Why PAT techniques are key to digital transformation
EDITOR ’ S CHOICE PROCESS ANALYTICAL TECHNOLOGY ( PAT )
OPTIMAL
Why PAT techniques are key to digital transformation
Countless businesses have set their sights on achieving a true ' digital transformation ' over the past decade , but many have struggled to turn these ambitions into reality . The exact reasons for this will vary from company to company , but one common thread is that they ’ re held back by poor planning and a lack of a solid framework . However , a key tool has emerged that can help manufacturers make these plans a reality : Process Analytical Technology ( PAT ).
As with so many business concepts , the exact definition of digital transformation is not the same for everyone . When it comes to the manufacturing sector , however , the generally agreed upon meaning is a business-wide transformation that leverages process and product data to boost business profitability , product quality , and manufacturing efficiency .
This approach puts data at the heart of any digital transformation effort . That means that if a project is to succeed , it must incorporate three key factors that link the digital world with the physical :
• Accurate , traceable , and verifiable data : Every piece of data must be correct , traceable back to its source , and confirmable for authenticity . Accurate data helps in making informed decisions , traceability allows for quick issue identification and resolution , and verifiable data ensures that the information used is trustworthy and can withstand scrutiny .
• Data with batch context : Even accurate data loses a lot of value if it ’ s presented out of context . Ideally , it should come bundled with all the associated metadata — information like time and date stamps , machine ID , batch number , etc . This ensures that any data-driven decisions made downstream are made with a full understanding of the wider picture .
When used as the backbone of digital transformation projects , PAT can help businesses achieve substantial productivity and optimisation gains .
• Total data integrity : The completeness , accuracy , and reliability of data must be maintained throughout its lifecycle . Data integrity ensures that information remains unaltered in unauthorized ways , accurate , and consistent from the moment it is recorded until it is archived . This is essential for regulatory compliance and making reliable , data-driven decisions .
How PAT can help If you haven ’ t encountered it before , PAT is a framework for designing , analysing , and controlling manufacturing processes through real-time measurements of critical quality attributes ( CQAs ) and performance parameters .
By focusing on this data , PAT ensures that product quality is consistently monitored throughout a process , rather than relying on traditional manufacturing methods that use empirical testing carried out after its completion . By collecting and analysing data as it is generated , manufacturers can make swift adjustments to maintain optimal quality , reduce waste , and enhance efficiency .
This ability to systematically collect , categorise , and learn from product quality data helps to distinguish PAT from more conventional PLC / SCADA and DCS setups .
While these systems are effective for basic control and monitoring tasks , they typically lack advanced data analysis , real-time quality management , and adaptive control capabilities . In contrast , PAT-based systems are capable of systematically collecting , categorizing , and learning from product quality data , enabling more sophisticated process control and optimisation .
It also makes it an ideal tool for businesses looking to launch digital transformation projects .
44 PECM Issue 72