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March 18, 2024

AI-driven capabilities to transform planning, decision-making & collaboration across manufacturing organizations

With the rapidly evolving landscape & technology capabilities driven by AI, we are seeing immense value in a number of industry verticals and solutions.

Notable examples include how organizations are: 
1. Improving portfolio management, phase-in/phase-out and advanced demand capabilities for industrial manufacturers 
2. Optimizing the supply chain in logistics & manufacturing using a combination of heuristics and LPs to drive operational excellence & out-performance 
3. Improving the integration between S&OP and S&OE to drive decision-intelligence amidst ongoing demand and supply volatility 
4. Driving improved supplier collaboration across the extended supply chain 

Manufacturing organizations are increasingly leveraging technology and AI capabilities to transform planning, decision-making, and collaboration capabilities as portfolios rapidly change, supply chain networks evolve, and external events create volatility. Learn how leading organizations are creating supply chain resilience and attracting talent to manage the new normal for manufacturing companies.

Watch this webinar with o9 and Microsoft to learn:
- How organizations are managing ever-increasing and changing product portfolios 
- How manufacturers are leveraging optimization to improve operational efficiencies 
- How manufacturers are synchronizing S&OP and S&OE to improve decision-making 
- How organizations are improving supplier collaboration and improving supply chain resilience 

First of all, thank you for all those that are joining, wherever you might be, you know, whether it's morning, afternoon, or evening. My name is Brent Hazingham from o nine, and excited to be here with David Bria from Microsoft today. So if you wanna go to the next slide, What we really wanna cover during this hour as we went through this across both organizations was really to make it informative across a number of different topics. So we'll start out. We'll do some introductions.

Give some context across each of our respective roles within our organizations.

And then we have, structured Q and A from both David and myself in terms of what are some of the challenges, as it relates to planning decision making and collaboration that are new in twenty twenty four, and what are some of the, pervasive issues that we continue to see over the past several years. That'll also then, cascade into a couple of use cases. We wanted to provide, variety of use cases across the manufacturing landscape.

So the goal was to be relevant for a number of different participants.

We still, you know, couldn't give the whole portfolio of what our organizations are working on, but hopefully there'll be some good, applicable learnings for, a variety of different organizations on the call. And then we'll spend a little bit of time talking about some of the path forward opportunities, how we typically see organizations getting started as we go through that transformation journey.

There will be, you know, time hopefully at the end for Q and A. So if there are any questions that the audience has throughout the session, please log them in the chat, and we will try to, either address them during the session or at the very end, we will consolidate and, go through that at that point in time. And then we'll also end with just some other opportunities to stay engaged with both o nine and Microsoft throughout the year.

So with that, we can go right into introductions.

So, like I mentioned before, my name is Brent Hazeencamp. I lead our industry solutions team for manufacturing, within o nine. I've, been at O nine coming up on three years now. And, before that, spent over fifteen years in a variety of different consulting industry and private equity roles, all in manufacturing and supply chain.

And Yeah. Really looking forward to the discussion today because the perimeter that we cover is everything from high-tech through to vertically integrated companies. And, you know, my team, we have a number of different industry, vertical experts as well. So with that, David, would you like to do an introduction as well?

Sure. Great. Thank you, Brent, and thank you everybody for joining.

My name is David Bria. I'm part of Microsoft's manufacturing and mobility practice. I've been with Microsoft for about five years.

This year, I'm leading a new team focused on process industries, so chemicals, metals, glass, cement, steel, and even semiconductor.

I think we're gonna give a good flavor of some of those industries. It's typically things that aren't the focus in these type of supply chain discussions.

But, just a quick background on myself. I I grew up in the automotive industry. Was born and raised in Flint, Michigan. So was, product engineer that had to go lay out a plant to build some new products around drive by wire. I think they're called Adaptive Cruise now.

Spent some time before a lot of these tools were available doing some consulting at AT Carney and then got hired by a company called I two Technologies. That was Sanjeev Sadoo's first unicorn, so it's, really interesting to see how a lot of the things that we were dreaming about in the late nineties and early two thousands are becoming true today.

I did do ten years at SAP. So I'm familiar with that back end system, and we'll talk a little bit about some of the opportunities and challenges of leveraging ERP type datasets, and then actually ran my own firm for a couple of years working with GM trying to, figure out supply chains to get midsize vehicles from Asia over to the North American market and spent a little bit of time with Volkswagen working in their service parts operations. So It's been interesting to see, like I said, a lot of the trends, come together, and hopefully today we can share some very pragmatic opportunities that we're seeing customers take advantage of across a lot of the sub verticals that we'll be highlighting.

So I think we'll kick this off, Brent. Why don't we jump right into the discussion. I think one of the first things that we wanted to get your perspective on are what are I think the the longstanding challenges most people are aware of, but wanted to start with what are some of the new challenges, right? What are some of the things that we're engaging customers on that are different in twenty twenty four than what we've seen previously.

Yeah. Thanks, David. And I think what is really critically important here, is even rewinding back to really at the onset of a lot of the COVID induced disruptions and the subsequent, you know, activities that occurred, whether it was the Texas winter freeze, that had a huge impact on the chemical industry, some of the, disruptions from various, you know, the soas canal, etcetera.

The the focus really during that period of time was all around, how do I get my arms wrapped around visibility? And not just within our organization, but into the extended supply chain. So so much of the focus was around visibility, decision intelligence, control tower, type solutions.

And if you fast forward you know, into the last twelve to twenty four months, there's been an ever increased focus on given the inflationary environment that we sit in today and what's happened, again, during those periods of time, there's been ever more increased focus on productivity, but also how do you bring in financial and commercial aspects into the planning process so that Still protecting margin, but remaining competitive from a, market perspective.

And the dynamics, they're critically important to maintain and grow from a profitability and efficiency perspective.

And those two areas while they remain just as important today as in twenty twenty four, there is a bit more stability, I think, than what we've seen, even in the past four to five years. But as organizations are looking into twenty twenty four, It's a lot of the, how do I now build an agility and an agile and responsive supply chain and take advantage of the technology that is coming to bear and is moving so rapidly, whether you think about gen AI, large language models, the ability to take advantage of automation at scale, and how do you build an organization of the future that can take advantage of that technology, but also be able to future proof your organization because technology is advancing so rapidly, in making sure that, you know, organizations are placing their bets into these platforms that can harness, you know, those use cases.

So I think that's probably the biggest change that we're seeing within our customers and various prospects over the last six to twelve months is some of the organizational principles. And also how do I rapidly prototype and get use case value out of some of these emerging technologies. So would also love to hear your thoughts on what you're seeing that's different in twenty twenty four than maybe what we've seen in the past.

Sure. Well, I think we've all seen the headlines.

Supply chain has been front and center probably more so than any other time in in our careers.

It has definitely taken a front seat on the CEO agenda.

And large large in part due to the financial impact of some of these disruptions. Right? It seems like more and more. It's going from million dollar to billion dollar type type impacts.

With the the technology, I think we're also seeing the ability to integrate different parts of the the value chain in the past. There was largely planning systems, that were, informing execution systems, but they were largely disconnected. And so there was a lot of work to be able to close some of those feedback loops and trying to figure out did things get executed on time at high quality?

And be able to feed that back into planning systems. And so the, I think the way that we're approaching it from Microsoft is supply chain is really the way to tie a lot of the new R and D and product development changes. There's more velocity around when products are coming out, whether they're brand new or whether their variations on existing ones, The integration of software in these products is pretty important and, a big challenge for companies that are traditionally making hardware.

But really the biggest opportunity is to make this more of a customer facing process, and we're seeing more and more companies look at customer engagement or customer experience as a core pillar for their transformation strategies. So I think the core principles of what dictates supply chain excellence has not changed. It's just that the scope in the way that this is integrated across the enterprise is is something that we wanted to see coming, but it's already here.

Yeah. Absolutely. And I think you've already touched on it a little bit as we move into the next question. But what we also wanted to talk about is, you know, what are the things that are not changing and what are some of those consistent challenges in both opportunities as organizations look to deliver, maintain operational performance, and customer expectations in twenty twenty four.

So David would love to get your thoughts there, and then I can add in a few things as well.

Yeah. I think the way that you talked about the impact of of the whole COVID and other activities that have been unplanned around the world, along with a lot of increases in just trade challenges and even regulation has imposed a whole new set of constraints that has created the need the need to really integrate the planning layers. Typically, things were done in layers, which created a lot of data latency, in a lot of companies, there is not a contiguous supply chain organization. There's a lot of process disconnects between the various silos that span the typical, score score type framework.

I think the the other thing that we're seeing is that in the past, you could place an order and largely expect things to show up on time. And so when you get into Eoq calculations, safety stock calculations, a lot of the variables were given That's not the case anymore. And so the planning processes when you walk the actual workflow has moved beyond trying to optimize around seven or eight variables to hundreds of variables, and that's very difficult for humans to do, even the most experienced planners.

In some cases, Excel is still probably the most popular.

Planning tool may not be the optimization tool, and I think we used to say that it's not it's not the fighter jets, it's the fighter pilots.

We have an opportunity to bring some of those things together.

The the other thing that I think is fundamentally different is that data was always a challenge back in the APS wars, like I said, twenty years ago, but most of that data was anchored on historical data set. It was trying to get into order histories, trying to look at shipment histories.

And so a lot of that was challenging when you're trying to forecast the future and predict what is going to happen when you're looking in the rearview mirror. And so the ability to connect to, not just the order events along a value chain, but real time signals, being able to extend some of the datasets to trade partners, third party logistics providers directly into robotics and factories and warehouses.

Is something that has created really the need for an entire new architecture.

And then, you know, the I think the leaders are moving beyond control towers to the concept of digital twins across the value chain. It's not a piece of equipment or a single line. It's really trying to take the control tower concept that's very effective for short term reaction to unplanned events, to be able to really simulate and conduct a what if scenarios across a dynamic network.

Yeah. I think that's, really touches on a lot of the similar themes that we continue to see as well.

There is an ever increasing acknowledgment that the best planning processes out there still have a level of uncertainty, and there continues to be that uncertainty that is going to permeate and exist moving forward even throughout the rest of twenty twenty four where you need to have the agility and the ability to be able to do you know, base case, upside, downside scenarios, and to be able to respond accordingly as the market unfolds, throughout each and every day, we etcetera.

And to kinda lean into the data and information, aspect of this, One of the things that we've also continued to see is that there continues to be an ever, growing investment in systems that are outside of your core ERP. So we talk a lot about the CRM investments that organizations are making track and trace information, various aspects that have really important utilization and aspects within various organizations, but how do you bring all of that intelligence in and and also there's you know, syndicated market information that organizations may be subscribing to, to get a better view of demand, pricing variables, etcetera.

And we continue to see an ever growing interest to be able to enact the core, aspects of planning from demand supply inventory, IVP, RS and OE control tower capabilities, but also how do you bring in this information that may be coming from your CRM, that may be coming from your syndicated market information, the collaboration that your customers and or suppliers are providing is a two way data feed track and trace, etcetera. And how do you bring it together to be able to really consistently execute all the planning activities that organizations need to be able to do.

In in ever increasing, complex operating environment. And those are the things that I think we're gonna continue to see ever more of, not less. Okay. And, I think, David, if we were to go into the next question because I think it'll be a good segue into some of the use cases, but would love to Again, hear your thoughts in terms of what are some of the top use cases organizations are are using and what are you seeing, you know, in in today's market?

Yeah. There's, I think the the opportunity, I'll kinda talk more structurally, and then we can get into some specific use cases, but in the past and eve even today when you go in and look at the way that most of the underlying technology systems are architected to support supply chain.

It's the the traditional kind of plumbing diagram. Right? There's boxes and lines, and they're different colors.

And so that creates a lot of onus on being able to manage point to point interfaces upgrades and try to wrap it around to protect the data and, the enterprise from cyber threats What we're really trying to do is move towards more of an open platform, where you can bring together connectivity, storage, compute power to enable, some of these more advanced analytics automation and AI capabilities that, are in the headlines today.

And so where we're seeing I think the hotspots in that, is demand planning, being able to look beyond some of the historical records I said, even being able to go into user reviews, market sentiment, taking a look at feedback from customers and contact centers.

Those are things that can be manipulated into a demand forecast at a micro segment level, so being able to look at different levels of granularity around customers, being able to look down to SKU and geography, level plans, and then being able Not just at the demand, but what could be the revenue uplift and the margin impact you know, of some of those plans. So it's really uniting the financial plan with the the operational plans there's there seems to be more focus on the demand side from an advanced planning standpoint because a lot of companies are reevaluating their networks. Right?

There's been a lot of, tax dollars trying to repatriate manufacturing, which has created a pretty interesting shift of production locations but that's also cascaded into suppliers and trying to be able to shorten supply lines to eliminate risk to be able to provide some more transparency to to what's happening. So I think we'll see the demand side of that. Go into the supply side, but we're gonna share some examples where even for, most companies, there is a blend between process manufacturing and discreet manufacturing.

There is not an enterprise app that can be able to handle the supply complexities of being able to look at that type of a hybrid, value chain.

And and then the the other piece is just around execution.

So, like, like I said, the the best way to respond to a customer request to say, hey, where's my order? I going to get it on time? Will it be in full?

Is still a challenge in a lot of companies typically the data exists, but it's hard to go piece that together and provide an answer beyond inventory or something that sits in a production schedule.

And that's where we're seeing the concept of control towers and ultimately digital twins be able to bring a lot more intelligence into not just available to promise type commitments, but being able to allocate capacity, inbound materials, even logistics, right, where there's capacity could go down to container levels.

Those are some of the things that, you know, with with o nine and Microsoft, we are addressing today.

That's great.

I think just some of the additional thoughts that I have as we kind of think about what are some of the top use cases, piggybacking on a couple of the points that you made from a demand side, where manufacturers continue to be quite upstream from the final end use of the respective product we continue to see an ever increasing focus in on what we call triangulation.

So we have a stat based model, We likely have customer forecasts that are coming in. We have sales and marketing intelligence in the field saying here's what we should be expecting.

And even sometimes having external data feeds like I mentioned before, and to be able to triangulate that into a consistent signal and to be able to post game against that to understand what feed consistently is adding forecast value at and what actually may detracting.

Those continue to be really, really core capabilities that organizations, manufacturers that are further upstream really wanna unlock because like you said, the past isn't always the best indicator of the future, but there are certain intersections and areas where we do need, you know, on the ground intelligence.

We do need to understand from our customers, what are they seeing further downstream, and how do you bring that together to be much more up to speed in terms of what is the market, you know, doing and what is it anticipating, you know, moving forward?

And then on the supply side, especially in the vertically integrated organizations, we see a lot of, different use cases depending upon where you sit in the vertically integrated supply chain. So far downstream, they're, not to overgeneralize, but where you see organizations need to respond very fast to customer orders that may be coming in today with a delivery, you know, here later this week is how do we make sure that we have the right capacity allocated how do we have all the right materials, the postponement strategies so that you can rapidly respond to those customer needs. But then as

you think ever more upstream many of these organizations where you whether you think cement aggregates, paper packaging, agribusiness metals, etcetera, the ability to win is to be as operationally efficient as we possibly can. So how do we understand what are the optimizations that we need from a manufacturing and distribution perspective and how do we cascade those signals upstream to get a reliable picture of what the downstream providers need, and what are those use cases at different time horizons.

So looking forward to diving into a couple of those examples later on, but those are the things that I think we're seeing as the biggest interest areas in twenty twenty four.

Yeah. There's a couple of emerging models that I think we're still.

We're we're actively working, but I I think we'll see them manifest more broadly.

Most companies that build hardware or machines or vehicles are architecting connectivity.

Into those products. Right? And so being able to leverage that streaming data as the voice of the customer is something that's very interesting that hits demand supply inventory planning processes, with with the, the focus on process industries there is increased scrutiny on cost, and that's driven by energy.

But, beyond some of the sustainability visions that companies have have laid out carbon is pretty hard, but we're also looking at water, we're looking at waste, we're looking at some of the other things that impact the environment, and it's also creating an opportunity to look at circularity.

So it starts with the product design, but it ultimately gets delivered based on the supply chain network and the way that these processes are gonna operate in the future. So to me, that's very exciting. Like I said, we're We're working on it, but I think we're gonna see those become more mainstream models, probably sooner than than what anybody expected.

So should we jump into some examples?

Yeah. Let's do it. Do do you want to, segue with this slide. Otherwise, I can jump in.

Yeah. I I spoke to this. I mean, given that I'm I'm with Microsoft, it's probably worth putting in, our perspective around AI and how we're seeing companies adopt AI.

We spent a lot of time over the past several months really thinking about what are the deeper industrial scenarios and how can AI help help enable them.

The reality today is we're seeing more and more companies try to leverage co pilots as a way to take advantage of the data that exists today. Like I said, earlier, we still see a lot of supply chain planning happening in the Office suite, and so with the launch of the M three sixty five co pilot, it can be a good productivity savings.

Not only within some of the applications that that most office workers use, but being able to try to convert that productivity into collaboration through platforms like teams. Right? It could even be outlook. And so not only being able to generate content, but being able to synthesize insights from existing, assets that have been digitized, and then being able to use that as a way to drive creativity, to drive, new innovations within those functions. I think the challenge is twofold. One is how do you really, convert an ROI based on productivity savings when you're saving ten minutes here, twenty minutes there. What we're seeing is that where companies have invested in these type of co pilot capabilities, with density in different parts of their organization, you can start very quickly to tie the value.

Two process metrics. Most companies know how to try how to tie improvements in cycle time, in conversion rates, in into, more financial metrics.

The, the other one is okay. Well, Most of these are feature extensions of assets that companies already own. How do we extend that into looking into ERP data in MES data into WMS type data so that we can enable not just the back office workers, but the people on the front lines. Right? It could be planners, it could be people working in a warehouse.

It could be the line side operators, or the indirect support people in production sites. And so I think that's gonna be a huge catalyst to be able to get some of these more advanced capabilities in the hands of people, and and where it really is helpful is it becomes a new UI it can't be enabled in a way where you have a natural conversation. And so I think, we're seeing companies really explore the boundaries of how do they move beyond structured workflow, standard processes, being able to enable co pilots to get access to unstructured data, to be able to, bring in things that are well known based on tribal knowledge into, you know, more more of a science driven optimization.

So looking at temperature humidity, barometric pressure. Some of those things are super important across supply chains.

And so I think it's early days, don't forget that M three sixty five co pilot launched on November one of last year, so We have been able to extract a lot of insights from some of the customers that were part of the early access program.

And even though that, that these capabilities are new, it's interesting to see the different strategies that companies are taking in terms of their own adoption. Some are focused on productivity alone.

Some are looking at trying to pull data together across the enterprise into a single source of truth, which has always been the the holy grail of supply chain.

Others are going direct, right, and they're really looking at, we're gonna leverage AI as a new way to create a market for our products.

Yeah, David. That's, such a important topic right now. And like you mentioned before, I think both of our organizations are, you know, while the GenAI large language model co pilot digital assistants are still, relatively new and in their infancy. I think the early results of these continue to be immense sleep powerful.

And I still firmly believe that within planning, the ability to digitize and institutionalize the inherent tribal knowledge that exists at so many different organizations, unlock so many, opportunities. And we we see as you think about the the complete, cycle from education and awareness through implementation running tools and applications and continued improvement, there's a applicability across all of those aspects, but where we're starting is really this, digital assistant of, you know, be able to query what are my top five variances you know, what are my five highest performing areas, you know, aspects of that, and to be able to bring that into an environment that is secure and is using the organizational data, but can take advantage of the scale that Microsoft has and o nine has, there's a lot of exciting opportunities that are just coming to bear in q one of twenty four.

But I think in many of the other, you know, aspects, we still don't even fully understand all of the different use cases that will ultimately be able to unlock here.

Yeah. Supply Chain is notorious for three letter acronyms. So the the new one that I've been, presenting and sharing with customers is NBO.

So when something happens unexpected, how do I know what the next best option is?

And if you think about that cost supply chain, those are the areas to target.

I think we we tend to like to Call out what are the questions that are easy to ask and hard to answer? Those areas are really ripe for some of the things we just talked about.

Absolutely.

Okay. So just a bit of a time check, we have twenty five minutes left.

We'll go through a couple of use cases to, you know, bring some of these aspects to life.

And we'll try to spend just a a couple of minutes on each one. So, going back into the theme of, you know, translating demand into good view of supply and understanding the interconnectedness of this. We wanted to highlight one case study that we see within the aggregates and cement sector so those that may be a little bit less familiar with the industry, aggregates, you know, you're ultimately working in queries and minds to be able to blast rock, to be able to process rock.

And there is this inherent view of process versus cell.

And there's also dynamics of depending upon the region that you're in One is that they, you know, the the value relative to the transport makes it a very local type of business. But at the same time, the seasonality, whether you especially in areas where you typically have colder periods and warm, you need to be able to anticipate, because when the heavy months pick up, you ultimately just can't you know, fulfill all the demand. So what the setup ultimately and then on the cement side, you also have an aspect of optimization, you can, distribute those effectively from further regions, but they all have to come together into the final, you know, applications downstream, whether you see ready mix, block plans, you know, other asphalt applications, etcetera.

So some of the work that we're doing right now is when you think about the aggregates piece, there is a co product optimization problem that we're looking to be able to understand the various inputs from I have these different options, from both how do I set up my aggregates facility in terms of what co products I'm running, but also what are the changeover times when I do that what is the expected demand and ultimately looking to make sure that one, we maximize the amount of product that is high value and will, be the runners and repeaters that we can ultimately support while at the same time mitigating and minimizing the amount of excess inventory that we have, and then also look to do this in the most cost efficient way to minimize the amount of changeovers that we're ultimately working to go through.

So that's one specific case that is in the optimization view. And as you think about on the demand side, there is obviously the historical piece of what have we done in the past, but also how do we drive better intelligence from all the sales and marketing associates that are using different systems, whether it may be a CRM or other aspects to make sure that, one, we have the proper accountability in the right aspect of that, but then also how do we translate that into a single source of truth?

In an environment that is partially hyper local, but also very global from some of the optimization pieces. So it's an exciting journey. The organization, that Microsoft and O nine have been able to work with is, you know, really forward in terms of what they're able to do in this, sector and, you know, we appreciate the journey that we've been on with them so far.

So with that, I will turn it over to David to talk about the next one in Metals.

Yeah. So I think you did a good setup on, cement and aggregates. A lot of the same characteristics are true in the metals, whether it's steel copper aluminum or anything, anything relative to that. In in this case, this is a good example of a hybrid type type, value chain, where it's a multistage process.

Most of these production sites are running twenty four seven.

They tend to get scheduled in sequenced around batches, and then those batches get converted into products. It could be big roles. It could be bars.

Ultimately, it gets delivered to customers either in those forms or it goes to a regional distribution center. And so the order management distribution to be responsive to customers is a big challenge in that part of the process. And so in this case, This is an excellent example of the open architecture.

When you go into, a lot of these process industries, there's very well established incumbents that do exactly what is needed to optimize production at the highest quality level.

The challenge with some of these systems is that it's hard to get the data. It's hard to integrate the data with some of the other things that are critical to not only the production process, but the the customer delivery process.

And so in this case, working together, we were were able to integrate with a third party solution at this site and be able to look at that not only as a technical solution, but really as a path forward. So there is a lot of envisioning use case ideation to ultimately come up with the right concept and solution for for this company.

I think I'll I'll kinda close with the punch line that no one provider can do it all. In so fostering this ecosystem goes beyond Microsoft and o nine, especially when we're getting into.

Some of some of these niche sub subverticals, both then discrete in process.

Yeah. It's a great point, David, for sure.

And then if we move into the next use case, We also wanted to give a couple of, use cases more on the discreet, manufacturing side. So, one notable example is an a very large engineered lighting manufacturer and distributor. So think about all of the lighting that you will see in commercial buildings, airports, but also even some of the lighting that you pick up in various locations.

They support a very, very broad and diverse customer set. That even extends into certain sectors such as automotive.

And the parts that they are supplying there is some level of commonality when you think about the fabricated metal, the lighting, the lumens, There's even sensors that have semiconductor dependency, etcetera.

There is only so many options there.

Not trying to minimize the the supply chain complexity because there's a lot. But where the challenge lies evermore is the fact that so much of the business is configured to order or engineer to order. Because that specific project is going to have, key design aspects that They've worked through this with an architect, with the general contractor, etcetera. So how do you demand plan in that environment And then how do you reliably translate that into a consistent supply plan that has an understanding of what we need to move, manufacture and procure, especially with the procurement activities that may have very, very long lead times that are coming from APAC and other regions, you know, such as that.

So part of the exciting, you know, opportunity here is, as David mentioned before, A lot of this this information does require a lot of ingestion and integration of a lot of different source systems everything from what have we historically done, but also what are we doing from product life cycle management perspective? What are the new products that are coming to bear?

What cannibalization impact do we expect to have on the existing portfolio?

And how do we really get ever more closely connected with the design and engineering teams with the planning organizations, etcetera?

And then once the demand plan is is well understood, being able to translate that into a reliable supply picture that can take a very, you know, complex demand signal in terms of here's my expected volume Here are the expected usage of these different materials while acknowledging the fact that, many of these things are still forecasted. It's not necessarily a firm order yet. And then how do we scenario plan on the supply side and having very good utilization to be also do inventory optimization integrated business planning and also some of the more execution control tower focus activities.

So that's, I think, a really good use case where you know, the organization up to this point.

They've seen a lot of value on the demand, planning side as they've gone through a number of cycles now for just getting into the go live pieces on the supply side, but really, really positive, developments there. And It's another example of a great partnership across, Microsoft ten o nine and also with that organization that has really transformed the way they go through planning, and decision making at scale.

And then the final one, I think, David, you've got, also in the high-tech space.

Yeah. This one's a good capstone. I think what you just walked through, you know, what's particularly challenging as a lot of, a lot of that work was around project based requirements.

This one's really more around high volume, lines of products where there's some high runners that are that are, pretty much build a stock, and then there's a long tail where it drops off and every every single product is a one off. So in this case, it's around high-tech electronics, servers that go into data centers, some of the data centers that run Azure.

It could equally equally applied to aerospace, automotive, building materials like windows where things are configured to order. And so the challenge here was really trying to tie, millions of opportunities in a CRM system into the planning tools that o nine provides and being able to really try to navigate hundreds of thousands of sellable skews and be able to tie that back into the supply plans.

The focus was more on how to automate and accelerate the deal flow.

So being able to integrate this with CPQ type systems where you can go configure price and quote.

In some cases, these are long standing customers with contractual opti, obligations.

So there's gives and puts on both sides and being able to, really harmonize the demand to supply netting across the value chain. In this case, working with o nine, there was a digital twin across the supply network to be able to model the nodes, the lead times, the cost, the constraints, and some other critical variables to be able to not just accelerate the deal flow, but be able to start playing up what if scenarios. So do I take this order in mis mis losing revenue on other orders since So I think the results kinda speak for themselves. Right? The the target is over a billion dollars between reductions in inventory.

A lot of that was E and O. So pre building units where the order gets canceled or changed. Usually, results in a in a write off.

Even if things could be built directly to customer requirements. There's a lot of expediting required. So there's a lot of premium costs that are also experienced. And so by being able to bring all of this together.

Like I said, the the number is over a billion for this company alone.

Awesome.

Great, David. And, yeah, hopefully for everybody, in the audience that gives a good, cross sector illustration of a few different use cases that our organizations are working on.

And there there were many more, but we had to, you know, triangulate this down. So in the kind of final section here, that we have in terms of thoughts and, you know, considerations in terms of how to get started. I think many organizations see better integrated business planning, better FP and A integration, as some of the most compelling value creation opportunities within an organization but are still continued to be challenged by, do I have the data available?

Do I have the organizational construct to take advantage of these digital tools and capabilities?

You know, how do I mitigate some of those risks? So David, you know, if if you could, it'd be great to maybe kinda talk about how do we think about these different pillars And how do we get started in a meaningful way that also derisk the, you know, some of the challenges that we continue to see in the in, you know, the path to get started?

Yeah. Very good. You know, I think there's kinda two types of conversations that we're having. It's it's with customers that have already been through strategy efforts, you know, largely with a lot of the business consulting firms, and they're ready to get to work. And so being able to prioritize use cases, try to deliver some value from the ones that are immediate and can take advantage of the data that does exist is something that, we can do in a series of weeks, right, in two months.

But for companies really looking at a broader transformation, I think this is the blueprint, as we call it, for how to how to get in front of that is not mutually exclusive with a bias for action, but I think a couple of things that we're seeing is quite different is The business strategy clearly dictates where to invest.

Unfortunately, I've been been involved with some companies that are trying to digital twin everything.

And that doesn't make sense even for our greenfield site. Right? The, core principles of excellence across supply chain, and other operations have not changed. So in the business strategy, you gotta pick your spots and try to really anchor on what are the key leverage points to unlock that value, mitigate risk, create competitive differentiation?

In in the past that would usually get thrown over to an IT team to go figure out the solution.

And what we're doing is really trying to harmonize that effort. So steps one and two, while they may may be sequential, the actual architectural design happens simultaneously, where there's a business architecture. It can go right down to roles or personas if you're taking a design thinking approach.

That gets integrated into the new, technology platforms that that are being provided in our in and, are the keys to being able to not just make this real, but be able to do it at scale.

In three is where you're kinda going from a program level view down to projects. And so where it does involve software, where it does involve, more of the technology capabilities that we provide, more and more of the breakthroughs are happening where cross functional teams are coming together.

Whether they're doing pure agile dev ops, I think the bias is to try to stand up an MVP like solution, to not necessarily demonstrate that the that the technology works, but that it it it can be deployed to drive change which will result in value. And so there's, actually been a little bit of a loosening around traditional project management type type capabilities. Right? This involved daily stand ups. There's still governance, but when cross functional teams come together, it's an opportunity to unleash innovation.

And then in four and five is where companies have kinda gotten through the MVP stage, and they're looking to scale this. It could be taking a use case to different value chains, different sites, different parts of the organization.

It could be trying to look at, okay, I've gone from connectivity and visibility to really looking at automating the way that work gets done to try to optimize the entire system. Right? The the other kind of golden unicorn and in supply chain is a single button to optimize the entire network.

Being able to have the right organization set up in the right governance process requires a lot of these work streams to come together in the right sequence to make that a reality. And so, every every journey is a little bit different, but like I said, I think this blueprint allows you to navigate both at the strategic level. It allows you to establish long term vision but it also provides an investment profile, where you can deliver capabilities along the way.

That mobilize the organization and deliver value. So it's quite different from some of the big bang ERP deployments that I'm sure many of us are familiar with, right? It's it's I'm gonna invest a lot of money and I'm gonna keep my fingers crossed that something good is gonna in five years from now, that is not at all what we're talking about in this model.

No. I think I think that's well said, David. And I appreciate we have five minutes left. I I think the two points I would just add is that, we continue to see you know, the challenge of the legacy thinking of people process then technology, and instead trying to do those in a very orchestrated way because technology can influence those two pieces, but we do need to get the foundations in place.

So organizations thinking through that in those particular areas in terms of how do you get the foundations in place and then to your point to be able to extend ever further And that goes into the second topic is that what is really interesting is that, you know, especially from a planning system perspective, the job is really never done, and it never should be done because the organization is always evolving. There's always dynamics, whether it might be M and A events or there might be different, restructuring activities or incremental data feeds. And we continue to see the center of excellence to product innovation of how do you continue to make sure that those changes are being manifested into the tool, and you're still being able to get minimum value as some of the key components that we continue to see successful organizations going through.

So, in the last couple minutes that we have, two quick, slides on how to stay connected with our organizations.

David, if you wanna just spend one minute on this. I'll spend one minute and then try to get through just the quick Q and A that we have.

Yeah. I'll just do a quick quick plug for the technology center. So we have a whole network of Microsoft technology centers. These serve multiple purposes, but essentially we're built for customers.

There are some of our most seasoned field practitioners that are part of the team there, and they offer a series of services to help customers along the way. We, also host many, what, many multiple customer events We've got two that are, scheduled this week, actually, in Chicago and Detroit in May, and that's where we can come in and help help establish a vision and a roadmap. We can get into some of the technology architecture. We can even We've got like a garage concept in these centers.

We can go right into rapid prototyping or build on a prototype and go into a hackathon type event. So I wanted to make everyone aware of that. That's also available to partners. We work with o nine.

Some of their, more advanced capabilities are showcased in these centers So I think that that's a way that we can move fast and bring the best people from our our respective organizations right into some of the ideas and plans that you have.

Absolutely agree. Amazing venues to understand what's full and and get started as you mentioned before.

And then on the next slide, the the final one, we continue across o nine and Microsoft, really, really value the network effects of how we bring, best practices to bear across so many different organizations. So our digital event is happening next week, two days of fantastic speakers anticipating over fifteen thousand attendees.

Just know that if you register, and aren't able to make the event, you will still get the recordings after the session.

And we also do a number of in person events, Chicago in June, and then Dallas and September too in in the US.

But we have eleven globally. So that's a little bit about, you know, ways to stay connected with our two organizations. I appreciate we have one minute left.

But I think some of the security questions, there's one question around, you know, how do you maintain the the security aspect of some of the new technologies.

I think what's really critically important is that while we have access to the LLM models that are more open source in public, where there is proprietary information, those types of chats and information are maintained specifically within your ecosystem, your environment, and where there is evermore regulated GDP, etcetera, you know, those limitations and restrictions are understood so that we're taking advantage of technology, but doing in a way that allows us to not be at risk from a data confidentiality breach, ex you know, etcetera.

Yeah.

What what goes on on that, Brent, I think, you know, part of the partnership that Microsoft has with, the Open AI is we're trying to leverage some of the algorithms from the large learning models, but we're putting an enterprise wrapper around that And so when we talk about some of these use cases, while we can take advantage of the open, large learning models, the way we deploy could be a small learning model.

So the data is private. We don't use customer data to train the large learning models and, not only are we protecting data and IP, we're architecting in a way that is not gonna risk downtime of operations.

And then also looking for some of the cyber threats. So Microsoft has really made, huge strides in overall enterprise security. So a lot more we can do in going into that, but that's my one minute response. We don't sign mail time time allowed.

Thanks, David. Well, hey, I know we're already a minute over. But have really appreciated the hour with you, David. You know, I hope everybody on the call took away something from this webinar. And, yeah, have a great rest of the day and week. And, and we'll, be in touch soon. Thanks, David.

Thank you.

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