 Good morning, good afternoon, and good evening. Hello, global supply trainers, and welcome to our second SE1X live event. Today, we are honored to have Dr. Ladi Lapidi with us. Dr. Lapidi has over 30 years of experience in industry, consulting, business research, and academia. He was the director of the management at the MIT Center for Transportation and Logistics. He currently teaches at the University of Massachusetts Boston Campus. He holds a PhD in operational research from the University of Pennsylvania Watson School, a master's in electrical engineering from MIT, and a bachelor's in electrical engineering from the Cooper University. OK, welcome, Larry, to this event. Thank you for being here. And thank you for inviting me, and thank everyone for joining this session. As you mentioned, my background is pretty extensive. It's basically 35 years of industry experience. I have done teaching as well. I have done business research. But my focus area is that I started my career in marketing. The first 15 years of my career was marketing. Next 2025 is supply chain. So I'm able to see the connection that is needed between marketing, commercial side of the house, and the supply chain side. And the thing that connects them the most is the main forecast. Because all supply chain has, in terms of determining what to be doing in supply chain, we have to understand what customers we have to service. And so the main forecast becomes the thing that drives the supply chain planning. It also drives the demand planning, which is sales and marketing planning as well. But that's the connection. The connection is we get a demand forecast, and everybody can plan around it. So we have what's called the one number of forecasting concepts. I decided to give you a thing I wrote. One of the things I do is I write two columns. I've been writing a column for 20 years in the Journal of Business Forecasting. A case study, the article you read, is called the best and the worst forecasting year. And I'll describe why I called it that. But I've been writing for them for 20 years, mostly about not too much about statistics, more methods, but more about the business process. Forecasting is a business process in a company. And you have to set it up as a business process. Of course, you need the methods that you learned in this course to do the statistical forecasting that needs to be done, and the quantitative forecast that needs to be done. But if you have a very good quantitative statistical forecast, if you don't know how to sell it to the company, inside the company, nobody's going to pay attention to it. You're just going to produce it. Nobody's going to use it. People need it to do that planning going forward. So this is a good example for you. So what I want to do now is move to the slide I'm going to show. And basically, this is just to give an introduction to what was going on when I experienced this. I experienced this in probably the late 1980s. So it's quite a bit further back, but it's still true in today's world. Because it talks about the business process. It's less about the technology. So you should be looking at a slide that's called Supply Chain, I'll put in my glasses on. Supply Chain SE1X, live event. And I'll read from here, but I'll elaborate as we go. As you read in my article, LDPD, the best in the world forecasting year. Now, why did I call it the best in the world forecasting year? The job I had at this time was what was called the marketing decision support group. I actually ran a marketing decision support inside of a marketing department. And we were basically the quantitative arm of marketing in terms of doing analysis around what we had to do from a marketing perspective. But one of my roles was to do the revenue forecasting. Of course, at the beginning of the year, to do the budgets. And then from then on, I had to update it monthly. So we had a monthly forecasting process in place. We basically, one big thing we did was obviously as written in the article, we had to drive the budget because the budget has to be driven by what we think we want to do from customers, what business we're going to get. So that's what this is about. It was the first time I had to do it for that. Now, to this point, I had been a very successful forecasting manager. Our forecasts were very accurate, but it was, as I mentioned in the article, it wasn't that hard because we were trying to forecast the number of customers and the amount of equipment on what we call repair, field service, which meant when things had to be repaired, reports, or things had to be preventive maintenance for computers and peripherals. We were the ones that did it for our customers. So 90% of the time, the customer would redo every year. So that piece of the business was very easy to forecast. However, we had to forecast the new customers that would come on board. And then we had to subtract the customers that left us. Either the computer broke down or the customer went to somebody else to do the service. So it's pretty easy. You don't get a lot of demand variation or revenue variation in that. My first several forecasts were growth. Everything was double digit percent growth. And it was relatively straightforward because that's what we had been doing for 20 years. And so my three years, we didn't just do simple extrapolation, but we basically were able to get the numbers down for what the revenues were going to be year by year. And this next year was the year that is the best and worst forecasting. It's the best because I learned a lot about what it takes to do forecasting in a big corporate environment. Because I had been successful, that's great, but you learn more from your harder times. And so it was the worst of my forecasting years because I had to go to my company and tell them that we were at an inflection point. Now, in the world of business forecasting, it's relatively easy to forecast when you're growing for a couple of reasons. One is you're following a trend many times. You're following seasonality. You're following some maybe promotional effects, some new product effects, that kind of thing. So those things are pretty straightforward and we have a lot of methods to do that. And so that's one thing. And the other thing is when you forecast and grow, it's good news for everybody. It's not bad news. But when you forecast a deflection point, I call it a turning point. Here I would say, forget the fact that we just last year grew 15%, the year before in double-digit percent as well, the year before that and for 20 years before that. This year, we're gonna be flat. That astounded everybody. It was a big surprise to everybody. Not to me because I realized that what had to be done is when you lose contract because computers go away, you need to be selling more new computers to go on to be serviced. And my company that I work for called Beta General Computers, and they're mini computers, I didn't sold many the year before. Well, I knew that was gonna impact service revenues later on the next year. So when I did the forecast, I went to my senior VP who was the head of the division and we put in a forecast of flat and everybody said, how can it be? It cannot be, right? We've been growing all along. So this is a write up about my one year that I learned a lot, but it was a struggle as you read in the article here, right? So let me read this. As you read in my article, best and worst forecasting year. Journal of this forecast spring to 2005. You can see I just recently wrote it many years later, but now I got perspective. I needed to get buy-in of the forecast from the whole organization. One thing you have to understand when you do forecasting, it's great to be doing the statistics and we all love the quantitative analysis, but then people have to live with that. People have to plan around it. It's what we call the one number forecast. Now you have to go to your organization and sell it to them. And why is it right? Because basically, forecasts are always wrong, right? You always have errors. You can't forecast 100% accuracy. So therefore you have to mirror it. And so now you gotta go and tell them that things are gonna get worse. In this case, flat, trauma growth. And you have to commit something. That's the case. And so one of the things I always talk about whenever you have to get into buy-in from the organization, you have to have full transparency on how you got the forecast. You can't use sophisticated mathematical models that no one understands. You have to be overly transparent. So I call it, you have to convince people by facts, figures, and assumptions. Every forecast has some assumptions in it that you make. So therefore, if you're transparent about that, that's what you focus on. Fact, figures, and assumptions. And when people have doubts about what you're doing, for whatever reason they have it, and that's what we're gonna be dealing with today, what are some of those doubts that we had, you have to talk to them about why you got the number you got and also what the errors would be for those numbers. So they recognize that this is uncertainty. We're trying to forecast something that's uncertain. Yes, it's that, but just because it's uncertain doesn't mean it's not the best forecast we could use. And that's what you position. And you position it with facts, figures, and assumptions. Always the key thing, right? Now, we'll go back to what the statement says here. This was hard because everyone had preconceived notions and opinions about what next year's revenues would look like. They doubted my group's forecast from the very get-go, all the way up to the executive level, right? They doubted mine because they use what we call simple heuristic forecasting. So what's a simple heuristic forecast? Well, a simple heuristic forecast, we've grown every year. Of course we're gonna get another growth this year. That's a simple forecast, right? You learned that. You learned about exponential smoothing, maybe moving averages. All picks up trends, right? What can you pick up? The turning point, the inflection point is much harder to forecast. You have to get into the depths of what's going on. And in this case, if you read the, when you read the article, you see that backfills was the big issue. When a piece of equipment goes on contract, it doesn't get in the system right away. And therefore you have to backfill the customer. We had a lot of backfills the year before and that was most of the growth last year, the prior year. And so I had to do a lot of analysis with the financial organization to dig into how much backfills we had, whether we're gonna get them again and all that stuff we had to do. So that's all I'm describing, but everybody in the company had an idea of what was gonna happen next year in their mind. And it's based upon, they didn't do analysis. They just saw things, right? So one thing they saw is growth and they assumed that was gonna happen. Why did they doubt mine? They should have accepted mine because I'm using what's called system two thinking. I'm doing a very detailed analysis of what's happening. They don't do that, they just observe. They don't do formal forecasting, right? So my formal called quantitative forecast methodology was able to forecast this inflection point and that was good news. The bad news was it's very hard to sell the organization because now it's gonna feed the budget. And what does that mean? It's gonna affect next year people's jobs, next year people's headcount, emotions, everything else, right? So it's really bad news for an organization when you do this. How do you get around it? That's what this case is about. So as a case study, what we want you to do is discuss the factors. One of the factors was we had grown along. There are other factors that's mentioned in the article that were things that drove people to have opinions about what the forecast would be next year without having to do any critical analysis. It's just they had their thoughts in their mind. So I want you to briefly discuss the executive team, what they had in their mind and why they doubted my forecast, right? And it's in the case, if you pull it out, why they doubted it. And then all the other managers, why did they doubt my forecast? Even though I did a very sophisticated forecast, why did I have to convince them? What were they basing their forecast on? And they're basing their forecast on what we call heuristics. It's not a formal forecast, it's just what we see, what we observe, what we think, what our opinion is, all of that, okay? So if you could, we want you to break out into breakout rooms and come up with answers to these two questions. What was the executive team's doubts, right? And when they used to develop their forecast and what was all the other managers' doubts and why they all doubted my forecast? Eventually they went with my forecast and by the way, it was pretty accurate, it was 1%. 1% what I'm talking about? Yeah, of course, we actually, the primes 1% instead of flat, okay? Anyway, so that is what we'd like you to do. Okay, excellent. So if you guys will have 20 minutes in the breakout session, just hopefully join the breakout room and we'll be back in 20 minutes. So you'll have the questions and we'll be back.