{"id":"W4221082525","doi":"10.18280/mmep.090132","title":"Improving Efficiency for Retail Warehouse Using Data Envelopment Analysis","year":2022,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Management and Optimization Techniques","field":"Business, Management and Accounting","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universitas Islam Indonesia; Universitas Indonesia","keywords":"Benchmarking; Data envelopment analysis; Productivity; Warehouse; Analytic hierarchy process; Variable (mathematics); Order (exchange); Operations management; Lead time; Computer science; Operations research; Supply chain; Economic order quantity; Business; Quality (philosophy); Engineering; Marketing; Economics; 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":[0.003535671,0.0005976571,0.0009946253,0.001985849,0.0004801105,0.001914001,0.0004509178,0.0004908286,0.0009620907],"category_scores_gemma":[0.005877741,0.0002610554,0.001100591,0.003626863,0.0002469476,0.002060154,0.000698714,0.0004836646,0.000236798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001380667,"about_ca_system_score_gemma":0.001623431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005211172,"about_ca_topic_score_gemma":0.002904344,"domain_scores_codex":[0.9978344,0.0007421546,0.0002096414,0.0001713133,0.0008883749,0.0001542531],"domain_scores_gemma":[0.9981364,0.001003199,0.0002237866,0.0001627568,0.000453668,0.0000201498],"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":[0.0001531762,0.000276019,0.01872868,0.0004658423,0.0002151012,0.0001077037,0.0003829261,0.7763498,0.00899795,0.01663877,0.0009556611,0.1767284],"study_design_scores_gemma":[0.00001499674,0.0001703844,0.00890955,0.0000556968,0.00004335446,0.00003433616,0.0003145383,0.9745374,0.008713612,0.005309696,0.001861901,0.00003451535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3451263,0.0007354972,0.6433443,0.000410406,0.00002501166,0.0002058711,0.0003874053,0.0003699116,0.009395282],"genre_scores_gemma":[0.8975363,0.0004409792,0.1006935,0.00003049566,0.00000553939,0.0001368894,0.0003281586,0.00003648707,0.0007915719],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005211172,"threshold_uncertainty_score":0.01869863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06463260445527377,"score_gpt":0.2230700647900833,"score_spread":0.1584374603348095,"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."}}