Logistics faces demand for speed and reliability. AI and hybrid computing help optimize operations, increase efficiency, and drive growth.
by Chris Arrasmith, Senior Vice President and General Manager of Enterprise Computing Solutions (ECS), Unisys
The logistics industry is at a crossroads. Clients expect faster, more reliable deliveries for a surging number of shipments, leaving shipping and cargo companies with an urgent need for greater visibility and efficiency. As supply chains grow more complex, it’s evident that many existing systems struggle to keep pace. To address today’s shipping demands, the industry needs solutions that can operate dynamically and adapt in real time, allowing rapid responses to changing circumstances.
Organizations are currently hampered by inconsistent real-time data tracking, inefficient loading and unloading processes, and challenges in securing materials during transit. These operational inefficiencies often lead to delayed shipments, increased costs, diminished customer trust, and ultimately, jeopardized growth.
Regulatory requirements and environmental concerns compound this challenge, adding complexity to an already intricate landscape. At the same time, clients’ growing need for visibility into these issues intensifies expectations, increasing pressure on organizations to adapt.
Companies must embrace cutting-edge technologies to address these challenges and seize new opportunities. Innovations such as AI and hybrid computing – combining classical and quantum methods – offer transformative potential, promising enhanced efficiency, better resource management, and improved business outcomes by leveraging the most suitable resources for each task.
AI-driven analytics and hybrid computing enable logistics companies to become more efficient, optimize resources, and reduce errors.
This is accomplished through AI algorithms enhancing predictive capabilities by analyzing historical data and current trends. This allows companies to anticipate disruptions and adjust strategies proactively. The use of AI facilitates more precise decision-making and agile responses to market demands.
AI and hybrid computing enable near-real-time, cost-effective decisions by optimizing load planning and route selection, reducing waste, and improving throughput.
Clients’ top logistics and supply chain management priorities are real-time visibility into their package’s location, on-time delivery, and customer service. By incorporating AI and hybrid computing technology into their systems, companies can better meet their clients’ needs.
This also helps address the unique pain points of supply chain challenges. Through this technology, organizations are better able to navigate supply chain hurdles by making faster decisions considering all possible outcomes, enhancing efficiency and customer satisfaction globally.
AI and automation can drive better client outcomes and growth. By optimizing processes, companies do more with existing staff, equipment, and infrastructure, lowering costs and increasing profits.
Advanced technologies can also support scalability and agility, enabling organizations to adapt more effectively to market changes and disruptions. The ability to quickly scale operations up or down based on real-time data ensures that businesses can respond to fluctuations in demand without compromising service quality. By adopting these technologies, companies enhance their current operations and secure a competitive edge, fostering long-term growth and success.
Modernizing supply chains requires the implementation of advanced technologies. Outdated practices hinder growth and customer satisfaction. AI and data computing methods in logistics optimization are the future for supply chains to achieve growth in the coming years. Staying ahead requires being proactive in technology adoption, with rewards found in sustained success, competitive offerings, and customer loyalty.
About the Author:
Chris Arrasmith is Senior Vice President and General Manager of Enterprise Computing Solutions (ECS) at Unisys, where he drives growth through innovative hybrid computing and AI solutions. With over 20 years of experience in IT and transformation roles, Chris holds an MBA from Bradley University and a BA from the University of New Mexico. He also advises Clemson’s AI Research Institute and has served on various non-profit boards.
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