How AI Reduces Drug Supply Chain Disruptions

Summary
AI is being used to reduce pharmaceutical supply chain disruptions in several ways, including integrating into modeling software to produce simulations, conducting risk assessments to prevent likely disturbances, improving counterfeit detection, and more.- Author Company: ReHack
- Author Name: Zac Amos
- Author Email: zac@rehack.com
- Author Website: https://rehack.com/
Drugmakers, academics and logistics professionals are using artificial intelligence to address the industry’s most pressing issue — drug shortages. Is this technology as successful as many claim it is? Could it make pharmaceutical product scarcity a thing of the past?
1. Predictive Analytics Makes Disruptions Avoidable
According to the Senate Committee on Homeland Security & Governmental Affairs, drug shortages increased by around 30% from 2021 to 2022. At the year’s end, it reached a five-year high of 295 active shortages. Even though things have settled since then, this report serves as a reminder that scarcity is all too common.
While pharmaceutical professionals cannot see the future, predictive analytics lets them come close. This technology uses historical and current data points to forecast likely outcomes with surprisingly high accuracy, enabling organizations to anticipate supply chain problems. If disruptions become predictable, they become avoidable.
2. Packaging Sensors Verify Product Information
As the margin for error shrinks, robust pharmaceutical packaging becomes increasingly important. Experts project this global market will increase from $99.9 billion in 2021 to an estimated $196.8 billion in 2026, achieving a 14.5% compound annual growth rate.
Since the pharmaceutical packaging market value is rising, it is the ideal setting for unexplored AI solutions.
AI-powered sensors embedded in containers can monitor temperature, ensuring cold chain goods remain intact. Machine learning vision systems can scan labels, blister packs and bottles to verify lot numbers and expiration dates. This way, firms can identify defects, errors and omissions before products reach consumers.
3. Simulations Outline the Outcome of Disruptions
Integrating AI into modeling software enables realistic, data-driven simulations. Even a well-trained large language model can simulate events. Users do not need to learn to code instructions — they simply input plain language prompts.
Industry leaders can simulate rationing policies, demand surges, company closures or export restrictions to identify the impact, extent and length of potential disruptors. They can use the results to enhance resilience against potential disturbances.
4. Image Recognition Enhances Quality Control
AI-powered image recognition technology has a place on the production line and in refrigerated trucks. It can autonomously identify drug defects or machine faults, depending on how it is implemented, helping manufacturers and distributors maintain pharmaceutical product quality.
5. Risk Assessments Prevent Likely Disturbances
Most pharmaceutical supply chains are not diversified enough to maintain resilience in the face of disruptions. Around 7 in 10 drug shortages in the United States are comprised of generics. The issue is that a single manufacturer supplies 4 in 10 generics. This overreliance cannot be helped in every case. When it can, risk assessments are crucial.
A machine or deep learning model can assess risk, assigning low, medium, high or critical scores to suppliers, distributors or drugs. It can offer insights or reasoning when questioned in plain language, aiding even nontechnical users. This enables industry professionals to choose resilient vendors to minimize the impact of disruptions.
6. Automation Logs Significantly Lower Error Rates
With AI-enabled automation, warehouse managers can automate inventory management, and logistics professionals can automate shipment monitoring. Whatever the application, the algorithm will provide a near real-time log of drug availability. The records can be kept in a centralized data storage system like the cloud to enable remote access.
Already, some organizations have trialed supply chain AI solutions. One study shows they reduce fulfillment mistakes by 25% and accelerate order fulfillment cycle time by 6.7 days on average. These improvements can counteract the effects of supply issues.
AI-powered managed inventory systems for pharmaceutical supply chain management show similar benefits. In one case study, the average annual inventory error rate dropped from 0.425 per thousand to 0.025 per thousand, increasing supply chain efficiency by 42.4%.
7. Recommendations Improve Decision-Making
Large language models and chatbots can offer recommendations based on trends hidden in massive datasets. These insights enhance decision-making and problem-solving. It could help leaders respond effectively to unexpected disruptors in the pharmaceutical sector.
8. Counterfeit Detection Prevents Drug Recalls
Although the U.S. drug supply is one of the safest in the world, thanks to its closed drug distribution system, counterfeiting still occurs. If retailers are affected, recalls must occur, limiting supply and skewing demand. Organizations can use AI-powered sensors and machine vision systems to detect fake pharmaceutical products before they reach shelves.
9. Demand Forecasting Mitigates Panic Buying
Demand forecasting can leverage industry, regional and local data to predict fluctuations in demand for specific drugs. This way, leaders can anticipate and respond to early signs of panic buying, preventing shortages before they begin.
Preventing Drug Shortages in Pharmaceutical Supply Chains
Although industry leaders cannot anticipate every unforeseen disruptor, AI can help them detect and avoid most. This technology’s unparalleled ability to sift through massive datasets makes it a powerful technology for pharmaceutical professionals who want to mitigate drug shortages.