AI in Pharma Supply Chain: Improving Logistics and Inventory Management

As the pharmaceutical industry becomes increasingly saturated, the need for innovative solutions is becoming more pressing. Artificial intelligence (AI) has emerged as a promising solution for many problems. From identifying drug candidates and optimizing manufacturing processes to simplifying documentation and regulatory submissions, AI has shown exceptional promising applications in all areas of the pharmaceutical industry—including the pharma supply chain.

AI-powered supply chain management has several advantages: It can improve logistics, demand forecasting, inventory management, supply chain visibility, and quality control. It can also speed up product distribution and decrease costs. All in all, AI in pharma supply chains sounds like a solution most pharma companies need. But how exactly can AI help realize so many advantages? How can AI be implemented to improve logistics?

The Role of AI in Pharma Supply Chains

Enhancing demand forecasting and inventory management

AI models can analyze historical data and seasonal trends to predict demand for upcoming quarters. These predictions can be used to optimize inventory levels across all distribution centers to ensure there are no stockouts or product losses. Furthermore, the predictions can also be used to set up production targets or timelines, reducing waste and holding time.

Increasing supply chain visibility and traceability

The pharma supply chain is a complex network of partners, vendors, suppliers, and distributors. AI-powered tracking systems and appropriate tracking technologies can be integrated into the supply chain to monitor the movement of both raw materials and drugs. Such systems can also enhance the transparency of the supply chain, preventing the chances of counterfeit addition and improving trust among all partners.

Improving logistics and distribution

AI models and predictive analytics can also improve logistics and product distribution by optimizing delivery routes and schedules. Such implementations will reduce transportation costs and delays and ensure the on-time delivery of materials. Internet of Things (IoT)-powered technologies can improve cold chain logistics by allowing the continuous monitoring of environmental conditions during transportation and storage, which will enable the company to ensure the integrity of environment-sensitive drugs.

Disruption mitigation

Unexpected changes such as supplier delays, raw material shortages, and even road closures can disrupt the entire supply chain. AI-powered predictive analytics tools can be used to identify potential risks and develop alternative solutions. For example, if a supplier is out of a particular raw material, AI models can identify potential suppliers who may provide the same. AI models can also simulate disruption scenarios and develop contingency plans, improving supply chain resiliency and reducing operational downtime and risks.

Fraud prevention

AI-powered authentication systems can be integrated to prevent fraud. AI algorithms like blockchain can verify serial numbers or packaging details at every stage of the supply chain. Hence, if counterfeit drugs are identified later in the supply chain, they can be easily traced back to the source. These systems can monitor the supply chain for irregularities and unauthorized deviations, which can be checked to ensure no fraud attempts.

These are only five cases of AI use in pharma supply chains. As more and more companies understand the advantages of AI-powered supply chains, they’re bound to find new use cases that will benefit themselves and the entire industry.

AI Technologies in Pharma Supply Chains

Various AI technologies have already been developed and implemented in pharma supply chains, and they are showing promise. Here are some popular AI-powered tools being implemented by pharma companies in their supply chains:

SAP Integrated Business Planning software

SAP Integrated Business Planning software is a cloud-based solution allowing users to complete various tasks. It will enable the user to predict demand, collaborate on planning processes, and complete multilevel supply planning using AI-powered algorithms. Its key features include:

• Forecasting and demand management (demand planning, advanced demand sensing, and time-series analysis)
• Response and supply planning (multilevel planning, rough-cut planning)
• Sales and operations planning (scenario simulation and comparison, real-time planning, and performance monitoring)
• Inventory planning and optimization (inventory optimization, forecast error management, and embedded analytics)

Kinaxis Maestro

Kinaxis Maestro (formerly RapidResponse) is an AI-powered supply chain orchestration platform that facilitates intelligent decision-making. It offers a digital view of the supply chain to keep all stakeholders in sync. Maestro has predictive tools that can run scenarios, create digital twins, and capture historical data for decision-making. It has the following features:

• Decision intelligence: Using available data and previous analysis, Maestro provides key performance indicator (KPI)-driven recommendations for decision-making.
• Scenario runs: These can determine the effect of a change on the company’s supply chain, which will minimize risk before implementing any change.
• Automation: It automates tasks, improving the company's responsiveness and efficiency.

Blue Yonder Enterprise Supply Chain Platform

Well-known pharma players like Bayer use the Blue Yonder Enterprise Supply Chain Platform. The platform aims to increase visibility across the supply chain and provides AI and machine learning solutions for planning and execution. It can be used for various applications:

• It can be used to manage cold chain logistics.
• It can be used for delivery optimization, such as delivery route and schedule optimization.

Who’s Already There?

Various companies have already adopted AI in pharma supply chains. Here are two pharma giants who’ve already entered the playing field:

• Novo Nordisk has implemented AI-powered demand forecasting models to improve its supply chain. This implementation led to a 50% reduction in forecast errors and an estimated cost savings of $20 million annually.
• AstraZeneca uses AI and ML models to optimize its inventory management. They can predict demand variations and inventory levels, which has reduced their inventory carrying costs by 40%.

What’s Stopping Others? Challenges Facing AI in Pharma Supply Chains

Unfortunately, implementing AI in pharma supply chains is not very straightforward. Pharma companies must overcome challenges to implement AI-powered supply chain tools to improve logistics. Some of the most prominent challenges are:

Data challenges

The lack of historical and complete data limits the effectiveness of models. Furthermore, data is usually obtained from multiple stakeholders and is of different natures, making the creation of a unified data pipeline challenging and expensive.

Financial challenges

Developing and deploying AI algorithms is expensive, and small and mid-sized pharma companies may not have the resources to implement advanced systems. Furthermore, AI systems require continuous monitoring and updating, which increases operational costs.

Ethical and regulatory challenges

Pharma companies must overcome various concerns when handling data, such as data privacy concerns and cybersecurity risks. They also have to ensure their data practices are compliant with regulatory guidelines.

Conclusion

AI is slowly finding promising applications in pharmaceutical supply chains. Many prominent companies, such as Novo Nordisk and AstraZeneca, have already implemented AI tools and solutions to simplify supply chain management, and many others are exploring the same. However, implementing AI still faces three significant challenges that companies must overcome individually to deploy their AI models. These are financial challenges, regulatory and ethical challenges, and data challenges. Thankfully, there’s a large incentive for solving these problems: AI-powered supply chain management can significantly reduce costs and increase profits by improving logistics. Hence, in the near future, we may see many pharma companies implement AI.

FAQs

1. What are the benefits of AI in pharma supply chains?

AI can improve logistics, demand forecasting, inventory management, and supply chain visibility, allowing pharma companies to track their products throughout the supply chain.

2. Can AI be used to prevent counterfeit drug infiltration?

AI-powered technologies can be used to prevent counterfeit drug infiltration. In the case of swaps, AI algorithms can track possible deviations to identify the point of infiltration.

3. Why can’t all pharma companies implement AI solutions?

The implementation of AI in pharma companies is complex due to stringent regulatory requirements, data quality and security concerns, and financial constraints.