Simulating The Impact Of Operational, Logistical And Contractual Factors In The Financial Performance Of Aerospace Supply Chains
Bibliographic record
Abstract
One of the key drivers in successful outsourcing decisions is the achievement of cost benefits for stakeholders across the supply chain. It is well established that enhancing the efficiency of operational and logistical activities can improve financial performance and there is an increasing awareness of the need to understand the relationship between the financial performance of supply chains, the activities that occur within them and the external factors that can influence them. To address this, the cash conversion cycle (CCC) is becoming a popular metric in supply chain management as it connects the activities performed by supply chain members to the cash flow between them. This paper uses a Design of Experiment methodology to identify the influence of the operational, logistical and contractual variables encountered in manufacturing supply chains on financial performance, specifically the CCC, inventory holding costs, and free cash flow (FCF). A discrete event simulation model of an aerospace subassembly line is used as an exemplar. The results lend credibility to using the Taguchi Method although its suitability depends on the format of the metrics used. The results also suggest the need to model variable cycle times for operations and logistics depend on inventory levels in the system.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".