Automation in M&A

This is a conversation with Dr. Carl Michael Popp, Senior Director, Corporate Development at SAP. He's a huge advocate for automation and has written a book called Automation of Mergers and Acquisitions. In this interview we talk about automation, M and A. We covered how automation can be used in strategy, machine learning and M and A tools. Augmenting technology with decision making Incorporating data modeling into organizational culture this episode is sponsored by our business lines the M and A Science Academy Elevate your team skills with the M and A Science Academy. With over 60 courses, template library playbooks, it's one of the largest M and A knowledge bases out there. Hear from our alumni about how our corporate training plans have streamlined their deal making processes. Visit mascience.com academy and get your team ready for bigger, smoother deals in 2024. Deal room get ready for M and a acceleration in 2024 according to BCG's Global M& A report, the stage is set for a robust year ahead. And what's the secret weapon for success? Deal Room Be at the forefront of the action. Streamline your processes and seize the opportunities. Don't just follow the trends, be the one to set them. M and A acceleration starts with Deal Room your key to a successful 2024. Learn more by visiting dealroom. Net Again, that's Dealroom. Net Firm room secure your end of year deals with the ease of using Firm Room. Fast setup in just two minutes. With up to 80% cost savings and no hidden fees. Experience the difference for yourself with a free trial at firmroom. Com. Firmroom isn't just about security. It's about efficient, reliable and straightforward document management for your deals. Try it for yourself with our free trial. Visit firmroom.com Again, that's firmroom.com let's get to our discussion with Dr. Karl Pop. I'm Kisan Patel and you're listening to M and A Science where we talk with deal professionals and learn valuable lessons from their experience. This podcast focuses on stories, strategies and what actually happened during M and A deals. Foreign A Scientists Here at M and A Science, our goal is to continuously expand our understanding of M and A and use that knowledge to create top notch training programs and resources. By visiting mascience.com you'll find all the information you need to take your M and A skills to the next level. Get started by signing up for our free weekly newsletter to stay up to date on our latest courses, upcoming events and expert interviews. Again, that's mascience.com I'm your host Kisan Patel, CEO and founder of M A science. Joining me today is Dr. Carl Popp, Senior Director, Corporate Development at SAP. SAP develops enterprise software to manage business operations and customer relations. SAP is the world's leading enterprise resource planning software vendor traded on Frankfurt Stock Exchange under SAP. Today we're talking about automation, specifically with M and a strategy. Herr Dr. Karl Popp. How are you today? Guten Tag.

Guten Tag Kieson. Thank you for having me. Welcome to Germany.

Thank you for hosting. We are live in Waldorf, Germany at SAP Global Headquarters. Excited to be here and have this conversation with you.

Thank you for coming over and I'm looking forward to our discussions.

Can we get an introduction on your background?

I'm an economist by education. Then I turned into AN Information Systems PhD. Started at SAP right after my PhD working on process modeling and process modeling tools. Then I moved to a small security software startup in Munich during their efforts to go public, which they did. Then the bubble burst and I returned to SAP working on product management for Portal Software and BW as well as working with partners. And in 2007 I was educated on post merge integration and also did a first M&A project and integration of a company in Norway. And in 2008 I moved to the corporate development and since then I've been running M and A projects but also ongoing M and A process improvement.

Started off computer science, became a process expert, got into integration. Now it seems like you're involved with all parts of M and A. I.

Collected some skills and hopefully now I can use a lot of them in my daily work.

I wanted to have this conversation because you have a book that is coming out soon that will probably be out by the time we publish it and the book is titled Automation of M and A. M and A Strategy, Processes, Theory, Task and Automability. What was your inspiration for putting this book together?

It was a simple motivation. So strategy for me was always very fluffy and unstructured. A lot of PowerPoint and Excel based shuffling of documents and I wanted to have, let's say my usual structured view. I wanted to have a data model, I wanted to have a process model of M and a strategy. Over the last three years I worked on this model and I also worked on the book and interestingly enough it has some very positive side effects that we will talk about.

I was very intrigued by this book. I'm really thankful you sent me a preview version to look at. I did have some biases before from our last conversation that we had a couple years ago and I thought Dr. Popp lives pretty far in the future. And how relevant is this stuff now and then I was thinking a lot of the academic books tend to be pretty theoretical, but you have a lot of practical experience that you based this off of. I think that's what made it really intriguing how you took this approach of strategy and broke it down into a quantifiable format, which I thought was unique because you actually explained how. I done a number of these interviews and we talk about what's the best way to evaluate strategy. And it does turn into a lot of fluffy stuff at the end of it. But you didn't let that hold you back. You broke it down into an actual science. So I'm hoping to cover as much of some of the ideas from it. Now, I must tell anybody listening, you gotta actually read through the book because there's very specific examples and it gets a little technical because it's Dr. Karl Popp, he's a German engineer. This is pretty specific. But I think some of the concepts would be good to discuss. I know the first idea that you talk about is measuring strategy. Maybe we can talk about how it lends to more of the specifics in the model.

So one thing I did is to look at what entities actually we find there, what concepts, ways to carve and define strategy. And there are some very obvious parts there which are of course, strategic goals and strategic assumptions which allow to define and test drive strategy. I looked at that and then I started to think about how can we put more structure in that. Why wouldn't we say that a strategic goal to increase the market share in China by 10% in 2023 could be defined in a structured format? And market share in China is like an entity in an entity relationship model. That's basically what I did. In addition, I also have a process model. What are the different tasks like target search and processing the long and short list. I defined the different tasks and then I looked at the information or the data that are used by the different tasks and then came up with, with this model that has structure and relationship between the different parts that make up a strategy that could also be used for defining consistency and completeness. And also they provide some red tape to say, what does this strategic goal relate to? And I can look it up by following the red tape.

What was the problem that you saw with the current approach with strategy? That people, once they get to a point, they are pretty confident with it? I mean, was there certain gaps that you found or that strategy didn't hold? When you start executing deals, there are.

A number of issues you face today. So strategy is pretty much separate from the usual business operations. So some extracts of data are used, then they are massaged and altered during the very manual strategy process. And I wanted to change that. I wanted to have the same underlying the, not theoretical, but more like a data model for strategy as well, that I could also define a relationship between the data I need in strategy work and that might be just generated from the data I have in market information, in databases out there with company data, et cetera. So it would make automation in the strategy process a lot easier by having the data structures and the tasks and everything for the M and A strategy.

Now in your model you have some building blocks, strategic entity is one of them. And then there's also strategic assumptions and strategic goals around those. Could you maybe walk me through that briefly?

The strategic entities are, let's say high level concept, like markets, like customers, like solutions, suppliers, like partners that are very often used to define certain strategic goals. Like we want to have more partners or we want to double the revenue from indirect sales via partners, whatever. So there you have the objects in the statement, so revenues and partners, et cetera. So that's what I took and made it part of the model. And as soon as you have these objects, then you can define the goals as I just explained it. Yeah. And strategic assumptions are basically assumptions about any of these strategic objects. The market will grow with a certain compound annual growth rate or market access will be possible for a company in a restricted market. And other assumptions that you could take, which are the hopefully solid foundation for a strategic plan and for the strategic goals.

You talk about metamodel in your book, is that where that would fit in?

Yeah, the meta model defines the underlying spider web of objects and relationships. You have a strategic goal, it has to relate to a strategic entity always. And that structure can also be used then to do some consistency checking, to walk along the relationships to look if things are okay, if they are correct, consistent, complete even, and that hopefully would make strategy also better.

And then part of that is you talk through structuring unstructured data, which I thought was unique.

One of the motivations to get this book is get from fluffiness to a structured data model, which is always interesting. So one of the key goals was to go from A, let's say PowerPoint filled with strategic goals, assumptions and plans to let's say a structured data model of strategic assumptions, plans. And that's one of the key drivers to who work on an overall data model for the M and A strategy phase.

Can we Walk through an example.

Take for example the Strategic entity Go to Market which contains all parts of a Go to Market endeavor. Starting with of course the people working Go to market, the salespeople, the pre salespeople, but also the different markets they act in. And all of this is screen wrapped in the Strategic entity Go to Market. It also contains the application systems that are used in Go to Market in different ways like a CM system and other things and tries to provide a full and detailed picture of what Go to Market entails. But it's springdraft in this Strategic entity Go to Market. So you have a very high level model as well as a very detailed model if you want to drill into details.

I like that you picked Go to Market. I think you can end up with a lot of fluffing and a lot of ambitious assumptions when it comes to go to market with that. And I know some of the examples you had of how do you break this down into actual quantifiable data sets? Maybe can we click down into some examples of that? Cause I feel like the assumptions are really vague when it comes to Go to Market. A lot of times you don't get this information early to get a sense of where do customers overlap, likeliness of products being able to be cross selled and things like that. So how do you start quantifying some of this metadata to be able to get to some predictability there?

The key theme in general in Go to Market is also based on SAP solutions. The level of fluffiness decreases dramatically. There's a lot of good ways to leverage existing information about what's happening, Go to market about customers, about what customers might need, what we could sell to customers that you could use to predict numbers with machine learning models better than by asking your, let's say regional sales lead. How much more revenue can you generate if we buy that company? There are already ways to use, I would call it advanced analytics to predict revenue that could be generated by acquiring a certain company.

That gives you a lot of interesting insights to help with your decision. But does it weigh in on other areas? Is this getting fed back into the model? Like what are other impacts? Are you utilizing this type of insight?

Yeah. So the model itself can be used to set up and also change the strategic plans accordingly. And we talked about that, that over the course of the MMA process, there's a lot of information coming in. The amount of information increases over time. So you start with like a very early draft of the business case. Then you have more information, you have more information. You acquire the company, you have even more information, etc. So it's an ongoing, evolving plan, from an M and a strategy to integration, and through integration, a lot of changes, et cetera. So the model itself would allow for that ongoing and frequent change, but it's not yet implemented in a tool or an application system that we could really.

Prove that that's the next endeavor to collaborate on. But that's the right philosophy of having that iterative approach so that you do end up course correcting as you adopt new information and it requires you to do so. The completeness of strategy. I think that was an interesting thing that you talked about in your book. Once you start breaking down your strategy into these quantifiable components, all of a sudden it becomes pretty clear how complete your strategy is. Because again, when it's fluffy and you put the story behind it, it can sound great. But then when you start clicking down to these details, I thought that was pretty interesting. What was the thinking there?

Yeah, just imagine you have a set of three strategic entities. You have customers, you have suppliers, and you have solutions. And you say, okay, I want to do an M and a strategy. Whatever you do might impact all these three strategic entities. One way to have completeness is that you say, aha, we want to enter a new market. We want to have new solutions for that specific market and have strategic goals for that. But you don't have a strategic goal for customers. Very obviously. This is somehow an incomplete strategy because how do you get into markets by maybe getting new customers in that specific market. So there is some inconsistency in there. And by reducing the fluffiness, you have ways to easily check that kind of completeness and consistency within the model.

You don't get feedback that this is overkill. Did he drive people crazy in your M and A team?

For us, let's say a structured way of doing the things we do anyway. So we have a very detailed discussion in strategy. We have a very high effort in detailed due diligence with a lot of plans. What exactly we want to do, what we want to avoid in due diligence and in post merge integration. So it's more like structuring what we do anyway. And to avoid the overkill, there's the abstraction layer of strategic entities. You don't care what's inside the strategic entity market. Let's maybe 20, 30 data objects in there. But on the high level, you don't care about that. You just say, I want to raise the market share. That's a strategic goal. And later, if you want to cascade and execute on the strategic goal, then you can use the more detailed level to figure out what does it mean to raise the market share. It might mean to do more advertising or to hire additional salespeople or whatever might be appropriate measures to reach that goal. Then be cascaded or detailed out on a more detailed level. But you have this no overkill level with strategic entities to define strategic goals.

When it comes to the strategic fit analysis, how are you fitting this in and then utilizing it to identify targets out in the world?

That's a very important piece of target search as well as long and short list processing. And as a products and solutions provider, of course you have a focus on products and solutions to look at the fit, but it's not sufficient because give you a simple example, you might be a manufacturing company and your goal is to be a high quality but also high price tag provider things. And you buy another company which is price leader and then you cannot just say oh, the product fits very well. But there are some things to be done because that company has a completely different pricing and quality strategy. So you have to look at all the, let's say strategy aspects and compare the companies that you can get to a proper statement, what the strategic fit is. In our case, you would have to decide should the target continue the price leader strategy or also become a high quality, high price tag.

You did a bunch of research on tools available for automation and the data sets, the big topic and how you approached identifying targets. What did you learn from all that research? What's the best data set to use?

Let's take for example target search. I've had a look at 17 different tools and also the data they are using. And data is of course a big topic. So you have like use all the data that's in the Internet at one end of the spectrum. At the other end of the spectrum you have solutions like Embryonic from Eye where they use all the big shot expensive company databases out there. And both approaches have their pros and cons. If you use several databases, you have to do curation because some of the data, like revenue data, might be better from Pitchbook than from Crunchbase and for technologies vice versa. And on the other hand, getting information and making sense of information that's on the Internet is also yet another approach. And there are goods and bads to both approaches. But what they have, of course you have a huge demand for data and you have also huge demand to create proper, let's say strategic assumptions and strategic goals that you want to make sure. You can also execute. There's a high need of data access as well as let's say a, a way to harden and really make the strategy consistent and complete that you can also execute it.

So what's the best data set?

It depends. There are a lot of providers out there that claim to have like global company data sets. Then there are many companies out there who say, yeah, we are local, we are in Scandinavia, we know all the Scandinavian company startups a lot better than the global databases because they're not present in the countries. Let's say we have all the Chinese startups covered a lot better than others. So it really depends on the exact scope of companies markets that you're searching in. It's always a good start to use one of the well known large company databases, but you might have to look to other vendors as well for like very specific industries or countries who have the best data set.

I had a feeling you're going to say that it does depend on the industry, sector focus and et cetera. So you got to do your diligence there. One of the concepts that I found fascinating in your book was the concept of a cascading strategy.

The cascading of strategy is a ever returning issue in all strategy work. The CEO says let's become a high growth company and we will extend our market share in the US by 25% within two years. Okay, so everybody knows what the strategic goal is, but what does it mean for the sales guy in Cincinnati and what is his share of getting there? And to make sure that the sales guy in Cincinnati does the right things to also feed the overall high level strategic goals, you need to cascade the strategy. He needs to know what does it mean on his level of the company hierarchy. And for this reason strategic goals can be broken down and should be broken down as well to make it easier, digestible and also executable by people in whatever function in the company. And in my model there is a direct connection from these high level strategic goals with always connected to these high level entities to a more detailed inside view of the strategic entity which allows them to break down their strategic goals into more detailed goals on a more detailed level of the organization and data. So for the Cincinnati guide might mean that his focus should be on high price items, for example, or on specific customers who are in desperate need to transform their business that should in the end feed the high level strategic goal with increasing the market share.

Then would this cascade all the way to the task to ultimately execute your diligence and integration.

Yeah, Absolutely, yes. The good thing is that you cannot only cascade, but you also have the red tape to go back and show to the Cincinnati guy what is exactly your heart in the overall game to reach the 25% market share. And that's a good thing that you can go both ways. It's also a motivational thing for the Cincinnati guy to know what his share is in this overall goal and that he has also proven to be part of the overall aspiration of the company.

I like this. This is something that we advocate for with the Agile M and a framework to essentially build that continuity between diligence and integration. What about the thing we talked about a little bit before around the new information coming in? Sometimes you get information that prompts you to change your strategy. Then in turn if everything LinkedIn cascading that create a bunch of rework, what's the approach to do that? And especially a large organization like yours, how do you reorient so many people?

Yeah, it's always iterations and at certain points in the planning process you have to be aware that there might be changes. So at some points you need to have some buffers in your planning, in the budgets and in the revenue planning, whatever, that you can easily decrease later as soon as you have more information. So it's always iterative. There are certain snapshots where you have decision points like going to the executive board to approve that. We enter into the negotiations and due diligence process. We take a snapshot of the plans and provide some caveat saying this is just the current snapshot and there might be changes and there will be an update in the final decision meeting. And yeah, as soon as I think it's very important to have everybody aware that at a certain point in time it's not the final plan and there might be changes that we need to make to the plan to be successful at a later point in time.

It sounds like you come up with something even rough in the beginning and you essentially have a feedback loop and just keep iterating on it as you go.

Yeah, absolutely. Yes.

One of the things that we talked about before is adopting the best practices from the target company. I want to talk about this one because it's probably one of the most easier said than done things in M and A. But the target company acquirer can be doing things better than the parent company that doesn't always get integrated in that way to benefit the overall organization. Can we talk about your experience around that and are you seeing things change?

The importance of this question, what can we learn from the target gets more and more importance and more and more presence in discussions, the more experience you have in acquiring and integrating companies. And the key thing is that you have to ask yourself the question, why exactly did we buy this target? And we had many cases where we said we want to acquire this target because they are in a new market where our company hasn't been before, or they're running a business model or a platform business model that we haven't done before. And then it's clear you bought this company because they know how to run that specific business and you don't want to tell them how to run that specific business because you simply don't know. You have to learn from them. And that thought process and the consequences from that is a very important ingredient of success. Because if you imagine that you would try to quickly and fully integrate a company whose business and sales model and operations model as an acquirer, you simply don't know how to operate, you would basically break this company and it would not be a successful integration. So for us, and that's just one example, having a business model that as acquirer is not capable of immediately executing, we refrain from, let's say fast or complete integration, at least for some time, until SAP is an acquirer, is capable of and has learned from the target how to run execute that business model successfully.

Ideally, you want to identify this early, adjust your integration strategy. In this case, let's not do full integration. We'll do a partial integration in the core things that we need, but give them autonomy to do their secret sauce in a way that preserves it. What about these things that come up later? Maybe you've set to do a full integration, but then you start identifying things, maybe even past diligence when you're integrating the company that hey, this is actually something that they're doing better or may be one of those secret sauce things that you don't want to break.

Simple things like there's a company that always has the maximum priced models of computers for the developers. SAP does not buy the highest price, just like the second highest might leave people frustrated and you have to rethink. You want to frustrate the developers on day one or do you on an exceptional basis by the more expensive computers who welcome them and not reduce their capacity. These are the things and the changes that you should definitely consider. And that's just a very simple example. Overall, we try to predict as many obstacles that we might run into as possible and also think and plan and budget the mitigations that we would need to overcome these obstacles in pmi.

That's a really good example. So that you've got to be the ad hoc and change plans pretty quickly. And it sounds like partly is having the culture to be open minded to be able to do that.

Yeah, absolutely. And it's an ongoing work as well. Change is always hard to achieve in let's say working environments like big companies. And this is why we support the acquired businesses for a good portion of time in PMI until we think they're ready to continue in a, let's say, day to day environment and not something that's under the umbrella of post merge integration.

I want to go back on quantifying strategy. We talked a little bit about the soft fluffy stuff, but I want to tackle the softest, fluffiest part, which is culture. How do you quantify culture? Can you quantify culture?

Culture? People might hate me for that, but there are quantifiable parts or at least parts that you can put in a data model which to some degree describe strategy. And you can easily see that in any surveys that are done with the acquired team, for example, to see how they are doing, if they're motivated, if there are any issues due to the integration. And there you find things like attitudes, like norms, beliefs and other things that you should look at when you look at culture, but it's not all quantifiable. So a lot of things in the cultural exchange between target and acquirer are also based on experiences. So we have a team that only is focused on the experience of the acquired. Yeah, a lot of things about role models, about good examples that you support and really help the acquired employees to feel home with the acquirer.

I wanted to ask you about the future of M and A and one of the things specifically around data you mentioned was a lot of this is around having an open API to enable leveraging some of these new emerging technologies. Can you walk me through that? Then maybe we can expand on what the actual impact's going to be today.

I think the predominant vendors in the M and A tools market out there are the data room vendors and they are expanding the scope beyond the data room functionality and due diligence, so into pmi, but also into earlier phases. So these from my point of view will be the established platforms for M and A automations. But I also believe that they will not be the sole providers of innovation. So they will certainly have to integrate, let's say other very focused but also very innovative solutions into their offerings and how you do that best today. Is by providing open APIs to have other solutions plug in. You might have a data room, but you might want to use a very, let's say highly skilled automatic contract analysis from another vendor and would then just plug it into the data room solution to make use of it.

I think that's a really fair point. I am obviously biased here because we own a data room product and what we call lifecycle management platform. But that is the vision is to have it as a platform. So then you can utilize these APIs to one connect with tools that may already be in the ecosystem of the customer. But I think what you're addressing is add in these additional or enhanced capabilities, which I think you're absolutely right because the data resides somewhere and that's where these capabilities need to be built around. Let's talk about what all these capabilities are going to be if we look through the lifecycle. We talked about strategy today. Even now you're giving me a bunch of ideas. So it's probably going to open up to continue conversation. There might be a collaboration opportunity here. Dr. Pop, can we build a strategy module on the platform to start building this stuff out and giving that strategic fit analysis, strategy analysis. We're seeing ways to really automate this. The quality and thoughtfulness and the fit on that side. What else do we see?

What I see is that the different platform vendors, the data room vendors, are starting to provide machine learning based functionality in different flavors. What I miss, but I see it in other industries, is the machine learning enabled analytics, which for example could mean that information, let's say information about growth rates, revenues, et cetera, could be automatically collected and maybe combined with the acquirer's information to make predictions on how much revenue we could achieve with the acquirer Salesforce selling a target solution. And we see glimpses of that already. So for example, there's a company called Modeliza that does automatic sales predictions. So if you go to a new customer and you have many solutions that you could sell to the customer, what is the one solution that would give you the highest likelihood to strike a deal? And there are solutions like Modelizr that can actually do that. And we're currently working on using that to not just predict the sales to one customer, one specific customer, but more to determine the overall revenue potential for an acquired product for the acquirer.

We're moving away from assumptions to predictions.

Absolutely. Yes. Yeah. And you can do better predictions with more formalized assumptions. One idea behind the book is also that having structured the domain of M and a strategy makes it Easier to build solutions and to pinpoint the data that I need in certain situations and work on top of that, we talked.

About the target list that could be potentially automated with the sets of data and using those metadata. From your strategy against it, there's contract analysis and summarization technology. I'm cheating. I'm using notes from your book. By the way, there's due diligence. We chatted previously about emerging technologies like ChatGPT and being able to autocomplete diligence requests. Those follow up clarification questions. The decision support was an interesting one. Can you talk a little bit about that one?

We know that there are maybe thousands of decisions which are not the typical day to day decisions you have to take during an M and A process. As soon as you have proper data, this data could be used to augment the information that you use to take a decision. And currently in the different contexts there are solutions out there that if you run a car tire company and you're in the process of ordering tires for the winter season, that there are analytical solutions, decision support solutions out there that based on new car sales, on weather predictions, et cetera, give you a proposal of how many tires to order for the winter season. And that's exactly the type of technology we should be using in the M and A process as well to make better decision. As soon as you know what this decision to take is, there might be machine learning based algorithms who collect information, make proposals and help you do better decisions than you could do before just because there are augmented pieces of information that help you to do better decisions.

I can see it being really valuable for both the broad use cases and M and A specific when it's critical to make fast paced decisions and fundamentally that's good leaders good at making those decisions. And then I also noted the summarization of our diligence findings, reports, progress reports and et cetera. Why hasn't any of this stuff progressed? I feel like when I look at like sales and marketing I always see so many emerging cool AI technologies and look at M and A and it really feels like we are literally 20 years behind. I think even people listening to this 80 plus, maybe 90 plus percent are still using Excel to run their whole process and not doing any of the stuff that we talked about. My hypothesis, I'm curious if you agree or disagree. I think this industry profitizes so much off of this inefficiencies and I'm thinking of like billable hours that nobody's incentivized to be innovative because they're making money off of all this inefficiency. What's your take?

Yeah, you're certainly right with your point, but there's also an additional point. So I think that people in corporate development are not techie people, are not nerds, at least not from a technology point of view. Maybe in financial models and everything all fine, but not in what is the technology out there. And we talked about the predictions use case that you can use. So it's what Kahneman called what is all there is. So if you're not a techie, you cannot claim that you want to have a certain technology used in the M and A process because you simply don't know. You only know what you know. And this is also why I put a section in the book that explains different technologies and how we could make use of it in the M and A process. We talked about predictions, we talked about summarization automatically and other things. And as soon as then you can create some pressure from the customer point of view and says, hey, why don't we have this fancy machine learning based thingy in the M and A process as well? And then we'll see a much higher degree of innovation in the M and A tool landscape.

I like that. M and A needs better tech leaders to drive the aspiration to innovate so we can have better NA outcomes and better people experience for everybody.

Absolutely, yes. Yeah.

I'm glad you're one of those leaders helping to carve the way. I gotta ask you though, what's the craziest thing you've seen in M and A?

M and A provides a lot of surprises and uncommon decision situations. And one of the, let's say the funny examples is that we were once in acquisition discussions with a company in Germany and we knew this company for a long time, good partner. We said, okay, then let's go forward. Let's have due diligence discussions. And this company, we were happy because we thought, okay, there's no international travel needed. It's all local, it's all German people will be a thing we can easily execute and manage. And then in the first meeting they told us, yeah, welcome, we have two product lines and one product line we care for in Germany, the other one we care for in Ho Chi Minh City, Vietnam. Without any glimpse of what's coming. We certainly had the international project we didn't plan for. So that was one of the interesting surprises and crazy things happening in M and A that you just cannot plan for.

Yeah, that's a big surprise. That kind of throws a wrench. Did you ultimately get the deal done?

Yeah, we got the deal done and it also created some efforts for us. We didn't have a subsidiary in Vietnam, so we had to send somebody over there to establish subsidiary to hire the people who were locally in Ho Chi Minh City and other things. Yeah, but we did the deal and it was also a successful one.

No deal goes without its surprise. Yeah Dr. Karl Popp this has been great. I enjoyed our conversation. I learned a lot. Those of you who've found this conversation interesting and want to learn more about engineering M and A Strategy, check out Dr. Popp's new book, Automation of M and A M and A Strategy, Processes, Theory, Task and Automability should be available now or soon after we publish this podcast. Dr. Pop, thank you again for taking the time helping me become a better M and A scientist here in Waldorf, Germany. Those of you still listening to thank you. Till next time. Here's to the deal. Thank you for taking the time to explore the world of M and A with our podcast. We love hearing feedback. Tag us on a LinkedIn post. Add a review on Apple Podcasts. We'd love to hear from you. If you you need help standing up an M and A function or optimizing one that you already have, we're here to help. And if we can't help you, we probably know someone that can. You can reach out to me by email Kisan K I S o n a science.com or you can text me directly at 312-857-3711. If you just want to keep learning at your own pace, visit MA for a lot more content and resources. That's where you can also subscribe to our newsletter. Again, that's mascience.com here's to the deal. Views and opinions expressed on M and A Science reflect only those individuals and do not reflect the views of any company or entity mentioned or affiliated with any individual. This podcast is purely educational and is not intended to serve as a basis for any investment or financial decisions.