Project management approaches and their selection in the digital age: Overview, challenges and decision models
Bibliographic record
Abstract
Digital transformation is a challenge that also impacts the selection of tools for implementing projects. Which tools are suitable for handling complex digital twins? Project management must respond to this with suitable approaches. The challenge for decision-makers is to choose the right one. Based on literature research and a case study, influencing factors are derived and practice-relevant project management approaches are collected. Furthermore, a decision model is developed that, on the one hand, supports the decision-maker in selecting tools before and during the project, and on the other hand makes empirical values from past projects usable for future decisions. The results show that the number of influencing factors is large, and the approaches are di-verse. In complex projects, this can lead to complex decision-making situations that require appropriate decision models. The developed “Supervised Decision Model – L5” is based on five levels (L): (L1) Building a database; (L2) Derivation of algorithms; (L3) Initial approach selection; (L4) Review of the initial selection; (L5) Using experiences for future decisions. In practice it turns out that complex projects – like Digital Twins - often fail. Modified decision models for selecting suitable approaches should therefore take the following as-pects into account: (a) decision-makers are actively supported in the initial decision phase; (b) initial decisions once made are checked in the early phase of the project and corrected if necessary; (c) the lessons learned are recorded in the database as empirical value and used for future decisions.
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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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".