The global pharmaceutical industry is having to adapt to a new challenge like never before. Many healthcare systems are falling behind due to frequent stock-outs of critical medicines. Despite attention being placed on the end product, the underlying issue is often a lack of resilience within Active Pharmaceutical Ingredient (API) supply chains. This results in high fluctuation in product quality and poor forecasting of stock.
Pharmaceutical supply chains have historically followed a Just-In-Time approach to manufacturing.
However, if there is any disruption to your raw materials supplier or any temperature issues with your cold chain then it can take months to get everything back to normal. To overcome this we need to move from a reactive supply chain to a predictive one.
The Real Risks of Manufacturing Drugs
The pharmaceutical industry has many strict regulations around the supply chain which means even a small variance in raw material quality or storage can result in a full batch quarantine.
There are three main areas causing issues for drug supply chains:
Lack of Visibility in Raw Material Supply Chains
Most drug manufacturers will have a complex supply chain. They will source their chemical raw materials from multiple levels of suppliers globally. This means there may be little visibility around what is happening at each stage. This causes issues if there is a hold on the product in another country or a shortage of raw materials.
Batch Quality Issues
Traditionally quality issues happen at the end of the line. If there is an issue during manufacturing then quality teams will only find out about the issue once the product is at the end of the line. If it fails it will have to be quarantined and this means losing money.
Manual Documentation of Regulations
To remain compliant with GMP throughout the world there is a huge amount of data. This means processing batch documents, audit trails and environmental monitoring information can cause delays.
Using Machine Learning & Predictive Analytics
Top drug manufacturers are using machine learning and predictive analytics to help solve these issues. Using real-time data, drug manufacturers can predict issues within their supply chains and make changes to optimise their processes.
- Predictive API Shortages
Using predictive supply chain analytics allows you to analyse the data coming from your global supply chain. This includes looking at trade data, geopolitical risks and supplier performance. This helps identify any potential issues with your raw materials well in advance. This means procurement teams can make changes to improve your supply chain.
- Predictive Batch Quality
Using sensors and machine learning analytics means you can monitor your critical process parameters. This means you can identify any small changes in the environment or chemistry in real-time instead of waiting until after manufacturing.
- Predictive GxP Compliance
With modern compliance solutions you can use Natural Language Processing and automated audit trails. This identifies any issues with protocols, checks batch records and ensures products are compliant with serialisation. This reduces the workload significantly.
Drug manufacturers are looking to improve their processes. Using enterprise solutions with supply chain intelligence and predictive analytics can help reduce batch failures and improve visibility across your business.
3 Ways to Improve Your Supply Chain
Biopharmaceutical companies can improve their manufacturing and supply chain processes by:
Mapping your multi-level supply chain – Understand what your secondary and tertiary suppliers are doing.
Real-Time Batch Monitoring – Reduce waste by using real-time monitoring instead of sampling.
Predictive Inventory Balancing – Use predictive analytics to understand demand based on disease trends in certain areas, delivery delays and storage capacity.
By using predictive analytics you can protect your margin, remain compliant and ensure patients have continuous access to their essential medicines.
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