VGU RESEARCH REPOSITORY
Please use this identifier to cite or link to this item:
https://epub.vgu.edu.vn/handle/dlibvgu/2117| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Tran Chi Quyet | en_US |
| dc.date.accessioned | 2026-01-08T07:37:50Z | - |
| dc.date.available | 2026-01-08T07:37:50Z | - |
| dc.date.issued | 2024 | - |
| dc.identifier.uri | https://epub.vgu.edu.vn/handle/dlibvgu/2117 | - |
| dc.description.abstract | Data analytics has become a cornerstone of Industry 4.0, enabling manufacturing firms to enhance productivity, optimize decision-making, and strengthen competitive advantage. This thesis investigates the adoption of data analytics across automotive, electronics, and textile manufacturing sectors, assessing industry-specific maturity using the Impuls model. Findings reveal that automotive and electronics firms lead adoption due to complex data, intricate supply chains, and high precision requirements, while textiles lag because of resource limitations and slower digital adaptation. Internal factors, such as top management support, goals to improve productivity, and cost reduction, were identified as more influential than external pressures in successful data analytics integration. Key internal challenges include technological infrastructure, employee skills, and organizational culture. To address these barriers, this study proposes a structured adoption guideline integrating PMBOK project management principles and Diffusion of Innovation theory. The guideline emphasizes readiness assessments, pilot projects, KPI monitoring, and scalable implementation to foster a data-driven culture and sustainable growth in manufacturing firms. | en_US |
| dc.language.iso | en | en_US |
| dc.rights | https://creativecommons.org/licenses/by-nc/4.0/ | * |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc/4.0/ | * |
| dc.subject | Data analytics adoption | en_US |
| dc.subject | Industry 4.0 | en_US |
| dc.subject | Organizational capability | en_US |
| dc.title | A guideline for implementing data analytics adoption in the industry 4.0 era, emphasis on techniques, key drivers and challenges | en_US |
| dc.type | Thesis | en_US |
| item.fulltext | With Fulltext | - |
| item.languageiso639-1 | other | - |
| item.grantfulltext | restricted | - |
| Appears in Collections: | Global Production Engineering & Management (GPEM) | |
Files in This Item:
| File | Description | Size | Format | Existing users please Login |
|---|---|---|---|---|
| A guideline for implementing data analytics adoption in the industry 4.0 era, emphasis on techniques, key drivers and challenges.pdf | 2.4 MB | Adobe PDF |
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