After just two months of its introduction, ChatGPT received 100 million users. That's the fastest user base growth on record for this class of AI, another sign that businesses see a big opportunity in using  artificial intelligence as a means to build better operations. The pharmaceutical industry, too, stands on the brink of transformation with its adoption of AI.

AI can be useful for MedTech and pharma companies in a number of ways; it can make processes more efficient, help companies personalize interactions/solutions, unlock creativity, and merge unconventional creativity with an exact science. AI can improve access to various enterprise data and a global knowledge base, allowing a new way to create value.

First, let us understand what AI can do for MedTech and pharma supply chains and its specific use cases.

Understanding AI in Supply Chains

Artificial Intelligence (AI) simulates human intelligence processes through natural language processing, including learning, reasoning, contextualizing, and self-correction. This helps to enhance supply chain and logistics control, consistently improving operations for MedTech and pharma companies.

Unlike CPG or e-commerce, the MedTech and pharma supply chains have seen limited growth and investment in agility or technology over the last decade. AI's introduction to these supply chains helps to gain a pivotal shift towards more agile, responsive, and efficient operations. AI enables companies to predict trends, understand customer needs, manage inventory better, and optimize logistics for time and cost savings, laying a solid foundation for the transformation.

AI's Impact on Med-Tech and Pharmaceutical Supply Chain Management

Med-Tech and pharmaceutical supply chains will face very different challenges when compared to other supply chains. Challenges like stringent regulations and compliance, temperature-controlled logistics, or absolute precision in inventory management are unique to them. AI-based technologies are specifically positioned to tackle these issues head-on and improve supply chain performance.

In fact, AI looks to hold the most promise in the healthcare vertical in the near term, growing at an 85% CAGR through 2027 to reach $22 billion, the fastest of any industry. Using AI for demand forecasting, supply chain executives can reduce waste and ensure that life-saving drugs and devices are where and when they are needed, without either excess or shortage in inventory.

For MedTech & pharma companies, the main advantages of AI are as follows:

• Increased efficiency through reduced manual work or higher-speed processes
• Unprecedented personalization of customer interactions
• Improved creativity to develop novel designs and products
• More comprehensive use of enterprise data and knowledge

There are already many case studies that illustrate AI's remarkable impact: predictive analytics for vaccine distribution, AI-driven platforms for real-time monitoring of temperature-controlled shipments, and AI-led regulatory documentation. Many more such AI-led innovations streamline operations and significantly reduce the risk of any errors, ensuring compliance and product integrity across vast, complex medtech and pharma supply chains.

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Use Cases of AI in MedTech & Pharma

As we discussed briefly, there are multiple use cases of AI in the MedTech and pharma industries, such as enhancing drug development, diagnostics, clinical trials, supply chain management, and more. Here's how these use cases are making a significant impact:

Product Development

AI's prowess in analyzing vast datasets is instrumental in designing drugs with the right structure and predicting their bioactivity, toxicity, and physicochemical properties. This accelerates the development process and ensures that drugs provide the optimal therapeutic response upon administration. This application is equally crucial in MedTech, where AI can contribute to developing innovative medical devices and personalized treatment plans while improving the efficacy of the existing ones based on clinical data inputs.

Diagnostics

In diagnostics, AI identifies image characteristics beyond human perception, playing a crucial role in diagnosing severe health conditions like cancer. For instance, research from the National Cancer Institute highlights AI's potential to enhance cervical and prostate cancer screening and pinpoint specific gene mutations from tumor pathology images. Other commercial applications are already in use, like AI-led heart risk diagnosing, where AI could detect the risk 93% of the time. The future is looking bright for other health concerns like diabetic retinopathy, paving the way for early detection and extended healthier lives. 

Clinical Trials

The pandemic has transformed clinical trial design and execution. Given that research and development costs constitute a significant portion of total spending for the pharma industry, leveraging commercially available platforms to decentralize trials can enhance efficiency and reduce costs. This shift is equally relevant for MedTech firms as they develop new devices and treatments. For example, BenevolentAI uses computational and experimental technologies and processes to draw on vast quantities of mined and inferred biomedical data to improve and accelerate every step of the drug discovery process.

Supply Chain Management

AI analyzes longitudinal data to identify and highlight systemic issues in manufacturing processes, pinpoint production bottlenecks, forecast corrective action completion times, and continuously monitor drug safety and quality. AI strengthens confidence in manufacturing companies and ensures compliance with regulations, especially when sourcing raw materials. The pandemic tested the resilience of MedTech and pharma supply chains, highlighting AI's importance for life-saving products.

Inventory Management

Thanks to biomarkers, personalized medicine is becoming more common. The downside is that drug companies now need to keep a wider variety of treatments in stock, but in smaller amounts. AI helps pharmaceutical companies with their inventory control by predicting which medicines will be in demand and when, monitoring their delivery to patients, and quickly arranging replacements in case of delays or problems.

For example, OptumRx relies on AI to process the data it gathers. Since it started, the AI system has been self-improving by examining data and outcomes, operating independently without human input. Initial observations suggest that AI is already making the supply chain more efficient by minimizing drug shortages and reducing surplus inventory.

Warehouse Automation

Integrating AI into warehouse automation improves efficiency and minimizes mistakes in picking and packing areas. AI helps by forecasting which items will stay in storage longer and arranging them to optimize space. According to a recent McKinsey survey, AI adoption companies experienced higher revenues, with 44% of them proclaiming cost reductions. For instance, Lineage Logistics, a company specializing in cold storage and distribution, increased productivity by 20% using AI strategies. Similarly, AI assists in arranging frequently needed items in easily reachable places, enhancing accessibility without causing bottlenecks.

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Starting with AI: A Simple Guide for Medtech & Pharma Companies

Unlike other emerging technologies, AI can quickly enhance efficiency and effectiveness. But how to get started? Here are three steps to begin integrating AI effectively:

1. Identify, Test, and Expand Key Applications Across the Value Chain

Initially, companies should identify areas of maximum impact and AI applications with significant potential to enhance it. This process involves assessing the AI impact, considering factors like model and data availability, error tolerance, data security, cost, and market demand.

From MedTech and pharma companies, we've identified several applications with immediate potential:

1. Customer Service: Use advanced language understanding to support customers with orders or administrative queries, primarily through self-service models, enhancing the customer experience.
2. Sales: Leverage past customer interactions and external data to identify personalized actions for customers, automating the creation of sales materials like emails or briefs.
3. Software Development: Speed up software development from coding to testing, addressing the bottleneck in MedTech and pharma industries and quickly bringing differentiated products to market.
4. Knowledge Management: Improve access to company knowledge, integrating AI into daily tools (such as Microsoft Office) to enhance employee productivity.
5. Operations: In manufacturing and quality control, synthetic data for model training is used to speed up deployment and improve accuracy. This is particularly relevant in pharma and MedTech, where precision is paramount.

2. Embed AI into the Overall Enterprise Strategy for a Sustained Competitive Edge

Today's novel AI applications will soon become standard as adoption grows. Companies need a strategy to identify where AI can provide the most significant competitive advantage, whether enhancing current products and services or introducing new ones. This may involve reevaluating product portfolios and customer/patient engagement processes, acknowledging that AI could substantially alter value chain activities and operational models.

3. Implement Appropriate Policies and Foster Talent

After proving their value, it's vital to expand AI applications organization-wide to leverage their benefits in terms of policies and talent fully.

1. Policies: Set frameworks for responsible AI use to mitigate risks like data bias, intellectual property issues, improving product efficacy, or reducing cybersecurity threats. Companies can proactively develop governance structures to address ethical, legal, and technological challenges, ensuring AI is used responsibly.
2. Talent: Prepare the organization and its members to thrive with AI. This may require adjusting organizational structures, defining roles and responsibilities, and updating job profiles. A revised talent strategy is essential, equipping employees with the skills to use AI-enabled tools effectively and to deploy advanced applications, such as enhancing machine-learning expertise.

AI presents MedTech and pharma companies with opportunities to refine internal processes and disrupt the market with innovative products and services.

Enhancing Patient Outcomes Through Supply Chain Innovations

The ultimate beneficiary of these AI-driven supply chain improvements is the patient. Efficient, reliable supply chain networks ensure that medical products are available and reach their destination in optimal condition, directly impacting patient care quality. For example, timely delivery of medications can significantly affect treatment outcomes for chronic diseases, while adequately handling sensitive products, such as bleeding management or trauma products, can be the difference between life and death.

Data integrity and security, enhanced by AI, also protect patient information and ensure that healthcare providers receive accurate, timely data. Supply chain management is critical in maintaining trust and efficacy in the healthcare system. If done and integrated properly, the end result is customer satisfaction.

Holocene's Approach to AI in Supply Chains

At Holocene, our commitment to AI in the supply chain is reflected in our broader mission to provide solutions for seamless, efficient global businesses. We offer Med-Tech and Pharma companies the tools they need to manage the complexities of international logistics while ensuring compliance, efficiency, and reliability. Our solutions are designed to meet these sectors' unique needs and customer demands, from predictive demand forecasting to real-time shipment monitoring.

We greatly focus on the Med-Tech and Pharma sectors to add value with AI and automation and transform the supply chain model for the future. Reach out today to discuss how Holocene can help your specific business needs.

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