An exploratory investigation of the effects of supply chain complexity on delivery performance
Bibliographic record
Abstract
As just-in-time delivery has become increasingly commonplace and customer demands continue to tighten, the importance of fast, reliable delivery cannot be overstated. This is particularly true for firms competing internationally, where the complexity of the supply chain must be managed within a global network. To explore the linkage between supply chain complexity and delivery, a two-dimensional framework is proposed that conceptualizes the degree of complexity embedded in a supply chain along two major dimensions: (1) form of technology and (2) nature of information processing. Technology is characterized using a conventional operations strategy framework of structural and infrastructural elements. In contrast, information processing captures both the level of complicatedness and of uncertainty that exists in the supply chain. Collectively, these two dimensions create a two-by-two framework that defines supply chain complexity and provides a strong theoretical basis for linking different aspects of complexity to delivery performance. An exploratory empirical investigation using an international database focused on immediate upstream and downstream echelons of a supply chain at the firm level. Results show strong support for the linkages between delivery performance and both complicatedness of the product/process and uncertainty of the management systems. In contrast, little evidence was found that greater product variety and more complicated supply networks adversely affected performance. Thus, management initiatives to improve delivery performance are best focused on improving informational flows within the supply chain and leveraging new process technologies that offer flexibility to respond to uncertainty.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".