Due to its demanding regulations, the pharmaceutical industry is very controlled. Pharmaceutical companies must follow stringent guidelines for manufacturing, testing, and distribution of new drugs to ensure their safety for humans. Traditional manual methods of monitoring processes, analyzing data, and creating reports on key performance indicators (KPIs) are no longer sufficient in today’s dynamic environment. To drive profitability analysis, modeling and optimization of production processes, many pharma manufacturers are adopting automation and AI-based solutions across the value chain from R&D to final product distribution. Let’s see how it can help your organization in this article.
A brief history of the pharmaceutical industry
The pharmaceutical industry has evolved from being a ‘craft-like’ business to a science-driven process over the last century. While the first modern drug was discovered in the 1890s, mass production of the drugs was only established in the 1960s. The following decades saw the rise of new systems for manufacturing and quality management, like batch processing and Quality by Design (QbD). Now, the Industry 4.0 is upon us. It aims to connect everything in an organization digitally and provide decision support based on real-time data.
The Value Chain in the Pharmaceutical Industry
The process of developing a new drug and bringing it to the market is long and complex. It takes 10-15 years, on average, to bring a new drug to the market. The figure below shows the value chain from R&D to marketing and distribution. The first step in the process is R&D. It is followed by manufacturing and quality control. The last step is marketing and distribution. There are several processes in between that involve procurement and manufacturing of raw materials, production and quality control of intermediate products, transportation of goods, and the management of logistics to reach the final customer. During the manufacturing phase, the production process is monitored and controlled with sensors, data recording devices, and computers that collect and analyze data. Once the products are ready, they are transported to the next stage in the value chain, i.e., quality control. Here, the quality control team analyzes the data and ensures that the products meet the required quality standards. Then, the products are transported to the marketing and distribution phase, where they are sold to the final customer.
Why is automation important for the Pharmaceutical Industry?
The pharmaceutical industry is highly regulated. Pharmaceutical companies are required to follow stringent guidelines for manufacturing, testing, and distribution of new drugs to ensure their safety for humans. Because of these stringent regulations, the industry has been slow to adopt automation. The pharmaceutical industry relies heavily on manual methods of production and quality control. Traditionally, manual methods are preferable for the industry because of the risk of introducing new technology that could compromise the quality of pharmaceutical drugs, which are critical for human health.
Key benefits of automation in the pharmaceutical industry
While the importance of quality control cannot be overstated, the industry has much to gain from the adoption of automation in other areas of the business. One area where automation is expected to make a significant difference is production and logistics.
Key requirements for automation in the pharmaceutical industry
Although the pharmaceutical industry is slow to adopt automation, there has been considerable progress towards bringing technologies like AI and blockchain into the business. One of the biggest challenges, as we saw above, is the need to ensure compliance with strict regulations. Automation and AI-based technologies allow processing, storing, and accessing substantial amounts of information. This makes it easy to manage critical quality and compliance data.
How AI can be leveraged to drive profitability analysis, modeling and optimization
As we mentioned above, the pharmaceutical industry is a highly regulated business. Hence, the first step towards analyzing profitability and modeling optimization is collecting and managing data. Manual data collection and management can be error-prone, time-consuming, and expensive. When traditional data management systems are used in the pharmaceutical industry, the organization must employ data analysts to manage and secure data. A centralized database, either on-premises or in the cloud, is vulnerable to cyber-attacks. To address these challenges, organizations are adopting AI-based solutions for data collection, storage, analysis, and modeling. Data coming from various sources, like sensors and laboratory instruments, is automatically sent to a centralized database. Here, AI-based technology automatically analyzes and compiles the data. That data can be instantly accessed by different systems. The key benefits of AI-based solutions for the pharmaceutical industry include data collection automation, real-time data analysis, centralized data storage and management, and minimized cybersecurity threats.
Conclusion
The pharmaceutical industry is slow to adopt automation. However, it has made progress towards automation, thanks to novel technologies like AI and blockchain. As organizations move towards adopting automation and AI-based technologies, they will be able to drive profitability analysis, modeling, and optimization. Additionally, they can also reduce cybersecurity threats, simplify data management, and improve customer satisfaction.