{"id":"W2016589390","doi":"10.1016/j.jom.2006.04.006","title":"Characterizing and structuring a new make‐to‐forecast production strategy","year":2006,"lang":"en","type":"article","venue":"Journal of Operations Management","topic":"Product Development and Customization","field":"Business, Management and Accounting","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"Tellabs (Canada)","funders":"","keywords":"Production (economics); Computer science; Build to order; Structuring; Order (exchange); Variety (cybernetics); Product (mathematics); Matching (statistics); Generalizability theory; Stock (firearms); Operations research; Risk analysis (engineering); Industrial organization; Business; Economics; Microeconomics; Artificial intelligence; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002496354,0.0005590144,0.0005177902,0.001536162,0.0005492143,0.004505742,0.0007634007,0.001092096,0.002145742],"category_scores_gemma":[0.01242402,0.0003709589,0.0004032353,0.0008326652,0.0008895852,0.002573862,0.0007041566,0.0006880243,0.0002898009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001629728,"about_ca_system_score_gemma":0.00183469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004173896,"about_ca_topic_score_gemma":0.003571452,"domain_scores_codex":[0.9980715,0.0005125101,0.0001416025,0.0003930231,0.0006318306,0.0002496329],"domain_scores_gemma":[0.9938068,0.003266959,0.001235876,0.0005310847,0.0008853471,0.0002739459],"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":[0.0002701521,0.0003806231,0.04912818,0.0001223091,0.0001276545,0.0004944332,0.0006602339,0.6306415,0.009708134,0.1231134,0.001837299,0.1835161],"study_design_scores_gemma":[0.00001792912,0.0001685774,0.007622868,0.0000358532,0.00002967486,0.0000726137,0.0003749585,0.9389873,0.004322588,0.04561253,0.002721285,0.00003391943],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5915623,0.000187155,0.3896553,0.0007425445,0.00002987269,0.0003408984,0.0001921565,0.0002446832,0.01704505],"genre_scores_gemma":[0.9480521,0.00006979571,0.05044281,0.00004508938,0.0000121967,0.00005173375,0.0001078257,0.00002345743,0.001194889],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004505742,"threshold_uncertainty_score":0.01320213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01317861945297624,"score_gpt":0.2092366729143331,"score_spread":0.1960580534613569,"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."}}