World Class Supply Chain 2021: Vision 2030: SCM in a New Decade
Notice bibliographique
Résumé
The Fifth Annual World Class Supply Chain Summit on May 5th, 2021 was momentous in at least threes ways: First, it marked a return to having the annual summit following the cancellation of the 2021 summit because of the COVID- 19 pandemic. Second, in light of ongoing pandemic-related restrictions on in-person gathering, the 2021 summit was convened virtually –this meant leveraging needed digital technology platform and technological savvy of the summit planning team personnel. Third, in light of how the pandemic has affected supply chains, the summit theme (Vision 2030 -SCM in a New Decade) and, the industry representation of the speakers, and the content aligned with the kind of thinking needed for supply chains to survive the pandemic and thrive in the years ahead. In particular, because container ports are reliable barometers of economic activity such as how well an economy is recovering from the pandemic, the summit’s panel discussion was expressly designed to shed light on insights from panelists who are executives at key North American ports: Halifax; Mobile (Alabama); Vancouver; and Prince Rupert. The panel discussion focused on eight questions that ranged from the initial pandemic impacts on container ports through to actions and ideas to handle both immediate and imminent challenges. Three of the most prominent issues in the panel discussion were (i) technology, (ii) collaboration/partnerships, and (ii) human capital. A noteworthy synopsis of some of these issues is the following quote from panelist Brian Friesen (the Prince Rupert Port Authority’s Vice-President of Trade Development & Communications, Prince Rupert Port Authority):\n"It’s a people business; organizations don’t do business with one another, people who work at those organizations do business with one another".\nTo help transition the summit’s deliberations from reflection on the pandemic’s supply chain effects and towards development of ideas for facilitating recovery, the chosen keynote topic was the road ahead. In the keynote address by Professor Glenn Richey (the Harbert Eminent Scholar and Chair in Auburn University’s Department of Supply Chain Management) explained several trends. For practitioners, those included building redundancy in supply chains (i.e., based on companies critically examining the efficacy of extremely lean supply chains for their unique circumstances), some shift to regional sourcing, chains, pandemic-induced renewal of emphasis on ecological sustainability, global supply chain complexity, and technologies that, currently, are not widely adopted. In looking at these trends from the perspective of supply chain scholars, Professor Richey discussed them as some of the areas in which research is needed in order to answer critical questions such as how to responsibly and sensitively approach cultural barriers to artificial Intelligence/machine learning. This rest of this white paper provides further elaboration on these points as well as the aforementioned points from the panel discussion.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,011 | 0,005 |
| Communication savante | 0,022 | 0,012 |
| Science ouverte | 0,002 | 0,008 |
| Intégrité de la recherche | 0,018 | 0,017 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,028 | 0,008 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».