Artificial Intelligence (AI) Implications for Facility Design

This focus has proven insufficient, prompting the Biennale to offer a solution by re-exploring holistic intelligence within architectural methodologies, specifically through the integration of "natural, artificial, and collective" forms of intelligence, emphasizing that design should not be confined solely to digital technologies.

The notion of AI needs to be viewed in its entirety, in reference to the comprehensive functioning of a facility, considering not only enhancements in operational production processes and compliance with good manufacturing practices (GMP) but also the optimal integration of a building within its environment.

AI could revolutionize the standard of operational excellence in the design of pharmaceutical production facilities, granting strategic benefits in facility layout and process flow, improving compliance with GMP and boosting innovation, aiding the production of pharmaceuticals that are safer, more sustainable, and more efficient.

The architectural design process is anyway a cyclical one, implying that it often requires revisiting various stages as new data is acquired or modifications are implemented in the design.

Therefore, the challenge lies in comprehending how architects can integrate AI into the architectural design flow.

The main scope of this essay is not giving a definitive answer about that but it’s a reflection of the status of the industry and, specifically, the process architecture discipline.

Facility Design Optimization

Process architects typically help to create a cost-effective facility that operates efficiently, minimizing activities that do not add value.

AI could support on that, to create more efficient, compliant, and economically affordable facilities, utilizing predictive and simulation modelling to pinpoint bottlenecks, enhance layouts, and model workflows, evaluating historical and real-time data process modelling and process simulation tools.

For example, models incorporating travel distances and varied transport modes can compute total travel distances along with related costs for a specific layout when the facility operates or can measure the necessary staging spaces for efficient operation avoiding disruptions or calculate the best adjacency layout regarding material, waste and personnel flows.

For example, it would be useful to develop algorithms capable of simulating the flows of personnel, materials, and waste, identifying possible risks of cross-contamination and the adjacency of non-optimal rooms.

Additionally, for energy and resource efficiency, AI tools assist in optimizing HVAC, lighting, and cleanroom systems for sustainability and cost reductions. Some scripts can enable us to calculate the lengths of the pipes and electrical cables in order to optimize the distribution paths.

Up to today, the tools utilized include Dynamo, Excel, Revit, and several specific prompts.

Dynamo is a visual programming tool that works with Revit to automate tasks in building information management software using scripts (sets of programmed instructions).

While Dynamo includes built-in samples, many engineering companies developed specific scripts tailored specifically to the company’s workflows and they could be automatically available to all Revit users.

Some scripts automate the creation of branch connections from main duct lines, additional scripts handle the automated creation of bends for ducts when clashes occur in the model, other import room data to all elements to maintain data accuracy and create and place wall openings for penetrations such as pipes, ducts, or conduits. These tasks can now be developed through AI and completed more efficiently and with greater consistency.

For process, real-time monitoring through AI maintains consistent, reliable, and efficient production, ensuring higher quality. Sources of variability might comprise manual tasks, batch errors, equipment malfunctions, and even alterations to the production timetable, reducing variability.

The demand for reduction of timelines and budgets for construction projects is growing, and AI and digital services can bring potential benefits, indeed.

In the data center business line, recently has been launched first robot specifically designed to drill vertically into concrete as part of a fleet operation. In the data center environment, for example, drilling thousands of precise holes is crucial for securing server racks and supporting overhead mechanical and electrical systems. This task is not only repetitive but also demands high precision, often causing delays and bottlenecks in the production process.

Finally, a valid tool could be the virtual reality, that can help train personnel and finalize design review. It’s an immersive, navigable environment, that can help verify flow consistency, reducing the risk of errors.

Process Architecture

As anticipated in the previous paragraph, process architects aim to develop a cost-effective facility that will be efficiently operated. The ideal is described in Lean manufacturing terms as minimizing non-value-added activities, for example reducing waste, using a model and simulations and supporting architects and engineers to refine current Good Manufacturing Practice (cGMP) manufacturing facility designs by analyzing extensive databases and developing innovative solutions.

AI could enhance process architecture by enabling smarter, more adaptive manufacturing systems and flexible layouts, for example flow process optimization and optimal adjacency layouts, building and keeping live electronic projects reference database and compliance lessons learnt, helping the designers to release decisions in a fastest way.

Lean design in pharmaceutical manufacturing is a systematic approach to building facilities and processes that minimize waste, reduce cycle times, and improve flow while ensuring compliance with regulatory standards. It involves designing facilities for supporting continuous flow, optimizing the more valuable one and applying timing principles to create a more efficient and cost-effective production system.

A key aspect is designing the facility with the correct flow from the beginning, which is far more cost-effective than trying to change it later. During the feasibility and concept phase is by far easier to implement some adjustments.

From the perspective of process architecture discipline, the two main targets currently are:

1. Eliminate waste: Identify and remove non-value-added activities, such as waste, overproduction, not counted disposable items
2. Continuous flow: Optimize processes moving smoothly through each step without delays or bottlenecks. This often involves shifting from batch manufacturing to a more continuous, single-unit flow that can improve sustainability by reducing waste, emissions, and water usage.

Through generative design, AI can create numerous design variations based on constraints such as regulatory requirements, productivity goals, and spatial restrictions. Rather than starting from sketching options, designers need to define the process and the main room adjacencies. Then, through specific algorithms, the resulted layout is not produced by humans using a sketching tool but is automatically generated by a series of instructions, variables, and parameters.

The main target should be to develop a design tool, leveraging computational design to create various facility configuration alternatives, within a certain range of complexities.

Quality and Compliance

Operational excellence in pharma is tightly linked to GMP compliance, where AI plays a critical role:

The transformative potential of AI in enhancing the design and construction of cGMP facilities is yet to be fully realized.

The pharmaceutical and biotechnology industries operate within stringent regulatory frameworks, necessitating facilities that meet rigorous standards of safety, quality, and compliance. AI-driven tools and methodologies have emerged as indispensable assets in achieving these goals, such as:

• Process Monitoring: AI systems detect anomalies and suggest corrective actions during production
• Environmental Monitoring: AI automates microbial colony counting and contamination detection
• Regulatory: AI helps maintain data integrity, collecting them and keeping benchmarks
• Smart Facilities: AI enables proactive installation management, for example predicting HVAC failures before they occur

Computational design methodologies are utilized for HVAC applications, specifically for calculating weight and cost. The dynamo script facilitates the automatic input of the system's airtightness classification, ensuring compliance with ISO EN standards, which allows for precise cost evaluation and immediate extraction of essential data (airtightness category, area, mass, thickness, expense).

Conclusion

The rapid growth of AI has led to concerns among some individuals about the possibility of job displacement.

Due to the nature of work in the industry, the variability involved means that AI cannot fully replace process architects. If certain variabilities are discovered to have a significant effect, initiatives can be justified to focus on reducing that variability. The architectural design process is inherently cyclical, often requiring us to revisit different phases as new data emerges or design changes are made

Considerations like transparency and the quality of reference data are important when discussing the use of new technologies.

In essence, AI is a transformative force that can enhance innovation, improve efficiency, and ensure regulatory compliance in the design of cGMP facilities. This, in turn, plays a crucial role in making pharmaceutical production safer, more sustainable, and more efficient.

While it's essential for users to approach these advancements with caution, the advantages often far outweigh the potential downsides.