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Supply Chain Innovation, Transformation, and Sustainability

How can leaders and finance teams enable business growth, innovation, and resilience through supply chain management (SCM) and digital transformation? And, how does sustainability affect supply chains? To answer these questions, we spoke with Jon Chorley, Chief Sustainability Officer and Group Vice President of Oracle, and Tony Nash, CEO & Founder of Complete Intelligence.

 

This video interview first appeared and originally published at https://www.cxotalk.com/video/supply-chain-innovation-transformation-sustainability on April 17, 2021.

 

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The conversation includes these topics:

 

Jon Chorley is group vice president of product strategy for Oracle’s supply chain management (SCM) applications and leads the team responsible for driving the business requirements and product roadmaps for these applications. Chorley is also the chief sustainability officer for Oracle.

 

Tony Nash is the CEO and Founder of Complete Intelligence. Previously, Tony built and led the global research business for The Economist and the Asia consulting business for IHS (now IHS Markit).

 

 

Show Notes

 

Michael Krigsman: We’re discussing supply chain innovation and transformation and sustainability with Jon Chorley of Oracle and Tony Nash of Complete Intelligence. Jon, tell us about your role at Oracle.

 

Jon Chorley: I run the supply chain management strategy group at Oracle, responsible for our overall investment priorities and directions for our supply chain solutions. I also have the chief sustainability officer role at Oracle where I help coordinate all of our sustainability policies and practices for the Oracle Corporation and help drive some of those ideas and thoughts into the products and services we deliver to the market.

 

Michael Krigsman: Tony Nash, tell us about the focus of your work.

 

Tony Nash: Complete Intelligence, we’re a globally integrated and fully automated artificial intelligence platform for cost and revenue proactive planning. We do forecasting for enterprises and markets in areas like continuous cost budgeting, continuous revenue budgeting, automation of certain, say, forecasting tasks. We also offer agile budgeting and forecasting.

 

We measure our error rates, so that’s important that someone is planning, especially around supply chain. We’re trying to help people reduce the risks around their future costs.

 

Supply chains are very complex: time, cost, quality, all sorts of considerations. Our focus is on the cost element of it, and there are many other things and why we’re working with Oracle. They have so many other things to bring to the table that try to complement them on that side.

 

Michael Krigsman: You met Jon through the Oracle startup program. Just briefly tell us about that.

 

Tony Nash: Oracle for Startups program is a fantastic way for early-stage companies to integrate with the Oracle ecosystem. There is the Oracle technology product side of it, but there is also meeting people like Jon, meeting people like his colleagues, and the Oracle marketing team, Salesforce, and product teams. Amazing opportunities to understand how an organization like Oracle works and how a company like Complete Intelligence can come alongside them and enhance Oracle’s end customer experience for the better.

 

 

How did supply chains function during the disruptions of 2020?

 

Michael Krigsman: Jon, during the last year, supply chain became a household topic for pretty much everyone.

 

Jon Chorley: Yes.

 

Michael Krigsman: What did the last year tell us about the nature and the reality of supply chains?

 

Jon Chorley: Well, that they’re central to everything that makes the modern world. When you see an empty shelf and realize it’s an issue with the supply chain. Or you see a run on a product as some shortage or some challenge in some way. People now understand that the complicated infrastructure that brings those products to them is the supply chain.

 

As we’ve gotten into the more recent months where we’re looking at the vaccine distribution, people understand that yes, it’s a technical problem to produce the vaccine, but it’s also a supply chain problem to get it in people’s arms.

 

All of those things, I think, have helped take the supply chain from the back office, from the folks like Tony and I who work in it day-to-day, into the board room, which I think is very important. But also into the dining room. People now understand the importance and centrality to efficient supply chains.

 

Michael Krigsman: Jon, give us some insight into the kinds of weaknesses that this last year exposed in how we handle supply chains.

 

Jon Chorley: I think that there are a couple of areas there that I’d point out. One is we had a very uncharacteristic demand shock. There was a real change in short-term demand.

 

Some of that was upside. A lot of charcoal sold to power the grill. A lot of toilet paper.

 

Some of it was downside. Restaurants challenged, hospitality, and so on.

 

Those demand shocks forced people to look at different ways to look at their traditional forecasts. That is supportable by the kind of technology Tony and I can help deliver, but it does require people to look carefully at how they’re forecasting their demands. That’s one angle.

 

Another angle, I would say, is the overall concern about resiliency. A lot of folks looked at ways of single sourcing, for example. Maybe relying on goods out of Western China, for example.

 

All of those things had a lot of challenges, and that forced people to look at, was the single-sourcing strategy driven by cost only the right answer? Did they need to look at A) maybe simplifying their product lines a little bit, so they had more flexibility, and B) looking at alternate sources of supply? I think resiliency came a lot more to the fore.

 

Tony Nash: We’ve had even companies like semiconductor companies (who have been based in Asia) start to build facilities in the U.S. so that they can regionalize some of those supply chains and de-risk the downturn impacts of future shocks like this. Electronics manufacturers, other people who are assembling goods, or even some primary goods, are regionalizing their supply chains so that they don’t see huge impacts or any future issues like COVID or other shocks.

 

There’s at least a little bit of a buffer by region, which saves. It’s greener in terms of saving on the sea freight fuel and that sort of thing, but it also helps cushion any shocks on the supply side so consumers can get what they need when they need it.

 

 

Challenges associated with overseas manufacturing operations

 

Michael Krigsman: Jon, I’ve heard you talk in the past about the inherent challenge of manufacturing goods overseas (in China, for example) and the timeliness of getting them here in the U.S.

 

Jon Chorley: It has a lot of advantages in terms of costs, scale, and so on. But it does bake into your supply chain a certain fixed amount of time. That is fine if you have predictable demand. But if you have variable demand, it becomes a lot trickier to manage.

 

The same is true really of the innovation cycles. The speed with which you may want to innovate can be constrained by working those things from points of consumption (let’s say Europe, North America) and points of production (let’s say the East, China, Vietnam, and so on). Those are factors folks are considering.

 

I think, in some areas, certainly advances in things like automation and technologies like 3D printing, rapid prototyping, those things are changing the equation a little bit in terms of what constitutes the most cost-effective or the most efficient, or the most responsive approach to manufacturing. I think you’re going to see those factors gradually have more and more of a play as people develop new ways to balance those equations.

 

Tony Nash: Michael, that’s interesting because, as we look at how the history of supply chains have evolved from keeping POs on 3×5 notecards 30 years ago to the digitization of that, it started with EDI (electronic data interchange) from, say, the ocean lines and the airfreight firms so that you knew where your package was, all the way down to today where you have everything kept, let’s say, in a bill of material within an ERP system or a supply chain system.

 

What people have been doing for the past few years is really bill of material versioning, where you’re running scenarios on the same product configuration, of bill of materials for multiple locations, to understand where they should make a certain good. Those considerations are allowing people flexibility. They can make the time and cost tradeoffs to look at when they can have goods in a market, whether it’s seasonality or whether it’s some disruption or whether it’s some demand pop for some reason people may not know. Allowing people to run multiple bills of material or versions of bills of material allows them the flexibility to identify what they should produce where and what it should be made of.

 

Michael Krigsman: It sounds like this is a data and analytics problem.

 

Tony Nash: It is, and the way things have been done typically is, as a manufacturer, you sign a longer-term agreement for your raw materials with a vendor. They provide that for you to a certain point. You make it in factory A somewhere and then ship it out. Of course, there is not necessarily a single factory for any large company, but it’s a well-worn path.

 

We’ve had an atomization of that with mini manufacturing, or regional manufacturing, flexible manufacturing, so people can have localized versions or, like I said, seasonality. These sorts of things. Manufacturing and finance teams can only make those types of decisions with data and with automation. It’s a simpler way on how to make better business decisions.

 

 

Digital tansformation and sustainability in supply chain

 

Michael Krigsman: You need clarity around the goals and the strategy. You need the right kinds of data. Then you need the cultural willingness to innovate and do things differently. Is that an accurate way of summarizing?

 

Jon Chorley: I agree. I think you need to have some idea of where you’re going. Although, that probably is going to change. But you need to have that idea. You need to have the information, as Tony has discussed, that helps you navigate that path.

 

Then you need to be able to course-correct because we live in the real world, and nothing quite goes the way you expect it to. You need to be able to constantly course-correct.

 

Like I say, if you have a great set of headlights, you can see what’s coming. You’re coming to a cliff. If you have no brakes and no steering wheel, it’s a huge problem you’d rather not know.

 

The ability to course correct is like having brakes and a steering wheel. You need to be able to make those adjustments as things change around you. That means flexible systems, flexible processes, a willingness to look at new ways of doing things, cultural changes. All of those things become important.

 

Michael Krigsman: Tony, I have to imagine you spend a lot of time thinking about the sources of data as well as the machine learning models and other types of models that you create.

 

Tony Nash: I get excited about things like data governance, but most people don’t. I get excited about it because I understand that it helps to have much better forecasting applications and tools to make those decisions.

 

Yes, we’re thinking about the granularity, the frequency, the level of detail people have. Are they using the data that they have to make decisions today because it’s not just, let’s say, a cultural change of let’s rely on automation of things like forward-looking views or forecasting or proactive planning? It could also be a cultural change: are we looking at our data to make our decisions? How much of our data are we looking at? Are we looking at maybe the error rates of the way we plan? Are we looking back on that from time to time?

 

Although that may seem mundane and small, it’s actually very big for things like digital transformation because you have to take inventory of what you’re doing today so you can plan where you’re going tomorrow. As Jon said, it’s never going to go exactly to plan – never. I wish it would, but it never does. You have to understand yourself well today so that you can identify what’s possible.

 

Michael Krigsman: Jon, we’ve been talking about the complexities of supply chain. Let’s shift gears slightly and talk about the complexities of sustainability. How does sustainability intersect supply chain?

 

Jon Chorley: Most people would agree that supply chains are about making and moving physical goods around the world. That is a huge part of what’s impacting the environment. It’s a huge impact on sustainability.

 

The way we design those supply chains, historically, has been what I would call a linear supply chain. Which is we make a product, we sell a product, we forget the product. We then make another product, sell that product, and forget that product. It’s a fire and forget mentality, if you like – to some degree.

 

If we want to be sustainable, we need to think about the full lifecycle of those products and how they get recycled back into the forward supply chain. As we progress into the future and start thinking about these things more — and we’re required to by the markets, by regulations (potentially), and by what constitutes good business — we will increasingly move towards adjusting our supply chains to be more circular. That is, looking at the full lifecycle of the product.

 

That begins with how you design it. That’s going to be a fundamental change in the way we think about all supply chains.

 

Advice on supply chain transformation for business leaders

 

Michael Krigsman: As we finish up, Tony, can you offer advice to business leaders and finance teams who are listening to this who say, “Yes, we want to change, transform our supply chain, but where do we even begin? It’s such a daunting challenge.”

 

Tony Nash: I would say, really start with the easy stuff. Get some successes. Do a pilot. Then you can accelerate it very quickly.

 

Data scales very quickly. Technology scales very quickly. But your team may be uncomfortable with digital transformation, especially around supply chains. Help them see some quick wins and then push forward as quickly as possible after that.

 

Michael Krigsman: Jon, you discussed earlier the cultural dimensions of supply chain transformation. It’s really important, so just share some further thoughts on that and advice that you have for folks who are listening.

 

Jon Chorley: I think any change is at least as much cultural as it is technological, and the people who implement those changes are key to its success. I think part of what’s needed is a willingness to understand that the way you did things in the past may not be the way you need to do things in the future.

 

Quite often companies, for example, feel that they have a certain special way of doing a process that’s absolutely required, and they hold onto that even though there is really no business differentiation for them to do it that way. They’ll invest a lot of time and energy to duplicate that on a new platform.

 

We always encourage people to step back a little bit and leave behind some of those preconceptions. Not everything is your secret sauce. Your secret sauce is a little bit on the top. It’s not stuff on the bottom.

 

Leave behind those preconceptions. I think that’s probably the single biggest cultural shift.

 

Then the other point we mentioned earlier is board support. I think that’s top-down. Having that support from the upper levels of the business is critical to any large-scale transformation.

 

I think the great thing, if there is a great thing from 2020, is that boards are aware now of the criticality of supply chains in their business and are probably more open to those kinds of conversations. Those difficult conversations from supply chain professionals with their board. Now is the time. The folks that make the investments now are the folks who are going to benefit from the uptick that we all hope is coming.

 

Michael Krigsman: Jon Chorley and Tony Nash, thank you both for sharing your expertise with us today.

 

Jon Chorley: All right.

 

Tony Nash: Thanks, Michael.

 

Jon Chorley: Thank you so much. Great talking with you all.

 

Tony Nash: Thank you.

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News Articles

How to Make Cloud Pricing More Transparent

This article on “How to Make Cloud Pricing More Transparent” is originally published at https://www.eweek.com/cloud/how-to-make-cloud-pricing-more-transparent

 

eWEEK CLOUD PERSPECTIVE: It used to be nearly impossible to compare cloud costs because different providers typically have their own nomenclature for cloud features, define services differently and offer different tiers of services that don’t line up with one another. Forget apple-to-apple comparisons, cloud price bake-offs were more like contrasting apples to peach cobblers. But help is here.

 

Cloud has inspired almost as much evangelical fervor as open source computing, particularly in the heady 2000s. The advent of cloud computing seemed to render traditional enterprise software vendors as out-of-date as telegraph operators. The monolithic process of releasing software every 18 months wasn’t fast enough for business, running your own servers became as fashionable as generating your own electricity, and the expense involved restricted technology access to the wealthiest businesses.

 

Cloud computing represented a true democratization of enterprise IT, allowing small companies to compete with bigger rivals without breaking the bank to buy servers, storage and software. Tens of millions of dollars for the right to walk onto the playing field were no longer required.

 

The other promise of cloud computing was of a more transparent and equitable business model.

 

In one of my first interviews as an IT reporter, in 2003, I asked the chief technology officer of a large health IT organization to define enterprise software. “It’s when they can’t tell you the price of the software upfront,” he said.

 

Sure, this lack of transparency reflected the complexity of the forecasting applications on offer, but also showed that the dominant sales model gave more power to vendors than customers.

 

The emergence of profitable cloud-native businesses both threatened existing business models and inspired business transformation. The agility and innovation made possible by cloud computing inspired many businesses to move their IT stacks from their own server rooms or data centers to the cloud.

 

 

The law of universal gravitation as applied to the cloud

 

By 2020, however, the low-hanging fruit has been picked. Businesses have reaped the benefits of relatively lower costs and more frequent innovation. And with the lion’s share of IT spending at most companies moving into the cloud, cost – and cost transparency – matters. Yet, the transparency promised by the cloud revolution has largely failed to materialize.

 

As was the case with the previous generation of technology, obfuscation isn’t a bug, it’s a feature, and it begins with Newton’s Law of Universal Gravitation. Pricing structures at legacy cloud providers punish moving data from one cloud to another. By intentionally making the cost of putting data into their clouds as low as possible, while making it prohibitively expensive to move data out to interact with systems in different clouds—a concept known as data gravity—they are walling in their customers.  This is an explicit strategy to make their clouds “sticky” and keep forecasting applications from moving to other clouds.

 

But the reality is that businesses want and need to operate in different cloud environments for many reasons. Not to mention, who wouldn’t want to cut 10, 30, or even 80 percent of cloud costs if possible?

 

 

 

Newton’s law of motion applied to the cloud

 

It used to be nearly impossible to compare cloud costs because different providers typically have their own nomenclature for cloud features, define services differently and offer different tiers of services that don’t line up with one another. Forget apple-to-apple comparisons, cloud price bake-offs were more like contrasting apples to peach cobblers.

 

There is help available. For one example, Oracle Cloud Workload Cost Estimator is a new tool now available for obtaining empirical cost information. It lets customers assess comparative costs of Oracle Cloud Infrastructure and Amazon Web Services in as close to a real apples-to-apples comparison as possible.

 

The calculator prices not only computing and storage costs, but that of IOPS (data input/output per second), and data transmission out of the cloud as well. That last factor, also known as data egress, is usually a wild card because traditional cloud companies start charging a markup after a given amount of data flows out. So once you hit a monthly target—1GB for AWS, according to the cost estimator—data egress charges kick in. At Oracle the meter doesn’t start until after 10,000 times more data egress—or 10 TB—per month.

 

IT leaders can enter the parameters of proposed workloads and then run their own OCI vs. AWS comparisons. In the end, they may discover that one cloud provider offers services that are closer to Newton’s third law (that for every action in nature, there is an equal and opposite reaction) than to his first

 

 

 

A few examples

 

Cost and performance go hand in hand, especially as software-as-a-service providers rely on third parties to serve their software to customers. Data technology firm Complete Intelligence, for instance, provides real-time risk management and forecasting services for its customers. It needs to know how much it will spend providing that service on an ongoing basis, and also be sure that its customers get the responsive service their businesses need.

 

“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,” said Tony Nash, CEO of the Houston-based company, which picked Oracle Cloud Infrastructure.

 

Another example of how modern businesses use the cloud is data integration provider Naveego. The company helps customers parse data from a myriad of sources. It cleans the data, deletes duplicates, provides a trail of sources, and then provides a clean golden record of data that is ready for analytics in real time.

 

“To do that, we run instances of our product in multiple availability zones. AWS charges for communications back and forth between those availability zones. Oracle doesn’t, and the cost difference ended up being huge for us. So, we decided to move our research and development, and some production, cloud tenancies to Oracle Cloud,” wrote Naveego CEO Katie Horvath in a blog post.

 

The company saved 60 percent on its costs since moving to the Oracle cloud, while being able to do more research and development. “Oracle’s claims that Oracle Cloud Infrastructure is 65 percent more cost effective on computers have also proven to be true for Naveego,” she says.

 

We’re starting a new decade on an awkward footing, and businesses need technology to help make smarter decisions. They may still want to fail fast, but they will also want to know what went wrong fast, what the fast road looks like to the promised land – and at long last, what it costs to get there. They’ve long known the cost of sending a telegram, and they can finally figure out the cost of using the cloud.

 

Michael Hickins is a former eWEEK and Wall Street Journal editor and reporter.

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Houston startup uses artificial intelligence to bring its clients better business forecasting calculations

This article is originally published at https://houston.innovationmap.com/houston-based-complete-intelligence-changing-the-business-forecasting-game-2643180609.html

 

The business applications of artificial intelligence are boundless. Tony Nash realized AI’s potential in an underserved niche.

 

His startup, Complete Intelligence, uses AI to help on how to make better business decisions, which looks at the data and behavior of costs and prices within a global ecosystem in a global environment to help top-tier companies make better business decisions.

 

“The problem that were solving is companies don’t predict their costs and revenues very well,” says Nash, the CEO and founder of Complete Intelligence. “There are really high error rates in company costs and revenue forecasts and so what we’ve done is built a globally integrated artificial intelligence platform that can help people predict their costs and their revenues with a very low error rate.”

 

Founded in 2015, Complete Intelligence is an AI platform that forecasts assets and allows evaluation of currencies, commodities, equity indices and economics. The Woodlands-based company also does advanced procurement and revenue for corporate clients.

 

“We’ve spent a couple years building this,” says Nash. “We have a platform that is helping clients with planning, finance, procurement and sales and a host of other things. We are forecasting equity markets; we are forecasting commodity prices, currencies, economics and trades. We built a model of the global economy and transactions across the global economy, so it’s a very large, very detailed artificial intelligence platform.”

 

That platform, CI Futures, has streamlined comprehensive price forecasting and data analysis, allowing for sound, data-based decisions.

 

“Our products are pretty simple,” says Nash. “We have our basic off the shelf forecasting application which is called CI Futures, which is currencies, commodities, equities and economics and trade. Its basic raw data forecasts. We distribute that raw data on our website and other data distribution websites. We also have a product called Cost Flow, which is our procurement forecasting engine, where we build a material level forecasting for clients.

 

“Then we have a product that we’ll launch next year called Revenue Flow, which is a sales forecasting tool that will use balance of both client data and publicly available data to forecast client sales by product, by geography and so on and so forth. So we really only do three things: revenues, costs and raw data forecasts.”

 

Forecasting across industries

Complete Intelligence’s Cost Flow and Revenue Flow products are specific to direct clients. They are working with clients in the food and beverage sector, the energy sector, the chemical sector, and the technology sector.

 

“Anybody that manufactures a tangible good, should use our product,” says Nash. “Because we can take their historical data we can configure their bills of material and they can see the exact cost and exact revenue of those products by month over time.”

 

CI is not a consulting firm, so they offer their clients an annual license, which allows them to receive updated forecasts every month to understand how markets will iterate over time.

 

“We’re integrating with the client’s enterprise data,” says Nash. “Whether it’s their ERP system or their procurement system or their CRM, we’re integrating with client’s enterprise data, and we’re creating forecast outlooks that are perfectly contextually relevant for client buying decisions.”

 

Called out by Capital Factory

 

As a business solution, CI has garnered widespread industry confidence and accolades, such as Capital Factory’s coveted “Newcomer of the Year” award, which recognizes innovative companies from a pool of 110 startups in Texas.

 

“Honestly, I couldn’t believe it because with a startup like ours, there’s so much hard work that goes into it, there’s so much time, there’s so much persistence,” says Nash.

 

“And the types of startups that Capital Factory attracts are very competitive startups, so for us to receive this award, it’s given us a huge amount of credibility in the market and it’s really encouraged the team inside the company to understand that what we’re doing is being recognized, it’s meaningful and we’re really going places.”

 

From consulting to billions of monthly calculations

 

Nash is no stranger to going places. Before setting up shop in his native Texas, he lived in Singapore for 15 years where he started his career in sourcing and procurement for American retail firms.

 

“I became very sensitive to costs, cost inflections and I got very involved in global sourcing and international trade and then I did a couple of corporate turnarounds and start ups and so with that you see costs as an issue with those types of firms,” Nash says.

 

He then worked with the Economist running their global research business. There, he grew familiar with how clients and customers use data. At IHS Markit, a global information provider.

 

“When I was working with those firms, those firms helped companies with planning,” says Nash. “The problem is that those firms have very large errors in their forecasts. It is not just the internal forecasts that have a 30 percent or higher error rate in their forecasts, even the industry forecasters typically have around a 20 percent error rates in their forecasts.

 

“Even the people who should actually know where prices are going are not very good forecasters. With Complete Intelligence, we wanted to use data and use artificial intelligence to machine learning to create a better way to identify where costs and revenues will go for companies.”

 

Every month, CI runs billions of calculations. They test their error rates and record them for clients that request them. With 700 assets that they show publicly, CI their average error rate is 3.7 percent, which is dramatically lower than both corporate procurement professionals and industry experts.

 

“With us doing billions of calculations, it allows us to run simulations and scenarios that your average analyst just can’t do and most companies haven’t even thought of. We’re able to run a comprehensive view of activities in the world to understand how things directly and indirectly affect a cost. In Houston, for example, that could be crude oil or natural gas or something like that.”

 

Proving its value

Last year, the company tested its platform with a natural gas trader. After reviewing the data, CI revealed to the client that natural gas would fall by 40 percent over the next year.

 

“They looked at our forecast and said they couldn’t work with us because it didn’t make sense,” says Nash. “A 40 percent fall didn’t make sense, so they didn’t subscribe to us. That was 2018. What has happened over the past 12 months? Natural gas prices had fallen by 49 percent. You would look at our forecasts and say, ‘Wow, that’s a dramatic drop over 12 months.’ But reality was even more dramatic than that and there weren’t analysts out there saying what our model was telling us.”

 

That natural gas trading company never admitted its faux pas, but if they had listened to CI, they could have positioned themselves to negotiate their vendors down for their cost base, which helps the margin of their business.

 

“Nobody ever admits mistakes,” says Nash. “But when you think about the numerous materials that require natural gas, especially things that are manufactured in Houston, it affects a lot of costs.”

 

Houston roots — by way of Asia

The missed opportunity with the natural gas trader notwithstanding, Nash is happy that he brought Complete Intelligence to Houston.

 

“I went to Texas A&M and grew up in Texas, so I moved back to Texas knowing how good Americans are with planning, with math and with data. I like Houston because people make stuff in Houston,” Nash says. “We just found Houston to be perfect after spending 15 years in Asia given the global centrality of Houston. The industry’s here and there’s a lot of diversity in Houston.”

 

Nash’s expectation was that he would be able to work with Western multinationals to improve their analytics and their artificial intelligence processes because he has learned that there is a lot of pressure in American financial markets and analysts communities to really know what is happening within companies.

 

“We want companies to be able to really tightly plan their costs so they can better improve their profitability,” says Nash. “That’s what I wanted to do when we moved to the U.S. and we’re finding that there’s a lot of interest from companies.”