“People have to change their thinking on CAPA. They can’t think that CAPAs are burdensome or take too much time. They have to see how powerful, how proactive this tool is and how it can shape the future of quality in your company.” 

Zillery Fortner, Product Advisor, QA/RA Life Sciences, Sparta Systems, a Honeywell company

Welcome to Forging Connections, a podcast from Honeywell about the convergence of IT and operational technology for industrial companies. We’ll talk about the future of productivity, sustainability, safety and cybersecurity. Let’s get connected.

Michelle Dawn Mooney (00:20):

Hello and welcome to Forging Connections, a Honeywell podcast. This is another installment of the series, Shaping the Future of Quality. I am your host, Michelle Dawn Mooney, and I am thrilled to bring on our two guests. We’re talking about flipping CAPA on its head with prevention and understanding the root cause. To bring more information to that topic, it is my pleasure to bring on Bethany Kearney. She is the Director of Enablement for Sparta Systems, a Honeywell company; and Zillery Fortner, who is product advisor, QA/RA, Life Science for Sparta Systems, a Honeywell company. Thank you both for joining me.

Zillery Fortner (00:54):

Thank you for having us.

Michelle Dawn Mooney (00:56):

So, before we dive right in, let’s get a little bio. Bethany, Zillery—can you give people a little bit of a background?

Bethany Kearney (01:03):

I’ll start with my bio. I’m a life sciences expert. I started in pharma and quality systems, in quality control specifically, and progressed through quality assurance in compliance roles. In clinical, in working with medical devices, I’ve worked with multiple quality systems across my over 15 years of experience and most recently joined Sparta in 2019. I help our customers move from TrackWise to TrackWise Digital, using SaaS solutions and offer my expertise in life sciences to really make sure that the customers get what they need. I most recently joined the services team as a Director of Enablement to make sure that they’re trained appropriately on our software as well.

Zillery Fortner (01:51):

And I am Zillery Fortner. I’m the Product Advisor for the QA/RA life sciences at Sparta Systems. I got my bachelor’s degree in Health Science from South University, and I have over 20 years of experience in the medical device arena related to quality assurance, regulatory affairs, surgical arena and JACHO. In addition, I also served 10 years in the military as a certified surgical tech. I’m actively involved in groups including RQA, RAPS, PDA and ASQ. That’s me in a nutshell.

Michelle Dawn Mooney (02:38):

Very, very lengthy resumes there. A lot of numbers Zillery you’ve got going on and I am really excited to have you both here because you obviously know a lot about the topic at hand. And before we get into that, I guess we could say this is kind of that old saying “an ounce of prevention is worth a pound of cure.” We never want problems to happen, but unfortunately they are often inevitable. And before we go into some ways to hopefully prevent them, let’s take a look at some examples and how costly they can be, not just from a monetary standpoint.

Zillery Fortner (03:10):

So, I can give an example from the medical device side on not looking at the preventive measures and not determining the true root cause of what was going on. We had some products that were constantly getting complaints. A CAPA was opened. There were nonconformances because the product wasn’t wearing well. It was a reusable medical device, like a gown you would wear in surgery to protect you during surgery. The gown would just rip. If it was repaired, patches wouldn’t stay on. They were supposed to last 100 lifecycles. So, it should have been able to be used a hundred times approximately. We were getting like 30 to 40 uses out of this when they would try to repair it, the barrier, which makes it a certain level gown would not meet those criteria anymore. Once we finally took the time to dig into truly what was going on, truly what was happening, we noticed that the complaints or the MDRs that were reported for safety with patients that they really stopped at a high level. They didn’t dig down deep to truly find out what was going on. It was human error, human error, human error. They didn’t inspect something right. They didn’t repair it right. It was never truly looked at, to see what was going on. And there wasn’t a global communication between the different facilities. There were multiple issues going on multiple times. It was closed with CAPA saying it was fixed. And when it actually came down to it, the true root cause was a chemical reaction. So, the chemical was reacting with the material, which degraded in, in the barrier and wouldn’t allow you to repair it. This could have saved millions of dollars if we would’ve just taken six months and done it right and truly, truly understood the problem.

Bethany Kearney (05:25):

I have an example like that from the pharma industry where human error was determined to be the root cause time and time again. The background is that it’s a similar kind of issue with the product itself, but the analytical staff was initially blamed for not mixing the product properly. So, there were OS results that were found and the root cause was determined to be human error, human error, human error. And it wasn’t until it went through manufacturing to the full OS investigation, and then they pushed it back to R&D and they figured out that our API in the product is inadequately distributed. So, it really mattered the way they were collecting the sample from the packaging itself. So, the affinity of the product to the packaging was the true root cause.

And there were numerous human error OS investigations prior to that. Finding the true root cause is very complex. It’s easy to blame a human. And if the firm is a hundred percent focused on metrics, then finding the true root cause is going to be very challenging because in some cases it is costly. But it’s much more costly to not know that true root cause. So, I understand that it’s a paradigm shift for organizations but putting in the time to do it right is really where the value is.

Michelle Dawn Mooney (07:04):

Yeah. Not just costly as we talked about from a monetary standpoint, obviously you mentioned millions of dollars there in that particular example, but just the time and the stress level of trying to figure that out and in hitting your head against the wall of why isn’t this working. I mean, that really takes its toll on your mental state as well. So, let’s move on to how we can reduce these issues to have better safety, better quality, and prevent these things from happening. Is that even possible?

Zillery Fortner (07:35):

It’s very possible. And it’s all about prevention. It’s all about constantly continually improving something. You know, when I look at myself every day, how can I make one improvement, one adjustment to make myself better. Right? You take that same mentality. There’s a simple thing that’s been out there for a while. It’s called plan, do, check, act (PDCA). You have to have support—or like you were saying, you would beat your head against the wall. If you have no support, no involvement from the key and the right stakeholders, you’re going to struggle. They have to promote quality, strong quality, and they have to prioritize improvement plans. And it also has to be cross-functional. Everything might sound great to me. It’s my fault. It’s my plan. Yeah, it’s perfect. But does it sound great to the other team members? To people that actually have to put this in place and not playing the blame game? When you look at CAPAs it’s very easy to point a finger. You can’t do that. You can’t blame Bethany for this problem. I, I really need to understand why it happened and fix it. And you also have to make sure that you’re ensuring what you fixed worked, and that doesn’t mean for a week. That means you kind of stand there and watch and make sure that this isn’t happening yet again, because if it’s happening again, you didn’t figure it out and you need to start over.

Bethany Kearney (09:08):

Yeah, that resonates a lot with me too. And I do want to dig in further with that effectiveness piece that you were just mentioning, because you really do need to ensure effectiveness, making sure that you not only have the right time allocated for an effectiveness check, but your sample size. And is that appropriate to identify when those failures occur? Don’t be so close minded to think that this can only happen for a single product. If it could be something that’s happening that you didn’t initially take into account in your CAPA, but it, it could be across your portfolio. So, yeah, I think one of the things that are challenging to the teams that I work with across different organizations is what to do when your CAPA is not effective. And what I would, what I guide them is don’t hesitate. Don’t delay opening a new CAPA.

You see that there’s a piece of your effectiveness check that failed. Then you don’t have your right root cause, or you didn’t appropriately respond in your actions. So, it’s okay to do it again. And I know there is a big metrics game, but if your effectiveness check didn’t go as expected then open a new CAPA, do new actions that are more meaningful as far as finding resolution to that true root cause. I do get pushback on that sometimes. So, I’m open to feedback on that. I guess the only other thing here is that culture piece that you mentioned with effectiveness checks, you make sure there happen and you have those right stakeholders involved because there is going to be bias. So, if it happens in your manufacturing site, at your lab and your team completed those CAPA actions, make sure you’re accounting for some of that bias. And everyone knows that you want things to work out great and do it right the first time. Sometimes it doesn’t happen. You have to be open to know that the effectiveness check might not pass, and you need to have that clear criteria in that effectiveness check. So, you trust, but you also need to verify.

Michelle Dawn Mooney (11:28):

Yeah, and effective being the key word, because if it’s only 50% or 75%, or even 95%, there’s still that 5% that’s going to come showing its ugly head in there and is, is going to be not what you want to have happen. So, let’s dive a little deeper into this. What specific elements are needed for an effective CAPA process?

Zillery Fortner (11:50):

On paper? This is easy. I mean, and Bethany can kind of dig into them further, but on paper there’s considered to be seven steps to a CAPA process. The first is identify—how did this happen? I’m identifying my problem. Next, I’m evaluating this. I’m basing this on data on history, on the impact of the problems, costs, quality, safety of customers. That’s huge to determine the risk. Then, number three is develop and investigate. Four, analyze the problem. Five, I create my action plan. Six, I implement it. Seven, analyzing the effectiveness. That’s our perfect seven steps to a CAPA. But you know, you have your companies, you have to believe in true quality. They have to promote this. They have to live and breathe these seven steps.

Bethany Kearney (12:52):

Yeah, it’s great that’s on paper. And we like to apply what’s on paper to our electronic systems too. And thanks for covering that, Zillery. I love that it’s the Greenfield’s perfect world version. And we do adapt our quality systems to what it could be. And of course, there’s the differences between pharma and medical device as well. So, I just wanted to kind of backtrack a little bit with the initiation part where some of the initial CAPA steps are determined, or perhaps your predecessor, like you’re out of specification investigation of a quality event or complaint that is capturing your initial information. So, you can expedite through those initial steps of your CAPA process, given that maybe some of that root cause is already determined and jump into CAPA planning and execution. In pharma, this is your typical use case for that, where your investigation step occurs on that initial event.

And what we see in the SaaS solution world is that those will be automated for your CAPA to really make those steps abbreviated so you can jump right into your action once your root cause is known. The other way that we see folks influencing the CAPA process and abbreviating it where possible, is using that Make CAPA Cool program from the MDIC group that was sponsored by the FDA and they’re creating this fast track CAPA. So that’s typically where the product is internally controlled, low risk issues that occur. So internally controlled, meaning didn’t escape to market. So, would it be a customer complaint? And they’re able to get through the process and sometimes justify not having an effectiveness check. So, there we’ve seen different adaptions on the CAPA process depending on risk. So, it’s pretty interesting and dynamic, although we have the regulatory requirements to push us to always to do the right thing and make sure that there isn’t recurrence where possible.

Michelle Dawn Mooney (15:05):

Let’s talk about the flip side for a moment, what happens when CAPA goes wrong? So, what factors contribute to basically the opposite of outcome of what we are hoping to see?

Bethany Kearney (15:19):

So, I see that where issues persist when the quality teams don’t conform or don’t confirm the root cause. So, they can’t address the root cause in the CAPA actions. They’re just not finding the true root cause. So that’s going back to our example with blaming people and this kind of mentality, or issue with the CAPA process that can cause catastrophic harm to a patient and to your product by issuing a recall and causing brand damage to your company. In the case where I talked about earlier, where human error was blamed, this was a major issue in the lab itself that caused people to go on the improvement plans. And they just wanted to leave the company because they knew they were doing it the right way. They were following the procedure and they were still being blamed for something that ended up being a product issue. And that really tarnishes their relationship and their trust with the company. And we’ve lost a lot of great talent from that.

Zillery Fortner (16:22):

And you also have smaller companies that won’t survive. Some of these major CAPAs that have been opened, not finding the true root cause, going through a recall, and suffering from brand damage. They just can’t bounce back like a larger company can. And some CAPAs are very simple and then some of them are very complex. It can be as simple as poor documentation, but then they can come to not tracking and tracing. Maybe you didn’t validate something, your effectiveness plan was poor and you continued, you used that thinking of “I already know the problem.” So, you didn’t look at it deeper. And I go back to the one with poor top management, the understanding true quality.

Michelle Dawn Mooney (17:16):

When we talk about CAPA, obviously every facet of what we’ve discussed here today, involved a lot of time. There’s thinking, there’s planning, there’s replanning, there’s trying to do problem solving repeatedly and bringing the cases up and closing them and bringing more cases up. It’s very, very time consuming. But what is the payoff for taking that time to make sure that CAPA is done right?

Zillery Fortner (17:44):

I think it’s always interesting when people understand how much a recall actually costs. And a recall is just one part of doing something wrong or, or not really truly understanding the problem. And it is a little hard to estimate, but McKinsey gave a statement last year that states a single recall process can cost up to 600 million dollars. We’re not including lawsuits, we’re not including legal. We’re just including fixing that situation. The medical device industry spends around 5 billion dollars a year on recalls. It is expensive. And this expense, we can’t ignore it because lives are literally at risk. Every minute that a defective product stays out on that market is another opportunity for somebody to be injured. I always tell people to think if that was your loved one, would you be quick to act, would you want to do this right? So, the payoff to get a CAPA right? I don’t think you can measure it. It’s huge. It might be time consuming in the beginning, but once you get on this cycle of plan, do, check, act and that continuous improvement, your CAPA should go down, right? You should just be preventing things, finding things you can always do better.

Bethany Kearney (19:25):

The numbers are startling, and I totally agree with you. Consider if that was a device that was implanted. So, I’m not sure how to put a price on sanity or a life for that matter. I guess from my experience, I wanted to just offer that there are so many hidden costs unless you’ve been through it. I was just astonished by all the intricacies and the steps of a recall. When I had to go through this process with the company I was with, where you have your receiving of the recall materials, your processing, you have another team processing refunds, you’re shipping your recalled material, you’re reconciling that material.

And then you have all these other complexities. If the material is DEA controlled well, who’s with it? And when you run out of space in your containment area you have to ship it somewhere for deconstruction. So, it’s very complex. And I can see how these figures are easily in that seven-figure range, just thinking downstream as well. If your inspection didn’t occur already and that might have attributed to why you’re having a recall, you better get ready because this is cause for shutdowns and fines. And of course, once this is on the news, you have brand impact when you your public announcement is launched. So, when the organization moves from that reactive mode to being preventive, there’s a lot of impact with your manufacturing, your lab, your overall supply chain, to make sure that you have materials and products available. So, it really does matter when it comes to that customer experience with your product.

Michelle Dawn Mooney (21:31):

It reminds me of that, I think it was a credit card commercial, that had baseball tickets or an experience and the impact was priceless. And I think about the last answers that you both gave. That’s what we’re really talking about. We’re talking about human lives, potentially. We’re talking about not even just from a business standpoint, but just a real impact from a human standpoint. And then on top of it, you’ve got a huge payout with the business side of things and recovery costs. So, any final thoughts as we wrap this up today?

Bethany Kearney (22:03):

I I’ll just start with saying that simply whatever level you find yourself in, in any position, because there are lives at stake, because you are your company’s livelihood, all of this is at stake. Just make sure you’re doing the right thing. And I think the folks in quality like myself and Zillery, we’re here for the right reasons. If you’ve been in the industry long enough, you’re there for the right reasons. You’re going to do the right thing. It’s hard at lower levels to escalate things. And if you’re being blamed for issues, you don’t want to point the finger at management. I know that’s a tough situation, but it’s worth it. It’s worth flagging that. And calling your compliance helpline or whatever you have in your back pocket to support you to do the right thing. Don’t hesitate to do that. If, if your management team is pressuring you to get through an investigation, as fast as possible to keep their numbers green, that’s not doing the right thing. You need to do it the right way. So, challenge that and then throw the flags when you need to.

Zillery Fortner (23:11):

And then I’ll just add on with something we haven’t said yet—Quality 4.0. Quality 4.0 has to be there for companies to survive in the future. They’re going to need to be able to leverage their analytics, not just quality data, but data from all systems. They’re going to need that connective capability across their organization. And that’s how they’re going to get the transparency that they need. When it comes to Quality 4.0, I think it’s important for people to also remember that it doesn’t replace your traditional methods. The whole point is to harmonize the people, the culture, the technology, and your processes to achieve what your company’s goals are. And the other thing is that people have to change their thinking on CAPA. They can’t think that CAPAs are burdensome or take too much time. They have to see how powerful, how proactive this tool is and how it can shape the future of quality in your company. You have to use your CAPA system for the gains, not just flaws. So you have to look at it for improvement reasons also, not just for the mistakes. And then you remember that a successful corrective and preventive action system is not one size fits all. So, what worked for me, isn’t going to work for Bethany or anything like that. It’s not a reactive, overburdensome process.

Michelle Dawn Mooney (24:49):

A lot of information there. And as Bethany said, once you do the right thing for the right reason, you get the right results. And pretty much every time that happens. And that’s really what the discussion is about. We’re talking about flipping CAPA on its head with prevention and understanding the root cause. And I want to thank you both for joining me today. Bethany Kearney, who is the director of enablement for Sparta Systems, a Honeywell company, And Zillery Fortner, who is product advisor, QA/RA life sciences for Sparta Systems, a Honeywell company. Thank you both for joining me today.

Zillery Fortner (25:22):

Thank you.

Bethany Kearney (22:24):

Thank you.

Michelle Dawn Mooney (25:24):

And thank you so much for listening to Forging Connections, Honeywell podcast, and this is another installment in the series, Shaping the Future of Quality. And of course, if you’d like to learn more about Honeywell and its affiliated businesses and companies, you can go to honeywell.com. I’m your host, Michelle Dawn Mooney. Thanks so much for joining me and we’ll see you soon.

Closing (25:46):

This has been Forging Connections, a podcast from Honeywell. You can follow Honeywell Forge on LinkedIn and download new episodes from our website honeywellforge.ai. Thanks for listening.

Resources About Root Cause Analysis:

• Blog: 5 Steps to Performing an Effective Root Cause Analysis
• On-Demand Webinar: Root Cause Analysis – Getting to the Root of the Issue and Getting it Right
• Blog: Uncovering the Root Cause and Overcoming Challenges Along the Way
• Blog: Root Cause Essentials: Unleash the Power of Your Mind with Mind Mapping
• Blog: Root Cause Essentials: Using the PDCA Approach to Support Continuous Improvement

The post Flipping CAPA on its Head with Prevention and Understanding the True Root Cause appeared first on Honeywell.

Introduction (00:02):

Welcome to Forging Connections, a podcast from Honeywell about the convergence of IT and operational technology for industrial companies. We’ll talk about the future of productivity, sustainability, safety and cybersecurity. Let’s get connected.

Michelle Dawn Mooney (00:20):

Hi and welcome to the Honeywell Forging Connections podcast brought to you by Sparta Systems, a Honeywell company. I’m your host, Michelle Dawn Mooney, and we are happy to have you join us for another installment of a very special series entitled: Shaping the Future of Quality, where we follow the path of thought leadership and how it can lead to exclusive ideas, strategies and, most importantly, execution. So, we are focusing on real-world stories, innovations and trends that impact life sciences and spark transformation that allow us to develop a forward-looking, quality culture together. So, let’s get connected. I am very happy to bring on our guest today. Kathleen Brunner is the president and CEO at Acumen Analytics. Kathleen, thanks so much for joining us.

Kathleen Brunner (01:05):

Thanks so much for having me. I’m really excited today, Michelle.

Mooney (01:08):

We are very excited about this conversation. So, let’s start off with just having you tell us a little bit about yourself and your history and working with Acumen, as well as a couple other things on your resume that you’d like to share.

Brunner (01:22):

Sure. So, I’m very fortunate to be with Acumen as president and founder and CEO. We’ve been in business now going on 18 years. I’ve really always loved numbers and, and technology, and I’m really an idea person. So, when someone says that it can’t be done or there’s a challenge or a problem we can’t solve— that’s the first thing that I really want to take on and find out “what is the problem”? And then what is it that we do know, what are we lacking in information? What don’t we know? And then where do we go to find out how to solve the problem? I’m really more focused on problem solving, not just problem identification. The key there is making sure that we figure out how to get the right information to the right people to make the right decisions when they need to make the decision, not post decision timeframe. And that’s where data comes in to play a key role.

Mooney (02:28):

And what a great spin to want to solve problems, because we’re always going to have those problems, but without the problem solvers, we’re not getting very far. So, let’s jump right into it. We are talking about data, and we are inundated with information—where do we get that information from data? So, let’s give a little background if you can, with just how much data is out there, how much data  companies are potentially dealing with on a regular basis, and then how are they able to navigate through it all?

Brunner (02:56):

It’s the cliche phrase now, right? Data is everywhere. Everything that we have, things that we wear, things that we buy, things that we drive, products that help improve our lives—including those products that come out of the life sciences space—all create some form of data. And right now, companies are inundated with data, and the key is smart data. So not just data and the volume of data that we have, but having data that’s effective, efficient, formatted in a way that it can be acted upon and used for decision making and leveraged in order to improve outcomes. And, in our particular case, our company, Acumen, is focused on patient outcomes. It’s about everything that we do. And so I think that’s the key for companies to begin to understand: where is it coming from and how do they harness it for improvement.

Mooney (03:55):

With so much data out there, as we talked about, how can businesses collect the right data and do it efficiently?

Brunner (04:03):

It’s a great question. And there’s no one answer. There’s not a one size fits all solution. Every business, every customer, every patient has a somewhat unique—and I’ll use the term personalized—need for a solution that fits them. It’s the whole concept behind personalized and precision medicine. But the key here is that when I talk about smart data, I’m talking about the ability to use advanced analytics and the power of data and technology. And it’s no longer a nice to have; it’s really a differentiator, and it’s a transformational capability that organizations can leverage. Whether for reducing cost, improving outcomes, accelerating their ability to make decisions, and getting patient products to market faster, safer. Also, quite frankly, even addressing things like we all know about now, post pandemic, supply chain issues, everything that we can do with data to improve outcomes is really the key here.

Mooney (05:11):

And let’s talk about the flip side. So what happens when data is poorly collected, poorly utilized. If you’ve got bad data, you’re making bad decisions and there’s cost involved… what are companies looking at there?

Brunner (05:25):

So, the key things with regard to all of that are data quality, data integrity, and cost. So, data quality just means that the way that your data is being acquired is in a manner that’s usable. Data integrity is more about whether the data that you’re collecting is clean. Is it usable? Is it in the format that you can leverage in order to make better decisions? And then when we talk about the cost, cost in these different areas can be the hard dollar cost to get reported on financial statements where you understand the cost of rework and the cost of acquiring systems and applications, and there’s things like technical debt, which is where you may have legacy applications that are not optimized. And you’ve got change that you need to make in your technology, but it’s not yet in need, but where I think it really hides and the opportunity that businesses can really let’s say, get…where do we get started? So, taking a look at those areas where it’s hidden—like I said, siloed systems, data repositories that don’t communicate manual processes—taking a look at a way to optimize, not all at once, but start small and take a look at where you can begin. This is the key to the entire topic of digital transformation and all those 4.0s—life sciences 4.0, manufacturing 4.0, factory 4.0—it’s all about transformation. It’s about collaboration, and it’s about accelerating better patient outcomes. And it’s all through innovation. And that’s really the lifeblood of what it is that we do here at Acumen for our customers.

Mooney (07:21):

The good news is we’re seeing a lot of businesses going through growth spurts, which is wonderful. So, they’re having mergers and acquisitions, but let’s hit on something that’s really important to keep in mind when you have a lot of systems that may be coming into play: How important is it for those systems to then be harmonized?

Brunner (07:38):

Right? You know, you cannot talk about that enough, because, as I said, siloed systems can be a real money pit where you just have data that’s unavailable, or that you need to recreate data, or you have to massage data to transform it into a usable format. Like I said, for reporting advanced analytics, all those key decision-making capabilities. And I think the key to success is understanding where and which technology is relevant and then make your decision on investing. And for me, ultimately that lens is what it means to the patient. Again, our efforts here at Acumen are always focused on improving patient outcomes. But looking at where to improve with regard to wasted efforts and resources, impeding organizational success and removing those barriers, and ultimately impacting the patient outcomes.

Mooney (08:35):

So, Kathleen, we’re talking about changes; we’re talking about how to find solutions. But when people are making changes, there is usually risk involved. So, how much risk is associated with making changes necessary to better optimize data outcomes?

Brunner (08:51):

Yes. So again, a key component in decision making about which systems and processes you’re going to target first. Second, risk management is critical in both cost containment, as well as good planning and about being future ready. So, it’s about how we can transform and make technology not the driver, but rather the enabler. So, making it part of the portfolios of solutions that you leverage as a tool—just like in manufacturing, if you were to use a machine of some sort—technology is a tool. Mitigating risk around the transformation of technology really has to be a planned process. And I think a key is a blended solution. Again, I said a little earlier, not a one size fits all solution. And what that means is meeting customers where they are. We’re going to continue to see these blends of on-premises solutions, cloud solutions, and modern mixes. And we need to be able to meet the needs by providing partners we partner with and collaborating with customers so that we tailor the solutions to meet their needs at the time that they need them. Thus, mitigating risks, having implemented strategies, and plans, and processes in place so that it’s a navigation across a journey rather than a point in time solution. And that’s where it becomes future ready, future proof and really crafting a resilient solution.

Mooney (10:31):

So, speaking of journeys, we’re talking about companies trying to maybe overhaul, maybe they’re minor changes, big changes, but what is the probability that even small moves, when you’re starting out, how much can those baby steps make a difference?

Brunner (10:48):

I would say that that is probably one of the key points that I would try and drive home with everyone. It is most impactful to take small, well-thought-out steps to create impactful wins. Taking a look across the organization, to go back to your point earlier, harmonization—are we harmonized? Can we collaborate across business units? Can our data and people communicate effectively, where are the roadblocks? If I save 15 minutes, across 30 people, across 30 days, out of every month, times 12 months… that small 15-minute improvement in the process—by leveraging technology or taking a data solution and perhaps looking into predicting things like a maintenance issue and reducing those things, or just eliminating them—15 minutes adds up. Whereas when you try and bite off something massive, it almost becomes paralysis by analysis, and you just never get started. I think the key is taking the first step, because it’s a journey, so it’s going to evolve, and you’re going to shift. A year and a half ago, who would’ve thought that we would be thinking about the things that we’re thinking about today with this speed at which we’re leveraging technology embedded in things like wearables and robotics. So, it’s about getting started.

Mooney (12:21):

It’s a process, definitely a process. Any final thoughts as we’re wrapping up here, Kathleen?

Brunner (12:27):

I think the key for me is that listening carefully to the customer—and that doesn’t necessarily mean the customer who is the entity or the organization but the user—the person who’s trying… what is it, the big pie in the sky? “I wish” … forget all things that would contain that “I wish”. What do I wish that I could do that would make my job easier, or faster, or better, or, again, with quality, do it right the first time and hearing the answer to those questions. Sometimes it’s not a big solution. It’s more about, oh, let’s just pull together again. Like I said, collaboration, and then what is the problem or challenge we’re trying to solve? Who can we pull together? What is it that seems insurmountable? And again, just kind of brainstorm, and think outside the box and almost always there’s a path, there’s a solution, and more and more today it’s leveraging data to come up with that solution.

Mooney (13:34):

Love hearing that word again: solutions. Solutions are great. Kathleen Brunner, president and CEO at Acumen Analytics. Kathleen, thank you so much for joining me.

Brunner (13:43):

Great. Thank you so much for having me. I really enjoyed the show today, Michelle.

Mooney (13:47):

Great conversation. And once again, not only putting out information to be daunting and overwhelming to companies but putting out information so that we can identify those problems and, more importantly, get solutions, which everybody wants to hear those solutions. I want to thank all of you for joining us today for the Honeywell Forging Connections podcast. This has been a special podcast series by Sparta Systems, a Honeywell company entitled, Shaping the Future of Quality, a great series. If you’d like to find out more podcasts, you can of course, subscribe to the Honeywell Forging Connections podcast. And if you’d like to find out any information about Honeywell, you can go to honeywell.com. I’m your host, Michelle Dawn Moony. Thanks so much for joining us today. We will see you soon.

Conclusion (14:33):

This has been Forging Connections, a podcast from Honeywell. You can follow Honeywell Forge on LinkedIn and download new episodes from our website: Honeywellforge.ai. Thanks for listening.

The post Kathleen Brunner: What Is the Cost of Not Being Digitally Smart? appeared first on Honeywell.

What success looks like is when everyone in an organization understands the importance of truth in data, through all levels of the organization, from the senior executives to the newest employees.

Kip Wolf, Head of Technical Operations & Portfolio Management, X-Vax Technology, Inc

View all Forging Connections episodes

Honeywell Introduction (00:02):  

Welcome to Forging Connections, a podcast from Honeywell about the convergence of IT and operational technology for industrial companies. We’ll talk about the future of productivity, sustainability, safety and cyber security. Let’s get connected.  

Michelle Dawn Mooney (00:19):  

Hi, and welcome to Forging Connections, a Honeywell podcast. Thank you for joining us for the second installment of “Shaping the Future of Quality.” I’m your host Michelle Dawn Mooney and today we are talking about data integrity, why it’s so important and everything you need to know about it. I have a great guest who is going to break it all down for us today. We are joined by Kip Wolf, Head of Technical Operations and Portfolio Management at X-Vax Technology. Kip, thank you for joining me today.  

Wolf (00:48):  

It’s my pleasure.  

Mooney (00:50):  

So, when it comes to data integrity, there’s a lot to talk about. We only have a short time, and I’m sure you could probably take a couple of hours or days to go over the information there, but let’s just start with the basics. What is data integrity and why is it so important?  

Wolf (01:06):  

The U.S. Food and Drug Administration defines data integrity in guidance published in December 2018 as the “completeness and consistency and accuracy of data.” I prefer to simply think of data integrity as truth in data throughout the entire data lifecycle. The data lifecycle is very important to consider. The Medicines and Healthcare products Regulatory Agency in the UK includes the lifecycle in their definition, talking about maintaining data throughout the entire lifecycle. That is the key point I want to make in today’s messaging.  

Mooney (01:42):  

Kip, let’s talk about how we can ensure or how does the company ensure data integrity?  

Wolf (01:49):  

It must be an integral component of the organizational culture. What success looks like is when everyone in an organization understands the importance of truth in data, through all levels of the organization, from the senior executives to the newest employees. And this is, again, instantiated in the U.S. Food and Drug Administration’s guidance where they mentioned for the first time in writing quality culture. This was the first time that FDA mentioned quality culture and they mentioned that it is the executive’s responsibility to create a quality culture where employees understand that data integrity is an organizational core value. I was so happy when that guidance came out because that’s something that we’ve been saying in my field for a long time, that it’s really about the personal commitment to data integrity. It is important to see it as an organizational core value and reinforce it all along the lifecycle of the data and across the entire organization.  

Mooney (02:52):  

With the importance of data integrity, let’s talk about ALCOA—what does ALCOA stand for?  

Wolf (03:00):  

It is great that we have this acronym to remind ourselves of what data integrity looks like. Data integrity is not simply limited to the items of the acronym. As I mentioned, it’s important to understand it as a core value across your organization.  

ALCOA stands for:  

A – Attributable  

L – Legible  

C – Contemporaneous  

O – Original  

A – Accurate  

Mooney (03:26):  

So, we want to talk about regulations because when we think of data and there’s so much to it, obviously major regulations there. Let’s talk about what the regulations are when it comes to data integrity and then let’s look at how that may vary between the United States and the EU.  

Wolf (03:44):  

Michelle, that is a good question because a lot of folks misunderstand that data integrity maybe is a new concept or there are new rules about data integrity. Which couldn’t be further from the truth. What we like to refer to as the predicate roles in life sciences in the U.S., that’s 21 CFR Parts 210 and 211. There are elements of those regulations that have been around for decades that speak to data integrity. I’m coming back again to the FDA guidance in that that guidance does a very good job of citing those predicate rules and previous regulations that relate to data integrity. The rules have been around for quite some time, but the way we think about them is what has changed.  

21 CFR Part 11, which is the electronic records and electronic signatures rule that the FDA produced became effective in August of 1997. It’s been around for a long time and is something that people regularly cite as relevant to data integrity and they’re not wrong. But data integrity is not simply limited to U.S. market Part 11 or just the predicate rules. It’s broader than that. And of course, I mentioned the MHRA guidance in the UK. There are other guidances in other regions imposed by health authorities, but the regulations have been around for some time. These guidances are very useful because they help interpret the regulations that may have been around for some time in current thinking in a distributed networking environment where we now have cloud computing, distributed networks and things like that. The guidance is very important to help interpret the rules and regulations that have been around for some time.  

Mooney (05:26):  

Kip, when it comes to life sciences organizations, how can they take a risk-based approach to data integrity?  

Wolf (05:33):  

Risk-based is a term that’s very often used in the life sciences industry and folks that are probably watching this podcast understand how to assign risk and do risk assessments. The important part with data integrity is to consider, again, data integrity as a core value across the organization and specifically across the entire quality management system. Some companies will simply put in place a singular policy for data integrity and consider themselves safe from a regulatory perspective and a practical, operational perspective thinking, okay, now we’ve spoken to data integrity that’s all we need to do. That’s certainly a good first step, but what we find is what’s better is to really interrogate the entire quality management system and almost take kind of a maturity model approach—assessing all elements of the quality management system, all elements of your operations and consider data integrity in each of those elements across each of the functional areas and within each process because you may find pockets in your organization where data integrity is very robust, yet other places where it could use a lot of help.  

Wolf (06:43):  

Considering a cross-functional, cross-divisional, and cross-organization approach is very important. Data integrity from a risk-based approach is not simply an IT problem. Think of the traditional business process management as a threefold “people, process, technology” approach. What we find is that technology has advanced most of the IT support that you get or what you’ve implemented in your company is probably very sufficient in terms of security and data protection. This is something the IT folks know very well how to manage. The processes may even be rather robust where the individual processes are well defined and regulated. The people become the biggest risk, and that comes from things like workforce changes, mergers and acquisitions, and business expansion, where data integrity and maturity kind of ebbs and flows as there are changes within the organization. It is terribly important to take a risk-based approach, not just once and not just periodically arbitrarily, but continually along the evolution of your organization.  

Mooney (07:51):  

Speaking of evolution, it is ever-changing, with more people, more information, and more data—so, with that, can you recommend any resources for data integrity guidance?  

Wolf (08:01):  

Absolutely, Michelle. I’ve mentioned the FDA and MHRA guidance, there are other guidances out there and you can easily find them in a search engine. The FDA guidance, as I mentioned, is very useful to read because if you’re not familiar with regulations and guidance, it’s an easy read. It’s structured in a question-and-answer kind of FAQ format. Even if you are familiar with regulations and guidance, it’s a much easier read than previous regulations. The MHRA guidance is very robust. It’s much larger than the FDA guidance on the topic, but it too is a very good read. A number of sections have discreet definitions and explanations of terms and practices for data integrity. There are other guidances out there. PIC/S guidance and a forthcoming industry, an agnostic technical report that’ll be coming out of the American Society for Quality that’ll include guidelines for collecting and recording and retaining data within a QMS.   

Mooney (09:00):  

Great information that you presented Kip.  

Let’s round things out with your final thoughts, because we went over a lot of information, a lot of important information that companies will want to find out more about. Any final thoughts on this?  

Wolf (09:15):  

Yeah, just most importantly, the data informs our lives, right? It informs our simplest and most complex life-altering decisions. Therefore, we need truth in data. Whether some of these topics resonated with you because you’re responsible for them in your company or whether you don’t have organizational responsibility, but you want to internalize some of these concepts personally. A lot of the references that I mentioned are complex and might be difficult to find. In the “Additional Resources” list below are references where you can find more information and read at your leisure.  

Mooney (09:50):  

Wonderful! If you would like more information, of course, you can go to the Honeywell website and find more information under the Forging Connections podcast link. I want to thank you Kip for joining me. Kip Wolf, Head of Technical Operations and Portfolio Management at X-Vax Technology, Inc. Thank you for being with me today.  

Wolf (10:08):  

It’s my pleasure.  

Mooney (10:10):  

And thank you all for joining us for Forging Connections, a Honeywell podcast, and this is the second installment of “Shaping the Future of Quality”. Once again, be sure to follow for more of that podcast information, and as Kip was saying some great links there with even more information on what we discussed today. I’m your host, Mooney. Thanks for joining us. We’ll see you soon.  

Honeywell Exit (10:31):  

This has been Forging Connections, a podcast from Honeywell. You can follow Honeywell Forge on LinkedIn and download new episodes from our website@honeywellforge.ai. Thanks for listening.  

Additional Resources  

Additional references are described in detail in an article by the interviewee at: https://www.pharmaceuticalonline.com/doc/the-data-integrity-body-of-knowledge-expands-with-new-pending-guidances-0001.  

These include:  

1. 21 CFR PART 11—ELECTRONIC RECORDS; ELECTRONIC SIGNATURES. U.S. Food and Drug Administration (FDA), March 20, 1997.  
2. Data Integrity and Compliance With Drug CGMP Questions and Answers Guidance for Industry. U.S. Food and Drug Administration (FDA), December 13, 2018.  
3. Guidance for Industry Part 11, Electronic Records; Electronic Signatures — Scope and Application. U.S. Food and Drug Administration (FDA), August 2003.  
4. EudraLex – Volume 4 Good Manufacturing Practice (GMP) – Annex 11: Computerised Systems. European Commission, January 2011.  
5. Guidance on Good Manufacturing Practice and Distribution Practice: Questions Answers – Data Integrity Section. European Medicines Agency, August 2016. https://www.ema.europa.eu/en/human-regulatory/research-development/compliance/good-manufacturing-practice/guidance-good-manufacturing-practice-good-distribution-practice-questions-answers#data-integrity-(new-august-2016)-section.  
6. GXP’ Data Integrity Guidance and Definitions. Medicines and Healthcare Products Regulatory Agency (MHRA), March 2018.  
7. WHO Expert Committee on Specifications for Pharmaceutical Preparations, Fiftieth Report. World Health Organization, 2016.  

The post Expansion Of The Data Integrity Body Of Knowledge appeared first on Honeywell.

It’s not just making sure that you’ve got the right pill of the right color and the right bottle, it’s safety and efficacy and reliability of supply.

Dr. Oxana K. Pickeral, Sparta Systems President and CEO

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Intro (00:02)

Welcome to Forging Connections, a podcast from Honeywell about the convergence of IT and operational technology for industrial companies. We’ll talk about the future of productivity, sustainability, safety, and cyber security. Let’s get connected.

Steve McCarthy, Sparta Systems VP of Digital Innovation (00:18)

Okay, good morning, good afternoon, and good evening listeners. Welcome to the first episode in our Shaping the Future of Quality Podcast, part of the Honeywell Forging Connections series. Thank you for joining us today. It’s my pleasure to introduce to you Dr. Oxana Pickeral, CEO of Sparta Systems and GM of Honeywell Connected Life Sciences. Welcome, Oxana.

Dr. Oxana K. Pickeral, Sparta Systems President and CEO (00:43)

Thank you, Steve. I’m delighted to be here and excited about the Shaping the Future of Quality Series and of course our conversation.

McCarthy (00:49)

Awesome. I would like to start by asking you to share a little bit about yourself, your background and your career before taking the helm at Sparta Systems.

Pickeral (01:11)

Sure. Happy to. I would say from the start I’ve always loved numbers. So initially, I was focused on math and physics and then I got really excited about the mathematical part of biology, which at the time was known as genetics. Times have changed since then and biological science has become more computational. I am excited to have been part of the human genome project at the National Institutes of Health (NIH). My Ph.D. thesis was on bioinformatics of human retrotransposons, what a mouthful. We were able to answer some interesting questions about biology through analysis of DNA sequences. Now, we are focusing on the biomedical research of the biopharmaceutical industry, figuring out how to apply modern technologies to improve the development of medical products, be that traditional small molecule, biologics, medical devices, and increasingly going digital with the digital therapeutics. I also spent some time in pharma at Human Genome Sciences. Before coming to Sparta, I spent five years leading the global healthcare life sciences partner ecosystem business, at Amazon Web Services (AWS). This was a fantastic opportunity to learn about innovation and collaborate with technology companies that are using the cloud. Sparta was one of the partners that I worked with and when the opportunity presented itself, it immediately piqued my interest.

McCarthy (03:06)

That is fantastic, especially since my background is in healthcare and biology. It’s clear that you have a lifelong passion for healthcare, the patient and life sciences, but you also have an equal passion for technology and innovation. You mentioned a little bit about what brought you to Sparta but what were you most excited about when the opportunity arose?

Pickeral (03:42)

The combination of Sparta with Honeywell’s capabilities is extremely intriguing. There are not too many companies that can do what we can do especially now with Honeywell connected life sciences. We have a solid software with a modern software offering, and the option to work on-prem. With our modern SaaS software business, we can combine world-leading industrial automation and industrial controls.

Plus, the additional innovation that’s happening under a Honeywell umbrella. I honestly didn’t know much about when Honeywell was acquiring Sparta, because there’s a lot more that’s going on in advanced materials, like the Aclar bottles that became a very important component of the pandemic response. We are in the midst of the software, hardware and industrial world connection and it is an exciting idea of optionality to be a partner with Honeywell. I’m a builder at heart. So, I am very excited to be building more and delivering solutions that are truly unique in the industry.

McCarthy (05:06)

I agree. I would like to come back to the topic of leveraging Honeywell’s innovation, but let’s shift gears maybe a little bit, and talk about quality in life sciences. I think one of the most challenging aspects of that subject is the topic of value and what is the true value of quality and how do you articulate the importance of quality in life sciences? How can a business put a value proposition on quality? That’s a tough thing to do.

Pickeral (05:49)

I think first and foremost, if you don’t wake up every morning caring about the patient, then you’re in the wrong business. For me, ultimately the lens on quality is what it means to a patient. Many industries care about product quality, but I think very few feel it can have immediate implications on a clinical outcome or on somebody getting or not getting a lifesaving drug. It’s an intrinsic component for the patient to trust the medical product. When we talk about putting the dollar value on quality it is a multiple dimension in my mind. First, anybody who has ever failed an FDA audit will have an immediate perspective on the cost of poor quality. You don’t want to learn that way by digging out of a hole. But also, we’re living in a world that increasingly has more complex, specialized, expensive products hitting the market. A lost batch of an expensive biologic has an immediate financial impact on the ability to deliver in a timely manner, which has an impact on the patient, the physician and the pharma company, especially in a competitive therapeutic category. That means a loss of share.

McCarthy (07:34)

I think we pride ourselves on having the patient at the center of things we do and I don’t think it’s a stretch for Sparta because as you said if you think patient centrically, you think about product quality, safety and efficacy of those medicinal products. The quality system has such a critical role in making sure that the patient has the right product on the right shelf at the right time and that is really what it’s all about.

Let’s talk about this topic of digital transformation. The reality is Industry 4.0 is huge and digital transformation is real and having a significant impact on all industries, but certainly on life sciences. It’s also very clear that there’s an increasing number of ways in which industry can meet these complex needs of quality and supply chain. You mentioned something important that this is this idea of optionality. It can be daunting with the complexity of choice that industries have between broad platforms, solution stacks and different technological ways of approaching digital transformation. I think certainly in our experience, we’ve seen that maybe we could talk a little bit about that complexity and the importance of this idea of optionality.

Pickeral (09:03)

Steve, that’s a good observation. I’m going to expose some of the Amazonian bias that, has rubbed off on me. Customers like a choice. I firmly believe that. In an industry like this, that is both innovative, but also in some ways, very conservative. This is a very controlled environment and to meet all the regulatory standards for an industry continuity in how you build and develop and go to market with a product and an industry like this, by definition, has to be somewhat conservative while it continues to innovate. I think it would be naive of us to think that the digital transformation switch is going to happen overnight. I’m a firm believer that we need to meet customers where they are, and that means we’re going to continue to see a blend of on-premises solutions, cloud solutions, variety of modern mixes. We need to meet that need with the products and services we deliver.

McCarthy (10:16)

Absolutely. There’s been an enormous investment over many years into Industry 4.0 and if you think about the complexity of these businesses and the idea of forcing change, forcing digital transformation it is not appealing. We must make sure that we help our customers along with that journey towards transformation.

Pickeral (10:38)

I’ll add that it’s not just the new possibilities that come with digital we also need to understand the risks that happen along the way and help bridge those risks. For example, you know, the more connected equipment that we have on the manufacturing floor the increased opportunities for cyber-OT events. We need to think very differently now about both discoverability of OT assets and about security concerns and how to manage these concerns proactively. This potential risk may not have existed so prominently in the past, but now we need to be able to manage it well.

McCarthy (11:25)

I often talk about this bimodal challenge, right? We can’t, as an industry, take our eye off the core, fundamental, basic capabilities of quality and compliance, but at the same time, we must balance those with the reality of not only the digital transformation but the transformation that’s happening in the medical product such as biologics and combinations and gene cell therapy. It is a tough balancing act for everybody, right?

Pickeral (11:51)

Yes, it’s tough, but it’s doable and that is the exciting part, doing it in a smart way, but also in a way that’s measured and can be done safely.

McCarthy (12:01)

When we think about digital transformation, I think of data process technology. Data first and foremost, and there’s a greater value placed on making data-driven decisions. One could say, this idea of advanced analytics and the power of data and technology is certainly no longer the rate-limiting factor, as you just said, it’s doable. Right? So, the technology of things like artificial intelligence, machine learning, IIoT and other aspects sort of outpaces its application in a regulatory environment. That technology can do far more than we are able to apply it to in life sciences. Let’s talk about how we position disruptive technologies like AI and the importance of those technologies in analyzing huge volumes and complexities of data. Actually, I want to pick up on a word you mentioned earlier which is trust. I think one of the hurdles, maybe that’s too strong a word, but one of the challenges the industry faces is how we trust things like artificial intelligence and other technologies that are certainly disruptive, particularly in such a highly regulated space.

Pickeral (13:25)

The way I’m thinking about it is it definitely has to be led by the business need. Technology is here to support. I think all of us have had any number of conversations over the recent years of whatever the latest technology is, how do we apply it? That’s really not the approach that we’re taking here because I can recall any number of conversations saying, “hey, I want some blockchain.” And then “what do you want blockchain for”? And there is no answer. The same with AI/ML, I want some, AI/ML, —but what do you want do with it? That’s clearly not the approach that we’re taking here. I would much rather be talking about, real time batch release and batch rescue for high value medicine. Aggregate reporting is one of the areas that we’re also looking at getting a lot more proactive, predictive, developing those capabilities and risk management solutions and tools before the problem actually happens and allowing for technology to give humans the right level of assist in making the right decision.

Pickeral (14:38)

Do I think over time, the technology can take on a larger role? Absolutely. Because we’re going to learn, we’re going to figure out what technology can and cannot do, but in the early days, it’s that human assist positioning of AI/ML tools and decision support tools that is going to, again, walk us down that path of becoming empowering humans to make the right decisions in the most, meaningful way.

McCarthy (15:05)

So, we are augmenting human decision making, not replacing it and solving real business problems, improving efficiency of process and effectiveness of process. The technology is just a means to an end. And, as you said, it could be seen as this, you know, sparkly jewelry that everybody wants when in reality, what they want is a better business outcome.

Pickeral (15:30)

Yeah. And realistically, do we all dream of a time when we have a self-healing system that saves every batch—absolutely. You know, signal on the manufacturing floor gets fed immediately into a quality management system and gets automatically classified or auto-categorized. That signal then goes back to equipment that self heals. I would love to see that. Absolutely. Are we there today? No, but there is definitely a path that we’re working on that will start getting us closer to that.

McCarthy (15:58)

Yeah, absolutely and it may be obvious, but I think this is really important as well, right. If we recognize the critical importance of data then this topic of data readiness is really critical and is still often overlooked. In my experience industry must make sure that their data is complete, concise, accurate and accessible. So let, let’s talk about that a little bit.

Pickeral (16:32)

Across data sets, right. You, you don’t want to compare apples and oranges and different measurement units across different systems.

McCarthy (16:40)

So, let’s talk a little bit about the challenge of data readiness and where within this broader architecture of enterprise systems is this critical data that we long to get access to?

Pickeral (16:55)

Yeah, absolutely. And I think that is a challenge that we see many of our customers struggling with even the more sophisticated organizations that already have complex data lakes built in their own environments for R&D, clinical, manufacturing, or post market, there is that next level problem that appears of different systems of record that are not necessarily talking easily to each other. If you look at a quality event, for instance, a lot of the information that will categorize and help catch quality events in a proactive, predictive way can live in LIMS, can live in batch, in historian, in other systems in MES. The ability to pull reliably and well understood data sets across those systems and then deliver tools and analytics and decision support mechanisms that draw from this in an easier way is a very significant and untapped opportunity.

McCarthy (18:05)

100%. And that architect sector becomes increasingly complex as the way in which we manufacture becomes more complex and how much outsourcing is going on. Often that architecture is not yours. Now it belongs to a third party, a supplier, a contract manufacturer, a CRO. So those levels of external complexity add another whole flavor to the challenge as well.

Pickeral (18:34)

And that question of who the data belongs to is a whole separate line of debate. Any number of opinions out there will say, ultimately, if we’re talking about the clinical trial, does the data belong to the patient? It belongs to the company, does it belong to the clinical institution that participated in this, clinical phase of research? If there are analytics companies stepping in and owning data sets and figuring out how to monetize those data sets, that becomes the next challenge. And at least in the architecture, in the approach that we’re taking customer owns the data, we don’t try to monetize and sell it back to them. We are looking to provide enabling solutions that will help the customer make decisions. We have transient access to data to do something useful, but we definitely don’t need to own, we don’t need to hold any limiting license terms for the customer to get access to their own data, to put it in their data lake. I much prefer taking this customer-friendly approach and let them own the data and, ultimately own what they do with it.

McCarthy (19:55)

 So, data democratization versus data acquisition. We don’t want to copy it. We don’t want to move it. We don’t want to take it from a data lake and put it somewhere else. We just want to be able to democratize to free up that data so that we can provide the analytical power that’s so important. This is a great segue to the Honeywell connected life sciences vision. Let’s talk about that a little bit, but also let’s talk about some of the key principles and elements of the vision. Let’s start with why—why is our vision, what it is, what are some of the core challenges and value gap that, that the Honeywell connected life sciences vision addresses?

Pickeral (20:37)

 I would say it’s pretty simple. Really one of the core principles is the principle of optionality and meeting customers where they are, and that integrates several levels of optionality. So, it’s an optionality of what systems of record are used to feed the analytics and the decision support tools that we are going to deliver. So we are never going to come in and rip out the system of record because we have a different one at Honeywell. What we are going to provide is an ability to tap into the portfolio and the systems of record that a customer has in place in order to drive and feed the analytics and inform the analytics to help make better human decisions. The other key principle here is the customer owns the data. So, we only tap in for the minimal level of access that is required to provide the analytic, feed it back, and then the customer owns and controls it. Ultimately, do we think that we all need to be here and be a true partner to help customers get ready for that digital journey? Absolutely. But until we get there that optionality between on-prem and modern cloud solutions is another key component of how and what we are delivering.

McCarthy (22:05)

And digital is more than cloud, right? Digital absolutely is mobility. And as we’ve said, enabling IoT smart sensors, the connected factory, the connected laboratory, things like AI, even things like how we connect the worker. The worker is, is, is no less important, right. We have to still connect the human to the digital increasingly digital factory. So, augmented reality and human machine interface, all of those things can sound like science fiction, but in reality, we know that they’re happening already in industry and certainly in life sciences.

Pickeral (22:44)

They’re certainly happening. And one of the things that we did at Sparta earlier this year that I was super excited about was we ran a customer obsession challenge. And one of the things that our own employees are so excited about is, what does augmented reality on a manufacturing floor look like? And I think that that next wave of innovation is here and we are going to figure out smart ways to feed it and support it and deliver value.

McCarthy (23:13)

100%. And I think it is exciting. And, you know, to be part of it now is a privilege. We talked about cloud, we talked about SaaS. Let’s talk a little bit about service. How do we serve the customer while focusing on how we help them along this journey of digital transformation? I see it often as a complex journey from this current state that is variable to a future state. It’s like a convergence strategy to this north star of digital transformation. How do we focus on the customer and be customer obsessed as they take that journey towards the rapidly approaching future of digital transformation?

Pickeral (24:04)

Yeah, the that’s something that I know both myself personally and also other people on the team think of quite a lot. Every time we have a chance to work with the customer, both in the early process of understanding what digital transformation means to them, but also hands on in implementation and daily operations. It’s very informative. So, one of the things that, I’m picking up in recent conversations is how do we focus on the most value added features, how do we deliver packageable insights, if you will. So, for example, when regulatory climate changes and country A, B or C, how do we capture those new requirements in our product? Do we deploy it? So we don’t have to have the customer double guess or, you know, have to have to rely on additional expertise. Some of the work that we’re doing around QPA, our quality process accelerators in my mind is one of the ways to combine that simpler implementation with infusing industry expertise and regulatory know how with doing things faster, cheaper, and on a greater scale.

Pickeral (25:14)

In my mind, that’s one of the paths on which we’re going to be successful. And another one is learning about what, what those value adding capabilities next stage capabilities are, and co- innovating co-deliver with customers. That’s where our recent conversations about real-time batch release come in. That’s where we are talking from, in aggregate reporting, going from a once a year occurrence that takes months to, delivering those types of support in a much more predictive, proactive, real time way. And I think that’s how we’re going to get better and deliver better over time.

McCarthy (25:58)

So, to be customer obsessed and to think of the reality of the challenges that they face. And that idea of data, process, and then technology, it’s not only about technology, we put data and process first and, and think about value. Okay. So let’s wrap up a little bit. We’re going to talk about digital transformation in summary, because it’s not about if in industry will transform it’s about how and when, and as we’ve just said, the value of transformational quality management and its impact on industry and the patient is very clear and it’s significant, and we have to think differently about what quality management means in this rapidly approaching future for the life sciences industry. So that’s why Sparta and Honeywell think so much more broadly across the entire supply chain, right?

McCarthy (26:53)

Not just in that traditional mode, one quality sense. So, let’s talk about that a little bit. Let’s talk about the ultimate customer of the industry, primarily the patient. And let’s go back to that word that we mentioned earlier on, which is trust. I want to just kind of wrap up with your thoughts on how a partner to the industry such as us can really play a role in making sure that the patient has the right product of the right quality, right. Efficacy, it’s safe, it’s on the shelf when they need it to be. What are your closing thoughts on this?

Pickeral (27:30)

I think the components that you’ve listed Steve are exactly what the quality management system brings to the table. It’s not just making sure that you’ve got the right pill of the right color and the right bottle, it’s safety and efficacy and reliability of supply. And that’s where certainly in customer conversations, I’m hearing a lot of interest in other advanced analytics work. Qualitywise.ai, for example, support that we deliver around predictive and proactive around supply or quality management that allows you to better understand your broader ecosystem of suppliers and be able to both analyze and to a certain extent control and adjust in near real time. And then on the right side of the value chain the work that we’re already doing in analytics around complaints, that’s a very natural bridge for us to go deeper in proximity to the patient in what experience patients and physicians have with pharma and med-tech products.

Pickeral (28:38)

And then again, feed that back into the next set of products and applications that we build, you know, on the supplier side. What I think is it, it’s important to understand how complex our world has become. And, and I think we all had a demonstration of that during COVID when a single event can disrupt—be that, a pandemic, but the same thing can happen with a major cyber event. You can cause a systemic disruption to a significant percent of your supply chain. And as you’re scrambling to find alternatives, having visibility, and in options that you have in your supply chain can actually be a factor of competitive advantage because every single one of your competitors is, is scrambling in the same way you do. So if you have a better set of capabilities in understanding and managing the quality of your supply network, that can quickly translate into dollars and cents, and also in your ability to deliver to the patient.

McCarthy (29:37)

Okay, Oxana, quick fire round. First thing that comes to your mind—what was your pandemic lockdown discovery?

Pickeral (29:48)

All right. Sailing! An activity that you can do outdoors and beyond the water in, in the weather and in beautiful places. So, yeah that was mine.

McCarthy (30:02)

Awesome. Is the outdoors part of your passion?

Pickeral (30:07)

Yes. For sure.

McCarthy (30:10)

Okay. Next one. Superpower. So, if you could be a superhero, what’s the one superpower you’re going to want?

Pickeral (30:17)

Teleportation

McCarthy (30:19)

Teleportation. I love that

Pickeral (30:20)

Still so many places to see and not enjoying air travel as much as I want to get around. So yeah.

McCarthy (30:32)

Having spent so many countless hours in the airport and on that airplane, I can certainly share that one with you. Okay. Last one. Hogwarts house.

Pickeral (30:43)

I’m going to say Ravenclaw.

McCarthy (30:46)

So that’s what the hat would choose for you?

Pickeral (30:49)

I think I’d end up in Ravenclaw, pretty sure. But one of my favorite characters ever is in Ravenclaw, which is the delightful loony Luna Lovegood. So yes Ravenclaw is it for me I think.

McCarthy (31:04)

I’d end up in Slytherin. Unfortunately, I have an evil side to me.

Pickeral (31:05)

Oh no!

McCarthy (31:07)

Well, Oxana, thank you so much for your time and for your insight and for your leadership. And thank you, our audience, for your attention. I hope you enjoyed our conversation today. I hope you look forward to the rest of our podcast series and I’m sure I’ll see you there along the way. So again, thank you all very, very much.

Pickeral (31:28)

Thank you, Steve, and everybody who listened. We welcome any questions that you have afterwards and look forward to engaging in a dialogue.

Outro (31:39)

This has been Forging Connections, a podcast from Honeywell. You can follow Honeywell on LinkedIn and download new episodes from our website, Honeywellforge.ai. Thanks for listening.

The post The Patient-Centric Passion Behind Sparta: Meet Dr. Oxana K. Pickeral, Ph.D., MBA, President and CEO of Sparta Systems appeared first on Honeywell.