What is undocumented, has not been done. This certainly applies to the discipline of regulatory compliance. Nevertheless, organizations still miss opportunities if they fixate on documents and hereby lose sight of the data – and maybe of quality. How do they take the step towards ‘data-based quality’?
The disciplines of quality management and documentation are intrinsically connected. A company is quickly faced with thousands of documents in which, for instance, Standard Operating Procedures (SOPs), work instructions and policies are defined and outcomes and test results are recorded. Documenting audits and ‘corrective and preventive actions’ (CAPAs) means the stream of documents only further increases.
These documents are necessary to demonstrate the quality of products, processes and services and compliance with laws and legislations. But, a lot more is possible with the data from quality systems. Just think of the advantage of being able analyse quality data, so that trends become visible and threatening issues can be quickly identified. A company can then intervene before matters are noticed by customers and the cause of the threatening problem can also be retrieved from the data. If the data is reliable enough, enterprises can even leverage this for true knowledge-based decision-making.
Companies should be prepared to implement several changes in how they execute quality. Many of these changes relate to a ‘digitalization of quality’. These include:
- Focus on data
Ample enterprises have already taken the step from paper documents to documents in an electronic format. Yet they still run into problems with this change as it is not true digitalization. Unstructured documents, for instance PDFs, are difficult to search and leveraging data from these is cumbersome. Organizations can overcome this if data is digitalized, accessible through structured documents and available via one common platform.
- Digitalize processes
Digitalization in quality also means digitalizing the workflows and processes associated with the documents and quality activities. If development, for example, changes a parameter, then this can have consequences for the entire production process and the product lifecycle. These changes should automatically be visible for everyone, so that the consequences for all involved are obvious and adjustments can be made easily and quickly. If the mutual dependencies are transparent, the cause of a problem can also be found more rapidly.
- Improve data quality
Everyone should be able to rely on the data. This data should be of a high standard, and thus complete and correct. Organizations can drive data quality by immediately entering generated data into the system, and not by first writing it down on a piece of paper. Eliminating this step allows automated flagging of deviations and collaborative sharing and reviewing of information.
- Monitor data integrity
Data integrity goes hand-in-hand with data quality. Has the used data been changed – voluntarily or by mistake? Is the data fully unaltered and can it be proven? And are all those involved certain that they are working with the correct version of a document? Data and information should be traceable and reliable during a product’s entire lifecycle. It is important to be certain that everyone at all times works with the same data set available from one single data source, a “single source of truth”. This is ensured by digitally connecting the dots.
- Invest in technology
The step towards a data-centric approach means imposing higher standards on the quality of data. This data should be of good quality, reliable, and should be in context in order to be meaningful. And should remain this way throughout a product’s entire lifecycle. This requires integration of processes and systems with can be achieved by implementing a virtual experience platform, such as Dassault Systèmes’ 3DEXPERIENCE platform. A system like this helps collect and validate data in real-time, makes it centrally available and provides capabilities beyond digital continuity – true enterprise wide digitalization.
- Create a quality culture
Digitalization of a quality system is only one part of the required change, it also requires that everyone feels responsible for quality. This is not the responsibility of just one single department, but affects the whole enterprise. Documentation is important but not enough. The understanding of the impact of any activities on quality and the relevance of data quality and integrity must be part of the mind-set of every employee.
Digital quality is in brief much more than having the documents available in an electronic format. It concerns digitalizing and connecting both the processes and the data, which should be reliable and of high quality. Ideally they are supported by advanced enterprise-wide technology and a company-wide culture of quality. With this approach, quality as well as regulatory compliance are easily met. And organizations have a good foundation on which to base sound decision-making.
For more information about Dassault Systèmes work in the Life Science industry please see here.