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Supply chains under pressure: How can data science help?

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  • Thierry Warin

Abstract

The world has changed and companies are facing a perfect storm, with catastrophic risks that have a very low probability of occurrence, but for which the consequences are enormous. The war in Ukraine is impacting global supply chains already constrained by the COVID-19 pandemic. Ukraine is responsible for about 70% of the world's neon and Russia controls 44% of the world's supply of palladium, both of which are essential inputs in semiconductor production. Semiconductors are themselves essential to the manufacture of cars, smartphones or even medical equipment. With Taiwan producing nearly two-thirds of the world's semiconductors, China's move to reunify with the island of Taiwan raises significant concerns. In this complex geopolitical context, some companies are considering reshoring or nearshoring, i.e. the repatriation of specific activities within national ou regional borders. Is this the right solution or not? In this short text, Thierry Warin, Fellow CIRANO and responsible of the CIRANO Pole on Data Science for Trade and Intermodal Transportationfor argues that the solutions to recent complex supply problems must themselves be complex. We need to use the tools we have access to today: massive data, computing power and new methods of analysis. The global trade system must adapt to a new technological paradigm, that of artificial intelligence and data science. The alternative of using the same mental patterns as in the past and proposing binary solutions is no longer acceptable today. There is no more time to lose. Le monde a changé et les entreprises sont confrontées à une tempête parfaite, avec des risques catastrophiques dont la probabilité d'occurrence est minime, mais pour lesquels les conséquences sont énormes. La guerre en Ukraine a un impact sur les chaînes d'approvisionnement mondiales déjà limitées par la pandémie de COVID-19. L’Ukraine est responsable d’environ 70 % du néon sur la planète et la Russie contrôle 44 % des approvisionnements mondiaux en palladium, deux intrants indispensables dans la production des semi-conducteurs. Les semi-conducteurs sont eux-mêmes indispensables à la fabrication de voitures, de téléphones intelligents ou même d’équipements médicaux. Avec Taïwan qui produit près des deux tiers des semi-conducteurs du monde, la velléité de la Chine de procéder à la réunification avec l’île de Taïwan soulève d’importantes inquiétudes. Dans ce contexte géopolitique complexe, certaines entreprises envisagent le rapatriement de certaines activités à l’intérieur des frontières nationales — le reshoring — ou régionales — le nearshoring. Est-ce, oui ou non, la bonne solution ? Dans ce court texte, Thierry Warin, Fellow CIRANO et responsable du Pôle CIRANO en science des données pour les échanges commerciaux et le transport intermodal, soutient que les solutions aux problèmes complexes d'approvisionnement récents doivent elles-mêmes être complexes. Nous devons utiliser les outils auxquels nous avons accès aujourd'hui : les données massives, la puissance de calcul et les nouvelles méthodes d'analyse. Le commerce mondial doit s'adapter à un nouveau paradigme technologique, celui de l'intelligence artificielle et de la science des données. L'alternative qui serait d'utiliser les mêmes schémas mentaux que par le passé et de proposer des solutions binaires n'est plus acceptable aujourd'hui. Il n'y a plus de temps à perdre.

Suggested Citation

  • Thierry Warin, 2022. "Supply chains under pressure: How can data science help?," CIRANO Papers 2022pe-06, CIRANO.
  • Handle: RePEc:cir:circah:2022pe-06
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    File URL: https://cirano.qc.ca/files/publications/2022PE-06.pdf
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    1. Benoit Aubert & Nathalie de Marcellis-Warin & Thierry Warin, 2021. "Le Québec économique 9 - Chapitre 11 - Science des données, réseaux sociaux et politiques publiques," CIRANO Papers 2020ch-11, CIRANO.
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    1. Thierry Warin, 2022. "Supply chains under pressure: How can data science help?," CIRANO Papers 2022pj-06, CIRANO.

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    Keywords

    Supply chains; data science; relocation; international trade; global value chains; Chaînes logistiques; Science des données; Relocalisation; Commerce international; Chaînes de valeur mondiales;
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