Matthew Bardell: 0:03

Normally, the, the, the companies with the biggest excesses are also the companies with the biggest shortages or the most shortages and, that seems counterintuitive, but the, the, the more you work in the area, you, you understand that all of the time spent producing the wrong thing is wasted time that could have been spent producing the right thing

Tom Raftery: 0:25

Good morning, good afternoon, or good evening, wherever you are in the world. This is the Digital Supply Chain Podcast, the number one podcast focusing on the digitization of supply chain, and I'm your host, Tom Raftery. Hi everyone and welcome to episode 363 of the Digital Supply Chain podcast. My name is Tom Raftery and I'm excited to be here with you today sharing the latest insights and trends in supply chain. Before we kick off today's show, I want to take a moment to express my gratitude to all of our amazing supporters. Your support has been instrumental in keeping the podcast going and I'm really grateful for each and every one of you. If you're not already a supporter, I'd like to encourage you to consider joining our community of like minded individuals who are passionate about supply chain. Supporting the podcast is easy and affordable with options starting as low as just three euros or dollars a month. That's less than the cost of a cup of coffee and your support will make a huge difference in keeping the show going strong. To become a supporter, simply click on the support link in the show notes of this or any episode, or visit tinyurl. com slash dscpod. Now, without further ado, I'd like to introduce my special guest today, Matthew. Matthew, welcome to the podcast. Would you like to introduce yourself?

Matthew Bardell: 1:46

Sure. Thanks very much, Tom. I'm Matthew Bardell, the Managing Director of nVentic,

Tom Raftery: 1:52

Okay. And what's nVentic?

Matthew Bardell: 1:55

NVentic is a specialist inventory optimization company. Our background is in management consulting, but we have advanced analytical software and we're actually on the verge of launching some of that in a software as a service format probably early next year.

Tom Raftery: 2:12

Okay, cool. And why, what, what made you get into this space? What, you know, what launched, what launched you on the path uh, creating this product?

Matthew Bardell: 2:23

Mm-hmm, well, nVentic is the brainchild of Dan Weil, the founder of nVentic. And I've, I've been working with Dan for about six years now. And his perception really is that inventory optimization is a significantly untapped resource in the business world. Everyone sort of is aware of the concept. Most people are doing something in that direction. But we regularly see that there is a a very large gap between what is theoretically possible and what people are actually doing in day-to-day terms. So I think our mission is to, is to help everyone take more advantage of the great work that's been done in inventory science and apply that to their real life situations.

Tom Raftery: 3:12

Okay. And I mean, you were doing that as consultants, so why the shift from that to a product offering?

Matthew Bardell: 3:21

Well, two reasons. I mean, first of all, the reason for setting out on our own is that our perception was that most management consultants had limited ability themselves when it came to if you like, the more scientific approaches to inventory optimization. I mean there, there's plenty of good, simple approaches. Look at what you've got. Try and order a bit less . You know, it's not difficult to reduce inventories if that's all you're looking at, but really people who understood the science and who understood how you can apply that to, to very big, complex, messy real life situations with very few and far between. In fact, we tended to find that there was more capability client side than within the, the management consultants themselves. So we saw that as being a, a, a gap in the market. Right from the start Dan developed the analytical tools, which really have taken all of the academic work on inventory science and condensed it into a set of diagnostic tools that give clients Very rapid, robust, and deep insights into where their biggest opportunities lay in inventory optimization. But our, our, our initial philosophy was that there's already quite a lot of inventory optimization technology on the market. And actually quite a lot of it is very good. And that the bigger problem is more that organizations for various reasons are struggling to take advantage of that capability. So, our starting point was we didn't really just want to be a me too, you know, we've got a magic tool that will solve all of your problems in this area. So we've been delivering what we call asset based consulting. So we use the analytics, but there's a, there's a big focus on helping clients understand what the, what the data's really telling them and what they can do about it. However, as, as time has gone on you know, whilst that is a successful approach and, you know, some, some very impressive results from some of our clients. In terms of sustainability they need, they need the ability to continue to have those insights. And we, we position what we're launching as a decision support system. So it's not there to replace planning tools. It's it's rather designed to complement them and and, and help clients understand some of the limitations and sensitivities of their existing planning technology and get more value from them.

Tom Raftery: 5:57

Okay. And just What would be kind of typical issues that customers would have and what would be kind of that they could use to overcome them or to optimize their, their inventory?

Matthew Bardell: 6:16

Yeah, great question, Tom. I, I think you know there, the, the, the reason that inventory continues to be sort of an, an under realized potential for most organizations is that there are, there are so many facets that influence it. The first challenge actually is, is just the data that even if you find a client who has you know, a few inventory specialists who understand statistics well and who could perhaps do something well. There's a challenge in getting data outta systems and being able to, to then get that visibility of what's really going on. The automation tools, the inventory planning tools themselves, their Achilles heel is, is twofold. First of all, they're very reliant on the quality of data in the systems and, you know, it won't surprise you to hear that, that, you know, there's very patchy data quality in most people's ERP. And the other Achilles heel is really the complexity of some of those tools. If, if, you know, for instance, if your tool is asking you to set a service level and it's asking you to choose between, for instance, fill rate and cycle service level, then by the time you are down at the individual planners, they frequently don't have the knowledge to even understand what difference that makes. So you, you can get wildly mistaken, you know, parameter setting. So, so there, there's, there's a whole technical aspect that makes it difficult for people to really understand how far from optimal they really are. And, and that's where we've developed our technology. But over and above that there are many human challenges because, you know, within most organizations, there's perhaps a na, a natural tension between sales and operations, where, you know, sales You know, really don't want to run out of inventory

Tom Raftery: 8:18

Sure

Matthew Bardell: 8:18

You know, they have a hard enough life trying to develop sales already without them worrying. Well, if we've got a willing buyer, then we can't ship. And operations who perhaps look at some of the forecasts, they know that there's significant forecast bias in there. But then there's a, there's a political thing of, well, if I've got enough to cover forecast, And we've got too much. That's not my fault, that's the forecast's fault. Whereas if I sort of stick to my convictions that we're never going to need that much and we run short, then, then that's my fault. So, you know, that, that's a simplification, but it's also a very real problem that, that, that most supply chain people are very familiar with. And, you know, having an excellent and robust, data-driven based influence, some of those conversations is very useful, but it's still just the start. You have to have organizations that are willing to embrace change and who, you know, ideally understands that inventory isn't a simple either or equation. Normally, the, the, the companies with the biggest excesses are also the companies with the biggest shortages or the most shortages and, that seems counterintuitive, but the, the, the more you work in the area, you, you understand that all of the time spent producing the wrong thing is wasted time that could have been spent producing the right thing. So it's not about, well, we need a lot and then we'll be fine. Or if we, if we start reducing, then we'll run out. It's no, we need to understand the demand patterns. We need to understand the variability. We need to understand the quality of our forecasts. And then we need to have inventory policies and approaches that are adapted to those, those different circumstances. And, you know, every single item that you stock has its own particular personality. It's lead time variability. It's, it's demand, uncertainty, all the rest of it. And organizations you know, especially if they're managing many hundreds if not thousands of items You know that, that they take quite broad brush approaches or, you know, we're going to try and have two months cover and, and you know, so, so, so that's often where the, the biggest cause of, of the issue is. And, and that's deeply ingrained on a behavioral level. So moving organizations past that is, is often a bigger challenge than sort of the, the, the, the clever analytical part of it.

Tom Raftery: 10:53

Sure. I mean, that's, that's more a, a change management issue rather than a, a software issue.

Matthew Bardell: 11:02

Yes.

Tom Raftery: 11:03

how, how do you overcome that

Matthew Bardell: 11:07

Well, I, I think, you know, Rome wasn't built in a day, so, so you, you address it over time and, and, and this is really where our services come in, is that one, one of the one of the other challenges with inventory optimization is its complexity that there are so many variables, all of which most of which are in an almost constant state of fluxx, that it can be a bit overwhelming. And what we find is very successful is to have a, a progressive approach where you sort of try and tie down one thing at a time, and then once you've made good progress on that, you move on to the next topic. So, You know, a a, a classic example with where we talk to new prospective clients, they'll often ask us about lead time variability. And our answer to that, which normally, you know, sets them back a little bit in surprise, is ignore it for now. Just take your maximum lead time, use that, optimize on that basis, and then when you've tied down some of the other things that are, you know, probably easier to fix. Then come back to your lead time variability, because actually lead time variability, it's, it's actually not that statistically difficult to deal with. You need a certain volume of data and you need to know what you're doing, but it's not really that big a thing. But people get a little bit fixated with, well, we think this is our problem. Our problem is our forecast, or our problem is our, you know, uncertainty of how soon suppliers will deliver and . , you know, the, the, the scientific approach says, well, those are just givens. You know, if, if your forecast, you know, if, if your demand is very difficult to forecast, then it is very difficult to forecast. You just need to understand how difficult to forecast it is. You need to understand if your forecast that you are using are adding or destroying value relative to using a, a naive forecast. And then you need to set all of your parameters accordingly. But people are typically trying to solve everything at the same time and sort of breaking it down and and moving it through. First of all, it makes it digestible. You can take the whole organization with you, people can understand what you're doing. And then typically some of the simplest things will deliver very big results in the first year. So you can also then build on that success and say, well look, you know, we've been doing this for a year. We address the following things. We reduced inventories by 15%. Service level actually improved slightly. Now let's try the next thing. And you know, we, we can, you know, address some slightly more complex topics, but, there is no silver bullet when it comes to inventory optimization. You know that everyone, has this vision of, well, maybe I, AI is going to solve it for us and it, it can deal with all of that complexity and then we can take the humans outta the equation. And our view is that, you know, we are, we are a long way away from that being a realistic proposition in, in inventory optimization. That isn't say that AI hasn't got a role to play, but, the bitter truth is, is that if you want to make progress in this area, you do have to grip your teeth and roll up your sleeves and do some hard work and you know, take, take people with you as you do it.

Tom Raftery: 15:08

Interesting. And just to kind of set some context, what are the potential issues that arise if you don't get your inventory optimization right. You know, what are the kind of catastrophic potential outcomes versus what are the potential gains for actually getting it right?

Matthew Bardell: 15:27

Mm-hmm.. Well, I think the. The if, if you are brand new to the whole concept of inventory management. You know, the way we always sum it up is that there are two rules. One is don't run out. And number two is don't have too much. And if in doubt, apply the first rule before you apply the second rule.

Tom Raftery: 15:50

Okay.

Matthew Bardell: 15:52

But then what happens is that if you, if you do run out, obviously then you know, you've got lost sales at the worst end of the spectrum. But then a lot of secondary costs, things like you know, rush orders flying things that could have traveled by sea otherwise. You know, that there's a range of things. Plus, of course, all the management time spent dealing with shortages. If you look at everything that ships requires a very small management effort compared to everything that's late or missing or delayed. So, so, so that has ripples throughout the whole organization. Also, if you are, if you are constantly playing catch up in that way, then production is constantly stressed and, you know, you, you have a whole range of suboptimal outcomes. At the other end of the spectrum, we, you know, excessive rapidly lead to obsolescence. You know, with C O V I D, everyone, you know, supply chains were facing often impossible situations and, you know, no one was doing a bad job. But nevertheless, lots of organizations have ended up with a lot of obsolete stock that then goes into landfill. Not all of it goes into landfill, of course, but, but, but a lot of it does. So that that's both money that you are wasting. And it's, you know, it has an environmental impact that, that, that organizations are, are more and more aware of these days. That if you think about the classic power struggle being between sales, who want plenty of inventory and operations, who, who want to run a bit leaner, then You know, maybe the new entrant in that discussion is the sustainability people who, who are looking at, not just we know how much CO2 are we producing, you know, how are we dealing with any hazardous materials that we have to dispose of? How are we using more and all of those things. But also, well hold it. Why do we consistently produce more than we need Now, you know, this, this can vary a lot by industry, if you know, one of the worst offenders is probably apparel where, you know, it's a very difficult thing, guessing what's gonna be fashionable, what's not going to be fashionable, and, and then a huge amount of end ends up being destroyed. And, you know, again, it's not because anyone in that industry's doing a bad job or doesn't understand this, it's just, well, if we don't have the fashionable things that people want on the shelves at the right time, then we lose market share quite fast. So, you know, there, there's, there's a lot of quite obvious downsides to getting it wrong. I mean, so much so that we, you know, we sometimes wonder, well, why, why don't organizations put more emphasis on inventory optimization? Because finding that right balance is so obviously attractive, you know, for financial sustainability and, and, and, you know, just calmness of operation reasons. But perhaps part of it is just the difficulty that people have tried. They run up against some of the barriers that we've been discussing earlier. And you know, once bitten, twice shy, you can put a lot of effort into something. Maybe you see a short term gain, but then, you know, six to 12 months later, the same, the same problems come back again. So it's, it's not an easy nut to crack.

Tom Raftery: 19:19

Sure, sure, sure, sure, sure. And you mentioned AI, and you said that AI does have a part to play. What part does it have to play? You know, where does AI fit into the inventory optimization scene?

Matthew Bardell: 19:35

Mm-hmm.. Well, as with any discussion of AI, it's, it's always helpful to say, well, what do we mean by it? By

Tom Raftery: 19:44

there's lots of

Matthew Bardell: 19:45

You know, you too often you see, especially in sort of sales and marketing messages, you know, we use AI to, you know, insert whatever it is that, that you are selling. And you say, well, okay, fine, but what do you mean by that? And actually, in our area, a lot of a lot of what we do is using well-documented scientific principles that we have turned into algorithms. So, we don't describe that as AI because we've taken an equation, we've built it into a set of routines that will apply that to, to large data sets. And the benefit of that compared to, let's call it an AI approach or an AI black box, is that you can explain to people exactly what's going on. This is, this is what the calculation is doing, this is what it's looking for. This, this is, you can go and look it up, you can read for it. It for yourself. And, and that's what you can do. However, there are certain situations where you know, perhaps you haven't got enough data points you if you need to do things like bootstrapping or, or hill climbing, where, where you use more advanced techniques that are currently described as AI. And you know, again, AI is a bit of a moving, a moving definition, isn't it? I can't remember who the quote's from, but they said, you know, AI is always what we used to describe what we can't yet do ourselves. But regardless of that, if, if we broaden our understanding AI to include sort of everything that's now possible with Industry 4.0, I think our point is that it's very good at doing things that humans find difficult. So applying quite advanced statistical approaches, for example, you know, without making errors, without getting tired, processing huge volumes in very quick periods of time. So, you know, it would be mad to not want to take as much advantage of that as possible. You know, that's really at the, at the heart of our whole approach. But at the same time, there are, there are two key things for success, and both involve humans, first of all, is that, at the end of the day, inventory optimization's going to feed through to your planning process. There are people whose job it is to ensure that stuff is moving, that you are getting the supplies that you need, that you have the products that you need to ship. Now, for a human to trust an AI or a piece of technology, they need to trust it. They need to say, well, okay, I understand enough about how it works. I need to know that it will perform reliably and predictably in a range of circumstances for me to even use it. And the fact that we are not necessarily there yet is shown by the fact that time and time again, you see people who are, you know, they're licensing quite advanced optimization technology and they're not using it. You know, we, we often have that conversation. People say, oh yes, we have technology. It can do optimization. And we say, yeah, but you're not using it, are you? They say, oh, how did you know that? And we say, well, 'cause everyone's the same that, even within ERPs themselves, I mean, SAP has some quite advanced optimization capability, but in our experience, people are rarely using it. And the reason is, is that it gives them a multitude of choices. They select the one or two that they actually kind of understand how it works and they work with them. And if they can't make it work with them, they go back to their spreadsheets, they work it out for themselves, and then they feed that data back into SAP or whatever other tools that they're using. So you're saying, well, okay, the. You, you're not taking advantage of what the tools can give you here because you don't have that human trust. So that's sort of one key issue. That's that, that's holding it back. The other, the other thing is that you know that the best technology in the world is highly reliant on what data it has available to it. And that's where humans are very good. Humans know things that your data sets don't necessarily. And you know, again, one of the big things holding inventory optimization back at the moment is often the humans don't even know which data their tools are using to make the recommendations that they are. So, you know, we, we, we see it as a, as a compliment that the, the technology and AI and everything that's coming with it, should, and I'm, I'm sure will help humans to do their jobs better. It's, it's not gonna replace them. It's going to massively enhance them.

Tom Raftery: 24:38

Yeah, no, absolutely. You have some very cool testimonials on your website. Can you talk to me about some customer success stories you've had and some successes you've had, maybe in inventory reduction or, you know, similar metrics that you might be able to pull out?

Matthew Bardell: 25:00

Sure. I mean, when a new client comes to us, they, they normally have one of two problems and, and sometimes even both. Normally, either they've got too much inventory and they want to reduce it, but without sacrificing service level. Or conversely, they're, they're facing shortages and they want to you know, fix that.

Tom Raftery: 25:24

Yep.

Matthew Bardell: 25:25

I mean, one of the, one of the testimonials on our website was from a, a tool manufacturer. And, and they were actually suffering with both. They were saying, you know, we are, we're struggling to get beyond about 85% fill rate, and yet we still feel that we've probably got far too much inventory. And we are, we are just missing the ability to work out how we address that and you know what to do with it. So, Our approach with a new client is pretty much always the same, is that we, we, we take you know, very large data sets from them. We want to look at every single transaction for every single item that they stock for at least 12 months. And then we apply all of our algorithms to that. And what we are doing is we are measuring what they have item by item with what they should have, item by item. And along the way, calculating some key planning parameters like safety stock level, order size, that sort of thing. So what that then gives the clients at the end is they get, they get both a high level overview of well, you know, you've got, you've got this much too much stock. And almost invariably clients fall somewhere in the 20 to 50% too much range. We, we've had one client in 10 years of operation who had less than 20%. That's our, that's our star pupil. That was a, that was an automotive client. And in fact, many clients who have more than a 50% reduction potential. And, you know, coming back to our tool manufacturer here, they, they had I, I can't remember exactly, I think it was about a 40% reduction potential, even whilst increasing their fill rate from 85% to 99%. And it all came down to applying differentiated approach to different items. And so with, with what we gave them, they were able to say to their planning team, okay, go and look at these item. Go and look at this analysis. You can filter it in terms of where are you the shortest, where, where have you got the biggest excesses? And then you can rebalance, you can increase your inventory levels, maybe review your safety stock policies. Where you can, and then where we've got too much, you can start draining straight away. So start canceling or postponing orders again, review what parameters we are using for balancing that. And so with that client, it took them three months to reduce 10% of their inventory. And then after six months, they'd got another 10% out and their service level was already running up in the high nineties. So once, once, people get that level of information, which they normally, you know, can't produce themselves, or if they can, it's a very long, laborious and error prone exercise. So, so, you know, the, the, the results are very concrete, really, quite quickly. And then, you know, actually five years later we're still working with that client and they're, you know, they're, they're, they're applying new things now and You know, there, there, there's always, there's always potential to do more, we find.

Tom Raftery: 28:33

Cool, cool, cool. And for yourselves, I mean, I know you're launching your SaaS product early next year. Where do you see yourselves going after that? If you look out on a kind of three to five to even 10 year horizon, what are your plans for as I say often here in the podcast, global domination,

Matthew Bardell: 28:54

Yeah, I mean, we are, we are not really a global domination minded organization.

Tom Raftery: 29:01

Okay.

Matthew Bardell: 29:02

You know, Dan and I do this because we're passionate about it. We enjoy it. And we, we believe that it adds great value to, to our clients. You know, we, we don't really want to grow at a breakneck pace such that, You know, the, the, the challenge I've, I've worked in management consulting for a good while. Tom and I, I dunno how much exposure to consultants you've had over your career. But, you know, they're, they're normally distributed. You, you get, you get some very good ones, you get some bad ones and most people are somewhere in the middle.. But then what we are doing is really at the expertise end of the market. What, what we want to give to our clients is the feeling of, you know, these guys know about more about inventory optimization than, than anyone that we've got. And you know that, that's why we work with Professor David Pike as well. We have that academic sort of check if you like that helps us with everything that we do. So in that sense, it, it's very difficult to imagine massive scaling on the consulting side without losing the quality. So that's not really where our ambitions lay, and that again, is a reason for, for going down the, the software as a service route because that's much more scalable and, and what we are really trying to, we're trying to find that sweet spot of something that's detailed and accurate enough to add more value than anything else on the market in terms of technology, but being simple enough for, for people to use with minimal, with minimal guidance if, if you like. So I think we, we, we will see how it goes with the launch of our first first products next year. We already have a pipeline of what comes next and it sort of, it, it, it, it increases in complexity as it goes. And I think whilst our initial market is quite broad, you know, anyone in supply chain planning would find value from it. The further down the road, the more specialist we will get in terms of more tools for the, for the specialist data analysts in, in organizations.

Tom Raftery: 31:12

Okay, for someone who might just be starting out on the road to inventory optimization. They're to your point, either having too much inventory or not enough or both, as you said as well can happen, what would be first steps to try and rectify the problem or problems?

Matthew Bardell: 31:36

I, I think that the first step that, that, that we always advise clients, and, and this is often counterintuitive to a lot of people, is for a second stop looking at the future. And look at what's, what's already happening because the, you know, this again, is one of the key challenges that maybe I, I passed over earlier, is that planning by definition is a future looking activity. So you're looking at, well, how much do we think we're going to need? How much have we got? You know, and then we're placing orders accordingly.. And then the problem is, is that in that sense, the future is always changing. That forecasts are being refreshed all of the time. Okay. You know, with good reason. Again, it's, it's not a, it's not a, a, a silly way of doing it. It's, it's the right way to do it. But what that means is that you are losing sight of why you've got the inventory imbalances that you have, because, By the time that inventory shows up, if your forecast has been refreshed five or six times since you actually placed the order,

Tom Raftery: 32:54

Yeah.

Matthew Bardell: 32:54

you're, you're no longer really sure of, well hold it. But, you know, is, is this what we, is this what we really needed? So what we say is that, you know, obviously you need to consider your forecast and you need to find something that works with that, but, before you go there, just look at what's, what's just happened over the last period of time. And look at how much, look at how your inventory behaved, whether you had, you know, quite a, quite a smooth transition, whether you've got spikes up or down. You know, where did you run short? Why did you run short? Where have you got your biggest successes? What can you do about that? And so that, that taking a step back and looking at that and then saying, well, okay, . How do we need to adapt the way we plan that? That's kind of just getting that philosophy embedded rather than constantly. Well, you know, we, we just need to feed our forecast into an optimization engine and then everything should be perfect. Which is kind of like the, the overriding philosophy, but then people know it, it never ends up perfect. But then the answer is, well, it's just our forecast. And the thing is . The forecast is an important lever, but it's far from being the only lever when it comes to inventory optimization. So that's sort of the, the, the, the single most useful concept to take on board when you're setting out.

Tom Raftery: 34:20

Cool. Cool. We're . Coming towards the end of the podcast now, Matthew, is there any question I didn't ask that you wish I did or any aspect of this we haven't touched on that you think it's important for people to think about?

Matthew Bardell: 34:34

I am not sure. I, you know, excellent questions. Tom Thank

Tom Raftery: 34:38

Thank you.

Matthew Bardell: 34:39

I, I think that, you know, there may, may be one thing that we could talk about a little bit more because I know that it's something that you are interested in as well is sustainability.

Tom Raftery: 34:48

Cool.

Matthew Bardell: 34:49

I mean, to, to, to, to mention something that, that we're involved in is a nonprofit collaboration called the Sustainable Medicines Partnership. That, that is looking at the broader problem of how many medicines each year go to waste. You know, there's a lot of medicines each year destroyed, unused, and there are many reasons for that. And there are different work streams looking at, you know, things like packaging and and what have you to, to try and reduce waste in the, in the medical supply chain. And we're also working with the SMP on topics related to what we've just been discussing. So, you know, whether it's a hospital or a pharmacy or in particular the big manufacturers they all suffer from the same challenges that we've been discussing today in terms of inventory optimization and, and for them, of course, frequently it's life critical that, you know, where you have patients who literally may rely on a medication to stay alive or at the very least for quality of life.

Tom Raftery: 35:54

mm-hmm.

Matthew Bardell: 35:55

You know, if, if no one wants to run short of things that their customer wants, within the medical supply chain, that's doubly true. So, you know, you have to apply very high service levels, you have to have you know, dual supply for critical things and all the rest of it. So it, it's it's a particularly challenging example, but at the same time, there's still a lot of wastage there. There's you know, a huge amount of medicines that are manufactured and never even leave the, the manufacturers. So that, that's, that's something with the SMP that, that, that we're, you know, hoping to try and get some white papers out in the, in the next year that, that will help help people understand you know, where, where, where the improvement can come. Because you know, what I said before is equally true of medicines. Whilst you don't want to run short of anything, all the time that you spend manufacturing things that people don't end up using

Tom Raftery: 36:51

It is a waste.

Matthew Bardell: 36:52

You could have spent manufacturing something else. So, and again, you know that, that there's, there's particular challenges in terms of forecasts. Medicines work to quite short patent windows. So there's high pressure. You know, you are, you are waiting for regulatory approval. You're gonna get a, you know, there there's a lot of things that are specific to that industry, like other industries. But the potential for, for making significant reductions in the amount of, of medicines that are, that are going into the incinerators is, is possible.

Tom Raftery: 37:24

Fascinating. Fascinating. Cool. If people would like to know more, Matthew, about yourself or any of the things we discussed on the podcast today, where would you have me direct them?

Matthew Bardell: 37:35

Well, the, the simplest place is our website, nVentic dot com. You know, there's, there's, there's a, there's a profile of Dan and me and professor Pike on there. And also you can find us on LinkedIn and Yeah, always happy to hear from anyone who's just a bit of an inventory geek, to be honest, because I I think that there, there's, there's a lot of people out there who understand what we're trying to do, and they have the same frustrations in terms of, well, how do they get that voice heard and how do they get real change to happen in their broader organization? So, always happy to talk to new people about about it.

Tom Raftery: 38:10

Fantastic. Fantastic. Great. Matthew, that's been really interesting. Thanks a million for coming on the podcast today.

Matthew Bardell: 38:17

Thank you for having me, Tom.

Tom Raftery: 38:19

Okay, thank you all for tuning in to this episode of the Digital Supply Chain Podcast with me, Tom Raftery. Each week, over 3, 000 supply chain professionals listen to this show. If you or your organization want to connect with this dedicated audience, consider becoming a sponsor. You can opt for exclusive episode branding where you choose our guests or a personalized 30 second mid roll ad. It's a unique opportunity to reach industry experts and influencers. For more details, hit me up on Twitter or LinkedIn or drop me an email to tomraftery at outlook. com. Together, let's shape the future of the digital supply chain. Thanks. Catch you all next time.