The pandemic identified pressing needs for resilient pharmaceutical supply chains and the ability to produce next-gen medicines faster and at scale - two challenges Resilience’s Digital Factory is designed meet. Our 20-minute topic will present a case study of our Digital Factory at Marlborough, MA, sharing the vision of the Resilience Digital Platform, the journey to bring the vision to life, and lessons learned from the cutting edge.

The pharmaceutical industry, like all industries, is being swept inextricably forward on the digital tidal wave - where the flood of data will continue to transform and underpin the future of the industry. Successful companies will be the masters of these seas – where technologies to collect large volumes of data and automate practically every segment of the pharmaceutical value chain are now mature and quickly becoming common. And where newer data analytics and artificial intelligence technologies are quickly becoming realistic tools to revolutionize how the industry distills new value out of the rising sea of available data. Data integrity, like clean water, will be paramount to survive. Quality organizations will need to make strategic shifts in process and approach and skills and mindset, and redefine how data is used to sustainably and efficiently achieve the quality mission. New paradigms will be required to sustainably achieve data integrity, demonstrate compliance and assure product quality.

One of the principal tenets of Pharma 4.0™ states that “Digitalization will open new horizons to achieve new levels of connectivity, transparency, agility and productivity through the application of faster and more accurate information for decision-making.”
The main question is: how to start the transition from Pharma 3.0 towards Pharma 4.0™?
We start with well known process mapping, which is called process-centered process mapping, followed by the concept of data-oriented process mapping, which will lead to full understanding of your data flow, ready for the digital world. The data-oriented process maps, are placed in the RAMI architecture, specifically designed for the industry 4.0 environment, resulting in an analysis of the current situation. The analysis provides input for the working plan describing how to reach the desired data integrated level, needed for the transformation towards Pharma 4.0™.

This presentation provides insights into process maps, data flows as well as system integration in the pharmaceutical industry with a focus on data integrity issues using the context of the RAMI model. In addition, an emphasis is placed on the practical aspect of how to access the necessary data, and what this means from an engineering and system integration perspective.

The pharmaceutical industry is subject to numerous standards, laws and guidelines. Process plants and other operational infrastructure form a functional whole. An understanding of these parameters is necessary in order to develop, and implement, an overarching, tailored decarbonization concept. 

For the pharmaceutical industry, production is the essential core element. Therefore, following a process-focused project management approach, in which the production process is at the center of consideration, allows a company to scrutinize existing standards and define potentials for energy optimization. There are numerous possibilities to achieve the goal of optimizing energy consumption of bioprocessing plants and such control models can often be created with little physical effort. The decisive factor is that the corresponding requirements are already defined in the preliminary planning of a project. Finding a strategic and structured approach to energy optimization and decarbonization is a major challenge for companies - it needs to be ambitious, but, at the same time, realistic. 

The goal is to align an external perspective with an internal perspective of the company. The result is a structured cost-benefit analysis showing how much CO2 is saved by each measure and the incumbent costs. This provides a clear picture of which reduction potentials are possible.

Sustainability continues to be a high priority for pharmaceutical manufacturers. Many have committed to ambitions environmental targets while recognizing their production processes often require large volumes of water and energy. Digitization has proven helpful for companies to drive sustainability improvements without compromising on product quality. Nalco Water, an Ecolab company, will share our experience with GSK in leveraging digitally enabled water management to improve sustainability. We’ll explain how GSK uses digital services to save 98 million gallons of water, 93 billion BTUs of energy, and 19,000 metric tonnes of CO2 emissions per year. 

We will discuss operational efficiency benefits too. These services help GSK save hours of labor per year and achieve total annual cost savings of $1.5 million. 

Take-aways: 

1. Digitally enabled water management can help pharma manufacturers meet their sustainability targets
2. Digitally enabled water management can yield significant operational efficiency benefits

The presentation describes a case-study showing machine-learning capabilities in reducing environmental impacts and costs, with a very limited investment in instrumentation and software. VTU Engineering has designed the expansion of a cooling towers system for an API plant in Northern Italy (~1200 MWh/year, OPEX>100 000 /year): targets are to maintain the T-range of the cooling water, reduce pressure fluctuations in the distribution network. Six 6 induced draft cooling towers, two pumps (without VSD), and little instrumentation resulted in a plant with little controllability, available 24/7, not energy efficient.

The proposed (and simulated) solution installs a weather station, few temperature and pressure sensors, and will equip the motors with VSD to manage the power absorption. The whole system will be connected to an AI unit which follows the L.I.C.O. approach: Learn: the AI unit monitors the system. Integrate: the algorithm is tested against different scenarios (off-line), identifies variables correlation, defines control strategies (data-analysis, machine training). Control: AI unit is put on-line as a surrogate model for the plant and is used in an optimal control scheme with a tailored objective function (energy saving). New set-points are proposed to the DCSOptimize: the control strategy is continuously improved.

In recent years, many pharmaceutical companies have been piloting advanced data analytics techniques to discover insights on the manufacturing processes. In this talk we will discuss a commercial biologics product case study in which we have consolidated disparate data sources that were previously not accessible in aggregate, including raw material specifications, manufacturing equipment, manufacturing process information, etc. and applied advanced data analytics including statistical modeling and machine learning. The goal is to unearth and understand the key drivers of an intractable cell culture challenge, and be able to improve the yield based on predictive control during the manufacturing process. 

We will discuss the findings, share lessons learned, and continue building advanced data analytics capability to enable more efficient and reliable production of our critical medicines for the patients.

This presentation will include a brief review of key regulations as they relate to the need to use the data collected for trending, process control, investigation and root cause analysis. The digitization is still in process, pharma industry due to the regulatory scrutiny are sometimes late adopters but the need to digitize is ever more important based on the evolution of the regulations encouraging trending and using data.

The discussion will cover the key contamination control processes including pharmaceutical water, HVAC, and gas monitoring, as well as environmental and personnel monitoring, also touching on cleaning validation and cleaning process monitoring. The talk will relate to the different trending tools, when they should be used, and what they mean. 

Examples of trends will be included in the presentation.

As we gather more information from our processes, we need mathematical models that provide flexibility and accuracy for complex systems. Classical statistical modelling and prediction often fall short in these situations. Bayesian statistical methods, however, are fundamentally flexible and can address these shortcomings. They can be leveraged to improve outcomes from complex systems/relationships made possible with digital transformation.

These advanced methods are not part of typical statistical training received by engineers and scientists. However, it is critical that engineers are aware of these options, so that the data they collect can be leveraged for maximum value. This presentation will cover common situations (e.g., hierarchical models, risk-based stability modelling, staged testing) where innovative modelling provides answers when classic approaches fail.

Often, new technology implementation is driven by early adapters in a project/production team, with limited business case foundation or broad stakeholder sponsorship. As a result, the implementation outcome varies widely and traceability from early decisions to proven productivity gains are lacking. Recognizing this challenge, the project management team, responsible for delivering a 2.8 Billion USD API manufacturing facility, developed a decision-tool for linking manual operations to relevant technologies to reduce the combined manning requirements. The result has been a process that clearly links a business case to each technology candidate and established full traceability for the decision process. 

This paper will present the process developed, the stakeholder mapping process and the roadmap for ultimately securing success of the initiative.

This presentation will discuss what tasks need to be solved in connection with digitization of workflows in cleanrooms to achieve an appropriate setup. Digital Transformation and Pharma 4.0 in clean rooms are a strong trend, that is driven by a need for IT applications all the way to the operators in cleanrooms to collect, process and share data. How can the IT hardware help solve the tasks in the clean rooms while being in compliance with the regulatory requirements and at the same time let IT operation gets the necessary stability, flexibility and mobility? How to create a good ergonomic working environment for the operators so that productivity is kept high? Hear about experiences through a customer case.

Description Coming Soon!

The move from Historical to Active Energy Management is an ongoing transition throughout all industries. This transition is even more impactful in the Life Sciences Industry
Components that encompass Active Energy Management Include:
DIGITIZATION & ANALYTICS
• Onsite/remote energy measurement
• Enterprise energy data management
• Energy usage analytics
• Reporting

RESOURCE-EFFICIENT OPERATIONS
• Energy efficiency
• Carbon footprint reduction
• Process optimization
• Facility modernization
VALUE-FOCUSED RESOURCING
• Traditional energy sourcing
• Renewable energy sourcing
• Microgrid & onsite solutions
• Risk management

SUSTAINABILITY AMBITIONS
• Corporate commitments
• Decarbonization strategies
• Scope 1,2,3 optimization
• Financial enablement
• Incorporation of innovation

In this session we plan to highlight many of the real world challenges that we have encountered in the Life Sciences market and how the challenge were successfully addressed.