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Automation ensures ‘Right-First-Time’ production output: Sanjeev Dharwadkar
Interviews- Oct 28,24
In this interview with Sanskriti Ramachandran, Sanjeev Dharwadkar, CEO, Pharma Tech Consulting LLP, explores the intersection of AI and pharma in today's world.
Intro-The goal of automation is to improve both product quality and productivity while minimising human intervention, which helps maintain a competitive product cost. Building automation increases the confidence of regulatory authorities & in turn establishes higher quality standards and customer expectations, says Sanjeev Dharwadkar, CEO, Pharma Tech Consulting LLP and Former Director, Sanofi.
How are you integrating automation into your production process?
In the pharmaceutical manufacturing processes, automation plays a crucial role in minimising human intervention & errors arising out of it. The objective of building automation is to ensure the products meet high-quality standards. Specifically, in pharmaceutical manufacturing, automation ensures ‘Right-First-Time’ production output, meaning the product is manufactured correctly on the first attempt. This is essential because small errors causes interruptions and increase the costs because of corrective measures, such as reprocessing and discarding defective products. These errors also lead to higher rejection rates.
The goal of automation is to improve both product quality and productivity while minimising human intervention, which helps maintain a competitive product cost. Building automation increases the confidence of regulatory authorities & in turn establishes higher quality standards and customer expectations. Every time we adopt automation, we keep in mind that minimising manual involvement will not only, reduce risks but also enhance operational efficiency, allowing us to deliver high-quality products to patients.
What challenges have you faced while adopting these technologies?
Although automation is not new to the pharmaceutical industry, its implementation is becoming increasingly essential over the years. Regulatory authorities are driving this shift. These authorities are keen to see how automation helps to minimise human intervention, improves product quality, and ensures compliance with the highest standards. Automation tools have become a key focus for regulators, who closely examine how manufacturers use technology to enhance quality and build data transparency.
One of the primary challenges is ensuring that the automation systems comply with regulatory standards at every step of the manufacturing process. Regulatory bodies require evidence that automated processes are free from risks such as data falsification or manipulation of production records. This means that all data generated by automated systems must be available in real-time and accurately reflect the production process. Regulators are highly vigilant in scrutinising these systems to ensure they function reliably and transparently.
Another challenge lies in selecting the right automation tools that not only improve efficiency but also meet regulatory requirements. Companies need to develop in-house expertise to validate these automated systems, proving their reliability and compliance to regulators. This validation process is essential to demonstrate that the automation tools consistently deliver the expected benefits, such as higher product quality and greater efficiency. Without this expertise, the adoption of automation can become a complex and resource-intensive task.
How does AI play a role in quality control?
Artificial intelligence (AI) is set to become a transformative force force pharmaceutical industry, particularly in quality control and supply chain management. AI leverages historical data to predict potential failures in production processes or quality control measures. By identifying these risks early, manufacturers can address them proactively, preventing the production of substandard products. This predictive capability ensures that errors are minimised, reducing the likelihood of product rejections and improving overall cost efficiency.
AI also plays a key role in optimising supply chain management by accurately forecasting future demand. It analyses trends based on seasonal patterns, patient needs, and socio-economic factors, enabling companies to plan production more effectively. Accurate forecasting allows manufacturers to adopt Just-in-Time (JIT) inventory practices, producing only what is needed and reducing excess inventory costs. This not only improves financial performance but also enhances customer service by ensuring timely delivery of products.
The benefits of AI extend beyond manufacturing. It can enhance marketing, sales forecasting, and operational planning by identifying trends and predicting customer needs. This allows pharmaceutical companies to align their production schedules with market demands, minimising disruptions and improving customer satisfaction. Overall, AI improves the efficiency and precision of the production process, enabling companies to consistently meet regulatory standards and deliver high-quality products to patients.
What future advancements in AI and automation are you most excited about in the pharma sector?
One of the most promising advancements in the pharmaceutical sector is the use of AI for predictive data management. AI tools will help manufacturers identify potential noncompliances in production and quality control before they occur, enabling proactive problem-solving. For example, AI can predict process deviations, quality failures, or building accuracy in supply forecast. This will allow companies to take corrective action in advance, preventing disruptions and improving efficiency across all operations.
AI will also play a critical role in redefining supply chain management methodology. With more accurate sales forecasting, companies can align their production schedules with demand, reducing lead times and inventory carrying costs. This enables manufacturers to adopt a Just-in-Time approach, producing only what is necessary, which optimises favourable financial outcomes and reduces product writeoff.
However, the adoption of AI brings its own challenges. Regulators will need to ensure that AI tools meet stringent compliance requirements. It will be essential for manufacturers to validate AI-driven processes to demonstrate that they are reliable, ethical, and free from manipulation. Transparency in data management and adherence to ethical standards are critical, as the pharmaceutical industry is heavily dependent on maintaining regulatory compliance and ethical practices at all times.
Looking ahead, AI will help pharmaceutical companies build more sustainable and compliant operations by enhancing transparency and accountability. It will ensure that all activities are performed ethically, with a focus on improving patient outcomes and meeting regulatory expectations. With the growing importance of AI, companies that successfully integrate it into their operations will be well-positioned to lead the industry into the future.
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