Over the years, the pharmaceutical industry has undergone a huge transformation with production moving from small-batch manufacturing to mass production of complex molecules. Additionally, the industry has seen three distinct generations over the last 100 years.

Industry 1.0 (the early 19th century) was the starting point of pharmaceutical evolution. In this period, manual processing of a botanical, animal, and mineral-derived materials was mostly produced by simple hand-operated tools to commercial-scale machinery for crushing, milling, blending, and pressing larger quantities of medicines. The large-scale production of drugs was carried out by using traditional chemical engineering.

Industry 2.0 brought with it electronic equipment with pre-programmed controls that included fundamental automation and process control which allowed manufacturers to monitor manufacturing processes and drug production.

In the recent past, Industry 3.0 came in with computers and communication technologies, such as the internet, networked computing, and wireless communications. These technologies incorporated automation in pharmaceutical manufacturing enabling continuous manufacturing and active process control.

Today’s Industry 4.0 or pharma 4.0 integrates traditional chemical and biological manufacturing with cognitive computing to bring about a system of intelligent automation.

The COVID-19 public health emergency has emphasized the need for manufacturing technologies that are responsive to changing consumer demand and which minimize human involvement. The emerging fourth revolution has the potential to produce a novel intelligent automation concept system allowing for the timely production of medicines and vaccines. This can be achieved by integrating digital data from all sources and fields to create a uniform platform for process control and decision making. Here, we discuss the core technologies that are being used in pharma 4.0 and the challenges faced during the implementation of these technologies. We also explore how they are expected to revolutionize production in the future.

Emerging Pharma 4.0 Technologies

Industrial Internet Of Things (IoT)

IoT consists of physical objects such as machines, sensors, and wearable devices that are embedded with electronics, software, sensors, or network connectivity to enable them to connect and exchange data. The IoT requires data digitization which is the representation of real-world objects or events in digital form to enable their processing, exchange, analysis, and visualization. Then data can be collected directly from equipment through sensors. Full digital maturity enables data connectivity between manufacturing equipment over the internet. These devices are capable of querying operational parameters, diagnosing equipment conditions, and reporting on events. Real-time data exchange is possible between machines through networks, which allow for automation of production lines or remote monitoring capabilities.

The integration of IoT with industrial control systems (ICS) enables manufacturers to monitor and process real-time information from their manufacturing facilities in an efficient manner with minimal manpower. The IoT has multiple applications such as predictive asset maintenance, scheduling orders, forecasting demand, etc., allowing manufacturers to improve their overall operation management by optimizing profit margins and reducing costs. It also provides reduced energy consumption and a reduced environmental footprint.

IoT offers immense potential for pharma 4.0 by connecting various devices within an automated production environment with cloud computing providing real-time information about materials, capacities, and quality issues. Monitoring these variables can help manufacturers produce better quality products and improve the final product.

Artificial Intelligence(AI)

AI is a series of technologies that enable machines to perform tasks with human-like intelligence. In pharma 4.0, AI is most commonly used in the form of cognitive computing systems that can understand, predict and respond with minimal human involvement. Cognitive computing systems have been developed based on machine learning algorithms and deep neural network architectures which allow them to adapt to changing situations and environments by learning from past experiences across multiple domains. The growing availability of large amounts of diverse data makes it possible for these systems to learn from experience without explicit programming.

AI applications are also being used in quality control. AI has been applied as a classifier system for detecting faults in pharmaceutical products before they reach the consumer markets. In this system, AI works in conjunction with machine learning algorithms to detect minute changes in pharmaceutical product images related to the quality parameters. On the basis of the degree of change detected in various image pixels, a decision can be made regarding whether or not to label an image for human inspection.

AI could also be applied in real-time visual analytics for providing insights into data gathered from automated data collection systems. This process could allow manufacturers to improve their production efficiency and service levels by informing key decisions at the right time. For example, monitoring a patient’s vital signs can help predict health conditions allowing healthcare professionals to plan ahead for administering medication.

Machine Learning(ML)

Machine learning(ML) is a branch of AI that enables computer programs to learn without being explicitly programmed. This process is accomplished by building algorithms that can receive data which they use to make inferences and predictions in real-time. It assists in processing raw data into information which leads to automated decision-making. Machine learning has major applications in pharma 4.0 intelligent production systems for process monitoring, fault diagnosis, inventory management, etc.,

Digital Twins

According to recent research, companies are now investing in developing digital twin technologies to understand the behavior of their assets better. A digital twin refers to an accurate virtual replica of a physical asset as used in real-world scenarios. It is hoped that the use of these twins will help reduce downtime and improve process efficiency.

A digital twin can be used for predictive maintenance so that facilities engineers are alerted when there is likely to be an issue with the equipment before it fails. This saves money through reduced downtime and helps ensure compliance with industry regulations.

Robotics

In the pharmaceutical industry, robotics has been applied to a variety of sectors from manufacturing and warehousing to distribution. As more companies adopt robotics as an operational tool, new challenges arise as well. For example, many warehouse operations use robotic systems to pick items from shelves and package them for shipment.

In other areas such as process manufacturing or distribution, robotics offers significant potential for improving operations and reducing costs.

PAT(Process Analytical Technology)

Pharmaceutical companies are also looking at ways to improve their existing automation tools such as Process Analytical Technologies (PAT) systems. These systems rely on a variety of in-line and off-line analytical techniques that help control and monitor the manufacturing process.

As part of pharma 4.0, the inclusion of real-time data collection and analysis will give manufacturers more insight into how they can streamline operations and reduce variability. PAT systems provide an ongoing, real-time picture of how a production process is performing.

As such, quality assurance and control engineers can target potential problem areas of the process. This helps reduce waste and ensures greater efficiencies.

Challenges To Implementing Industry 4.0 In The Pharmaceutical Industry

Regulatory concerns, impacts of the transition from batch processing to real-time processing, and technical hurdles in deploying new technologies are barriers to implementing Industry 4.0 in the pharmaceutical sector.
Cybersecurity is a significantly growing concern in the industry as an increasing number of operations become dependent on automation and connectivity.
Physical facility concerns as warehouses may be required to store components as well a master site plan may be necessary to ensure that operations comply with local zoning laws.
The management and disposal of single-use solids waste might become a burden when attempting to stay balanced with ever-increasing sustainability standards.
Facility Site Master Planning is required to accommodate Industry 4.0 technology equipment and processes.
Potential workforce concerns if manufacturers are to successfully implement Industry 4.0 in pharma.

Pharmaceutical Companies have come a long way over the years in terms of productivity, quality, safety, and patient care. With pharma 4.0, the industry will continue to evolve.

The future of Facilities is aimed toward lean manufacturing and flexibility. This is where Industry 4.0 comes into focus as it integrates both human and equipment resources for optimal functionality and performance. Successful adoption of Industry 4.0 in pharmaceutical manufacturing will increase the quality of pharmaceutical products and significantly benefit patients with higher quality products and more dependable supply chains.

Facility Management: Pharma 4.0