An integrated framework of enterprise information systems in smart manufacturing system via business process reengineering
Bibliographic record
Abstract
Enterprise information systems play a significant role in the Industry 4.0 era and are the crucial component to realize smart manufacturing systems. However, traditional enterprise information systems have some limits: (1) lack of complete information, (2) only satisfy limited business needs, and (3) lack of seamless integration, business intelligence, value-driven processes, and dynamic optimization. Clearly, the existing enterprise information systems are unable to satisfy the requirements for smart manufacturing systems: (1) autonomous operation, (2) sustainable values, and (3) self-optimization. In addition, smart manufacturing systems have become more efficient and effective, demanding for seamless information flow in enterprise information systems, knowledge, and data-driven accurately decision. Therefore, a new enterprise information systems framework is needed to bridge gaps between the requirements for traditional manufacturing system and smart manufacturing system. In this article, the integrative framework is proposed based on the business process reengineering, lean thinking, and intelligent management methods, with inclusion of six enterprise information systems aspects to provide upgrading guidelines from traditional manufacturing to smart manufacturing. The procedure of this method contains three steps: (1) it identifies requirements and acquires best practices using AS-IS model, (2) it redesigns six aspects of enterprise information systems using TO-BE model, and (3) it proposes a new enterprise information systems framework. Finally, the proposed framework is validated by real cases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".