Smart Warehouse Research Articles

Smart Agriculture Crop Management Warehouse

In a developing country like India, agriculture is one the most important sectors in terms of financial gain and providing healthy foods to survive. Agriculture is the primary occupation in our country for ages and most of the population depend on it. Now-a-days farmers are facing huge losses due to some storage requirements, while storing agricultural products. A Farmer should have knowledge about the condition of their agricultural crops. Farmers are keeping their agricultural crops in warehouses to increase the lifetime of food grains. Food plays a significant vital role in life, once it involves food security that is littered with each food loss and food wastages. Reports suggests that almost fifty proportion of the food made world-wide doesn't even reach the individuals as they're wasted throughout harvest, transportation, or storage. One in all the point that threats to the farmers is that the loss throughout storage of crops in granaries and warehouses. If crop losses are reduced, it'll mechanically increase the number of food accessibility. To solve this problem one solution is that this project includes smart warehouse management and monitoring farming field, which includes temperature maintenance, humidity maintenance, fire alarms, stock measurement, Pest control. To assist and help the farmers who protects the crops by storing in warehouse, a fully automated enabled observation system is planned to be deployed in remote areas wherever the accessibility is incredibly minimum for farmers with smart storage facilities to scale back food losses and increase food safety. Keywords: Agricultural crops monitoring and controlling, fully automated technology, Sensors, Warehouse management system.

Towards the design of a smart warehouse management system for spare parts management in the oil and gas sector

IntroductionThe oil and gas production industry requires rapid changing. As oil and gas companies around the world are called upon to decarbonize, several sectors within the industry are attempting to find new ways of working to achieve decarbonization. One of those sectors is spare parts warehousing. During the past decades of oil and gas production, spare parts warehousing has not been prioritized in optimization measures. Warehouses have been filled with material, all activities have been performed manually, and financial expenditure has been high. Now, a large oil and gas production company in Norway is looking to optimize its spare parts warehousing to streamline its logistics activities and increase sustainability.MethodThis study addresses the optimization ambitions of the company and proposes key design elements of a smart warehouse management system using Industry 4.0 technology implementation. The study is conducted by performing a review of relevant literature and company ambitions, before embarking on a qualitative design measure to contribute to the company’s innovative success.ResultsThe study proposes optimization of the warehouse activities goods receipt, issuing of stocked goods, and inventory count. Additionally, the digital supply chain of tomorrow and the technical architecture of a track and trace system in the warehouse is suggested.DiscussionThe study’s results can be used in practical implications and provide a foundation for future research. Future research is suggested to include extensive visual simulations, practical implementation studies, and studies on long-term effects of implementation.

Leveraging Artificial Intelligence for Optimizing Logistics Performance: A Comprehensive Review

Objective - Artificial Intelligence (AI) has become a pivotal technology in transforming logistics performance. This paper aims to comprehensively understand how AI-enabled solutions improve efficiency, accuracy, and responsiveness in logistics operations. The study focuses on synthesizing current research to explore AI applications across various logistics domains, such as predictive analytics, autonomous vehicles, and smart warehousing. Methodology/Technique – A systematic review approach was used to gather and analyze existing literature on AI applications in logistics. The review covered studies published in recent years, highlighting the advancements and impact of AI on logistics processes. The methodology included selecting relevant sources, categorizing AI applications, and assessing their effects on different logistics functions. Finding – The findings reveal that AI adoption substantially improves logistics operations, including enhanced operational performance, cost reduction, and increased customer satisfaction. Specific AI applications, such as predictive analytics for demand forecasting, autonomous vehicles for transportation, and smart warehousing for inventory management, were identified as key contributors to these improvements. However, challenges such as data privacy concerns and integration complexities were also noted. Novelty – This study's novelty lies in its comprehensive analysis of AI applications across various logistics domains, offering a holistic view of AI's role in optimizing logistics performance. This paper highlights the benefits of AI adoption and addresses the associated challenges, providing insights into future research directions and practical implications for leveraging AI in logistics. Type of Paper: Review JEL Classification: C61, C62, D83. Keywords: Artificial Intelligence; Logistics; Performance Improvement; Predictive Analytics; Autonomous Vehicles; Smart Warehousing Reference to this paper should be referred to as follows: Fatorachian, H. (2024). Leveraging Artificial Intelligence for Optimizing Logistics Performance: A Comprehensive Review, GATR-Global J. Bus. Soc. Sci. Review, 12(3), 146–160. https://doi.org/10.35609/gjbssr.2024.12.3(5) _____________________________________

THE IMPACT OF LOGISTICS 4.0 ON SMART WAREHOUSE MANAGEMENT: EXAMPLES FROM COMPANIES IN TÜRKİYE AND A BIBLIOMETRIC ANALYSIS FOR FUTURE TRENDS*

This study examines the applications of smart warehouse systems in Türkiye from the perspective of Logistics 4.0. In the context of the advancements in digitalization and automation brought about by Industry 4.0, smart warehouse systems play a pivotal role in optimizing warehouse operations and enhancing supply chain management. The study provides a detailed account of how leading Turkish companies, including Arçelik, Yemek Sepeti, and Borusan Lojistik, integrate these technologies and highlights the advantages of such integration. The study underscores the strategic importance of smart warehouse systems in Türkiye’s logistics sector, emphasizing findings that these technologies increase operational efficiency, enable real-time monitoring, reduce costs, and enhance customer satisfaction. Another significant aspect of the study is a bibliometric analysis conducted using the keywords "smart warehouse" in the Web of Science database. The objective of this method is to conduct a systematic review of existing scientific literature in order to identify new research trends and areas of interest. This analysis significantly contributes to the deepening of the existing knowledge base in academic circles and illuminates future research directions in this field. In conclusion, the objective of this study is to provide a comprehensive analysis of the current state of smart warehouse systems in Türkiye and to offer strategic recommendations for digital transformation processes in the logistics sector.

The Adoption of Smart Warehouse Technology in Thailand

The Adoption of Smart Warehouse Technology in Thailand

A simulation-based Digital Twin for smart warehouse: Towards standardization

Cyber–physical systems are developed to meet the need to improve process flexibility, optimality, and transparency through Digital Twin (DT). Unfortunately, the application of DT is still limited in practice, and there is no standard way to achieve integration. An Asset Administration Shell (AAS) appears as a promising concept for realizing DT in a standard manner. A literature review shows that most studies only reached static DT and only considered a few specific assets, especially unmovable ones. This study contributes to DT development by proposing 3D-based computer simulation technology as dynamic DT based on the AAS Framework. The proposed concept enables DT to conduct dynamic monitoring, optimization, and direct controlling. A smart warehouse in Taiwan is used to verify the proposed concept. Assets considered include Automated Guided Vehicles (AGVs), Operator, Conveyor, Forklift, and Storage Rack. AAS structure, simulation model built in FlexSim software, and system integration architecture have been constructed. Two advantages of using simulation for DT are demonstrated: multi-objective simulation–optimization for AGV capacity planning and creating dynamic dashboards. Based on the proposed concept, industry 4.0 scenarios can be integrated comprehensively.

A digital twin-based modularized design approach for smart warehouses

ABSTRACT The rapid emergence and widespread adoption of next-generation information technologies have led to a growing recognition of the need for digitalized warehouse designs. However, creating a digital replica of a physical warehouse in a virtual environment is a complex task. This research introduces a framework for designing smart warehouses based on digital twins, consisting of four key steps: (1) defining the dimensions of the digital twin, (2) establishing a digital twin framework that encompasses the physical warehouse, digital twin, and design processes, (3) implementing modularization techniques for the digital twin, and (4) operating the smart warehouse based on the digital twin. To validate the proposed framework, we present a detailed case study involving a semiconductor manufacturing plant. The results demonstrate the effectiveness of the digital twin-based smart warehouse design and its operational processes.

A decision-making framework for determinants of an organisation’s readiness for smart warehouse

Demanding, competitive business dynamics drive organisations to continually evaluate and adopt automation in their structural setups, particularly warehouses, for growth and profitability. Industry 4.0 technologies are revolutionising warehouse operations, resulting in cost savings, increased efficiency, and a more environmentally friendly approach for businesses. The readiness of organisations to adopt these technologies is evaluated with the help of hard and soft determinants selected through a theoretical framework of Technology–Organisation-Environment (TOE). In this study, a unique method is used that combines the Modified Delphi method, content validity for scale validation, expert opinion consensus tests for agreement, ranking determinants using the Best Worst Method (BWM), and rank validation through sensitivity analysis for warehousing industry experts and organisations. Warehouse service providers who convert their warehouses into smart modes to serve fast-moving consumer goods companies in Tier-2 cities are chosen for the structural framework. Results present the role of the ‘operational efficiency of technology’, ‘top management’, ‘employee’s education’, and ‘technical background’ as the most effective driving determinants. This research provides valuable information to researchers and managers trying to upgrade their warehouse into a smart warehouse.

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.

Early Detection of Onion Spoilage Utilizing IoT and AI during Storage and Transportation

This paper introduces an innovative approach to reducing crop wastage by converting traditional onion warehouses into smart warehouses using IoT and AI technologies. We propose an IoT-based monitoring solution that continuously analyses and communicates the health status of onion stock in real time. Additionally, our methodology aims to support farmers by providing them with essential information about their onion stocks, enabling informed decision-making. Traditionally, farmers rely on sensory cues such as onion odor to detect decomposition. However, we propose a technological intervention utilizing image processing and AI-driven sensory mechanisms to enhance the onion detection process. Specifically, cameras are employed to identify sprouted onions, while AI algorithms analyze sensory data to detect rotten onions. Our prototype achieves an impressive 87% efficiency in identifying sprouted onions using camera-based systems. Furthermore, the response of the gas sensing system in detecting rotten onions under prescribed chamber dimensions yields encouraging results. Through the integration of IoT and AI, our methodology offers a promising solution to minimize crop wastage and promote sustainable agricultural practices.

Order Picking Optimization in Smart Warehouses With Human–Robot Collaboration

Order Picking Optimization in Smart Warehouses With Human–Robot Collaboration

Multi-agent simulation of autonomous industrial vehicle fleets: Towards dynamic task allocation in V2X cooperation mode

The smart factory leads to a strong digitalization of industrial processes and continuous communication between the systems integrated into the production, storage, and supply chains. One of the research areas in Industry 4.0 is the possibility of using autonomous and/or intelligent industrial vehicles. The optimization of the management of the tasks allocated to these vehicles with adaptive behaviours, as well as the increase in vehicle-to-everything communications (V2X) make it possible to develop collective and adaptive intelligence for these vehicles, often grouped in fleets. Task allocation and scheduling are often managed centrally. The requirements for flexibility, robustness, and scalability lead to the consideration of decentralized mechanisms to react to unexpected situations. However, before being definitively adopted, decentralization must first be modelled and then simulated. Thus, we use a multi-agent simulation to test the proposed dynamic task (re)allocation process. A set of problematic situations for the circulation of autonomous industrial vehicles in areas such as smart warehouses (obstacles, breakdowns, etc.) has been identified. These problematic situations could disrupt or harm the successful completion of the process of dynamic (re)allocation of tasks. We have therefore defined scenarios involving them in order to demonstrate through simulation that the process remains reliable. The simulation of new problematic situations also allows us to extend the potential of this process, which we discuss at the end of the article.

Investigating the impact of smart manufacturing and interconnected emerging technologies in building smarter supply chains

PurposeIndustry 4.0 has put forward a smart perspective on managing supply chain networks and their operations. The current manufacturing system is primarily data-driven. Industries are deploying new emerging technologies in their operations to build a competitive edge in the business environment; however, the true potential of smart manufacturing has not yet been fully unveiled. This research aims to extensively analyse emerging technologies and their interconnection with smart manufacturing in developing smarter supply chains.Design/methodology/approachThis research endeavours to establish a conceptual framework for a smart supply chain. A real case study on a smart factory is conducted to demonstrate the validity of this framework for building smarter supply chains. A comparative analysis is carried out between conventional and smart supply chains to ascertain the advantages of smart supply chains. In addition, a thorough investigation of the several factors needed to transition from smart to smarter supply chains is undertaken.FindingsThe integration of smart technology exemplifies the ability to improve the efficiency of supply chain operations. Research findings indicate that transitioning to a smart factory radically enhances productivity, quality assurance, data privacy and labour efficiency. The outcomes of this research will help academic and industrial sectors critically comprehend technological breakthroughs and their applications in smart supply chains.Originality/valueThis study highlights the implications of incorporating smart technologies into supply chain operations, specifically in smart purchasing, smart factory operations, smart warehousing and smart customer performance. A paradigm transition from conventional, smart to smarter supply chains offers a comprehensive perspective on the evolving dynamics in automation, optimisation and manufacturing technology domains, ultimately leading to the emergence of Industry 5.0.

제3자 물류 기업 활용에 영향을 미치는 공급체인 리스크에 관한 연구

The recent increase in supply chain risk due to global supply chain issues has caused logistics costs such as fuel and labor costs to rise for shipper companies engaged in import and export activities. Therefore, shipper companies are outsourcing logistics activities to third-party logistics providers(3PLP) to manage supply chain risk and solve management problems caused by rising supply chain risk. In addition, as third-party logistics companies have begun to adopt and utilize 4th industrial technologies such as smart warehouses and autonomous driving, it is necessary to examine the relationship between these technologies and firm performance. Therefore, this study categorizes supply chain risks in a logistics uncertainty environment and identifies the causal relationship between risk factors, 3PLP utilization, and firm performance. To conduct the study, a questionnaire was constructed based on previous studies and interviews with current practitioners, and a survey was conducted based on the list of member companies of the Korea International Trade Association, the Association of Small and Medium Venture Businesses, and the Korea Trade-Investment Promotion Agency. The hypotheses were tested using PLS-SEM methodology, and the results showed that among supply chain risks, only demand risk had a significant impact on 3PLP utilization, and 3PLP utilization had a significant impact on shippers' financial performance and logistics performance.

Optimized IEEE 802.11ax for smart warehouses

Industry 4.0 aspires to digitize factories processes wholly. Wireless technologies represent one of the elements of this goal. However, the current standards and proprietary solutions need to meet the industry’s tight requirements for various use cases, such as smart warehouses, where robots manage and control entire tasks. One of the key research challenges toward replacing wired fieldbuses with wireless links is the design of wireless systems that address short packet transmissions and multi-user environments. Nevertheless, this challenge is partially obstructed by the limits of the current proposals and the industrial channel models. The authors detect as a potential solution the optimization of IEEE 802.11ax that, due to the use of OFDMA, could offer a high-performant system for smart warehouses. This article proposes an ad-hoc wireless system based on IEEE 802.11ax for smart warehouses that comprises optimization for MAC and PHY layers. Furthermore, two retransmission schemes based on frequency domain are described. This article proposes a methodology to evaluate the latency and reliability performance. Compared to previous proposals, the system shows improved performance.

Mixed Palletizing for Smart Warehouse Environments: Sustainability Review of Existing Methods

Mixed palletizing constitutes one of the problems in the logistics domain aroused from the need for fast product movement to satisfy the continuously increasing number of customers with the demand for highly personalized goods. In a demanding environment, such as warehouses, where break bulk and the consolidation of loads take up most of the working time, the automation of mixed palletizing can lead to increased efficiency and speed while keeping errors to a minimum. Space utilization of pallets enables savings in storage and transportation costs, boosting the overall sustainable role of the Supply Chain sector. This paper presents the proposed approaches to mixed palletizing stemming from different fields, with a focus on recent developments in the application of Industry 4.0 technologies. Our research highlights quite a few areas that require attention from researchers.

Personalized Federated Learning for Goods Classification in Wireless Sensor-Assisted Smart Warehouses

Personalized Federated Learning for Goods Classification in Wireless Sensor-Assisted Smart Warehouses

FAIR: Cyber Security Risk Quantification In Logistics Sector

For many industries, the fourth industrial revolution has brought technological advancements in the form of advances in the integration of physical and digital technologies, particularly for cyber-physical systems, that go far beyond speed, scalability, storage, and cost-effectiveness. Smart manufacturing, smart factories, smart warehousing, and smart logistics are every aspect of the enabling Fourth Industrial Revolution. As a result of these improvements and advantages, new attack surfaces have been created that benefit malicious actors. For effective cyber security risk management in the face of converging cyber attacks, it is essential to quantify cyber risks across or within organisations that estimate cyber security risks in monetary value. This study explored and integrated the FAIR methodology (a well-recognized approach for quantitative cyber security risk assessment) to quantify cyber risk. A case study was conducted with one of the largest logistics companies in Scandinavia.

Deep Reinforcement Learning for Task Assignment and Shelf Reallocation in Smart Warehouses

Deep Reinforcement Learning for Task Assignment and Shelf Reallocation in Smart Warehouses

Collaborative Target Search With a Visual Drone Swarm: An Adaptive Curriculum Embedded Multistage Reinforcement Learning Approach.

Equipping drones with target search capabilities is highly desirable for applications in disaster rescue and smart warehouse delivery systems. Multiple intelligent drones that can collaborate with each other and maneuver among obstacles show more effectiveness in accomplishing tasks in a shorter amount of time. However, carrying out collaborative target search (CTS) without prior target information is extremely challenging, especially with a visual drone swarm. In this work, we propose a novel data-efficient deep reinforcement learning (DRL) approach called adaptive curriculum embedded multistage learning (ACEMSL) to address these challenges, mainly 3-D sparse reward space exploration with limited visual perception and collaborative behavior requirements. Specifically, we decompose the CTS task into several subtasks including individual obstacle avoidance, target search, and inter-agent collaboration, and progressively train the agents with multistage learning. Meanwhile, an adaptive embedded curriculum (AEC) is designed, where the task difficulty level (TDL) can be adaptively adjusted based on the success rate (SR) achieved in training. ACEMSL allows data-efficient training and individual-team reward allocation for the visual drone swarm. Furthermore, we deploy the trained model over a real visual drone swarm and perform CTS operations without fine-tuning. Extensive simulations and real-world flight tests validate the effectiveness and generalizability of ACEMSL. The project is available at https://github.com/NTU-UAVG/CTS-visual-drone-swarm.git.