Efficiency Analysis of Large Global Manufacturing Companies by Data Envelopment Analysis Approach
Bibliographic record
Abstract
The development process of the manufacturing industry is a foundation for establishing many large enterprises around the world.The purpose of this study measures the performance of eight large manufacturing companies from past to future by a data envelopment analysis (DEA) approach.First, the super-SBM model was used to calculate the efficiency score in the previous term.Second, the resampling model with Lucas and weights applies to compute the forecasting values based on the historical data from 2016 to 2020; notably, this model can calculate the efficiency score in the future period of 2021-2025, based on integrating super-efficiency.The empirical results of the past, current, and estimated scores reveal that Toyota, Apple, Samsung, Honda, and Cardinal always obtain the performance above one number.Whereas Cardinal is the best manufacturing company with a consistently high score based on the efficiency qualification in the whole term, Ford is the worst manufacturing company as its efficiency score under one number.Finding results figure out an overall picture of the operational process of large manufacturing companies.The analysis result suggests a direction for improving the inefficient cases in future terms.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".