Iot based laundry services: an application of big data analytics, intelligent logistics management, and machine learning techniques

Chang, Liu; Feng, Yan; Lin, Dongtao; Wu, Liang; Guo, Min

doi:10.1080/00207543.2019.1677961

Cited by 110 publications

(46 citation statements)

References 27 publications

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“…[ [680][681][682][683][684][685][686][687][688] EMM: IoT-enabled smart indoor parking system for industrial hazardous chemical vehicles, IoT-enabled solutions monitor perishable cargo for temperature, humidity, and other environmental factors, and humanitarian assistance disaster response scenario. [680,[688][689][690] WCOA: warehouse and yard management system (IoTcontrolled safe area), inventory management.…”

Section: Convergence Of Disruptive Technologies In Logisticsmentioning

confidence: 99%

Industry 4.0 Disruption and Its Neologisms in Major Industrial Sectors: A State of the Art

Bongomin

Yemane

Kembabazi

et al. 2020

Journal of Engineering

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Very well into the dawn of the fourth industrial revolution (industry 4.0), humankind can hardly distinguish between what is artificial and what is natural (e.g., man-made virus and natural virus). Thus, the level of discombobulation among people, companies, or countries is indeed unprecedented. The fact that industry 4.0 is explosively disrupting or retrofitting each and every industrial sector makes industry 4.0 the famous buzzword amongst researchers today. However, the insight of industry 4.0 disruption into the industrial sectors remains ill-defined in both academic and nonacademic literature. The present study aimed at identifying industry 4.0 neologisms, understanding the industry 4.0 disruption and illustrating the disruptive technology convergence in the major industrial sectors. A total of 99 neologisms of industry 4.0 were identified. Industry 4.0 disruption in the education industry (education 4.0), energy industry (energy 4.0), agriculture industry (agriculture 4.0), healthcare industry (healthcare 4.0), and logistics industry (logistics 4.0) was described. The convergence of 12 disruptive technologies including 3D printing, artificial intelligence, augmented reality, big data, blockchain, cloud computing, drones, Internet of Things, nanotechnology, robotics, simulation, and synthetic biology in agriculture, healthcare, and logistics industries was illustrated. The study divulged the need for extensive research to expand the application areas of the disruptive technologies in the industrial sectors.

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Section: Convergence Of Disruptive Technologies In Logisticsmentioning

confidence: 99%

Industry 4.0 Disruption and Its Neologisms in Major Industrial Sectors: A State of the Art

Bongomin

Yemane

Kembabazi

et al. 2020

Journal of Engineering

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“…The demand forecasts also have significant impact on inventory ordering policies in production and retail (Doganis, Aggelogiannaki, and Sarimveis 2008), and several models are presented in the literature which highlight the importance of demand forecast for the inventory management (Erlebacher 2000;Priore et al 2019). Moreover, demand forecasts also helps in planning the distribution, routing and logistics management in retail (Sillanpää and Liesiö 2018;Winkelhaus and Grosse 2020;Liu et al 2020). Such importance of demand forecasts requires the retailer to achieve maximum accuracy in forecasting as that will lead to less uncertainty and better decisions.…”

Section: The Forecasting In Retailmentioning

confidence: 99%

Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail

Punia

Νικολόπουλος

Singh

et al. 2020

International Journal of Production Research

112

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This paper proposes a novel forecasting method that combines the deep learning method -long shortterm memory (LSTM) networks and random forest (RF). The proposed method can model complex relationships of both temporal and regression type which gives it an edge in accuracy over other forecasting methods. We evaluated the new method on a real-world multivariate dataset from a multichannel retailer. We benchmark the forecasting performance of the new proposition against neural networks, multiple regression, ARIMAX, LSTM networks, and RF. We employed forecasting performance metrics to measure bias, accuracy, and variance, and the empirical evidence suggests that the new proposition is (statistically) significantly better. Furthermore, our method ranks the explanatory variables in terms of their relative importance. The empirical evaluations are replicated for longer forecasting horizons, and online and offline channels and the same conclusions hold; thus, advocating for the robustness of our forecasting proposition as well as the suitability in multi-channel retail demand forecasting.

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“…e purpose of this paper is to define the components, system structure, system function, and operation mode of the Internet-based manufacturing green innovation system and analyze the open innovation of the manufacturing green innovation system by sorting out and drawing on the research results of economic theory and innovation theory, manufacturing green innovation system, and open innovation in the context of the Internet and combining the new situation of the integration and development of the Internet industry and manufacturing industry [2]. e meaning and motives of the open innovation of the manufacturing green innovation system are analyzed [3]. It e progress of society, along with the development of science and technology, has also made the retail industry undergo radical changes.…”

Section: Introductionmentioning

confidence: 99%

Optimization of IoT-Based Sporting Goods Consumer Service Management System

Bao-bin

2021

Complexity

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With the rapid development of computer technology and information industry technology, the use of these technologies has been deeply applied to the management of various industries. Data time-sharing transmission can solve the problem that the system is not affected by the network; that is, it makes good use of the network and is not bound by the network. Even when the network speed is low or the network is not smooth, the system of each store can run normally, and when the network is smooth, the data will be uploaded in packets and automatically received by the server. The system is well designed with functional modules’ management, including sports goods import and export management, inventory management, membership management, and ledger management. System operation can well solve the coordination work between the head office and stores; the head office can master the operation of each store, check the generated ledger at any time, form statistical reports, and guide the operation status of each store so that the stores can adjust their operation methods and learn from each other at any time. The system network management can make valuable data information aggregated, rather than single data. The head office can make overall data backup promptly to provide good logistic support for each store.

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Iot based laundry services: an application of big data analytics, intelligent logistics management, and machine learning techniques

Cited by 110 publications

References 27 publications

Industry 4.0 Disruption and Its Neologisms in Major Industrial Sectors: A State of the Art

Industry 4.0 Disruption and Its Neologisms in Major Industrial Sectors: A State of the Art

Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail

Optimization of IoT-Based Sporting Goods Consumer Service Management System

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