My first interview with Hans Læssøe last month covered a wide-range of topics, including background on what led Hans’ former company to adopt ERM and a few factors key to its success.
In this interview, we discuss a topic I’ve touched on briefly in previous articles but haven’t elaborated on much, until now.
Monte Carlo simulation is a tool or approach that was named after the famed city on the French Riviera. It was developed by an English prisoner in World War I to calculate or find decimals of PI, which as you may remember from Statistics of Physics class, is a mathematical constant approximately equal to 3.14159.
The model wasn’t widely used until computer systems became commonplace. It helps or rather allows companies to create models of their performance to predict a range of outcomes to expect. There are literally an infinite number of things to model, but in our interview, Hans used the example of sales.
Your company may budget for sales of 2 billion, but you know you’re not going to hit that number exactly, so you put ranges around this goal…this is your uncertainty. You then ask the computer to come up with random scenarios, typically a number around 10,000. Once you have this model, you can then start adding risks in and understand how they will impact the target. This is where the model becomes more advanced, more real life, and very useful for guiding decisions.
To use Monte Carlo simulation effectively, you need to have some basic knowledge of statistics (i.e., ranges, stochastic distributions, probabilities, etc.) or someone on your team who understands these things. Two books that can help revive your statistics memory include Douglas Hubbard’s The Failure of Risk Management: Why It’s Broken and How to Fix It and How to Measure Anything: Finding the Value of Intangibles in Business.
Much to my surprise, Hans explains any organization can use Monte Carlo simulation…
Remember, you can simulate risks, but in the end, what executives care about is performance. How can you measure performance criteria?
The more data you have at your disposal, the better, but if no data is readily available, you can ask specialists in different business units to develop ranges. This should be seen as a last resort though.
This sounds great, but my company has always done qualitative assessments. How can I convince executives that Monte Carlo simulation is worth it?
Although you should get approval from your direct boss, Hans doesn’t think executive approval is needed beforehand.
Instead, you as a risk professional can begin leveraging Monte Carlo simulation using a variety of free tools and tutorials that are available. With a little trial and error and consultation with peers, you should be able to develop a model around a certain performance metric and then report the likelihood of meeting the target to executives.
The key to remember is to start small…
Don’t rollout Monte Carlo simulation to the whole company before testing or “piloting” it out in one area. See how a simulation for a particular metric will work in one business unit, say sales or R&D. If you find out your model is not valid, it’s much easier to start over than if you just rolled it out to the whole company.
Also, when looking for areas of the company to try out a Monte Carlo simulation, consider applying it to a project in an area of your company that is used to dealing with numbers like operations, manufacturing, or engineering, if applicable.
Wherever you begin using Monte Carlo simulation in your company, remember to always begin with your target in mind.
Wrapping up the interview, Hans explains how Monte Carlo simulation is a tool that is just as important to a risk manager as the screwdriver is to a carpenter or mechanic. Successfully harnessing this tool can be a real game changer for companies over heat maps and other qualitative approaches. While it is something I’m learning about more and more, many of the companies I work with are not prepared for this advanced level of ERM.
Have you attempted to use Monte Carlo simulation or other quantitative methods to support decision-making?
A huge thank you to Hans for his time and insights on this fascinating topic…check out the video at the top or the full interview transcript below for more. To share your thoughts, leave a comment below or join the conversation on LinkedIn. And please purchase his latest book, Decide to Succeed, for details on how to use modeling results to impact your organization’s decision-making process.
If your company is looking for options to better understand risk, uncertainties and opportunities, please contact me to discuss your specific situation today!
Topic #2: Using Monte Carlo Simulation to support decision-making
Hans Laessoe (00:02):
Hi Hans. It is so good to see you again. Everyone, this is Hans Laessoe from Denmark and he has a business called, Aktus, which stands for …
Hans Laessoe (00:16):
Hans Laessoe (00:17):
Fantastic. And that is really key, right? The active involvement around uncertainty and really embedding that
Hans Laessoe (00:25):
leverage. Yeah. The most important risk management strategy you will ever have is exploit.
Carol Williams (00:32):
And people hate that word sometimes because it has a negative connotation to it. Leverage. I like it. I like it. All right. So today we’re gonna talk about how to use Monte Carlo simulations to support decision making. And I know that this is a favorite topic of yours because it talks about using the data and the quantitative side of things. So yes, we’re going to have fun with this one.
Carol Williams (00:57):
Describe to you what, what is Monte Carlo simulation and modeling?
Hans Laessoe (01:04):
Um, it’s a tool. It’s an approach. Um, it’s named after the city and it was actually developed by a World War One prisoner, English prisoner in 1912, 13, 14 or 14, something like that. That’s going to be between 14 and 18. Where he used the approach to calculate or find decimals of PI.
Hans Laessoe (01:31):
I mean there’s nothing else you can do. So we are, he was working with that one later. Computer systems came on and a lot of things are happening and it is used widely. What it does is that it allows you to make a model of your performance. Could be a budget and then you put uncertainties on the different levels. We have a sales, we budget for a sales of 2 billion fine. You know, you’re not going to sell 2 billion comma zero zero zero. So it’s going to be somewhere between 1900 and 2200; probably 2 billion. Okay. So we have that uncertainty. We have one on the discount structure and the cost of goods sold and distribution got all the different things you can model these different put ranges on top of that. Now some of these things will be good and some of them will be bad and there will be things coming in.
Hans Laessoe (02:24):
And what do you then do is you ask the computer to come up with 10,000 random things. Could be fighter. Doesn’t matter. But I normally use 10,000 different scenarios. Now you tell me what’s the likelihood we have a budgeted bottom line of $250 million? What’s the likelihood that we’ll actually meet that? What’s the likelihood it will be 400? What’s the likelihood that we will be in red figures? And now we have a discussion about the performance of the company and then once we have that model, we can start adding in the risks. This could happen. We can get a strike on a factory or a fire on a factory. What will then happen? How would that impact, and build that into the model and making the model gradually more and more advanced, more and more real life
Hans Laessoe (03:12):
and ending up being a tool to predict within which range your outcome will be. And yes, some of the ranges will be very wide, wider than you think. Yes, that’s life. It will happen. And if executives challenge you and say there’s no way we can get red figures were sold, well consolidated was doing so well, you just take one or two or three of the scenarios that actually did come up with a negative bottom line and show, okay, if this, this, this and that happened the same year and occurred, not likely but occurred then you’re in red figures. Even if that and that good thing happened at the same time, you’re still be in red figures yeah, okay…maybe. Most companies are much closer to being lost or losing their profitability so they will have a significant risk of being negative. But now you have a tool that you can show that based on the data that you have., and you have the facts so you might as well leverage them and put them into the model.
Carol Williams (04:14):
That makes total sense. I know in a previous conversation we were talking about some of the skill sets people need to bring to the table and you had mentioned some basic statistics. Yes. Be able to handle the data. That is where this is coming in is to be able to extract all that data.
Hans Laessoe (04:33):
Yes, you need to have a gut feeling – have some basic training that tells you in this case I actually can do with a triangular distribution. That case I need a normal distribution. You need to have that basic understanding of ranges and stuff, stochastic distributions and stuff, probabilities and stuff like that. If that is all mumbo jumbo to you, you’re in trouble.
Carol Williams (04:59):
We have a 90% confidence interval that we are going to hit this range. That’s your statistics coming out and saying you’re 90% confident that we can hit this goal.
Hans Laessoe (05:13):
Yes. Yeah. It’s not very complicated, but you need to have that approach that allows you to leverage the Monte Carlo simulations.
Carol Williams (05:23):
And if you as a risk manager have more of the people side and the not so, quantitative skill sets, the statistics side of things, go get some for one, but then you’ll get somebody on your team even if even if you yourself don’t have it, that’s where you can get someone on your team.
Hans Laessoe (05:47):
You can also help yourself, which is what I did because I knew it’s statistics that I just didn’t know when I started. I just didn’t know I needed it. Get Douglas Hubbards, The Failure of Risk Management: Why It’s Broken and How To Fix It. And you get another one of his called How To Measure Anything. You read those two books. Once you have those, you understand the basics of how you’re doing it. Then you find some software package that you put on top of excel. The most common ones are At Risk from Palisades and Moderate Risk. The easy first step is to get the basic basic edition of the model risk because that’s for free. And you could work with that and get acquainted with it before you decide whether you actually want to pay for something and it’s not for free for a trial. It’s free for good, the basic edition, but there’s a lot of things you can do but others can but you don’t need that to begin with. And then you start experimenting and then you start working with it. I’m an engineer by training. I love solving issues. When I was introduced to Monte Carlo simulation, I had so much fun. My wife hated me because suddenly my private budget was much more complicated our pension plan was complicated because I thought it was, it was so much fun and I was supported by the executive committee because I was starting working with that and explaining how I was doing it. I was consolidating these risks and the CEOs that that’s the right way to doing which meant everybody else shut up. Nobody opposed the CEO on that one, and I thought that was fast. He came up with that. How did he know that? And coming back from the meeting back to my office, I said, Oh yeah, that’s right. The guy has a PhD in mathematical game theory. This is kids kid stuff. He actually knew exactly what I was doing.
Carol Williams (07:49):
That’s a bonus. Probably a rare circumstance for you.
Hans Laessoe (07:53):
That was a real, that was a lift.
Carol Williams (07:57):
Are there certain types of organizations that are better equipped to take advantage of Monte Carlo Simulations?
Hans Laessoe (08:07):
No. That would actually assume that some of them couldn’t. And there are no organizations that cannot leverage it. They may not every take the hospitals again and say our purpose is to save lives or our scores, our purposes to turn out graduates and stuff like that. They may not simulate our money, but they may have other metrics, but any metric can be measured. And any metric it can be used. It doesn’t have to be money.
Carol Williams (08:37):
So one of the constraints though is going to be data and having it and having it organized right. To be able to use it in your simulations. Yeah. So,
Hans Laessoe (08:49):
but if you keep up, if your peak key performance indicator. How many patients do we run through, um, ER, um, in a week or in a month or something like that. And how many do we turn down or do don’t we get through? And the fact is your key risk indicators then you can start Monte-Carlo simulating what will happen to, what would it take for this number to go up or down or how’s the, what’s the risks and the likelihood of that happening. And you can model that. There’s a doctor sick, uh, pure, fewer through and all that stuff. Longer waiting lists. So you can, you can Monte Carlo simulate anything that you can measure and you can, as Dr. Hubbard said, “measure anything.”
Carol Williams (09:32):
So where would information from the Monte Carlo simulation come from? I know you were just talking about the key performance indicators. Are there other pieces of information or sources of information that you would recommend? Especially for companies starting out?
Hans Laessoe (09:52):
Yeah, starting out. Depends on where you’re starting, what are you starting with? And then you start asking, how do you know you’re successful? What’s your metric for success? That is a master metric, you can Monte Carlo simulate. I mean, there’s no point in simulating we have 18 risks, somewhere between 18 and 25 risks. Who cares? I mean, what does that mean to us? What you do care about is performance. So you say, my performance criteria is this. How do I measure that? I mentioned it this way. Okay, so now you know whether you’re successful or not successful. And then you start measuring all of your risks and all of your uncertainties based on that key parameter and go from there. And you will have data on a lot of stuff. In any organization, they have a load of different data. Ask specialists, people actually working with us and say, okay, how do you measure that?
Hans Laessoe (10:40):
How do you know that? Where do you gauge that? Oh, we have these data. We have those data. Okay, don’t start looking at those patients. In the end, if you really don’t have anything, and you do have companies that walk in, walk through life in total ignorance because they never catch data on anything, they’re there not for long, but they’re there. You can start asking the specialists, what do you think are you, this number here is 500 what is the minimum number you are 95% certain it’s going to be, it’s narrow. There’s a very small 5% 2% chance it’s below 350. What’s the best it can be? Whether there’s a very small chance in awkward places, it could be more. Well it could be 650 could be 620 and then you challenge that and you talk about them with that. But now you have a range and you can use what is most not commonly used, a case approach distribution with those three parameters. And you have your distribution, you have your rollout, but that’s your last resort. Your first resort is data. The second result data, the third is data. And then then the fourth, if you have nothing else, then you ask people.
Carol Williams (11:55):
Well, and even then they’re going to have experience and having numbers, right? So even if it’s a paper copy of a report, you can say, well over the last two years, here’s how the data has come out so we can tell you, you know, here’s 24 months worth of data right here.
Hans Laessoe (12:15):
One of my key points is that when you’re looking at projects and how they are running through, why are it projects always linked? I started in IT in 1981 that’s 39 years ago and at that point in time I was told that IT project management, they should take a huge project. You cut it up. It’s like in bits, your plan every bit, you find out how much time the whole thing is and you multiply it with three because it’s always going to take more time. What does that multiply by? Three? What would, what would happen if you were able to multiply by two or just add 10% because you knew what was happening and they still haven’t gotten it. They’re still multiplying by three delivering late . Building projects…the same thing.
Carol Williams (13:05):
They multiply by three and then they still deliver late after the multiplying by three. That’s the crazy part.
Hans Laessoe (13:10):
Yeah. Building projects. They always over budget. Why? Well, because to get the contract in the beginning where they come up with an estimate that they know is too low and then once they’ve started, they assume that they can ask for more money because now you have your hand on a cooking plate anyway. So it’s kind of a business model. I would kill the company that did that to me if I had a building project and say, okay, this is what you believe. Yeah. Okay. Now you put your hand on it on the plate. If it’s anything here, the first thing we do is we take every extra dollar that we need to build their thing from your bottom line, not from my bottom line. Once you’re exhausted and on the brink of bankruptcy, then I started feeding in money. But not until that. So yes,
Carol Williams (13:59):
I’ve actually seen companies do it and it’s amazing how well it works to get an accurate budget. Yeah.
Carol Williams (14:06):
What skill sets does a company need to utilize, in order for them to be able to utilize Monte Carlo simulation? Outside of the statistics that we just talked about?
Hans Laessoe (14:21):
Nothing. There were no extra skills they need. They just need the insights from people who were there in the first place and people know where the data are. Oh, you want to make sure that we have a figure right over here, you want to see, you haven’t want to have a predictive indicator of world growth. Tricky. Yeah. Look at the number of orders which have big trucks.
Hans Laessoe (14:47):
The number of orders and pick trucks precedes the GDP development where they’re about six to nine months. So if the order drops down as it did in the financial crisis, by the way before the housing price went bust six months before that, orders for big trucks plummeted and then went the market. So yes, you can get data, very good insights in the two books from Dr. Hubbard on where to get, how to get data and software. Also the fact that you need less than you think.
Carol Williams (15:17):
You always need less data than you think you do. Right.
Hans Laessoe (15:20):
The more data you have, the more safe you are statistically and the better distribution you could find to be accurate. Sure. But you need very few data to get a distribution that is accurate enough. Dr. Hubbard talks about the value of information.
Hans Laessoe (15:40):
Now you can get an information that is actually, you can spend a ton of money on getting an information that is absolutely spot on. For 10% of that money you can get an almost accurate, the number a range out of it. And if you look at the two ranges, the decisions that comes out of the executive office based on the two, exactly the same, then the total accuracy is a waste of money. It’s a waste of money to try to be that precise. It makes you feel good. Yeah. Rate how much money you’re going to pay for feeling good. But sometimes you do. But yeah, not in business. Basically, yes, you need data enough to ensure that you drive the right decision.
Carol Williams (16:33):
And you save the company that much money, right. In time…
Hans Laessoe (16:36):
Or make a bolder step. But I’ve been involved in decisions where, product launches has been postponed because when we looked at the product development, they have made a plan. There was this was it, there was a buffer put in top of it and say that’s the launch day we aiming for. And then when, we looked at the uncertainties and the risks in the product development phase. We found that there was a 10% likelihood that we’re going to meet that date, even the prolonged date and that wasn’t much, nope. But there are two, three very huge risks, very likely risks that will severely postpone your development phase. If you want to make sure too that you’re able to deliver at that point in time, you need to postpone your launch date.
Carol Williams (17:23):
So they did.
Hans Laessoe (17:24):
So they did in other cases, the Lego group is very much up against Christmas and there was no such thing as performing a launch. which means that sometimes it costs a ton of money. Yes, and it does.
Carol Williams (17:40):
But that’s the price.
Hans Laessoe (17:42):
That’s the price they pay to be able to meet, b But you cannot have the marketing campaign running the TV spots, running, the shelf space allocated in the stores, and the products are not there. It’s so expensive. It’s an absolute debt no-go.
Carol Williams (17:58):
And not just at that instance, but also the reputation.
Hans Laessoe (18:02):
Oh no, no, no. It’s a lot worse than that. If you were saying your launch March 1st on the new product line, and there’s like two foot of shelf space available in stores and the product is not there. Storekeepers have an allergy. They eat empty shells. So they would take whatever they have and put them on your empty shelves to have a vibrant business and have ripe and a store. One week later, your product arrives just four days later, your product arrives and your shelves are full. Then they’re gonna take what they are put in there, away to put your new stuff in. They’re going to wait until it’s sold and then they put them and stuff in. But that means your four days of delay suddenly becomes a three months delay and by then all the kids have forgotten all about the new, TV commercials and let’s stop talking about the product because nobody has it. So you’ve lost it. Sorry. So yes, generally not just the labor group, generally in what is called fast moving consumer goods markets, you bend over backwards to deliver on time.
Carol Williams (19:14):
Yeah, I agree. I can see that. Definitely see that. And even, I mean that’s really just all about meeting expectations
Hans Laessoe (19:22):
Having a consolidated view using Monte Carol Simulation to consolidate all the uncertainties and give you a vantage frame of valid reference point to what’s the likelihood you will be able to meet your delivery day. What are key issues that you can address in order to support that coming out of the other part of the tornado inMonte Carlo analysis with what is called a tornado diagram, where you get a ranking of the uncertainties that you have by how important they are to your end result.
Carol Williams (19:56):
Well, and that really gets back to you, to use your example of they’re willing to spend the money in order to meet the deadline. That’s because they have a very low tolerance for missing that deadline. So they are, taking action, making decisions based on their tolerance. But it’s all based from the data that you’ve been able to provide them with the Monte Carlo Simulation.
Hans Laessoe (20:19):
We have been at the Lego group, we have been flying products, finished products, toys to the US or to Asia, to meet with the trucks that drove them directly into the stores. I mean, we have to bend over backwards to make it.
Carol Williams (20:32):
Carol Williams (20:34):
Well, so what you have to do is what you have to do.
Carol Williams (20:39):
So we were talking about the skill sets and you said you don’t need any additional skill sets. You need to have that statistics background. If people feel like they’re going to be overwhelmed with data that they don’t feel like one person can manage or just trying to get their arms around it. Are there other areas within an organization that can be leveraged without having to add to the ERM stuff?
Hans Laessoe (21:06):
No, but I don’t think you need to. What do you do out of the Monte Carlo simulation, you don’t present them onto Carlo Simulation (inaudible). I mean, consultants are very bad at this. They start telling you how hard they’re working, how good they are, and how much your references they have. You have hired them. You basically don’t care. You’re looking in the, into this and now you’re telling them not how you got to the results. You basically assume they trust you to do the right thing. So what you tell them is the likelihood that you’re going to meet your track, it is. There’s a 30% likelihood that you will meet your strategic objective. Are you happy with that? Nope. Okay. Here’s what you can do about it. Here’s the key issues that you should address to do something about it. And I’ve looked at the top two and talk to some specialists and some of the ideas that we’ve gotten up with are these and those
Carol Williams (21:56):
And that was a great example of a framework for a conversation of how ERM needs to be talking with the
Hans Laessoe (22:04):
Don’t report on risks. Nobody cares. A report on likelihood of meeting targets.
Carol Williams (22:11):
It’s a twist in the frame of reference.
Hans Laessoe (22:14):
Hans Laessoe (22:16):
It’s talking business language executives don’t care about risk. They care about performance.
Carol Williams (22:21):
Right. Even though in our mind it’s like, well, we’re doing enterprise risk management, so we need to talk about the risks out there. Getting managed, right? No. It’s actually about just managing the enterprise.
Hans Laessoe (22:36):
It’s actually about performance. Managing the enterprise. Yeah. Yes. I would like to, I would like to, and it’s going to be an uphill battle and I’m going to make it in my lifetime, change the concept of risk management into one of, performance assurance.
Carol Williams (22:54):
That’s a very laudable goal. I’m with you on that one. I can help you. We’ll work on that. We’ll talk about that.
Carol Williams (23:04):
What are your thoughts on convincing executives that Monte Carlo simulation is worth it? And I’m thinking about it from the perspective of companies who have historically done qualitative assessments. We have not used data. Now they’re going to have to shift in their way of approach. Is there any convincing that needs to be done?
Hans Laessoe (23:28):
No. They don’t have to change anything you do, you leverage. Go ahead and do it. I mean, a moderate risk is for free. Get the tool, look at the, tutorials. They’re actually rather good for both of the software packages, both at risk and moderate risk. Look at the tutorials, try it out, figure it out, use a model, have it validated and so forth. And maybe talk to a peer or somebody or even ask them or even ask the consultant or the vendor company about this and have client, and then apply. And then go to the executive and say, instead of using these humanly biased qualitative things where you know, you don’t agree on how difficult this is and how big this is and so forth. We have started using data instead and facts instead. And we have analyzed this usually at a mathematical approach. I don’t want to bother you with what we come out with this result, what we can tell you, there’s a 30% likelihood that you will meet your target.
Carol Williams (24:29):
So a great point in what you just said is basically not having to go to the executives ahead of time and get their approval to change our approach instead it’s just change our approach and do it and tell them, you know, that you didn’t like this part.
Hans Laessoe (24:47):
The only approval you need to get is from your direct boss. I mean, don’t step on his toes and present something new he’s never heard about. Tell him what this is and talk about to him. Show him, I mean, give him an example. Show him this is what we can do now. Get them excited about this reporting likelihood of meeting targets and say, okay, but this agreement, I will now do it here and then go to an area to a project to a decision wherever and talk to those guys and convince them, let’s try this. Let’s see what it brings us and then go ahead and do it, with that one and start reporting on it.
Carol Williams (25:24):
And I think the outcome of those conversations will be mind blowing, not just for the ERM professionals because it is a shift in reference, the mental reference as well as the decisions that will get made instead of the, conversations focusing on, well I don’t agree with this score or this score and where it falls in the ranking. Instead it that doesn’t matter. It doesn’t matter.
Hans Laessoe (25:50):
I was working with one company that was looking for their sales and operation planning and they were away working with ranges that were too narrow, way too narrow. They were looking at this has a 5% upside and a 10% downside. Then you looked at the product lines that they have had in the past years and say, what did you budget for and what you could actually get. And you found that on product level, on product line level, the uncertainty was plus/minus 30% so if your plan for 100 million, it was going to be somewhere between 70 and 130. It was not going to be between 90 and 105 it was way bigger than that.
Carol Williams (26:39):
So it’s not just looking at what you think is a good range, but also look at your history and let history tell the story.
Hans Laessoe (26:48):
When you have the data, you have your history and stuff like that, you can use that to validate. You may also be in cases where you have two wide ranges and say, I can narrow that a lot because I actually know.
Carol Williams (27:00):
There you go. Okay.
Carol Williams (27:02):
How do you address potential bias in the quantitative approach?
Hans Laessoe (27:12):
How can you have bias quantitative?
Carol Williams (27:15):
Well, I have heard, I have not experienced that myself, but people inputting numbers to basically get the result that they’re looking for when they say, here’s the target that we want. And that’s because they know it’s an easy target. So, you know, versus let’s give us a stretch goal that we want to achieve. So how do you get around that bias?
Hans Laessoe (27:41):
Collaborating and talking, challenging. Have one guy challenge the other guy, have a conversation about is this really the minimum? Is it really the most likely? Is this really the maximum? And have them discuss that and say, okay, we have these five cases from the past where this was the range. Now you say that is the range, why? How did you get to that? Or, but I believe that and short voice and say, but with this insights, do you still believe it or should we just maybe change it a bit? And, well then they were generally, because they’re specialists, they’re not politicians, they’re not executives. They are more afraid of being wrong than of losing face. Many executives are not all of them, many executive are more worried about losing face than being wrong.
Carol Williams (28:35):
So when you get the specialists in the room, they are more focused on being right.
Hans Laessoe (28:39):
Yes. They have a professional pride in their specialty. They want to be right. So they are adaptable. They are changeable. They are motivated to get the best part. If you show me this, yeah okay, I will truly consider it. So they, it’s not like, it’s not like a, a one my wife talked about and say, I have made up my mind. Don’t please don’t confuse me with facts.
Carol Williams (29:08):
I think one of those elements that you just brought up is that the asking the questions, challenging assumptions. Again, those two major roles of why ERM exists, to ask questions and challenge. But not just with the executives and everything, but also with the specialists.
Hans Laessoe (29:28):
Specialists don’t ask executives questions and then give them the answers they have just given you, I mean, it’s the wrong approach. They don’t serve you, you serve them.
Carol Williams (29:37):
Right. Well, the other element is with the let them know that you are their voice to the executives. So you’re going to help you know, champion their causes. You see a problem. But let’s talk about that. Let’s figure out how we can communicate that with the executives. And by the way, we’re going to use the data that you are so well familiar with that executive see reports and the certain context all the time. We’re going to apply that data in a totally different way. Yeah. Right. And apply it to the performance piece of it. Indicator.
Hans Laessoe (30:12):
Yeah. We want to leverage the facts that we have.
Carol Williams (30:14):
What are some common challenges that organizations experience when they try to use Monte Carlo simulation?
Hans Laessoe (30:25):
I think actually, I don’t know. I have to say bluntly, I don’t, I don’t know what kind of challenges they have.The ones that I have seen started using it. the risk manager came back to me and said it was a game changer, complete game changer for them. They could struggle with organizational push back and stuff like that, but when you start small, start with somebody who will actually buy into it. The, you won’t get that kind of push back. You will perhaps get on a project where you failed. I mean, your model was simply not valid. Okay. I ruined , go ahead, do it again.
Carol Williams (31:09):
I mean, try and maybe with a pilot, always all about pilot
Hans Laessoe (31:14):
Start small. I mean, you don’t start Monte-Carlo Simulation with you ERM. I mean it’s ridiculous. It’s like being a new guy in finance with a new approach and then you say, Hey, we want to do activity based costing. Let’s do it for the entire company. No, let’s just try it out for engineering and see how it works over here. So that we have our approach and we know that it works and then trust the approach.
Carol Williams (31:39):
So avoid any challenges by starting small.
Hans Laessoe (31:43):
Have good challenges you can manage. Yeah. Okay. It seems that you shouldn’t start so small, but you don’t get any value out of it. I mean, the field, to me, the field won’t accomplish emulation. It’s a simulation that comes out with a result. And this is the likelihood that you will not meet targets and the executive sets. Exactly. That’s, I knew that then you’ve given them nothing. You’ve made an effort, you have nothing to show for it because he knew that
Carol Williams (32:13):
and he still decided to proceed with that knowledge.
Hans Laessoe (32:17):
Carol Williams (32:18):
But then, but then you guys asked, so what prompted you to go ahead and proceed knowing that the chances of failure are this high? What’s the opportunity side? So there’s still some questions that can be asked, but not necessarily.
Hans Laessoe (32:33):
But if you, have executives or leaders who are the Richard Branson type, they will push hard to win on a 40% chance of winning….60 40 against, let’s do it. Let’s just be diligent about, let’s know what we’re doing. I can beat that. Let’s do it.
Carol Williams (32:57):
Yeah. I can switch the odds to be in my favor.
Hans Laessoe (33:00):
No, I can hit the 40%.
Carol Williams (33:05):
That too. All right.
Carol Williams (33:07):
For companies that want to start using Monte-Carlo, we’ve talked about having this, the small projects, um, and getting one of either at risk or model risk, um, to kind of start small. Is there any other recommendations for organizations that do want to start?
Hans Laessoe (33:27):
Start with a project in a part of the organization that they’re used to numbers. Operations, manufacturing and engineering. These guys are engineers. They’re used to numbers anyway. Don’t start with HR. HR is not used to numbers and they will be looking at them say, what is this thing, Monte Carlo simulation, uh, new applicants, forget it. Um, They won’t understand what you’re doing. They have, haven’t their minds is looking at something else, which is good for HR, but it’s bad for statistics. So stay in a place where they understand numbers. I used to work quality departments. Uh, yeah, flow planning a pipe and stuff like that. But I used to numbers leverage their own data. So you have this insights. How are you working with, Oh well not really. Well, I can give you a hint of what we could do. Take the data, make the analysis and go back and say, this is what we actually can, this, I can deliver this for you, I can train you to do this and embed that in your organization. And then you are there.
Carol Williams (34:32):
Fantastic. And always start with the target in mind. Have your target,
Hans Laessoe (34:38):
Have your business target. It’s about business performance.
Hans Laessoe (34:42):
Well thank you so much for talking about Monte Carol. I think that it’s like you said, it’s a game changer for companies to get some qualitative
Hans Laessoe (34:53):
As a risk manager. It’s a tool, but it’s a tool that is just as important as a screwdriver is to a carpenter. You cannot consolidate risk without Monte Carlo simulation. You can not look at a risk portfolio without consolidate, without using Monte Carlo Simulation. Heat maps are useless and that’s on a good day.
Hans Laessoe (35:14):
So you need to have a concert, proper tool for consolidation and that’s Monte Carlo Simulation. That’s it. There’s nothing else.
Hans Laessoe (35:22):
Well, you know what, that I’m going to bring up one other quick question. Since you mentioned heat maps. Board members they like their pictures, right, so how, and they’ve gotten so adapt, they’ve adapted to reading heat maps. How can risk managers go from that process and that type of report out to this new output that they are going to be providing? How do they manage that?
Hans Laessoe (35:51):
Let’s assume the company have five strategic targets or five business targets for the coming year. Now you show them five bars, red, yellow, green, whatever, just yet a red and yellow or green and red if you want and say, okay, on target one the likelihood of meeting that one is 65%, target two 28%, target three 51%, on target four 92%. Those are the heat map they need. This is a better performance. Now they start discussing what does target two it to 28% we need to do something about that. The key issues we can address are these and those and we’re looking at this and we are looking at that, you need to do more. We have to go in this direction or something like that. Or you may even get back this, is that target too stretched ? Or can’t we do it or has something else happened or whatever? But then they can start discussion having six red risks. I wonder what discussion they have with the board committee. If you have one red risk and 17 yellow risks, are the 17 more dangerous than the one? Doesn’t make sense. So give them another picture.
Carol Williams (37:05):
That’s a great point. And I love, the practical advice because that’s what it should be all about. It’s not about the machinery, it is about the practice of it.
Hans Laessoe (37:15):
I’m not an executive. I’m a practitioner. I want to just do it.
Carol Williams (37:19):
Yeah. You and me both. That’s the fun part is the doing not just the talking about it. Right. All right. Well thank you so much, Hans. I really appreciate it.
Hans Laessoe (37:30):
Speaker 3 (37:31):