Tag: Industry 4.0

What is a Digital Twin?

A digital twin is a virtual reflection of a physical object, generally driven by marvels of:

• Internet of Things (IoT),
• Cloud, and
• Advanced Analytics.

A digital twin constantly collects real-time data and simulates it into the virtual replicate of the physical object. This virtual replicate then can be used to provide solutions to the problems experienced by the physical object.

The term ‘Digital Twin’ was coined by Michael Grieves in 2002. However, the concept of Digital Twin is as old as Apollo 13 (the 1970s). Though Apollo 13 was a failed moon mission, it hinted towards the inception of virtualization of the physical world. On its way, around 330,000 km from Earth; the Kennedy Space Centre received an SOS: “Houston, we have a problem”. The oxygen levels in the spacecraft had started declining fast. The dramatic rescue mission was started to bring the onboard astronauts back to the Earth. The key to this mission was that NASA had a physical replica of Apollo 13 on Earth. The Engineers performed a series of troubleshooting measures on the replica and came up with the best possible solution for bringing back the quickly declining Apollo 13. Rescuing all three members onboard was done successfully. This mission revolutionized the future of Space Exploration and it is also popularly known as a successful failure.  

“Houston, we have a problem”.

Unlike Apollo 13, all the replicas of current NASA programs are digitally and virtually monitored. NASA has been continuously using the real digital twin technology. It is used to solve the day-to-day problems encountered in the operation and maintenance of its space programs; without actually being physically present.

Another milestone in the history of digital twins was the launch of Predix by GE Digitals (a subsidiary of GE Electric). Predix is an Internet of Things (IoT) platform that secures cloud computing and data analytics. Used for improving the operational efficiencies of the machines. In 2015, Collin J Paris, Vice President of GE Global Research Center; demonstrated to the world:

• how a computer program could predictively diagnose malfunctions in the operation of a steam turbine and,
• even could perform the maintenance activities remotely.

GE has been continuously monitoring hundreds of such turbines using their digital twins for over a decade now.

Working of Digital Twin

• The physical object, also known as an asset, is designed to have many, sometimes hundreds, of sensors. These sensors capture real-time data (about almost everything) and send it across to its digital twin.
• The digital twin analyses this useful information. Further, mixes this information with the hundreds of other similar assets, using:
  • the Internet of Things (IoT),
  • cloud connectivity, and
  • predictive data analytics.
• Additionally, the information shared with the digital twin is simulated to the various design features of the asset.
• The simulation is used to answer two important questions viz.,
  • What could go wrong?
  • What could be done about it?
• This knowledge is used to build a learning platform that makes digital twins smarter every time additional information is added.

Applications of Digital Twin

Use of digital twins in patient care

Philips is pioneering on the concept of what is referred to as the virtual representation of a patient’s health status, i.e., each patient would have a digital twin that enables the right type of treatment in the right way and at the right time. For example, if a patient presents with a particular symptom, its digital twin uses medical diagnosis data in combination with the patients’ medical history along with a variety of medical information available to build a digital model that recommends the patient-specific treatment modality with the best possible outcome. The digital twin also enables simulation of the treatment modality on the patient before implementing the procedure on the patient in the real case scenario. During the performance of the procedure, it ensures fidelity of the procedure and can even predict any unforeseen complication that can be averted in the first place. Moreover, all this information is stored in the cloud and can be retrieved anywhere at any time.

Use of digital twins in manufacturing 

The digitalized twin of a manufacturing process uses IoT sensors that collect real-time process data continuously. The IoT sensors enable uninterrupted monitoring of the process. This increases the overall performance of the manufacturing process. Continuous monitoring also allows anticipation of the maintenance needs through the use of advanced analytics. This could reduce the possible process outages and downtimes that save millions of dollars. The amalgamation of advanced analytics and IoT can be used to manage the performance of the manufacturing process and which, in turn, improves the quality of the final product. It is important to note that the digital twin of a manufacturing process is not a single application but hundreds of interconnected applications. The communication between all these applications puts the process into a state of control.

The virtualization process is also taking over the most vital component of the manufacturing industry, i.e., supply chain management. The digital twin of the supply chain can automate the organizational processes. The twin can automate the purchasing and tracking of the assets and consumables based on the anticipated usage. If there is a shortage of raw material, the twin can assess the possible impacts on the operations and also offers the best-case scenario and solutions. This makes an organization prepared for overcoming the logistic challenges and hence improve the overall productivity of the organization

Future of Digital Twin Technology

The digital twin technology is rapidly expanding its applicability in almost every industry and, in fact, almost everywhere. Due to the adoption of the fourth industrial revolution, Industry 4.0, the market of the digital twin is expected to grow enormously. The global market of digital twins was valued at $3.1 billion in 2020 and is projected to grow $48.2 billion by 2026 at a Compound Annual Growth Rate (CAGR) of 58%.

The outbreak of COVID 19 has upheld the implementation of digital twins in business models, particularly in the biotechnology and pharmaceutical industries. The industry is gearing up to upgrade the existing infrastructure and adopt the digitalized technologies to avoid crippling losses due to frequent lockdowns. The Governments are also very keen on adopting the technology as can be seen in the design of smart cities across the world. The smart city initiative of Singapore is the best-fit example for the application of digital twin technology. This model combines different technologies to develop a digital version of the city’s resources, processes, and procedures. The digital version of the city enables superintend of the city using a simple computer program.

Conclusion

The new normal of the pandemic has redirected and reinforced the adoption of Digital twin technologies into every aspect of our lives. Digital twin technology is going to be a game-changer in the fields like continuous manufacturing. There are innumerable advantages of the adoption of the technology like cost leadership, environmental sustainability, economic stability, energy efficiency, etc. This is going to change the way our businesses have ever been managed. Let’s Excel Analytics Solutions LLP can support your organizational needs to develop digitalized tools for reinventing the business.

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Introduction

ISPE stands for International Society for Pharmaceutical Engineering, founded by a group of experts to discuss new challenges faced in pharmaceutical manufacturing. ISPE is a non-profit organization that provides technical and non-technical leadership for managing the life cycle of pharmaceutical products. In 2017, SIG (Special Information Group) was appointed to create a roadmap to facilitate “Industry 4.0” for pharmaceutical manufacturing. The prime objective of SIG was to reinvent Industry 4.0 for the adoption and leverage into the Pharmaceutical Industry. ISPE “Pharma 4.0” is based majorly on similar concepts and ideologies as that of Industry 4.0, it additionally has regulatory aspects based on  ICH guidelines, specifically ICH Q8 and Q10.

History of Industry X.0

Smart Factories: The various components involved in the smart factories communicate with each other and mark the inception of total automation. These components are known as Cyber-Physical Systems that employ advanced control systems operated using softwares capable of internet connectivity {Internet of Things and Internet of Systems}, cloud computing and cognitive computing. The efficient communications and availability of information have enabled the digitization of manufacturing systems.

The Germans were the firsts to adopt the Fourth Industrial Revolution, named it I 4.0 when they initiated the projects that promoted the digitization of Manufacturing Systems.

Barriers of Industry 4.0 into Pharmaceutical Industry

It’s very right to say that the pharmaceutical manufacturing industry is not keeping up the pace with the advancing technologies. It is attributable to the stringent regulatory requirements that have slowed down the implementation process. For regulatory agencies, compliance with the existing standards matters more than the adoption of new technologies. It is believed that the pharmaceutical industry is highly regulated, and it can’t be left to machines. But the industry has started to realize the benefits of advanced technologies that can enhance productivity and improve quality at the same time. This hints at the inception of automation in achieving regulatory compliance in pharmaceutical manufacturing.

Evolution of Industry 4.0 to Pharma 4.0

• Very often, Pharmaceutical organizations experience quality shortcomings that eventually lead to 483 observations and warning letters from regulatory agencies. Every year, approximately 4500 drugs are recalled alone in the USA. This recalling costs a great deal to the organizations.
• Currently, the pharmaceutical industry is trying to adopt new strategies that can mitigate quality-related incidents. Lean Six Sigma tools are employed to improve product quality in pharmaceutical manufacturing.
• In 2004, the US FDA published a guidance document entitled “Quality Systems Approach to Pharmaceutical Current Good Manufacturing Practices Regulations” that insisted manufacturers implement modern quality systems and risk-based approaches to meet the expectations of the regulatory agencies.
• In 2009, ICH Q8 guidelines were revised to incorporate the principles of “Quality by Design”(QbD); it stated that the quality cannot be just monitored but should be built into the product. Despite these measures, quality violations of pharmaceutical products continue to be unabated.
• The best solution to these problems is the digitalization of platforms. What is required is, the model for the implementation of digitization to the operations. ISPE has pioneered to restructure Industry 4.0 to fit the Pharmaceutical Industry, which is now known as ISPE Pharma 4.0 Operating Model.

Pharma 4.0 Operating Model

Framework of ISPE Pharma 4.0 Operating Model

The above table depicts the basic structure and framework of the ISPE Pharma 4.0 Operating Model, which consists of two broad components:

• Enablers
• Elements

ICH defined Enablers: Knowledge Management and Risk Management

ICH defines knowledge management as a systematic approach to acquiring, analyzing, storing, and disseminating information related to products, manufacturing processes, and components.

The different sources of information include:

• Product design and development
• Technology transfer
• Commercial manufacturing, etc.

The knowledge management of the product and product-related process needs to managed right from the product development through commercial manufacturing up to product discontinuation. It has to be digitalized in the form of  databases and should be connected directly to the raw data sources, which will ensure the data integrity of all GxP and non-GxP data, that  helps in making better choices and build regulatory confidence.

Various In-line, At-line, and On-line tools as used for :

• Analysis of raw materials.
• In-process monitoring
• Final product analysis

These tools can be directly integrated into database systems for real-time data management.

ICH Q9 (Quality Risk Management), also known as the ICH Q9 model, is a fundamental guideline that describes the potential risks to quality that can be identified, analyzed and evaluated.

This guideline is supported by ICH Q10 (Pharmaceutical Quality Systems) which describes a model for an effective quality management system.

The ICH Q10 implementation has three main objectives:

1. Attain Product Realisation
2. Develop and Maintain a state of process control.
3. Ensure continuous improvement.

ICH Q10 provides guidelines regarding critical quality attributes (CQAs) that should be within a specific range to ensure desired product quality. The variables, process parameters, and material attributes that affect the critical quality attributes are referred to as Critical Process Parameters (CPPs) and Critical Material Attributes (CMAs) respectively.

ICH Q12 appends on ICH Q10 to include those parameters which are not critical to quality but are responsible for the overall performance of the product. These attributes are known as Key Process Indicators (KPIs) and continuous efforts should be made to bring the KPIs under six sigma control.

Any excursions or changes in the CQAs, CPPs, CMAs, and KPIs should be communicated to the respective regulatory authorities; prior approval is required in certain cases before the implementation of the changes.

Pharma 4.0 Enablers: Digital Maturity and Data Integrity by Design

The first enabler in Pharma 4.0 to make an organization a smart factory is, Digital Maturity. It specifies the ability and the path of implementation of Pharma 4.0 for an organization. The model is developed in a way such that, an organization can perform gap assessment in terms of its position in digital maturity, improvisations in its capabilities, and based on what future capabilities would be. The basic requirement to achieve digital maturity is computerization and interconnectivity across all the quadrants of the operating models. After fulling these requirements, the organization can move towards advancement by capabilities like data visibility, predictive capacity, and adaptability.

• Data visibility: A strategy where an organization can acquire, display, monitor, and analyze the data generated across all the sources in the organization.
• Data Transparency: The ability to access the data no matter what generated it and where it is located.
• Data Predictability and Adaptability: The ability of the data to predict future outcomes and improve on the predictability as more data is added to enhance the accuracy of the predictions.

These functions of the data help an organization to make a statistically calculated decision as they are based on real-time data.

ICH Q6 (Good Clinical Practices) defines data integrity as the extent to which data is complete, consistent, accurate, trustworthy, and reliable throughout the data lifecycle. The regulatory approval of the drug and all the related process are dependent on the quality and integrity of the submitted data. In the year 2016, USFDA issued a guideline, entitled “Data Integrity and Compliance with Drug cGMP”  that focuses on developing effective strategies for data integrity throughout the life of the drug product.

These strategies should be bases on quantitative risk assessments for patient safety.  Moreover, data integrity should be built into the products and related processes during the design and development; this could be done by introducing digitalization of data integrity known as ‘Data Integrity by Design’. When digitalization will be introduced, every process will have a defined workflow to avoid any silos of information and data integrity relates issues.

Pharma 4.0 Elements:

Resources:

Resources of an organisation refer to the physical and intangible assets owned by an organization, majorly categorized into:

• Human Resources
• Machines
• Products

The Machines employed in Pharma 4.0 should be highly advanced and developed based on Artificial Intelligence and Machine Learning. They would be highly automated and adaptive to the ever-changing business needs of the organization. These machines can be connected to PAT tools for in-line, online, and at-line monitoring during the manufacturing of the products. Such capabilities enable machines in taking their own decisions. But to run these machines, a new generation of highly skilled people is required, these people would be called Workforce 4.0. The success of Pharma 4.0 would largely be dependent on the engagement and continuous upskilling of Workforce 4.0 and the choice of Artificial Intelligence and Machine Learning Platform.

Information

The information system is an integrated set of components for collecting, storing, and processing data and for providing information, knowledge, and digital products. By this means the components relate to each other. This integration forms a basis for:

• How data is interfaced
• How processes are Automated
• How processes have the power for predictive analysis.

The predictive analysis enables the real-time release testing of the products known as “ ad hoc reporting”, which is already being used by some organizations.

The other benefit of integration into information systems is the preventive maintenance of equipment. The equipment takes ownership of its maintenance by analyzing daily data and let the potential maintenance activities be known in the first place and in some cases rectify the abnormalities, this reduces the equipment breakdown time significantly, thus increasing overall productivity. There is more potential area of integration into the information system, but they should adhere to global standards like GAMP5, ISO, etc.

Organization and Processes

An organizational structure needs to be developed which builds processes for substantiating prospective business challenges. Pharma 4.0 is a huge task for the organization and its outcomes are also uncertain, hence a sound and step-by-step organizational structure is required to be developed. The Organisational process needs to be developed across all elements of the holistic control strategy, such that each element functions collaboratively.

Culture

Culture refers to the shared beliefs and values of an organization that help achieve common organizational goals successfully. It should promote collaborative contributions as collaborations drive innovations. A culture where people understand the importance of each Pharma 4.0 element and which percolates down to each stage in the product lifecycle, from the early development to technology transfer and commercial manufacturing, should be developed.  New collaborations should be sought every time to improve on the existing capabilities and acquiring new capabilities. People should be encouraged to adapt to the new changes as upgradation is the requirement of sustenance in the ever-changing market.

Existing Control Strategy vs holistic Control Strategy

• The existing control strategy was once a game change, which improved quality oversights in the manufacturing, however, to note it just reports quality, i.e, it can tell what has gone wrong, but it cannot predict when and what can go wrong. It puts process control by continuous monitoring of manufacturing processes for the process-related excursions.
• The Holistic Control Strategy as described by ISPE is based on ICH and Pharma 4.0 enablers and elements that provide control over the production process to ensure a flexible, agile, sustainable, and reliable manufacturing system with lower risks to patients, processes, and products. However, its success depends on the mutual consensus between industry and regulatory agencies.

Barriers to Pharma 4.0

Even though the Pharma 4.0 model might initiate a new era of smart pharmaceutical manufacturing, there are several barriers to the adoption of this model.

 The main barriers involved are:

• High cost of digitization
• Time-consuming
• Skilled and trained workforce
• Uncertainty of the Outcomes

Despite all these barriers particularly the cost factor, Pharma 4.0 is going to be a reality and the desperate business need for sustainability. At Let’s Excel Analytics Solutions LLP we have developed cloud-based platform technologies that drastically cut down on digitalization costs. Hence, the barriers will be quickly offset by the tremendous increase in productivity and significant reductions in downtimes.

Summary

Pharma 4.0 digitalization is an imperative and inevitable transition that Pharmaceutical Industry is undergoing. To support the smooth transition to Pharma 4.0.

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