{"id":"W4400099665","doi":"10.20944/preprints202406.1825.v1","title":"The Synergy Between Supply Chain Agility and Marketing Flexibility: A Qualitative Study of Adaptation Strategies in Turbulent Markets","year":2024,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Supply Chain Resilience and Risk Management","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Supply chain; Flexibility (engineering); Business; Adaptation (eye); Marketing; Process management; Competitive advantage; Dynamic capabilities; Supply chain management; Resilience (materials science); Demand chain; Industrial organization; Knowledge management; Service management; Computer science; Economics","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":[0.01022325,0.0004414345,0.0005369966,0.001177742,0.006421336,0.003214021,0.001084197,0.001365778,0.003488271],"category_scores_gemma":[0.0125737,0.0005103277,0.0003033656,0.001281645,0.008349775,0.004023583,0.003607437,0.002451127,0.0003734309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004590542,"about_ca_system_score_gemma":0.004971429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00880772,"about_ca_topic_score_gemma":0.01516285,"domain_scores_codex":[0.9935189,0.004949496,0.0001265998,0.0003152921,0.0003423401,0.0007473973],"domain_scores_gemma":[0.9867834,0.01058529,0.0007311077,0.0002493581,0.0007620439,0.0008889039],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00003768979,0.00005950952,0.002535605,0.0001056004,0.000002659186,0.0006284271,0.9897642,0.00007796074,0.0009059876,0.00218692,0.0004823863,0.003212993],"study_design_scores_gemma":[0.000003819221,0.00003324917,0.000926641,0.00008363082,0.000001950312,0.0001216014,0.9925291,0.0001186322,0.0003039642,0.0005474466,0.005319652,0.00001032436],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9870139,0.0002711128,0.004443727,0.002294462,0.00004437133,0.0002193395,0.000185703,0.0000133601,0.005514098],"genre_scores_gemma":[0.9946196,0.0003453115,0.001463523,0.0006653122,0.000009961243,0.000290901,0.00006198118,0.00002241479,0.002520942],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01022325,"threshold_uncertainty_score":0.05406636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09782598102594495,"score_gpt":0.3590578019081622,"score_spread":0.2612318208822173,"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."}}