Simulation-based analysis of a supplier-manufacturer relationship in lean supply chains
Bibliographic record
Abstract
Purpose This paper aims to analyze a conceptual framework of supplier-manufacturer relationship in a lean supply chain environment, which considers two different configurations for the integration of information and material flows, aiming to better understand the applicability of such kind of approach to realistic cases. Design/methodology/approach Two different configurations for the integration of transport and material flows will be comparatively simulated and tested, aiming to better understand scientific implications and the applicability of such kind of approach to realistic cases in terms of performance of delivery service level and lead time. Findings The findings indicate that the conceptual model provides a framework to define threshold values of production variability to support the decision-making process regarding finished goods lean strategy. Furthermore, as the conceptual model considers as inputs the process variability of both supplier and customer’s production rates, it allows for the verification of the influence of supplier’s efficiency in the inventory sizing adopted in each case. Originality/value This study contributes to the body of literature on lean supply chain by proposing a simulation-based model that analyzes two different theoretical scenarios enabling the assessment of trade-offs among delivery service level, inventory strategy and production stability. This analysis provides theoretical arguments that may be extrapolated to real case situations, and considered other sources of instability that can impact the performance.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".