{"id":"W4384434830","doi":"10.1007/978-3-031-28863-0_26","title":"Reducing the Supply-Chain Nervosity Thanks to Flexible Planning","year":2023,"lang":"en","type":"book-chapter","venue":"AIRO Springer series/AIRO Springer Series","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval; Transport Canada","funders":"","keywords":"Supply chain; Economic shortage; Bullwhip effect; Production (economics); Lexicographical order; Operations research; Range (aeronautics); Operations management; Computer science; Distribution (mathematics); Production planning; Distribution center; Business; Mathematical optimization; Supply chain management; Economics; Engineering; Microeconomics; Commerce; Mathematics; Marketing","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":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.001428633,0.001830499,0.001434835,0.00131535,0.00163832,0.001801831,0.002252441,0.0006601121,0.002274538],"category_scores_gemma":[0.0001989529,0.001659743,0.0007340761,0.0007070772,0.0005043747,0.002692062,0.004365622,0.001455153,0.004337396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003480794,"about_ca_system_score_gemma":0.0001151409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001075958,"about_ca_topic_score_gemma":0.0006855614,"domain_scores_codex":[0.9931128,0.00003785831,0.001432594,0.002092025,0.001512571,0.001812124],"domain_scores_gemma":[0.9958649,0.0001336143,0.0009187044,0.002574838,0.0003373583,0.0001706294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009389754,0.0001343154,0.003932002,0.002384364,0.001283708,0.0007438406,0.003337702,0.003446027,0.0004876727,0.7726958,0.2001679,0.01044769],"study_design_scores_gemma":[0.0004676209,0.0001070825,0.004281108,0.001179095,0.0003691725,0.00001744836,0.00138018,0.0001196152,0.0004471779,0.008735269,0.9807703,0.002125874],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.01135535,0.001402407,0.0004546253,0.01748196,0.01476366,0.003943759,0.00008149941,0.004717781,0.9457989],"genre_scores_gemma":[0.04971766,0.0003928309,0.0007355416,0.007063586,0.009401649,0.0005155202,0.0002368105,0.001025379,0.930911],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.7806024,"threshold_uncertainty_score":0.9996614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03550432182061139,"score_gpt":0.2302053265999628,"score_spread":0.1947010047793514,"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."}}