Convergence of logistics planning and execution in outsourcing
Bibliographic record
Abstract
Background: Logistics service providers (LSPs) are becoming increasingly involved in their clients’ businesses. Beyond just providing vehicles and buildings, LSPs are now becoming involved with knowledge-related work that is connected to the traditional services provided.Objectives: To investigate the likelihood and potential value of LSPs extending their range of services to their clients by means of a convergence of planning and execution activities.Method: In the research through a literature review and empirical study presented here, attention is given to the practical impact that convergence planning and execution functions have on business success, as well as how selected clients of an LSP (referred to in this article as logistics company A or LCA) perceive the impact of increased integration of LCA within its businesses. The results should assist LCA and other LSPs considering the same objective to ascertain the opportunities and key requirements associated with a strategy to converge planning and execution activities for their clients.Results: The study found that the vast majority of respondents see value in the convergence of planning and execution activities.Conclusion: Such convergence will be challenging, owing to the importance of the planning function for clients, as well as key collaborative and measurement requirements that will have to be put in place for successful business integration.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".