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  • Team ODA

How AI is revolutionizing Pharmaceutical Supply Chain management

Emerging as a workhorse for pharmaceutical businesses, Artificial Intelligence (AI) has opened new pathways to driving innovation and reengineering supply chains. Let’s see how AI as an enabler can assist pharma companies to build new-age efficient and productive supply chains.

AI is already valued at $902.1 million in the life sciences market, and it is anticipated to continue to expand at the rate of 21.1 percent through 2024 and beyond, generating significant advancements and contributions, particularly to the pharmaceutical supply chain.

AI-powered apps organize the supply chain data, enabling manufacturers, distributors, and pharmacies to more accurately forecast and assess procurement, delivery methods, and overall costs. As a result of their increased knowledge of all their options, all stakeholders ultimately experience lower loss percentages.

Like in most other domains, Artificial intelligence (AI) and machine learning (ML) have emerged as ground-breaking technologies in the pharmaceutical supply chain too. The immense potential of AI is compelling pharma companies to become AI-enabled. Working in conjunction with the internet of things (IoT) and the cloud, AI and machine learning are offering a strong foundation for more automation and reengineering opportunities and enabling better and quicker decision-making.

So, innovations are unfolding fast in pharmaceutical businesses, with AI being at the top of all choices of pharma leaders.

Here, we dive into details of how AI and machine learning are creating value for pharma businesses.

Implementing AI in the pharmaceutical supply chain – How businesses are reaping benefits

One of the most celebrated examples of how AI can bring revolution in pharmaceutical supply chains has been demonstrated by CVS. As a leader in hyper-automation, CVS Health has long used RPA, AI, and other tools for business process automation to enhance its support operations.

To enhance the rate of prescription refills and decrease treatment gaps, CVS is utilizing machine learning personalization.

The pharmacy retailing giant has used machine learning techniques to identify which approach is most likely to enhance the user experience. For instance, the pharmacy may recommend side-effect counseling, sync numerous prescriptions to be ready on the same day or have medicines prepared as soon as they are needed. It uses advanced analytics to comprehend the behavior of clients who don't follow their treatment regimen.

So, right from improving store efficiency to ensuring timely availability of inventory to ensuring excellent drug availability, CVS is trying to revolutionize its supply chain. This is being done by identifying important junctions and implementing and introducing AI at critical junctions.

Where do the applications of AI lie in the pharma supply chain?

Artificial intelligence (AI) and machine learning are continuing to penetrate pharma supply chains and offering the promise of making a positive impact on core aspects. Here, we look at some of the areas where AI is paving the way for transformations.

Inventory Management

When it comes to tracking when a product is delivered to a patient, AI-based inventory management can identify which product is most likely to be needed. The seamless accuracy to track inventory levels can ensure optimal safety stocks of drugs, thereby reducing the instances of ordering delays, thereby ensuring smooth replenishment.

Intelligent drug supply-demand management

In pharmaceutical logistics and supply chain management, demand forecasting is crucial. Pharma businesses are aware that making investments in this area makes them more responsive to changes in patient or customer demand. AI can enable them to analyze data that they were previously unable to do due to its complexity or volume. This enhances decision-making efficiency and makes them more resilient.

Disposal of excess medicines

Artificial intelligence (AI)-based chatbots, data collected from the (IoT) internet of things, image identification and classification algorithms, and web-based expert systems are some AI and Ml tools that can be employed to dispose of excess medicines. AI-driven web-based expert systems decrease hazards associated with improper disposal by offering valuable guidance on the proper disposal of unused medications.

Process Improvement

AI may be applied in many ways to increase manufacturing efficiency, resulting in quicker output. AI and ML not only guarantee that activities are carried out with extreme precision, but also examine the procedure to identify potential areas for streamlining. As a result, there is less material waste, production speeds up, and consistency is maintained in the Critical Quality Attributes of products. AI-based temperature control improves the efficiency of cold chain logistics, as medicinal products demand strict handling of drugs.

Drug Discovery

AI can impact the identification and validation of target-based, multi-target, and phenotypic drug discovery and biomarker identification. The main advantage for pharmaceutical businesses is the potential for AI to shorten the time it takes for a medicine to get approved and reach the market, particularly when used during drug trials.

Drug repurposing

Repurposing medications appears to be one of the most practical applications of AI-based technology for pharma businesses. One of the niche applications of AI includes repurposing existing medications or late-stage drug prospects for use in new therapeutic areas. AI algorithms can help in reducing the possibility of unforeseen toxicity or adverse effects in human trials and probably require less R&D spending.


As a pharma business, your digital strategy should be centered on AI and machine learning. A strong shift is what pharma companies need to create value from AI and machine learning.

The initial first step is always to begin by determining the use cases for AI and machine learning that are most likely to bring improvements to your business.

Consider your business’s emphasis areas, customer value propositions, and long-term expansion objectives when setting priorities. As you look to transform your pharma supply chain with AI, continuously track the proof-of-value (PoV) performance.

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