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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.

Categories
Visual (Videos)

A Mission-Critical Focus to Enable Growth

This article originally published at https://www.admentus.com/podcast/a-mission-critical-focus-to-enable-growth-with-tony-nash-of-complete-intelligence/ on March 26, 2021.

 

 

Every company wishes they have a crystal ball when it comes to making business decisions, and while a physical iteration of that wish is not possible, Tony Nash has developed the next best thing for his clients at his startup, Complete Intelligence.

 

Tony is the CEO and Founder of Complete Intelligence. Before founding Complete Intelligence, Tony was the global head of research for The Economist and the head of Asia consulting for IHS Markit.

 

Complete Intelligence is a fully automated and globally integrated AI platform for smarter cost and revenue proactive planning. Using advanced AI, they provide highly accurate cost and revenue forecasts fueled by billions of enterprise and public data points.

 

Key Takeaway: As a growing, scaling business, you must know what you are good at, what you do, and what you do not do. Maintain your mission-critical focus on the most important aspects of your business and outsource the parts that you are simply not good at or are outside of your mission.

 

Lessons Learned:

 

• Put Significant Thought into Your Senior Hires – hire low first, then hire the upper levels as they will be the ones that have to share your mission and must be the right hire.

• Know what You Do Not Do – Knowing what you don’t do is just as important as knowing what you do do.

• Define Your Culture – Define the culture you are building and continually and intentionally reinforce it.

 

Show Notes

 

JC: Hello everybody, Jeff Chastain here with the building to scale podcast again, where I get the opportunity really to speak with entrepreneurial business leaders growth-minded leaders who are working to grow and scale their own companies. And some of the we’ll discuss some of the challenges. Some of the successes as they’ve had over the years working through that.

 

Today’s guest with me here is Tony Nash with Complete Intelligence out of the Houston, Texas area. So first off Tony welcome to the show and thank you for taking a few minutes out of your busy day to join us here.

 

TN: Thanks, Jeff. I appreciate the opportunity.

 

JC: So give us a little bit about what Complete Intelligence is and what you guys have got going on there?

 

TN: Sure. We run an artificial intelligence platform. We use it to forecast market activity say currencies, commodities, equities for investors. We also help people companies understand their costs and their revenues which are really important on the budgeting side. So we help people de-risk their future business decisions by understanding where their costs are going to go and where their revenues will likely go.

 

JC: Okay, so I’ve got a background in technology and we kind of talked about AI and stuff beforehand but if we were to bring that down. And say okay I put you on the spot here but it was well the networking questions I’ve heard before like. Okay, if you describe that to a five-year-old what do you really do? So I know we kind of talked beforehand that this is typically big enterprise focus but for those that are not into that industry or not dealing with 9 10 figure dollar budgets, kind of a thing. Proactive budget planning. What does that really mean from a obviously from a company your size or your perspective?

 

TN: Sure, if I have to describe it to a five or ten year old. It’d say look, if you run a lemonade stand you have to understand how much the lemons are going to cost. How much the water is going to cost. How much the sugar is going to cost you. Also want to understand how many customers you’re going to have. How much money they’re going to spend. How much money you’re going to take in through the lemonade stand, right?

 

So we work with customers to understand all of those things. Now when companies themselves forecast this stuff and we know this from talking to our clients. They typically have 30 error rates or worse, even for raw materials costs. So their planning is way off, okay? When you look at industry experts investment banks economists, industry experts, these sorts of things. Their error rates are typically 20% off, okay? Our error rates are typically about around 4.6 percent, okay? And that’s on an absolute percent error basis. So we’re not gaming the pluses and minuses, okay?

 

So if you’re buying those lemons and that sugar and that sort of thing you can pay a dollar 20 for it. For us maybe a dollar five or something like that, right? So we’ll help you save 15 cents a lemon, okay? And you’ll understand where those costs are going. And so when you scale that up to very large customers who have you know 2 billion, 5 billion, 20 billion dollars in turnover or more. They’re buying in tens and hundreds of millions of dollars.

 

So let’s say a 17% improvement in their ability to forecast things, those are very large numbers. And so we’re working with enterprise scale data in the cloud and helping them understand where their business is going. And I would say probably better than just about anybody else out there. And so it doesn’t have to be the biggest company in the world doing this stuff. We work with mid-sized companies as well, okay? Because we’ll take data out of their enterprise planning system or something like that. And we’ll use it on our platform to help them make better decisions. We’re not telling them what to do, we’re just telling them where the data tell us that things are going to go.

 

So the real problem we’re solving aside from the obvious of what’s going to happen in their markets and their costs. Every company has a very painful budgeting process, okay? Some companies it takes a month or two or three months. Some companies some of our customers it takes six or seven months. And they’re going through in a very meticulous way of proactive planning their budgets. And there are hundreds of people involved and at the end of the day it goes up to the CFO and the CPO the chief procurement officer or the CFO and the head of sales and it’s a verbal agreement on what’s actually going to happen. This is actually one of the CFO pain points.

 

Not all that data driven, right? And so what we do is we give them a straw man to base it on so they can a very meticulous and detailed straw man. So that seven month process is taken down to a couple days, okay? From data transmission processing to sending back. And they also get a continuous budgeting exercise, okay? Every month we’ll reforecast their budgets for them so if something like Covid happens as it did last March, April. We help them understand what’s likely to happen uh in their business.

 

JC: Now that makes sense and that’s really one of those things that regardless of the business side that it’s like, okay having actual real data not seven month old data actually having it on a monthly basis or even closer kind of a thing. You can actually make real decisions on it at that point rather than just thinking like you said one code would happen. Everybody had their budget set January, February for what 2020 was going to be. And now two months later they’re completely invalidated that either the like you said earlier some some businesses are up, some are down, some are pulling back the the expenses. So it may have turned out okay but all the proactive planning they did initial on is completely out of window at that point.

 

TN: Right and most of those guys their revenue budgets were blown out like they had no idea what was going to happen there. They were saddled with their cost budgets that they had to continue paying for all this stuff. They didn’t know what was coming in on the top line. And so they then had to be very reactive on the on the cost side. And initially it was just a lot of you know arbitrary cost cutting and no disrespect to anybody. They were doing the best they could right but a lot of these big companies initially were just like, we don’t know what what we’re going to be in three months.

 

We were initially told covered was four to six weeks. And you know it’s still going on right and so what we saw is a lot of companies cut costs in the second quarter and the third quarter and by the end of the third quarter the management views looked up and said, well we’ve cut it as much as we can through the first three quarters let’s not release any more budget in Q4. So that just helped them on the income side so that they you know their bottom line looked better than it probably would have if they would have been a status cooperation.

 

JC: Yeah

 

TN: But still what we’re doing is using actual live data to help clients make the actual decisions that they need to make to run their businesses.

 

JC: Yeah and that’s really to me the key whether you’re got the small business that you simply just don’t have that much data to be processing all the way up to the enterprise. It’s still the same thing of saying, okay making those decisions on the numbers rather than, like you said with with Covid where it’s almost an immediate knee-jerk panic reaction of, hey we’ve got to cut things or hey everything’s going to be down. It’s like okay let’s look at the numbers and hopefully by a Q2 Q3 et cetera we’ve got some actual real data that we can start looking at.

 

So but yeah that’s that’s interesting so going back to Complete Intelligence then take us back. And say I think you said it 6 to 7 years old for the company itself. So how did this how did this kind of come about from a entrepreneurial standpoint.

 

TN: Sure, yeah, I used to run global research for a company called The Economist based in the UK, publishing company. And then I moved to a company called IHS Market which was just bought by S&P about six months ago. I was their Asia head of consulting. I was working with clients on a lot of data-driven decisions. And what clients were telling me were two things first the forecast that everyone was doing not just stuff, us were wrong and there was no accountability for that, okay?

 

The second is they could never get a forecast for their exact decisions. Forecasts were always too high level or not the right thing or something. So I rolled out of IHS market saying I want to have a data driven company that actually helps people make real decisions about their business. And so we started as a consulting firm for our first few years we were a consulting firm. And I was trying to understand the types of decisions that people needed to make I knew it from my consulting days with bigger firms but I wanted to understand what we could actually do.

 

About three years in we decided to turn into a product firm. Which is a very different type of business and so you know we built an initial platform that was very customizable but then to productize it out to build it to scale really is a very different skill set. Aside from a little bit math and a little bit of code it’s a very different same marketing and sales operation. It’s a very different you know infrastructure and all that stuff, right?

 

So a couple years ago we decided to productize with some subscription online subscription data products. And then we’ve got more specific with say cost and revenue products. So, I started the company in Asia in Singapore and then in 2017 we moved to Texas. So part of our, my calculation there was the talent in my mind is better here in the US. The customers are much easier to access here in the US and the business environment is pretty friendly. So it was a pretty easy decision for us to decide to come to Texas.

 

JC: Interesting. Okay. So what kind of challenges or what did you face in going from I guess I don’t necessarily know what your role was when you were saying with the economist except I’m assuming you’re you’re managing a team but you’re not necessarily managing a company. At that point to now owning and running your own company here with you said what 10 11 something employees up to now?

 

TN: Yes, that’s right that’s right, I think. So you know first is always the administrative part of it, right. I mean I think every new business owner just isn’t aware of the administrative stuff. And also the fear of missing something, right. What have I not done. what what tax filing have I not done or you know something like that, right? So there’s always that which was not a major issue but it was an additional burden.

 

When I think the biggest part of it was, I was just doing everything. And you come as a as a business owner you come to a point where you’re doing everything and you’re involved in everything. And then you’ll come to a point where you have to delegate stuff. And finding the right balance of when to do that and how to do that is I would say it’s more art than science. And other things like scaling RIT infrastructure that’s never really a decision I’d make before. I’m a math nerd and economics and data nerd, right.

 

So you know those types of decisions were really new but also on the customer side. Although, I had been customer facing when and this is kind of a no-brainer of course but when you don’t have a big brand behind you. Getting to the right people is a much more difficult process. And so we, I knew that coming out of the gate but I underestimated how hard it would be.

 

We started talking with some of our sales partners right away. Knowing that they wouldn’t give us a yes, right away but starting the relationship so guys like oracle guys like Bloomberg, Microsoft, Refinitive Tompson, Reuters these guys are all major partners for us now. Major sales channel partners and it took us four to five years to get those relationships moving and commercialized. So for a small business owner who is looking at channels as a major part of their business strategy. I would recommend you have to start talking to those partners right now like a year or two or three before you intend on getting your first dollar.

 

And so the other part as we’ve grown is we’ve had to think through, what do we do well as a company. And what’s best for us to outsource so things like HR. You know what, we don’t have an HR team. We have an outsourced HR firm, right, that’s a no-brainer but you know I can’t do it all myself. I don’t know the laws and stuff so we have outsourced HR. As I said with our channels we are scaling up our sales force but to have that as a kind of a force multiplier is huge for us, right. And things like marketing we have a marketing team in the Philippines and we have some marketing here but where can we get great skills at the best price really, right. And so we have to look around to find out you know what that stuff looks like.

 

We don’t have any of our data science team or any of our developers offshore. They’re all here in the US and part of that is for our client base. We don’t want things going to Eastern Europe or Asia or whatever but where we can push things off and make sure that we keep our core business. We’re happy to push things off. And so what I mean is we are a technology company, okay. We are not a human resources company we are not a marketing company and we’re not a consulting firm. And so we partner or outsource so that we can stay small and scale but do it very very well.

 

JC: Yeah and really even still that’s giving you the ability to scale because you’re not having to hire in like you said a whole team of HR. It’s a lot more cost effective especially for a smaller business to say hey we’re going to go pay a much smaller fraction of that to an outsourced group still allows you to scale and grow the business but at a much slower cost at that point.

 

TN: Right.

 

JC: So kind of what was that did you just walk into that and say day one we’re just not going to do HR. We’re just not going to do marketing etc. or was that kind of a a transition process because I know a lot of people will try to do some of it before they finally throw up their hands. And say okay, yeah this is not us or how quickly did you make that handoff there.

 

TN: That was immediate. I knew we didn’t want to do that from the start. Just from my corporate experience I knew that that wasn’t something I knew that we would spend a lot of money there not necessarily get good value. And so when somebody is a vendor you can you know you need some output, you need some outcomes. And so we just chose to make some of those guys vendors instead of making them full-time employees.

 

JC: So I’m curious since obviously you’re a numbers driven company accounting stuff like that. What does your relationship with some of these vendors look like how much of a numbers kind of basis relationship are you doing with them or are they is that more free flowing?

 

TN: Well, U think when you say numbers basis what what do you mean by that? I’m sorry.

 

JC: A lot of times. I’ll work with companies to sit here and say okay we’ve still got to measure our return on ROI kind of a thing on everything. So do we have specific numbers do we have specific milestones measurables et cetera tied to outside vendors the same way as we’d have tied to an employee?

 

TN: Oh, yeah absolutely. So like with our HR you know our outside stage our vendor. What we get from them on a monthly basis, I would probably have to hire a couple people to do internally. It just doesn’t make sense for us the the fully loaded FTE costs are just way too much. On the marketing side, unless somebody has absolutely stellar marketing skills, a lot of the direct marketing campaigns, social media marketing all that stuff for a firm our size at least it just doesn’t make sense to hire somebody. We can direct that activity manage it every day that sort of thing but the execution of it is better outsourced because we can do better with an outsourced vendor like dramatically better than we can by hiring those people directly, right. And so and so and we’re not talking a small kind of we’re saving 20% we’re saving a lot more than that by hiring marketing people directly.

 

JC: Yeah, that makes sense.

 

TN: Yeah and so I think again with most of the decisions we make. We really question how core is that to our business does it add to the technology, does it add to the customer relationship? And that’s really what it comes down to so I think we’re you know we’re at a place with things like video calls. And with a lot of the other technology that’s come around over the last 10 years. Where you don’t necessarily need that you don’t need everything in house it’s just not necessary. And if I have a vendor then I don’t necessarily have to pay for them to learn. If somebody is on staff I have to pay for them to learn. And so it’s not necessarily all fully productive time, right. And so again we’re very results oriented company. And so again we think through all that stuff. So for the guys who are watching your podcast. I would say look you know if you’re growing a company you really need to think through what your head count expectations are. What are they doing can you get that outsourced do you absolutely need to hire that person or can you turn it into an invoice.

 

JC: Yeah and that’s that’s really the the key because I see a lot more today of having a lot more availability and options of those outsourcing kind of a thing. That it’s not just necessarily the one big accounting firm that you had to be local face to face meeting somebody with the technology these days. I can have my account on the other side of the country kind of a thing and it’s just no big deal or I can have a marketing firm like you said all the way over the Philippines. It’s no big deal at that point so it’s almost it’s driven competition in those fields for sure. So it’s really almost like you said a no-brainer that okay why would you why would you want to go build your own in-house marketing firm when you’re a technology company or when you’re a financial services company something like that. It’s like that’s not your core business but still really identifying that core business is obviously the key there.

 

TN: Right.

 

JC: So talking about that core business you said you kind of made a an evolutionary change there with within your own company of saying okay consulting to now today being the the 100 product focus. What did that process look like or I guess for that matter? Why did you necessarily say because a lot of people I was that was my own background coming out of corporate America was, okay we’re going to be a consultant kind of thing. So how did you go from the consultant to saying okay we need to do something different or something transitioning towards the product side?

 

TN: Yeah, it’s very simple. As a consultant my upside is limited. I only have so many hours in the week and I can only bill against those hours. And if I hire people the upside is limited for them, right. So and if I want to grow a large revenue base I then have to hire a lot of people and then add x percent on top of their cost. And you know if their time isn’t sold then I can’t hire them anymore, right.

 

So I just got really tired of being the main guy consulting and you know billing against my hours. And so we productized because you know I wanted to make sure we could scale the kind of intellectual property that was in my head. And build that out as much as possible. Now that process was a it took a lot longer than I thought and a lot longer than I had hoped. That transition really took 18 months to two years. So you because you know, I had resources that were helping us on client engagements. I had to take them off of client engagement so they weren’t revenue generating to develop the IP around our product business because they can’t do both, okay. They can’t serve clients and develop IP because the development of  IP always gets put off. And so I had to make as a business owner, I had to make a very hard decision to say we’re going to stop you know selling, right now, okay.

 

And I’m going to pay the cost on these resources to develop this capability so that we can then productize it in 18 months time. And that was a very very hard decision but we did it because we had to otherwise I would have been flying all over working you know 90 hours a week, all that stuff. And we did it we bit the bullet and we came out with some pretty amazing capability.

 

JC: Oh and that’s really the key to me of saying, yes it’s a longer term vision you’re playing the longer game there even like you were talking about with the channel partners. Okay, you gotta start investing in things now looking towards that that longer term goal. And if you’re only looking towards next quarter, next month even next year. You might not necessarily have made that change to go product because you’re just looking at okay how can we get more billable revenues here in the next quarter.

 

So yeah it’s looking at that so kind of going down that direction. What does what does the vision look like for Complete Intelligence? Well how do you define vision from a company perspective and what’s your what’s your bigger picture vision there since it obviously sounds like you’re one to look longer term than just focusing on the immediate short term?

 

TN: Yeah I think so so our focus is really to continue to build out what we’ve started to do which is licensing sales for our core capability and aligning with other products. So how do we get built into core let’s say core erp software or core e-procurement software or you know something like that. So that a client doesn’t even have to think about working with us it’s just all baked into that software, right. And so that’s part of the vision.

 

The other part of the vision is how do we ensure that the results of our efforts are easy for a client to work into their internal processes. So just producing data or just producing something. If it’s an extra step then it’s a hassle for people, right. So how do we make sure and part of this is integration with other software that sort of thing but how do we make sure what we’re doing is really really easy for our customers to use. So that it helps them rather than adds more tasks to their day.

 

JC: Makes sense. So a lot of times I’ll see this where the the company owner. I’m not saying you are but the company owner has the vision there the ideas going forward how do you bring that down or how how do you bring that down in your own company to the team to say okay there. How do you get them bought into that vision or them understanding that vision internally?

 

TN: I think anybody doing that has to be comfortable with a lot of kind of a lot of mistakes and ongoing iteration of processes. I may have a short-term view of things that may not be right my team may be doing stuff that ends up wrong. I have to be okay with that and we have to learn. So and it’s not that’s not a luxury if you’re doing something like we’re doing we have to be a learning organization that is always seeing things that aren’t just right. And say okay that’s not right let’s take a couple days fix it. And then we’ll you know we’ll roll it out again or something like that, right. So as a software company we can do that. If we were making something physical it could, it would be different.

 

JC: Yeah.

 

TN: But as a software company we can iterate as we’re going, right. And so I think delivering that vision is really helping people understand on an ongoing basis. What the original vision is but then adjusting incrementally on a regular basis. And those regular adjustments they may be technology issues where we can’t actually do what I want to do, okay but that’s fine we iterate and we move along toward that path.

 

JC: Makes sense. So running a little long here running out of time. I always like to kind of come back and we we’ve talked about a bunch of different things over time but still what is kind of the best tip the best strategy that hey if I had known this six years ago. When we started the company or if I had this in mind this path in mind things might have been easier? What comes to mind as being your kind of your top idea here that wish I’d known this or thought about this or done this earlier?

 

TN: I think you know the biggest thing that I would have done is really thought through what I needed in a management team. If you’re scaling and you’re building the people who you put in place in a management team are really really critical. So what I would say is higher lower levels first and then make sure that the senior level management team that you’re hiring is somebody that you can really trust and someone who can really manage a team.

 

So put off those senior hires as long as possible. And it’s going to be painful and it’s going to mean you’re going to have to work a lot. And you know that sort of thing but higher low first then higher the upper levels, okay. And that’s almost the opposite of what say a venture capital investor or something would tell you. They want to see a management team but the fact is you need execution and then you need to build into those senior people that you can really trust to execute on the vision.

 

JC: That makes sense that’s interesting since we hadn’t touched on that one yet. I was figuring you’d go different directions but yeah I know a lot of times I’ll see that especially with the small ones if you’re don’t not having to do venture capital or stuff like that because I do agree there but a lot of times it is. Still it’s almost more the challenge that was what I run into of you start building out the lower levels. And you’re still trying to wrap your arms around it for honestly too long before you start introducing that management but yeah it’s doing that lower level and really understanding what’s going on first. And making sure you’ve got to keep handle on it before you can start bringing in people and really focusing at that point on.

 

Okay, what even going back to like what you were saying. Okay, what’s our core focus in the business this turns into. Okay, what’s your core focus as a leader to say. Okay, what are the aspects that I don’t want to do that I don’t enjoy doing that I don’t do well etc to hire on but yeah I like that from the focus on on building out the lower level team first that makes a lot of sense because a lot of times you’ll see startups said hey here’s our full sweet sea level
suite all these people we brought in it’s like. Okay, who’s actually doing the work at this point so yeah very cool, right?

 

TN: That’s right.

 

JC: So the listener wants to learn more about uh your company about Complete Intelligence about yourself where can they go find some more information here?

 

TN: Sure, so you can find us on on the web at completeintel.com. On social media on twitter we’re @complete_intel and you know just look us up online and we have a lot of interviews. A lot of resources on our website to find out more.

 

JC: Okay, we really appreciate it so thank you for taking time out.

 

TN: Thanks Jeff.

 

JC: Thank you.

 

TN: Thanks have a great day.

 

Categories
News Articles

AI for Supply Chain Forecasting and Proactive Planning

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

 

 

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

 

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

 

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

 

Many industries are especially sensitive to supply costs:

 

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

 

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

 

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

 

#SupplyChain #AI #EconomicForecasting

Categories
Visual (Videos)

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

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….

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News Articles Uncategorized

Startup makes superforecasting possible with AI

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

 

 

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

 

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

 

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

 

 

Massive datasets across 1,400 industry sectors

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

 

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

 

 

Superforecasting brings a modern mindset to an old industry

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

 

Cloud power and partnership

 

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

 

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

 

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

 

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

 

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

 

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

 

 

Beyond mining: superforecasting futures with AI

 

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

 

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

 

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

 

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

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Visual (Videos)

Oracle for Startups Featuring Complete Intelligence

Complete Intelligence is in partnership with Oracle for Startups, and here’s a Youtube interview featuring our CEO and founder, Tony Nash, where he explained what the company does and for whom. Get to know the technology behind the superforecasting for manufacturing firms and learn how CI helps them be more profitable specially in a highly volatile market like in the Covid pandemic. There’s also a section on how CI uses the Oracle Cloud Infrastructure to better serve its clients around the world.

 

The video above is published by Oracle.

 

❗️ Besides Oracle, Complete Intelligence is also in partnership with Bloomberg, Refinitiv, and Microsoft. Learn more about our Partnership program here.

❗️ Discover how CI can help your company in future cost projections, revenue forecasting, budgeting, and more. Book a demo here for your agile budgeting and forecasting.

 

Show Notes

 

WD: Can you tell me a little bit about what Complete Intelligence does and for who?

 

TN: We work with global manufacturers and we help them better understand their cost and revenue environment. We’ll work directly with their ERP data. Work with IT in the cloud and help them understand the forecast for their costs and for their revenues. So, they’re using their exact data in their exact environment to make great decisions for their clients.

 

WD: I’ve heard what you do referred to as super forecasting, which sounds so cool. Which industries
are best served by the super forecasting that Complete Intelligence offers?

 

TN: It’s mostly manufacturers. We work with chemicals firms, mining firms, electronics manufacturers, industrial manufacturers. So people who make stuff or people who work with firms who make stuff have to know how much that stuff’s gonna sell for, how much it’s gonna cost. Anybody who has risk associated with the future cost or future price, would need what we do to really help them de-risk their future decisions and their proactive planning processes.

 

WD: How are the forecasts that you provide impacted by volatility caused by unprecedented global events, say a pandemic?

 

TN: When Covid came around, when markets were hit dramatically in February, March and into April, we increased the frequency with which we update our forecast to our clients. But we also folded in a lot more volatility-specific algorithms, so that clients would understand what the path back would be like. In a normal year, let’s say the cost forecasts for a major manufacturing firm can be off by up to 30 percent. In some cases even more. So, if you’re planning those expenses and those budgets. You have a huge variance that you’ve got to pad in your budgets.

 

On average, we’re looking at a four to seven percent error rate. We’re helping people in a dramatic way to really de-risk their future outlook on the cost side. What we’re doing is a fully automated process. That guesswork of people sitting around the table saying, “let’s push this number up, let’s take this number down,” that’s a long budgeting process for people. And we really put that in the cloud. We have the machines learn and work through the data and calibrate and reduce that error for clients.

 

WD: Working with global markets and currencies, you must have massive data sets. Increasing the frequency of running those data sets probably requires quite a bit of computational power. How does Complete Intelligence manage that?

 

TN: Wee do that with cloud solutions. We work with OCI and the current generation of OCI to expand our computing capability. Many companies work across clouds. They work across on-perm and cloud and so we’re flexible with all of that. The frequency of those updates, the frequency with which clients want an updated view of the future for different companies changes. You have really fast moving companies who want that on a really high frequency basis. You have slower moving companies who are looking at it maybe monthly. That’s fine. We adjust to all of them.

 

WD: So, flexibility and multi-cloud are two really interesting considerations for dealing with enterprise customers like you do. What are some of the other unique challenges that face startups, like yours right now?

 

TN: With the pandemic, we’ve seen clients be very, very risk-averse. The the risk of taking on a new small company as a vendor is a problem for major companies. They’re trying to figure out how to adjust their business to an uncertain environment. For us, partnering with Oracle has helped to de-risk that decision for major companies. Oracle says Complete Intelligence has a viable solution, let’s talk about how we can help you. And the credibility that Oracle has when we go into a client is really really important for that situation.

 

WD: Aligning with a credible brand that’s been around for 40 years like Oracle is absolutely something that a startup can use to hack their growth. I’m curious about your use of Oracle Cloud and solutions that are open source Cloud native like Kubernetes. Can you talk a little bit about how you work with those Cloud Native Solutions?

 

TN: Kubernetes is a great one where our solution is containerized. We throw it onto Oracle Cloud and we can use it with clients. So, whether it’s the database we use, whether it’s the scheduling languages we use, whether it’s containerization, all of that is flexible on Oracle Cloud. And we can use the open source infrastructure that we have within our specific configuration on Oracle Cloud.

 

Over the last year, OCI has changed a lot in terms of enabling some of the very specific solutions that we’ve had. And very kind of high performance computing solutions that we’ve needed. Accommodation has really given us a lot of confidence with OCI.

 

WD: Your startup has had a pretty unique trajectory. You started the company in Asia and now you’re based in Houston, Texas. What inspired such a significant change?

 

TN: I guess the biggest thought behind there, is this is where the customers are. And to be honest this is where the talent is. The people who are doing the leading edge work in what we’re focused on are here. And the context around manufacturing and the need to automate some of the decisions around manufacturing really are happening in the U.S. and Europe, in a big way.

 

Of course that’s happening in Asia but it’s different in Asia. I spent 15 years in Asia. We conceived of and started Complete Intelligence there but we really utilized as much as we could there. And I came to a point where we just had to move the company to the U.S. to find the resources we need to build the company.

 

It’s been great moving to Texas, has been great. It’s a fantastic business environment. The manufacturing clients here are fantastic. Oil and gas is seeing a lot of headwinds right now which is a real opportunity for us.

 

WD: So the forecast is looking bright for Complete Intelligence?

 

TN: Oh absolutely. Again, with the right partners, we can move into the right clients and any startup trying to go it alone today is going to have a really hard time. It’s possible and it’s probable with the right amount of work put in, but building the right partnerships like our partnership with Oracle has been huge in helping us to accelerate our commercialization and our presence in the market.

 

WD: Absolutely and I know that if startups want to learn more about working with Oracle they can go to oracle.com/startup. If they want to learn more about the exciting work that Complete Intelligence is doing, where should they go?

 

TN: They can go to completeintel.com. We’ve got all of the resources there. We have a weekly newsletter. We have regular video interviews with industry experts, similar to what you’re doing. There are a lot of resources. Our twitter feed is complete_intel as well, there’s a lot there.

 

WD: Great, any secret market intelligence you want to share with our viewers?

 

TN: The changes we’ve seen over 2020 and the risk and volatility we’ve seen over 2020, unfortunately we don’t see a return to normal soon. The challenges that we’ve faced as startups and the challenges that our customers have faced in 2020 aren’t necessarily going away. This type of up and down environments and the persistence that we’ve had to have as startups, 2021 is not going to bring a normal back. We’ll see a little bit more, but as startups we’re going to have to continue to push very, very hard to get the mindshare within those endpoints.

Categories
News Articles

New AI Planning Tool Is a First in FinTech, Modernizes Procure-to-Pay Process

This article is originally published at https://www.businesswire.com/news/home/20190924005671/en/

 

HOUSTON–(BUSINESS WIRE)–Houston-based FinTech firm Complete Intelligence is turning heads in the industry with its new Artificial Intelligence offering. The revolutionary system allows companies to visualize their product lines, analyze their cost centers and forecast changes in costs, all in one platform.

 

“Based on feedback from our clients, we’ve been astonished to learn that typical industry forecasts have error rates over 20%. This makes it difficult for companies to plan effectively,” said CEO and Founder Tony Nash. “We set out to reduce that error rate and are thrilled to demonstrate that we can dramatically reduce client forecasting risk by 5 – 10x – bringing error from 20-30% down to 2-5%. That can help our clients find millions in cost savings. This platform has created tremendous excitement because the business impact is meaningful and there isn’t another machine learning technology like this in the industry.”

 

CostFlow offers comprehensive procurement data analysis coupled with cost forecasting, so companies can reduce risk for more intelligent proactive planning. Users can easily identify exactly how costs will change within the supply chain and procurement process. Additionally, the tool can forecast a product substitution to see how a change in supplier or raw material may affect overall cost and performance.

 

Companies most set to benefit from the new technology are those in the food and beverage, natural resources, technology hardware, consumer goods, and industrial manufacturing sectors.

 

“It encourages me to see Houston-based Complete Intelligence leading the charge in FinTech innovation. It’s proof that Houston has the talent, expertise and client base to support a thriving tech market,” said Gina Luna, Complete Intelligence Board Member and CEO of Luna Strategies.

 

CostFlowTM is the first of four planning modules the company has slated for launch between Third Quarter 2019 and Second Quarter 2020.

 

About Complete Intelligence

 

Using advanced Artificial Intelligence, Complete Intelligence provides highly accurate cost and revenue forecasts fueled by billions of enterprise and public data points. Its innovative cloud platform gives companies insight into their future, so they can plan for success. Complete Intelligence helps companies to stop guessing, start proactive planning and succeed. Find out more at www.completeintel.com.

 

Contacts
Jacqueline Voss
jv@completeintel.com
(713) 710-9131 x707
www.completeintel.com