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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 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.

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.




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….