{"id":"W2093303877","doi":"10.1080/07408170208928902","title":"Integrating advance order information in make-to-stock production systems","year":2002,"lang":"en","type":"article","venue":"IIE Transactions","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":122,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Build to order; Computer science; Stock (firearms); Queue; Stock control; Order (exchange); Supply chain; Operations research; Production (economics); Optimal control; Information structure; Risk analysis (engineering); Mathematical optimization; Microeconomics; Business; Economics; Engineering; Mathematics; Finance","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001710371,0.0001328829,0.0001186665,0.0005043437,0.0001714153,0.0002143104,0.0001149942,0.000039821,0.0005200328],"category_scores_gemma":[0.00004862606,0.0001333004,0.00003698244,0.001005572,0.00001599253,0.002263354,0.000008632132,0.0001422178,0.0008297854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008887525,"about_ca_system_score_gemma":0.000004112949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004216726,"about_ca_topic_score_gemma":0.0004136316,"domain_scores_codex":[0.9990891,0.000009064195,0.0003207899,0.0001746189,0.0001852769,0.0002211731],"domain_scores_gemma":[0.9995871,0.000009308366,0.00008643133,0.0001886301,0.0001155517,0.00001303441],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001212391,0.0007877641,0.003096097,0.001421018,0.00009480652,0.00001077189,0.003765139,0.5067768,0.0006183163,0.01959586,0.0419186,0.4217936],"study_design_scores_gemma":[0.0005487772,0.00001676591,0.001187283,0.0001734209,0.00003164487,0.000002781816,0.002864278,0.1988209,0.00003403493,0.0001420842,0.795832,0.0003460848],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1221001,0.0002685415,0.6641613,0.015093,0.01066154,0.004542745,0.0000124657,0.001216954,0.1819434],"genre_scores_gemma":[0.9952233,0.00001403262,0.0004606749,0.0009383526,0.0004121315,0.0002402423,0.00001379222,0.00001529053,0.002682224],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8731232,"threshold_uncertainty_score":0.9999482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0207735321565556,"score_gpt":0.2148019828782036,"score_spread":0.194028450721648,"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."}}