Governing Digital Transformation: An Integrated Framework for Project Management, Scheduling, and Change Leadership
Bibliographic record
Abstract
This article proposes an integrated framework for managing complex digital transformation projects within large multinational corporations. It critiques siloed approaches and argues that success hinges on the confluence of robust project governance, adaptive scheduling, and structured change management. This conceptual paper employs a single, in-depth case study of the Hilti Group's migration from SAP to Salesforce. The analysis is structured around a tripartite framework evaluating the project through the lenses of (1) the PRINCE2 methodology for project governance, (2) Critical Chain Project Management (CCPM) for scheduling, and (3) the ADKAR model for change management. The analysis demonstrates that digital transformation projects are not merely technical upgrades but strategic organisational changes. The recommended integrated framework ensures strategic alignment through PRINCE2's business case focus, mitigates resource-based risks via CCPM's buffering system, and actively manages human resistance using ADKAR's staged approach to change readiness. This paper synthesises three established but often separate project management domains into a single, cohesive framework. It provides project managers and organisational leaders with a practical, holistic model for navigating the technical, logistical, and human complexities inherent in major digital transformation initiatives, thereby enhancing the probability of project success and strategic benefit realisation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".