Journal of Autonomous Intelligence

Shaping the Next Generation Pharmaceutical Supply Chain Control Tower with Autonomous Intelligence

Published on
2019-06-18
Updated on
2019-06-18


Summary: Technologies such as AI can play a valuable role in the decision-making processes within a CT environment.

Today’s pharmaceutical distributors are faced with several key strategic priorities. These include retaining and managing operating margin, acquiring business agility and controlling pharmaceutical counterfeiting and fraud. Overall, the control tower (CT) concept can transform how healthcare and pharma industries lead and manage their supply chains by shifting to a model in which real-time information gathering, analysis, and decision making are possible.

In essence, a CT is a center of excellence that facilitates a coordinated network to continuously manage complexity and execute at levels that cannot otherwise be managed easily by humans. It must provide fundamental capabilities to enable the levels of visibility and awareness to achieving this mission.

Matthew Liotine made a research on the next generation pharmaceutical supply chain control tower, and the research result published on the journal of autonomous intelligence.

This paper summarizes the findings of an industry panel study evaluating how new Autonomous Intelligence technologies, such as artificial intelligence and machine learning, impact the system and operational architecture of supply chain control tower (CT) implementations that serve the pharmaceutical industry. Such technologies can shift CTs to a model in which real-time information gathering, analysis, and decision making are possible. This can be achieved by leveraging these technologies to better manage decision complexity and execute decisions at levels that cannot otherwise be managed easily by humans.

Overall, a CT serves as a command center to enable a firm to act more closely with suppliers and be more proactively provide customer service, and ultimately improve profitability. Technologies such as AI can play a valuable role in the decision-making processes within a CT environment. Such decision-making requires learning based on high-quality transaction-based data, less tangible data and prior human-based operator decision behavior patterns. The supply chain professionals of the future will evolve from the management of exceptions to creating more strategic value through new ways of working. Firms might have to reorganize or reconstitute functional roles within supply chain and perhaps information technology to accommodate machine-based decision-making.

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https://en.front-sci.com/index.php/JAI/article/view/34