The world we live in is one of constant production. There are many things that people would say we don’t really need in our day-to-day lives but there is one thing that we cannot go without – pharmaceutical products.
From needing medicine for a cold to having access to insulin to treat diabetes, pharmaceutical products are a necessity in society. These products, however, just like almost every other kind, are produced in huge quantities on assembly lines in factories.
Unlike toys or accessories, pharmaceutical products are vital for the people who use them which means the process of manufacturing them has to be as efficient as possible.
Making the manufacturing process run like a well-oiled machine is the goal of real-time process support software and predictive analytics.
In order to learn precisely how these two methods can also be implemented in a way that doesn’t ignore GMP (Good Manufacturing Practice) compliance, BGO Software has interviewed an expert in the field – Harry Birimirski,
The miracle of real-time process support
As our expert says, real-time process support used to be done directly by people. Someone would observe how the entire assembly line was functioning and report if there was anything wrong with the machines, the materials, or even the end results.
There is, however, the aspect that no one is perfect, and human error is a definite possibility. Having a person monitor whether everything is going smoothly is not the most efficient way manufacturing can be handled.
A natural question a couple of decades ago would have been how exactly would people be replaced when it comes to keeping an eye on the proper function of the manufacture of any type of product, nevertheless pharmaceuticals.
Thankfully, there are now almost limitless amounts of such a process that we can keep track of through sensors and devices. Most important of all, we have software capable of analyzing information from these sensors enabling immediate detection and response to deviations.
When considering manufacturing GMP guidelines must be taken into account. The reason why real-time support is so efficient even when it comes to adhering to the rules of good practice is that the sensors that gather data can store it into electronic records.
Such records are far less prone to being tampered with or lost. A fundamental part of GMP compliance is the integrity of data and through digital records, real-time support provides this integrity.
Another point that has to be made for such technology and practices is that they are not stagnant and can be constantly improved and scaled up when the time comes. This way the method that improves efficiency can be optimized itself.
The impact of predictive analytics
“Why aren’t predictive analytics rocket science and why despite that it’s still not something all companies can easily implement?”
To understand the impact of predictive analytics we must first learn more about what the term really means. Our expert explains the concept very clearly. Predictive analytics are intended to optimize the manufacturing process of not just pharmaceuticals but any kind of product.
The practice of gathering data, processing it with statistical algorithms, and utilizing machine learning techniques to make future predictions is what predictive analytics is meant to do.
Harry mentions how this practice isn’t rocket science but nevertheless, not all companies take full advantage of it. The main reason behind this is that while the level of complexity isn’t as high as what a rocket engineer would encounter it is on a larger scale of implementation.
The manufacturing process can never be viewed as one thing happening in just one place. There are many different aspects that come into play before the actual product is released and ready to be used.
From the raw materials, the supply chain, the equipment, and all the way to the quality testing and assurance. The use of predictive analytics has to be taken into account at all of these phases of the manufacturing process and more. This issue is exactly what makes the implementation of such optimization difficult for most companies.
Another fundamental concept our expert explains is that of the “golden batch”. What a golden badge in terms of manufacturing means is the perfect case of producing a product where all aspects of the development were excellently executed.
Having such a golden standard is what is especially valuable for machine learning software as giving the algorithms example cases is exactly how they learn what the system they manage has to strive for. Any deviation from this badge can be an error and the software should account for how such an error can be dealt with.
Predictive analytics in pharmaceutical manufacturing
Every product that is manufactured has to be carefully tested and validated, but this goes double for pharmaceuticals because they are intended to have a certain effect on the human body and no manufacturer would want their medical products to influence the health of a person in a negative way.
Predictive analytics is a practice that can help us in this process in a couple of ways:
- Quality control and assurance. Predictive analytics helps pharmaceutical manufacturers maintain high standards of quality. By analyzing historical data and real-time information, it can predict potential quality issues before they occur. This proactive approach minimizes deviations, reduces waste, and ensures that products consistently meet GMP and regulatory standards.
- Supply chain management. The practice is valuable for managing the pharmaceutical supply chain. It can forecast demand for raw materials and predict potential supply chain disruptions. This helps manufacturers ensure a stable supply chain, avoid shortages, and maintain production schedules.
- Process optimization. Predictive analytics optimizes manufacturing processes. It enables pharmaceutical companies to predict the most efficient process parameters, such as temperature and pressure, which can lead to improved yields and reduced production costs. By identifying areas for improvement, predictive analytics contributes to the development of more efficient processes.
- Risk management. In pharmaceutical manufacturing, risk management is essential. Predictive analytics can predict potential risks associated with product quality, compliance, and supply chain issues. By identifying and addressing risks early, manufacturers can reduce the likelihood of costly incidents or recalls.
- Regulatory compliance. Predictive analytics aids in maintaining regulatory compliance. Monitoring and predicting deviations in real time allows for corrective actions before compliance issues arise. This is crucial for meeting GMP and other regulatory standards.
The way to stay up-to-date with advancements in predictive analytics
To keep pace with advances in predictive analytics, BGO Software implements a comprehensive strategy focusing on continuous learning and development. This entails motivating employees to pursue ongoing education, whether through in-house training programs, or external courses.
Collaboration and networking are pivotal in this journey. Encouraging employees to engage with peers, both within and outside the organization, fosters a dynamic learning environment. Forming partnerships with universities, research institutions, and data analytics companies opens doors to the latest technologies and insights as well.
Staying informed about the latest trends and techniques in predictive analytics is facilitated through following influential blogs, publications, and journals. Another method of keeping current with advancements in such practices is encouraging employees to contribute to industry publications which can greatly help spread the company’s knowledge and insights to a wider audience.
Lastly, BGO fosters internal knowledge-sharing sessions, cultivates a culture of mentorship within the organization, and promotes the exchange of findings, experiments, and insights related to predictive analytics among employees.
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How BGO Software implements real-time process support and predictive analytics?
One of BGO’s clients, a global pharmaceutical company, had some issues with optimizing their manufacturing process. These issues are usually the result of having too much data that is gathered through sensors and an inability to process all of it in a way that would merit any kind of progress.
What BGO and our expert Harry did in this case was develop software that would take in all the necessary data and analyze it, using predictive analytics and real-time process support.
As sensors and measurement tools become increasingly available, the volume of generated data is expanding at an unprecedented rate. The crucial step in fully harnessing this data’s capabilities is its transformation into actionable insights using data intelligence. It’s equally important to create effective business procedures to guarantee the efficient use and upkeep of this system
The solution that was implemented to overcome this challenge was to create a robust global system that integrates diverse mathematical methodologies for data assessment. This integration empowers data-informed decision-making and expedites the future implementation of projects throughout the network.
The software harnesses multivariate statistical techniques like Principal Component Analysis (PCA), Partial Least Squares (PLS), and Artificial Neural Network (ANN), among others, to derive insights.
By amalgamating these methodologies with regression approaches, it becomes feasible to anticipate the progression of processes or essential Key Performance Indicators (KPIs), thus enhancing the efficiency of process monitoring.
Unlike conventional univariate methods, this software allows for the simultaneous evaluation of numerous multivariate process parameters, facilitating a swift determination of whether the process remains on course. This data-centric analysis promotes more knowledgeable decision-making and doesn’t replace good manufacturing practices but only supplements existing procedures.
Since the pandemic, GMP regulations haven’t been as strict about technologies that utilize such approaches as machine learning and predictive analytics as before. In order to keep up with changing regulations, however, the software that Harry Birimirski teaches us about has been made to be scalable and easily changeable.
The programming language, called R by the R-Project, was specifically designed to accommodate statistical computing and graphics allowing for the more efficient use of these practices.
Managing vast amounts of data can be difficult for any company. The bigger the manufacturing process, the larger the information gathered surround it.
BGO Software has had a lot of experience in the field of utilizing new technologies to come up with solutions for such challenges and endeavors to learn more on the subject of how even their software can be optimized and made more efficient in the future. Striving to be compliant with regulations is also a task that should be prioritized in the development process and experts like Harry helps us better understand exactly how we can accomplish that.
Whether you’re a startup, a Fortune 100 company or a government organisation, our team can deliver a solution that works for you.