Warehouse to Wellness: Bob Mauch on Modern Pharmaceutical Distribution

Cencora President and CEO Bob Mauch shares insights into the company’s innovations that enhance pharmaceutical distribution, ultimately improving patient outcomes. As a former pharmacist, Mauch emphasizes the vital role of distributors in delivering medications efficiently to patients, especially in response to new healthcare challenges.

With the rise of complex biologic therapies and evolving demands in healthcare, Cencora is leveraging advanced technologies such as AI, machine learning, and automation. For instance, the implementation of AI in inventory management allows the company to utilize predictive analytics. This capability helps determine the most efficient routes for products entering the network, ensuring timely access to medications—sometimes within just one or two days. Such improvements not only enhance service levels but directly contribute to better patient outcomes.

Additionally, Cencora has expanded its cold-chain logistics, essential for transporting sensitive cell and gene therapies. They conduct over 12,000 cryogenic shipments annually, relying on technology to monitor conditions, thereby assuring the integrity of the products.

Mauch highlights that integrating these digital capabilities is an ongoing journey, essential for managing complexity in healthcare. Looking forward, he aims for Cencora to remain a purpose-driven organization that harnesses AI and analytics, ensuring its position as a leader in the specialty market for advanced therapies. His vision reflects a commitment to continuous improvement, emphasizing patient access and the importance of delivering medications reliably and efficiently, ultimately fostering healthier futures in healthcare.

Supply chain management is on the brink of a revolutionary transformation, as detailed in a recent article by Andre Calmon and colleagues in the Harvard Business Review titled “The Age of Autonomous Supply Chains Has Arrived.” This research highlights the profound impact of generative AI on supply chain operations, moving beyond mere automation to achieve true autonomy.

Utilizing AI models like Llama 4 Maverick 17B with refined prompts and data-sharing protocols has shown remarkable results. In a study employing the MIT Beer Distribution Game, AI systems outperformed over 100 human participants from Georgia Tech, achieving a staggering 67% reduction in total supply chain costs. This demonstrates a clear advantage of AI-driven decision-making.

Traditional supply chain systems are typically limited by rigid, human-created rules. In contrast, the autonomous agents examined in this study learn and adapt in real-time, significantly enhancing coordination and efficiency across multiple functions. Success hinges on four key elements: selecting strong reasoning models, instituting guardrails to avoid errors, orchestrating data effectively, and refining performance-driven prompts.

Calmon emphasizes the strategic benefit of this technology, stating that it positions the Scheller College of Business at the forefront of AI in supply chain innovation. Businesses that leverage generative AI alongside appropriate data and policies stand to gain a significant competitive edge. Beyond costs, this technology allows human managers to concentrate on high-level strategic tasks, such as network design and supplier relationships. As global uncertainties increase, the strategic integration of AI-enabled tools will become vital for achieving supply chain resilience and success.

How AI is Streamlining the Food and Beverage Supply Chain

Artificial intelligence (AI) is revolutionizing the food and beverage supply chain by enhancing efficiency, reducing waste, and supporting sustainability goals. As brands like Tyson, Conagra, and General Mills embrace AI, they are transforming logistics, inventory management, and research and development.

AI excels at predicting disruptions and analyzing vast amounts of data. For instance, simple AI applications, such as large language models (LLMs), can structure unorganized information from sources like PDFs and emails, drastically reducing data processing time from hours to seconds. Machine learning models reveal patterns in data that help companies foresee supply chain disruptions sooner, leading to better decision-making.

Operationally, AI agents take over manual tasks like adjusting schedules or managing customer orders, enabling companies to react swiftly to changing market conditions. This proactive approach results in minimized waste and reduced emissions, transforming sustainability aspirations into measurable outcomes.

From a practical standpoint, a restaurant chain can use AI to analyze weather data alongside inventory levels, preparing for sudden shifts in customer demand. This integration of AI allows firms to create a unified source of information, facilitating real-time, data-driven decisions, which in turn enhances forecasting accuracy and curtails overproduction.

AI also impacts consumer behavior, enabling brands to respond quickly to emerging trends and improve product offerings based on real-time insights. This agility boosts customer satisfaction, ensuring better stock availability and faster innovation.

In essence, AI strengthens human judgment rather than replacing it. By streamlining repetitive tasks and unifying data, AI equips food and beverage professionals to focus on strategic decision-making, ensuring their operations stay ahead in a dynamic marketplace.

The U.S. Department of Transportation (DOT) is enhancing its operations through an extensive integration of artificial intelligence (AI) and Salesforce's customer relationship management (CRM) software. This initiative, described as an "expanded agencywide transformation," aims to modernize key functionalities within the DOT while improving citizen safety via real-time data sharing.

One concrete application of AI in this strategy is the deployment of Agentforce, Salesforce’s AI platform, which will automate routine tasks and provide 24/7 support for citizens. This system will handle complaints and service requests, enabling human staff to concentrate on more complex issues. Additionally, AI agents will analyze intricate datasets—such as traffic trends and weather patterns—to issue real-time alerts and propose strategies to mitigate accidents and injuries, ultimately enhancing transportation safety.

Furthermore, the DOT's use of AI will streamline the management of federal grants. By quickly reviewing applications and ensuring compliance, these agents can flag inconsistencies and prepare approval recommendations, facilitating faster access to vital funding for infrastructure projects.

This transformation addresses challenges posed by outdated systems and administrative burdens, allowing the DOT to significantly accelerate its operational speed. As Pavan Pidugu, Chief Digital & Information Officer of USDOT, stated, automating these processes will save considerable time and resources. The strategic shift to incorporate AI at the core of DOT operations not only empowers the agency to work more effectively but also prioritizes the safety and reliability of the nation’s transportation infrastructure.