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CI Futures Expands Market Forecasting Platform to Cover All S&P 500 Stocks

Houston-based Complete Intelligence Technologies, Inc (CI) has announced the expansion of its CI Futures platform, which now includes all stocks in the S&P 500. 

CI Futures is a globally integrated cloud-based AI platform that provides accurate market forecasts for over 1,200 assets including 700 currency pairs, commodities, market indices, and economics.

“With the addition of all stocks in the S&P 500 to our CI Futures platform, we are continuing to lead the market in providing reliable, accurate, and comprehensive financial forecasting,” said Tony Nash, CEO and Founder of Complete Intelligence. “This expansion will give our clients even greater insights to make informed long-term investment and trading decisions.”

CI Futures is already used by leading financial institutions, corporations, and investors around the world. 

Besides CI Futures, Complete Intelligence also offers RevenueFlow™ and CostFlow, designed to provide companies with reliable, automated forecasts of revenues, costs, and expenses to become more efficient and profitable. 

RevenueFlow™ augments and accelerates the budgeting process with AI while improving accuracy and profitability. It transforms the annual budget process and transitions to continuous monthly forecasting to eliminate the disruptive annual budget drama. 

CostFlow™ streamlines planning and reduces costs with AI-driven expense forecasting. With a transparent, organized, and accurate planning platform, teams can forecast costs and expenses with ease.

For more information about Complete Intelligence and the CI Futures platform, visit https://completeintel.com/futures/.

About Complete Intelligence
Complete Intelligence Technologies, Inc (CI) is a Houston-based company that offers AI-powered financial forecasting and planning solutions to businesses and investors worldwide. Its flagship platform, CI Futures, is a globally integrated cloud-based AI platform that provides accurate market forecasts for over 1,200 assets, including all S&P 500 stocks, commodities, market indices, and economics. The company also offers RevenueFlow™ and CostFlow™, which provide automated forecasts of revenues, costs, and expenses to improve efficiency and profitability. With Complete Intelligence, businesses and investors can make informed decisions and stay ahead in finance.

Contact:

Complete Intelligence
Rick Nash
info@completeintel.com

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How AI-based ”nowcasts“ try to parse economic uncertainty

This post was published originally at https://www.emergingtechbrew.com/stories/2022/06/17/how-ai-based-nowcasts-try-to-parse-economic-uncertainty?mid=13749b266cb1046ac6120382996750aa

This month, the S&P 500 officially hit bear-market territory—meaning a fall of 20+ percent from recent highs—and investors everywhere are looking for some way to predict how long the pain could last.

Machine learning startups specializing in “nowcasting” attempt to do just that, by analyzing up-to-the-minute data on everything from shipping costs to the prices of different cuts of beef. In times of economic volatility, investors and executives have often turned to market forecasts, and ML models can offer a way to absorb more information than ever into these analyses.

One example: Complete Intelligence is a ML startup based outside Houston, Texas, that specializes in nowcasting for clients in finance, healthcare, natural resources, and more. We spoke with its founder and CEO, Tony Nash, to get a read on how its ML works and how the startup had to adjust its algorithms due to market uncertainty.

This interview has been edited for length and clarity.

Can you put the idea of nowcasting in your own words—how it’s different from forecasting and the nature of what you do at Complete Intelligence?

So Complete Intelligence is a globally integrated machine learning platform for market finance and planning automation. In short, we’re a machine learning platform for time series data. And nowcasting is using data up to the immediate time period to get a quick snapshot on what the near-term future holds. You can do a nowcast weekly, daily, hourly, or minutely, and the purpose is really just to understand what’s happening in markets or in a company or whatever your outlook is right now

And what sort of data do you use to fuel these predictions?

We use largely publicly available datasets. And we’re using billions of data items in our platform to understand how the world works…Macroeconomic data is probably the least reliable data that we use, so we use it for maybe a directional look, at best, at what’s happening. Currencies data is probably the most accurate data that we use, because currencies trade in such narrow bands. We use commodities data, from widely traded ones like oil and gold, to more obscure ones like molybdenum and some industrial metals. We’re also looking at individual equities and equity industries, and we track things like shipping times for goods—shipping times…are usually pretty good indicators of price rises.

Who are your clients, and how are the nowcasts used in practice?

Our clients range from investors and portfolio managers, to healthcare firms and manufacturing firms, to mining and natural resources firms. So they want to understand what the environment looks like for their, say, investment or even procurement—for example, how the current inflation environment affects the procurement of some part of their supply chain.

In fact, we’re talking to a healthcare company right now, and they want to nowcast over the weekend for some of their key materials. In an investment environment, of course, people would want to understand how, say, expectations and other variables impact the outlook for the near-term future, like, days or a week. People are also using us for continuous budgeting—so revenue, budgeting, expenses, CFOs, and heads of financial planning are using us…to understand the 12- to 18-month outlook of their business, [so they don’t have to have an annual budgeting cycle].

Tell me about how the AI works—which kinds of models you’re using, whether you’re using deep learning, etc.

There are basically three phases to our AI. During the pre-process phase, we collect data and look for anomalies, understand data gaps and how data behaves, classify data, and those sorts of things.

Then we go into a forecasting phase, where we use what’s called an ensemble approach: multiple algorithmic approaches to understand the future scenarios for whatever we’re forecasting. Some of those algorithms are longer-term and fundamentals-based, some of them are shorter-term and technical-based, and some of them are medium-term. And we’re testing every forecast item on every algorithm individually and in a common combinatorial sense. For example, we may forecast an asset like gold using three or four different forecast approaches this month, and then using two forecast approaches next month, depending on how the environment changes

And then we have a post-process that really looks at what we’ve forecasted: Does it look weird? Are there obvious errors in it—for example, negative numbers or that sort of thing? We then circle back if there are issues…We’re retesting and re-weighting the methodologies and algorithms with every forecast that we do.

We’ve had very unique market conditions over the past two years. Since AI is trained on data from the past, how have these conditions affected the technology?

You know, there’s a lag. I would say that in 2020, we lagged the market changes by about six weeks. It took that amount of time for our platform to catch up with the magnitude of change that had happened in the markets. Now, back then, we were not iterating our forecasts more than twice a month. Since then, we’ve started to reiterate our forecasting much more frequently, so that the learning aspect of machine learning can really take place. But we’ve also added daily interval forecasts, so it’s a much higher frequency of forecasting and in smaller intervals, because we can’t rely on, say, monthly intervals as a good input in an environment this volatile.

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China’s Belt And Road Has Failed. TONY NASH In Conversation With Daniel Lacalle

Tony Nash joins Daniel Lacalle in a discussion on the rise of the machines in a form of AI and machine learning and how Complete Intelligence helps clients automate budgeting with better accuracy using newer technologies like now casts. How GDP predictions are actually very erroneous yet nobody gets fired? And how about China’s GDP as well, and why it’s different from other economies? All these and so much more in markets in this fun discussion.

 

The video above is published by Daniel Lacalle – In English.

 

Show Notes

 

DL: Hello everyone and welcome to this podcast. It is a great pleasure to have somebody that you should actually follow in social media on Twitter, Tony Nash. He is somebody that you definitely need to need to look for because it has very very interesting ideas. Tony, how are you?

 

TN: Great, thanks Daniel. Thanks so much for having me today.

 

DL: It’s a tremendous pleasure as I said I was very much looking forward to to have a chat with you. Please introduce a little bit yourself. A little bit to our audience and let us know what is it that you do.

 

TN: Sure, thanks Daniel. My name is Tony Nash. I live in Houston, Texas. I’ve spent actually most of my life outside of the U.S. I spent most of my 20s in Europe, North Europe, the UK, Southern Europe and from my 30s to almost the end of my 40s I was in Asia. And so you know being in the U.S., Europe and Asia has really given me personally an interesting view on things like trade economics markets and so on and so forth.

 

During that time I was the global head of research for the economist out of London, I was based in Singapore at the time. Led the global research business. I moved from there to lead Asia consulting for a firm called IHS Markit which is owned by S&P now.

 

And after that I started my current firm Complete Intelligence which is a machine learning platform. We do global markets currencies, commodities, equity indices, economic concepts. We also do corporate revenue and expense forecasting so we’ll automate budgeting for large multinational firms.

 

DL: Wow! amazing. Truly amazing. You probably have a very interesting viewpoint on something that a lot of the people that follow us have probably diverging views. Know the situation about the impact of algorithms in the market the impact of high frequency trading and machines in markets.

 

We had a chat a few months ago with a professor at the London School of Economics that he used to invite me to his year-end lectures to to give a master class. And he mentioned that he was extremely concerned about the almost the rise of the machines. What is your view on this?

 

TN: I think so an Algo is not an Algo, right? I mean, I think a lot of the firms that are using Algo’s to trade are using extremely short-term algorithmic trading say horizons. Okay? So they’re looking at very short-term momentum and so on and so forth. And that stuff has been around for 10 plus years, it continues to improve. That’s not at all what we do we do monthly interval forecasts, Okay?

 

Now, when you talk to say an economist they’re looking at traditional say univariate and multivariate statistical approaches, which are kind of long-term trendy stuff. It’s not necessarily exclusively regression, it gets more sophisticated than that.

 

When we talk to people about machine learning, they assume we’re using exclusively those kind of algorithms. It’s not the case. There’s a mix we run what’s called an ensemble approach. We have some very short-term approaches. We have some longer-term traditional say econometric approaches. And then we use a configuration of which approach works best for that asset or that revenue line in a company or that cost line or whatever for that time.

 

So we don’t have let’s say, a fixed Algo for gold, Okay? Our algorithm for the gold price is continually changing based upon what’s happening in the market. Markets are not static, right? Trade flows economics, you know, money flows whatever they’re not static. So we’re taking all of that context data in. We’re using all of that to understand what’s happening in currencies, commodities and so on, as well as how that’s impacting company sales. Down to say the department or sub department level.

 

So what we can do with machine learning now. And this is you know when you mentioned should we fear the rise of the machines. We have a very large client right now who has hundreds of people involved in their budgeting process and it takes them three to four months to do their budgeting process. We’ve automated that process it now takes them 72 hours to run their annual budgeting process, okay? So it was millions of dollars of time and resources and that sort of thing. We’ve taken them now to do a continuous budgeting process to where we churn it out every month. So the CFO, the Head of FP&A and the rest of the say business leadership, see a refresh forecast every month.

 

Here’s the difference with what we do, compared to what a lot of traditional forecasters and machine learning people do, we track our error, okay? So we will as of next month have our error rates for everything we forecast on our platform. You want to know the error for our gold price forecast, it’ll be on there. You’ll know the error for our Corn, Crude, you know, JPY whatever, it’s on there. So many of our clients use our data for their kind of medium term trades so they have to know how to hedge that trade, right? And so if we have our one, three month error rates on there, something like that it really helps them understand the risk for the time horizon around which they’re trading. And so we do the same for enterprises. We let them know down to a very detailed level to error rates in our forecast because they’re taking the risk on what’s happening, right? So we want them to know the error associated with what they’re doing with what we’re doing.

 

So coming out of my past at the economist and and IHS and so on and so forth. I don’t know of anybody else who is being transparent enough to disclose their error rates to the public on a regular basis. So my hope is that the bigger guys take a cue from what we’re doing. That customers demand it from what we’re doing. And demand that the larger firms disclose their error rates because I think what the people who use information services will find is that the error rates for the large firms are pretty terrible. We know that they’re three to seven times our error rates in many cases but we can’t talk about that.

 

DL: But it’s an important thing. What you’ve just mentioned is an important thing because one of the things that is repeated over and over in social media and amongst the people that follow us is well, all these predictions from the IMF, from the different international bodies not to the IMF. Actually the IMF is probably one of the one that makes smaller mistakes but all of these predictions end up being so aggressively revised and that it’s very difficult for people to trust those, particularly the predictions.

 

TN: Right. That’s right.

 

DL: And one of the things that, for example when we do now casts in our firm or when with your clients. That’s one of the things that very few people talk about, is the margin of error is what has been the mistake that we have made in the in that previous prediction. And what have we done to correct it because one might probably you may want to expand on this. Why do you think that the models that are driving these now cast predictions from investment banks in some cases from international bodies and others? Are very rarely revised to improve the prediction and the predictability of the of the figures and the data that is being used in the model.

 

TN: It’s because the forecasters are not accountable to the traders, okay? One of the things I love about traders is they are accountable every single day for their PNO.

 

DL: Yeah, right.

 

TN: Every single day, every minute of every day they’re accountable for their PNO. Forecasters are not accountable to a PNO so they put together some really interesting sophisticated model that may not actually work in the real world, right? And you look at the forward curves or something like that, I mean all that stuff is great but that’s not a forecast, okay? So I love traders. I love talking to traders because they are accountable every single day. They make public mistakes. And again this is part of what I love about social media is traders will put their hypothesis out there and if they’re wrong people will somewhat respectfully make fun of them, okay?

 

DL: Not necessarily respectfully but they will.

 

TN: In some cases different but this is great and you know what economists and industry forecasts, commodity forecasters these guys have to be accountable as well. I would love it if traders would put forecasters up to the same level of criticism that they do other traders but they don’t.

 

DL: Don’t you find it interesting? I mean one of the things that I find more intellectually dishonest sometimes is to hear some of the forecasters say, well we’ve only made a downgrade of one point of one percentage point of GDP only.

 

TN: Only, right. It’s okay.

 

DL: So that is that we’ve grown accustomed to this idea that you start the year with a prediction of say, I don’t know three percent growth, which goes down to below two. And that doesn’t get anybody fired, it’s sort of like pretty much average but I think it’s very important because one of the things. And I want to gather your thoughts about this. One of the things that we get from this is that there is absolutely no analysis of the impact of stimulus packages for example, when you have somebody is announcing a trillion dollar stimulus package that’s going to generate one percent increase in trendline GDP growth it doesn’t. And everybody forgets about it but the trillion dollars are gone. What is your thoughts on this?

 

TN: Well, I think those are related in as much as… let’s say somebody downgraded GDP by one percent. What they’re not accounting for, What I think they’re not accounting for is let’s say the economic impact kind of multiplier. And I say that in quotes for that government spending, right? So in the old days you would have a government spending of say you know 500 billion dollars and let’s say that was on infrastructure. Traditionally you have a 1.6 multiplier for infrastructure spend so over the next say five years that seeps into the economy in a 1.6 times outs. So you get a double bang right you get the government spending say one-to-one impact on the economy. Then you get a point six times that in other industries but what’s actually happened.

 

And Michael Nicoletos does some really good analysis on this for China, for example. He says that for every unit of say debt that’s taken out in China, which is government debt. It takes eight something like eight units of debt to create one unit of GDP. So in China for example you don’t have an economic multiplier you have an economic divisor, right?

 

DL: Exactly.

 

TN: So the more the Chinese government spends actually the less GDP growth which is weird, right? But it tells me that China is an economy that is begging for a market. A real market, okay? Rather than kind of central planning and you and Europe. I’m sure you’re very familiar with the Soviet Union. I studied a lot of that in my undergrad days very familiar with the impact of central planning. China there’s this illusion that there is no central planning in China but we’re seeing with the kind of blow-ups in the financial sector that there is actually central planning in China.

 

And if you look at the steel sector you look at commodity consumption, these sorts of things it’s a big factor of china still, right? So but it’s incredibly inefficient spending. It’s an incredibly inefficient way and again it’s a market that is begging for an open economy because they could really grow if they were open but they’re not. They have a captive currency they have central planning and so on and so forth.

 

Now I know some of the people watching, you’re going to say you’ve never been to China, you don’t understand. Actually I have spent a lot of time in China, okay? I actually advise China’s Economic Planners for about a year and a half, almost two years on the belt and road initiative. So I’ve been inside the bureaucracy not at the high levels where they throw nice dinners. I’ve been in the offices of middle managers for a long time within the Chinese Central Government so I understand how it works and I understand the impact on the economy.

 

DL: Don’t you think it’s interesting though that despite the evidence of what you just mentioned. And how brutal it has been because it’s multiplied by 10. How many units of debt are required to generate one unit of GDP in a little bit more than a decade? Don’t you find it frustrating to read and hear that what for example the United States needs is some sort of central planning like the Chinese one. And that in fact the the developed economies would be much better off if they had the type of intervention from from the government that China has?

 

TN: Sure, well it’s it’s kind of the fair complete that central bankers bring to the table. I have a solution. We need to use this solution to bring fill in the blank on desired outcome, okay? And so when central bankers come to the table they have there’s an inevitability to the solution that they’re going to bring. And the more we rely on central bankers the more we rely on centralized planning. And so I’ve had so many questions over the last several years, should the us put forward a program like China’s belt and road program, okay?

 

We know the US, Europe, the G20 nobody needs that, okay? Why? Because Europe has an open market and great companies that build great infrastructure. The US has an open market and although European infrastructure companies are better. The US has some pretty good companies that build infrastructure in an open market. So why do we need a belt and road program? Why do we need central planning around that? And we can go into a lot of detail about what’s wrong with the belton road and why it’s not real, okay? But that type of central planning typically comes with money as the as kind of the bait to get people to move things. And so we’re already doing that with the FED and we’re already doing that with treasure with money from the treasury, right?

 

And if you look at Europe you’re doing it with the ECB. You’re doing it with money from finance ministries. The next question is, does the government start actually taking over industries again? And you know maybe not and effectively in some ways they kind of are in some cases. And the real question is what are the results and I would argue the results are not a multiplier result they are a divisor result.

 

DL: Absolutely. Absolutely it is we saw it for example. I think it’s, I mean painfully evident in the junk plan in Europe or the growth and jobs plan of 2009 that destroyed four and a half million jobs. It’s not easy to to achieve this.

 

TN: You have to try to do that.

 

DL: You have to really really try it, really try.

 

I think that you mentioned a very important factor which is that central banking brings central planning because central banks present a program of monetary easing of monetary policy. And they say well we don’t do fiscal policy but they’re basically telling you what fiscal policy has to be implemented to the point that their excuse for the lack of results of monetary policy tends to be that the that the transmission mechanism of monetary policy is not working as it should. Therefore because the demand for credit is not as much as the supply of money that have invented. They say, well how do we fill in the blank? Oh it has to be government spending. It has to be for planning. It has to be so-called infrastructure spending from government.

 

You just mentioned a very important point there is absolutely no problem to invest in infrastructure. There’s never been more demand for a good quality infrastructure projects from private equity, from businesses. But I come back to the point of of central banks and a little bit about your view. How does prolonging easing measures and maintaining extremely low rates affect these trends in growth and in these trends in in productivity?

 

TN: Well, okay, so what you brought up about central banks and the government as the transmission mechanism is really important. So low interest rates Zerp and Nerp really bring about an environment where central banks have forced private sector banks to fail as the transmission mechanism. Central banks make money on holding money overnight, that’s it. They’re not making money necessarily or they’re not doing it to successfully to impact economies. They’re not successfully lending out loans because they say it’s less risky buying bonds. It’s less risky having our money sit with the Fed. It’s less risky to do this stuff than it is to loan out money. Of course it’s less risky, right? That’s goes without saying.

 

So you know I think where we need to go with that is getting central banks out of that cycle is going to hurt. We cannot it… cannot hurt, well I would say baby boomers in the West and and in Northeast Asia which has a huge baby boomer cohort. Until those guys are retired and until their incomes are set central banks cannot take their foot off the gas because at least in the west those folks are voters. And if you take away from the income of that large cohort of voters then you’ll have, I guess I think from their perspective you’ll have chaos for years.

 

So you know we need to wait until something happens with baby boomers. You tell central banks and finance ministries or treasuries will kind of get religion and what will happen is behind baby boomers is a small cohort generally, okay? So it’s that small cohort who will suffer. It’s not Baby Boomers who will suffer. It’s that small cohort who will suffer. It’s the wealth of that next generation that Gen x that will suffer when central banks and finance ministries get religion.

 

So we’re probably looking at ten more years five more years of this and then you’ll see kind of… you remember what a rousing success Jeff Sax’s shock therapy was, right?

 

DL: Yeah.

 

TN: So of course it wasn’t and it’s you know but it’s gonna hurt and it’s gonna hurt in developed countries in a way that it hasn’t hurt for a long time. So that kind of brings to the discussion things like soundness of the dollar, status of the Euro that sort of thing. I think there are a lot of people out there who have this thesis. I think they’re a little early on it.

 

DL: Yeah, I agree.

 

TN: So economists you know these insurance people see it from a macro perspective but often they come to the conclusion too early. So I think it’s a generational type of change that’ll happen and then we start to see if the US wants the dollar to remain preeminent. They’re going to have to get religion at the central bank level. They’re going to have to get religion at the fiscal level and really start ratcheting down some of the kind of free spending disciplines they’ve had in the past.

 

DL: Yeah, it’s almost inevitable that you’re in a society that is aging. The net prison value of bad decisions for the future is too positive for the voters that are right now with the middle age, in a certain uh bracket of of age. Me, I tried the other day my students I see you more as the guys that are going to pay my pension than my students. So yeah…

 

TN: But it’s you and me who will be in that age bracket who will pay for it. It’s the people who are 60 plus right now who will not pay for it. So they’ll go through their lives as they have with governments catering to their every need, where it’s our age that will end up paying for it. So people our age we need to have hard assets.

 

DL: Absolutely.

 

TN: You know when the time comes we have to have hard assets because it’s going to be…

 

DL: That is one of one of the mistakes that a lot of the people that follow us around. They they feel that so many of the valuations are so elevated that maybe it’s a good time to cash in and simply get rid of hard assets, I say absolutely the opposite because you’ve mentioned a very important thing which is this religious aspect that we’ve that we’ve gotten into. And I for just for clarity would you care to explain for people what that means because…

 

TN: I say get religion? I mean to become disciplined.

 

DL: I know like you because that is an important thing.

 

TN: Yes, sorry I mean if anybody but to become disciplined about the financial environment and about the monetary environment.

 

DL: Absolutely because one of the things that people tend to believe when you talk about religion and the the state planners religion and and central bank’s religion is actually the opposite. So I wanted to write for you to very make it very clear. That what you’re talking about is discipline you’re not talking about the idea of going full-blown MMT and that kind of thing.

 

TN: No. I think if there is if there is kind of an MMT period, I think it’s a I don’t think it’s an extended period. I think it’s an experiment that a couple of countries undertake. I think it’s problematic for them. And I think they try to find a way to come back but…

 

DL: How do you come back from that because one of the problems that I find when people bring the idea of well,  why not try. I always, I’m very aware and very concerned about that thought process because you know I’ve been very involved in analyzing and in helping businesses in Argentina, in Hawaii, in Brazil and it’s very difficult to come back. I had a discussion yesterday with the ex-minister of economy of Uruguay and Ignacio was telling me we started with a 133 percent inflation. And we were successful in bringing it down to 40 and that was nine years.

 

TN: Right. So, yeah I get how do you come back from it look at Argentina. look at Zimbabwe. I think of course they’re not the Fed. They’re not you know the EU but they are very interesting experiments when people said we’re going to get unhinged with our spending. And we’re going to completely disregard fundamentals. Which I would say I would argue we are on some level disregarding fundamentals today but it’s completely you know divorced from reality. And if you take a large economy like the US and go MMT it would take a very long time to come back.

 

DL: Absolutely.

 

TN: So let’s let’s look at a place like China, okay? So has China gone MMT? Actually, not really but their bank lending is has grown five times faster than the US, okay? So these guys are not lending on anything near fundamentals. Sorry when I say five times faster what I mean is this it grew five times larger than the bank lending in the US, okay? So China is a smaller economy and banks have balance sheets that are five times larger than banks in the US. And that is that should be distressing followers.

 

DL: Everybody say that the example of China doesn’t work because more debt because it’s growing faster what you’ve just said is absolutely critical for for some of our followers.

 

TN: Right, the other part about China is they don’t have a convertible currency. So they can do whatever they want to control their currency value while they grow their bank balance sheets. And it’s just wonderland, it’s not reality so if that were to happen there are guys out there like Mike Green and others who look at a severe devaluation of CNY. And I think that’s more likely than not.

 

DL: Yeah, obviously as well. I think that the the Chinese government is trying to postpone as much as it can the devaluation of the currency based on a view that the imbalances of the economy can be sort of managed through central planning but what ends up happening is that you’re basically just postponing the inevitable. And getting a situation in which the actual devaluation when it happens is much larger. It reminds me very much. I come back to the point of Argentina with the fake peg of the peso to the dollar that prolonging it created a devastation from which they have not returned yet.

 

TN: Right. And if you look at China right now they need commodities desperately, okay? Metals, they need energy desperately and so on and so forth. So they’ve known this for months. So they’ve had CNY at about six three, six four to the dollar which is very strong. And it was trading a year ago around seven or something like that. So they’ve appreciated it dramatically and the longer they keep it at this level. The more difficult it’s going to be on the other side. And they know it these are not stupid people but they understand that that buying commodities is more important for their economy today because if people in China are cold this winter and they don’t have enough nat gas and coal then it’s going to be a very difficult time in the spring for the government.

 

DL: And when you and coming back to that point there’s a double-edged sword. On the one side you have a currency that is out to free sheet are artificially appreciated. On the other side you also have price controls because coal prices are limited by the government. And therefore you’re creating on the one hand a very big monetary hole and on the other hand a very big financial hole in the companies that are selling at a loss.

 

TN: That’s true but I would say one slight adjustment to that things like electricity prices are controls. When power generators buy coal, they buy that in a spot market, okay? So coal prices have been rising where electricity prices are highly regulated by the government this is why we’ve seen blackouts and brownouts and power outages in China. And why it’s impacted their manufacturing base because they’re buying coal in a spot market and then they’re having to sell it at a much lower price in the retail market.

 

And so again this is the problem with central planning this is the problem with kind of partial liberalization of markets. You liberalize the coal price but you keep the electricity price regulated and if you don’t have the central government supporting those power plants they just blow up all over the place. And we’ve seen the power generators in the UK go bankrupt. We saw some here in Texas go bankrupt a couple years ago because of disparities like that and those power generators in the UK going bankrupt that’s the market working, right? So we need to see that in China as well.

 

DL: Yeah, it’s a very very fascinating conversation because on the other hand for example in Europe right now with the energy shortage we’re seeing that a few countries Spain, France, etc. are actually trying to convince the European Union, the European Commission to try to get into a sort of intervened market price in the in the generation business. Which would be just like you’ve mentioned an absolute atrocity very very dangerous.

 

TN: This creates a huge liability for the government.

 

DL: It creates a massive liability for the government. This is a key point that people fail to understand the debate in the European union is that, oh it’s a great idea because France has this massive utility company that is public. And therefore there’s no risk it had to be bailed out twice by the taxpayers. People tend to forget that you’re paying for that.

 

TN: But again this is what’s that block of voters who doesn’t really care about the impact 10 or 20 years down the road. That’s the problem. There’s a huge block of voters who don’t really care what the cost is because the government’s going to borrow money long-term debt. And it’s going to be paid back in 10 or 20 years and the biggest beneficiaries of this and the people on fixed incomes they actually don’t care what the cost is.

 

DL: Yeah, yeah exactly, exactly. There’s this fantastic perverse incentive to pass the bill to the next generation. And that obviously is where we are right now. Coming back to the point of the infrastructure plans and the belt and road plan. What in your view are the the lessons that we must have learned or that we should be learning from the Belgian road initiative?

 

TN: So here’s a problem with the Belton road and I had a very candid discussion with a senior official within China’s NDRC in probably 2015 which was early on, okay? And this person told me the following they said the Belgian road was designed to be a debt financed plan. What’s happening now, and again this was six or seven years ago, very early on in the in the belts and road dates. They said the beneficiary countries are pushing back and forcing us to take equity in this infrastructure, okay?

 

Now why does that matter well the initial build out of infrastructure is about five percent of the lifetime cost of that asset, okay? So if you’re if China is only involved in the initial build out they’re taking their five percent, it’s a loan and they get out. If they’re equity holders in that let’s say they’re 49 equity holders in an Indonesian high-speed rail then they become accountable for part of that build-out. And then they have to maintain the other 95 of the cost for the next 30 to 50 years. So they thought they were going to be one and done in and out. We do this infrastructure we get out they owe us money and it’s really clean what’s happened is they’ve had to get involved in the equity of those assets.

 

And so I’ve since had some uh government officials from say Africa ask me what do we do with the Belton road with china? Very simple answer force them to convert the debt to equity, okay? They become long-term involved on a long-term basis. They become involved in those assets and then they’re have a different level of interest in them in the quality maintenance and everything else but they’re also on the long-term basis accountable for the costs.

 

So they don’t just build a pretty airport that and I’m not saying this necessarily happens but they don’t just build a pretty airport that falls apart in five years, okay? They then have to think about the long-term impacts and long-term maintenance costs of that airport, right? And so but you know the original design of the Belton road was debt financing. Mobilizing workers and so on and so forth what it’s become is a mix of debt and equity financing. And that’s not what the Chinese government has wanted.

 

So I’ve been telling people for three or four years the Belton road is dead, okay? And people push back me and say no it’s not, you know think tank people or whatever. But they don’t understand the fundamental fact of how the Belton road was designed it was designed as a one-and-done debt financed infrastructure build out it’s become a long-term investment all around the world. So it’s a different program. It’s failed, okay?

 

They’re not going to make the money they thought yes they’ll keep some workers busy but they’re not going to make the money they thought. All of those assets, almost all those assets are financed in US dollars, okay? So they’re not getting their currency out. It’s not becoming an international unit like they had hoped. They’re it’s not they’re not clean transactions and so on and so forth. So this is what’s happened with the Belgian road. So the lesson learned is they should have planned better. And they should have had a better answer to you become an equity owner. And uh

 

I think you know if any western governments want to have kind of a belt and road type of initiative. They’re going to have to contend with the demand from some of these countries that they become equity owners. And I think that’s a bad idea for western governments to be equity owners in infrastructure assets so you know this is this is the problem.

 

Japanese have taken a little bit different because of where the Yen is and because of where interest rates are in Japan. Japanese have basically had kind of zero interest or close to zero interest on the infrastructure they’ve built out. And so they haven’t gone after it as aggressively as China has. They’ve had a much cleaner um structure to those agreements. And so they’ve been, I think pretty successful in staying out of the equity game and staying more focused on the debt financing for their infrastructure initiatives.

 

DL: Oh, absolutely big lesson, big lesson there because the we see now that the vast majority of those projects are impossible to the debt is impossible to be repaid. There’s about 600 billion dollars of unpayable debt out there. And we also have the example from from the internationalization of the French, Spanish, Italian companies into Latin America that they fell into the same trap. They started with a with a debt-financed infrastructure build type of clean slate program that ended up owning equity. And in some cases with nationalizations hopefully that will not…

 

TN: And watch for debt to equity conversions in these things. It’s good. There’s going to be huge pressure because the Chinese say the exit bank the CDB. A lot of these organizations are going to be forced to convert that debt to equity and then unload it on operating companies in China. They’re not going to want to do it but we’re going to start to see more and more pressure there over the next couple of years.

 

DL: Great! Well I’m absolutely convinced that will happen. Tony, we’ve run out of time so it’s been an incredible conversation lots of things that are very very interesting for our followers. We will give all the details to follow you and to get more information about your company in the details of the of the video. And thank you so much for your time. I hope that that we will be able to talk again in a not too distant future.

 

TN: Thank you Daniel. Anytime. Thank you so much.

Categories
Tutorials

How to find the forecast price of silver?

In this short video, we teach you how to see the forecast price of silver using CI Futures. For more information about this app, go to the CI Futures page.

 

How much has silver risen in 10 years? This video also shows price of silver over the last 10 years as well as the silver price trend chart. CI Futures is an AI/ML app for forecasting thousands of assets including commodities like silver and gold.

 

Our engine also forecasts currency pairs for Forex, different equity indices in the world, and economic indicators like GDP and inflation. For a complete list of what we forecast, please go to the Forecast Assets list. 88.6% of the items we forecast have more than 95% accuracy.

Categories
News Articles

AI for Supply Chain Forecasting and Proactive Planning

This article originally published at https://www.linkedin.com/pulse/ai-supply-chain-forecasting-cas-milner/ on January 27, 2021. It talks about one of the CFO pain points, which is planning.

 

 

How much confidence do you have in traditional price forecasts for the components of your supply chain? Your answer is probably “not much”, if you have been in business for over a decade — or even just during 2020! But AI can do better — much better — at price forecasting than the standard statistical technique of linear regression most of us learned in college.

 

Complete Intelligence has built a comprehensive platform for making very accurate supply chain ingredient forecasts. The forecasting Saas have done the hard work of aggregating (and cleaning!) billions of data points from many high-quality sources, including import/export trade data, all feeding the AI algorithm engines to produce amazingly accurate predictions. You should follow the postings of Tony Nash , for his economic commentary based on many forecasts for exchange rates, basic commodities, and supply chain components important for world economies and local business operations.

 

Many companies have antiquated, inaccurate processes for forecasting costs in their supply chain. Their standard statistical forecasting is usually done with linear regression – a straight-line projection of historical costs, into the future. But the price behavior of most commodities is not linear, it is non-linear. Artificial intelligence algorithms are especially suited to making accurate forecasts using non-linear data, which is why they are increasingly applied to dynamic financial forecasting.

 

Many industries are especially sensitive to supply costs:

 

  • Manufacturing (electronics, energy equipment, automotive, health supplies, pharmaceuticals, metals, plastics, papers)
  • Extraction operations (oil and gas, forestry, mining)
  • Services (transportation, shipping, hospitality, food and beverage)

 

Supply chain cost planning is a core process, and AI tools are destined to become key ingredients, deeply embedded in operations.  They enable automation of proactive planning and monitoring to digitally transform the organization. The licensing cost for these financial forecasting tools or financial projection software is a small fraction of the operations cost – and potential savings. It is also worth noting that having reliable forecasts of future price trends can create a rational basis for supplier negotiations. Simplify financial planning with AI and machine learning.

 

I’m excited about the AI-driven digital transformation of micro-economic forecasting, and would eagerly discuss the benefits with you.

 

#SupplyChain #AI #EconomicForecasting

Categories
News Articles Uncategorized

Startup makes superforecasting possible with AI

This article originally published at https://blogs.oracle.com/startup/startup-makes-superforecasting-possible-with-ai on December 1, 2020.

 

 

Here’s a mathematical problem: The sum of all the individual country GDPs never equals the global GDP. That means forecasting models are flawed from the start, and it’s impacting global supply chain economics in a big way. Entrepreneur Tony Nash found that unacceptable, so he built an AI platform to help businesses “understand the sum of everything” through a highly automated, globally data-intensive solution with zero human bias.

 

Complete Intelligence, Nash’s Houston-based startup, uses global market data and artificial intelligence to help organizations to visualize financial data, make predictions, adjust plans in the context of a global economy, all on the fly. The globally-integrated, cloud-based AI platform helps purchasing, supply chain planning, and revenue teams make smarter cost and revenue decisions. It’s a way on how to make better business decisions.

 

“The machines are learning, and many times that has meant deviating from traditionally held consensus beliefs and causality models,” said Nash. “Causal beliefs don’t hold up most of the time—it’s human bias that is holding them up—our AI data is reducing errors and getting closer to the truth, closer to the promise of superforecasting.”

 

 

Massive datasets across 1,400 industry sectors

More than 15 billion data points run through the Complete Intelligence platform daily, making hundreds of millions of calculations. Average business forecasting saas software models use 10-12 sector variables. Complete Intelligence, on the other hand, examines variables across 1,400 industry sectors. The robustness gives businesses insights and control they didn’t have before.

 

“We’ve seen a big shift in how category managers and planning managers are looking at their supply chains,” said Nash. “Companies are taking a closer look at the concentration of supply chains by every variable. Our platform helps companies easily visualize the outlook for their supply chain costs, and helps them pivot quickly.”

 

 

Superforecasting brings a modern mindset to an old industry

 

Australia-based OZ Minerals, a publicly-traded company, is a modern mining company focused on copper with mines in Australia and Brazil. OZ says their modern mantra is more than technology, it’s also a mindset: test, learn, innovate. They wanted to better navigate and understand the multi-faceted copper market, where the connectivity between miner, smelter, product maker, and consumer is incredibly complex and dynamic. They turned to Complete Intelligence.

 

“I need a firm understanding of both fiscal and monetary policies and foreign exchange rates to understand how commodity prices might react in the future because a depreciating and/or appreciating currency can impact the trade flows, and often very quickly, which might influence decisions we make,” said Luke McFadyen, Manager of Strategy and Economics at OZ Minerals.

 

“Our copper concentrate produced in Australia and Brazil may end up being refined locally or overseas. And then it is turned into a metal, which then may be turned into a wire or rod, and then used in an electric vehicle sold in New York, an air conditioner sold in Johannesburg, or used in the motor of a wind turbine in Denmark,” he explains. “The copper market is an incredibly complex system.”

 

With Complete Intelligence, McFadyen has a new opportunity to test for a bigger-picture understanding and responsiveness. Previously, he updated his models every few months. Now he could do it every 47 minutes if he needed to.

 

McFadyen points to the impact of COVID-19 as a “Black Swan” event that no business forecasting saas software could have predicted, but is nonetheless impacting currencies, foreign exchanges, and cost curves throughout global copper market and supply chains.

 

“If your model isn’t dynamic and responsive in events like we are experiencing today, then it is not insightful. If it’s not insightful, it’s not influencing and informing decisions,” he said. “Complete Intelligence provides a different insight compared to how the traditional price and foreign exchange models work.”

 

McFadyen says early results have reflected reductions in error rates and improved responsiveness.

 

 

Cloud power and partnership

 

Complete Intelligence needed a strong technology partner but also one with global expertise in enterprise sales and marketing that could help boost their business. They found it with Oracle for Startups.

 

“We have lots of concurrent and parallel processes with very large data volumes,” said Nash. “We are checking historical data against thousands of variables, anomaly detections, massive calculations processing, and storage. And it’s all optimized with Oracle Cloud.”

 

Nash, who migrated off Google Cloud, says Oracle Cloud gives him the confidence that his solution can handle these workloads and data sets without downtime or performance lapses. The partnership also gives him a credible technology that is native to many clients.

 

“As we have potential clients that come to us that are using Oracle, having our software on Oracle Cloud infrastructure will make it easier for us to deploy and scale. A seamless client experience is a critical success factor for us.”

 

Nash says the Oracle startup program‘s free cloud credits and 70% discount has allowed them to save costs while increasing value to customers. He also takes advantage of the program’s resources including introductions to customers and marketing and PR support.

 

“We’ve been impressed by the resources and dedication of Oracle for Startups team,” he said. “I’d recommend it, especially for AI and data startups ready for global scale.”

 

 

Beyond mining: superforecasting futures with AI

 

Beyond mining, Complete Intelligence is working with customers in oil and gas, chemicals, electronics, food and beverages, and industrial manufacturing. From packaging to polymers and sugar to sensors, these customers use Complete Intelligence for cost and revenue planning, purchasing and supply chain proactive planning, risk management, and auditing teams, as well as general market and economic forecasts.

 

The error rates for Complete Intelligence forecasts in energy and industrial metals performed 9.4% better than consensus forecasts over the same period, and Complete Intelligence continues to add methods to better account for market shocks and volatility.

 

OZ Minerals’ McFadyen said, “This is the next step in how economists can work in the future with change leading towards better forecasts, which will inform better decisions.”

 

Nash and Complete Intelligence are betting on it – and building for the future.

Categories
Podcasts

Behold the Power of Superforecasting

This podcast first appeared and originally published at https://soundcloud.com/user-454088293/behold-the-power-of-superforecasting on August 26, 2020.

 

In just 2 minutes, you’ll learn why superforecasting is so much better than forecasting. Hear how automated, data-intensive AI with no human bias can help make predictions and adjust strategy on the fly, and how startup Complete Intelligence is making it happen.

 

Is forecasting enough when you need to analyze and take action? Meet the startup that says “no.” What’s needed is superforecasting.

 

Hi, it’s Mike Stiles, and this is Meet the Startups for the week of August 26th, brought to you by Oracle for Startups.

 

How can you be happy with forecasting when there’s something out there called superforecasting?

 

Startup founder Tony Nash and his company, Complete Intelligence are making super forecasting possible with a highly automated, data intensive A.I. solution.

 

Part of what makes it so SUPER is there’s zero human bias. No spin or wishful thinking allowed.

 

Complete intelligence is helping organizations visualize financial data, make predictions and adjust strategy on the fly. That gets you things like smarter purchasing, better supply chain planning, smarter cost and revenue decisions.

 

But it’s intense.

 

More than 15 billion data points are run on Complete Intelligence’s platform every day.

 

To get where they needed to be on performance and price, the company moved from Google Cloud to Oracle Cloud. That did it. Computing is at peak performance and Complete Intelligence’s global customers are reaping the benefits. That’s super.

 

We asked Complete IntelligenceCEO Tony Nash what this pandemic has done to forecasting and supply chains.

 

”We’ve seen a big shift in how managers are looking at their supply chains. As a result of Covid-19, companies are eager to understand their cost and revenue risks, things like concentration risk and the timing of their cost, that sort of thing. We’re helping our customers with timely and accurate information to make smarter cost and better revenue planning decisions.“

 

What startup doesn’t like better performance and lower costs? Oracle has a startup partnership for you at Oracle.com/startup.

Categories
Podcasts

Stories from the Cloud: The Forecast Calls For…

Tony Nash joins veteran journalists Michael Hickins and Barbara Darrow at the Stories from the Cloud podcast to talk about the forecast calls for businesses, and how AI and machine learning can help in predicting the futures in budget forecasting. How does his company Complete Intelligence dramatically improve forecast accuracy of companies suffering from a huge 30% error rate. He also explained the AI technology behind the CI solutions and strategic toolkit, and how this practically applies to global companies. How can they benefit from this new technology to better be prepared in their budget planning and reducing risks in costs?

 

Stories from the Cloud description:

It’s not easy to predict the future. But when it comes to business and cash or financial forecasting tool or software, the right data and the right models are better than any crystal ball.

 

Tony Nash, CEO and founder of Complete Intelligence, explains how AI and the cloud are giving companies better cash forecasting software tools to see into their financial futures.

 

About Stories from the Cloud: Enterprises worldwide are turning to the cloud to help them thrive in an ever-more-competitive environment. In this podcast, veteran journalists Michael Hickins and Barbara Darrow chat with the people behind this massive digital transformation and the effects it has on their work and lives.

 

Show Notes

 

SFC: Hey, everybody, welcome back to Stories from the Cloud sponsored by Oracle. This week, I am here, as always, with Michael Hickins, formerly of The Wall Street Journal. I am Barbara Darrow. And our special guest today is Tony Nash. He’s the founder and CEO of Complete Intelligence. And this is a very interesting company. Tony, thanks for joining us. And can you just tell us a little bit about what the problem is that Complete Intelligence is attacking and who are your typical customers?

 

Tony: Sure. The problem we’re attacking is just really bad forecasting, really bad budget setting, really bad expectation setting within an enterprise environment. Companies have packed away data for the last 15, 20 years, but they’re not really using it effectively. We help people get very precise, very accurate views on costs and revenues over the next 12 to twenty four months so they can plan more precisely and tactically.

 

SFC: It sounds like a big part of the mission here is to clean up… Everybody talks about how great data is and how valuable it is. But I mean, it sounds like there’s a big problem with a lot of people’s data. And I’m wondering if you could give us an example of a company, let’s just say a car maker and what you can help them do in terms of tracking their past costs and forecasting their future costs.

 

Tony: So a lot of the problem that we see, let’s say, with the big auto manufacturer, is they have long-term supply relationships where prices are set, or they’ve had the same vendor for X number of years and they really don’t know if they’re getting a market cost, or they don’t have visibility into what are those upstream costs from that vendor. And so, we take data directly from their ERP system or their supply chain system or e-procurement system and we come up with very specific cost outlooks.

 

We do the same on the sales revenue side. But say for an automaker, a very specific cost outlook for the components and the elements that make up specific products. So we’ll do a bill of material level forecast for people so that they can understand where the cost for that specific product is going.

 

Before I started Complete Intelligence, I ran research for a company called The Economist and I ran Asia consulting for a company called IHS Markit. And my clients would come to me and say, there are two issues at both companies. Two issues. First is the business and financial forecasting tool or even strategic toolkit that people buy off the shelf has a high error rate. The second issue is the forecasts don’t have the level of context and specificity needed for people to actually make decisions. So what do you get? You get very generic data with imprecise forecasts coming in and then you get people building spreadsheets and exclusive models or specific models within even different departments and teams and everything within a company.

 

So there are very inconsistent ways of looking at the world. And so we provide people with a very consistent way and a very low error way of looking at the future trajectory of those costs and of those revenues.

 

SFC: So I’m curious, what is the what is the psychology of better business forecasting software? So on your customers and I’m thinking, if I’m a consumer, so this is maybe not a good analogy, but if I’m a consumer and I look at the actual costs are of a phone that I may have in my pocket, I may think, jeez, why making a thousand dollars for this? But then part of me says, such things mark up and well, I guess so. There are uncertainties in financial projections. So on me, I mean, I don’t need a financial projection software to tell me that the components of the pocket computer have don’t add up to what I paid for them. But I kind of understand that there needs to be money made along the way. I just I want it. Right. How does that translate on a B2B perspective? What are the people’s attitude about price and how do they react to the data that, as you said, I mean, heretofore, it’s kind of been unreliable.And all of a sudden, I think you say a lot of procurement projections have been around 30 percent, which is huge. Right. So how does that happen and how do people react to something that seems more trustworthy?

 

Tony: Well, I think that expectations depend on the level within a manufacturing company that you’re talking to. I think the more senior level somebody is, of course, they want predictability and quality within their supply chain, but they’re also responsible to investors and clients for both quality and cost. And so at a senior level, they would love to be able to take a very data driven approach to what’s going on. The lower you get within a manufacturing organization, this is where some of the softer factors start to come in. It’s also where a lot of the questionable models are put in as well.

 

Very few companies that we talk to actually monitor their internal error rates for their cost and revenue outlooks. So they’ll have a cost business forecasting software model or a revenue forecasting model that they rely on because they’ve used it for a long period of time, but they rarely, if ever, go back and look at the error rates that that model puts out. Because what’s happening is they’re manually adjusting data along the way. They’re not really looking at the model output except for that one time of the year that they’re doing their budget.

 

So there really isn’t accountability for the fairly rudimentary models that manufacturing companies are using today. What we do is we tell on ourselves. We give our clients our error rates every month because we know that no no business forecasting software model is perfect. So we want our clients to know what the error rate is so that they can understand within their decision making processes.

 

SFC: And it’s kind like a margin of error in a political poll?

 

Tony: Yeah, we use what’s called MAPE – mean absolute percent error. Most error calculations. You can game the pluses and minuses. So let’s say you were 10 percent off, 10 percent over last month and 12 percent under this month. OK. If you average those out, that’s one percent error. But if you look at that on an absolute percent error basis, that’s 11 percent error. So we gauge our error on an absolute percent error basis because it doesn’t matter if you’re over under, it’s still error.

 

SFC: Still wrong, right.

 

Tony: Yeah. So we tell on ourselves, to our clients because we’re accountable. We need to model the behavior that we see that those senior executives have with their investors and with their customers, right? An investment banking analyst doesn’t really care that it was a plus and a minus. They just care that it was wrong. And they’re going to hold those shares, that company accountable and they’re going to punish them in public markets.

 

So we want to give those executives much better data to make decisions, more precise decisions with lower error rates so they can get their budgeting right, so they can have the right cash set aside to do their transactions through the year, so they can work with demand plans and put our costs against their say volume, demand plans, those sorts of things.

 

SFC: I have to just ask I mean, Michael alluded to this earlier, but I want to dive into a little more. You had said somewhere else that most companies procurement projections are off by 30 percent. That’s a lot. I mean, I know people aren’t… I mean, how is that even possible?

 

Tony: It’s not a number that we’ve come up with. So first, I need to be clear that that’s not a number that we’ve come up with and that’s not a number that’s published anywhere. That’s a number that we consistently get as feedback from clients and from companies that we’re pitching. So that 30 percent is not our number. It’s a number that we’re told on a regular basis.

 

SFC: When you start pitching a client, obviously there’s a there’s a period where they’re just sort of doing a proof of concept. How long does that typically last before they go? You know what? This is really accurate. This can really help. Let’s go ahead and put this into production.

 

Tony: Well, I think typically, when we when we hit the right person who’s involved in, let’s say, category management or they actually own a PNL or they’re senior on the FPNA side or they’re digital transformation, those guys tend to get it pretty quickly, actually. And they realize there’s really not stuff out there similar to what we’re doing. But for people who observe it, it probably takes three months. So our pilots typically last three months.

 

And after three months, people see side by side how we’re performing and they’re usually convinced, partly because of the specificity of projection data that we can bring to to the table. Whereas maybe within companies they’re doing a say, a higher level look at things. We’re doing a very much a bottom up assessment of where costs will go from a very technical perspective, the types of databases we’re using, they’re structured in a way that those costs add up.

 

And we forecast at the outermost leaf node of, say, a bill material. So uncertainties in financial projections are solved. A bill Of material may have five or 10 or 50 levels. ut we go out to the outermost kind of item within that material level, and then we add those up as the components and the items stack up within that material. Let’s say it’s a mobile phone, you’ll have a screen, you’ll have internal components. You’ll have the case on the outside. All of this stuff, all of those things are subcomponents of a bill of material for that mobile phone.

 

SFC: So I am assuming that there is a big role here in what you’re doing with artificial intelligence, machine learning. But before we ask what that role is, can you talk about what you mean by those terms? Because we get a lot of different definitions and also differentiations between the two. So maybe talk to the normals here.

 

Tony: OK, so I hear a number of people talk about A.I. and they assume that it’s this thinking machine that does everything on its own and doesn’t need any human interaction. That stuff doesn’t exist. That’s called artificial general intelligence. That does not exist today.

 

It was explained to me a few years ago, and this is probably a bit broader than most people are used to, but artificial intelligence from a very broad technical perspective includes everything from a basic mathematical function on upward. When we get into the machine learning aspect of it, that is automated calculations, let’s say, OK. So automated calculations that a machine recognizes patterns over time and builds awareness based on those previous patterns and implies them on future activities, current or future activity.

 

So when we talk about A.I., we’re talking about learning from previous behavior and we’re talking about zero, and this is a key thing to understand, we have zero human intervention in our process. OK, of course, people are involved in the initial programming, that sort of thing. OK, but let’s say we have a platinum forecast that goes into some component that we’re forecasting out for somebody. We’re we’re not looking at the output of that forecast and go, “Hmmm. That doesn’t really look right to me. So I need to fiddle with it a little bit to make sure that it that it kind of looks right to me.” We don’t do that.

 

We don’t have a room of people sitting in somewhere in the Midwest or South Asia or whatever who manually manipulate stuff at all — from the time we download data, validate data, look for anomalies, process, forecast, all that stuff, and then upload — that entire process for us is automated.

 

When I started the company, what I told the team was, I don’t want people changing the forecast output because if we do that, then when we sit and talk to a client and say, hey, we have a forecast model, but then we go in and change it manually, we’re effectively lying to our customers. We’re saying we have a model, but then we’re just changing it on our own.

 

We want true kind of fidelity to what we’re doing. If we tell people we have an automated process, if we tell people we have a model, we really want the output to be model output without people getting involved.

 

So we’ve had a number of unconventional calls that went pretty far against consensus that the machines brought out that we wouldn’t have necessarily put on our own. And to be very honest, some of them were a little bit embarrassing when we put them out, but they ended up being right.

 

In 2019, the US dollar, if you look at, say, January 2019, the US dollar was supposed to continue to depreciate through the rest of the year. This was the consensus view of every currency forecaster out there. And I was speaking on one of the global finance TV stations telling them about our dollar outlook.

 

And I said, “look, you know, our view is that the dollar will stabilize in April, appreciate in May and accelerate in June.” And a global currency strategist literally laughed at me during that interview and said there’s no way that’s going to happen. In fact, that’s exactly what happened. Just sticking with currencies, and for people in manufacturing, we said that the Chinese Yuan, the CNY, the Renminbi would break seven. And I’m sure your listeners don’t necessarily pay attention to currency markets, but would break seven in July of 19. And actually it did in early August. So that was a very big call, non consensus call that we got months and months ahead of time and it would consistently would bear out within our forecast iterations after that. So we do the same in say metals with things like copper or soy or on the ag side.

 

On a monthly basis, on our base platform, we’re forecasting about 800 different items so people can subscribe just to our data subscription. And if they want to look at ag, commodities, metals, precious metals, whatever it is, equities, currencies, we have that as a baseline package subscription we can look at, people can look at. And that’s where we gauge a lot of our error so that we can tell on ourselves and tell clients where we got things right and where we got things wrong.

 

SFC: You know, if I were a client, I would I would ask, like, OK, is that because you were right and everyone else is wrong? Is that because you had more data sources than anyone else, or is it because of your algorithm or is it maybe because of both?

 

Tony: Yes, that would be my answer. We have over 15 billion items in our core platform. We’re running hundreds of millions of calculations whenever we rerun our forecasts. We can rerun a forecast of the entire global economy, which is every economy, every global trade lane, 200 currency pairs, 120 commodities and so on and so forth. We can do that in about forty seven minutes.

 

If somebody comes to us and says, we want to run a simulation to understand what’s going to happen in the global economy, we can introduce that in and we do these hundreds of millions of calculations very, very quickly. And that is important for us, because if one of our manufacturing clients, let’s say, last September, I don’t know if you remember, there was an attack on a Saudi oil refinery, one of the largest refineries in the world, and crude prices spiked by 18 percent in one day.

 

There were a number of companies who wanted to understand the impact of that crude spike on their cost base. They could come into our platform. They could click, they could tell us that they wanted to rerun their cost basis. And within an hour or two, depending on the size of their catalog, we could rerun their entire cost base for their business.

 

SFC: By the way, how dare you imply that our listeners are not forex experts attuned to every slight movement, especially there’s no baseball season. What else are we supposed to do? I wanted to ask you: to what extent is the performance of the cloud that you use, you know, important to the speed with which you can provide people with answers?

 

Tony: It’s very important, actually. Not every cloud provider allows every kind of software to work on their cloud. When we look at Oracle Cloud, for example, having the ability to run Kubernetes is a big deal, having the ability to run different types of database software, these sorts of things are a big deal. And so not all of these tools have been available on all of these clouds all the time. So the performance of the cloud, but also the tools that are allowed on these clouds are very, very important for us as we select cloud providers, but also as we deploy on client cloud. We can deploy our, let’s say, our CostFlow solution or our RevenueFlow solution on client clouds for security reasons or whatever. So we can just spin up an instance there as needed. It’s very important that those cloud providers allow the financial forecasting tools that we need to spin up an instant so that those enterprise clients can have the functionality they need.

 

SFC: So now I’m the one who’s going to insult our readers or listeners rather. For those of us who are not fully conversant on why it’s important to allow Kubernetes. Could you elaborate a little bit about that?

 

Tony: Well, for us, it has a lot to do with the scale of data that’s necessary and the intensity of computation that we need. It’s a specific type of strategic toolkit that we need to just get our work done. And it’s widely accepted and it’s one of the tools that we’ve chosen to use. So, for example, if Oracle didn’t allow that software, which actually it is something that Oracle has worked very hard to get online and allow that software to work there. But it is it is just one of the many tools that we use. But it’s a critical tool for us.

 

SFC: With your specialization being around cost, what have you looked at… Is cost relevant to your business and so on cloud? How so?

 

Tony: Yeah, of course it is. For us, it’s the entry cost, but it’s also the running cost for a cloud solution. And so that’s critically important for us. And not all cloud providers are created equally. So so we have to be very, very mindful of that as we deploy on a cloud for our own internal reasons, but also deploy on a client’s cloud because we want to make sure that they’re getting the most cost effective service and the best performance. Obviously, cost is not the only factor. So we need to help them understand that cost performance tradeoff if we’re going to deploy on their cloud.

 

SFC: Do you see this happening across all industries or just ones where, you know, the sort of national security concerns or food concerns, things that are clearly important in the case of some kind of emergency?

 

Tony: I see it happening maybe not across all industries, but across a lot of industries. So the electronics supply chain, for example, there’s been a lot of movement toward Mexico. You know, in 2018, the US imported more televisions from Mexico than from China for the first time in 20 some years. So those electronics supply chains and the increasing sophistication of those supply chains are moving. So that’s not necessarily sensitive electronics for, say, the Pentagon. That’s just a TV. Right. So we’re seeing things like office equipment, other things. You know, if you look at the top ten goods that the US receives from China, four of them are things like furniture and chairs and these sorts of things which can actually be made in other cheaper locations like Bangladesh or Vietnam and so on. Six of them are directly competitive with Mexico. So PCs, telecom equipment, all these other things.

 

So, you know, I actually think that much of what the US imports will be regionalized. Not all of it, of course, and not immediately. But I think there’s a real drive to reduce supply chain risk coming from boards and Coming from executive teams. And so I think we’ll really start to see that gain momentum really kind of toward the end of 2020 and into early 2021.

 

SFC: That is super interesting. Thank you for joining us. We’re kind of up against time, but I want to thank Tony for being on. I want to do a special shout out to Oracle for startups that works with cool companies like Complete Intelligence. Thanks for joining us. Please try to find Stories from the Cloud at on iTunes or wherever you get your podcasts and tune in again. Thanks, everybody.

Categories
QuickHit Visual (Videos)

QuickHit: China is not going to stop being China

Panama Canal Authority’s Silvia Fernandez de Marucci joins us for this week’s QuickHit, where explains why China is not going to stop being China. She also shares first-hand observation on the global trade trends — is it declining and by how much, what’s happening in cruises and cargo vessels, where do gas and oil shipments are redirecting, why June was worse than May, and what about July? She also shares the “star” in this pandemic and whether there’s a noticeable regionalization changes from Asia to Europe, and when can we see it happening? Also, what does Panama Canal do to be up-to-date with technology and to adapt the new normal?

 

Silvia is the Canal’s manager of market analysis and customer relations. She has 20 years of experience studying all the markets for them and is responsible for their pricing strategy, their forecasting of traffic and customer relations.

 

Panama Canal opened in 1914 with annual traffic of 14,702 vessels in 2008. By 2012, more than 815,000 vessels had passed through the canal. It takes 11.38 hours to pass through it. The American Society of Civil Engineers has ranked the Panama Canal one of the seven wonders of the modern world.

 

***This video was recorded on July 30, 2020 CDT.

 

The views and opinions expressed in this QuickHit episode are those of the guests and do not necessarily reflect the official policy or position of Complete Intelligence. Any content provided by our guests are of their opinion and are not intended to malign any political party, religion, ethnic group, club, organization, company, individual or anyone or anything.

 

Show Notes

 

 

TN: Recently, the CPB of the Netherlands came out and said that world trade was down by double digits for the first five months of the year. Obviously that’s related to COVID. Can you tell us a little bit about what you’ve seen at the Canal and really what you guys have been doing? Everyone’s been in reactionary mode. So what have you seen happening in the market?

 

SM: There are some trends that had been present before COVID like the movement of production from China to Eastern Asia and we think this is going to be accelerated by this pandemia. But I don’t think that China is going to stop being China. It will keep the relevance and the importance in global trade as they have today.

 

We think that probably, yes, we will see more regionalization. We saw the signing of the renewal of the NAFTA trade between Canada, the US, and Mexico. So we think that there may be something happening in that area. However, we don’t see that trade is going to stop. I mean trade is going to continue growing after this pandemic.

 

This is something that I would say very different from anything that we have experienced before because once it is solved, I don’t know if the vaccine appears and people start going back to the new normal, there will be changes probably to the way we do things and the consumer is going to be very careful and probably will change his habits in order to prevent contagion. But I think trade is going to continue.

 

We see some of these trends becoming more and more important or at a faster pace. It is not an economic crisis per se. Once the people are going back to work, the industry will restart their operations, people are going to be rehired. The economy should start recovering faster. We are not sure because there is no certainty with this situation.

 

We first heard about it early in the year with the cases in China. But then, it looked so far away. It was happening to China. It was happening to Italy. We didn’t think about it as something that was so important or so relevant. The first casualty was the passenger vessels. The whole season for cruise ships at the Canal was cut short in March and Panama went to a total lockdown on March 25.

 

It really started for us when we received the news of a cruise ship arriving in Panama with influenza-like disease on board that wanted to cross, which was the Zaandam, and the first one that we had with the COVID patients on board.

 

TN: And how much of your traffic is cruise ships?

 

SM: It’s very small, to be honest. It’s less than two percent of our traffic. But still, we see it as an important segment, not only because of the traffic through the Canal, but also because of what it does to the local economy. We have a lot of visitors, a lot of tourism, and that is a good injection of cash coming to Panama. It was the probably the end of the season but it was shorter than what we would have wanted.

 

TN: When we saw the first wave of COVID go through Asia, did you see a a sharp decline in vessel traffic in say Feb, March? Or was it pretty even? Did we not see that much? Because I’ve spoken to people in air freight and they said it was dramatic, the fall off they saw. I would imagine in sea freight, it’s not as dramatic but did you see a fall off?

 

SM: It started in January, which is the very low season for containers, which is the most important market segments in terms of contribution to tolls. When we saw that there was this COVID happening in Chinese New Year, everything was closed. We were in a slow season. So we didn’t see much of an impact.

 

And for the Canal, there is a lagging effect because we are 23 days away in voyage terms. So whatever happens in China, we feel it probably one month later. We expected January and February to be slow because of the normal seasonality of the trade. But then after March, I would say that April was probably the worst month for us. We were hit April then May was worse than April and then June that was even worse than than May.

 

TN: June was worse than May? Okay.

 

SM: June was worse than May. We‘ve seen four percent, ten percent, fourteen or sixteen percent decline each month. It was like, “Oh wow! This is really thick. This is really getting worse.” We had reviewed our forecast in April. And I think so far, it is behaving as we expected back then. But there’s nothing written about COVID. We are learning as we go.

 

I would say that container vessels were also affected these three months of the year. We have LNG vessels that were supposed to deliver natural gas to Japan, Korea, and China. And LNG had been behaving very badly all year. That is kind of a peak season for LNG and LNG has been having a hard time because the market were supplied and the prices were very low, so many shipments that were supposed to end up in Asia, ended up in Europe or other destinations that were more profitable for the owners. But when the price of oil collapsed and went negative, the prices of LNG were affected in the Middle East and became more competitive than the US prices.

 

We saw a harsher decline in LNG shipments. We see, for example, 30 percent less than we expected to see and by COVID in April, it was probably 50 percent below what we were expecting. It was major and Iguess it’s a matter of demand because since the whole Asia was locked down, there was no demand.

 

TN: When industry stops, you don’t need energy. It’s terrible.

SM: Exactly. It’s really terrible. It was terrible. But we had some stars in our trade that supported the situation like LPG, the cooking gas and obviously people were cooking more at home so the demand was high and we saw an increase in trade for LPG. It’s a good market for us, for the neopanamax locks, so in a way we are grateful that our trade has not suffered as much as we have seen in other areas.

 

TN: You said you declined into June. How have things been in in July, so far?

 

SM: July seems promising. We came from a from a very bad June that was closed probably 16 percent below what we expected to have. But July is about maybe seven percent below our expectation. But we are very concerned about a potential W-shape recovery because of the new cases that we have seen in the US.

 

TN: When we saw factories close across Asia in the first quarter and in some cases stay until the second quarter, did you see some of the folks who were shipping through the Canal start to pivot their production to North America?

 

SM: It’s probably too early to say. We will see the effects of COVID probably in terms of near shoring maybe in two years. I don’t think that the companies or the factories are so quick as to move the production especially during this period in which everybody is still trying to cope with the situation.

 

TN: And manage their risks, right?

 

SM: Yes. So I don’t see that happening anytime soon. But it’s probably something that the factories and the companies are going to start speeding up and diversifying their production.

 

TN: And as you said earlier, China’s still going to be there. China’s not going to disappear as an origin, right? What I’ve been saying to people is it’s incremental manufacturing that may move. It’s not the mainstay of Chinese manufacturing that’s going to move or regionalize. They’re still going to do much of the commoditized manufacturing there because the infrastructure is there.The sunk cost is there, and they need to earn out the value of those factories. I like your timeline of two years before you really start to see an impact because we may see some incremental movement and maybe some very high value, high tech stuff or something like that move first but the volume of things probably won’t happen for at least two years. Is that fair to say?

 

SM: I would say so and I would add that we have seen these shifts to Vietnam and Malaysia and other countries in Asia, but we still see containerized cargo shipping from China. The volumes are still not high enough to be shipping directly from those countries. The container may come from Vietnam and or from Malaysia and they come to Shanghai or to another port in China. They consolidate the vessel there and the vessel departs from those ports. So in terms of Canal, for us that is good news. And I would say that probably Korea is trying to attract that tradition as well. So the long voyage will start in China or in Korea or in Japan instead of these other countries that are further away from our area of relevance.

 

TN: That makes a lot of sense. Just one last question. How do you see transit changing over the next five to ten years? What are you seeing from the Canal perspective in the way your operations will change?

 

SM: We are still adjusting to what is happening. We have always been very regulated in the best way. What I mean is that we have always had our protocols and codes for attending every situation. We have our protocol for infectious diseases that was the basis to start working with COVID. We think that at the canal probably, what we will see in the future is more technology to improve the operation. I’m not sure exactly how, but definitely there are machine learning and artificial intelligence that may help us be more accurate in our forecasts and probably organize our traffic in a way that is faster or we make better use of the assets. The canal is 106 years old. We have been adjusting every time to the new ways of the world, and we’ll continue to do so as a trade enabler.

 

TN: That’s right. Silvia, thank you so much for your time. This has been very insightful. I really do hope that we can connect again in some time and and just see how trade recovers and what we look like maybe going into 2021 or something like that. Okay. Thank you so much.

 

SM: Thanks to you.

Categories
Podcasts

Fixing terrible forecasts and the lack of context

Tony Nash joined Geoffrey Cann in Digital Oil and and Gas podcast to talk about his revenue forecasting and predictive intelligence analytics startup company Complete Intelligence — how does the company solve the problem of terrible forecasts and the lack of context around data?

 

Geoffrey Cann joined us in QuickHit: 2 Things Oil & Gas Companies Need to Do Right Now to Win Post Pandemic.

 

This podcast originally appeared at https://digitaloilgas.libsyn.com/159-interview-with-tony-nash-of-complete-intelligence?utm_campaign=interview-with-tony-nash-of-complete-intelligence

 

Digital Oil and Gas Description

 

 

Jul 22, 2020

Today’s podcast is an interview with Tony Nash, CEO and founder of Complete Intelligence. Specializing in revenue forecasting and predictive analytics, Complete Intelligence develops artificial intelligence solutions. In this interview, we discuss predictive intelligence analysis, how Complete Intelligence works, and what value these forecasts can generate. 

 

Show Notes

GC: Welcome back to another episode of Digital Oil and Gas. My name is Geoffrey Cann, the host of the podcast. And I’m joined today by Tony Nash, who is the CEO and co-founder of Complete Intelligence. Tony, welcome to the podcast.

 

TN: Thanks, Geoffrey. It’s good to be here.

 

GC: You and I met probably a bit of a month ago. We did a short video exchange, and it was so much fun, we agreed that we should probably do something a little more involved, and here we are today. Of course, my interest is how digital innovation and digital strategic toolkit are transforming how the oil and gas world operates.

 

Your area of interest and expertise, the focus of your startup is in the application of smart technologies in agile budgeting and forecasting and market modeling. And that’s a big area of interest for oil and gas. That’s the reason why I thought you’d be a terrific guest to come on the show today and talk a little bit about that.

 

TN: Thank you very much.

 

GC: What’s your background? You were with The Economist, is that right?

 

TN: I was with The Economist. I led their global research business for a while. And I built what’s called the Custom Research business. It was a small niche business when I joined. It was a pretty sizable revenue by the time I left. Great organization. Had a lot of fun there. I then moved to a company called IHS MarkIt. Information services firm. I led their Asia consulting business. And from there, we started Complete Intelligence. I’ve been in information services off and on for way too long, since the late 90s.

 

GC: And what’s your education background? Did you start out in computer science or something?

 

TN: I was a graduate at Texas A&M in business and my grad work was in Boston at a school called The Fletcher School, which is a diplomacy school. So I was trained to be a diplomat, although I’m not very diplomatic at the moment. I have my moments.

 

Part of the reason I started going down this road is because in grad school, I had a trade economics professor who was amazing, great guy. I started my career after undergrad at a freight forwarder and customs broker. I didn’t have a glamorous first job. I was actually working the night shift in a warehouse at a freight forwarder, receiving exports and typing out airway bills and all that stuff. I got to know the nuts and bolts of world trade pretty specifically and pretty firsthand. I don’t know of any other trade economists who have started the way I have. I look at trade data differently than almost every other economist that I know of. I look at it somewhat skeptically. It’s that skepticism that I realized in grad school with this fantastic professor that my skepticism was an asset. My skepticism was an asset within statistical, mathematical models, within economic discussions and so on, so forth.

 

I had used it in business before that, but I didn’t think that I necessarily had the ability to apply it in this big world before I had this experience in grad school. So I then took it and I joined The Economist. I kind of conned them into hiring me, which was great, and then within a year or so, I was heading their global research business. From there, we just kind of took off.

 

GC: What are some of the products out of The Economist? Because I buy the magazine every week. And The Economist publishes an occasional handbook of global statistics, GDP by country and balance of trade and so forth. Were you involved in those kinds of products or were the products you were involved in much more specific to a client or customer requirement?

 

TN: I wasn’t. A lot of those are extracts from, say, IMF data. That’s part of The Economist publishing, which is a slightly different business to what I was doing. A lot of what I was doing was really applied work with clients. Solving real problems, figuring real things out. Some of this was corporate forecasting, looking at costs, looking at revenues, those sorts of things. Some of this was doing work for example the World Health Organization in places like Cambodia, comparing different treatments for mother-child transmitted HIV.

 

We had all kinds of cool, different approaches. And from my perspective, we could really play with different methodologies. We could really understand what was working and what wasn’t working. It was a huge sandbox for me. Again, really great smart people. That really started a lot of this kind of true love for me, which is what I’m doing now.

 

GC: What is the business problem that you saw that was sufficiently vexing that you decided to devote a lifetime in a career to trying to solve?

 

Because your career builds you to a point and then you say, “You know what? This is the problem I think I’m going to aim to solve.“ And you know what? You may go on to solve other problems, but at that moment, why would you become a founder to go solve something unless it was so big and so vexing, it was worth your time?

 

TN: I think I became a founder because I underestimated how hard it would be to build a business. Almost every founder will tell you that. When I was with both The Economist and IHS Markit, I had two really consistent feedback points that people gave me.

 

First is the quality of forecasting within information services, within corporate, say, strategy, finance, forecasting units, is pretty terrible. Most people forecast through, let’s say, a moving average approach. Some of the largest companies in the world will forecast using a moving average. If they are super sophisticated, they’ll use a very small maybe regression model or something like that.

 

But what mostly happens is one of two things. Either they look at last year’s and add a small percentage. “We’re just gonna have three percent on this year.“ That’s pretty common. The other one is really just a gut feel like, “I really think it’s going to be X this year.“ If a Wall Street analyst understood how unscientific the way outlooks are done within large companies, they’d be pretty shocked.

 

I mean, there is a belief that there is a lot that goes into the sausage machine. Traditional forecasting is terrible. Any forecast you buy off the shelf? Pretty terrible. Any forecast you’d get within a company? Pretty bad. Even the data scientists that are on staff with a lot of these big companies, really brilliant people, but they’re not necessarily fine tuning their forecasts based on error. And this is the key.

 

Companies who forecast should be required to disclose their error for every forecast they’ve done historically. That’s what we do for our clients. Because the number one problem was the quality of forecasts. So we spent our first two and a half years focused on that problem. We continue our approach to that.

 

GC: When you say “publish the error,” do you mean error in hindsight? How bad were we last year or do you mean here’s what we think our forecasted error is likely to be this year?

 

TN: Every year, any forecaster on planet Earth should say, this is what we forecast last year and this was our error rate. When we look at consensus forecast, for example, for energy like crude oil, natural gas, industrial metals, consensus error rates are typically double digits. Typically double digits. We just did a calculation. When I talk about error rate, I’m talking about absolute percent error. I’m not talking about gaming off pluses and minuses because that’s really convenient. But you look at a plus 10, you look at a minus eight, and that becomes a nine instead of a one.

 

People who forecast should be required to publish their error rates. Companies, especially energy companies, are paying hundreds of thousand dollars, if not seven figures to buy data. Those guys [forecasters] know they’re between 15 and 30 percent off in their forecasts regularly. Businesses are making decisions based on these data.

 

That’s the thing that, as someone who’s run businesses, not just analysts businesses, but run real proper businesses in different spaces, seeing planning people make decisions with a 30 percent error rate or 50 percent error or whatever it is, but no accountability from the information services provider? That’s a problem.

 

That’s a 1990s business model where you could play with the opacity around data. But in 2020, that should not be the case at all. We regularly show our prospects and our clients our error rates because they deserve it. They deserve understanding what our error rates are line by line.

 

GC: In oil and gas, when I’m building up a forecast, particularly for, say, an oil project, I’m having to forecast currency exchange rates, interest rates for my borrowings, the price of certain critical commodities like cement and steel. I’m having to forecast project delivery timeline and schedule. I’m having to forecast future market demand like, where’s my product likely to go? If each of these has a 15 to 30 percent error rate built into them and I’ve added them all up to get to a :here’s my forecasted economics for the year.“ Have I built in and basically had an accumulated error rate that makes my forecast pretty unreliable at that stage? Or these different errors, all sort of stand alone?

 

TN: That’s the budgeting process.

 

GC: I’ve been in that process. Right.

 

TN: Anybody who’s worked on a budget like that, they understand it. Maybe they don’t want to admit it, but we talk to people all the time who tell us. We have a client in Europe who admitted to us that some of their core materials that they buy, they know internally that their forecasts typically have a 30 percent error. And when we say that to people, to other companies, that’s feedback we get consistently that the people who actually know, the data know that their companies have error rates that are 20 to 30 percent or in some cases worse. They’re that far off.

 

When you think about it from a finance perspective, you’re over allocating resources for the procurement of something and that resource could have been used for something else. That’s one of the reasons why it’s really important for us to help people really narrow that down.

 

We check ourselves all the time and we looked at some industrial metals and energy stuff based on a June 2019 forecast for the following twelve months through the COVID period, comparing some consensus forecasts and our forecast. On average, we were 9.4 percent better than consensus. This is a Complete Intelligence forecast. It’s an aggregate looking at one of our manufacturing clients.

 

When you look at the different horizons, we look every three months, what was the error every three months, even up to the COVID period. On average, we were 9.4 percent better on a MAPE (Mean Absolute Percent Error) basis. If you’re buying off the shelf forecasts from some of the typical service providers, you’re looking at a pretty large disadvantage. They’re not using machine learning. They’re not using artificial intelligence. If they are, it’s typically very, very simple.

 

Now, part of what we’ve done through the process is we’ve removed the human process, human involvement in every aspect of data and forecast. From the data sourcing to the validation to anomaly detection to processing, to forecasting, we do not have human analysts who are looking at that and going, “that just doesn’t look right.“

 

GC: OK. It’s all done by machine?

 

TN: Right. We have machines that apply the same rules across assets. Because if we have human beings who gut check things, it just inserts bias and error through the whole process. And with no human intervention, we have a massive scale in terms what we do. We forecast about 1.1 million items every two weeks. Our forecast cycles are every two weeks. And we do it very, very quickly.

 

GC: And nine percent less error rate or a lot lower error?

 

TN: For the ones that we checked for that one client, yes. I would say in general, that’s probably about generally right. In some cases, it’s better.

 

GC: So a few things. One is the huge range of things that you can forecast when you remove all the humans out of it gives you these scale-ups. And then the fact that you can do it over and over and over again in much tighter cycle times than someone who just does it annually, once for a budget. And third, you’re testing your accuracy constantly to improve your algorithm so that you’re getting better and better and better over time.

 

TN: Exactly. When you consider something like crude oil, there are hundreds of crude forecasters who know that, they know that they know the six things that drive the crude oil price, right? And I guarantee you those crude oil forecasters who know what they know, what they know what those six things are, manually change their output once their models run. I guarantee you.

 

GC: I remember working for an oil company in Canada where the coming of the oil sands, but it was the monthly oil sands production expectation and would come into the finance function, where I was working, and the numbers would come in the spreadsheet and the finance people go, “add five percent to that.” Because they would say, they’re wrong every month, we’re tired of being embarrassed about being wrong. And they’re wrong because they undersell their performance. So just add five percent. And that was the number that would go to the market.

 

TN: And then that’s the error, right?

 

GC: And was that even the final error? There may or may not be on top of that?

 

TN: Probably not. And there are very few companies, we have some German clients, so they’re pretty good about doing this. But there are very few companies who actually track their error. And so most companies Are not even aware of how far off they are, which is a problem.

 

Here’s the second problem. The first one is forecasting quality is terrible. So we’ve developed a fully automated process. We measure our error, that sort of stuff. The second one is the context of the forecast. What I mean by that is, let’s say you’re making a specific chemical. You can go to some of these professional chemical forecasters, but they’re not making the chemical exactly where you make it. They don’t have the proportion of feedstocks that you have. Because we’ve built this highly iterative forecast engine that does hundreds of millions of calculations with every run, we can take a bill of material for that microphone in front of you or a chemical or a car, And we can forecast out the cost of every component to that every month for the next 12 to 24 months.

 

GC: Really? So, at any scale or any, I mean, you do it for a phone, you can do it for a car?

 

TN: That’s right. So if you look at a bill of material with, say, a thousand levels in it. Not a thousand components. But, you know, if you look at the parent-child relationship within a bill of materials, these things get really sophisticated really quickly. Some of the largest manufacturers have this data. They have access to it and we can tap into that to help them understand their costs, the likely trajectory of their costs over time. What does that help them? Helps them budget more accurately. It helps them negotiate with their vendors more accurately.

 

If you’re a, let’s say you’re a 20-billion dollar company and you have one percent on your cogs, how additive is that to your valuation if you’re trading at 15 or 20 times EBITDA.

 

GC: Yeah. And just right to the bottom line at that level.

 

TN: Exactly. This is what we’re finding. For the high context as of the second kind of business problem that we’re solving, and so we do this on the cost side. We do this on the revenue side. For that second problem, which is high context, again, the platform that we’ve built allows the scale, because if we had analysts sitting there scratching their head, rubbing their beard for every single thing we’re forecasting, there’s no way we could do this scale.

 

But because it’s automated, because it’s scalable, we can actually do this. And so it adds a whole level of capability within major manufacturing clients and it adds a whole level of risk protection or error mitigation to those guys as well.

 

GC: Just think about the current year that we’re in, which would include, at least in Canada, a pipeline constraints and the potential for rail expansion activity south of the border to either curtail production, the behavior of OPEC. When you think about getting into forecasting world of commodity prices… I can understand a manufacturer bill of materials and get into cost of goods sold and forecasting quite precisely what their forward manufacturing cycle will look like. I can use the same thing, though, in the oil industry, though, and probably gas, too, I would suspect.

 

TN: Yeah, yeah. Absolutely.

 

GC: And what’s the industry’s reaction to it? Because there’ll be people inside oil and gas who are doing forecasting today and they’ll be fairly proud of the models that they built that delivering a forecast. You’re walking in and saying ”I’ve got a whole new way to do this that is so many more cycles faster than what you can manually do, looking at many more products than you practically can. And if I show you that you’re nine percent off, 10 percent off with it.” I can imagine a negative reaction to this. I can also imagine for some organizations, pretty positive reaction on balance. How companies react when you told I can sharpen up your numbers?

 

TN: OK. So I’ll tell you a story about a gas trader. October of 2018, we went into a natural gas trader here in Houston. We showed them what we do. Gave a demo, give them access for a couple weeks so they could poke around. And we went back to them later and they said, “Look, you are showing a like a 30, 31 percent decline in the price of natural gas over the next 6 months. There’s no way that’s going to happen. So thanks, but no thanks.”

 

GC: This was your data telling them? All right. Refresh my memory. What was going on in October of 2018?

 

TN: Nothing yet. But Henry Hub prices fell by forty one percent within six months. So these guys were completely unprepared. The kind of conventional wisdom around natural gas prices at that time were unprepared for that magnitude of fall. But we were showing that that was going to happen. And so when you look at that, we had an 11 percent error rate at that point, which seems kind of high. But conventional wisdom was a 30 percent error rate.

 

We don’t expect to be the single go to source when we first go into a client. That’s not our thought. We know we’re a new vendor. We know we’re offering a different point of view. But we’re in a period of history where you have to think the unthinkable. And this is 2018, ‘19.

 

With the volatility that we’re seeing in markets, you really have to be thinking the unthinkable, at least as a part of your possibility set. It’s really hard. I would think it to be really hard for really anybody who’s trading any magnitude of oil and gas product to put something like this outside of their arsenal of strategic toolkit that they use.

 

GC: Well, certainly, if you had that gap in expectation of gas prices, the gas producer should have been thinking about hedging at that moment. And if their conclusion was, you’re completely wrong and I’m not going to bother with hedging, then shame on them really, because they should have done a far better job of managing to the curve. That’s a great story because it illustrates the challenge.

 

TN: That’s normal. It’s kind of the “not invented here” approach. And I see a lot of that within oil and gas.

 

We see a bit more interest in chemicals. They have to understand the price of their feedstocks. They have to understand their revenues better. And so we see a bit more on the downstream where there is a lot more interest. But midstream, upstream, it’s just not really there.

 

GC: What’s the untapped potential here to sharpen up forecasting? If you’re talking with a company and you say, “I can sharpen up your forecasts and your estimates and tighten up your variability and your business plan.” How does that translate to value and how do you extrapolate that to here’s the the slack, if you like, that’s built up economically within the system and as a whole that we stand potential to extract out and it’s going back to the misallocation of capital, the inadequate negotiations with suppliers, the margin left on the table because of the numbers aren’t just that reliable.

 

TN: We just went through this exercise with a manufacturer with about 20 billion dollars of turnover to help them understand. If you look at, say, the nine percent difference that we had in that exercise that I told you. So let’s say we’re working with the manufacturer with a 20 billion dollars and a PE ratio around 20, which is kind of where they’re trading. If instead of a nine percent or even four and a half percent improvement, let’s just say we had a one percent improvement in their materials. That one percent improvement in their costs translates to a three percent improvement in their net income. That’s three percent improvement in their net income translates to a 1.1 billion dollar improvement on their market capitalization.

 

We’re not going out there saying, “hey, we’re gonna help you save 10 percent of your costs.“ We’re not going out with statements that are that bold. We’re saying, “OK, let’s run a scenario where we help you with a quarter of a percent,“ which would help them add 280 million dollars on to their market capitalization. So procurement management and planning is kind of that tightly calibrated that if we helped this company with 0.25 percent improvement in their costs, keep in mind we’re nine point four percent better than consensus, that actually helps them add 280 million dollars onto their market cap. It’s just exponential.

 

GC: Well, it’s the leverage effect of earnings per share as you drop those earnings to the bottom line. And so anybody who’s actually measured on EPS or stock price should take a very interested look at this because you’re not selling a hardware, big capital investment, stand up a big department, not stuff. This is about taking the current process, that’s their budgeting, and squeezing out the variability or the error rate and trends that translates directly to value. When you think about it, it’s a complete no-brainer. Like, why would you not do this?

 

TN: It is. And we’re not going to charge them 280 million dollars to do it. But we could charge for this agile budgeting and forecasting. But we’re not going to.

 

GC: What you would do is you’d say, we’ll take shares in your company.

 

TN: I mean, that’s been suggested many, many times.

 

GC: Yeah, no, I totally get It. I say to oil companies, I’ll sell my services to you based on the price of oil. But the shareholder actually values the volatility on oil pricing. So they’re not prepared to give that away. And I’d be the same. I wouldn’t do that. But on the other hand, the back to this question of untapped potential. The ship, the bulk of the economy is operating off of wildly inaccurate consensus estimates. I think that’s fair to say, I don’t know if that’s accurate or not, but that would be my my conclusion. The bulk is operating off of inaccurate assessments. And so over time, what should happen is we should see a considerable improvement in that, which in turn translates into much better performing economy, allocation of capital and supply chains and so forth.

 

So you’ve been an entrepreneur now for how long’s it been three years?

 

TN: It’s five. We started as a consulting firm. It’s been about five years now. We actually started the company in Singapore. I moved it to Texas at the end of 2018. I couldn’t really find the coding talent and the math talent in Asia. I know this sounds really weird, but I couldn’t. And so I relocated the business to Texas in 2018.

 

GC: Yeah. And the talent pool is rich enough in the United States to fulfill this ambition?

 

TN: Yeah, yeah. Yeah. Totally fantastic.

 

GC: And what lessons have you taken away from all of this experience? Would you do it again?

 

TN: I would do it again. But I would do It differently. Anybody who starts a business has to realize that markets aren’t necessarily ready for radical new thinking. And it really takes a long time to get an idea of this out there. The kind of AI industry and the talk about automation has been around for a long time. But things like this, companies aren’t really ready to just let go of. It takes a lot for them to consider letting go of this stuff.

 

If your idea is pretty radical, it’s probably to take a while to socialize with an industry. But I would say it’s also, we as a company, we had a staff issue about a year ago, actually, that really shook us. And out of that, we developed our principles and our values. For anybody who wants to do this, you really have to understand what your own principles and values are from early on. It’s not something you wait until you’re 100 people to develop.

 

That issue a year ago was a very clarifying moment for us as a company. It really forced us to think about what kind of business we wanted to build. And I’m grateful for it, although it was really terrible at the time. I’m grateful for it because we have our values. It’s actually posted on our website. Whenever we recruit new people, that’s one of the first things I send to them and say, “Look, this is who we are. If you’re not comfortable with this, then this is not the right place for you. I’m sure you’re talented, all that kind of stuff. But we really live by this stuff and and those things are important.“

 

The other thing I would recommend for anybody who’s doing this is you’ve got to play nice with everyone on the way up and you got to play nice with everyone on the way down. It’s easy for tech entrepreneurs to really think a lot of themselves. And I think that’s fun. But it’s also not really helpful in the long run.

 

There’s a lot that I’ve learned about recruiting leadership teams, finding fit, looking for investors. I have the Asia experience. I have the U.S. experience. The math and the tech around A.I. is almost the easiest issue to solve. With technology, as long as you think big but retain humility, you can do a lot. You have to be bold, but be comfortable with mistakes.

 

The trick is getting the right team and the right investors who are comfortable with that environment. And if you get the right team and the right investors who are comfortable with that, then it can be much more fun. You actually have a chance at being successful because so many startups just fail. They don’t last a year or two years, much less for five. It’s really, really critical to get the right people.

 

GC: Yeah, I completely agree. The people and the money, it’s both sides. If the investors don’t have the patience or they’re marching to a different drum like they want short term results, and that’s as much of a death knell for for many startups as a talent talent deficit.

 

Tony, this has been excellent. Thank you very much for taking the time to join me today on Digital Oil and Gas. And if people want to learn more about Complete Intelligence, where do they go? What’s your website?

 

TN: Our website is completeintel.com. And we’re on Twitter. We’re on LinkedIn. There’s a lot of information there. And like you did about a month ago, we have a lot of five-minute interviews we do with industry experts and a weekly newsletter. There are a lot of ways to get to learn about us.

 

GC: Fantastic. Tony, thank you very much. This has been another episode of Digital Oil and Gas. And if you like what you’ve heard, by all means, press the like button and the share button and add a comment, and that helps other people find the show. And meanwhile, tune back in next week, Wednesdays, when we’ll issue another episode of Digital Oil and Gas. This is Canada Day week. So happy Canada Day to my all my Canadian listeners.

 

And Saturday is Independence Day. It’s July 4th. So, Tony, have a great time on Independence Day. Be socially distant and be safe out there. Thanks again.