From supply chain learning to the learning supply chain: drivers, processes, complexity, trade-offs and challenges
Bibliographic record
Abstract
Purpose The view that supply chain learning (SCL) has become a fundamental capability that supply chains must employ to innovate and improve their financial, technological, operational, environmental and social performance is widely accepted. However, the SCL phenomenon is still understudied and not fully understood by scholars, decision-makers and government representatives. This article aims to make sense of the existing literature and to identify important research directions that require further attention. Design/methodology/approach This article reviews the diversity of SCL in the literature, proposes a typology of such a phenomenon, provides an overview of key articles in the literature and identifies a series of recommendations for the future development of the field. Findings This article combines two fundamental dimensions from the literature (i.e. SCL driver and SCL network) to produce a typology of four types of SCL: Captive, Consortium, Selective and Distributed. Practical implications The typology proposed here offers an important framework for supply chain decision-makers to rely on when implementing SCL initiatives. The implications of each type of SCL offer a robust rationale for decision-makers to adopt the most appropriate type of SCL or combinations of SCL types, given each situation. In addition, the typology supports policy-makers in further understanding the SCL phenomenon and creating effective innovation, economic development and sustainability policies through supply chains. Originality/value This article offers a novel typology that the authors hope will help scholars to advance the field of SCL in order to understand this important phenomenon. There is no good/bad/better/worse SCL type in the proposed typology, but the critical element for the success of SCL efforts is the level of fit between the type of SCL, the type of knowledge to be created and diffused, and the outcome supply chains aim to achieve with that learning effort. In addition, the authors coin the construct of “the learning supply chain”, which refers to a supply chain that learns constantly by employing all four types of SCL simultaneously.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".