In my previous blog I looked at the role of data integrity. In this blog, I’d like to focus on the move to integrated quality management.
It’s a trend that we’re seeing right across the Life Sciences sector. Quality is being built into key enterprise-wide business processes that go beyond individual departments to encompass the company’s ecosystem. How can this help drive innovation?
A top line figure from a Life Sciences survey from LNS Research caught my eye. When asked about their financial objectives, Life Sciences companies placed growing revenues as far and away their major driver (over 55%) with cutting costs being mentioned by virtually no one (under 5%). I take this to mean that, while quality management is vitally important to reduce product defects and product recalls, companies want to use quality as a means to deliver improved product development and customer service.
The LNS survey supported my thinking by showing that those surveyed placed both compliant operations and customer services above improved manufacturing efficiency. Interestingly, the research also showed that Life Sciences performed better than other industry sectors when it came to successful new product introductions.
Breaking Down the Silos
For quality management to drive innovation across a Life Sciences company, it has to become integrated into the ecosystem of enterprise applications that underpin business operations for the enterprise and its supply chain. That means overcoming two information silos.
The first silo is quality management itself. Many organizations operate quality management on a departmental level: there are separate quality and compliance systems for production, finance, SHE and marketing. These need to be brought together to provide an enterprise-wide quality management system. Improving quality and compliance while reducing risk can only occur when you have visibility and control across quality information, workflows and business processes.
The second silo to break down is the connection between the quality management system and other enterprise applications such as ERP, CRM, PLM, SCM and MOM. These systems all contain valuable data that is required to make better decisions on quality. Your enterprise-wide quality management system must be able to quickly access and analyze the data wherever it resides. No only that but it must be able to incorporate structured and unstructured data to provide a ‘single version of the truth’.
Quality Beyond Manufacturing
We are all aware of the effect that social media is having on business within every sector. The unstructured conversations and information on social networks can inform product development and improve customer service if it can be properly captured and managed. In addition, enterprise applications that once only handled structured information – such as SCM and CRM – are now handling much more unstructured information.
More information held in more places and in more formats spells more risk, more compliance issues and more problems with quality. Data integrity is just one challenge. From the perspective of innovation, data capture, analysis and distribution are equally important. You need to be able to get the right information to the right people (and, increasingly, machines!) across the product lifecycle from design to production to sales and service.
The flip side is that if you can successfully manage all this information, quality really can move from a business enabler – something necessary to keep you doing what you’re doing – to a business driver – something that underpins innovation and revenue growth. I believe that creating a centralized quality platform using an Enterprise Information Management solution is the most effective way to achieve this.
We’re seeing more and more Life Sciences companies using their EIM platforms to deliver an environment where quality can be pro-actively managed. They are introducing automation and advanced analytics to exploit quality data for product and process improvement. For example, the data created within Preventative Actions to address a product failure can feed the early design of new product types.
As we finish up summer and head into fall, I will further discuss how analytics and AI will and can be leveraged to improve processes across the life sciences value chain.