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DESCRIPTION:Machine learning is transforming supply chain management by ena
 bling businesses to make faster\, data-driven decisions in an\nincreasingl
 y complex and volatile market. By analyzing vast amounts of historical and
  real-time data\, ML helps companies improve demand\nforecasting\, ensurin
 g better alignment between supply and customer needs while reducing stocko
 uts and overstocking. It also plays a\ncritical role in optimizing invento
 ry by balancing stock levels\, cutting down holding costs\, and enhancing 
 overall efficiency. Supplier\nevaluation is another area where ML proves i
 nvaluable\, as it helps assess pricing trends\, delivery performance\, and
  quality metrics to\nidentify the most reliable partners. Additionally\, l
 ogistics and transportation benefit significantly from ML-driven route and
  schedule\noptimization\, reducing fuel costs and improving delivery times
 . By integrating machine learning into supply chain operations\, businesse
 s\ncan enhance agility\, reduce inefficiencies\, and gain a competitive ed
 ge in an increasingly dynamic market.\n\nVirtual: https://events.vtools.ie
 ee.org/m/475810
LOCATION:Virtual: https://events.vtools.ieee.org/m/475810
ORGANIZER:dileep.kumar.rai@gmail.com
SEQUENCE:24
SUMMARY:AI/ML in Supply Chain Decision Making
URL;VALUE=URI:https://events.vtools.ieee.org/m/475810
X-ALT-DESC:Description: <br /><p>Machine learning is transforming supply ch
 ain management by&nbsp\; enabling businesses to make faster\, data-driven 
 decisions in an<br>increasingly complex and volatile market. By analyzing 
 vast amounts of&nbsp\;historical and real-time data\, ML helps companies i
 mprove demand<br>forecasting\, ensuring better alignment between supply an
 d customer needs while reducing stockouts and overstocking. It also plays 
 a<br>critical role in optimizing inventory by balancing stock levels\,&nbs
 p\;cutting down holding costs\, and enhancing overall efficiency. Supplier
 <br>evaluation is another area where ML proves invaluable\, as it helps&nb
 sp\;assess pricing trends\, delivery performance\, and quality metrics to<
 br>identify the most reliable partners. Additionally\, logistics and&nbsp\
 ;transportation benefit significantly from ML-driven route and schedule<br
 >optimization\, reducing fuel costs and improving delivery times. By&nbsp\
 ;integrating machine learning into supply chain operations\, businesses<br
 >can enhance agility\, reduce inefficiencies\, and gain a competitive&nbsp
 \;edge in an increasingly dynamic market.</p>
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