Design and application of Internet of things-based warehouse management system for smart logistics

Lee, C. K. M.; Lv, Yaqiong; Ng, Ka Man; Ho, William; Choy, King Lun Tommy

doi:10.1080/00207543.2017.1394592

Cited by 314 publications

(196 citation statements)

References 27 publications

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“…In the future, this research could be extended to not only deriving material flow patterns in smart factories, but also deriving worker movement and patterns in factories using techniques such as crowd sensing [60,61]. In addition, this research could be integrated with numerous Smart X studies, including smart logistics, smart cities and smart streets [62][63][64], beyond smart factory fields.…”

Section: Discussionmentioning

confidence: 99%

Development of Scalable On-Line Anomaly Detection System for Autonomous and Adaptive Manufacturing Processes

2019

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Featured Application: The proposed framework is capable of analyzing the flow of numerous objects in real time on the basis of a new process anomaly detection approach that considers the manufacturing process of an autonomous and adaptive smart factory.Abstract: Factories of the future are foreseen to evolve into smart factories with autonomous and adaptive manufacturing processes. However, the increasing complexity of the network of manufacturing processes is expected to complicate the rapid detection of process anomalies in real time. This paper proposes an architecture framework and method for the implementation of the Scalable On-line Anomaly Detection System (SOADS), which can detect process anomalies via real-time processing and analyze large amounts of process execution data in the context of autonomous and adaptive manufacturing processes. The design of this system architecture framework entailed the derivation of standard subsequence patterns using the PrefixSpan algorithm, a sequential pattern algorithm. The anomalies of the real-time event streams and derived subsequence patterns were scored using the Smith-Waterman algorithm, a sequence alignment algorithm. The excellence of the proposed system was verified by measuring the time for deriving subsequence patterns and by obtaining the anomaly scoring time from large event logs. The proposed system succeeded in large-scale data processing and analysis, one of the requirements for a smart factory, by using Apache Spark streaming and Apache Hbase, and is expected to become the basis of anomaly detection systems of smart factories.

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Section: Discussionmentioning

confidence: 99%

Development of Scalable On-Line Anomaly Detection System for Autonomous and Adaptive Manufacturing Processes

2019

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“…This evidence even underpins the reason why a lack of empirical studies failed to support the TBL. Most of them put the attention exclusively on the economic dimension, underlining the impact on the remaining dimensions as a positive externality [6,56]. This raises the implication to study antecedents of I40 adoption to shed light on how they influence the actual adoption, and the impacts they have on the TBL.…”

Section: Implications For Researchersmentioning

confidence: 99%

Exploring Organizational Sustainability of Industry 4.0 under the Triple Bottom Line: The Case of a Manufacturing Company

2018

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There is an increasing interest in sustainability practices for organizations. Organizations act sustainably when they support the three dimensions \of the triple bottom line. Industry 4.0 (I40) promises to afford organizations to act sustainably. However, few empirical pieces of research targeted the impact of I40 on the social, economic, and environmental dimensions of sustainability. Our investigation considered the adoption of I40 in a manufacturing company which we analyzed as a single case study. We describe the level of I40 adoption and the process through which the unit has adopted them. Our case confirms that I40 applications support the triple bottom line through the improvement of productivity and product quality (economic), continuous energy consumption monitoring (environmental), and safer work environment and less intense work-load and job enrichment (social). We contribute to the literature by identifying two trajectories of interaction among the three dimensions of the triple bottom line in the shift from a traditional manufacturing company to a knowledge-intense organization. In the trajectories found, the three dimensions of sustainability influence and reinforce each other.Sustainability 2019, 11, 36 2 of 17 investigations of sustainability in I40. Section 3 introduces the research design. Section 4 illustrates the single case study analysis. Section 5 illustrates the results of the case analysis discussing them regarding sustainability and the interaction among the TBL dimensions and I40. The article concludes in Section 6, proposing implications both for research and practice. Related LiteratureOur work is framed within the concept of sustainability and develops on the literature discussing I40 applications, and the sustainability potential of I40 applications. SustainabilityThe United Nations World Commission on Environment and Development first introduced the sustainability concept in 1987. In that year, the commission defined sustainability as an economic development model that allows to "meet the needs of the present generation without compromising the ability of future ones to meet their own needs" [11]. Sustainability is a multi-dimensional concept encompassing environmental, social, and economic dimensions. Such dimensions form the TBL view of sustainability (as shown in Figure 1) [2,3].Sustainability 2018, 10, x FOR PEER REVIEW 2 of 17 the single case study analysis. Section 5 illustrates the results of the case analysis discussing them regarding sustainability and the interaction among the TBL dimensions and I40. The article concludes in Section 6, proposing implications both for research and practice. Related LiteratureOur work is framed within the concept of sustainability and develops on the literature discussing I40 applications, and the sustainability potential of I40 applications.

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“…The increased complexity of manufacturing processes led to increased complexity of component portfolios which led to complex warehousing processes. In the case of real-time data and contextual information, these complex warehouse processes can be improved through Industry 4.0 technologies [16]. The success of applied Industry 4.0 technologies depends on the characteristics of manufacturing environment [17].…”

Section: Content Analysismentioning

confidence: 99%

Introductory Chapter: Industry 4.0 and Its Impact on Logistics - A Retrospective Review

Bányai¹

2020

Industry 4.0 - Impact on Intelligent Logistics and Manufacturing

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Design and application of Internet of things-based warehouse management system for smart logistics

Cited by 314 publications

References 27 publications

Development of Scalable On-Line Anomaly Detection System for Autonomous and Adaptive Manufacturing Processes

Development of Scalable On-Line Anomaly Detection System for Autonomous and Adaptive Manufacturing Processes

Exploring Organizational Sustainability of Industry 4.0 under the Triple Bottom Line: The Case of a Manufacturing Company

Introductory Chapter: Industry 4.0 and Its Impact on Logistics - A Retrospective Review

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