Realising the full potential of artificial intelligence in pharmaceutical supply chains:
Why AI without a solid network foundation will fail
Shirell James, Vice President, Blue Yonder
Artificial Intelligence (AI) offers enormous potential value to the life sciences supply chain. However, without a robust strategy centred around data and use cases that drive the most value, AI will not meet expectations. Artificial intelligence brings a fascination unmatched in recent years.
1. Enterprise vs. Network Data Pharmaceutical supply chains are increasingly outsourced and extended, involving multi-enterprise manufacturing processes, shorter lead times, and more complex products being sourced, manufactured, and delivered across a multi-tier supply chain. Most AI initiatives are built on an enterprise's data lake, which provides visibility to the enterprise data and, at best, one tier of supply. Without a comprehensive view of what is happening across the extended network, AI lacks access to the data it needs to make optimal decisions, ultimately failing to enable the required levels of supply chain resilience.
2. Use Cases That Drive the Most Value AI has predominantly been used for enterprise-focused use cases such as inventory optimisation and customer service. Other use cases, such as supply chain visibility, risk management, and quality management, can drive more value. However, due to the limitations of enterprise versus network data described above, it is impossible for AI to be effectively utilised for use cases that span the network and have the potential to drive the most value.
Where to Focus for Better Returns on AI
A networked ecosystem of partners with a common unified data model is foundational to unleashing the potential of AI in life sciences. When AI is enabled on a multi-party network the following use cases become possible:
- Supply Chain Visibility and Risk Management: AI can identify and resolve operational and strategic risks by analysing vast quantities of real-time network supply chain data, external risk data, and connections between trading partners. This enables companies to rapidly identify, for example, potential shortfalls in product availability or risks due to single sourcing of critical items.
- Manufacturing Planning and Optimisation: Pharmaceutical manufacturing is increasingly outsourced to contract development and manufacturing organizations (CDMOs). AI can help pharmaceutical companies and OEMs analyse network data (such as production capacity) to improve multi-enterprise planning and assist CDMOs in making faster and better production planning decisions to mitigate constrained capacities.
- Logistics and Distribution: AI can optimise route planning, fleet management, and load scheduling by adapting to fluctuating demand, yield outputs, health crises, and packaging needs. And when extended to include network chain of custody and traceability, AI can identify contraindications, adverse reactions, and expiry data, enabling rapid, targeted recalls and localised batch distribution. This reduces the potential impact on patients and the brand while minimizing reverse logistics costs.
- Quality Control and Assurance Custom drug formulations and cold chains add complexity to manufacturing. AI can monitor vast quantities of batch-related data at each stage and across multiple enterprises to identify potential batch failures, giving companies time to react. This improves yield, enhances availability to patients, and reduces costs.
- Personalised Medicine Personalised medicines operate on a completely different supply chain, with the patient as a supplier and lead times so short that products cannot be stored. This requires advanced AI-enabled networks to provide an automated supply chain that eliminates system lead times, connects all parties involved in execution, and makes real-time decisions throughout the execution process to resolve issues as they arise.
Interconnected multiparty networks, such as the Blue Yonder Network, are of tremendous importance in enabling AI-driven supply chains that can improve yields, reduce costs, and ultimately enhance patient centricity and health equity.
Prerequisites for a Successful AI Implementation in Life Sciences Supply Chains
To ensure scalable and effective AI adoption, companies must:
1. Enable a Digital Supply Chain Network AI cannot function in enterprise silos; it requires data from an ecosystem of trading partners to drive the most value.
2. Use GenAI to Drive Network Adoption A highly connected ecosystem of CDMOs, suppliers, and carriers improves the quality of real-time data. GenAI can increase network adoption by reducing the time and effort required to integrate partners into a network.
3. Score Early Wins Demonstrating quick successes fosters user adoption and accelerates machine learning curves.
4. Prioritise Organisational Change Management Internal and external communication about AI’s value is critical for stakeholder buy-in and seamless implementation.
Life sciences companies should proactively address AI challenges by establishing a dedicated team and a "value office" to track progress. Focusing on high-value AI use cases and partnering with an experienced supply chain AI provider will ensure long-term success.
