article

How Manufacturing Companies Can Improve Their Competitiveness: : Research on Service Transformation and Product Innovation Based on Computer Vision

Authors:

Yongling Zhang,

Sang-Bing (Jason) TsaiAuthors Info & Claims

Journal of Global Information Management (JGIM), Volume 32, Issue 1

Pages 1 - 26

https://doi.org/10.4018/JGIM.336485

Published: 17 January 2024 Publication History

Abstract

As the global market continues to evolve and competition escalates, the business environment becomes increasingly competitive. How manufacturing companies improve their competitiveness has always been a topic of great concern. Service transformation and product innovation are key factors and are considered to be important ways for enterprises to stand out in the market. Traditional service transformation and product innovation processes often face complex problems, including the diversity of customer needs and fierce market competition. This makes it difficult for companies to accurately capture market opportunities, provide personalized solutions, and respond quickly to changes. At the same time, many companies also face problems with product quality control and production efficiency, which further weakens their competitiveness. It is against this background that the importance of computer vision technology has become increasingly prominent.

References

[1]

Alhichri, H., Alswayed, A. S., Bazi, Y., Ammour, N., & Alajlan, N. A. (2021). Classification of remote sensing images using EfficientNet-B3 CNN model with attention. IEEE Access : Practical Innovations, Open Solutions, 9, 14078–14094.

[2]

Alt, R., Beck, R., & Smits, M. T. (2018). FinTech and the transformation of the financial industry. Electronic Markets, 28(3), 235–243.

[3]

Amazon. (2018). Amazon customer reviews dataset. Amazon. https://registry.opendata.aws/amazon-reviews/

[4]

ArnabA.DehghaniM.HeigoldG.SunC.LučićM.SchmidC. (2021). Vivit: A video vision transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 6836–6846). IEEE.

[5]

Chang, S. E., & Chen, Y. (2020). When blockchain meets supply chain: A systematic literature review on current development and potential applications. IEEE Access : Practical Innovations, Open Solutions, 8, 62478–62494.

[6]

COCO. Common Objects in Context. (2019). [Dataset]. COCO. https://cocodataset.org/

[7]

Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., Liu, Y., Topol, E., Dean, J., & Socher, R. (2021). Deep learning-enabled medical computer vision. NPJ Digital Medicine, 4(1), 5. 33420381.

[8]

FangH. S.WangC.GouM.LuC. (2020). Graspnet-1billion: A large-scale benchmark for general object gras**. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 11444–11453). IEEE. 10.1109/CVPR42600.2020.01146

[9]

Gao, X., Zhou, Z., Wang, L., Shao, G., & Zhang, Q. (2023). Feature selection and text clustering algorithm based on binary mayfly optimization. Journal of Jilin University Science Edition, 61(3), 631–640.

[10]

García-Morales, V. J., Garrido-Moreno, A., & Martín-Rojas, R. (2021). The transformation of higher education after the COVID disruption: Emerging challenges in an online learning scenario. Frontiers in Psychology, 12, 616059. 33643144.

[11]

Gonzalez-Sabbagh, S., Robles-Kelly, A., & Gao, S. (2022). DGD-cGAN: A Dual generator for image dewatering and restoration. arviv preprint arviv:2211.10026.

[12]

GrahamB.El-NoubyA.TouvronH.StockP.JoulinA.JégouH.DouzeM. (2021). Levit: A vision transformer in convnet’s clothing for faster inference. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 12259–12269). IEEE. 10.1109/ICCV48922.2021.01204

[13]

Han, K., Wang, Y., Chen, H., Chen, X., Guo, J., Liu, Z., Tang, Y., Xiao, A., Xu, C., Xu, Y., Yang, Z., Zhang, Y., & Tao, D. (2022). A survey on vision transformer. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(1), 87–110. 35180075.

[14]

Han, K., & Yuan, S. (2023). Short text semantic similarity measurement algorithm based on hybrid machine learning model. Journal of Jilin University Science Edition, 61(4), 909–914.

[15]

Hermes, S., Riasanow, T., Clemons, E. K., Böhm, M., & Krcmar, H. (2020). The digital transformation of the healthcare industry: Exploring the rise of emerging platform ecosystems and their influence on the role of patients. Business Research, 13(3), 1033–1069.

[16]

Huang, Y., Pan, G., Li, X., Sun, Z., Koyama, S., & Yang, Y. (2021). Mining potential requirements by calculation of user operations. [JOEUC]. Journal of Organizational and End User Computing, 33(6), 1–14.

[17]

ImageNet Large Scale Visual Recognition Challenge. (2009). Home. ImageNet. http://www.image-net.org/

[18]

Juma, H., Shaalan, K., & Kamel, I. (2019). A survey on using blockchain in trade supply chain solutions. IEEE Access : Practical Innovations, Open Solutions, 7, 184115–184132.

[19]

Kaggle. (2020). Statistical process control (quality control). Kaggle. https://www.kaggle.com/

[20]

Klinker, K., Wiesche, M., & Krcmar, H. (2020). Digital transformation in health care: Augmented reality for hands-free service innovation. Information Systems Frontiers, 22(6), 1419–1431.

Digital Library

[21]

Liu, W., Quijano, K., & Crawford, M. M. (2022). YOLOv5-Tassel: Detecting tassels in RGB UAV imagery with improved YOLOv5 based on transfer learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 8085–8094.

[22]

Loey, M., Manogaran, G., & Khalifa, N. E. M. (2020). A deep transfer learning model with classical data augmentation and CGAN to detect COVID-19 from chest CT radiography digital images. Neural Computing & Applications, •••, 1–13. 33132536.

[23]

Lundvall, B. A. (1985). Product innovation and user-producer interaction. The Learning Economy and the Economics of Hope, 19, 19–60.

[24]

Naderi, M., Karimi, N., Emami, A., Shirani, S., & Samavi, S. (2022). Dynamic-Pix2Pix: Noise injected cGAN for modeling input and target domain joint distributions with limited training data. arviv preprint arviv:2211.08570.

[25]

Ng, C. Y., Law, K. M., & Ip, A. W. (2021). Assessing public opinions of products through sentiment analysis: Product satisfaction assessment by sentiment analysis. [JOEUC]. Journal of Organizational and End User Computing, 33(4), 125–141.

[26]

Pham, T. N., Nguyen, V. H., & Huh, J. H. (2023). Integration of improved YOLOv5 for face mask detector and auto-labeling to generate dataset for fighting against COVID-19. The Journal of Supercomputing, 79(8), 8966–8992. 36619832.

Digital Library

[27]

RibaE.MishkinD.PonsaD.RubleeE.BradskiG. (2020). Kornia: An open source differentiable computer vision library for pytorch. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 3674–3683). IEEE. 10.1109/WACV45572.2020.9093363

[28]

Russell, E. W., & Bvuma, D. G. (2001). Alternative service delivery and public service transformation in South Africa. International Journal of Public Sector Management, 14(3), 241–265.

[29]

Sarker, I. H. (2021). Deep learning: A comprehensive overview on techniques, taxonomy, applications and research directions. SN Computer Science, 2(6), 420. 34426802.

Digital Library

[30]

Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(3), 160. 33778771.

Digital Library

[31]

Shahid, A., Almogren, A., Javaid, N., Al-Zahrani, F. A., Zuair, M., & Alam, M. (2020). Blockchain-based agri-food supply chain: A complete solution. IEEE Access : Practical Innovations, Open Solutions, 8, 69230–69243.

[32]

Tang, Y., Chen, M., Wang, C., Luo, L., Li, J., Lian, G., & Zou, X. (2020). Recognition and localization methods for vision-based fruit picking robots: A review. Frontiers in Plant Science, 11, 510. 32508853.

[33]

Tian, S., Li, L., Li, W., Ran, H., Ning, X., & Tiwari, P. (2024). A survey on few-shot class-incremental learning. Neural Networks, 169, 307–324. 37922714.

Digital Library

[34]

TorkzadehmahaniR.KairouzP.PatenB. (2019). Dp-cgan: Differentially private synthetic data and label generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. IEEE. 10.1109/CVPRW.2019.00018

[35]

Ulhaq, A., Khan, A., Gomes, D., & Paul, M. (2020). Computer vision for COVID-19 control: A survey. arv preprint arv:2004.09420.

[36]

Wang, J., & Dai, Y. (2022). The agglomeration mechanism of network emerging e-commerce industry based on social science. [JOEUC]. Journal of Organizational and End User Computing, 34(3), 1–16.

[37]

Wang, J., Yang, L., Huo, Z., He, W., & Luo, J. (2020). Multi-label classification of fundus images with efficientnet. IEEE Access : Practical Innovations, Open Solutions, 8, 212499–212508.

[38]

Yu, X. (2022). Global multi-source information fusion management and deep learning optimization for tourism: Personalized location-based service. [JOEUC]. Journal of Organizational and End User Computing, 34(3), 1–21.

[39]

Yu, Y., Si, X., Hu, C., & Zhang, J. (2019). A review of recurrent neural networks: LSTM cells and network architectures. Neural Computation, 31(7), 1235–1270. 31113301.

Digital Library

[40]

Yuan, L., Chen, D., Chen, Y. L., Codella, N., Dai, X., Gao, J., & Zhang, P. (2021). Florence: A new foundation model for computer vision. arviv preprint arviv:2111.11432.

[41]

Zhang, M., & Yin, L. (2022). Solar cell surface defect detection based on improved YOLO v5. IEEE Access : Practical Innovations, Open Solutions, 10, 80804–80815.

[42]

Zhao, Y., & Zhou, Y. (2022). Measurement method and application of a deep learning digital economy scale based on a big data cloud platform. [JOEUC]. Journal of Organizational and End User Computing, 34(3), 1–17.

Cited By

He JWu JSiau K(2024)Task Scheduling Strategy for 3DPCP Considering Multidynamic Information Perturbation in Green SceneJournal of Global Information Management10.4018/JGIM.35115632:1(1-23)Online publication date: 17-Sep-2024
https://dl.acm.org/doi/10.4018/JGIM.351156
Li RTsai FEscobar O(2024)Management Innovation, Digital Orientation, and External Knowledge SearchJournal of Global Information Management10.4018/JGIM.34805032:1(1-20)Online publication date: 7-Aug-2024
https://dl.acm.org/doi/10.4018/JGIM.348050

Index Terms

How Manufacturing Companies Can Improve Their Competitiveness: Research on Service Transformation and Product Innovation Based on Computer Vision
1. Applied computing
  1. Enterprise computing
  2. Operations research
    1. Computer-aided manufacturing
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision

Index terms have been assigned to the content through auto-classification.

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cover image Journal of Global Information Management

Journal of Global Information Management Volume 32, Issue 1

Aug 2024

1843 pages

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Copyright © 2024.

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IGI Global

United States

Publication History

Published: 17 January 2024

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Cited By

He JWu JSiau K(2024)Task Scheduling Strategy for 3DPCP Considering Multidynamic Information Perturbation in Green SceneJournal of Global Information Management10.4018/JGIM.35115632:1(1-23)Online publication date: 17-Sep-2024
https://dl.acm.org/doi/10.4018/JGIM.351156
Li RTsai FEscobar O(2024)Management Innovation, Digital Orientation, and External Knowledge SearchJournal of Global Information Management10.4018/JGIM.34805032:1(1-20)Online publication date: 7-Aug-2024
https://dl.acm.org/doi/10.4018/JGIM.348050

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