Executive Commentary |
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Descartes
Ken Wood
Executive Vice President , Product Management www . descartes . com
Artificial intelligence is at the forefront of innovation discussions , especially in supply chain management . Advanced technologies for warehouse optimization , transportation , network design , B2B multi-party digital communication / collaboration and other areas can be difficult for companies to fully adopt , realize value potential and sustain due to challenges such as expertise , data quality and business adaptation . AI promises to make these tools more accessible and sustainable by driving automation , improving data quality , enabling machine learning and incorporating natural language interfaces and adaptable workflows . This innovation allows companies to deploy existing supply chain technologies more efficiently . The industry can then build on this agile foundation with emerging tools such as affordable internet of things , augmented reality , edge AI and robotics , creating a more resilient and responsive supply chain .
Large language model ( LLM ) - based AI technologies are powerful tools for expanding the adoption of applications and techniques already shown to improve supply chain operations and performance .
technologies , fragmented systems and incomplete or inaccurate data , limiting visibility into the location and temperature of life-saving goods across the global supply chain .
For many , the global pandemic was a wake-up call to the urgency of digital transformation in supply chains , demonstrating that without real-time insights and automation , critical supply chains are at risk of costly disruption . Today , pharma leaders implementing real-time visibility technology are driving down costs , eliminating waste ,
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As networks and applications adapt to LLM interactions and data accessibility increases , we ’ ll see a broader use of AI agents in the supply chain . These agents will leverage existing applications , optimization engines and communication channels — enhanced by AI — to automate supply chain functions , all under the direction and supervision of human experts . These experts deeply understand business needs , customer expectations and AI-assisted supply chain analysis , guiding automation and ensuring optimal logistics operations .
Long-tail applications such as those related to complex B2B communications are another area where AI can offer powerful solutions . By streamlining cleansing , conversion and ingestion processes for both structured and unstructured data , AI can help make large-scale digital transformation and B2B integration projects much more achievable .
We have lots of technologies that have unrealized potential for transportation , warehousing , e-commerce , B2B communications , supply and demand management , and trade compliance . The list is long . AI is going to help unleash that potential .
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increasing efficiency and safeguarding patient safety .
Sensitive and complex treatments such as cell and gene therapies and personalized medicine take up an ever-larger share of pharma ’ s product portfolio . As the volume of these high-cost , high-complexity shipments rapidly grows , the only visibility that can future-proof the safety and sustainability of the pharma supply chain is real-time visibility .
Real-time visibility not only reduces waste but also provides a single source of
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truth through accurate and timely data , paving the way for automating processes subject to pharma ’ s strict quality controls . The automation of data-driven decision-making also enhances resilience by decreasing dependency on labor and other resources that may fluctuate in availability .
The next frontier in future-proofing pharma supply chains , particularly for delivering a new wave of treatments , will require innovations that enable end-to-end visibility , reliably tracking shipments from production to patient down to the unit level . Implementing this technology at scale presents new challenges , especially when the ambition is to do it sustainably and without compromising the planet or patient safety . The reality is that without realtime visibility , costs , risks and waste will rise , and patients may miss out on groundbreaking new treatments .
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DSV
Jesper Riis
COO www . dsv . com
In 2025 , visibility will no longer mean just tracking shipments in real time . It will evolve into a multi-dimensional view , where customers will expect the ability to monitor and analyze every aspect of the supply chain — from raw material sourcing to final delivery . At DSV , we are working hard to deliver on these expectations . This level of visibility enables quick adjustments when disruptions occur . Lead times will dynamically change daily , reflecting demand shifts and local / global supply disruptions . This is pivotal in times of geopolitical turmoil where flexible and resilient supply chains are key to mitigate changing trading patterns and new customer behavior . Predictability , on the other hand , will lean heavily on data and AI . It will mean not just forecasting demand but anticipating delays , risks and even consumer behavior shifts before they happen . In essence , it is about being proactive rather than reactive , ensuring a more resilient and agile supply chain .
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“ By streamlining cleansing , conversion and ingestion processes for both structured and unstructured data , AI can help make largescale digital transformation and B2B integration projects much more achievable .”
Ken Wood
“ In 2025 , visibility will no longer mean just tracking shipments in real time . It will evolve into a multidimensional view , where customers will expect the ability to monitor and analyze every aspect of the supply chain .”
Jesper Riis
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