Efficiency Of Warehouse Research Articles

Penerapan Metode Fifo (First in First Out) dalam Menjaga Efektivitas Warehouse pada PT. Mutiara Luwuk Bintang Lestari

Mutiara Luwuk Bintang Lestari still manages inventory data manually in notebooks, which are prone to loss, damage, and miscalculations, resulting in inaccurate data. This study aims to examine the application of the FIFO (First In First Out) method in maintaining the effectiveness of inventory management in the warehouse of PT. Mutiara Luwuk Bintang Lestari. The research method used is qualitative with a descriptive approach. Data was obtained through direct observation and analysis of documents related to the inventory management system. The results of the study show that the application of the FIFO method has proven to be effective in improving the accuracy of inventory data, speeding up the process of managing goods, and reducing potential losses due to recording errors. The application of this method resulted in an increase in profits, as seen in the calculation of the cost of sales of Olympic Cabinets of Rp 191,600,000, Plastic Chairs of Rp 51,450,000, and Frontliner Seats of Rp 887,200,000. Thus, FIFO-based inventory systems provide significant benefits in improving the operational efficiency of a company's warehouse.

An exact analysis and comparison of manual picker routing heuristics

AbstractThis paper presents exact derivations of the first two moments of the total order picking time in a warehouse for three routing heuristics, under the assumption of random storage. The analysis is done for general order size distributions and provides exact closed expressions in terms of the probability generating function of the order size distribution. We also indicate how the methods and insights in this paper extend to different storage policies and multi-block warehouses. The exact results derived in this paper are used to investigate effects of routing heuristics, order size distributions and layouts on warehouse efficiency. As illustration, we model the warehouse as a queueing system. By using approximations of the average order-lead time in terms of the first two moments of the order picking time, we are able to find optimal warehouse layouts and batch pick sizes.

YOLOv8 Powered Solutions for Box Identification in Warehouses

In modern warehouse management, the ability to effectively identify and track boxes is critical for optimizing operations and reducing costs. This research investigates the application of YOLOv8 deep learning model for real-time box identification in warehouse environments. Three different approaches were evaluated: using a pre-trained YOLOv8 model, training the model with a dataset obtained from the Internet, and training the model with a custom dataset designed for this application. For the second and third approaches, the model was trained using Google Colab, and image annotation was performed using Roboflow. Each approach is thoroughly tested to assess the accuracy and robustness of the model under various conditions. The results demonstrate the strengths and limitations of YOLOv8 in different scenarios, providing valuable insights into its practical implementation for warehouse automation. This study highlights the potential of YOLOv8 as a useful tool for improving warehouse efficiency.

Configuration based on Industry 4.0 technologies as a step towards an affordable smart warehouse

The rise of Industry 4.0, marked by its focus on digitalization and intelligent technologies, is redefining traditional warehouse operations within smart cities. This new era leverages tools like Artificial Intelligence (AI) and the Internet of Things (IoT) to create automated solutions that address the complex challenges faced by modern urban centers. While smart cities grapple with interconnected issues, traditional methods often fall short. Data-driven, technology-powered solutions offer a more efficient approach. In the field of logistics, innovative warehouse solutions are emerging to address the growing complexities of the supply chain. One major challenge faced by many small businesses is inaccurate inventory knowledge, leading to inefficient warehouse management. This research proposes a cost-effective solution that utilizes Industry 4.0 technologies, specifically AI and IoT, to optimize inventory control and reorder point forecasting. This approach aims to democratize warehouse efficiency in smart cities by making it accessible to businesses of all sizes. This paves the way for a “smarter” future, upgrading warehouses with intelligent yet affordable solutions.

Revolutionizing Warehouse Efficiency with Shared and Class-Based Storage

This study addresses inefficiencies in the warehouse layout of a paper production company in Pasuruan, East Java, which led to delayed deliveries. By implementing shared storage (SS) using FIFO and class-based storage (CBS), the research aimed to optimize space and improve material handling efficiency. Data collection involved observation, interviews, and company records. The proposed layout reduced space usage from 59.45% to 16.95% and achieved significant time savings: 17.84% for brown paper, 8.84% for white paper, and 28.51% for custom-colored paper. The findings suggest that a systematic layout can greatly enhance warehouse efficiency. Future studies should consider the cost factors associated with these improvements. Highlights: Space Efficiency: Reduced usage from 59.45% to 16.95%. Time Savings: Up to 28.51% for custom-colored paper. Data-Driven: Used observations, interviews, and records for layout optimization Keywords: Warehouse layout, material handling, shared storage, class-based storage, efficiency improvement

The Effect of Integrated Warehouse Operation Efficiency on Organizations Performance

In the contemporary business landscape, warehouse efficiency has emerged as a cornerstone of organizational prowess, serving both as a centre of expertise and a strategic tool for competitive advantage. A well-functioning warehouse not only facilitates swift fulfilment of customer needs but also significantly enhances overall business performance. This study seeks to investigate the impact of warehouse attributes on efficiency, focusing particularly on layout and operations. Through a comprehensive literature review, we explore the role of warehouse design and operational practices in driving efficiency gains. Our analysis reveals that the configuration of a warehouse plays a pivotal role in determining its operational effectiveness, with optimized layouts facilitating rapid response to customer demands. Furthermore, effective warehouse operations are identified as critical components in enhancing overall efficiency, working synergistically with layout design to bolster performance. Concurrently, inventory management has emerged as a paramount concern for organizations worldwide, directly influencing operational success. Efficient inventory practices not only enhance customer service but also mitigate stock-related challenges.. By synthesizing insights from both warehouse efficiency and inventory management domains, this study aims to provide a comprehensive understanding of the strategies employed by organizations to optimize operational performance and drive sustainable growth. Keywords: Seamless integration of inventory practices, Efficient warehouse operations, Technological advancements in inventory control, Inventory tracking systems, Optimization of holding costs Technology-driven warehouse management

The Effects of Increased Distance Travel on the Operating Costs of Freight Truck Vehicles

Transportation industry is an important part of most countries’ economies with significant effects on productivity and social welfare. However, refrigeration has dramatically altered global food transportation efficiencies in the cold supply chain. Hence, a key factor to optimize transportation is by lowering operational costs, with distance travel having a considerable impact. The paper examines how the operational costs of different truck classes is influenced by the distance travelled. The operating cost equation was utilized to make a comparison between vehicle operating costs and to assess how these costs change in relation to travel distance. The equation consists of influential parameters such as fuel cost, driver wage, and maintenance cost of a vehicle. The result showed that the operating cost rate varies among trucks of different classes. According to the results of the equation and effective parameter analysis, 18-ton trucks had the highest cost, followed by 5-ton trucks. This is due to fact that the cost is influenced by factors of travel distance, tolls, and fuel consumption. Furthermore, based on transportation costs, a green practice framework can be constructed to reduce truck costs for each delivery while reducing CO2 emissions to the environment. By taking into account transportation cost factors in the framework of green practices, effective and practical information can be generated such as product delivery schedules to customers and better routes to improve business performance. Finally, effective transportation management can improve inventory flow by enhancing warehouse efficiency, reducing overall lead time, and saving on storage costs.

Last‐minute coordination: Adapting to demand to support last‐mile operations

AbstractIn the highly competitive e‐commerce industry, customer‐facing warehouses are crucial as the “order penetration points” for e‐commerce last‐mile operations. This research examines how warehouses use last‐minute coordination, an unstructured mechanism, to ensure sufficient inventory at the order penetration points. Previous research has focused on structured mechanisms like contracts and inventory management systems to enhance warehouse performance. However, these mechanisms can be ineffective when faced with unforeseen local contingencies. To adjust inventory and adapt to changes in supply and/or demand, warehouses need to engage in unstructured, last‐minute coordination with other warehouses. Using coordination and loose coupling theories, we find that coordinating with many warehouses (i.e., large coordination scope) reduces the operational efficiency of individual warehouses. At the network level, we find that a centralized coordination structure improves the operational efficiency of the entire network. We also show that demand uncertainty reinforces the existing last‐minute coordination patterns, using the Separable Temporal Exponential Random Graph Model (ST‐ERGM). This research highlights the importance of last‐minute coordination and reveals its effects on both individual warehouses and the overall network.

Enhancing Warehouse Efficiency With Time Series Clustering: A Hybrid Storage Location Assignment Strategy

Enhancing Warehouse Efficiency With Time Series Clustering: A Hybrid Storage Location Assignment Strategy

A classification approach to order picking systems and policies: Integrating automation and optimization for future research

Picking system optimization is the main condition for improving customer satisfaction and warehouse efficiency. However, related literature does not present a standard classification for the different picking systems and warehouses. This issue implies a lack of scientific uniformity and the difficulty of conducting comparative studies on which the best approaches are, applied to picking problems and/or warehouses. Therefore, this paper aims to provide a theoretical framework on the picking system classifications most used by researchers. The research is descriptive in nature and focuses on the two major categories of picking systems: i) according to automation strategies and processes; ii) according to picking optimization policies and methods. These classifications have been revised so that researchers can combine them with a wide range of picking systems and warehouses. The main contribution of this paper is to provide a better understanding of the main specificities regarding classifications inherent to picking systems and warehouses. We also demonstrate that approaches integrating people, warehouses, technological advances, and adaptation of multi- objective algorithms should be the focus of researchers. The theoretical framework presented corroborates with future studies aimed at managing and adapting picking systems and warehouses. We concluded that integrating picking strategies and policies is critical to dealing with technological advances in warehouses.

Optimization of Storage Location Assignment for Non-Traditional Layout Warehouses Based on the Firework Algorithm

With the development of logistics, sustainable warehousing has become increasingly important. To promote the warehousing efficiency, non-traditional layout warehouses and storage location assignments have been proposed separately. However, they are rarely combined. Taking inspiration from the advantages of non-traditional layout warehouses and storage location assignments, a storage location assignment optimization algorithm for non-traditional layout warehouses is proposed to further improve the efficiency and sustainability of warehousing. By reducing the picking distance and picking time, this algorithm further boosts the warehouse efficiency and sustainability, saving energy in the process and resulting in positive effects on the environment and the economy. In the process of establishing the model, taking the order-picking efficiency and shelf stability as optimizing objectives, a multi-objective optimization model is derived. Then, a storage location assignment optimization algorithm based on the firework algorithm is developed using adaptive strategies for explosion and selection to enhance the convergence rate and optimization performance of the algorithm. With this approach, the storage location assignment optimization for non-traditional layout warehouses can be handled well. Finally, a set of comparative simulations is carried out with MATLAB, and the results show various positive effects for sustainable warehouse management, such as a higher order-picking efficiency, better shelf stability, time and resource savings, and so on.

Improving Warehouse Efficiency Through Effective Inventory Management Practices

Several business establishments have used inventory management strategies to strike a balance between responsiveness and efficiency. This study's primary objective was to assess the effect of inventory management practices on the warehouse efficiency of supermarkets and retail stores in Kedah and Perlis, specifically supermarkets. This study utilises a quantitative method by using primary data from questionnaires and secondary data compiled from prior studies' results. Statistical Packages for the Social Science (SPSS) version 25 software was used to analyse the data. The relationship between the independent and dependent variables was determined using regression analysis. The outcome showed that the inventory reorders point and supplier partnership management variable were accepted. However, the information technology hypothesis was rejected. This study recommends that supermarkets and retail stores utilise the best inventory management strategies to increase warehouse efficiency. It enables the tracking of stock unit movement in the warehouse, facilitates stock counting in the warehouse, maximises the use of available warehouse space, improves inventory data accuracy, reduces inventory theft, and enhances inventory cycle counting.

A Discrete-Event Simheuristic for Solving a Realistic Storage Location Assignment Problem

In the context of increasing complexity in manufacturing and logistic systems, the combination of optimization and simulation can be considered a versatile tool for supporting managerial decision-making. An informed storage location assignment policy is key for improving warehouse operations, which play a vital role in the efficiency of supply chains. Traditional approaches in the literature to solve the storage location assignment problem present some limitations, such as excluding the stochastic variability of processes or the interaction among different warehouse activities. This work addresses those limitations by proposing a discrete-event simheuristic framework that ensures robust solutions in the face of real-life warehouse conditions. The approach followed embraces the complexity of the problem by integrating the order sequence and picking route in the solution construction and uses commercial simulation software to reduce the impact of stochastic events on the quality of the solution. The implementation of this type of novel methodology within a warehouse management system can enhance warehouse efficiency without requiring an increase in automation level. The method developed is tested under a number of computational experiments that show its convenience and point toward future lines of research.

The Role of Warehouse Layout and Operations in Warehouse Efficiency: A Literature Review

The Role of Warehouse Layout and Operations in Warehouse Efficiency: A Literature Review

A fast and accurate RFID tag positioning method based on AoA hologram and hashtables

RFID-based item localization for large-scale warehousing and inventorying applications has been gradually developing over the years. In these settings, fast and accurate item localization with low deployment cost is vital for inventory visibility and warehouse efficiency. However, existing tag positioning methods based on a single antenna offer either fast inference with low accuracy or high accuracy with slow inference. In this paper, we propose a phase-based SAR tag positioning method that offers both merits. Firstly, the traditional holograms formed in existing SAR methods are completely replaced by a one-step version of the recently proposed hologram mask (Angle-of-Arrival hologram), to accelerate the tag positioning task. Secondly, a pair of hashtables is implemented to output directly hologram grids from phase measurements, reducing further the hologram construction time. Thirdly, a pre-processing cross-validation stage tests the measurements at each operating frequency to determine the one leading to the lowest positioning error. After this, the model is trained and tested solely on this frequency, limiting both the inference time and on-site collection time. Finally, we efficiently infer the height information of tags from a single antenna moving along a rectilinear horizontal trajectory. Our comparative experiments show that our model achieves similar positioning accuracy to the state-of-the-art accuracy methods while offering a much lower inference time, in both short-range and long-range settings.

Economic rationale for determining the optimal layout of racks and aisles in enclosed warehouses for single piece cargo

The purpose of this study is to substantiate the effectiveness of the selection of options for the placement of racks and aisles in covered warehouses for single-piece cargo. As part of the study, the storage capacities were determined, considering the peculiarities of storage and movement of such goods. To achieve this goal, an algorithm was proposed for selecting the effective placement of aisles and racks in the warehouse. This algorithm considers various factors, such as warehouse capacity, interaction with various modes of transport and reduction of downtime of vehicles waiting for loading and unloading operations at these warehouses. The practical significance of this research lies in the fact that its results make it possible to improve the interaction of various modes of transport through the warehouse, increase the capacity of warehouses and reduce downtime of vehicles waiting to perform operations in these warehouses. This helps to reduce time costs, increase the efficiency and efficiency of warehouses, as well as improve the overall logistics chain of transportation of packaged goods.

Picker routing optimization of storage stacker based on improved multi-objective iterative local search algorithm

<p style='text-indent:20px;'>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.</p>

Improving Warehouse Efficiency Using Lean Six Sigma: A Case Study of a Third-Party Logistics Provider

Improving Warehouse Efficiency Using Lean Six Sigma: A Case Study of a Third-Party Logistics Provider

Performance Analysis of Picking Routing Strategies in the Leaf Layout Warehouse

The routing strategy for order picking is an important factor in the efficiency of warehouse picking, and improvements to the warehouse layout provide more routing options for picking. The number of storage locations to be visited during the picking operation also has an impact on the selection of routing strategies. In order to achieve an effective improvement in the efficiency of picking operations in warehouse distribution centers, this paper focuses on the leaf warehouse layout based on the previous single-command operation strategy and extends it to study the multi-command operation strategy, in which three heuristic routing strategies, the S-shape, the return, and the composite, are introduced to solve the walking distance problem of picking operations, with the study of the selection of the routing strategy for different numbers of storage locations to be visited. Based on the distance equation between any two storage locations to be visited in the leaf layout warehouse, travel distance models corresponding to the three routing strategies in the picking operation are constructed, and the cuckoo search algorithm is introduced to solve and calculate the travel distance of the composite strategies for the experiments. In addition, the computational experiments of the three path strategies are carried out according to the different numbers of storage locations to be visited in the picking operation. By analyzing the numerical results, we find that the composite strategy has the best overall results among the three routing strategies, with the average values of optimization rates exceeding 30% (the S-shape) and 40% (the return), respectively. At the same time, the return strategy outperforms the S-shape strategy when the number of locations to be visited is less than seven. As the number of locations to be visited increases, the S-shape strategy gradually outperforms the return strategy. From a managerial and practical perspective, compared to the single-command operation strategy that is the focus of the current research, the multi-command operation strategy we studied is more relevant to the actual situation of order merging picking in warehouses and can effectively improve the efficiency of picking operations, the competitiveness of enterprises, and customer satisfaction of e-commerce enterprises.

Stochastic models of routing strategies under the class-based storage policy in fishbone layout warehouses

In order to improve the picking efficiency of warehouses, shorten the time cost and promote the development of the logistics industry, this study analyzes the routing strategies in fishbone layout warehouses under the class-based storage strategy. The fishbone layout was divided into three storage areas for class A, class B, and class C items according to the proportion using the straight line, to meet the classification requirements of items. Under the class-based storage strategy, to evaluate the performance of the return routing strategy and the S-shape routing strategy, the stochastic models of the expected walking distance of the two routing strategies in the fishbone layout warehouse are established by calculating the sum of the expected walking distances in diagonal cross-aisles and picking aisles. Finally, the stochastic models of the two routing strategies are simulated and verified, and the impacts of the two routing strategies on walking distances are analyzed by comparing the expected distances under different ordering frequencies and space allocation strategies. The numerical results show that the return routing strategy has an advantage over the S-shape routing strategy when determining the relevant parameters of the fishbone layout and picking orders. Meanwhile, it also provides a theoretical basis for research on stochastic models of routing strategies in fishbone layout warehouses under the class-based storage strategy.