A business process and portfolio management approach for Industry 4.0 transformation
Bibliographic record
Abstract
Purpose The purpose of this paper is to address the difficulties faced by manufacturing enterprises by providing a project portfolio management approach supporting the selection and prioritization of various Industry 4.0 projects where business process analysis is used to ensure the strategic alignment and value of the project portfolio. Design/methodology/approach The design research methodology, a mixed applied research methodology, was used to develop and test the proposed approach. Findings Despite the growing interest of the scientific and industrial communities in I4.0, it seems that there is no method by which manufacturing companies can select a large number of improvement projects. Moreover, studies tend to focus on the evaluation and implementation of a single technology, while the transformation of an intelligent plant requires the consolidation and coordination of many initiatives to achieve a global objective. Originality/value The proposed project portfolio management model offers support to enterprises during their digital transformation and improves their processes by integrating technology levers through consistent and achievable selection of I4.0 initiatives while meeting strategic goals and objectives.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".