A New Human Capital Development Framework in Logistics and Supply Chain Incorporating Industry 4.0
Bibliographic record
Abstract
When Industry 4.0 technologies are utilized in logistics practices, they create a Logistics 4.0 environment, and the key to successfully implementing Logistics 4.0 practices is the human capital of the logistics industry. The human capital of the logistics industry was found to be classified into four major categories, namely operative, supervisor, management, and government. This study aims to develop a new human capital framework that guides the logistics industry towards successfully implementing and managing Logistics 4.0 practices. The significance of this study is to utilize a new framework to shorten the gap between current basic logistics practices into Industry 4.0 practices. An integrative literature review technique was used thoroughly and reviewed and analyzed in order to create the Logistics 4.0 human capital development framework in a manner that satisfies the categories of logistics human capital. A combination of the business logistics management (BLM) framework and Industry 4.0 elements creates a new human capital development (HCD) framework in logistics and supply chains.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".