Since quality management’s inception at the turn of the 20th century, life sciences business leaders have predominantly viewed the field as necessary overhead, rather than a driver of ROI across the organization.
The sheer volume of data that quality systems have access to within the manufacturing, packaging, and distribution processes can now offer leaders the capability to proactively predict failures and incidents while simultaneously driving down overall costs.
It’s a revolution over 50 years in the making finally coming to fruition – thanks in large part to machine learning technology.
Quality Data, Not Your Typical Competitive Differentiator
Most quality organizations are reactive to situations that have occurred, or are in the process of occurring, due to their underlying objectives to ensure product and safety controls.
Consider the limitations of fully automated batch records and MES systems as a competitive advantage. Both systems provide a wealth of data and are vital for a modern quality organization in life sciences. Automated batch records reduce paperwork and provide the organization the ability to effortlessly trace products in case of a recall. An MES solution allows an organization to understand the status of an individual batch within the manufacturing process. Neither provides full visibility to where the overall manufacturing process is trending and where future non-conformances are likely to occur. Both are designed to inspire action after an error has already occurred and money has already been spent.
The current trend towards global systems compounded with the desire to integrate mergers and acquisitions quickly causes more businesses to experience issues in leveraging quality systems data, preventing your organization from effectively tying all your disparate systems together and bloating IT costs further.
In 2016, the benchmarking firm APQC surveyed quality executives on their quality views. Perhaps unsurprisingly, and perhaps because of the “necessary overhead” reputation, the top challenge cited by quality organizations was how they often had to compete “for resources in the organization.”
True competitive advantage doesn’t come from analyzing past events. It comes from being able to leverage the data from these solutions and your other quality systems to predict non-conformances before they happen.
The Future of Quality Systems
A typical life sciences quality system produces a massive amount of historical data on things like testing time and rework created by defects. This data, if leveraged correctly could provide predictive results and identify potential non-conformances based on existing trends. A quality professional could analyze raw materials and/or finished lots that are trending out of specifications and investigate potential issues before they even occur. How?
Enter SAP Leonardo.
SAP Leonardo was launched in 2017 to great promise. The platform combines a variety of cutting edge digital cloud services—including machine learning, big data, and the internet of things — into one platform. According to ComputerWorldUK, Caterpillar and Trenitalia are already looking to leverage it for predictive insights.
Of course, machine learning technology isn’t an entirely new concept—major competitors include IBM’s Watson and Microsoft’s Azule—but it is the first to seamlessly connect into an established ERP system. “We are working with APIs to have real-time connectivity into the execution system and we offer that [in] over 25 industries,” SAP’s head of products and innovation Bernd Leukert told ComputerWorldUK. “I am not aware of any company on the planet that can offer that connectivity and that comprehensiveness.”
Imagine the possibilities for your quality organization. Armed with a massive amount of existing data and predictive capabilities, quality experts could speed up production times and proactively avert failure actions. Already, businesses outside of the life sciences industry are exploring the quality benefits of these new tools.
Interested in an SAP Leonardo Proof of Concept project for your Quality organization? Contact us to learn more about our quality systems practice.
Engineering.com reported that machine learning allows food companies the ability to identify and grade produce by a rate of 12 pieces of fruit per second. Recently, Koehler Paper Group, an independent European paper company, completed a predictive quality pilot program. It built a data set from a variety of inputs—ERP, MES, production line sensors—and used machine learning to predict future quality ‘leaks’ and adjust the manufacturing process to ensure consistency. “Tracking and analysis from the sensors gives us much more control over the entire process,” Jörg Behnisch, CIO, Koehler Paper Group said, “which helps us get closer to the exact quality required at the lowest cost and risk to produce.”
Though these examples don’t speak directly to the intricacies of the life sciences manufacturing and production processes, it’s clear that Leonardo has the potential to fundamentally transform the quality function from necessary overhead to an area that actively drives cost reductions and ROI.
It isn’t hard to imagine companies making real-time decisions driven primarily from the quality group. Executives that invest in technology like SAP Leonardo and apply it to support their quality systems will gain a true competitive advantage in their industry. While these technology advancements may feel far away, building proof of concept projects today which leverage SAP Leonardo can create a competitive advantage in the future.
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