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Fixing terrible forecasts and the lack of context

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

 

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

 

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

 

Digital Oil and Gas Description

 

 

Jul 22, 2020

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

 

Show Notes

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

 

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

 

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

 

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

 

TN: Thank you very much.

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

TN: That’s the budgeting process.

 

GC: I’ve been in that process. Right.

 

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

 

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

 

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

 

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

 

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

 

GC: OK. It’s all done by machine?

 

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

 

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

 

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

 

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

 

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

 

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

 

TN: And then that’s the error, right?

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

TN: Yeah, yeah. Absolutely.

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

TN: Yeah, yeah. Yeah. Totally fantastic.

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

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News Articles Uncategorized

Transforming Capital Projects Using Digital

Complete Intelligence is mentioned in this article by digital innovation expert Geoffrey Cann. You can find the first and original version of this at https://geoffreycann.com/transforming-capital-projects-using-digital/. We thank Geoffrey for including us in this valuable piece that helps oil and gas companies in modernizing their operations and technologies. 

 

The oil and gas industry spends hundreds of billions each year on new capital projects. An effort by a group of international producers should eventually improve the efficiency of that spend.

 

DIGITAL CAPITAL

 

I was contacted recently by a trade association representing about 40% of the global production of oil and gas to discuss the role of digital innovation in upstream capital. Their brief states that while most oil and gas companies have programs in place to progress their internal digitalization agenda, some initiatives need to be tackled at the industry level to unlock value at scale. An example of an efficiency opportunity with industry-level appeal is the digitalization of the supply chain.

While their aim is to focus initially on capital projects, it’s probably safe to assume that the initiative will move to other areas of interest in time.

This post summarizes the survey that I submitted in response to the survey.

 

Question 1 — Scope of Digitalization

 

What are the key areas that you think of as being part of a Digitalization agenda?

 

RESPONSE TO SCOPE OF DIGITALIZATION

 

Rather than listing off a random set of possible digital technologies to frame the scope of digital, I set out the key elements of my digital framework which also incorporates infrastructure and work processes areas as integral to a digital game plan.

 

Question 2 — Business Impacts

 

How do you see Digitalization impacting Major Projects in the Oil and Gas Industry? What are your thoughts on the impact on key Capital Project areas?

 

RESPONSE TO BUSINESS IMPACTS

 

Oil and gas capital projects have slipped backwards in terms of productivity gains while most other industry sectors have advanced. At the LNG18 event in Perth in 2016, Shell presented their analysis which shows oil and gas capital has declined in productivity by 25% over the preceding decade whereas most other sectors had gained. The upside for capital is to capture this loss of productivity, and to catch up with other sectors (leading to an outsized gain potential).

 

Oil and gas spends hundreds of billions per year in capital. The IEA estimates that oil and gas stands to gain a minimum of 20% productivity improvement and 20% cost reduction through digital. The opportunity is in the range of $100B in cost savings, and $100B in capital avoidance. Substantial carbon emissions stand to be avoided. Every aspect of the capital cycle is able to leverage digital tools to capture these savings.

 

I contributed to a confidential government study in Australia that set out to understand how the competitiveness of their LNG sector could be improved. The modelling showed that a 25% reduction in schedule (from 4 years to 3, for example), would reduce the break even cost of a typical project by $1 per million British thermal units (MMBTU) for 20 years. To give a sense as to what this means, a 9 million ton LNG plant ships 441 trillion BTU per year. Do the math.

 

CAPITAL STRATEGY

New securitisation technologies (distributed ledger) could be used to transform capital access, and create a new capital asset class. New government crypto currencies (China, EU) may allow for capital market access that avoids US banking system and related sanctions abilities.

 

RISK ANALYSIS

Advanced ML tools can provide much better predictability to underlying volatile commodity assets (currencies, carbon, hydrocarbons, cement, steel, etc). See company Complete Intelligence. Better predictability to commodity risk can lower project capital costs and improve purchasing strategy.

 

SCHEDULING AND PROJECT CONTROLS

The industry routinely produces digital twins of operating assets, but how about creating a digital twin model of the schedule? Another possibility is the use of game tools to create the “game” equivalent of a capital project (see Real Serious Games), used for schedule tuning and post build auditing. Cloud computing can help create deeper virtual environments that span entire supply chains, not just one link at a time, so that schedule and carbon impacts can be visible.

 

ENGINEERING

It’s practically here, but the use of robotic tools to automate routine engineering work is still nascent. Data visualization tools can assist with engineering reviews (see Vizworx) across disciplines and suppliers, provided data is normalized. Open data standards can enable industry cooperation (see OSDU). Deeper virtualisation of teams working across time and location boundaries is enabled by cloud computing, digital twin tools, collaboration systems (zoom, slack). Finally, blockchain tools can be used to capture document versions, protect IP.

 

CONTRACTING

Some companies already use AI to read/interpret contracts, flag areas for review. Bot technology can then conduct alerts, notifications, payments using blockchain interface (smart contracts).

 

PROCUREMENT

The industry can leverage entirely new supply models for common procurement (see The IronHub). Blockchain technology can be used to track carbon content and asset provenance throughout the supply chain during sourcing, fabrication, and mobilization.

 

ON-SITE EXECUTION

There are already examples of robots being used on project sites to facilitate work execution—drones for visual inspections in both aerial and subsea applications. Advanced measurement tools are starting to close the gap between engineering and fabrication (see Glove Systems), which is handy when fabrication is modularised and distributed to multiple global shops. Leading companies create the digital twin of civil site works (see Veerum), allowing for continuous monitoring of site performance, and analytic tools to improve execution, reduce carbon. Safety analytics can identify and predict emerging safety hazards.

 

DIGITAL COLLABORATION

Large projects will leverage cloud computing to enable single source of truth about capital projects.

 

WORKFORCE MANAGEMENT

With most workers now carrying one or two supercomputers on their person, industry can now bring valuable data directly to the worker. Two-way collaboration using cameras and audio can connect workers to supervisors, sites to suppliers, builders to engineers. Game tools can be deployed to show individual performance (safety, time on tools) compared to team, shops, fabricators, best teams, best practice (See EZOPS).

 

MATERIAL MANAGEMENT

Blockchain technology is already in use in supply chains to provide for track and trace of materials in support of warranties, product specifications, certifications (see Finboot) to tighten compliance.

 

Question 3 — Longer Term Impact

 

How do you see Digitalization impacting the overall Oil and Gas Industry over the next 10 years?

 

RESPONSE TO LONGER TERM IMPACT

 

In my book, I set out the substantial headwinds to the oil and gas industry (decarbonization efforts, capital constraints, talent shortfalls, environmental activism, competitive alternatives for transportation). Digital innovations are the only known solution that addresses these cost, productivity and carbon concerns simultaneously.

 

Technology companies supplying the industry are already rapidly adopting digital tools to stay competitive. Brownfield assets are going to slowly adopt digital tools because of operating constraints (short outage windows to make change, management of change process). Capital projects have the opportunity to drive change precisely because they are greenfield, and specifically the short duration capital cycles in unconventional areas.

 

Over the next ten years I expect to see some oil and gas companies distinguishing themselves with new business models that are digitally led. With its substantial spend, oil and gas companies could become one of the leading advanced digital technology industries globally.

 

Question 4 — Key Drivers for Digital

 

What do you see as the key drivers and value areas behind a Digitalization program?

 

RESPONSE TO KEY DRIVERS FOR DIGITAL

 

There are many drivers for digital innovation, but here are four that are at an industry level.

 

TALENT.

The industry is at risk of becoming unattractive to talent (the Greta Thunberg effect). People in oil and gas are falling behind in companies that are falling behind in an industry that is falling behind. Digital tools can make junior resources as productive has highly experienced, as well as make the industry more “high tech” and attractive as an employer.

 

CAPITAL MARKET ACCESS.

Capital markets are shut off to much oil and gas investment. The top 7 largest companies by market cap are all digital (Amazon, Facebook, Alphabet, Apple, Microsoft, Tencent, AliBaba). Oil and gas has shrunk from 15% of NYSE to less than 5%. Apple alone is now larger than the combined oil and gas majors. Capital markets need to hear a thoughtful strategy about how the industry is embracing digital innovations.

 

CARBON MITIGATION.

The EU Green deal is driving carbon neutrality targets for oil and gas (see BP, Shell, Repsol). Oil companies and their supply chains will be unable to access markets without thoughtful carbon gameplan (track, measure, monitor).

 

COST AND PRODUCTIVITY.

Oil and gas spends hundreds of billions per year in capital. The IEA estimates that oil and gas stands to gain a minimum of 20% productivity improvement and 20% cost reduction through digital. The opportunity is in the range of $100B in cost savings, and $100B in capital avoidance. Substantial carbon emissions stand to be avoided. Every aspect of the capital cycle is able to leverage digital tools to capture these savings.

 

Question 5 — Biggest Challenge

 

What is the biggest challenge at implementing a Digitalization strategy?

 

RESPONSE TO BIGGEST CHALLENGE

 

As I see it, digital is not a ‘technology’ opportunity. It is a culture change opportunity. Oil and gas tends to view digital as something to purchase (buy and do digital), rather than as a lever to drive behaviour change (to be digital). Oil and gas companies underinvest in the necessary change management actions to create the conditions for digital success.

There is an inadequate amount of training on the digital basics for the front line workers who need to embrace this unknown technology. A reliance on engineering water fall methods of work instead of agile methods undermines the speed by which digital change can take place. By underinvesting in the user experience side of change, and placing the asset at the center of digital efforts, the industry increases the resistance to technology.

 

Question 6 — Foundational Capabilities

 

What foundational capabilities do you feel need to be in place for O&G companies to fully exploit Digitalization?

 

RESPONSE TO FOUNDATIONAL CAPABILITIES

 

I cover much of this in my book. For example, IT and OT need to be merged into a single organization. Systems need to be cloud enabled as much as possible. Enterprise solutions (SAP, Maximo) need to be upgraded to their digital versions (so that they do not block other digital efforts). An experimentation capacity to run digital trials must be in place. Funding for digital investments must be in place. Clear expectations for achieving desired outcomes (cost, productivity), must be expressed. Methods for doing work must follow agile principles. Better connections to the digital start up ecosystem should be in place.

 

Question 7 —Investment Candidates

 

Have you seen any Digitalization initiatives that should be carried out collectively or would be more effective if adopted in a common way across the industry (including the supply chain)?

 

RESPONSE TO INVESTMENT CANDIDATES

 

OSDU is a powerful illustration for enabling sub surface data management and exchange to accelerate the adoption of digital in the upstream. Something like this for capital projects would be valuable. The OOC is demonstrating the power of community of collaboration to drive blockchain-enabled initiatives forward.

 

CLOSING THOUGHTS

 

Building assets that last 20 years or more is just the first step in their lifecycle. Digital efforts in Capital Projects should enable must faster and more graceful commissioning and handover. For example, CSA Z662 and PHMSA 192 set out the new materials tracing for linear infrastructure (tubular, pumps, fittings, flanges) which can only be achieved by deploying digital in the capital project. Poor quality data about installed infrastructure destroys up to 40% of value in a transaction (and that data is largely generated and collected during capital spend).

 

The sooner the industry tackle capital project efficiency the better.

Categories
QuickHit Visual (Videos)

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

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.

 

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: 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.