Uncertainty regulation and adaptable supply chain planning
Bibliographic record
Abstract
Purpose Supply chain planning (SCP) as an important management intervention is highly relevant to operations and supply chain practice. While SCP processes have developed over time with the use of integrative and advanced analytics tools, the essential foundation and focus on restoring stability remain unchanged and limit the capacity of SCP to adapt to uncertainty. We aim to address this limitation and the way forward in research and practice of SCP through adaptable supply chain planning (ASCP). Design/methodology/approach Drawing on the current discourse on uncertainty regulation and SCP foundations and assumptions, a typology of SCP uncertainty and respective planning strategies are conceptualized. Findings To elevate SCP to account for today's volatile environment, we put forward various forms of uncertainty that organizations face with respect to their awareness and understanding of threats from uncertainty. We also consider how conditions require simultaneously using different planning strategies and navigating between the strategies to regulate the uncertainty, not only to mitigate it but also to create opportunities for progress and growth. Originality/value We propose a new ASCP paradigm with foundational principles, planning strategies and layers of potential research avenues.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".