Medtech Industry Revolution: How Digital Twins Unlock Game-Changing Insights

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Medtech Industry Inventory Management: How AI, data integration, and automation are changing the landscape forever[/et_pb_text][/et_pb_column][/et_pb_row][et_pb_row _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”]

We spend most days brainstorming ways to improve our customers’ lives. That includes thinking hard about leveraging our platform to help them improve their businesses and stand out in the medtech industry. What features do we need? What capabilities can be improved? What gaps need to be closed? What innovations should we focus on?

In the beginning, we started with efficient data capture and data retrieval. How do we make it easy to keep, store, and access critical data related to inventory as it moves from the warehouse to the field, and then through the sale and eventual use of those assets? How do we turn data into knowledge that can be leveraged whenever and wherever it’s needed? These questions remain essential for any company in the medtech industry looking to improve supply chains and field operations.

That quickly evolved to simplification and automation. How do we take the most cumbersome, complicated, and manual data-related tasks and make them truly easy? Better yet, how do we automate them? How do we remove as many clicks as possible? How do we make them frictionless? Can we make them enjoyable? These same priorities—automation, simplification, and user delight—are at the heart of digital transformation in the medtech industry.

Over the past several months, our thinking has been focused on AI’s power and utility. How do we leverage the most promising AI tools to make the things we’re already good at, even better; even faster; even easier? How do we turn data into analytics, and analytics into insights? How do we create a feedback loop that is self-reliant? Self-sustaining? Self-sufficient? AI is already reshaping how the medtech industry delivers care and manages field inventory, and we see limitless opportunity ahead.

Each of these things has, in turn, become core to our business and our value proposition. But each can (and should) also be considered distinct from our platform.

Every company, no matter how they ultimately do it, should be looking for ways to more effectively capture and manage key data related to their field ecosystem. In the medtech industry, that means unifying everything from manufacturing and regulatory compliance to on-site surgical support. Every company needs to simplify and automate. Every company should be actively engaged in figuring out how to leverage AI for their business to avoid being left behind. Those who don’t may not recover in today’s rapidly evolving medtech industry.

Again, these things are not strictly tied to our platform, and we know it. They’re fundamental to almost every business in the 21st century. That’s why we believe it’s important to think about (even grapple with) these things publicly, for the benefit of anyone considering similar issues, whether they use our tools or not.

So, what are we thinking about now?

We’re systems thinkers. We are focused on improving macro- and micro-ecosystems across the medtech industry.

From first-to-last-mile logistics and international sales operations to the individual sales rep working with a single surgeon at a local hospital, the amount of information changing hands can be massive. The complexity can span the entire value chain—something every medtech industry leader recognizes as both a challenge and an opportunity.

Our constant focus on this issue has led us to consider the role digital twins can/should play in the medical device field inventory ecosystem. Are digital twins possible? What does that mean for field inventory operations in the medtech industry? Can that even be a thing? And if it can, would it be worth the effort?

If you haven’t heard “digital twin” before, you may be asking, “What exactly is that?” The term “digital twin” has been around for over 3 decades. And the concept is even older than that.

For general context, according to the Digital Twin Consortium:

A digital twin is a virtual representation of real-world entities and processes, synchronized at a specified frequency and fidelity.

• Digital twin systems transform business by accelerating holistic understanding, optimal decision-making, and effective action.
• Digital twins use real-time and historical data to represent the past and present and simulate predicted futures.
• Digital twins are motivated by outcomes, tailored to use cases, powered by integration, built on data, guided by domain knowledge, and implemented in IT/OT systems.

An example of a common manufacturing use case is implementing digital twins as virtual representations of manufacturing equipment/assembly lines to track and manage their productivity, efficiency, and system health in the real world. Another example might be an entire electric vehicle.

As interesting as those examples are, this isn’t a deep dive into the concept of digital twins. Feel free to check out these articles from The Digital Twin Consortium and IBM if you want to learn more.

Why Are Digital Twins Important for the Medtech Industry?

It’s not difficult to come up with a handful of ways the concept of digital twins can generate value for industries like automotive and aerospace. But in the medtech industry, the stakes are even higher. The most important opportunity is creating the ability to move from individual, siloed stores of point-in-time data that have little meaning without significant intervention, to integrated, updated, multi-dimensional data that can be acted on with very little intervention to have a massive positive impact on the business.

That’s a mouthful. But you can think of it like this: A digital twin can act as a mirror, not a photograph, and it can do that without having a direct line of sight.

Imagine seeing in real or near-real time how your ecosystem is operating. Imagine getting data from multiple sources, reducing reliance on self-reporting, and even being alerted before things get off track. Imagine seeing your operation at 100,000 feet, 50,000 feet, 1,000 feet, and 50 feet, all at the same time. And then imagine that being updated all day, every day, forever. For companies in the medtech industry, that level of insight can reshape field inventory management and patient care.

That sounds awesome to me. It’s also hard to do (impossible even) without some version of a digital twin.

How Do Digital Twins Translate to the Field Inventory Ecosystem?

At the highest level, digital twins represent a real-world “thing” or “process” using bi-directional data that provide real-time and predictive insights to help optimize a business’s use of that “thing” or “process.”

So when we think about field inventory and field sales—specifically as they relate to medical devices—we are not considering digital twins of actual inventory items. We’re not talking about a digital twin of a spine implant the way an auto manufacturer might digitally twin a car’s drivetrain. That would be crazy.

Crazy? Well, yeah, mostly crazy. There are several reasons why, including:

• The nature and size of many small implantable devices
• The inability to sensor and, therefore, collect real-time data at the device level for most implantable items
• The marginal individual cost of many implantable items relative to the cost of creating a real-time data capture mechanism at the device level
• The lack of any general utility for digitally twinning most implantable devices or even instruments in most cases

Instead, what we’re talking about is creating a digital twin of a device manufacturer’s field inventory and sales ecosystem. While inventory is directly implicated in the management of that ecosystem, and even represents a component of it at the highest level, the individual devices are far less important than the health of the ecosystem itself. Don’t think 100 feet; think 100,000.

Rather than needing to know what is happening to a single inventory item in the field, we want to see how each of the processes that support getting those items to and from the field are working. Are they working? How well? Are they efficient? Wasteful? Costly? These are critical questions for anyone in the medtech industry trying to streamline operations and reduce risk.

Any manufacturer can tell you the cost of part 12345 with lot number ABX-DX. What is harder to tell you is how many times that item, or items just like it, was shipped and how much that cost the company before it was sold. They can tell you which distribution arms create bottlenecks in retrospect, but it is much harder to tell you whose performance is slipping in real-time, who is likely going to run out of inventory, and who is most likely to let valuable inventory expire before use. In the medtech industry, those blind spots can delay surgeries and cost millions.

Using system data, data integrations, open data, real-time user inputs, and user feedback, we believe you can design a digital twin that gets you to these insights not retrospectively, but proactively. Consistently. Perpetually. And that can be a game changer for your business and for the broader medtech industry.

But how do you do that?

We’re glad you asked that. It’s a great question. It’s an important question. It is also the question we will address in Part II of this post.

If you want to be notified when that post drops, please use this form to share your email address. In the meantime, have a great Fourth of July!

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