{"id":"W4213371707","doi":"10.55365/1923.x2021.19.17","title":"Efficiency Analysis of Large Global Manufacturing Companies by Data Envelopment Analysis Approach","year":2021,"lang":"en","type":"article","venue":"Review of Economics and Finance","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Data envelopment analysis; Manufacturing; Term (time); Process (computing); Operations research; Computer science; Efficiency; Envelopment; Operations management; Econometrics; Industrial organization; Economics; Business; Marketing; Engineering; Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004678892,0.000779943,0.0009803076,0.006017133,0.0003552637,0.001684643,0.0004498329,0.0005098566,0.0008183971],"category_scores_gemma":[0.00869439,0.0002399988,0.001715999,0.007697413,0.0005067639,0.001687886,0.0008711523,0.0005950227,0.0001746449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00200467,"about_ca_system_score_gemma":0.001503397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006058461,"about_ca_topic_score_gemma":0.003651567,"domain_scores_codex":[0.9967891,0.00132775,0.0002972524,0.0002967852,0.001080718,0.0002083763],"domain_scores_gemma":[0.9965999,0.001995908,0.0004221264,0.0003083227,0.0006337035,0.00004009414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007843785,0.0001272358,0.05986161,0.000877545,0.0008494652,0.0004152668,0.0005143518,0.6477852,0.002110906,0.09216525,0.002193497,0.1930212],"study_design_scores_gemma":[0.00001581698,0.0001583229,0.05742871,0.000346663,0.0002479975,0.0001754526,0.001173571,0.8521768,0.004471694,0.06830991,0.01540709,0.00008791035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3624618,0.009543241,0.6077878,0.001047014,0.00006051446,0.000225688,0.001485608,0.0001873418,0.01720088],"genre_scores_gemma":[0.9484951,0.00354151,0.04608631,0.00005286848,0.00002888487,0.0001222134,0.0008054779,0.00002560105,0.0008420765],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006058461,"threshold_uncertainty_score":0.02474463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07150039997612802,"score_gpt":0.3515835892565427,"score_spread":0.2800831892804147,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}