Advanced analytics in pharma is a fast-growing market, predicted to expand from $64.3 billion in 2023 to $226.2 billion by 2028, at a CAGR of 28.6%. It’s being stimulated by a combination of the rising adoption of big data tools, cloud and edge computing, and the need for solutions to pressing problems like rising costs, sustainability requirements, and fluctuating demand. 

This blog post will discuss advanced analytics in pharma and the potential benefits it can bring, as well as the challenges plants face in implementing it.

What is advanced analytics in pharma?

Advanced analytics refers to the use of artificial intelligence (AI) and machine learning (ML) to crunch enormous big datasets. Like other process manufacturing verticals, pharma plants produce massive amounts of data from sensors, Industrial Internet of Things (IIoT) devices, and plant monitoring equipment. The data is too big for human analysts to comprehend, but advanced AI and ML analytics tools can analyze them to produce valuable insights. 

Adoption of advanced analytics in pharma has been helped along by the rise of other technologies, such as cloud computing which can store such a vast amount of data, and edge networks and 5G and 6G networks that support close to instant data transfer for large datasets. Faster computing enabled real time and near-real time analysis, which increases the value of advanced analytics insights. 

Advanced pharma analytics covers many different applications and use cases. Predictive monitoring, digital twins, predictive maintenance, early alerts about part failures, demand forecasting, supply chain analytics, and quality control monitoring are all examples of advanced analytics in pharma manufacturing, and most plants have already implemented some or all of these use cases. 

Why do pharmaceutical manufacturing plants need advanced analytics? 

Like every industry, pharma manufacturing has been affected by the acceleration of digital transformation and Industry 4.0, as well as other trends including increasing demands for sustainability in manufacturing. McKinsey lists 6 major implications for pharma companies from today’s global trends: rising operational complexity, increasing risk, shifting capability requirements, higher capital expenditure requirements, variable-cost increases, and opportunities for savings

While some manufacturing segments saw demand fall during COVID-19, pharma experienced the opposite. The pandemic resulted in increased demand for many medications and saw pharma plants struggling to manage quality assurance and process optimization with reduced on-site employees. 

As a result, many pharma companies are ahead of the curve when it comes to remote operations, but they are still striving to future-proof plants for the next pandemic or natural disaster. At the same time, manufacturers need to increase operational efficiency and ruthlessly cut waste in energy, water, and raw materials, to limit their vulnerability to water shortages, power outages, and ongoing uncertainties in the supply chain. 

Pharma manufacturers are also facing amplified calls for personalized and small-batch, customized medications, which requires greater efficiency in changing between batches. They are looking for the best ways to keep up with calls for fast turnaround times and continuous manufacturing, while trying to comply with mushrooming regulations. Fortunately, advanced analytics can help with all these goals. 

The benefits of implementing advanced analytics in pharma plants

Pharma plants can see many benefits from adopting advanced analytics. 

  1. Process optimization

Advanced analytics allow pharmaceutical manufacturers to analyze data from various stages of production, delivering insights into possibilities for increasing efficiency and optimizing resource utilizations. Predictive analytics can provide early alerts about bottlenecks in production, allowing process engineers to clear them before they impact on product quality or processing costs. 

By identifying inefficiencies and improving resource optimization, pharmaceutical companies can reduce operational costs and improve their overall financial performance. It also empowers plant engineers to change from one production run to another more quickly, giving them greater agility to meet orders for small batches and personalized medications. 

  1. Predictive maintenance

Predictive maintenance tools can pick up on even minor anomalies that indicate nascent issues, and notify engineers before they escalate into serious incidents. Plant managers can then optimize maintenance scheduling and proactively plan downtime for times when it will minimize disruption to production. In this way, predictive analytics can anticipate equipment failures and reduce unexpected downtime. 

  1. Quality control

Quality control is vital in any type of manufacturing, but particularly crucial in pharma. Any lapse in quality standards could cause serious harm to the patient, while product recalls damage the plant’s reputation. 

Advanced analytics like predictive monitoring can track product quality through every step of the production process. Early detection of even slight deviations in quality means that engineers can correct processes or fix equipment before significant amounts of product need to be destroyed, ensuring compliance with regulatory standards. Real-time analytics can monitor batch processes, allowing for quick adjustments and minimizing the likelihood of batch failures or rejections.

  1. Supply chain management

Supply chain analytics and predictive monitoring can help pharma manufacturers to optimize the supply chain and reduce their vulnerability to supply shocks. Advanced analytics can predict demand more accurately to help plant owners to plan ahead for the materials they will need, and inventory management gives a clearer view of stock levels. With predictive analytics, it’s possible to ensure that the right materials are available at the right time.

  1. Risk mitigation 

Pharma plants are busy places with many inherent risks. Advanced analytics tools can help mitigate these risks in a number of ways. When all the processes are running smoothly, it helps raise safety levels in the plant and for nearby communities. Early alerts into anomalies make it possible to prevent potential health hazards like leaks or emissions. 

Because advanced analytics are able to gather data and assess risk across the plant, it also reduces and often removes the need for human employees to place themselves in potential danger in order to check on equipment and systems. 

  1. Sustainability

Sustainability is a major concern for pharma manufacturing companies. Predictive analytics can help them achieve sustainability goals in a number of ways. For example, more efficient processes means less waste for resources like energy, water, or raw materials and lower amounts of harmful emissions. Early alerts from predictive monitoring systems also allow plants to prevent environmental incidents such as leaks or flare events.

  1. Competitive advantage

All the above-mentioned benefits come together to provide pharmaceutical manufacturers with a competitive edge over their rivals. Pharma plants which implement advanced analytics can innovate faster, anticipate market demands, meet customer requirements for sustainability and quality, and lower costs to boost profit margins. 

The challenges of adopting advanced pharma analytics

Although the benefits of advanced analytics in pharma are clear, there are still challenges which plants need to overcome. 

  1. Data quality and availability

Ensuring that high-quality data is available for analysis can be a significant challenge. Inconsistent data sources, data silos, and data integrity issues can hinder the effectiveness of advanced analytics. It’s important to assess your data pipelines and ensure that all data is collected in a single data repository before introducing advanced data analytics. 

  1. Data privacy and security

Pharmaceutical manufacturing involves sensitive and confidential data, so confirming data privacy and security is a top priority. At the same time, data analytics tools must comply with data privacy regulations such as GDPR and HIPAA, which can add complexity. Pharma companies need to carefully choose advanced analytics vendors which take security and compliance seriously and embed robust cybersecurity policies into their products. 

  1. Integrating with existing infrastructure and technology

Advanced analytics can’t operate in a vacuum. You’ll need to integrate them with your existing tech stack and verify that they play well with the tools you already rely on. This could require significant investments in hardware, software, and IT infrastructure to bring your current systems up to date. Older manufacturing facilities may need substantial upgrades to support data collection and analysis.

  1. The skills gap

The wider labor shortage is affecting pharma plants too. There’s a general lack of skilled data scientists, analysts, and engineers who can work with advanced analytics tools. Manufacturing verticals tend to be unpopular among younger jobseekers, making it harder to compete for scarce talent, and training existing staff can be time-consuming and costly.

  1. Change management

Pharma plants can meet with resistance when they try to transition to a data-driven culture and convince stakeholders to embrace advanced analytics. Employees may be reluctant to change established processes and workflows, and/or wary of working with AI systems. It can be tough to alter an entrenched organizational culture to accept data-driven decision-making, especially in traditional or long-established pharmaceutical companies.

Precognize can help

With Precognize’s predictive monitoring solution, SAM GUARD®, pharmaceutical manufacturing companies can access the benefits of advanced analytics more quickly. SAM GUARD®’s predictive analytics provide visibility into plant processes, helping pharma companies to optimize resource allocation, boost operating efficiencies, mitigate health, safety, and environmental risks, and reduce waste and costs. 

SAM GUARD® delivers predictive monitoring that can detect the earliest signs of process inefficiencies, bottlenecks, fouling, or part failure. This enables process engineers to swiftly fix issues, and often to carry out minor repairs instead of larger replacements that necessitate unplanned downtime. With insights from SAM GUARD®’s advanced analytics, plant engineers can reduce the time spent on root cause analysis, maintenance, and resolution to slash both casts and waste. 

Adopting SAM GUARD® also helps pharma plants more easily overcome the challenges of adopting advanced analytics. It offers an Analytical Monitoring Service (AMS) which brings human intelligence together with machine learning capabilities (HI + ML). The system deploys human experts to review ML-generated alerts, selecting those which are the most urgent and pushing them to the on-site process engineers. The AMS helps pharma stakeholders to gain value from the solution more quickly, assisting the culture shift to ML analytics and data-driven insights.

Advanced analytics can be a boon for pharma manufacturing

Despite the challenges of adopting advanced analytics, many pharmaceutical companies recognize the value that implementing advanced pharma analytics can improve operational efficiency, product quality, and decision-making. With the right tools and a clear understanding of the benefits that advanced analytics can bring to pharma companies, pharma manufacturing plants can successfully adopt predictive analytics and other advanced analytics tools and see improvements in their bottom line.