{"id":"W3212859073","doi":"10.1016/j.ijpe.2021.108349","title":"Supply network design for mass personalization in Industry 4.0 era","year":2021,"lang":"en","type":"article","venue":"International Journal of Production Economics","topic":"Product Development and Customization","field":"Business, Management and Accounting","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Flexibility (engineering); Mass customization; Profitability index; Supply chain; Modular design; Computer science; Production (economics); Realization (probability); Function (biology); Personalization; Modular programming; Product (mathematics); Supply chain network; Manufacturing engineering; Industrial engineering; Business; Supply chain management; Marketing; Economics; Microeconomics; Mathematics; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.000766202,0.00009085434,0.0001388158,0.0002529651,0.00005027748,0.0001981346,0.0001778724,0.00008405436,0.0001350394],"category_scores_gemma":[0.0004162176,0.0001007308,0.00006059563,0.0001929107,0.00001470375,0.001551361,0.00002868566,0.0001711709,0.00001128636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000173604,"about_ca_system_score_gemma":0.0001850007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003349855,"about_ca_topic_score_gemma":0.00001345102,"domain_scores_codex":[0.9990943,0.00001340833,0.0004748228,0.0001738237,0.0001190478,0.0001245673],"domain_scores_gemma":[0.9984416,0.0000234246,0.0005126505,0.00006780971,0.000945667,0.000008840131],"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":[0.0008343684,0.0002541813,0.1202635,0.00007418703,0.0003272697,0.00002954667,0.000250732,0.7654142,0.002473769,0.03817252,0.05062098,0.02128476],"study_design_scores_gemma":[0.009106103,0.00007355382,0.09177101,0.0007159279,0.0002526247,0.0004771603,0.002021224,0.09039955,0.02156159,0.2538524,0.5281965,0.001572366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7226672,0.0004641347,0.2002634,0.04833992,0.02619908,0.000602241,0.000005640622,0.00004997092,0.001408384],"genre_scores_gemma":[0.9516937,0.0002137373,0.02838187,0.001647939,0.01686466,0.00001626819,0.0001085498,0.00003424515,0.001039016],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6750147,"threshold_uncertainty_score":0.4107682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02460169013496177,"score_gpt":0.2301593317042266,"score_spread":0.2055576415692648,"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."}}