Glossary Definition: AI Manufacturing

What is AI manufacturing?

AI manufacturing harnesses the power of artificial intelligence (AI) to automate complex processes, complete tasks, and develop products. To achieve this, it integrates advanced technologies such as machine learning and automation into industrial processes and workflows.

AI manufacturing optimizes production, enhances quality control (QC), and streamlines operations by leveraging data analysis for informed decision-making. This transformative approach improves efficiency, reduces errors, and accelerates innovation across various industries.

What are the benefits of AI in manufacturing?

There are various benefits to leveraging AI manufacturing. These include:

  • Less downtime: According to a recent McKinsey report, utilizing AI for predictive maintenance can reduce machine downtime by as much as 50%. AI can use data to determine predictive maintenance, allowing manufacturing companies to prepare in advance and reduce the likelihood of equipment failure.
  • Greater operational efficiencies: Machines are capable of working 24/7, require no breaks, and can use data to analyze processes, anticipate failures, and ultimately mitigate deviations. As a result, greater operational efficiencies are achieved throughout the production process.
  • Reduced costs: AI manufacturing automates much of the manufacturing process, meaning reliance on human workers is reduced. This decreases the expensive headcount and the associated costs.

What is AI manufacturing in pharma?

In pharma, AI manufacturing is the process of leveraging tech stack and machines to automate processes, analyze data, contextualize data, and improve workflows. By 2025, it’s estimated that 50% of life science companies will implement AI-powered tech. Currently, top pharmaceutical companies including Pfizer, Merck, AstraZeneca, GSK, Sanofi, AbbVie, Bristol-Myers Squibb, and Johnson & Johnson are leading the charge to adopt and implement AI.

The ultimate goal of AI manufacturing in pharma is to accelerate drug development.

AI achieves this by:

  • Performing Quality Control (QC) checks throughout the drug manufacturing lifecycle
  • Increasing compliance by minimizing human error factor
  • Automating various workflows
  • Tracking and monitoring data capture and transcription
  • Analyzing data and capturing potential errors in real time 
  • Providing continuous process modeling and simulations which identify improvements for time reduction or production yield

As a result, pharma companies can maintain compliance and safety, while also bringing medicines to market and consumer sooner, improving quality and reducing costs.

How is AI used in pharma?

Pharmaceutical systems can integrate with AI platforms to analyze and contextualize the data generated and controlled by those systems. Two common use cases for this integration are MES & LES systems:

Manufacturing execution systems: This system, also known as an MES solution, tracks and monitors the manufacturing process from start to finish. Data processing is completely automated, thanks to AI and machine learning.

Laboratory execution systems: Commonly referred to as an LES system, laboratory execution systems digitally capture data and support lab operations by optimizing workflows. Laboratory execution systems optimize quality control by providing scientists with the ability to manage samples and lab resources at scale, flag and resolve exceptions in real time, and execute test methods on devices.

Why is AI manufacturing important in pharma?

AI manufacturing is important in pharma because it reduces the time it takes to discover and develop therapies. On average, it takes 10 to 15 years to commercialize a new drug. But AI manufacturing is disrupting this narrative by enabling companies to simulate early-stage models and automate menial, time-consuming tasks, thus increasing productivity and providing a higher success factor on the end product.

AI based manufacturing optimization of the process flow can increase utilization of the equipment and improve predictive maintenance and manufacturing planning. Furthermore, AI in Pharma Manufacturing helps enforce regulatory requirements and results in higher compliance and expedited quality release processes.

As a result, new and existing products can be commercialized sooner — and with less risk.