain management faces before going over the many ways that robotics and automation are used at various points along the supply chain. Additionally, we go over the advantages of robots and automation in supply chain management, including higher efficiency, accuracy, lower costs, and improved safety. Lastly, we discuss some of the present drawbacks and difficulties associated with robots and automation in supply chain management and suggest some possible directions for further investigation. [ABSTRACT FROM AUTHOR]

Purpose: The aim of the article was to analyze why customers are less willing to use the services of the described company, at what stages of warehouse processes the company wastes the most time, what to do to save time and what solutions to introduce to improve all activities. Design/methodology/approach: An SMED analysis was performed, which allowed the entire warehouse process to be presented step by step, how much time each stage takes and which of them takes the most time. Then, it was determined what solutions should be made to improve the process. Again, using SMED analysis, the times were verified and at what stage the savings were noticed. Findings: The conducted research allowed us to determine how important information technology is in current logistics and how the investments introduced allow for a sudden change in the services provided by the company. Research limitations/implications: The practical part is intended to reflect the actual compliance of the company with the imposed rules. Practical implications: Analysis conducted before and after introducing the permitted changes determine whether the problem has been resolved and what benefits the company has experienced. Test the time of individual processes allowed for the implementation of a new IT system, which contributed to the development of the company and allowed us to save a large amount of capital. Social implications: The aim of the proper selection of the strategy is to increase the efficiency of the process, shorten the duration and reduce costs. The main strategies are discrete picking, group picking and zone picking. Discreet picking involves performing single orders, when an employee can assemble items from only one order in one cycle. This is the simplest strategy and is often used when orders have a large number of items relative to media units. Originality/value: The article draws attention to the importance of a non-routine approach to the dynamics of picking capabilities in an enterprise. This is important for management as a scientific discipline, but also for the enterprise, which indicates various possible development paths. [ABSTRACT FROM AUTHOR]

Recently, there has been a heightened emphasis on employing smart technologies for evaluating logistics performance, and hence we have considered four key technologies, namely blockchain technology (BT), warehouse automation (WA), smart routing (SR), and cloud computing (CC), and measured their aggregate impacts on logistics performance. We have considered these four technologies as exogenous constructs, and the logistics performance factors as endogenous constructs. First, we generate 23 items or indicators under those technologies and measure their loadings to determine how strongly they are linked to each technology. Next, we determine how strongly technologies are linked to logistics performance factors. Finally, we test our theoretical model using partial least squares structured equation modeling (PLS-SEM). Our findings confirm that the constructs and indicators in our theoretical framework meet the criteria, supported by PLS-SEM results and fit indices. This study advances logistics theory by highlighting smart technology adoption in practice and supporting institutional theory.

The article explores the potential benefits of combining automation and artificial intelligence (AI) in scientific laboratories. It discusses the use of self-driving labs and cloud labs to improve efficiency and speed in various scientific fields. While there is excitement about the possibilities of autonomous laboratories, the article acknowledges the need for human supervision and the challenges associated with implementing these technologies. It highlights examples of successful self-driving labs and emphasizes the importance of improved automation in leveraging machine learning in scientific research. The article presents a balanced view of the current state and future potential of AI in scientific research. [Extracted from the article]

The article on the effectiveness of using cargo warehouses at Soekarno Hatta Airport is a scientific literature review article within the scope of operational management science. This article aims to create a hypothesis regarding the relationship between factors, which can then be used for further research in the field of operational management. Descriptive qualitative research methodology was used in this research. The data used in this research comes from previous research which is still relevant to the current investigation. Data was collected from leading academic online platforms, including Publish or Perish, Google Scholar, digital reference books, and journals. The findings of this research are as follows: 1) Loading and unloading equipment influences the effectiveness of warehouse use; 2) The frequency of imported commodities influences the effectiveness of warehouse use; 3) International trade influences the effectiveness of warehouse use; and 4) The regulatory system influences the effectiveness of warehouse use. [ABSTRACT FROM AUTHOR]

This study analyzes the changes that have occurred in food logistics in the three years since the emergence of the COVID-19 pandemic and the one year since the war in Ukraine commenced. Food logistics companies are highly sensitive to demand shocks, energy prices, and staff availability. In this study, "first-hand" information was collected in the Iberian Peninsula, and it showed a process of Schumpeterian transformation. This crisis environment in which food logistics companies have been operating has opened a unique opportunity to renew operating procedures and seek new solutions, products, and markets. Therefore, food logistics companies have developed more effective communication strategies and innovative, profitable, and forward-looking commercial strategies to adapt to the new needs of their clients, applied more efficient transport planning and management methods, implemented new technologies to increase automation and digitization in warehouses, transport platforms, and trucks, and boosted market concentration and investment in infrastructure. Therefore, public authorities and top executives must focus on promoting and facilitating these improvements. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to delve into the effectiveness of third-party logistics (3PL) providers in inventory management for retail businesses operating in the e-commerce sector. Design/methodology/approach: The study focuses on analyzing a conducted case study. The case study aims to compare the currently functioning inventory management system with the system proposed by the 3PL provider for managing the location of SKUs in the warehouse. Findings: The research has allowed for a positive verification of the posed hypothesis (i.e., a 3PL company is capable of better inventory management for an e-commerce service recipient). The 3PL provider has the potential to implement an improved inventory management system based on location management and automated storage processes built upon the logistic service provider's know-how. Research limitations/implications: The primary limitation of the research lies in presenting results based solely on a single case study and the instance of a logistics operator with significant experience and an established market position. Originality/value: This article constitutes a significant contribution to the expanding spectrum of research concerning the role of third-party logistics (3PL) providers in enhancing inventory management processes, particularly in the context of e-commerce. The added value lies not only in confirming the positive affirmation of the research hypothesis but also in highlighting the immense potential that 3PL providers possess in implementing advanced inventory management systems based on location management strategies and warehouse process automation. [ABSTRACT FROM AUTHOR]

Direct Air Capture (DAC) offers a promising pathway for combating climate change by removing carbon dioxide (CO2) directly from the atmosphere. Here, we discuss Heirloom’s approach to DAC, which uses naturally occurring minerals, namely, calcium carbonate (CaCO3), in a cyclic process that leverages warehouse automation systems previously developed for large warehouses. The integration of DAC with warehouse automation systems unlocks a degree of manufacturability, scalability, operational efficiency, and financial viability. For successful scaling, DAC technologies and project developers must think through key scalability constraints, including modularity, constructability, supply chains, and leveraging existing infrastructure.

Warehouse/distribution center (DC) automation technology for the retail industry promises to reduce operational costs, improve flexibility and response time for customers, and help improve network productivity, thus making it very relevant for omni/multichannel (OC/MC) settings. However, the investment required to acquire the DC automation technology is high, and hence, the investment decision must be operationally and financially comprehensive. In fact, an automated DC has a network-wide impact: it can benefit players in the network, but in turn is exposed to network risks and the investment must be safeguarded. While the need for a comprehensive decision-making framework and safeguarding strategy is stressed by scholars, such a framework is lacking. Further, corresponding integrated sub-frameworks for key elements in the OC/MC value chain are also missing. In this paper, we address these gaps and contribute by providing a) generalized and integrated three-part framework, b) corresponding sub-frameworks, c) discrete event, economic, and math programming models, d) rapid-sizing/analysis tools based on: i) analysis at the DC-level, ii) network level, iii) economic/business level, and iv) contract level (sustainable supplier/distribution relationship). In this reference, we investigate a new generation ‘full-case’ technology that has been recognized as a key to warehouse automation. The insights from our research inform several strategic tradeoffs (extent of automation, investment in labor vs. capital, response vs. efficiency, and sustainable supplier management) relevant for decision-making and safeguarding an expensive asset such as an automated DC. Our analysis is based on interviews (retailers, automated and conventional DCs, and DC equipment suppliers), on-site observations, secondary data, and learning from analytical models. We also present an illustrative real-life application/case study of the framework and the modeling details in the E-component.

We consider the problem of optimal unsignalized intersection management, wherein we seek to obtain safe and optimal trajectories, for a set of robots that arrive randomly and continually. This problem involves repeatedly solving a mixed integer program (with robot acceleration trajectories as decision variables) with different parameters, for which the computation time using a naive optimization algorithm scales exponentially with the number of robots and lanes. Hence, such an approach is not suitable for real-time implementation. In this paper, we propose a solution framework that combines learning and sequential optimization. In particular, we propose an algorithm for learning a shared policy that given the traffic state information, determines the crossing order of the robots. Then, we optimize the trajectories of the robots sequentially according to that crossing order. This approach inherently guarantees safety at all times. We validate the performance of this approach using extensive simulations and compare our approach against 5 different heuristics from the literature in 9 different simulation settings. Our approach, on average, significantly outperforms the heuristics from the literature in various metrics like objective function, weighted average of crossing times and computation time. For example, in some scenarios, we have observed that our approach offers up to 150% improvement in objective value over the first come first serve heuristic. Even on untrained scenarios, our approach shows a consistent improvement (in objective value) of more than 30% over all heuristics under consideration. We also show through simulations that the computation time for our approach scales linearly with the number of robots (assuming all other factors are constant). We further implement the learnt policies on physical robots with a few modifications to the solution framework to address real-world challenges and establish its real-time implementability.

The aim of this work is to introduce a system for environmental monitoring within a 4.0 automated warehouse. In the era of globalization, the emphasis on meeting customer needs and establishing a sustainable supply chain has become essential for companies striving to maintain competitiveness in the market. Logistics, in this context, has emerged as a pivotal factor in gaining a competitive advantage. The challenge for companies lies in assessing the impact of ongoing changes and developing a robust logistics strategy to optimize operational efficiency, reduce costs, and mitigate environmental impact. The automation of warehouses is often the solution adopted by companies to meet these new market demands. Through a meticulous analysis of costs and benefits, many companies have recognized the advantages offered by automated solutions in designing their warehouses. However, it is crucial to evaluate not only the economic aspects but also those related to sustainability. European regulations and growing environmental concerns necessitate companies to adopt sustainable practices, reducing environmental impact, energy resource usage, and promoting social well-being. In this context, performance monitoring using specific key performance indicators (KPIs) becomes crucial. They allow for the assessment and monitoring of operational efficiency, resource utilization, and the environmental impact of the automated warehouse. This enables the identification of areas for improvement and the implementation of strategies for sustainable warehouse management. The availability of data that automated warehouses can provide through horizontal integration with Enterprise Resource Planning (ERP) systems further facilitates this process. These systems generate important logistics-related data, such as management control, budget, or production data. [ABSTRACT FROM AUTHOR]

This article explores the reorganization of warehouse activities to improve efficiency and minimize processing time. It discusses the different operations involved in warehouse activities, such as unloading, storage, order preparation, and loading of goods. The article identifies various problems that can occur during these operations, such as delays, resource unavailability, and issues with registration and traceability. It suggests solutions such as implementing surveillance and monitoring of goods, improving safety and working conditions, and implementing scheduling and organization methods. The article emphasizes the importance of efficient warehouse management to minimize time and cost in order processing. It also highlights the significance of control and surveillance to prevent theft of goods and the use of technology for automated inventory management. The conclusion suggests the potential for further research in diagnosing and predicting warehouse problems and improving decision-making. [Extracted from the article]

Purpose: The main goal of managing a simulation project in a manufacturing company regarding the optimization of warehouse preparation and placement of galvanized steel profiles using FlexSim software is to improve logistics processes in the warehouse and increase production efficiency. The aim of the research is to develop an effective package addressing scheme and to introduce a special code that will enable precise management of profile storage and will improve the process of completing customer orders. Design/methodology/approach: The simulation project management methodology is based on several key steps: defining goals and requirements, collecting data and modeling, designing scenarios, performing simulations, and designing an individual addressing scheme for parcels in the warehouse. Findings: Simulations allow you to find the optimal warehouse layout that ensures effective use of the available space. This, in turn, allows you to increase the storage capacity and better organize the storage of profiles, which translates into minimizing losses and costs. Simulations allow you to test different strategies for placing profiles in a warehouse to minimize the time and effort needed to find them. Optimizing the placement process allows parcels to be located faster and more precisely, which speeds up order picking. Research limitations/implications: Simulation-based process optimization requires model accuracy and reliability. Imprecise or inaccurate data can affect simulation results and lead to inappropriate decisions. This requires careful data collection and model validation before implementation. Further research may focus on optimizing routes inside the warehouse and optimizing internal logistics. Investigating the best routes and methods of transport will minimize the time your products pass through your warehouse. Research can introduce an element of random events, such as machine breakdowns or fluctuating demand, into the simulation model to better reflect real conditions and increase the reliability of the results. Practical implications: Improvement of warehouse processes and the introduction of a special code addressing the shipment can significantly reduce the risk of errors and material losses, which contributes to greater accuracy and efficiency of operation and improvement of customer relations. Social implications: Effective optimization of warehouse and production processes contributes to increased work efficiency. Reduction of redundant activities, more precise management of resources, and more efficient order picking can affect employee satisfaction as well as positively affect working conditions and security. Originality/value: The key element of originality is using advanced FlexSim software for modeling and simulation of storage processes and producing galvanized steel profiles. Simulations of this type integrated with real data and the implementation of a special package addressing code constitute a comprehensive approach to optimizing the entire process. The results of such a study can have a significant impact on the practice in manufacturing companies, enabling more effective warehouse management, shortening the time of picking orders, reducing costs, optimal use of resources, and improving the quality of customer service. It is an interdisciplinary approach that combines aspects of production management, logistics, process optimization, and the use of advanced IT tools. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of the presented research and analysis was to create design assumptions for a vision system supporting security supervision in the warehouse space to reduce threats. Design/methodology/approach: In the first phase of creating the project concept, two methods of hazard analysis (FTA and ETA) were used in the warehouse facility. Then, at the stage of selecting the devices included in the video surveillance system, a simple FOKUS program was used to scale the cameras. Findings: In order to create a vision system project for a selected warehouse facility, an analysis of its spatial structure, weak points and identification of possible threats was carried out, taking into account the specificity of work performed within the working space of the warehouse. Then, video surveillance zones were determined based on the classification of threats in the space of the facility. The devices included in the vision system were also selected according to the assumed functions of this system and their distribution in the tested object. In addition, the optimal parameters of the cameras (using a scaling program) and the characteristics of other necessary devices of the vision system were determined. Practical implications: The solution can be an aid in making an investment decision by the management in order to improve security management in the examined warehouse facility of company X. Originality/value: As a result of the analyses, an optimal and original design solution for the vision system was obtained for the tested warehouse in company X. [ABSTRACT FROM AUTHOR]

In recent years, with the improvement of people's living standards, online shopping has become an indispensable part of people's lives. The rapid development of e-commerce has brought unprecedented opportunities to the express delivery industry. Therefore, modern manufacturing enterprises must shorten the cycle from order to delivery to be successful. The study of machine learning (ML), which integrates computer science, statistics, pattern recognition, data mining, and predictive analytics, has become one of the most significant areas of research in the last few decades. It has also established itself as a cornerstone in terms of applications, making significant progress in modern information technology and practice. This paper used the capabilities of one of the powerful paradigms of ML called reinforcement learning (RL) and soft computing to improve the warehouse automation process while taking market demands into account. Since stackers and Automatic Guided Vehicles (AGV) are the main participants in this automation process, we focused on these two in our research to enhance the warehouse logistic scheduling process as a whole. To accomplish this, we collected historical data related to warehouse operation from the warehouse environment, such as AGV and stacker moments, inventory level, job execution time, and other pertinent factors. We first created an RF-based model using the Q-learning technique, one of the RF approaches, before using these data for the model training. The model designing is accomplished by first formulating the logistic scheduling problem as a Markov Decision Process (MDP), where the warehouse system changes between states and takes actions to maximize a cumulative reward over time. After that, we performed a number of operations, including state representation, action space definition, and reward design, to transform the problem into a format that the Q-learning approach can handle. In four experiments, the design model is trained using the data that has been collected up to 100 episodes. The proposed model is further improved with soft computing approaches such as fuzzy control methods. We utilized MATLAB and Plant simulation software to conduct the experiments. The results of the proposed model are thoroughly evaluated and compared with already existing approaches. [ABSTRACT FROM AUTHOR]

Order-picking optimization in a business sustainable competitiveness context is challenging due to prior studies focusing on theoretical model development with unrealistic assumptions in their algorithms and methodological validation, often neglecting practical concerns. This paper improves order-picking operations by employing combinatorial optimization as a travelling salesman problem and class-based dedicated storage models for the ATP company. The paper's originality and novelty lie in bridging the gap between academia and management, presenting an effort to connect theoretical concepts with practical optimization in order-picking warehouse operations in an environment of competitiveness. Realistic data and LINGO software were employed, revealing substantial improvements in the ATP warehouse operations through optimized pick path decisions embedded in warehouse layouts. This paper provides managerial tools for distance traveled optimization in the warehouse that yield competitive edges, enhanced supply chain efficiency and effectiveness, as well as other positive impacts on social, and environmental concerns such as labor safety, customer satisfaction, energy consumption, and CO2 emission. The paper also outlines future research directions to advance warehouse management and address sustainable competitiveness challenges, adding a new dimension to the original research. [ABSTRACT FROM AUTHOR]

This paper presents a detailed analysis of the critical role and complications surrounding withholding tax processing in the asset management industry. The paper first explores the substantial losses incurred by the industry due to a lack of proper attention and expertise in withholding tax matters, demonstrating the urgency of this issue. It then examines the current approach of relying predominantly on custodian banks for managing these issues, highlighting the shortcomings and risks inherent in this practice. The paper further investigates the current state of asset management companies, revealing their limited capabilities in managing withholding tax efficiently due to various factors including cost pressures, technological gaps and lack of awareness. To address these challenges, the paper proposes two primary options for asset managers: increased collaboration with tax advisers, and automation through software solutions. A detailed comparison is made on the pros and cons of these options. Collaborating with tax consultants, while beneficial in many ways, might fall short due to the complexity and volume of the withholding tax challenges. On the other hand, automation can provide significant benefits in terms of efficiency, accuracy, scalability, compliance and cost-effectiveness, although challenges related to implementation and integration exist. The paper concludes with a checklist of critical success factors for selecting, implementing and operating withholding tax processing software, providing practical guidance for asset management companies seeking to improve their handling of withholding tax matters. These findings underline the importance of a proactive approach and the necessity for asset managers to explore the potential of technology-based solutions in this crucial area. [ABSTRACT FROM AUTHOR]

This study investigates priority assignment rules (PARs) for transaction processing in automated warehouses featuring a shuttle-based storage and retrieval system (SBSRS). By incorporating real-time data tracking through agent-based modeling, the research explores the unique aspect of the SBSRS design, which involves flexible travel of robotic order picker shuttles between tiers. The paper proposes PARs under agent-based modeling to enhance multi-objective performance metrics, including average flow time (AFT), maximum flow time (MFT), outlier transaction AFT, and standard deviations of flow times (SD) within the system. Experimental evaluations are conducted with various warehouse designs, comparing the results against commonly used static scheduling rules. The findings demonstrate that real-time tracking policies significantly improve system performance. Specifically, prioritizing the processing of outliers based on transaction waiting time enhances MFT, SD, and other performance metrics, while minimizing adverse effects on AFT. Certain rules exhibit notable improvements in MFT and SD, while others achieve the lowest AFT values among all experiments. This paper contributes to the existing literature by presenting a multi-objective performance improvement procedure and highlighting the advantages of real-time data tracking-based scheduling policies in automated warehousing systems. [ABSTRACT FROM AUTHOR]

Technology is dynamically transforming the supply chain landscape, triggering various demands on managerial competence to meet the requirements. The global boom in warehouse technology is attributed to the surge in e-commerce. Rapid innovations in hardware and software promote automation. This study underscores the importance of these technologies in enhancing warehouse operations, acknowledging the critical decisions managers make in selecting appropriate technologies. While automation in warehousing has flooded the market with customized offerings, there is a dearth of standardized guidelines. This study evaluates proven technology, providing a guideline for its assessment. It aims to provide handy lessons that are helpful throughout a tech upgrade project. [ABSTRACT FROM AUTHOR]

Motivated by collaboration with a prominent fast-fashion retailer in Europe, the researchers focus their attention on the one-warehouse multistore (OWMS) inventory control problem, specifically addressing scenarios in which the demand distribution is unknown a priori. The OWMS problem revolves around a central warehouse that receives initial replenishments and subsequently distributes inventory to multiple stores within a finite time horizon. The objective lies in minimizing the total expected cost. To overcome the hurdles posed by the unknown demand distribution, the researchers propose a primal-dual algorithm that continuously learns from demand observations and dynamically adjusts inventory control decisions in real time. Thorough theoretical analysis and empirical evaluations highlight the promising performance of this approach, offering valuable insights for efficient inventory allocation within the ever-evolving retail industry. Motivated by our collaboration with one of the largest fast-fashion retailers in Europe, we study a two-echelon inventory control problem called the one-warehouse multistore (OWMS) problem when the demand distribution is unknown. This system has a central warehouse that receives an initial replenishment and distributes its inventory to multiple stores in each time period during a finite horizon. The goal is to minimize the total expected cost, which consists of shipment, holding, lost-sales, and end-of-horizon disposal costs. The OWMS system is ubiquitous in supply chain management, yet its optimal policy is notoriously difficult to calculate even under the complete demand distribution case. In this work, we consider the OWMS problem when the demand is censored and its distribution is unknown a priori. The main challenge under the censored demand case is the difficulty in generating unbiased demand estimation. In order to address this, we propose a primal-dual algorithm in which we continuously learn the demand and make inventory control decisions on the fly. Results show that our approach has great theoretical and empirical performances. Funding: The work of M. Gümüş was supported in part by research grants from the Natural Sciences and Engineering Research Council of Canada [Grant 217601 NSERC RGPIN-2019-06091]; and the Institute for Data Valorization [IVADO G254088]. The work of S. Miao was supported by the Strategic Management Society Strategy Research Foundation [Grant SRF-2015DP-0063]; the Social Science and Humanities Research Council of Canada [Grant 752-2014-0378]; and the Natural Sciences and Engineering Research Council of Canada [Grant G259160 NSERC RGPIN-2022-03247]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2021.0694. [ABSTRACT FROM AUTHOR]

The benefits of multi-robot systems are substantial, bringing gains in efficiency, quality, and cost, and they are useful in a wide range of environments from warehouse automation, to agriculture and even extend in part to entertainment. In multi-robot system research, the main focus is on ensuring efficient coordination in the operation of the robots, both in task allocation and navigation. However, much of this research seldom strays from the theoretical bounds; there are many reasons for this, with the most-prominent and -impactful being resource limitations. This is especially true for research in areas such as multi-robot path planning (MRPP) and navigation coordination. This is a large issue in practice as many approaches are not designed with meaningful real-world implications in mind and are not scalable to large multi-robot systems. This survey aimed to look into the coordination and path-planning issues and challenges faced when working with multi-robot systems, especially those using a prioritised planning approach, and identify key areas that are not well-explored and the scope of applying existing MRPP approaches to real-world settings. [ABSTRACT FROM AUTHOR]

The building industry is strongly reliant on materials, which account for 55%-60% of its expenses. However, due to outdated and time-consuming approaches, inadequate inventory management prevails. This is where developing technologies such as Deep Learning (DL) might help uncover solutions. Surprisingly, very little scientific research on DL has been conducted for this purpose. As a result, this study looks into the prospect of automating construction warehouse management by employing CNN for object detection and counting. During the initial investigation, 23 studies out of 26 used Convolutional Neural Networks (CNN) for image processing and object detection. Secondly, a model was developed and compared its accuracy to that of human counting and discovered that the model outperformed people. Industry professionals were interviewed to discuss the findings. Industry professionals highlighted the advantages and disadvantages of using such an automated system in construction warehouses. In conclusion, this study shows that the CNN base model outperforms people in counting materials, and the proposed automated inventory management system has significant industry potential.

In the process of warehouse automation, the efficient picker routing strategy is one of the key factors to improve warehouse efficiency. According to the order and the picking characteristics of the warehouse stacker, the picker routing of the stacker is regarded as the prize-collecting vehicle routing problem (PCVRP). According to the path and order for stacker picking, we designed a picker routing optimization strategy based on improved multi-objective iterative local search (IMOILS) algorithm. An elite pool strategy was set to save the high-quality solutions in the iterative process, and the nondominated solutions in the iterative evolution were stored and updated. Adding the adaptive enhanced perturbation strategy was beneficial to the current solution to escape from local optimum. In the local search procedure, to limit the excessively large search space, the epsilon constraint was adopted to limit the significant deterioration in another objective direction. The computational results showed that the proposed algorithm is superior to the comparison algorithms in the number of nondominated solutions, the hypervolume, and the spacing metric, and the stability of the algorithm is also good, which not only verifies the effectiveness of the proposed algorithm, but also provides a feasible optimization scheme for the storage stacker's picker routing problem. [ABSTRACT FROM AUTHOR]

Parts kitting is a frequently adopted method to feed parts from a warehouse to an assembly line. While kitting allows minimisation of space occupation and Work In Process holding cost at the workstations, also simplifying materials flow and supporting manual assembly, it is penalised by high workforce cost for manual kits preparation. For this reason, automated kitting systems have been also proposed although their practical application is somewhat limited owing to the relevant capital investment and scarce performances of robotised kitting systems. Moreover, economic models allowing a quantitative comparison of manual and automated kitting systems are not yet available in the literature. As a contribution to fill this gap, in this paper a cost model useful to compare manual and automation-assisted parts retrieval and kitting systems is presented. The model can be used as a decision-making tool when planning and justifying kitting systems. The model includes capital investment, workforce, space occupation and quality costs. A case study as an application example is also included to demonstrate the model capabilities. Results show that automation-assisted kitting systems can be competitive in comparison to manual kitting and that break-even conditions can be also identified by the proposed method. [ABSTRACT FROM AUTHOR]