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Smart warehousing aims at increasing the overall service quality, productivity, and efficiency while minimizing the costs and failures. For designing the reference architecture, we apply a domain-driven architecture design approach and use the architecture design knowledge as presented in the software architecture design literature. We first provid...
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... Section 3.1.1, we present the domain model of smart warehouses. Fig. 1 presents a feature diagram for a traditional warehouse. In this model, various common and variable features of the warehouse system are presented. A traditional warehouse has several toplevel features such as receiving, storing, track and tracing, picking, shipping, the up and downstream stakeholders, and a management system (Won and ...
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... smart warehouse relies on communication between and integration of systems. In the BPM of a smart warehouse, various systems are included to assist the warehouse management system in place, as shown in Fig. 11. First of all, APS software is included to guide the overall planning and scheduling processes. At the same time, an inventory management module or system is included to optimize stock management. Finance/accounting and sales management are included in the process of safeguarding all monetary flows. It is responsible for all task which ...
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... roles (a.k.a., swimlanes), namely Supplier/Client, Warehouse Management System (WMS), and the Warehouse are depicted in Fig. 11. The client initiates the request, and then, the supplier processes this request. Planning and management of the request are handled in the WMS. Warehouse operations are managed by the Warehouse role. Each role consists of different actions that might trigger other ...
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... from picking and preparing orders, the warehouse also has incoming goods, as shown in Fig. 12. As the new stock arrives, a (passive) recipients' confirmation is sent to the supplier. While the truck comes in and is unloaded, the system is updated. If needed, the quality of the products is checked, and then, the disassembled products are tagged at one of the levels, being from the truck until the product level. Once tagged, it ...
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... context diagram for smart warehousing is presented in Fig. 13. The central system here is the warehouse management system (WMS), guiding the process through all the operations of receiving, storing, picking, and shipping. First, it interacts with various operators such as corporate supervisors, warehouse managers, and warehouse employees. These operators register actions, activities, and guide ...
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... decomposition view for smart warehousing is presented in Fig. 14. This view presents all the modules required for smart warehousing. Besides the top-level modules for smart warehousing, the sub-level modules for the technologies are presented as well. These technologies are barcoding, AR, AGV, Internet of Things (IoT), WMS, scanning, RFID, and communication. For instance, for the IoT package, we ...
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... that can adapt to the needs and interests of the stakeholders in the context. Real-Time Information collected from sensors and devices in the smart warehouse is used during the execution of AI and AmI algorithms. Security is one of the most important components of the IoT systems, especially Industrial IoT systems. Another sub-package shown in Fig. 14 is the Security Module that provides security controls on the IoT ...
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... shown in Fig. 15, the two central modules are the WMS and the warehouse communication network. The WMS communicates with other systems, back-end modules, and the network. The network acts as a bridge between the WMS and the AR hardware or AGV. TMS, IoT, RFID, and external system also directly operate with the communication ...
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... Fig. 16, the identification of the software modules to the relevant hardware is presented. The module of data processing is deployed on the Warehouse Management Server and Warehouse Manager nodes. Other nodes are devoted to cameras, sensors, scanners, and AR hardware. AGVs can have their cameras, sensors, and scanners, and shelves can have ...
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... warehouse that we analyze closely resembles a traditional warehouse, as shown in Fig. 17. The warehouses' top features are receiving, storing, track and tracing, picking, shipping, retailer communication, and a warehouse management system. Currently, the warehouse operates with unloading the trucks and, if absent, tagging the products with a sticker on the pallet level. The barcodes are linear coded and are mainly used to ...
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... on the research, interview, and observations, a feature diagram for the proposed situation is presented in Fig. ...
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... the BPM for the case warehouse concludes that the current situation is similar to the traditional warehouse, as shown in Fig. 19. The case warehouse has one fluctuation as a TMS is integrated into the system. The restocking sub-process has some small alternations as the shipment is already shipped upon arrival. In the proposed system shown in Fig. 20, an automated storage and retrieval system (AS/RS) is argued for both the storing of the new shipments and for ...
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... Context diagram. Fig. 21 presents the context diagram for the case of the food industry warehouse. First of all, truck drivers are not applicable as they do not operate on the WMS. Furthermore, as smart warehousing is targeted, the warehouse employees are not included in this view. As the warehouse has one partner company, which is both the client and supplier ...
Citations
... Using blockchain technology, decision-making processes at strategic, tactical, and operational levels can be transformed, resulting in increased transparency, efficiency, and resistance to tampering. Furthermore, the seamless integration of AGVs into complex supply chain networks enhances their economic potential, as highlighted by Van Geest et al. (2021). The analysis of blockchain-based advancements in transportation and logistics can be summarized into three main categories. ...
The dawn of Industry 4.0, characterized by the convergence of digital and physical technologies, ushers in a new era of economic and management paradigms. Blockchain technology, known for its decentralized, transparent, and immutable characteristics, is at the heart of this transformation. This paper explores the potential and challenges of integrating blockchain into Industry 4.0, focusing on economic and management innovations. Blockchain’s impact on sustainability reporting and corporate performance monitoring is profound, offering unprecedented transparency and trustworthiness. It enables granular insights into operations, fostering a responsible business ecosystem and equitable ecological accountability. However, aligning blockchain incentives with value-based objectives is crucial to avoid unintended consequences. Environmental concerns regarding blockchain’s energy consumption, especially in public blockchains like Bitcoin, highlight the need to balance innovation with ecological responsibility. Scalability remains a challenge, necessitating innovative solutions such as proof-of-stake. Furthermore, regulatory uncertainties and trust dynamics pose significant barriers to blockchain adoption. The paper emphasizes the importance of addressing these systemic challenges for the seamless integration of blockchain into economic and managerial ecosystems. Blockchain technology holds immense potential to revolutionize Industry 4.0 but comes with complexities and challenges. As industries, researchers, and policymakers navigate this uncharted territory, optimism, caution, and curiosity must guide their approach. Embracing blockchain’s transformative power while addressing its limitations can pave the way for a transparent, accountable, and sustainable future in Industry 4.0.
... The IoT and artificial intelligence technologies are transforming warehouse management into an automated, integrated and more efficient mode to address these challenges (Choi et al., 2022). And the concept of the smart warehouse (van Geest et al., 2021) is rapidly developing with the aim of achieving better management structures. ...
Accurate storage location status data is an important input for location assignment in the inbound stage. Traditional Internet of Things (IoT) identification technologies require high costs and are easily affected by warehouse environments. A lightweight convolutional neural network is proposed for perceiving storage status to achieve high stability and low cost of location availability monitoring. Based on the existing You Only Look Once (YOLOv5) algorithm, the Hough transform is used in the pre-processing to implement tilt correction on the image to improve the stability of object localization. Then the feature extraction unit CBlock is designed based on a new depthwise separable convolution in which the convolutional block attention module is embedded, focusing on both channel and spatial information. The backbone network is constructed by stacking these CBlock blocks to compress the computational cost. The improved neck network adds cross-layer information fusion to reduce the information loss caused by sampling and ensure perceptual accuracy. Moreover, the penalty metric is redefined by SIoU, which considers the vector angle of the bounding box regression and improves the convergence speed and accuracy. The experiments show that the proposed model achieves successful results for storage location status perception in stereo warehouse.
... Leveraging IoT sensors and cloud computing, this system enables real-time inventory monitoring and optimization. Similarly, "Design of a reference architecture for developing smart warehouses in industry 4.0" by Van Geest et al. [16], proposes a reference architecture for smart warehouses, integrating IoT, AI, and cloud computing technologies. This architecture facilitates responsive inventory management, cost reduction, and customer-centric operations. ...
The evolution of Internet of Things (IoT) and sensor technology has been nothing short of remarkable, reshaping the very fabric of our existence across multiple domains. This comprehensive review encapsulates the dynamic landscape of IoT and sensor technology, underscoring their pivotal role in realizing the aspirations of Industry 4.0. We first explore the remarkable advancements brought about by these technologies, delving into their transformative impact on various aspects of our lives. Moving forward, we navigate through the intricate challenges posed by these innovations, spanning security vulnerabilities, privacy concerns, and data management complexities. Moreover, we illuminate the horizon with emerging trends such as edge computing and the advent of 5G networks, envisioning a future where these technologies catalyze unprecedented societal and economic transformations. By encapsulating the essence of IoT and sensor technology's profound impact, this review serves as a beacon guiding us towards a future defined by innovation, efficiency, and sustainable progress across diverse industries and sectors.
... [7]. Applying a domain-driven architecture design strategy is recommended, utilising the knowledge of architecture design as offered in the literature on software architecture design [8]. This paper proposes a multi-layer software architecture for facilitating cooperative missions involving a fleet of quadrotors in the context of electrical power line inspection activities. ...
This paper introduces a conceptual architecture for Fuzzy Risk-Based Decision Support Systems (R+DSS). This architecture is designed to provide a comprehensive and efficient approach to decision-making procedures in various domains involving assessing and controlling potential risks. The proposed architecture exhibits versatility in its applicability across multiple fields, such as finance, healthcare, engineering, and environmental management. It incorporates these components flexibly and scalable while also being user-friendly. The framework employs fuzzy logic principles such as membership functions, rule sets, and inference methods to facilitate a thorough evaluation of the risk that accommodates the inherent uncertainties and imprecisions characteristic of real-world risk scenarios. The Fuzzy Inference Engine is a versatile and resilient risk analysis tool capable of accommodating diverse data and systems, enabling effective risk mitigation strategies. The adaptability of this architecture to effectively handle complex, uncertain and dynamic environments makes it a promising tool for decision-makers looking to improve risk assessment and management protocols.
... Since most companies can now afford them, AS/RS technologies are among the most popular and beneficial options for investment, especially for requirements in warehouse systems [1,13]. AS/RSs are made up of a succession of computer-controlled systems for autonomously depositing and collecting goods from predetermined places for holding fresh shipments and picking orders [14]. By retrieving items for use or shipping and putting them back in the proper places within the production facility, these advanced technologies automate the handling of materials ( Figure 5). ...
... Industrial processes where large amounts of inventory enter and exit production or distribution processes are suitable for AS/RS. In that way, upgrading to an automated warehouse creates more secure, ergonomic working conditions while enhancing throughput and productivity with dependable, secure storage that prevents damage to goods and equipment [13,14]. Although AS/RS is primarily developed to increase warehouse productivity, it is regarded as a technology that substantially automates material handling in sustainable industrial processes. ...
... Although AS/RS is primarily developed to increase warehouse productivity, it is regarded as a technology that substantially automates material handling in sustainable industrial processes. Among the main benefits that AS/RS has achieved are [13,14]: ...
... The normal transportation devices used in industrial warehouse systems are AGVs, AMRs, self-driving forklifts, and belt conveyors [36]. Most of the research studies about AGVs concern the problems of the number of transportation tasks, AGV utilization, travel distances, and congestion [37]. ...
With the tremendous development of the logistics industry, the global market of automated warehousing has been growing rapidly. Meanwhile, the warehousing industry shows drawbacks, such as low storage capacity and poor efficiency. By comparing and analyzing the shuttle-based storage and retrieval system (SBS/RS), miniload automated storage and retrieval system (AS/RS), and KIVA system, a novel efficient parts-to-picker approach in flexible warehousing systems is proposed. Among them, buffer racks and access racks, associated with the access of automated mobile robots (AMRs) and stackers are used. The results show that compared with other parts-to-picker systems (such as the KIVA system), this system provides a significant increase in storage capacity (more than three times), and the picking efficiency is also very high at various layout scales, where the picking efficiency is no less than the KIVA system when the number of AMRs reaches the max. The novel system is suitable for small-, medium-, and large-scale warehouses in terms of showing high capacity and producing excellent space utilization. More importantly, this system can easily compete with its traditional counterparts by using a layout of high density without much increase in cost. This sustainable improvement realizes the efficient utilization of spatial resources and provides important support for the construction of green supply chains.
... Managers within supply chain management and warehousing are eager for their operations to have increased visibility and efficiency through such interconnectivity enabled by Industry 4.0 [9]. The smartness of Industry 4.0 technologies enables warehouses to increase service efficiency, productivity, and visibility of items across the supply chain-concepts that are of high interest to industries [10]. RQ2: What difficulties or challenges can an oil and gas production company face in implementing IIoT and other relevant Industry 4.0 technologies? ...
... Godina et al. [71] propose a framework and illustrate how additive manufacturing impacts sustainable business models. van Geest et al. [10] proved successful usage of Industry 4.0 concepts to develop an architecture for a smart warehouse. Klumpp et al. [72] establish the value of human-robot interaction and cooperation in decision making in production logistics. ...
Background: Spare parts warehousing in the oil and gas industry is essential for offshore production. With the introduction of Industry 4.0 and its subsequent technological tools, new functions are enabled in industrial logistics activities. Efficiency, visibility, optimization, and productivity are often mentioned as benefits of successful Industry 4.0 technology implementation in logistics activities. In this paper, the implementation of Industry 4.0 technologies such as the Industrial Internet of Things (IIoT) in spare parts warehousing in the oil and gas industry is studied. Method: 133 peer-reviewed journal publications indexed in Scopus and Web of Science are analyzed in a systematic literature review. The review is structured as frequency and content analysis. Aim: As there is limited research on this specific topic, the aim is for this paper to be a theoretical foundation that assists the industry with future solutions. Results: The list of benefits of implementation gathered from the literature is comprehensive. However, the list of challenges is particularly pertinent to the oil and gas industry and indicates a strong inclination towards limited and controlled implementation of some technologies. The safety demands of the industry mean there are many limitations to implementation currently. Conclusions: This paper reflects on the results, identifies research gaps, and gives proposals for future research.
... The subsequent sections outline recommendations for improvement in each of the specified areas. (van Geest, Tekinerdogan and Catal, 2021;(adj Sassi et al., 2021;Zaman et al., 2023): ...
An industrial water pump importing company relies on a network of distribution warehouses to efficiently manage the storage and delivery of its products to clients. This paper delves into the operational intricacies of the company, with a primary focus on sustaining a superior level of service to meet customer demands, all while attempting to minimize costs and achieve optimal inventory control. The central aspects explored in this research encompass the meticulous determination of the number of pipes needed and the optimal ordering times. To address this, the Probabilistic Economic Order Quantity (EOQ) method is used and supported by 5S concept, recognized for its ability to provide reasonably accurate estimates crucial for pivotal decision-making in inventory management. The utilization of the Probabilistic EOQ method in this context reflects the company’s commitment to adopting sophisticated and proven methodologies to enhance decision-making accuracy and the warehouse area is more suitable by the 5S implementation principles. The research outcomes not only assist in refining the determination of Safety Stock levels but also contribute valuable insights into the precise quantities of goods that should be ordered. This strategic approach aligns with the company’s overarching goal of achieving cost-efficiency without compromising on the ability to efficiently meet customer demands. These upcoming insights could encompass innovative strategies, technological implementations, or advances in supply chain optimization.
... e role of warehouses changed and specialized even during the first and second industrial revolutions. However, the term "smart warehouses" has been introduced with the advent of Industry 4.0 and it refers to the growing automation in traditional warehouses (van Geest et al., 2021a). e warehouse management system is a data system that combines software to identify, control, track and manages the storage capacity and warehouse decision. ...
In the food supply chain warehouses play a significant role in effectively fulfilling customer and industrial demand. To satisfy this need warehouse management system has been created for tracking, monitoring, and managing the warehouse performance. However, the growing food market needs more efficient management to handle the huge amount of data, inventory management, and customer desires. erefore, smart warehouses are being designed to enhance convenience, profitability, and quality by improving the cost, speed, and accuracy of operations. e evolution of a smart warehouse management system has the potential to monitor, track, control, and manage the stocks and their quality in the warehouse. ese systems are based on the Internet of tings, barcodes, radiofrequency identification tags, sensors, automated guided vehicles, etc. to keep a record of all activities in the warehouse and during the supply chain. Therefore, in this chapter, the role of different techniques in the automation of warehouse management systems has been discussed.
... Industry 4.0 denotes a paradigm shift in the manufacturing and industrial processes, defined by the combination of automation, data sharing, and digital technologies [1], [2]. It expands on previous industrial revolutions, which included the utilization of steam power and water, electricity, and computers, and now unites the digital, biological, and physical domains [2], [3]. Industry 4.0 is fundamentally based on networked systems. ...
... Real-time communication between machines, sensors, devices, and people makes it easier to transmit the data needed to make decisions. Informed decision-making is empowered by the comprehensive insights and analytics made possible by this connectedness in conjunction with information transparency [3], [4]. In Industry 4.0, technological innovations like artificial intelligence (AI), machine learning, and augmented reality are essential components [5]. ...
Industry 4.0 is fundamentally based on networked systems. Real-time communication between machines, sensors, devices, and people makes it easier to transmit the data needed to make decisions. Informed decision-making is empowered by the comprehensive insights and analytics made possible by this connectedness in conjunction with information transparency. Industry 4.0-based wireless sensor networks (WSNs) are an integral part of modern industrial operations however, these networks face escalating cybersecurity threats. These networks are always vulnerable to cyber-attacks as they continuously collect data and optimize processes. Increased connections make people more susceptible to cyberattacks, necessitating the use of strong cybersecurity measures to protect sensitive data. This study proposes a predictive framework intended to intelligently prioritize and prevent cybersecurity intrusions on WSNs in Industry 4.0. The proposed framework enhances the cybersecurity of WSNs in Industry 4.0 using a multi-criteria approach. It implements machine-learning and deep-learning algorithms for cybersecurity intrusion detection in WSNs of Industry 4.0 and provides prevention by assigning priorities to the threats based on the situation and nature of the attacks. We implemented three models, i.e., Decision Tree, MLP, and Autoencoder, as proposed algorithms in the framework. For multidimensional classification and detection of cybersecurity intrusions, we implemented Decision Tree and MLP models. For binary classification and detection of cybersecurity intrusions in WSNs of Industry 4.0, we implemented Autoencoder model. Simulation results show that the Decision Tree model provides an accuracy of 99.48%, precision of 99.49%, recall of 99.48%, and F1 score of 99.49% in the detection and classification of cybersecurity intrusions. The MLP model provides an accuracy of 99.52%, precision of 99.5%, recall of 99.5%, and F1 score of 99.5% in the detection and classification of cybersecurity intrusions. The implementation of Autoencoder with binary classification yields an accuracy of 91%, a precision of 92%, a recall of 91%, and an F1 score of 91%. The benchmark models, i.e., Random Forest (RF) for multidimensional classification and Logistic Regression (LR) for binary classification, have also been implemented. We compared the performance of the benchmark models with the models implemented in the proposed framework, revealing that the models in the proposed framework significantly outperformed the benchmark models. The framework presents an intelligent prioritizing methodology that is significant for effectively identifying and addressing high-risk intrusions. The proposed framework implements a proactive preventive system that functions as a strong defensive wall by quickly putting countermeasures in place to eliminate threats and increase network resilience.
