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Edge Devices In Logistics For Efficient Inventory Management

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Pdf) Intelligent Logistics System Design And Supply Chain Management Under Edge Computing And Internet Of Things

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By Daniel Y. Mo Daniel Y. Mo Scilit Preprints.org Google Scholar 1, *, Chris Y. T. Ma Chris Y. T. Ma Scilit Preprints.org Google Scholar 2, Danny C. K. Ho Danny C. K. Ho Scilit Preprints.org Google Scholar 1 and Yue Wang Yue Wang Scilit Preprints.org Google Scholar 1

Supply Chain Visibility: The Role Of Real Time Data In Logistics

Received: August 29, 2022 / Rev.

(This article belongs to the special topic of Sustainability, Innovation and Competition: Emerging Players and Challenges in Supply Chain Management)

Although logistics in turn allow service providers to achieve greater environmental and economic benefits, research and successful business cases are insufficient. This study designs a new reverse logistics system that uses Internet of Things (IoT) intelligence and business to optimize the reverse logistics process by designing suitable components for sustainable reuse operations. In addition, the classification scheme and the inventory analytical model were developed to identify the components failing to repair by assessing the return amount of the failing component, the repair rate of the failing component in the repair center, the resumption rate of the refurbished parts, and the appropriate costs. utility parts refurbished. In addition, a mobile application developed by IoT technology to streamline process flows and avoid the collection of simulated components. Finally, if a study of the company’s electronic products has been conducted, and the conclusion is that the proposed approach has allowed the company to facilitate the composition and benefit of cost savings. The results of this study demonstrate the importance of reverse logistics for companies to support their marketing activities.

Logistics Information Systems In Warehouses

The management of critical system components, such as high-tech systems, medical equipment, and office equipment, has developed worldwide [1]. Part of service management is essential for improving service quality, maintaining productivity and capturing opportunities in various sectors. Managing usable items is becoming a common industrial practice these days. However, it is challenging to ensure that the required parts are available at the appropriate points in the supply chain to achieve the desired level of service [2], due to obstacles such as the large variety of parts, the risk of inventory obsolescence [3]. , require irregular or lumpy patterns that distinguish many parts [4] and with great enthusiasm [5]. Additional complexity occurs when there is a strong need to effectively overhaul failed components to improve organizational environmental performance. Although the adoption of advanced information and communication technologies and a holistic approach to connect forward and backward logistics parts is expected to support operations that provide greater economic and environmental benefits, more research and successful business cases are required.

To address the challenges and the research gap in the sustainable management of service parts, the purpose of this research is to propose a framework of integrated logistics systems, which is enabled and ready by the Internet of Things (IOT) and business intelligence (BI). reverse and smooth flow for overhaul of failed components. The proposed system is unique on several fronts. First, it addresses the varying characteristics and uncertainty in service parts requirements, which is accepted in practice, by using parts usage to identify suitable parts for reuse and to avoid unnecessary overhaul. Second, to repair the operations of the defective parts from an economic point of view, an analytical model, supported by BI, is developed to estimate the operations required for the repair of the defective parts, considering the income factors of the amount of the defective parts, the repair. rate of failed components during repair. Center, reuse rate of remanufactured parts, corresponding costs, and profitability of remanufactured parts. Third, an IoT-powered mobile application solution is proposed to optimize reverse logistics processes and avoid the collection of composite simulators. Finally, the functionality of the proposed reverse logistics system is verified through a case study of a leading electronic product company in Hong Kong. An analysis of the data collected from the project’s resource planning support system showed that more than 200 units of inventory could be returned to avoid material waste and improve parts revenue flow to service parts management. Therefore, the proposed design framework not only contributes to fill the research gap in the literature, but also contributes to an industrial system for partial operations.

The rest of this article is organized as follows. Section 2 presents a review of the literature on the management of parts in service, BI with IoT, and reverse logistics systems. Section 3 describes the framework of a reverse logistics system integrated with IoT for service parts management. In addition, an analytical model is presented to identify the trade-off relationship between the utility of reuse and the return of sellers, reverse logistics, and repair to support reverse logistics operations and component reuse. Section 4 presents a study to demonstrate the advantages of the proposed reverse logistics system with IoT. Finally, Section 5 concludes the study and identifies areas for future research.

How Technology Can Help Redraw The Supply Chain Map

In order to better identify the reverse logistics system that is served by IOT and BI to the parts management, this study reviewed and integrated past studies in the fields of parts service management, BI with IOT, and vice versa for logistics systems.

Parts service management is a subset of the maintenance system and can be classified into two categories: normal maintenance and preventive maintenance. An analysis of the total maintenance costs between repair and preventive breakage [6] shows that maintenance repair costs in the reciprocal repair mode are about three times higher than in the preventive mode. Although conservative maintenance is an advanced method for ensuring the availability of critical systems, predicting error machine failures remains a major challenge. However, the integration of data with the supply chain is almost absent. Kasibi [7] highlighted the importance of extensive cooperation in the provision and innovation of supply chain partners. In supply chain collaboration, Boone et al. [8] commented that most companies do not have a systemic perspective for service management, due to weak customer relationship management and accurate forecasting requirements. Through interviews with several service sector managers, Boone et al. [9] identified the downstream requirements of the service component planning, forecasting and outbound distribution. The conclusion was that although parts management could be improved through preventive maintenance with better budgeting, information integration with supply chain partners was a core component. Moreover, the distribution of the service parts necessary for the customer’s needs. For example, medical equipment repair services often require a quick response within hours rather than days.

Garcia et al. [10] A system was developed that continuously monitors equipment performance and deviations of subcycle times, which provided an indication of potential failures to support machine failure prevention scheduling. The industry therefore needs to integrate further downstream supply chain partners and perform better preventive maintenance for advanced technologies such as IoT, machine learning and big data in Industry 4.0. [11]. In addition, IOT can be used not only to improve the operational efficiency, but also to improve the sustainability of the reuse of components, considering that the operational efficiency is increased in enormous savings [12, 13] to support the system integration of the downstream supply chain partners; current Thanks to the integration system of the downstream supply chain partners to share information [9], cleaner decisions can be made to handle breakdown problems based on real-time inventory [14] and obtain failed parts for reuse and recycling. Having a service management system with IoT-enabled network devices containing sensors to collect and exchange data is not enough by itself. To realize business value from big data, we need IoT solutions to connect to BI. However, studies on the application of BI with IoT in reverse logistics systems for service component management are insufficient.

Advanced Logistics Systems In The Factory Of The Future

BI systems are enterprise-sized data-compiled decision support systems that combine data collection and storage with advanced analytical functions.

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