Understanding why firms should invest in sustainable supply chains: a complexity approach
Bibliographic record
Abstract
This paper explores why firms should include sustainable development considerations in supply chains as a means of improving social and environmental impacts of production systems. The recognition of financial, social and environmental elements however creates greater complexity, which makes optimisation approaches to sustainable supply chain problems infeasible. We frame our analysis using Kauffman's (1993) NK theory, with interactions among financial, social and environmental elements identified through empirical research conducted in Brazilian oil and gas, sugarcane ethanol and biodiesel supply chains. We use a matrix of interactions (Baldwin and Clark 1999 Baldwin, C and Clark, K. 1999. Design rules: the power of modularity, Cambridge, MA: The MIT Press. [Google Scholar]) as a template, allowing for the identification of key financial, social and environmental elements and their interconnections within and between supply chains. We contribute by arguing that firms focusing on individual sustainable development elements independently are unlikely to find satisfactory solutions to their sustainable supply chain problems. We further argue that certain sectors have a propensity to be socially exclusive, whereas others are potentially socially inclusive; in such cases, firms operating in exclusive sectors may be able to find satisfactory solutions to their broader sustainability strategies by investing in the social and environmental performance of other 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.006 | 0.017 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".