An end-to-end big data analytics platform for IoT-enabled smart factories: A case study of battery module assembly system for electric vehicles

Kahveci, Sinan; Alkan, Buğra; Ahmad, Mus’ab H.; Ahmad, Bilal; Harrison, Robert

doi:10.1016/j.jmsy.2022.03.010

Cited by 35 publications

(14 citation statements)

References 32 publications

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“…Grafana is one of the most popular open-source platforms for interactive data visualization. [79] has used Grafana to create a dashboard for visualising energy data at the workstation level to show operational KPI and power consumption trends (Figure 8.b). Similarly, [81] has developed dashboards using Grafana and Amazon QuickSight for its compatibility with Spark to display the results of small-batch processing for the detection of anomalies on CNC Machines (Figure 8.c).…”

Section: F Data Visualizationmentioning

confidence: 99%

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Big Data Life Cycle in Shop-Floor–Trends and Challenges

et al. 2023

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Big data is defined as a large set of data that could be structured or unstructured. In manufacturing shop-floor, big data incorporates data collected at every stage of the production process. This includes data from machines, connecting devices, and even manufacturing operators. The large size of the data available on the manufacturing shop-floor presents a need for the establishment of tools and techniques along with associated best practices to leverage the advantage of data-driven performance improvement and optimization. There also exists a need for a better understanding of the approaches and techniques at various stages of the data life cycle. In the work carried out, the data life-cycle in shop-floor is studied with a focus on each of the components -Data sources, collection, transmission, storage, processing, and visualization. A narrative literature review driven by two research questions is provided to study trends and challenges in the field. The selection of papers is supported by an analysis of n-grams. Those are used to comprehensively characterize the main technological and methodological aspects and as starting point to discuss potential future research directions. A detailed review of the current trends in different data life cycle stages is provided. In the end, the discussion of the existing challenges is also presented.

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Section: F Data Visualizationmentioning

confidence: 99%

“…FIGURE 8: Data visualization. a) [85] Seaborn visualization b)[79] Grafana visualization c)[81] Grafana and QuickSight d)[47] web and mobile apps e)[86] HTML5, CSS, JavaScript web application…”

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confidence: 99%

Big Data Life Cycle in Shop-Floor–Trends and Challenges

et al. 2023

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“…End-to-end visibility, which offers timely and accurate coverage of the entire SC, plays a crucial role in helping companies receive early warnings of potential disruptions and recover quickly (Ju ¨ttner and Maklan, 2011;Ivanov, 2021;Dolgui and Ivanov, 2022). This, in turn, leads to increased productivity and efficiency (Holcomb et al, 2011;Dolgui and Ivanov, 2022;Kahveci et al, 2022). In the textile industry, which has a high variation in goods, volatile demand and small lot size, firms must respond quickly to consumer demand and enhance differentiated product collection, thereby promoting agile SC management (Pearson et al, 2010;Sumarliah et al, 2021).…”

Section: Dynamic Supply Chain and Inbound Logistics Capabilitiesmentioning

confidence: 99%

Impacts of inbound logistics capabilities on supply chain resilience: insight from Vietnamese textile industry

Thị

Khanh

Minh

et al. 2023

MBE

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Purpose This study aims to examine the impact of inbound logistics on dynamic supply chain capabilities and, subsequently, on supply chain resilience in the Vietnamese textile industry. Design/methodology/approach A conceptual framework based on a resource-based view was empirically tested using partial least squares structural equation modeling and data collected from 215 Vietnamese textile enterprises from December 2021 to March 2022. Findings The research shows that inbound logistics capability positively affects dynamic supply chain capabilities. In particular, the study has ratified reengineering as the chief factor that textile firms should consider when building a resilient supply chain. Originality/value This study considers the Vietnamese textile industry to assess the indirect effect of inbound logistics on supply chain resilience through dynamic supply chain capabilities in a theoretical sense while assisting managers in comprehending the functions of supply chain collaboration, agility and reengineering as the foundation for supply chain resilience in a managerial sense.

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“…One of the main challenges towards the digital transformation enabled by Industry 4.0 is the design and implementation of big data analytics solutions for industry. Big data analytics systems are one of the main drivers of smart factories, to support decision-making in business process steps (Kahveci, et al, 2022). Currently, big data analysis applications are still limited in the context of manufacturing companies.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Smart Manufacturing: Case Study of Workforce Management Process in an Italian Leather Goods Company

Pietroni

Marconi

2023

Proc. Des. Soc.

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Digitalization is one of the fundamental pillars of Industry 4.0. Within smart factories, Big Data Analytics systems play a key role in supporting the decision-making process of various stages of business processes. In this context, this research aims to identify solutions able to process large volumes of data from digital business processes with the final goal of adding value to the organisation. More specifically, the research deals with the implementation of a digital manufacturing tool able to digitize the workforce management process. The research has been applied in the case study of an Italian manufacturing company operating in the leather goods sector through the digitalization of the workforce management by a cloud-based platform. The implementation of the tool increases the efficiency of the production process, provides efficient management and integrates workforce data into one system. The implemented tool generates a large volume of data, the final goal is to make data user-friendly to support business decisions. Digitisation provides an exchange of information to support managers to make confident decisions.

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An end-to-end big data analytics platform for IoT-enabled smart factories: A case study of battery module assembly system for electric vehicles

Cited by 35 publications

References 32 publications

Big Data Life Cycle in Shop-Floor–Trends and Challenges

Big Data Life Cycle in Shop-Floor–Trends and Challenges

Impacts of inbound logistics capabilities on supply chain resilience: insight from Vietnamese textile industry

Data-Driven Smart Manufacturing: Case Study of Workforce Management Process in an Italian Leather Goods Company

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