Our Expert Weighs in on Platforms, Data and AI


If you’ve spent any time on our site, odds are you’ve heard us talk about “centralizing data” and avoiding “siloed systems.” You may also have read about the importance of getting everything on a single system, ditching manual or hybrid systems, and using data to glean actionable insights into your businesses processes. If you’re playing buzzword bingo, you probably have five in a row right now. But what do these phrases even mean? And why should you care?

Fortunately, we’ve got just the person to break it all down for you. MasterControl Product Management Director Sue Marchant gave us some insights into the platform approach, how it helps you get your data together, and why that’s important for an organization.

Q: Why is a platform approach so important?

A: Your ability to create life-changing products is dependent on data. You need data that helps you understand where you have risk, where you’re consistently running into problems and challenges, and where you have opportunities for efficiency and improvement. You also need data to understand current status and what you ought to be focused on right now, later on today and two weeks from now. A holistic view of all your data together, across your organization shows you, for example, how the quality of the materials you get from a particular supplier impacts your nonconformance rate, and how deviations in your process impact customer safety. You can’t do that if your data isn’t connected, and that requires a platform.

Now you may be thinking, “Well, I can import and export that data into Excel and eventually get to that level of insight.” But that’s inaccurate and slow, and you introduce error and risk with each transformation of your data. If you’ve ever tried to combine data from two Excel documents that are each presenting the data in slightly different formats, you’ve experienced this problem on a small level. Imagine that challenge stretched across an entire organization using multiple systems, each with their own versions of truth. You want that information to exist in one system as a single source of truth that guides your decision-making, and you want to have that information continually at your fingertips, as close to real time as possible. It’s essential for making decisions that safeguard your company and your customers.

It’s also quite a bit harder to understand current status if you’re dealing with a lot of different systems rather than a platform. If you have a deviation on your manufacturing line, it affects the safety and quality of your product. If that causes a permanent change in your work instructions, you need to make sure everyone gets trained on that change. If you’re then audited, you need to be able to show the appropriate training documentation to an auditor, preferably quickly. Every minute you make an auditor wait for a response seems like forever when you’re scrambling to find documents. If it takes you three hours to find that document, that doesn’t show an auditor that your process is under control. And the truth is, if it takes you three hours, it isn’t really under control. It’s under control when you can instantly access and understand the status of any component or data point.

Q: Which areas of a quality-focused organization benefit the most from connected data?

A: You know, there are benefits at every level of the organization. A training manager can check a dashboard and see exactly what training is coming due this week, and how a gap in completion might impact a manufacturing line. A complaints manager could instantly see that there is a sudden spike in complaints about bandages that were all produced on a particular line on a particular day and could begin taking proactive measures. A manufacturing line supervisor could be alerted when a particular step in the manufacturing process has been trending significantly longer than average ever since everyone was trained on a new work instruction.

Connected data represents a big benefit for company executives, giving them an opportunity to stay continually informed about company performance on critical KPIs, rather than waiting for managers from each section of the company to compile data they can review quarterly. Instead of making decisions months after something happened, they can be continually informed and alerted. We want to help leadership move from a reactive position to a proactive position, with the data and context to make critical decisions before the company reaches a crisis point. We think there will be exciting opportunities to use data and artificial intelligence (AI) to help management stay informed about what parts of their process represent additional risk.

Q: You mentioned AI. How does AI play into everything we’ve been talking about?

A: That’s actually the end goal. Everything we’ve mentioned so far are the baby steps leading up to AI. When we talk about AI in this context, we’re not talking about robots on the manufacturing line. We’re talking about an application using your data to tell you what improvements to make. And these are improvements that can help you in your everyday job, now. I think a lot of people don’t realize how applicable AI is to what they do every day. CAPAs (corrective and preventive action) are a good example. On a basic level, AI could be used in the CAPA and complaint realm in many different ways. It could be used to help you understand when you have an emerging issue that represents a risk to your or your customers. It could continually review incoming complaints and make a recommendation when an escalation to CAPA is warranted. It could instantly identify if all the complaints came from a batch that had the same deviation or ingredient substitute. It could help you understand how effective your CAPA was and where similar problems are still occurring on your manufacturing line. Using AI is about using the data we create and collect every day to solve real problems in every realm.

I think at an organizational level, it seems like every executive has a sense that they ought to be doing something with their data and with predictive analytics — they’re just not sure exactly how to get the data that will enable it and how to prioritize their efforts. Our processes generate so much data and there is a lot we can learn in every realm. We want to harness that data and give executives tools for making better decisions. The baseline of analytics capability should ensure that executives can forecast what will happen if the company continues on its current path. With AI, you can go a step further and have the system identify and recommend changes that could be made to improve performance. What can you improve in your current process to reduce yield loss at each phase? What ingredients from which suppliers are associated with the most deviations and should be reconsidered? When should a particular batch be inspected more closely or with a higher level of scrutiny because of the combination of factors on the line? When you can use AI to actually improve performance, safety, and quality — that’s huge.

Q: How do you see AI playing into MasterControl’s future?

A: We’re working on a project now that’ll give customers these benefits. The next generation of MasterControl products will include some of the things we’ve discussed. The initial insights will primarily be descriptive with some initial capabilities around trending, clustering and benchmarking. The insights will grow increasingly predictive and prescriptive. Our overall vision for the company is integrated AI in every workflow. This is the first step on that journey – giving customers meaningful access to connected data and the insights that result.

So, going back to your question, AI will play a huge role in our future. And not in a stereotypical Hollywood “robots go crazy and take over” kind of way. What AI really means for MasterControl is that our customers spend less time worrying about if they have all their data and what it means, and more time working on problems that require human ingenuity.

2019-bl-author-sarah-bealeSarah Beale is a content marketing specialist at MasterControl in Salt Lake City, where she writes white papers, website landing pages, and is a frequent contributor to the company’s blog, GxP Lifeline. Her areas of expertise include the nutraceuticals, cannabis, and food industries. Beale has been writing about the life sciences and health care for over five years. Prior to joining MasterControl she worked for a nutraceutical company in Salt Lake City and before that she worked for a third-party health care administrator in Chicago. She has a bachelor’s degree in English from Brigham Young University and a master’s degree in business administration from DeVry University.

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