Lean supply chain management: a contextual contingent reconceptualization and Delphi method study
Bibliographic record
Abstract
Purpose This research proposes and illustrates a conditional view of lean supply chain management (LSCM) based upon the contextual contingent alignment between lean performance objectives (i.e. a contextual factor) and supply chain management challenges (i.e. a contingent condition) in the selection of lean approaches (i.e. a contingent event). Design/methodology/approach Drawing on the notions of contingency-based practices and strategic fit, the authors’ LSCM reconceptualization jointly considers contextual and contingency factors in specifying what lean approaches to adopt. The authors illustrate the practical relevance of LSCM reconceptualization for the Canadian agri-food industry using the Delphi method. Findings The authors highlight that LSCM is founded upon alignment associations between specific lean performance objectives and supply chain challenges as well as their influence on the selection of suitable lean approaches. The empirical illustration shows that those alignment associations do not occur at random, which supports the conditional view of LSCM. Research limitations/implications The contextual contingent view of LSCM can inform future scholarly inquiry and can reframe practically relevant middle-range theorization on LSCM. Practical implications The Delphi method-derived descriptive model of LSCM provides guidance to managers in the Canadian agri-food sector in identifying suitable lean approaches to adopt given the specific performance objective(s) pursued and supply chain management challenge(s) encountered. Originality/value The authors advance scholarly theorization and managerial understanding of LSCM by providing a conditional conceptualization that jointly considers relevant contextual and contingency factors that hitherto have not been examined. In ascribing what lean approach(es) to adopt to the alignment associations influence between lean performance objective(s) pursued and supply chain management challenge(s) encountered, the authors provide compelling conceptual and empirical support for the joint conditional view of LSCM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.043 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".