In today's dynamic world, your supply chain must be agile, flexible, and fast to value.
-Robert Zwerling
Episode Summary
Ready to get back into the supply chain driver’s seat? Tune into a conversation with respected author and Managing Director of the Finance Analytics Institute, Robert Zwerling. Get his recommendations and insights on how agile analytics and artificial intelligence (AI) are paving the way for automation and scenario planning to help you anticipate a range of future possibilities. The exciting outcome? You will enable your business to transform from reacting to the future into a business that is able to orchestrate the future.
Episode Transcript
Intro/Outro (00:01):
Welcome to TEKTOK Digital Supply Chain Podcast, where we will help you eliminate the noise and focus on the information and inspiration that you need to transform your business, impact supply chain success, and enable you to replace risky inventory with valuable insights. Join your TEKTOK host, Karin Bursa, the 2020 Supply Chain Pro to Know of the Year. With more than 25 years of supply chain and technology expertise and the scars to prove it, Karin has the heart of a teacher and has helped nearly 1000 customers transform their businesses and tell their success stories. Join the conversation, share your insights, and learn how to harness technology innovations to drive tangible business results. Buckle up. It’s time for TEKTOK, powered by Supply Chain Now.
Karin Bursa (01:13):
Welcome back, supply chain movers and shakers, to the TEKTOK Digital Supply Chain Podcast. I am your host, Karin Bursa, and I am here to help you replace risky inventory with valuable insights. And today I am joined by Robert Zwerling. He is the author on the hot, hot, hot topic – that’s three hots – of predictive analytics. He’s also the co-founder of the Finance Analytics Institute. Robert and I are going to be talking with you today about agile analytics driven demand planning. More agility, more analytics to get you a better demand plan. This should be really interesting. If you’re a fan of the show, I want to ask you to subscribe to TEKTOK, that’s T-E-K-T-O-K, and leave us a review. And don’t forget to follow us on both LinkedIn and Twitter.
Karin Bursa (02:07):
Now, here’s something that I’m sure that you’ve thought a lot about over the last two and a half years, and that is how agile or how resilient your business really is. Now, although these two terms are used interchangeably, they do mean different things.
Agility is the ability to pivot and change quickly, or at the very least, quicker than you’ve been able to in the past. Agility is essential to help mitigate disruptions, but it’s also important because it gives you the ability to exploit some unplanned opportunities that you may see in the marketplace as well.
Resiliency, is the ability to avoid, contain, stabilize, or recover from a disruption. And we’ve been doing a lot of that over the last two and a half years.
Agility and resiliency for your supply chain are not just nice to have – they are critical to your business’ ability to thrive. However, Gartner tells us that only 40% of supply chains are set up for resiliency. That’s right, four – zero – only 40%. And I’ve got to tell you, I think they’re being generous with that number, but that means 6 out of 10 are structured for one thing.
Karin Bursa (03:34):
Sixty percent of supply chains are structured for predictability and cost efficiency, not resiliency, and certainly not agility. And the bottom line is that makes our businesses fragile, not agile. Clearly, we need another approach, which is why I am so glad to have Robert Zwerling with us today. Robert is going to share some of his recommendations and insights on the topic of agile analytics and incorporating artificial intelligence into the way you plan for the future. The exciting outcomes are going to be that your business is going to transform. And you are going to move back into the driver’s seat as your business transforms from simply being reactive to what’s happening around you, to actually orchestrating where your business is headed, and harnessing the latest and greatest demand signals. So, Robert, thanks for joining us today on TEKTOK.
Robert Zwerling (04:39):
Thank you for having me here.
Karin Bursa (04:41):
Robert, you have co-authored numerous articles and research papers. You’re very prolific on this topic of business analytics and have also written two groundbreaking books, Implementing an Analytics Culture for Data Driven Decisions. And I know that word culture is very important. We’re going to come back to that shortly. The other book is AI-Enabled Analytics for Business. Now, both of these are published by Wiley, which is very well regarded in the marketplace. Congratulations on these two books! You’ve also founded the Finance Analytics Institute and the Analytics Academy. So, I’m not sure when you’re sleeping, but it is safe to say, Robert, that we are going to get some expert insights here today. How did you get into this field of AI-enabled analytics?
Robert Zwerling (05:35):
Well, I think it goes all the way back to my college days where I was doing research in two-phase flow for nuclear reactor core re-criticality. So that heady kind of thing was really the application of mathematics and data to do predictions and find insights. And that’s a very important word, insights. I want to distinguish insights from information. Information we get by doing analysis. Analysis is applying arithmetic to data, add, subtract, multiply, divide. “Gee, our sales are up by 10%.” That’s analysis. It’s informative. Yes?
Karin Bursa (06:18):
Yes.
Robert Zwerling (06:19):
Analytics is applying mathematics on data. So, take something like we run a correlation. We find that consumer sentiment is a three-month leading indicator to men’s blue shirts in our San Diego department store. That’s an insight. An insight is something that we don’t know and when known will affect our decision-making. So, with that lead up, how do we get started, it just turned out – a number of years ago, I met a gentleman who’s become my colleague, my co-author, my friend, my collaborator. We’ve written those two books together. We’ve co-founded the Finance Analytics Institute and we built the Analytics Academy. And, it was all about our frustration. He’s a CFO for a software company in Europe. And we were frustrated about how everyone was talking about what analytics is and not how to do analytics. So, okay, I get it. I get what it is. Now, how do I do it? And so, we wrote the first book to really help the business analyst, manager, the director in implementation, and the second book we wrote for the directors and vice presidents and officers to give voice, vision and clarity to the value of analytics and learn how to implement analytics because 2 out of 3 analytics projects are failures.
Karin Bursa (07:48):
Did you say, “Two out of three analytics project fail?”
Robert Zwerling (07:50):
Yes, ma’am.
Karin Bursa (07:51):
All right. We’re going to dig into that a little deeper, I’m sure.
Robert Zwerling (07:55):
Yes, ma’am. And the whole idea is not what you have to do because I think we pretty well get the what; but now how do we do it? So, we convert that 2 out of 3 failures into a 100% success. So, that was the whole kind of journey to write the books, found the Finance analytics Institute, develop the Analytics Academy to teach how to, which is the book; the first book is the syllabus for the Analytics Academy and the second book is for the executives who, by the way, are the number one problem in analytics failures. Sorry, boys and girls, but it’s all on you. And, that’s the way it is. And so, we did a lot in the second book because everyone is writing about not only the what, but the successes. And we write more about the failures so people learn the mechanisms of failures. And there are three main mechanisms, which we call bandwidth, focus, and budget. But we’re not going to get into the book. The point is that was the journey on how to get here.
Karin Bursa (08:57):
That is a great background for our discussion. And I love your distinction on insights and understanding how to apply what we are uncovering in the marketplace and to predict what’s likely to happen in the future. Robert, you said executives are the “problem.” Right now, supply chain leaders, supply chain executives, are drowning in this environment of uncertainty and increased complexity. I was on the phone earlier today with a chief supply chain officer, and he must have said the word “chaos” five times in the course of our conversation. It’s chaos! There are too many moving parts. It’s too different from the way we’ve operated in the past. Tell me why NOW is the time that we should be looking to leverage more analytics-driven process to regain control of what’s happening in our global supply chains?
Robert Zwerling (10:05):
It really comes back to where we are in our technology journey. I mean, our technology or transactional systems, our demand planning systems, our ERP systems, our supply chain management systems, our CRM systems, all these systems were built upon a transactional concept and the overall business concept that we had stability in our supply chains and what we were seeking was financial efficiencies, right?
That’s all out the window. So, we have two things, one exacerbating the other. One is transactional systems were never built for the analytics driven, AI-driven complexities of supply chains that we have today. And our transactional systems were never designed or built for the kind of, as you said, chaos that we have today. And we’re not going to be changing these systems. They’re too big, they’re too expensive. All our processes are built around it. We don’t have a chance, but we can’t rip out the engines to the plane while we’re up at 36,000 feet. What we have to do is surround the systems now with user-compatible analytics tools that supplement and can provide the kind of predictions and forecasts and scenario planning that’s needed to be more flexible and to be more future driven.
Karin Bursa (11:41):
Let’s dig into that for just a minute, right? Can artificial intelligence, modern day analytics really identify new demand patterns or use new demand signals, say things that have happened in the last three months or the last four months versus having two or three years of history in order to project future performance? Let’s talk about that for just a minute in how we surround – I like the way that sounds, right? Because we don’t have time to do a complete replacement of supply chain planning for every industry. Now, that may be necessary, you know, for a business over time. But I think the point you’re making is we need answers now, right? We need to move product to market today. How do we do that better? So, let me back up because I just threw three or four questions at you. So, first question, can artificial intelligence use these short-term demand signals to help improve the quality of the demand plan?
Robert Zwerling (12:47):
Yes. Next question?
Karin Bursa (12:49):
That’s a pretty quick “yes.”
Robert Zwerling (12:51):
It is. I mean, because you have long range, you can have artificial intelligence for the long range and short range and medium range. I mean, there are AI enablements for demand signaling that are using what I call more fresh or recent data to give you a signal in what direction the trends are going. Yes. There are also ways of applying AI and analytics for longer range forecasting. When I do that, I would say 3, 6, 9, 12 months. And I won’t get into the mechanics of it because we don’t have time to, but, yes, you can do it and you can get very high accuracy by doing it.
Karin Bursa (13:31):
So, Robert, we talk in terms of digital supply chain, which includes people, process and technology. However, today with the data available expanding exponentially, data has become a fourth leg of the stool, if you will. Data is critically important. But most companies don’t have the time or tools to do a huge data cleansing and data warehouse and still digest that data quickly to become more agile or more responsive. How do we get started? Is there a quick way to see value that we can then fuel more momentum behind the effort for the business?
Robert Zwerling (14:22):
I wish, Karin, someone would write a paper called Agile Analytics-Driven Demand Planning. Well, wait a minute. We did that!
Karin Bursa (14:35):
Talk a little bit about that because, Robert, that’s how you and I met.
Robert Zwerling (14:38):
Exactly. You and I met because I was doing research for a follow-on book to AI-Enabled Analytics for Business and talking to supply chain leaders and thinkers. We met and then we decided we’d do this paper. And in that paper, we talked exactly about how to get fast value.
Robert Zwerling (14:59):
How do we get going today? And we had, you know, I think, it was five steps. The first step is always define the problem. Always. What are we trying to solve? We’re not boiling the ocean. What are we trying to solve for? Right? Are we trying to get better forecasting for the next quarter, next two quarters, next four quarters? What are we – and are we doing it at the customer level? Are we doing it at the customer product level? For example, what are we trying to solve for? Because that defines the data. It defines the data, the dimensions, the mathematics.
Robert Zwerling (15:30):
And the second part is to choose your, what we call the proof-of-concept software vendor. And as you know, this is not an IT function. Do not go there. Do not pass, go, do not collect $200. This is a user function because the only people that know their business are the users. And I always tell people, think of IT as the power company. You know, you don’t – power company doesn’t know your business. They know how to get power to your building and how to keep the lights on. Your IT knows how to build a network and knows how to build a computing infrastructure and keep it secure and safe, but they don’t know what the company does. Right? Only you know what you’re, if you’re in supply chain, what supply chain does, what you’re doing in inventory and what you’re doing in logistics. Only you know those things.
Robert Zwerling (16:23):
So, those are things that you have to do. You have to select the tool. And, unfortunately you’re going to have to fight the battle where IT may come back to you and say, “Well, you can only use Power BI or Tableau or something like that.” You’re going to have to push back on them. Like I said at the beginning, it’s the difference between visualization and analytics. Power BI, Tableau, Qlik, those are visualization tools. They’re descriptive tools. They’re not prescriptive predictive tools. Go out and find a modern analytic tool. There are many cloud vendors out there for modern analytic tools. And you pick it, match it to your skillset. You know, if you have data scientists, you might get a more sophisticated tool. If you don’t have data scientists, you’re going to need a more user compatible tool. But you select it and then you do a proof of concept.
Robert Zwerling (17:14):
All proof of concepts are small. They should be in that three to six week range. Don’t boil the ocean. Now, step three, don’t boil the ocean. Make it small, but make it measurable. Make it measurable and significant, meaningful, measurable and meaningful. ‘Cause it doesn’t have to be large to be meaningful. And then after you’ve done that, you benchmark your skillset. What did we learn? So, once we know what we learn and what skills we need in order to run analytics, now we can scale. Now, we can go to the next project, the next project, the next project, next project. And all of them are these, you know, small-to-moderate-sized projects because you’re going to string along a whole lot of fast value because that’s where you have to be, agile, flexible, fast value. We can’t wait a year. There’s a wonderful story we have in the book, AI-Enabled Analytics for Business, about a company that hired 150 people and they got a billion and a half dollar return on their money and they spent $100 million. Now, how many companies can do that?
Karin Bursa (18:22):
Not a lot.
Robert Zwerling (18:22):
Exactly. There are only 20,000 companies that have over 500 people total in their company, let alone hiring 150 of them. So, that’s why I say even for the largest Fortune 500 company, small is better, fast value is better. Get your return and get those kinds of things that you need to be agile and resilient in your supply chain.
Karin Bursa (18:46):
Robert, I want to make sure that our listeners understand the differences in the terms “predictive” and “prescriptive” analytics. So, predictive analytics are future focused to predict what’s going to happen in the future, but prescriptive may be a term that they’re not familiar with.
Prescriptive analytics include the ability to “prescribe” or recommend a course of action, right? Based off of the insights that are available, these are the next logical steps or likely steps to take. Talk a little bit about prescriptive analytics because that’s unique and different. Today, most analytics are looking backwards. They are valuable but can be seen as fancy reporting, right? Or reporting with drill down capabilities. Here, we are talking about charting the future with additional insights about market performance or business response. Give just a little more flavor around these prescriptive analytics.
Robert Zwerling (19:52):
Starting with hindsight or hindsight looking. So, what does that mean? It means that we’re reacting to the future when it arrives versus predicting the future so that we can plan to make the future happen. Planning to make the future happen is prescriptive or proscriptive, right? And because if we can predict, then we want to prescribe, right? That’s the notion. And I do it in the concept of the five key questions that every business has to ask of analytics – what happened, where it happened, why it happened, what will happen, and how to make it happen. So, when you think about the first two questions, what happened and where it happened, that’s descriptive analytics. And that’s what we use our reporting and our visualization tools for. When we get into why it happened, predicting what will happen and prescribing how to make it happen, that’s when we get into our analytics tools and now we’re applying mathematics on data to gain insights to affect our planning and decision making.
Karin Bursa (21:05):
Yep. Absolutely. Now, another really important thing that you said – I’m taking notes as you are laying this down for us. So, another important thing is that this proof of concept is not an IT-driven initiative.
Robert Zwerling (21:17):
Yes.
Karin Bursa (21:18):
It really needs to come from the business or led by a business leader that understands the business, the context, and the questions that we’re trying to solve for here. That’s really important. We have seen over the last four or five years that supply chain roles are taking more ownership of the supply chain master data. And I think that this is an important reason around; it’s because they understand the context of the data and what it indicates into the future so we can help to train and harness that to really be more responsive or more agile in the marketplace.
Robert, can you give us a business example, of a quick win in this area because you said we need to look at three to six weeks. That’s fast! That is a rapid time to value. Share a story from one of your research initiatives or perhaps a company that you’ve worked with that has been able to gain an important insight or an important capability in such a short period of time.
Robert Zwerling (22:32):
Exactly. And I’ll give you one right out of the box. I mean, the $460-million-dollar CPG manufacturer. And they had demand forecasting software from one of the major suppliers, so they’re not without tools. But, you know, again, these systems, your average demand planning system has a 35% demand forecast error. They were running 49% average demand forecast error. And they wanted to get that down to 30%. And one of the things they did is they selected a new demand planning system, which was fine, but then they had the idea, “Could we get – could we do better?” And so, then they followed the five steps that I mentioned before. They said, “We want to do better. We want to do it at the customer level and we want to do it a year in advance. We want to have not just 30, 60, 90-day planning, we want to have do 12-month planning.” Right? “We really want to know what we’re doing so we can really get control of our supply chain.” So, they went out. The business organization selected their POC vendor, and I think it was in three or four weeks, they did a head-to-head comparison of their demand planning’s current demand planning system with using AI-enabled analytics cloud tool, and they were able to get 95% accuracy on average across their tier one customers.
Karin Bursa (24:04):
Wow!
Robert Zwerling (24:05):
So that’s a 5% error over 12 months. They had 95% average accuracy over 12 months. It was so good. It was too good. And they went back to management saying, “We can’t say that. We’re going to say we got an average error of 15%, so they’ll believe us.”
Karin Bursa (24:24):
That’s remarkable.
Robert Zwerling (24:26):
It is.
Karin Bursa (24:26):
In all the years that I’ve been in supply chain, I have not seen a company consistently leverage a customer-level forecast for all customers.
Robert Zwerling (24:34):
Right.
Karin Bursa (24:34):
Right. So, item, customer-level granularity. So, we’re not talking large numbers here. We are down in the nitty-gritty granularity of the plan, but a 95% accuracy rate. And honestly, Robert, an 85% accuracy rate would be impressive at that granularity.
Robert Zwerling (24:53):
Exactly.
Karin Bursa (24:54):
With them backing off from that, it’s like, let me give you a lower number and then I’ll overachieve that number.
Robert Zwerling (24:59):
Right.
Karin Bursa (24:59):
So, a little sandbagging happening maybe in that particular example?
Robert Zwerling (25:03):
It did because they didn’t want to make it, you know, too unbelievable. And again, the numbers range, you know, you could have a whole series of ranges from, you know, 90% to 99%, but, you know, you average it out in over a year and that was great results. And they had residual benefits they could do. Again, by continuing to apply the analytics, not only were they able to reduce their inventory on hand, they were able to do dynamic inventory management. So, in other words, rather than having static Min/Max levels, they did dynamic Min/Max levels. And then, by having also better forecasting, they also reduced their E&O and they also could reduce their charges, you know, shortage and fill rate fines. And so, you put that all together and they’re at a $100 million of avoided costs or cost that could be reallocated to growth. So, it was a tremendous, and it was, you know, talking about a three-to-four-week fast value. There’s a perfect example of it, all under the control of the user group.
Karin Bursa (26:09):
Yep. That’s substantial to have the ability to use a more accurate forecast to leverage dynamic inventory policies. Right? Dynamic safety stock policies for your business really allows you to reduce the working capital or where – I’m assuming it was a reduction in working capital over time, but it may be that I’m increasing inventory for higher demand than I expected as well. But the bottom line is I’ve got these new insights that are going to help us make those adjustments over time and really get to an optimal performance and intelligently reduce that working capital.
Robert Zwerling (26:47):
You said “optimal.” So often when forecasts are done, it’s one and done and it’s a point, here’s a forecast, which almost never happens.
So, you wind up spending a lot of time explaining why it (that single number) didn’t happen. In the realm of analytics, we don’t do point forecasts. We do forecast ranges, which is a probabilistic range so that businesses can optimize about a forecast range rather than a probable range rather than a point number.
Karin Bursa (27:22):
That’s a really important point that you’ve drawn out. And certainly that’s a point that comes out in the article that we’ve collaborated on. Again, that article is called Agile Analytics-Driven Demand Planning. And you know what, we’ll put a link to it in the show notes as well. It’ll be easier for our TEKTOK listeners to access and download the article. Robert, you included really a roadmap to help get started quickly and then scale. And I know we’ve talked through a couple of the key points to help our listeners get a proof of concept done quickly – downloading the article will be a helpful reference as well. I love the fact that you and I are such strong believers in having these initiatives driven by the practitioners or by the businesspeople because they’re the ones that get the phone calls, right? Or, they’re the ones that solve the problems and face the challenges. We all know there are a lot of moving parts and parts of our supply chains are moving at very different rates than they have historically.
Karin Bursa (28:29):
When you are working with companies in this area, what’s standing in their way? What are the big stumbling blocks in really getting a proof of concept? You used the term POC which stands for proof of concept, POC. You’ll hear that term quite often as you are validating business use cases. So, Robert, question back to you. What’s getting in the way? What’s stopping supply chain professionals from embracing or doing a proof of concept?
Robert Zwerling (29:00):
Two things. One is their perception of their data. First is we need real, what they call real-time data or big data, or our data isn’t clean enough. And I can tell you that you have plenty of data. There’s no such thing as big data. It’s right size data. It’s whatever you need for what you’re solving. And there’s no such thing as real-time data. It’s right time data. Again, whatever you need for what you’re solving. If you’re doing demand planning, for example, on a weekly, monthly basis, you don’t need the data minute by minute or day by day. Right? And you don’t need a mountain of it. So, first is to stop the self-imposed “Oh, we can’t do it” before it even starts. The second big thing is the battle with IT. You know, they say, well, it has to do it or IT is forcing the tool on them, you know, like I said, and the tools tend to be your visualization tools and they just can’t – you know, they’re not – again, you can’t bring plumbing tools to do carpentry. Right? And the idea of bringing, and the use – by the way, the users know it. The users understand that these tools are visualization. They’re descriptive, they’re reporting tools, they’re not analytic tools. So, that’s the other big battle that has to be fought, which stymies a lot of projects even getting started. Those are the two things.
Karin Bursa (30:29):
I do think that focus on the predictive and prescriptive is important.
One other reminder is that if your company has the luxury of having a team of data scientists in it, you need to be aware that those data scientists enjoy solving a problem for the first time. They don’t necessarily enjoy doing that over and over, right? They want to be creative and exploratory in how they are evaluating a bunch of different business use cases. So, we need a way that we can institutionalize and get repetitive and momentum under our way that we’ve proven it works. We expand maybe the customer list or the products or the market or the channel, and then we get broader and broader in the way that we’re improving our overall demand plan. So, I think it’s a great use case in using agile analytics, but artificial intelligence or AI, which is just a fancy way of saying, new math and new methods to solving these business challenges.
I think it’s exciting! I really think, you know, it’s a great time to be in supply chain and it’s amazing to see the use of new science helping to not just solve the same old problems but address new data, incorporate new opportunities, and refine the process so that we can indeed be more resilient or more agile in the way our businesses respond.
Robert, I want to thank you for the opportunity to collaborate with you on the article. It’s been a lot of fun and great opportunity to get to know you. But if you could leave our audience with one final thought, what would that be on this topic of using agile demand analytics to really drive better decisions faster?
Robert Zwerling (32:35):
Right. I guess the best way to say is the Nike slogan, do it. Right? And then you do it. Don’t wait. Right? If you have to run a covert operation, run a covert operation. By the way, my research that brought us together shows that people are doing it from companies, whether they’re a hundred million or the top 50 Fortune 500 companies. They’re all doing it. People are engaged in surrounding their transactional systems with this kind of agile analytical thoughts and how to bring it to the table and how to solve the local problems with user-enabled tools that are analytic tools that they can bring. And they’ll learn them and they’ll find them and they’ll do them. And they are.
The best thing to do is do it. The best time to get started is now, and like I say, it’s low cost, it’s fast value, it’s high ROI. So, do it!
Karin Bursa (33:32):
Sounds pretty compelling. And that’s it for today for our TEKTOK Digital Supply Chain audience. Listen, I want to thank you Roberts Zwerling for joining me today and helping to inspire all the supply chain movers and shakers out there. Let’s make sure to check out the article that Robert and I collaborated on. It is called Agile Analytics-Driven Demand Planning. And I think you’ll really want this not only to inform your current initiatives, but it’s going to give you a few ideas for the future as well. So, Robert, what is the best way for our TEKTOK listeners to connect with you?
Robert Zwerling (34:08):
First of all, thank you for having me here. It’s been a great pleasure to be on this TEKTOK podcast. Great pleasure to work together. Look forward to doing more of it, and it’s easy to get a hold of me on LinkedIn at Robert J. Zwerling. And I’m there and you can say hello and we can then connect via email real fast.
Karin Bursa (34:29):
All right, terrific. Well, until next time, remember that this show would not be possible without all of the supply team movers and shakers in the marketplace and the fans of TEKTOK Digital Supply Chain Podcast. Please follow us on LinkedIn and Twitter and be sure to give us a comment or two on your feedback on today’s topic.
Our goal at TEKTOK is to help you eliminate the noise, focus in on the information and inspiration to help you transform your business and replace risky inventory with valuable insights. We’ll see next time on TEKTOK, which is powered by Supply Chain Now.
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Robert Zwerling is a high-tech serial entrepreneur with 30 years founding and growing software companies across telecommunication, manufacturing, distribution, high data availability, predictive analytics, and Artificial Intelligence. He is an accomplished business leader, globally recognized speaker on predictive analytics, inventor of the ground-breaking One-Touch AI for demand forecasting, and creator of Systematic Thinking™ the methodology for the application of analytics to solve business problems. He is at the vanguard of digital transformation and considered a thought leader in analytics. He has been a keynote speaker on AI and delivered highly rated lectures and courses attended by analysts, managers, and executives throughout North America, Middle East, and Europe. Mr. Zwerling has co-authored numerous articles and research papers in AI and analytics, as well as the groundbreaking books “Implementing an Analytics Culture for Data Driven Decisions”, and “AI-Enabled Analytics For Business” (published by Wiley). Mr. Zwerling is founder and managing director of Aurora Predictions, providing AI-Enabled analytics software with its intuitive/no-code platform and no need for data science/programming skills, which automatically delivers AI insights across the sales, finance, operations, supply chain, demand planning, and inventory organizations. He is co-founder of the Finance Analytics Institute, which teaches how to implement analytics through articles, research papers, surveys, benchmarks, and the Analytics Academy. FAI’s Benchmark Analytics Survey, is the first quantitative measure of the position and progress of an individual and organization on the Roadmap to an analytics culture. FAI’s Analytics Academy brings vision, voice, and clarity to the value of analytics and the Roadmap to implement a culture of data driven decisions. Mr. Zwerling’s career spans leadership and executive positions at Fortune 500 companies in power generation and high-tech. As founder and CEO of multiple software companies he managed global growth and exits to a major public company DXC Technology Company (NYSE:DXC), foreign public company, and a private party. He has a Bachelor of Engineering (magna cum laude) in mechanical engineering from Stony Brook University, and Master of Science in Mechanical Engineering (major in thermodynamics and fluid mechanics) from CSU Los Angeles. He is a member of the Tau Beta Pi engineering honor society, and a licensed Professional Engineer, Mechanical Engineering, in California. Connect with Robert on LinkedIn.
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