Tag: AI

Manufacturing 4.0: AI and Automation Strategies For Manufacturers Across Supply Chain

Post author By admin
Post date January 11, 2021

Complete Intelligence joins the AI World Summit 2020 and we had the honor to discuss Manufacturing 4.0: AI and Automation for Manufacturers Across Supply Chain. This event is organized by MyFinB group. This video is a recording of the event.

Introductions

As global supply chains are becoming more complex amidst the disruptive effects of Covid 19, the room for any inefficiencies becomes a matter of survival. Manufacturers need to maximize productivity and minimize costs by taking on new technologies and processes. Key questions remain how could AI perform demand forecasting production planning and predictive maintenance? How would AI-led tools help plan contingency events? This track reveals the power of AI in transforming the manufacturing landscape and revolutionizing the supply chain management for the next decade.

This session is chaired and moderated by Peter Kua who is based in Malaysia. Peter Kua is the head of data science.
Our EV media group formerly media prima digital. Our esteemed panelists comprise of Tony Nash, CEO and founder of Complete Intelligence. Tan Yet Mee the founder and director of Maypreen Sdn. Bhd. last but not least, we have Dr Ahmad Magad, Executive Director at Management Development Institute of Singapore (MDIS) former secretary general of Singapore manufacturing federation.

Tony is the CEO and founder of Complete Intelligence. Previously, he built and led the global research business for the economies and the Asia Consulting Business for IAIHS now known as IHS markit. He has also been a social media entrepreneur writer and consultant.

Tony is a public speaker and a leader of a closed-door dialogues with business and government leaders on markets, economics risk and technology. He is a frequent contributor to leading global media like BBC, CNBC and Bloomberg and has served as an advisor to government and Think Tanks in Tokyo, Singapore, Beijing and Washington DC.

Tony is an international advisory board member for Texas A&M University and a non-executive director with credit micro finance bank in Cambodia. He has a master’s degree in international relations from the Fletcher School of Law and diplomacy at Tufts University and a BA in a business management from Texas A&M University.

TN: Thank you, Peter. Thanks very much for the opportunity and thanks to MyFinB for asking me to speak today. I really appreciate this. Today, what I’d really like to talk about is how we’re helping companies use Artificial Intelligence and machine learning to better plan their manufacturing businesses, both on the cost side and on the revenue side. We’ll talk a little bit about some case studies. We’ll talk a little bit about kind of what the issues with the status quo, kind of ways of doing this are. And then we’ll talk a little bit about what exactly we’re doing with our products.

So, really what we’re trying to do is help companies become more profitable. We have built an Artificial Intelligence platform to focus on cost and revenue planning.

I started the company in Singapore. I lived in Singapore for 15 years. A few years ago, I moved back to the U.S., we’re now based in Houston, Texas. There’s a lot of oil and gas and manufacturing companies in the Central U.S. and Southeastern U.S. So, we’re really helping those companies here in the U.S.

What we’ve seen as we’ve entered the pandemic or as we’ve gone through the pandemic, we’ve seen a much more focused intention on proactive planning. People have realized that we’re at a very volatile environment. That’s probably not going away anytime soon, of course. We’re not in constant volatility. We have intermittent volatility and the human approach to understanding the future simply seems to in many cases extrapolate, today into kind of forever. We’re using machine learning to better understand how companies can look at costs at a very granular level. And how they can look at their revenue planning at a very granular level. As yet, we mentioned there are not many companies who are prepared for this. In fact, Gartner says 87% of companies aren’t prepared for basic analytics and business intelligence much less artificial intelligence.

So, some of our more recent activities, right now, we’re working with a global chemicals firm. And what we’ve done is we’ve taken data directly from their ERP system. We’ve helped them at a very granular level with their product revenues by geography, by very local geography, understanding what their sales will be by month over a forecast horizon, say 12 to 24 months. We’ve brought their revenue planning error down for that product to 4.4%. So, we’re helping them understand really pretty closely to actual what will happen with their revenue and when it will happen.

We’re also working with an Australian mining firm. They mine copper and gold and silver and a number of other things. We’ve helped them reduce their planning for their gold forecasts by 38%. We’ve done similar activities for copper high 20s. What this helps them do is better plan when to bring their goods to market. Better plan the revenues based upon the volume of say material that comes out of their mining sites and better report their numbers to public markets. So, our chemicals firm client their share price has risen by three times in 2020. Our morning firm client their share price has risen by two times in 2020. So, our clients are seeing some real results from the work that we’re doing for them.

Today there are a number of problems with proactive planning. So, industry forecast. So, industry experts consensus air this is say investment banks economists say industry expert firms who know metals or agriculture goods or something like that. They typically have an error rate of about 20% and this is on an absolute percent basis. So, if you’re buying industry forecasts to understand the price of steel or zinc or you know wheat or something. Those typically have an error rate of 20%, in many cases, it’s more than that. Our clients on the procurement side tell us that even for basic materials, their pricing forecasts are can be say 30% off on an absolute percentage error basis.

So, as proactive planning and finance teams, within companies, as buyers, as strategists look at markets. There really isn’t a precise view of where their costs will go or where revenues will go. And that’s where we come in we have a number of products where we’ve trained our models to understand, how markets will move and how companies can best plan transactions. And plan activities based upon where their revenues will go and where their costs will go. Our off-the-shelf product has about 800 assets across commodities, currencies and equity indices, that we forecast twice a month. We’ve trained our models based upon all of these activities and then as clients come in they typically work within, say the 1400 industry sectors that we have our models trained on.

The other part of the proactive planning process is kind of the spreadsheet aspect of it. We work with major multinational firms and some mid, small, mid-sized multinational firms. There are hundreds or thousands of spreadsheets moving around these organizations with differing approaches, differing conclusions. And what typically happens in planning meetings is there’s really a kind of a verbal agreement, rather than an analytical agreement on how the company will go forward. And we’re really helping companies come to a data-driven conclusion and recommendations around when they should take these transactions. Okay?

So, here’s what we’re doing, we’re taking data directly from clients, we’re working with. We’ve partnered with Microsoft, we’ve partnered with Oracle and others to actually bring our capabilities to market. We use data directly out of a client’s ERP system and other systems. We take it within our environment. We have billions of our own data items and publicly available data items within our environment. And then we deliver the data directly to the client’s context. So, how do they need to make those decisions. What are they looking at and in what context are they looking at those decisions and we just want to fit into their workflow, so that they can plan better? Whether it’s manufacturing. Whether it’s the sales cycle. Whether it’s to optimize working capital and so on.

On the product side our main product right now is called CI Futures. CI Futures is a subscription product where we’re looking at commodities, equities, currency indices. We have a number of clients who layer custom assets into here. Whether it’s say plastics or packaging or we have one client who has us forecasting their sugar costs globally. They’re uh their European confectioner so we take in data from them every month. We help them understand where those prices will be. So, they can not only come up with their procurement strategies but also come up with their hedging strategies for those raw materials.

On the enterprise planning side we have two different services one called CostFlow. CostFlow is a structured bill of material. We show costs from say the business unit level so a chief procurement officer or somebody from FPA or a CFO, can understand where costs are going according to budget across the organization. We solve CFO pain points. We go all the way down to the bill of material which you can see an image of a structured bill of material on the screen and then we go below that to the components and the elements. So, everyone is looking at the same interface not just for historical data and business intelligence. So, that they can see where things are going on a future basis all the way down to the granular level that they’re looking at for procurement on the revenue side.

We look at product sales at different geographies it can be a city level or a country level. We look at it across business units. So that, all of this kind of sums up to a greater whole globally. So, whether it’s a regional business unit or a product business unit. We’re helping people understand how their sales on a month-by-month basis will match up with their costs on a month-by-month basis, both of these activities today we’re um selling and working with clients on uh the user interface and these things will be worked on with in 2021 and we’ll release them early in the first half of 2021.

All of this stuff whether it’s cost flow or RevenueFlow or CI Futures uses the same engine the same cognitive global system to make these decisions and inform our clients for their cost and revenue decisions. So, really what we’re helping people do is do more with less some of our clients. There’s a major manufacturing firm here in the U.S. that has I think 40 people fully dedicated to revenue forecasts. Those people can be used for much more interesting things aside from working in excel spreadsheets all day. On the procurement side there are analysts, there are buyers, there are product people who spend a huge portion of their day in excel spreadsheets and trying to understand cost directions. We’re helping people take those resources focus on the core business and do more with less. We’re helping people increase operating margins and even look at their market cap.

So, these publicly traded companies it doesn’t take much on the savings side and on the revenue delivery side, given equity market multiples for stock prices to rise given the changes that we can help them make. So, we complement a lot of the other physical aspects of the industry 4.0 environment by helping with the proactive planning process months ahead of time. So, thanks very much I really appreciate the time today, Peter.

PK: What does the factory of the future look like to all of you? What about you Tony? What is your take on the factory of the future and how does it look like to you?

TN: I think the one of the first steps of the factory in the future is really is companies really looking at their own data today there are you know data governance is a is a great first step for factories to start to understand how they can better develop, say a machine-driven environment. If you don’t have good data on what you’ve done in the past and how you do things today. It’s going to be very difficult to transition into a next generation factory so I think the factory of tomorrow or the fact of the future really starts today with executives and firms understanding how they capture data how they capture their processes and how they can understand where to automate and understand what those steps are to get there. So, it is of course highly automated but there are a lot of things we do you know we did 20-30 years ago or 50 years ago that we just don’t do today.

So, that’s really it I think it has a lot to do with understanding what we do documenting what we do and checking out the data and making sure we have good data. When we work with customers for their own say cost and revenue data we find that in some cases 30 to 40 percent of the data the historical data that they have is unusable. Meaning it hasn’t been recorded consistently it hasn’t been recorded properly you know those AP people or finance teams or people in operations haven’t really taken the data seriously, it’s an inconvenience and so it makes the starting points of that transition very very difficult. So, I would say executives really need to start today on data governance and documentation to understand where they can go in the future.

PK: How can the industry revolution fall and AI cut across the supply chain and lead to competitive advantage? For example, you know having superior manufacturing capabilities and of course most importantly customer satisfaction. Would you guys, have any success stories to share?

TN: Yeah, okay so when we look at well, we have one uh customer who um using our data better understood the pricing environment for the products that they were bringing to market. And they realized that, gosh they just raised their prices for one of their products by something like 80 percent. So, they’re capturing a lot more revenue of course and margin based upon better understanding the dynamics of their environment and how much revenue they could capture. Their customers are happy and they’re a more profitable company. So, you know that’s one way where by better understanding the environment and automating some of these decisions rather than sticking with the human bias of previous ways of doing things they can actually be more profitable and their customers are just as happy or happier.

PK: What do you think are some of the issues or barriers with respect to realizing IR 4.0 for manufacturers?

TN: I think the main issue that we see is human bias so people are accustomed to doing something a certain way. For example, a company has a vendor that they’ve bought uh raw material x from for five years or ten years or something. Or they you know there are sales processes or sales say expectations that are put together through a negotiating process internally, right? But there are a number of ways that human biases get involved in all business processes. And it really holds companies back.

So, what we’re demonstrating to people is that by cutting out that human bias we can actually help them either optimize decisions or kind of come close to optimizing decisions, rather than relying on you know a vendor you’ve had for a long time. Maybe you rely partly on them and diversify to kind of a better price same quality environment. Something like that, rather than looking at production runs because maybe that’s what you’ve done, you know. You’ve come to that conclusion the same way for the last 10 years.

We’re helping them use machine learning to get around that human bias and better understand. When demand will hit the magnitude with which it will hit, so that they can have the product the right amount of product made at the right time. So, we find that the biggest barrier is human bias and it’s really fear of the machines kind of making mistakes rather than you know phasing things in gradually. People feel are afraid that it has to be some sort of big bang.

PK: What do you think the employee or the future look like to you?

TN: Thank you. I think, what’s been mentioned is right. I think a lot of the redundant activities that you know repetitive activities will be kind of lightened up. I think more of the critical thinking skills and more the collaborative skills will be much more useful. I also think as we have more kind of machine-driven AI driven capabilities within a company more is going to be expected from a company. So, people will have to focus more on the again as I said the business itself rather than the administrative aspects of it or the repetitive aspects of that business. So, we’ll have more sensors we’ll have other things that businesses are required to do just as a service expectation that they may not be doing today. So, I think it’s not necessarily all bad for employees. I think there’s a lot more to do as we have more tech enabled capability.

PK: What do you think the skills pipeline for developing the workforce for the future in manufacturing would look like you know to ensure that our people are ready for the disruption in manufacturing?

TN: That’s a great question. I think I don’t know that people will necessarily have to be more technical meaning the person on the shop floor isn’t necessarily going to have to be able to fix the device. I think they’re just going to have to be more specialized. They’re going to have to understand more specific aspects about their safe span of work but I also think we’ll actually have fewer white-collar workers. So, you have more people actually on the shop floor in the field customer facing so on and so forth and fewer people in the back office. A lot of the back office activity is repetitive and can be automated. So, I think many companies will see less in the back office. We’ll see fewer people with for example basic business degrees, okay? We’ll see more people with really applied degrees so that they can actually do stuff like i said talk to people put things together service people that sort of thing rather than work in software programs.

PK: How do you think Covid 19 has affected manufacturing and whether IRF or AI could have mitigated some of the risk?

TN: I think there are a number of risks that AI could help with first is I think sourcing and supply chains have been really impacted and we’re starting to see more regionalization of sourcing and supply chains. So, helping say supply chain planners, procurement teams, and finance teams understand where they can source in different regions and how that will impact their cost base is one way that could have been impacted. But I also think there are things like digital twins which we don’t do but I’ve seen a number of companies who are doing this. Where they’re monitoring physical spaces so that things like health and safety or operational procedures are being observed. These sorts of things reduce the number of people on a shop floor or in a warehouse and make sure that the companies aren’t missing out on things like safety. So, these types of things can be implemented they’re available today. And I think the pandemic has really opened up the need for it and helped people realize that it’s needed much more quickly.

PK: Great! Thanks Tony. Tony did you want to add something else on the Covid 19 impact on…?

TN: Yeah, Peter, thanks. You know one of the other things that we where we saw a really interesting use for AI is helping people understand the path back to kind of business opening and demand. So, what we saw in say March, April say February, March, April is just panic among manufacturers trying to reconfigure their supply chains but they didn’t really understand how demand would come back. And that’s one way that we really helped them both on the on the revenue side and on the cost side is proactive planning. When would those costs bounce back because there was no demand for a while? And then how would their sales bounce back. We did that extraordinarily well for clients and they were ready and they have been ready as they as demand has come back in different markets without over supplying or without say cutting their workforce too much as things were pretty negative.

PK: Very interesting. Now, let’s talk about the smaller manufacturers or the for the SME’s. Now, I know that Yat Mee has mentioned some of the problems faced by SME’s, when it comes to adopting AI and IR 4.0 but I wanted to really hear from also from both Dr Ahmad and Tony. For example, compared to the larger manufacturer counterpart right what are the specific challenges faced by SME’s when it comes to IR 4.0 and AI adoption. What about you Tony what are the specific AI challenges are faced by our small manufacturers?

TN: Aside from the optimizing working capital, which is which is a big deal for small companies but aside from that hurdle. I think small and mid-sized companies are actually much better placed than large companies because they don’t have a lot of the organizational hurdles and kind of status quo, kind of entrenched status quo activities. So, there’s a huge opportunity for small companies to deploy kind of industry 4.0 and AI assets to scale and to improve their performance. So, of course there’s always fear of course there’s always fear of changing things but I think in general they’re much better place for adoption than larger companies.

PK: Very interesting. Okay so it looks like we only have about three minutes left. So, I would really like to conclude this session with your thoughts of the future. Now, what I want to each probably, I want each of you to look into the look into the crystal ball, like five to ten years ahead and tell me what are one or two things that really excite you about the future of manufacturing and supply chain management?

What about you Tony? I’ll give you the last word.

TN: Great! Wow! Thank you. I think when you look inside out of the factory. I think what we’re looking at with a certain amount of automation. My hope is that it leads to happier employees. I think as they’re doing more interesting work, I think we’ll have a much happier staff base within manufacturing companies. I think from the client side we’ll have much better products and much more consumer choice and I think a lot of it may will be made regionally or locally so the manufactured goods will be more approximate to the consumption markets. And I think that’s better all-around for the environment and for the manufacturers themselves.

PK: Cool! thank you so much and with that we have come to the end of the session. Thank you so much for being part of a very enlightening panel of discussion and obviously the enthusiasm show and the knowledge chat right have really exceeded. I think everyone’s….

Tags AI, CFO pain points, finance teams, optimize working capital, proactive planning

News Articles Uncategorized

Startup makes superforecasting possible with AI

Post author By admin
Post date January 5, 2021

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.

Podcasts

Forecasting Global Markets with Artificial Intelligence

Post author By admin
Post date November 28, 2020

“Bitcoin Kid” JP Baric is joined by Tony Nash in this premier episode of Digital Gold.

Tony Nash is the CEO and Founder of Complete Intelligence. Using advanced AI, Complete Intelligence provides highly accurate market, cost, and revenue forecasts fueled by billions of enterprise and public data points. Previously, Tony built and led the global research business for The Economist in the Asia consulting business for IHS he’s also been a social entrepreneur, media entrepreneur, writer, and consultant.

JB: Tony, as I mentioned, you’re the founder of Complete Intelligence. Can you tell me a little bit more about what Complete Intelligence does and how you work with your clients?

TN: Sure, yeah. As you mentioned in the intro, I led global research for a British firm called The Economist and I led Asia consulting for an American firm called IHS Markit. In that time, over about a decade, I had a bunch of clients come to me saying, we have two problems. First, forecasts are terrible and that was a comment both on the work of the firms that I worked with as well as just the market generally and they said forecast error rates are terrible. There’s no accountability of the forecasting saas and nobody tracks their historical data, so we have to try to dig it out ourselves.

So forecast accuracy is a huge issue. The second issue is the appropriateness of a forecast. So if you make a chemical or a mobile phone or cake mix, there are specific items within that product that you need to know the cost of. But you may not be able to do that internally. Major companies have hundreds of Excel workbooks floating around with their forecast for sales or for costs or whatever and it’s just really confusing. So what ends up happening is people kind of manually estimate costs and revenues. And so, what we wanted to do was automate that entire process company-wide.

We wanted to take out the human bias that comes with the forecasting industry and internal forecasts and all that stuff and we really wanted to build products that allowed the machines to learn how markets move so that’s currencies commodities equities and so on as well as how company revenue and spend changes over time.

JB: So when doing some of my initial research on Complete Intelligence, basically just to paraphrase, you guys are taking the spot of what an analyst would do. Is that correct?

TN: Yeah. But here’s what we don’t do. We don’t put together a report on what’s going to happen in industry x or with commodity y because what we find is when that stuff is put together so when an analyst puts a report together on some aspect of an industry, it’s really loaded with a lot of, let’s say, a house view on something or a personal bias. And so we do have a weekly newsletter and we do kind of video podcast that sort of thing. But we don’t have industry notes because we don’t want our clients to feel like we have bias towards say the oil and gas sector or toward industrial metals or that we’re for or against gold or for or against crypto or something.

There’s so much of that loaded into forecasting today and it has been that way for decades, that we just want to let the data and the sophistication of the data… we’re doing billions and billions of calculations every time we run our process. Humans do this but they’re not aware of it. The humans also aren’t aware of the amount of bias that they put into their calculation. So what we do is we track this and we track it based on error rates and we allow the machines to correct based upon how they’ve made error over time. It’s just like an infant learns, right. You touch a hot stove and you learn not to do that again. It’s very similar the way we kind of reinforce the behaviors that we want within our platform.

JB: I guess my question to you is when it comes to these machines, they’re learning in the background so you don’t have a team of a thousand analysts. Instead you have a team of a thousand neural networks or machines basically working for you running these calculations 24/7 on all these different commodities and are they just making assumptions and then confirming if those assumptions are right and then the models that do better end up going end up kind of getting weighted more? How does that work, I guess? How do those questions and answers work in those data testing points, those AB testing that you mentioned.

TN: It’s a good question. So we’re running tens of thousands of scenarios for everything we forecast, every time we forecast. And then we’re looking at which ones best reflect the market as it stands right now and then we add in the different approaches on a weighted basis to make sure that they reflect where the market is. So it’s a multi-layer analysis. It’s not just a basic kind of regression correlations driver, that sort of thing. We’re also looking at the methodologies themselves.

Some of these are very fundamental, traditional statistical methodologies. Some of them are more technically-driven say decision trees, those sorts of things, types of machine learning models and we’re looking at how on a proportional basis those different methodologies best understand the market at this point in time. And so yes. I mean, that’s a long way of saying “yes” to your question.

JB: No. I think that was a great answer. So you guys are looking at currencies, equities, and in July you discussed gold and silver being nature’s Bitcoin. Can you explain to our listeners what you mean by that and provide your thoughts on bitcoin as a store of value and where you see that blockchain space going?

TN: Well I think one of the key aspects of cryptocurrencies is that there should be a fixed amount of it. If it really is immutable, then there’s only so much of it and if there really is demand for something that’s limited, then the value should rise or fall based upon the availability of that fixed good, right?

Gold is similar in that I can’t necessarily go and buy a car with gold. I mean I’m sure I could. I can’t buy a loaf of bread with gold. I think cryptocurrencies is becoming a bit more spendable than precious metals, a bit more useful depending on which cryptocurrency you’re looking at. But yeah, it is similar in that cryptocurrencies to date have been more of an asset than a currency. They’ve behaved more like an asset than a currency.

Meaning the value goes up and down pretty dramatically based upon the perception of scarcity. Currencies don’t necessarily act that way. Currencies act as units of value so that you can buy other stuff. And so, it is. Gold is on some level kind of nature’s bitcoin or nature’s cryptocurrency. But I think we’re coming to a point where there’s a division between those two, where cryptocurrencies are starting to be used as and when II say starting of course they have already been, but more broadly be used as vehicles to buy other stuff not just stores of value. So the former is a currency the latter is an asset.

JB: Yeah. I definitely agree with you on that point as we move down this line of utilization. We saw with the Paypal news that recently came out Square News. Hopefully people will start using bitcoin more as a day-to-day currency. It’s one of the biggest I guess questions I get is, you know, it’s too hard to use bitcoin or what am I going to use at the store less of actually bitcoin has a store of value especially from some of the retail clients coming into this space.

So regarding bitcoin and Complete Intelligence, are you guys forecasting anything in the digital currency space? Are you forecasting the currencies themselves maybe the mining profitability or any of the mining machines and can you speak a little bit further on that?

TN: We do. We started forecasting limited cryptos about six months ago and as I’m sure you can imagine there’s been a lot of volatility in cryptocurrencies over the last couple years. And because we’re a machine learning platform, it takes a while for the machines to understand how cryptocurrencies trade and move and so just because we started forecasting cryptocurrencies doesn’t necessarily mean that we would recommend people making trades or taking positions based upon what we forecast. You know, it’s different for things like, I don’t know, copper or whatever that we’ve been doing for a long time and those are also relatively stable markets say industrial metals, you know, that sort of thing. But cryptocurrencies very volatile, very new, and the market is still learning how to value them.

This is one of the key things about cryptocurrencies that I think is misunderstood is the market is still learning how to value them. That’s not a comment on whether I think they’re undervalued or overvalued right now. I just think the market isn’t really sure how to value them. And so, you know, in our platform we expect it to take really another couple months before we’re confident in where our platform is saying cryptocurrencies will go again because it’s such a complicated asset in the way it moves and because there’s so little institutional and historical knowledge about it. We have to iterate it, you know, a couple billion more times for us to really understand where it’s going.

JB: Are you seeing a lack of data or trading data, network data in making these decisions that making it harder than traditional markets or have you seen that the data in the bitcoin space is relatively open and well established?

TN: I don’t really see an issue with data. I think part of the problem with cryptocurrencies is that it doesn’t really trade on fundamentals. So what we’re utilizing is a configuration of methodologies that balance out fundamentals and technicals. You know, some months, certain assets lean more toward technicals. Some months, they lean more toward fundamentals.

Cryptocurrencies don’t really have fundamentals to lean on and so then you’re looking at a lot of relatively short-term and ultra-short-term approaches to understand the value of something. So the memory of the price, it’s either sticky or it’s not and I know that sounds a little bit silly but you know cryptocurrencies move in bursts or they languish. There’s really not a lot of in between and so understanding which technical approaches to take and within what configurations to take them is what’s really kind of confounding our platform right now and I would say our error rates for cryptocurrency is probably I think three times what our average error rate is.

So our average error rates for across our assets on an absolute percentage basis is between five and seven percent something like that. Across currencies, commodities, equities. For cryptos, we’re looking at probably a 15 ish to 20 percent error and so it might be a little bit lower than that now. But it’s settling within the range that we’re comfortable with. We’re really comfortable when things are say less than 10 percent error and we expect to be there, you know, very soon. But part of what’s different about what we’re doing is that we’re not afraid to talk about our error rates. We’ll be very transparent with people about what our current and historical error rates are and have been because our clients are making decisions based upon the data that we bring to them and the forecast that we bring to them.

So when I say to you, look our, you know, our error rates for cryptocurrencies is between 15 and 20 percent, I’m not really sure you can find many other people who would admit that publicly. But if traders are making decisions based upon the forecasts that we bring to market, then they need to know that, right? They need to know how to hedge against that error range.

JB: And so you’re referring to that the cryptocurrencies are much harder to predict. Is that keeping any of your current clients from moving over to the digital currency space? Are they looking at this space for growth opportunities or for potential revenue generating opportunities or even a way to hedge from the current macro environment?

TN: I think everyone is either involved and trading let’s say even at a small level or they’re very committed. I think the approach that we’ve tried to take, the number of firms that get very hypey about cryptocurrencies and almost feel like they’re trying to push it on to their clients. We’re not that way. We don’t care if someone invests in iron ore or investing cryptocurrencies. It’s really what is their profile and you know how well can we forecast it. But I think the interest in cryptocurrencies obviously is still very high because nobody really knows what’s happening there.

Nobody really knows what the future is there and nobody really wants to miss out. Actually, I know maybe two or three people who want to miss out on that and do and already at all but very few people want to miss out on it and so they’re keeping an eye on it or dipping a toe in if they’re not already in in a big way. And I think you know you have to be fair on these sorts of things you know. It’s not as if say the main cryptocurrencies have have kind of fizzled out. They’re still around. They didn’t fizzle out after say two years. They’re still around. People still trade them. You’re still trying to you know we’re still trying to figure out how to get them into some sort of monetary system or some sort of transmission mechanism. And until that’s figured out, I think that you know unless they fizzle out you know the main ones I think it’s still necessary to stay involved. So we’re not seeing a massive demand for what we’re doing in terms of forecasting and when I say forecasting I’m not talking about the next say five to seven days. I’m talking about the next 12 months, okay. Monthly intervals over the next 12 months.

So for something like cryptocurrencies that have a relatively short-term horizon because it has been pretty speculative from an investment perspective. It’s been pretty hard to to look at this stuff over a longer term. But we’re getting better at it and I think as these things become more predictive, there will be a lot more interest and that’s largely the market coming to agreement on what the various cryptocurrencies are actually worth.

JB: And following up on that you know, how do you value them this being a common trend it seems like in the analysis that you guys are doing as a large bitcoin miner in this space, we believe the stock to flow ratio is a huge component of giving value to underlying cryptocurrency and so that is when the when you know the having occurs did your models take that into account or did they do they how do they kind of work with that event?
Because I think the having is an event where you don’t really have that in any other industry where you’re losing half of your new coins coming in or half a new supply coming in on a daily basis.

TN: Well I think you you know, what you. You do see this a bit with say central bank money supply, you know that sort of thing. So and you do see, let’s say with the Dollar or the Euro, the Japanese Yen or something like that. You do see central bank money supply coming in and the pickup of that money supply is not fundamentally dissimilar from cryptocurrencies. Although I think with cryptocurrencies, it’s a it’s a fair bit more technical. But I think it’s you know understanding both the stock and the flow is critical to understanding where that value is. If there’s too much stock, then, you know, it’s obviously not valuable unless there’s the demand, the flow going into demand.

So yeah. I think it’s… But until people can have a normalized discussion around where it’s similar to say central banks, then I think it’s really hard for people to contextualize within their kind of trading and valuation framework. So look. You know, if you look for example, you know, the Chinese government introduced this coin into Shenzhen a few weeks ago, right. They effectively gave people the equivalent of thirty dollars in this Chinese crypto currency to spend and then it was gone. So they’re calling that a study on how widespread adoption of cryptocurrencies will work and I’m sure it was gone within a day, right. I mean if I’m given 30 bucks to spend for free then I’m going to spend it probably today.

So you know, I think until we have a better baseline for widespread adoption and I think the government endorsement on some level kind of matters because let’s look at that thirty dollar. It’s effectively like a voucher or a gift card, right, that they’ve given people. They gave people a thirty dollar gift card for free. It doesn’t matter what currency it’s in. Okay. It’s gonna get spent, right. I don’t necessarily think that that’s a valid test of the adoption of a cryptocurrency.

I think you have to have something more widespread and more enduring because there you have a fixed amount of stock that’s spent over a very abbreviated period. Doesn’t really mean anything, right. But I think until we have a wider spread adoption for spend, we’re not necessarily going to get a fundamental based value, okay. We’ll get that technically based value, meaning looking at the stocks and the flows and trying to understand based on stocks and flows but not necessarily based on the inherent value that you get with a legit currency. Not that cryptocurrency is illegitimate. That was probably a bad word choice but let’s say a central bank endorsed currency, we’ll say that much.

JB: And on the central bank, endorsed currency kind of chain of thought, when you see the United States and Europe and also China adopting these different types of cryptocurrencies or I guess you could say ways to distribute capital to individuals for stimulus. How are you seeing China and the US and any other major players kind of deploying these central bank currencies over the next two or three years? As you did mention, you know China is already doing it. In the US, I’m not aware of us doing any type of central bank currencies or deploying central bank currencies to citizens. But are you seeing… I guess, how do you see that playing out over the next two or three years, if not and maybe longer?

TN: Sure. So China, the China central bank did a first test of a cryptocurrency I think in January of 2017.

JB: Oh wow.

TN: So they’ve been trying to figure this out for some time and I think china sees it as a potential way to rival the US Dollar. The problem is, there is no trust in the the People’s Bank of China. Nobody outside of China really trusts it, okay. So the immutable aspect of a cryptocurrency doesn’t have validity outside of probably the walls of the center of the People’s Bank of China building. And without that, kind of limited supply, without the immutability of it, then again, it’s just a gift card. It’s just a voucher. Now I think the PBOC, the Chinese central bank has had but with each day it’s kind of passing I think they’ve had an opportunity to utilize cryptocurrencies for things like trade finance which is a really opaque aspect of international finance related to trade. And if they had, let’s say gone to some of their trade partners and said look in Europe or the Middle east or somewhere, you know, we can get around using the US Dollar by utilizing this digital, you know, Chinese yen or something.

I think there was a time when people would have been open to it especially if it made payments faster and less costly. But I think that window has passed at least for now. I think it’s really hard for China to insert itself. I think if they had done this say in 2015-16, I think they would have had a real opportunity and they could have done a lot to displace some US Dollar denominated trade finance and probably displace a lot of Euro denominated trade finance. But they didn’t do it. They’ll keep trying.

I’m not sure how successful they’ll be outside of those places that have to trade with them meaning North Korea, Iran and and those sorts of economies Venezuela and so on. With Europe and the US, I don’t think the central bankers fully understand what a cryptocurrency is and I don’t think that they really have say the patience to understand how to say deploy it in a credible way, if that makes sense. And so, I think you’ll almost have these parallel currency regimes with cryptocurrencies.

The problem though is, I don’t necessarily, at least for the next few years, see them displacing a currency like the Dollar. They may displace say secondary or tertiary currencies within say international trade, trade finance, cross-border payments, these sorts of things, and even domestic payments where say a central bank doesn’t really have credibility that makes a lot of sense but I’m not necessarily sure that I see it displacing say US Dollar or Euro transactions let’s say in kind of main say kind of day-to-day activities.

If you look at a government like Venezuela or Turkey or something like that where you see a real currency crisis, I think it’s possible. I’m not necessarily saying it’s probable at a place like Turkey but I think it’s possible that you could see adoption of something like cryptocurrency especially if the government puts a a restriction on US Dollar use.

JB: Tony, do you see… I mean it seems like you’re saying that the western, you know, China will have its own central bank digital currency and maybe the United States will try to deploy theirs as well. Do you think this is going to move the global economy into being a more closed system or do you think this will actually open up finance and trade and make it you know better for everyone? Or do you think we’ll end up having this almost finance war. We already do have that but like on the digital currency level now where it’s traceable and trackable by a single entity and the capital or the cost to deploy these systems is much lower.

TN: It’s a great question. I think the people who accept the digital Chinese Yuan are going to have to decide if they want a centralized authority in China, tracking all of their activities in that digital CNY, you know. I think that’s a real decision and a real trade-off that those people who trade in that currency are going to have to figure out.

Although dollars are traceable, you know you can kind of transmit them and other currencies. You can kind of transmit them, I wouldn’t really say in an anonymous way but you can kind of get around tracking of every single transaction. But with cryptocurrencies, you know, the ledger tracks everything. And so if you have say the PBOC in China tracking every single transaction for every single digital CNY, that’s out there.

That’s kind of next level of information out there, right it’s not just Google understanding what’s in your email and it’s not just Alexa tracking what you’re saying. It’s every single Penny you put out there being tracked by a central ledger.

JB: And I think you said that perfectly you know China will be tracking every transaction and that will help these Central Bank digital currencies. If it’s China, if it’s the U.S. if it’s you know somewhere in Europe and as these different currencies are deployed.

They’ll really be able to build almost a very well put together social graph of who you’re paying. I mean it’s very similar to Venmo. When Venmo had the kind of privacy era, when you could see every transaction. If you had your transaction on public that you sent all your friends, right?

This is almost like that but the Central Bank can see that for every single person. Now we know who interacts with who, where you go, you know if you’re going to get coffee at Starbucks every morning. Where you’re going to be you know it’s very interesting to see the amount of power that you know these Central Banks in my opinion are going to start are going to gain over deploying a currency. Where it’s traceable trackable and it’s on a single ledger.

TN: Right, well also imagine, you know right now we have macroeconomic data releases like gross domestic product or industrial production or retail sales, those sorts of things. Imagine you know right now the way that happens is a statistics ministry does an estimate of what that economic activity is and they release it like a month after it actually happens. And then they revise it four times before they finally give up and say that this macroeconomic variable is finished.

If you do have a centralized kind of ledger for this stuff, you can actually look at national and global economic activity on a real-time basis, right? So you could actually see through Covid. You could see the U.S. economy declining on a real-time basis or the Europe economy declining on a real-time basis which would be pretty scary actually but that’s the reality of it. If you have this centralized ledger you can see let’s say, the velocity of that currency grinding to a halt as people don’t spend money which from a Central Bank perspective can help you understand how to incentivize people to spend money if they have it.

So from a kind of centralized monitoring of the economy perspective. I could see that being beneficial from a consumer and an individual saver. Spender perspective, I can see that being a little bit scary.

JB: It is a little bit scary but I agree with you also with the Covid situation. You know, the stimulus, really in my opinion didn’t get to the people as well as it should have. And Central Bank digital currencies will allow the these Central Banks to give stimulus to those who are most affected, at least in theory. And to be able to provide you know potentially different access to credit for different types of individuals we’re taking different types of risk being business owners or just employees. But on the Covid kind of analysis and as you guys with CI were we’re doing the analysis on the equity markets and in oil. And different types of currencies. Did you guys see any indicators you know as Covid was picking up in the analysis of the market. And how did it affect your predictions in these you know kind of broadly over the different markets that you guys predict and watch.

TN: I think what we saw in the wake of Covid was, and this is no surprise to anybody I don’t think is. A move to very short-term thinking you know, what data points are coming out. What’s moving. What are people doing let’s track to day what’s actually happening. Also an eye on kind of what is the government doing. What stimulus is coming out. When is it coming out. How much is it. Where is it going that sort of thing.

So I think for the probably three to four months I would say until July or August, a lot of trading and forecasting was really done on that basis kind of the news moved the market. It was fear and news that really moved markets and we had to come to a place where the size of the dump truck of stimulus was bigger than the fear that people had of Covid. And when we got to a number big enough you started to see markets break higher. Which was I guess a positive thing for people who weren’t working but getting stimulus from government so they could kind of day trade and make some money in markets to shore up some of their bills.

Now that the stimulus has gone out and now that we see at least some markets coming back to I wouldn’t say normal but at least to a significant level. We’re starting to see or we’ve started to see over the past, say six to ten weeks, more fundamental basis put into markets and put into some of those those value decisions whether it’s in equity or whether it’s a commodity or something. It’s still playing out in a number of ways a lot of the texts still very sentiment and stimulus based.

We see things like you know some of the commodities that are still very much based on that or I would say kind of more than 50 based on that but we’re starting to see markets move back into a direction that’s a bit more traditionally based and I use that term very loosely traditionally based but with at least a bit of fundamental analysis. But you know look at something like Tesla for example the price to earnings ratio is around 1100, I think something like that. It’s just I mean you may love Tesla but that’s a pretty healthy multiple, right? So you know at some point and I’m not necessarily predicting Tesla will fall to earth but at some point something will catch up with the valuations of these things.

Whether they’re commodities or whether they’re equities and will start to value things on a more traditional again. That’s a loose application there but on a more traditional basis.

TN: One of the things that I’ve been noticing in just conversations is it seems like you know the stock market is almost I would say really turning into a casino. Where you have people just buying stocks they heard on the news. They’re getting the motley fool every week and they have so many decisions to make. So many different options and I’ve noticed that it seems to be just too complex for I would say normal retail robinhood traders. They get overwhelmed with so many decisions. I think one of the nice things you know about value as we talked about valuing crypto. Is at least with Bitcoin you know what you’re getting. You know that this is an asset with a stable monetary supply with a stable issuance rate over the next 100 years.

What are your thoughts on how bitcoin mining? I’m actually gonna change it up and move to a separate topic a different topic but what are your thoughts on Bitcoin mining and how it relies on as on the global supply chain starts in semiconductor factories in China and you mentioned the supply chain optimization a lot on your website as a function of Complete Intelligence. Can you walk through a little bit how you guys optimize supply chain and then I’d love to talk with you through potentially how the Bitcoin mining supply chain works on our end and see where you know optimizations are and and how Covid or any of these other things impact supply chains and what you guys are seeing on a worldwide basis?

TN: Sure, that’s great, I think with any supply chain you have really three factors. You have cost, you have distance, and you have time, okay? And so I mean there’s quality as well but if you assume that you can get equal quality in you know in multiple locations. You have cost, distance and time. And so we help people initially with costs, okay? We’re helping them to kind of arbitrage the best cost locations.

We have a client who manufactures confectionary that makes candies and sweets. And they buy sugar, I think at eight different places around the world and so we help them understand where the sugar price is because there’s not a single global sugar price, right? There are local factors so we we help them understand where sugar prices will change and at what magnitude they change.

So that their factories can be prepared and that they can have the right margin they need so that they can take in the right inventory. So that they can make the right transactions at the right time. So I think from a pure cost basis with commodities for example like sugar, it’s possible to do that. When you look at something like semiconductors with a very sophisticated manufacturing process.

Cost is probably not the only, well I can assure it’s not the only factor associated with the decision. So then you start looking at things like time and you look at things like distance and so when we go back to say March, April, May, a lot of semiconductors travel by air and we had air freight rates from Asia to the U.S. that were normally say a dollar fifty a kilogram. That had in many cases been jacked up to say 15 dollars a kilogram. So, 10 times or more of the normal price. So that’s where distance becomes or let’s say cost becomes a function of distance, right? And so that’s that chipset that semiconductor may cost the same x factory but getting it to the destination is increasingly critical and increasingly costly.

So, that’s where we help people also to understand what the cost of that distance is and what the cost of that time is because you could put it on a vessel and you could ship it and it could take three weeks to get where it needs to go. But in many cases the cost of those the finished goods are high enough that you can absorb some of that transport cost. Okay? So there are a number of ways that we help people understand those transactions but at the end of the day it all has to do with the cost of that bill of material, meaning the cost of the goods that go into that finished item that’s ultimately sold to a customer.

So when we look at semiconductors for example and you look at what has happened over the last, particularly last year and if you look at say TSMC Taiwan semiconductor. Moving one of their locations to I think it’s Arizona in the U.S. We’re starting to get more of that high value supply chain in the U.S. more as a function to de-risk supply chains in the wake of Covid meaning, factories in China closed during Covid people still had to make stuff and they had to still have their business open but they couldn’t because the factories in China were closed.

Once the factories in China opened. There was constrained transport capacity so it would cost them a lot more so they had goods that were late and they had goods that were a lot more expensive than normal. And so I think what a lot of manufacturers have done especially in the wake of Covid and said, look we need to diversify our supply chains and have multiple sources for some of these high-value goods and we Complete Intelligence have been talking about regionalization of trade since 2017. We wrote about it more formally in say starting Feb of 18 when the steel and aluminum tariffs were put on by the current administration but we’ve believed for years that we would start to see a re-regionalization of trade and that cuts out some of the risk associated with supply chains and some of those costs. Maybe, transport costs that may be lower are offset by maybe marginally higher say labor or taxes or something like that either in the U.S. or Mexico or something.

So one of the things that many people don’t necessarily understand is when China came into the WTO in 2000 the U.S. was in the first decade of the NAFTA agreement North American Free Trade Agreement at the time there were a lot of manufactured there was a lot of manufacturing for the U.S. done in Mexico. Part of the reason a lot of factories moved to China was because electricity in Mexico was really really expensive at the time, okay? And the electricity in China was really cheap. So a lot of these manufacturing especially energy intensive manufacturing firms moved to China to save on their electricity. Which was a large fun factor within their total cost. So what’s happened in Mexico over the last… I think four years is laws were passed to deregulate the electricity market in Mexico. So now you have power in Mexico that’s a lot cheaper than it was 15, 20 years ago. So the attractiveness of Mexico as a location at least from a cost basis is quite a bit higher than it was in the past and especially quite a bit higher than it was when firms were leaving Mexico to go to China.

JB: So Tony you mentioned the impact of of Covid on these supply chains and I want to talk a little bit about something that we have in in Bitcoin mining called the supply gap. And it basically what that is when the price of Bitcoin is is skyrocketing and is hitting an all-time high, like it did back in 2017. The underlying you know value of these Bitcoin miners really relies on the profitability of those machines and that is heavily relies on the price of of Bitcoin.

So what we see is that you know these supply chains they they shrivel up, almost. They you know there’s being able to order machines over a three-month period it ends up going out to six months. You won’t be able to get machines and you know until six months later. Do you see this sent not centralization but going from globalization back to Mexico. Back to these localized economies. Do you see that helping these kind of massive supply fluctuations or kind of I guess events that occur specifically you know with Bitcoin price and Bitcoin miners but I guess also globally with events like code that really do shock the system we know of today.

TN: Yeah, I do. I think that of course you know we’re going to have some difficulties in the early days of it. We’re going to have some awkward moments where things don’t work as people plan, that sort of thing. Whenever you have a large systemic change you always have some moments that are a little bit embarrassing and cause you to second-guess the decision. We’re going to have those that’s normal but I think over time. What we’re building is a more robust global supply chain you know. Something like 40 of all manufactured goods are made in Northeast Asia, China, Korea, Japan and as we have re-regionalization of manufacturing and that’s to North America, that’s to Europe and so on. We have a diversity of manufacturing locations and so if there is let’s say Covid in China or in Asia but it hasn’t hit the U.S. yet then you know it’s possible to use additional capacity in say U.S. or European factories to help meet the needs of Bitcoin miners, right? Depending on what we’re doing. Depending on the sophistication of those factories and the capacity of those factories but I believe that as we have regionalization of supply chains you have much more robustness in those supply chains.

I also think that in the wake of Covid… so I lived in Asia for 15 years. I just moved back to the U.S. in 2017. I lived through probably five or six pandemics in that time and so we got a little bit used to it. In the U.S. it’s relatively new and I think people here trying to figure out how to contend with it and kind of the calibration of risk in the U.S. to pandemics is it’s new. So people aren’t really sure what it means or doesn’t mean. So the global transmission of viruses is not something that’s really going away. So will we have more code like viruses coming out of Asia or coming out of Europe or the U.S. It’s likely and so we’re at a point where we have to have regionalization of supply chains.

So first we have robust supply chains where we can source from the U.S., Europe, Asia wherever we want as capacity as demand and as costs require but also we have the flexibility if there is one of those events whether it’s a disease event or whether it’s you know let’s say a war or something like that. We have the flexibility to make stuff in other parts of the world too. So if there was a devastating conflict in Northeast Asia today. Global supply chains would be paralyzed that’s just a fact and so the sooner we can get regionalized supply chains the better, we’re all off because the risk of a let’s say a conflict in Northern Asia, if it ever happens, it won’t impact everyone on the planet as much as it would.

JB: We definitely, I agree are seeing that de-risking and a big huge news with a semiconductor in TSMC moving to potentially the United States to build a facility you know hopefully reducing on that that distance for Bitcoin miners specifically. I found it very interesting that you mentioned about Mexico and the electricity prices there. To understanding that those manufacturers actually had to leave Mexico and went to China because it was too you know too expensive to extract or to complete that manufacturing process. I view Bitcoin mining as a way to almost extracting you know Bitcoin from the network through a manufacturing process where we’re using these Bitcoin miners and large amounts of energy to do just that.

So I wanted to talk farther about how you’ve worked with clients in either the natural gas or the energy sectors in the United States specifically and pricing out those markets and where do you see the future of this industry going the electricity market specifically and the cost of power in the United States?

TN: Sure, so I’m in Texas the cost of natural gas is very low and the abundance of natural gas is very high. So electricity prices to be honest is not really something we worry about here. I know in other parts of the country and other parts of the world it is a worry you know, electricity is something that has kind of always been very regional and it has been always been very feedstock specific if you’re burning oil to make electricity or coal or nuclear or whatever and you really have to look at that blended cost, right? but in Texas we’re looking at a lot of natural gas to fuel our electricity. So not that much of a worry for us and and in this region it’s not that much of a worry.
I think in places like Europe where they’re net gas importers, I think it’s more of a worry and there’s always a lot of discussion around importing gas from say Russia or from the Middle East or from the U.S. I think they have an abundance of choice there but it’s relatively more expensive there than it is say here in the U.S.

I think in Asia you have a lot of imports from the Middle East particularly places like Qatar, these sorts of things for natural gas. China uses a lot of coal something like 70 plus percent of their power generation is from coal and it’s really hard to um to wean themselves off of that. Japan is a very large LNG and natural gas importer because they shut off their nuclear power after the incidents in 2010 or 2012 sorry with the reactors the Fukushima reactors. So you know it really all depends on the local power generation capacity in feedstocks. But I think generally you know we’re not necessarily seeing a world where hydrocarbons become all that expensive for quite some time. When we look at what Covid did to demand the demand destruction that Covid brought about is is pretty shocking that applies to industries and that applies to consumers so we don’t see say oil prices or natural gas prices hitting let’s say the highs of 2008 for quite some time. And you know since they are relatively global commodities although there are differences in certain aspects of them it also pushes down the prices, let’s say in other parts of the world say the middle east and so on and so forth. So we don’t see electricity prices outside of say regulatory impacts or things like fixed investment requirements.

So let’s say there’s a regulatory requirement that a power station can only be say 20 years old you know that’s a significant cost that would add to electricity prices but other than that it seems to us that the feedstocks, although we don’t necessarily expect to see kind of negative 37 oil like we saw in April. We don’t necessarily see energy price inflation coming anytime in the next say 24 months. And if you look at things like gasoline I know this isn’t electricity but things like gasoline prices are down say 30 percent from where they were a year or so ago. And they’re expected to remain that low at least for the next six to 12 months. So it’s not just electricity it’s also gasoline or petrol as well where because of muted demand prices will remain relatively low.

JB: I think that’s that’s great news for for miners in the in the United States and you know I really cross the world as more and more energy generation comes online. We’re seeing that that cost to produce coins is continuing to get cheaper and which allows miners here in the U.S. to compete if not beat miners in China on the cost per kilowatt hour. Tony, was there any other trends that you guys are focusing on right now in regards in to your investment portfolio analysis that you wanted to highlight on the show today?

TN: JP, I think there are hundreds of trends we’re following but I think we’ve cut most of the main ones. I think really it’s you know understanding risk of any asset that we follow or our clients follow is really really important. Whether it’s cryptocurrencies or whether it’s oil and gas or whether it’s you know I don’t know the SP500. Understanding the risk there is really critical we’re always trying to figure out how to balance the risk and opportunity associated with the assets that we forecast and that’s I would say for any of your listeners that’s the really critical part to understand. So you know we could pursue this down any avenue and I’m sure we could talk for another hour on you know on just about any asset. So I really appreciated the time today it’s been a fantastic discussion, thank you very much.

JB: Yes, thank you Tony it was great to have you on. I want to offer you the opportunity to join you have any questions that you want to ask me about Bitcoin specifically that you want the audience to make sure they hear, anything that’s on your mind?

TN: You know, I guess what I am curious about Bitcoin is you know we saw a bump in 2017. I think largely driven by broad awareness or a more broad awareness of the opportunities in Bitcoin. What will drive the next bump in Bitcoin or crypto value? What do you see driving that next rise let’s say 30 to 40 to 50 rise in the value of of cryptocurrencies?

JB: So the way I view the cryptocurrency market and really Bitcoin specifically is I’m all about as the stock to flow ratio and how that bitcoin is created. So when that having event occurs I got into cryptocurrency back in 2013. So I’ve been through two of these having events now and when that have even occurred in 2016 we see that it kicks off like a real almost momentum. Moving into the space where the cost of creating these new coins is exponentially higher, makes it so that all these older machines have to come offline and it really does a disservice or really degrades the value of these mining machines it makes the profitability got cut in half. And so when that happens I think that there are these the lack of coins new coins coming into the system, creates the momentum which is needed to push the price up to those 2017 highs you were talking about or potentially you know 2021, 2022 highs, simply saying it doesn’t happen instantly because it does take a while to get there but I expect that to you know to happen in the next coming years. Not necessarily because of one event but simply because of the schedule of new coins coming out of the market.

TN: So sorry if I understood you correctly are you also saying that the age of the infrastructure that the miners are working on has an impact on the so the replacement cost of that infrastructure also puts upward pressure on the price of bitcoin?

JB: I would say that exactly so the fact that we have to replace machines that have less efficiency. So the joules per tera hash or how well they can turn one watt of energy into one terra hash of mining power is needs to be upgraded by 50 so if you have a machine that was running 100 joules per terahash like the s9 that machine is no longer and it was just barely making money that machine is no longer going to be even anywhere close to profitable because of this having event, you know now, you would need to go upgrade all of your machines so they run at the 50 joules per tera hash level or you need to find half the cost of electricity and that is very hard to do especially because these facilities are massive with hundreds of megawatts of power.

So that’s what I drive as the underlying driver to this Bitcoin price push that we see every four years if you look back on the chart it happens every four years. Simply because the miners place such they’re one of the biggest components of the ecosystem there’s about five billion dollars in mining rewards today every year and that’s a huge driver in a relatively small market where Bitcoin is currently sitting.

TN: Interesting, so that that replacement cycle like you said it’s and this is a question it’s not a statement that’s that’s about every four years give or take.

JB: Every four years give or take either have to replace your equipment with newer machines which now you’re waiting in line because you know everyone else in the whole bitcoin network has to do that or you’re moving to power where it’s half as expensive but all miners are always searching for the cheapest power so that’s something that’s always occurring.

TN: Okay, so with the kind of the supply chain hiccups that we saw with Covid does that push that replacement cycle back like are is that replacement cycle being pushed back by six to nine months so or is that do we have a pent-up kind of inflation meaning. Do you believe that the value of bitcoin being driven up will last for longer because of the supply chain issues we saw in Covid?

JB: So with this definitely the supply chain issues in Covid it affected our shipping rates as you mentioned those increased dramatically it affected how fast machines could get out it actually caused bitmain and some of the other major manufacturers to delay their shipping by two or three months. So if you were to buy a batch to be delivered in November it still hasn’t been delivered.

So there is that that pushback and we’ve seen that greatly affect the market regarding the deployment of these machines and kind of scaling with the recent bitcoin price-wise guys new machines are very hard to get. I would say about maybe 10,000 to 15,000 new machines per month are coming to the U.S. And that might be even on the higher range that’s about 50 megawatts of power per month coming to the U.S. and coming out of these factories. Which is is only 50 million dollars worth of capital. So we have huge constraints on the semiconductor themselves and being making those mining machines and when the price of bitcoin even jumps up like it has over the past couple of days up to the 13,000 mark that’s going to create even more external pressure even more interest in mining which makes it even harder to get those machines and will push out the timeline even farther.
So yes it’s a huge issue when it comes to supply chain management because of Covid and the Bitcoin price increasing investors appetite to get exposure the space.

TN: Fantastic that’s really interesting. Thanks for that.

JB: Of course Tony, well thank you for coming on. I appreciate it and I’m glad we’re able to have you on. Thanks again Tony.

TN: Thank you, hope to speak soon. Have a great day. Thanks JP, bye-bye.

Tags AI, artificial intelligence, bitcoin, bitcoin kid, cryptocurrency, digital gold, digital gold podcast, forecasting global markets, forecasting saas, Global Markets, ihs markit, Jp baric, podcast, the economist, tony nash

Podcasts

Behold the Power of Superforecasting

Post author By admin
Post date August 28, 2020

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.

Podcasts

Stories from the Cloud: The Forecast Calls For…

Post author By admin
Post date August 20, 2020

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.

QuickHit Visual (Videos)

QuickHit: China is not going to stop being China

Post author By admin
Post date August 7, 2020

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.

Podcasts

Fixing terrible forecasts and the lack of context

Post author By admin
Post date July 23, 2020

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

160 – Interview with Tony Nash of Complete Intelligence

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.

Podcasts

Economies are sputtering, which means trade war will intensify

Post author By admin
Post date June 26, 2020

Here’s another guesting of our founder and CEO Tony Nash in BFM Malaysia, talking about trade war between US and China. Can these two countries actually decouple? Or is the current supply chain too dependent to do that? Can the economy have the V-shaped recovery that everyone is dreaming of, or is it just an illusion? What can the policymakers do to improve the economic outlook for this year? What can his firm Complete Intelligence see happening based on the algorithms and AI?

We also discussed regionalization of supply chain as a result of the Trade War in this QuickHitQuickHit episode with Chief Economist Chad Moutray of National Association of Manufacturers.

BFM Description:

The trade wars between the US, China and the Eurozone seem to be gaining momentum. Tony Nash, CEO, Complete Intelligence, offers some insights, while also discussing European industrial activity.

Produced by: Michael Gong

Presented by: Wong Shou Ning, Khoo Hsu Chuang

Listen to the “Economies are sputtering, which means trade war will intensify” podcast in BFM: The Business Station.

Show Notes

This is a download from BFM eighty nine point nine. So is the station. Good morning. This is BFM eighty nine point nine. I’m considering that I’m with one shotting bringing you all the way through the 10:00 o’clock in the morning and Rano 76. We are talking about markets, but well above 50 bucks sort of because of that with about 15 minutes time, we’re talking to call you. Ling was an independent panel, a political economist at Ciggy and I’m advisers will be discussing palm oil.

BFM: So last night in America, the stock market slumped. Investors are cautious, right How did the markets do?

Not so well, because there’s been clearly a resurgence in virus cases in multiple states, which puts into question the economic recovery. So, unsurprisingly, the Dow closed down three percent and S&P 500 closed down 2.6 percent, while the Nasdaq closed down 2.2 percent. Meanwhile, in Asia yesterday, only Shanghai was up, which was up 0.3 percent, while the Nikkei 225 closed down marginally by 0.07 per cent. Hang Seng was down 0.5 percent, Singapore down 0.2 percent, and KLCI was down 0.3 percent.

So for more clarity into the whys and wherefores of markets, we’ve got it on the line with us Tony Nash, who is the CEO of Complete Intelligence. Now, Tony, thanks for talking to us. Trump’s getting tough on China rhetoric highlights, well, obviously, the American’s concerns about being too reliant on China. And, of course, we can see that being manifested in the list of 20 companies, which is deems suspicious. In your opinion, can the two economies decouple or other interests in supply chains too heavily aligned?

TN: Well, I don’t think it’s possible to completely decouple from China. I think the administration are really being hard on each other. And I think the hard line from the US, you know, it’s relatively new. It’s a couple years old. But I don’t think it’s possible, regardless of the hard line for those economies to decouple and for the supply chain to decouple. We had some comments over the weekend out of the U.S. saying that they could decouple if they wanted to. But that’s just the hard line and unaware of the possibilities. We’ve been talking about, for some time, probably two and a half, three years, is regionalization of supply chains. And what we believe is happening is the US-China relations have just accelerated regionalization. It means manufacturing for North America, moving to North America. Not all of it, but some of it. And manufacturing for for Asia is largely centered in Asia. Manufacturing for Europe, some of it moving to Europe. And that’s the progression of the costs in China. And some of the risks are relative risks to supply chains highlighted by COVID} coming to the realization of manufacturers.

BFM: U.S. markets corrected sharply last night. So is the market actually now waking up to the reality that COVID 19 is going to be a problem for economic recovery? And this V-shaped that what many investors thought is probably a pipe dream?

TN: I think what markets are realizing is that it’s not a straight line. Well, we’ve been saying for a couple months is that end of Q2 or early Q3, we would see a lot of volatility. Then people started to understand how the virus would play out. Until we’ve had some certainty around the path, we will have days like today. And we’ll have a danger with an uptick as optimism comes back, what’s happening is markets are calibrating. People are trying to understand not only the path of COVID, but what those actors mean—the governments, the companies, the individuals—will do to respond, how quickly the markets come back. But what are people going to have to do? What mitigations that we’re going to have to take? What monetary and fiscal policies will governments take as well? We’re not done in that respect. So more of that’s to come, but we don’t know what’s to come there exactly. Markets have moved a lot on new case count. I don’t believe that it’s the case counts itself because a lot of these are are really mild cases. It’s just the uncertainty around how long it will last. The magnitude and the mitigation that people will take around it. There’s more of this volatility to come.

BFM: Tony, you might have seen the IMF‘s growth forecast, which was just announced a few hours ago. They’ve now said that global growth will shrink 4.9 percent for 2020. That’s nearly two percent worse than what they originally thought. And I think the U.S. also marked by an expectation of a negative 8 percent, down from negative 6o.1 percent. Do you think this might cause the policymakers to have an even more vigorous policy response and liquidity into the system?

TN: It might. I think the U.S. has shown that it’s not really afraid to be pretty aggressive. I think you may see more aggressive policy responses in other places. Obviously, Japan is very active on the monetary policy side. But we need to see more actual spending and more direct support of individuals and companies to make it through this. So, I do think that, obviously, IMF’s forecast concern people and get policymakers attention. I do think that they’re probably a little bit overblown to the downside, though. So I wouldn’t expect 8 percent decline. I wouldn’t expect a global decline as acute as they’ve stated today.

BFM: If you look at oil prices declined last night and I think this is on the back of U.S. crude inventories increasing. But is this also a function of COVID-19 fears in terms of how that may impact the economy’s going forward and consumption of oil again?

TN: Yeah, that’s interesting. The oil price is our… I think there are a number of things. The storage, of course, as you mentioned. But there’s also how much are people starting to drive again? What do traffic patterns look like? Also, how much are people starting to fly again? We really need to look at like Google Mobility data. We need to be looking at flight data. We need to be looking at looking to really understand where those indicators are headed. So when we compare a $40 a barrel of oil at $39 s barrel for WTI today, compared to where it was a month ago. The folks in oil and gas are really grateful to have that price right now. And it’s a real progress from where we were a month or two months ago. So I think what people are looking at today is the progress and then the expectation. They’re not even necessarily looking at the real market activity today. It’s all relative to a couple of months ago and it’s all expectations about a couple of months from now.

BFM: Last question on perhaps the data that your algorithms generated, Complete Intelligence. What kind of signs and indicators does our technology and the AI tell us about the direction the market’s going forward?

TN: Yeah, well, this is where we we pulled our assertion of volatility. We we really expected things to be pretty range traded for some time. So, you know, crude oil is a good example. We were saying back in February, March, the crude oil would end the quarter in the low 40s. This is WTI and here we are. So, with volatility, we’re not necessarily trying to capture the high highs and the low lows. We’re just recognizing that the markets are trying to find new prices. So it’s interesting when you look at things like the dollar. The dollar is a relative indicator for, say, emerging market‘s uncertainty and troubles as well. We did expect a dollar rise toward the end of Q1, early Q2, as we saw. But we haven’t expected the dollar to come back to strengthen until, say, September. So there are a number of indicators around trade or on currencies. And what we’re finding generally with our client base, for global manufacturers generally, are the algorithms… We’ve found that our average-based forecasting has an error rate that is about nine percent lower on average than consensus forecasts. So when we had all of the volatility of the last three, four months, consensus forecasts in many cases were 20 to 30 percent off. Ours were about nine percent better than that. Nobody expected the COVID slowdown. If we look at that from a few months ago, the bias that’s in normally of doing things, negotiating, procurement, supply chain, the revenue, that sort of thing. We take that out and this passionate… I would suggest that there is a lot of passion in the analysis from day to day when you look at three percent fall in markets today, but you can’t extrapolate today into forever. And what we can do with AI is taking emotion out of this, take a rational view of things. And really remove, not all of the error, of course, nobody can remove the error. There area a lot of the error from the outlooks in specific assets, currencies, commodities and so on.

BFM: All right, Tony, thanks so much for your time. And that was Tony Nash, chief executive for Complete Intelligence talking from Texas, USA. Interesting that this kind of stuff that he does at his business, tries to remove the emotional, the emotive side of the markets and give something a predictor over the future. But I think that sometimes you can’t discount too much of human emotion because it’s all driven by essentially two emotions, right? Greed and of fear.

But you know, basically his nugget is it’s going to be volatile. Right. Hang onto your seats. Right. Because we really don’t know. There’s too much uncertainty out there at the moment. This is a scene where it’s for oil prices or even for equity markets.

QuickHit Visual (Videos)

QuickHit: 2 Things Oil & Gas Companies Need to Do Right Now to Win Post Pandemic

Post author By admin
Post date April 14, 2020

This week’s QuickHit, Tony Nash speaks with Geoffrey Cann, a digital transformation expert for oil & gas companies, about what he considers as “the worst downturn” for the industry. What should these companies do in a time like this to emerge as a winner?

Watch the previous QuickHit episode on how healthy are banks in this COVID-19 era with Dave Mayo, CEO and Founder of FedFis.

Show Notes

TN: Hi, everybody. This is Tony Nash with Complete Intelligence. This is one of our QuickHits, which is a quick 5-minute discussion about a very timely topic.

Today we’re sitting with Geoffrey Cann. Geoffrey Cann is a Canadian author and oil industry expert and talks about technology and the oil and gas sector.

So Geoffrey, thanks so much for being with us today. Do you mind just taking 30 seconds and letting us know a little bit more about you?

GC: Oh, sure. Thank you so much, Tony, and thank you for inviting me to join your QuickHit program.

So my background, I was a partner with Deloitte in the management consulting area for the better part of 20 years, 30 years altogether. I had an early career with Imperial Oil and I’ve spent most of my career helping oil and gas companies when they face critical challenges.

These days, the challenge I was focusing on prior to the pandemic was the adoption of digital innovation into oil and gas because the industry does lag in this adoption curve and yet the technology offers tremendous potential to the sector. I see my mission, and it still doesn’t change just because of the pandemic, as the adoption of digital innovations to assist the industry and to resolve some of its most intractable problems. That’s what I do.

TN: Wow. Sounds impressive. I’m looking at the downturn in oil and gas and the downturn in prices. There have been big layoffs and cost savings efforts and these sorts of things with oil and gas firms. And, typically, a pullback is an opportunity for the industry to re-evaluate itself and try to figure out the way ahead. Are we there with oil and gas? Do we expect major changes, and as we emerge from the current pullback, how do we expect oil and gas to emerge? We expect more technology to be there. Do we expect more efficiency in productivity? Are there other changes that we expect as we come out of this?

GC: I’m pessimistic about the prospects for oil and gas and it’s driven by this collapse and available capital and cash flow to the industry.

When the industry hits this kind of survival mode, there’s a standard playbook that you dust off. And that playbook includes trimming your capital, canceling projects, downsizing staff, closing facilities, squeezing the supply chain, trimming the dividend. Anything that is considered an investment in the future is put on hold until the industry can get back on its feet.

And this is the worst downturn. I’ve lived through six of these. This is the worst I’ve seen.

Certainly sharpest, fastest, and deepest and coupled it with the over excess production in the industry. When the industry comes out of the other end of the pandemic, what we’re going to see the industry do is devote its capital to putting its feet back on the ground and getting back into its normal rhythm. But what that means is all the changes that our potential out there are likely to have been set aside in the interim.

TN: If you were to have your way, and if you were running all the oil companies, and they were to make some changes in this time, what would those changes be? What would some of those key changes be?

GC: There is a gap between what other industries have discovered, learned, and are adopting, and where oil and gas is at. That gap is, first, needs to be addressed by raising the understanding and the capability and the capacity in oil and gas to deal with the possibilities presented by these technologies. And so there’s task number one that oil and gas companies can absolutely do even during a downturn. Just train people and get them across the newer concepts or newer ideas.

A second possibility is to embrace the foundational elements that have proven to be the key success factor for so many other industries. One of those would be cloud computing. The adoption of cloud-based infrastructure, moving data into the cloud, is not costly, it generates an immediate payback because cloud infrastructure is so cheap, and it puts the company into a solid position for when the normal day-to-day running of it gets back in gear, the investments it may have been making an in digital innovation can all now be brought back into stride because this foundational technology will be in place.

So those are the two things that I would do: Get people ready for the journey ahead and put one of these foundational steps in place to get ready.

TN: Those are really enabling technologies, right? They’re not substitutional. They still need people, they still need engineering skills. It’s really just enabling them to do more, right?

GC: Correct, yeah. And covering off that gap incapacity is the key thing. Somewhere down the road, there will be the adoption of artificial intelligence and machine learning tools to improve the performance of the business. Those are coming and they’re coming very quickly. We’re not there yet. The job is where the industry needs to move forward, and as I see those are the two steps.

TN: Do you see this as kind of a generational thing? Is this five-ten years away? Or is it an iterative thing where you see it changing bit by bit for each year? How do you see this on the technology side for them?

GC: Well, in my book, I actually sketched out a way to think about this problem. And I call it the fuse in the bang. The fuses, if you think about Bugs Bunny cartoons. Bugs Bunny and it would be a comically large keg of gunpowder. It’ll be jammed into the back of your Yosemite Sam. As they go racing off, they leave a trail of gunpowder and Bugs would just drop a match in it. It always ended in a comically large but not very terminal explosion. So imagine that the length of fuse, that trail of gunpowder is how much time we’ve got and the size of the keg of gunpowder is how big the impact is going to be. In my book, I could actually go through some ways to think about this.

But you have to think about it in these terms, oil and gas is principally a brownfield operations business. In other words, most of the assets predate the Internet Age and they’re continuing to run and they run 24/7, they’re extremely hard to change, and so as a result, the idea that we can quickly jam innovation into these plants is just nonsense. It’s not going to work. So it’s going to take quite a long time.

The generation is on two fronts. One is the technology is legacy and therefore it has generational barriers to adoption of change. We also have a workforce, which is tightly coupled to that infrastructure and it also has struggles to cope with change. So we have to come across these two generational shifts that have to happen and they basically have to happen at the same time.

TN: Very interesting. Geoffrey, I wish we could go on for another hour. There’s so many directions we can take from here. So, thanks much for your time. It’s been really great talking to you and I hope we can revisit this maybe in a couple of months to see where the industry is, how far we’ve come along, just with the downturn of first and second quarter, look later in the year just to see where things are and if we’re in a bit of a better place.

GC: It’d be great fun because this is, you know, as I’d like to tell people, this is not the time to actually leave or ignore the industry. It’s when it goes through these great troughs like this, this is where exciting things happen, so pay attention.

News Articles

Startups Step Up with Free Resources and Virtual Technology

Post author By admin
Post date April 11, 2020

This post on free resources was originally produced by Oracle and first appeared on the Oracle for Startups Blog: https://blogs.oracle.com/startup/startups-step-up-with-free-resources-and-virtual-technology

Startups are known to be adaptive, innovative, and agile. When there’s a crisis or disruption, these up-and-coming business are quick with a solution, and this situation is no different.

Despite being hit hard themselves, startups are stepping up to help by offering their virtual technologies and resources for free. Among them, we are proud to share, are several cloud startups from the Oracle for Startups community.

Here is a running list of some of the startups who are putting their ingenuity and inspiration into action.

Extending Help to Farmers and Growers

AgroScout

AgroScout’s software solution enables growers and farmers to turn a low-cost commercial drone into a digital agronomist, providing pinpoint detection of disease and pests, thereby protecting crops and increasing yield. During this economic crisis, AgroScout is offering its solution at discounted rates and including free use of a drone for 2 weeks in the case of growers who do not already own one, so the grower can try out the system without any cost.

“In these challenging times, we don’t want to ask farmers to put their hand into their pockets unless they are 100% positive it’s going to help them out,” said Simcha Shore, CEO of AgroScout. “In addition to our discounted offerings, we are also providing online demonstrations so growers can be acquainted with the system and understand the benefits.”

The solution accurately and autonomously detects, identifies, and monitors diseases, pests, and other agronomic problems in the field. Data is uploaded to the cloud and analyzed by AgroScout’s deep learning algorithms with the goal of sending growers accurate crop stress statuses, disease, and pinpointed pest locations, accompanied by treatment recommendation, directly to their computer or mobile device.

You can take advantage of AgroScout’s current offers here or by emailing sales@agro-scout.com

Patient Triage Via Mobile App

w3.care

Brazilian startup w3.care is focused on mobile emergency care through telemedicine and artificial intelligence solutions for ambulances, rescues, and healthcare units. The startup has developed a new and free service, TeleCOVID, which helps identify potential patients and calculates their severity into low- and high-risk profiles. Low risk profiles receive care instructions and best-practice procedures, as well as connections with medical professionals. In the case of high risk, the TeleCOVID will start the medical tele-orientation using the w3.care platform, which is HIPAA and HL7 compliant, to help better connect high-risk patients to immediate care. (No personally identifiable information is used during the process.)

“Telemedicine is critical right now and the ability to help triage via TeleCOVID is helping the general population and the many medical doctors and organizations we are working with,” said Jamil Cade, MD and CEO of w3.care. “We are helping medical professionals to tele-triage, tele-orientate, tele-monitor and use real-time data visualization to battle this pandemic.”

To access information on this free service, visit their website.

Real-time, Active Analytics Helping on the Front Lines

Kinetica

Kinetica is providing free access to its Active Analytics Platform for researchers, data scientists, and academics trying to analyze the impact of COVID-19. Kinetica helps organizations build real-time active analytical applications that react instantly to changing conditions. The platform leverages powerful GPUs to process and visualize complex streaming, historical, and location data at scale—layering on machine learning—to deliver real-time information for insight-driven actions and results.

“Our hearts go out to all those affected by the outbreak of COVID-19. I believe it is our duty to do all we can for the safety of our community,” said Kinetica CEO Paul Appleby. “Kinetica was founded on the idea that data can change the world. By providing our analytics platform for free we will help provide critical, real-time information to protect the most vulnerable, assist emergency responders, better care for the sick, and find a solution against this terrible virus.”

Use this form to provide a basic overview of your project, and access the platform free.

Throwing Studios and Artists a Lifeline

GridMarkets

GridMarkets, a cloud rendering and simulation company for studios, animation/visual effects, and other industries, is providing its service at a significant discount (and in some cases, at no cost) to studios and freelance artists in need. GridMarkets’ “COVID-19 Relief Program” (powered by Oracle’s VMs) can help studios and freelance graphic artists in many ways, including:

•   Enabling studios to continue work so they can preserve their cash and business
•   Providing a lifeline to the artistic community
•   Bootstrapping a freelance business (if they have been laid off by their studios)
•   Helping professionals refresh their artistic “reels”
•   Creating helpful community VFX 3D tutorials

“Visual effects studios and freelance 3D artists, who produce the world’s visual content, are being crushed by COVID-19. Demand is down and anyone fortunate enough to have a project is now, understandably, ultra-budget sensitive,” said cofounder Mark Ross. “Our visual effects cloud-based rendering and simulation service, powered and secured by Oracle, can be up and running for a studio or freelancer in minutes with no special skills required. We have cut our prices and made grants available as a way of giving back to the artistic community in their hour of need.”

Learn more about GridMarkets’ COVID-19 Relief Program on their webpage.

Helping Navigate Volatility in Markets and Supply Chains

Complete Intelligence

With economies around the would essentially being put on pause, there is a new level of uncertainty in markets and supply chains. As a result, manufacturers are quickly trying to pivot and make adjustments on the fly. Complete Intelligence is offering a free report and consultation call to help businesses adjust to volatility in markets and supply chains.

“We’ve seen a big shift in how category managers and planning managers are looking at their supply chains,” said Tony Nash, CEO and founder. “With entire economies being shut down with coronavirus, companies are taking a closer look at the concentration of supply chains by region. Our AI/ML software helps companies easily visualize their supply chains, and helps them pivot quickly.”

With Complete Intelligence, businesses can easily visualize their cost data, make predictions and plans, all in the context of a global economy. The company uses more than 15 billion data points in their AI/ML tool, so planning teams can see their cost projections in the context of market influences.

Contact Tony Nash at tnash@completeintel.com for more information.

Keep your storytelling fresh – even while working from home

Sauce

Video is paramount to brand storytelling, but creating great, engaging content when you can’t send out video crews or get face-to-face is a problem.

Sauce’s platform allows businesses to keep engaging with their audience, by transforming every organization’s community into a video creation team. The London-based startup enables video creation leveraging smartphone cameras, so anyone can become part of the film crew. The result is authentic user-generated content.

With features for editing, subtitling, and music – the platform is collaborative, fast, and robust.

“We’ve received an uptick in organizations needing advice and direction around video creation,” said Sauce cofounder Priya Shah. “We want to meet their needs with advice and technology resources so they can keep their video content and storytelling fresh and constant—even while we are all working from home.”

Contact Priya at priya@sauce.video for advice on capturing great video, even when your whole team is at home.

Chatbots triage customer service calls

BotSupply

BotSupply is a conversational AI company that helps organizations create engaging and relevant customer experiences using their bot platform. Today, the cutting-edge startup is providing its AI platform for free to public and non-profit healthcare organizations so they can do what they do best: save lives.

Triage and response teams across industries are being overloaded with customer calls. As call volume increases, so do wait times. Chatbots help these organizations provide information in a timely manner, automating the most repetitive queries and routing only the most critical ones to human agents.

“The beauty of chatbots is that they are so flexible and easy to implement that you can respond to any crisis in a matter of hours, not weeks. This is something other communication tools simply can’t do,” said BotSupply cofounder Francesco Stasi. “We are happy to offer these resources free while many are in need.”

To get started, contact Francesco at francesco@botsupply.ai

Mapping services for governments, healthcare, startups

TravelTime

TravelTime’s platform processes maps and data from across the globe and delivers optimized travel time mapping, so you know what’s reachable in minutes, not miles.

Today, TravelTime is offering its data and mapping services to governments, charities, health services, and NGOs for free. The startup is also covering mapping and data costs for other startups and small businesses.

“Although the current situation is disrupting our personal lives, our technology remains as solid and stable as always and so it is business as (un)usual for us,” said TravelTime cofounder Charlie Davies. “There is no time limit on this, there is no contract, there is no assumption for future use. We want to repurpose our data and services to help. Lots of people have helped us along our way, now it’s our turn to try and do the same for others.”

Any government, charity, health service, or NGO that is actively helping to address the crisis can get unlimited free access to data to help them plan their responses, including:

• Arranging visits to vulnerable patients

• Mapping the right locations for testing centers

• Communicating to the public which test centers are right for them

Small businesses and startups can also take advantage of these services. Access the request form here.

With virtual-AI platform, HR recruiting keeps pace

Jobecam

Brazilian-based Jobecam is offering free access to its virtual recruitment platform so human resource teams can continue recruiting. Jobecam is a 100% digital recruiting experience that brings agility, accessibility, and diversity through AI-driven video technology. A pioneer in video blind interviews, Jobecam’s solution improves the recruiting experience and makes it virtual in a time when face-to-face meetings aren’t possible.

“In this moment of uncertainty and social isolation, we all need to come together and help,” said Jobecam COO Thereza Bukow. “By making our solution free, we enable businesses to be more agile in their recruitment process and deliver a better experience that is secure and modern.”

Jobecam’s solution offers:

• Registration of unlimited job posts

• Automatic screening of candidates

• Recorded video interviews

• AI-based intelligent rankings

• Live interview room, cultural matching, and video curriculum

Contact Jobecam by emailing cammila@jobecam.com or thereza.bukow@jobecam.com.

Real-time employee feedback that’s simple and meaningful

Holler Live

Dutch startup Holler Live is offering their real-time feedback solution free to human resource managers, so employees can provide their opinions and feedback on various topics, including how they are adapting during this time.

“Employees across the world are working from home—many for the first time. Holler provides an easy way for employees to voice their opinions and feedback—allowing human resource managers to better understand how staff are handling the changes and challenges of remote working during this difficult time,” said CEO Rado Raykov.

With one swipe, Holler Live allows people to express their opinion in an easy and universally understandable way. Holler Live partners get specific and user-permissioned alerts, permitting them to promptly respond in real-time to the opinions of their target audience, whether it’s employees, customers, or other stakeholders.

To access Holler Live’s free solution, please email rado@holler.live or sign up here. Watch a video of the mobile employee engagement solution.

Keeping media rolling with AI-powered content tools

aiconix

German startup aiconix is offering its multilingual transcription and subtitling solutions for free and discounted rates. An AI-powered media and content creation platform, the technology enables media and entertainment professionals to produce better content more efficiently by automating routine workflows and creating new content from large amounts of unstructured audio-visual data.

“In these days, where everybody communicates online, it should be essential to reach also those who need barrier-free access, and provide searchability in audio and video files,” said CEO and cofounder Eugen L. Gross. “We want to provide our live transcription and live subtitling feature for free for the next three months to those who need it like hospitals, authorities and NGOs.”

From press conferences to media content, aiconix’s transcription and subtitle services can plug into any data stream in multiple languages allowing organizations to quickly repurpose and disseminate valuable content. The platform enables automated subtitling of videos, semantic text analysis, transcription of audio, automated recognition of faces and local celebrities, label detection, and much more.

Contact aiconix to access your discount and get started: Live@aiconix.ai or contact form.

Startups are also reducing operating costs by taking advantage of free and discounted cloud with Oracle for Startups. Learn more and join them at oracle.com/startup

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