An embroidered passive textile RFID tag based on a T-matched antenna

This paper addresses the design and fabrication of an embroidered textile RFID tag antenna. The main feature of this design is that we have embroidered an RFID chip on the textile support which avoids the use of metallic wires or soldering. The modeled equivalent circuit of the tag is presented to get physical insight into RFID tag antenna design. The detailed results given in this paper include the effect of the bending and the human body proximity on the antenna performance. It is shown that the bending does not introduce a conspicuous effect on the tags read range while the dissipative characteristics of the human body cause a gain and read range reduction. The proposed design may find applications in wearable devices dedicated to health monitoring applications.Peer ReviewedPostprint (author's final draft

Agent-Based Modelling and Heuristic Approach for Solving Complex OEM Flow-Shop Productions under Customer Disruptions

The application of the agent-based simulation approach in the flow-shop production environment has recently gained popularity among researchers. The concept of agent and agent functions can help to automate a variety of difficult tasks and assist decision-making in flow-shop production. This is especially so in the large-scale Original Equipment Manufacturing (OEM) industry, which is associated with many uncertainties. Among these are uncertainties in customer demand requirements that create disruptions that impact production planning and scheduling, hence, making it difficult to satisfy demand in due time, in the right order delivery sequence, and in the right item quantities. It is however important to devise means of adapting to these inevitable disruptive problems by accommodating them while minimising the impact on production performance and customer satisfaction. In this paper, an innovative embedded agent-based Production Disruption Inventory-Replenishment (PDIR) framework, which includes a novel adaptive heuristic algorithm and inventory replenishment strategy which is proposed to tackle the disruption problems. The capabilities and functionalities of agents are utilised to simulate the flow-shop production environment and aid learning and decision making. In practice, the proposed approach is implemented through a set of experiments conducted as a case study of an automobile parts facility for a real-life large-scale OEM. The results are presented in term of Key Performance Indicators (KPIs), such as the number of late/unsatisfied orders, to determine the effectiveness of the proposed approach. The results reveal a minimum number of late/unsatisfied orders, when compared with other approaches

A Region-Based Deep Learning Algorithm for Detecting and Tracking Objects in Manufacturing Plants

In today\u27s competitive production era, the ability to identify and track important objects in a near real-time manner is greatly desired among manufacturers who are moving towards the streamline production. Manually keeping track of every object in a complex manufacturing plant is infeasible; therefore, an automatic system of that functionality is greatly in need. This study was motivated to develop a Mask Region-based Convolutional Neural Network (Mask RCNN) model to semantically segment objects and important zones in manufacturing plants. The Mask RCNN was trained through transfer learning that used a neural network (NN) pre-trained with the MS-COCO dataset as the starting point and further fine-tuned that NN using a limited number of annotated images. Then the Mask RCNN model was modified to have consistent detection results from videos, which was realized through the use of a two-staged detection threshold and the analysis of the temporal coherence information of detected objects. The function of object tracking was added to the system for identifying the misplacement of objects. The effectiveness and efficiency of the proposed system were demonstrated by analyzing a sample of video footages

A systematic literature review on the benefit-drivers of RFID implementation in supply chains and its impact on organizational competitive advantage

Application of Radio Frequency Identification (RFID) in managing supply chains has witnessed significant interest in recent years. However, the current understanding of the potential benefits that act as the motivating factors/drivers in implementing RFID technology (benefit-drivers), its link to competitive advantage, is fragmented and scattered across the literature. This formed the motivation of this study which seeks to address this gap in the literature through a systematic literature review. Based on a rigorous screening of the literature (2006–2018), the study develops a comprehensive understanding of the various 1) corporate-driven and 2) customer-driven benefit-drivers from RFID implementation. The “2 C” categorization of benefit-drivers is novel and should provide more impetus for practitioners to leverage from RFID implementation. Further, the link between the benefit-drivers and competitive advantage is understood and proposed in the form of a conceptual framework. Finally, avenues for future research are highlighted. The study findings and the framework provide a good starting point for academics and practitioners to further explore the opportunities in supply chain afforded by RFID

Product Personalization and Customization: Proposing a System Architecture that Integrates Self-Transactional Materials with RFID and IoT Shared Database

This research paper presents a novel system architecture that integrates Self-Transactional materials with Radio Frequency Identification (RFID), Internet of Things (IoT), and a Shared Database to enable efficient product personalization and customization in supply chain management (SCM). By utilizing RFID tags to carry unique customer preferences or design specifications and leveraging IoT technologies for data communication and control, this proposed architecture aims to streamline the manufacturing processes and enhance customer satisfaction. The shared database serves as a central repository for storing customer-specific information and facilitates seamless coordination across the supply chain. Through the integration of these technologies, this system architecture demonstrates potential for reducing lead times, supporting flexible manufacturing processes, and achieving accurate and timely customization of products

ASPIE: A Framework for Active Sensing and Processing of Complex Events in the Internet of Manufacturing Things

Rapid perception and processing of critical monitoring events are essential to ensure healthy operation of Internet of Manufacturing Things (IoMT)-based manufacturing processes. In this paper, we proposed a framework (active sensing and processing architecture (ASPIE)) for active sensing and processing of critical events in IoMT-based manufacturing based on the characteristics of IoMT architecture as well as its perception model. A relation model of complex events in manufacturing processes, together with related operators and unified XML-based semantic definitions, are developed to effectively process the complex event big data. A template based processing method for complex events is further introduced to conduct complex event matching using the Apriori frequent item mining algorithm. To evaluate the proposed models and methods, we developed a software platform based on ASPIE for a local chili sauce manufacturing company, which demonstrated the feasibility and effectiveness of the proposed methods for active perception and processing of complex events in IoMT-based manufacturing

An Environmental-Based Perspective Framework: Integrating IoT Technology into a Sustainable Automotive Supply Chain

Purpose - Over the next decade, humanity is going to face big environmental problems, and considering these serious issues, businesses are adopting environmentally responsible practices. To put forward specific measures to achieve a more prosperous environmental future, this study aims to develop an environment-based perspective framework by integrating the Internet of Things (IoT) technology into a sustainable automotive supply chain (SASC). Design/methodology/approach - The study presents a conceptual environmental framework - based on 29 factors constituting four stakeholders’ rectifications - that holistically assess the SASC operations as part of the ReSOLVE model utilizing IoT. Then, experts from the SASC, IoT, and sustainability areas participated in two rigorous rounds of a Delphi study to validate the framework. Findings – The results indicate that the conceptual environmental framework proposed would help companies enhance the connectivity between major IoT tools in SASC, which would help develop congruent strategies for inducing sustainable growth. Originality/value - This study adds value to existing knowledge on SASC sustainability and digitalization in the context where the SASC is under enormous pressure, competitiveness, and increased variability

A systematic literature review on the benefit-drivers of RFID implementation in supply chains and its impact on organizational competitive advantage

Application of Radio Frequency Identification (RFID) in managing supply chains has witnessed significant interest in recent years. However, the current understanding of the potential benefits that act as the motivating factors/drivers in implementing RFID technology (benefit-drivers), its link to competitive advantage, is fragmented and scattered across the literature. This formed the motivation of this study which seeks to address this gap in the literature through a systematic literature review. Based on a rigorous screening of the literature (2006–2018), the study develops a comprehensive understanding of the various 1) corporate-driven and 2) customer-driven benefit-drivers from RFID implementation. The “2 C” categorization of benefit-drivers is novel and should provide more impetus for practitioners to leverage from RFID implementation. Further, the link between the benefit-drivers and competitive advantage is understood and proposed in the form of a conceptual framework. Finally, avenues for future research are highlighted. The study findings and the framework provide a good starting point for academics and practitioners to further explore the opportunities in supply chain afforded by RFID

Agent-Based Modelling and Heuristic Approach for Solving Complex OEM Flow-Shop Productions under Customer Disruptions

The application of the agent-based simulation approach in the flow-shop production environment has recently gained popularity among researchers. The concept of agent and agent functions can help to automate a variety of difficult tasks and assist decision-making in flow-shop production. This is especially so in the large-scale Original Equipment Manufacturing (OEM) industry, which is associated with many uncertainties. Among these are uncertainties in customer demand requirements that create disruptions that impact production planning and scheduling, hence, making it difficult to satisfy demand in due time, in the right order delivery sequence, and in the right item quantities. It is however important to devise means of adapting to these inevitable disruptive problems by accommodating them while minimising the impact on production performance and customer satisfaction. In this paper, an innovative embedded agent-based Production Disruption Inventory-Replenishment (PDIR) framework, which includes a novel adaptive heuristic algorithm and inventory replenishment strategy which is proposed to tackle the disruption problems. The capabilities and functionalities of agents are utilised to simulate the flow-shop production environment and aid learning and decision making. In practice, the proposed approach is implemented through a set of experiments conducted as a case study of an automobile parts facility for a real-life large-scale OEM. The results are presented in term of Key Performance Indicators (KPIs), such as the number of late/unsatisfied orders, to determine the effectiveness of the proposed approach. The results reveal a minimum number of late/unsatisfied orders, when compared with other approaches

Enhancing supply chain performance using RFID technology and decision support systems in the industry 4.0: a systematic literature review

Supply Chain processes are continuously marred by myriad factors including varying demands, changing routes, major disruptions, and compliance issues. Therefore, supply chains require monitoring and ongoing optimization. Data science uses real-time data to provide analytical insights, leading to automation and improved decision making. RFID is an ideal technology to source big data, particularly in supply chains, because RFID tags are consumed across supply chain process, which includes scanning raw materials, completing products, transporting goods, and storing products, with accuracy and speed. This study carries out a systematic literature review of research articles published during the timeline (2000-2021) that discuss the role of RFID technology in developing decision support systems that optimize supply chains in light of Industry 4.0. Furthermore, the study offers recommendations on operational efficiency of supply chains while reducing the costs of implementing the RFID technology. The core contribution of this paper is its analysis and evaluation of various RFID implementation methods in supply chains with the aim of saving time effectively and achieving cost efficiencies