The 4 Top Challenges in Deploying AI in Healthcare and Pharmaceutical Supply Chains

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How to avoid the most common pitfalls of implementing AI and how to ensure your initiative delivers real and significant value quickly

Artificial intelligence (AI), in all its various forms obviously offers enormous potential value to healthcare and pharmaceutical companies, in transforming their supply chains. But without a robust strategy, proper implementation, and rigorous prioritization of high value use cases in production, most of this value is lost.

Too often, AI implementations fizzle out, with little to show in terms of business value. Or if they do deliver value in some niche area, they are difficult to scale across the enterprise and trading partners, to really transform the supply chain.

The 4 Top Challenges in Deploying AI in Healthcare and Pharmaceutical Supply Chains Share on X

This article explores the practical challenges to be aware of when implementing AI in pharmaceutical supply chains. In the report I link to at the end of this article, I share a strategy in detail that generates high returns quickly, while minimizing implementation overhead and risk.

Due to the highly interconnected nature of pharmaceutical and healthcare supply chains, success is critically dependent on having an underlying network platform that takes into account the complexity and multi-tier nature of such supply chains.

Common Challenges and Pitfalls of AI in Pharma Supply Chains

Where do most health and life science companies go wrong when attempting to deploy AI in their supply chains? Having being involved in many implementations

1. Complex data landscapes and learning from both past and new data

This is a major problem: Trying to tackle the problem without a single integrated model therefore being unable to connect the dots. Or in other words, not having community master data and management system.

Supply chain is a heterogeneous mix of partners with a myriad of data models that most often don’t talk to each other. This is compounded by complex integration fabrics that makes it very difficult to trace a transaction across its life cycle. The ability to access data outside of the enterprise or, more importantly, receive permission to see the data that is relevant to your trading community, must be made available to any type of AI, e.g., deep learning or machine learning algorithms.

High performing AI systems should be able to assimilate past trends and continually learn from new data and incrementally adjust the output. AI systems in supply chain shouldn’t adopt a batch model where with every new variable or data point the entire algorithm needs a foundational shift in order to achieve a resilient supply chain.

Making AI work in supply chains: High performing AI systems should be able to assimilate past trends and continually learn from new data and incrementally adjust the output. Share on X

2. Ever changing GxP compliance guidelines and regulatory landscape

The life science industry, and particularly pharma, is heavily regulated and has strong compliance requirements, such GCP (Good Clinical Practice) and GMP (Good Manufacturing Practice). These are specific to formulations, therapy areas and geo-specific in terms of assay rules, and FDA guidelines. To make matters worse, these rules keep evolving. This requires complex text mining algorithms to figure out process implications of these ever-changing regulations.

3. AI use cases beyond the back-office and hyper focus on efficiency

While many life science companies have tried implementing AI and RPA (robotic process automation) in their back-office operations, the real value of AI manifests in engaging the users on the front end. For example, leveraging AI algorithms to predict therapies, disease occurrence prediction, and autonomous patient scheduling, as opposed to restricting AI to customer service and productivity-related use cases.

Many pharma companies fail, or are unable, to target other areas like revenue growth, patient compliance, risk, etc., and often have difficulty in establishing the business case for such areas.

4. Focusing on point outcomes without considering the propagation impact of AI-led decision making

Most major pharma companies have, at best, isolated AI pilots in the works on select areas such as demand planning, freight optimization, vendor screening. This has led to an array of proof of concepts across the various facets the supply chain., these projects struggle to scale and are unable to achieve the holy grail of supply chain: resilience. This challenge is especially difficult for the pharma industry, because its supply chains are heavily interconnected, from end-to-end and across the tiers, with complex manufacturing guidelines, and zeroing in on a set of network-wide objective functions is crucial for implementation success.

Where to Focus for Better Returns on AI

This is a broad and complex topic, that I can only touch on here, but I give details in the report listed at the end of the article. For now, I’d like to leave you with one key piece of the puzzle that we have found extremely useful.

Amidst all the chatter and hype around AI applications in supply chain, life science brands need to be careful in prioritizing the right use cases, and backed with the fit-for-purpose data and tech stack, so that they can see real and significant results quickly.

At One Network Enterprises (ONE) we interact with a wide range of life science companies, each at a different level of maturity. Yet, they all want the same thing: A set of use cases that offer the biggest return on investment.

In order to achieve that, we have found that the best way to do that is by establishing a “Value Office,” a dedicated team focused on value (a function at the intersection of customer success and value engineering). The reason this is so critical is because most of the use cases are not localized to a specific silo in the supply chain. Usually, the use cases are interconnected and have multi-echelon impact around cost, inventory and service levels. The Value Office team can monitor across functions and connect the dots in ways that localized teams will find difficult if not impossible.

AI Can Be High Impact, If Your Are Aware of the Pitfalls and Approach AI with a Proven Strategy

In conclusion, the implementation of AI in pharma supply chains presents a myriad of challenges, such as managing complex data landscapes, navigating ever-changing regulatory landscapes, expanding AI applications beyond back-office operations, and taking into account the wider implications of AI-led decision-making. However, these challenges can be addressed by prioritizing the right use cases, backed by fit-for-purpose data and technology. Companies should aim for AI systems that can integrate and learn from new and historical data, adapt to changing regulations, and provide value in diverse areas. A dedicated “Value Office” could be key in overseeing these complexities, as it can ensure that AI implementation takes into account the interconnected nature of the supply chain and its effects on cost, inventory, and service levels. Despite the potential pitfalls, with the right strategies and focus, life science companies can achieve significant returns on their AI investments.

And that report I mentioned, you can download it here.