How Manufacturing Simulation Helps Your Business

Dave McDermaid: Hello, and welcome to this session on solving real business problems with manufacturing simulation. You’ll be muted for the session, obviously, but you can chat with me on the event chat, not on the Zoom chat. You can schedule a meeting with me later on. And, of course, the presentation will be made available in PDF afterward.

So many of our customers, many of you, use simulation, or should cost analysis to help the business, but in what way? Each one of aPriori’s clients has different problems to solve, and we’re gonna explore them today. In my organization, I make drills and off-the-shelf items. I make custom drills that I need to bid for in the world of mining in forestry. I turn over about $350 million, of which $210 million of the cost of goods sold aPriori helps me with and have a gross margin of $42 million.

These are some of the other stats about my organization, including the stats around how much of the business and margin comes from custom work that I have to bid on against a specification. My products go into life for about five years on average, which is important to note because the decisions I make today could be far-reaching.

How aPriori’s Real-Time Manufacturing Simulation Software Drives Cost Savings

So, how do I solve problems? These are the top 10 that we’re gonna look at today. Of course, there are about 37 different classic ways in which aPriori can help. These are just the ones we’ll look at today. We’ll start with one of the most common ones, fact-based negotiation. How does aPriori help customers of aPriori and people in the world of manufacturing simulation do this?

Well, first of all, for those who know aPriori, it very quickly, very repeatedly, very precisely as precise as we need it to be, gives us manufacturing information and cost that we can use. So, if I look at the components that aPriori runs through very, very quickly, I can see from left to right all components with their should costs. I can also bring in information from ERP about the actual costs quoted by my suppliers, and I can see the differences between them. Crucially, aPriori does this very quickly, as we know, and the speed allows us to get this information at a very timely point in the process where we can see where the savings potential is. So, I can negotiate good contracts with my suppliers, and I can look at these savings by size or percentage.

This one here doesn’t have a big difference as a percentage, but this one does. So, this is a component I want to talk to the supplier about before I commit to production to see if we’re aligned on our thinking, to see if this is the right supplier for it, and get a good price for doing that. When we do, we look into the digital factor within aPriori. So, there’s many ways of looking at the information coming out. This is the most detailed one. This is where I see inside the factory in aPriori, the digital one that produced this component, and these digital factories made some assumptions around the optimal routing and process for this.

It selected the best machine for it based on the size of the component, raw material, and some other factors. But my supplier may have a slightly bigger machine, and I can just rerun the simulation based on that to make sure I’m aligned with my clients. So, if they’re gonna charge more money because of a bigger machine, well, I can test that to see what that should be. I can also break down this cost into more detail and look at the assumptions and calculations that are made inside the digital factory. Here, if my supplier and I are not aligned on the overhead cost, then I could see how aPriori came about calculating that at 56 pence here.

I could see the overhead rate that was calculated at 11.41 per hour. I can see the assumed yield and so on. And when my supplier tells me that the overhead rate is much larger, well, I can just re-run this simulation with a bigger number and see what the outcome of that would be. I can also see more detail about how we and aPriori assume the energy cost that’s based on electricity and machine power. These calculations are made inside the digital factory calculations that go beyond just negotiation. This gives us insight into sustainability as well. In fact, watch this space as aPriori is going to be using these numbers to generate much greater insight for our customers as sustainability becomes more important.

So, the information inside the simulation is very powerful. It gives us another layer of data to have a conversation with a supplier to get the best price based on the processes that they have in the real world. So, this is a classic way of using aPriori for fact-based negotiation. But what does it mean to our organization? Well, let’s look at those numbers again. There’s 147 million that we spend with the supply chain, and if we made just a 3.4% reduction in procurement costs, and I say 3.4 because our average with our clients is 5.4. I’m gonna take 2.0% to the next thing I’m gonna talk about, which is strategic sourcing. So, a small percentage difference yields 5 million a year, and over a year, find your production run that’s worth $25 million to us. This is why aPriori is used in this world most often because there are some very large savings to be had by just getting aligned with the supply chain to reduce costs.

Reducing Complex RFQ Processes and Evaluating Supplier Manufacturing Operations

The next area is reducing the amount of RFQs and quotation processes in the supply chain because these quotation processes take time, energy, and draw away from our efficiency. And it also creates a situation where the suppliers are starting to respond to quotes. When we are crying wolf. We are bidding… They’re asking to bid for things that they’re probably not gonna win; they’re gonna start putting their prices up accordingly. So, what we can do with aPriori is define digital factories inside here that replicate, much more precisely, our manufacturing suppliers. And inside these, we have the different skills that they have in their wage grades. And we can collaborate with these suppliers to get this detailed insight here because if we can understand what their costs are gonna be, we can give them more business, which is good for them and good for us, obviously.

And then, we replicate their different processes in here. They do machining, for example. And these are all the different operations that they can do in machining. These are all the different machines that they use. And each machine says for five access has its different power ratings. Its properties are around the size of the machine, what parts it can take, as well as the labor rate setup times. Inside this digital factory is everything we need to define the manufacturing strategy and process for one of our suppliers. And for key strategic suppliers, we can use this to get to a should cost with the margin, of course, so we can start production early on without going through the RFQ process. We only do this with strategic suppliers, of course. But that means we can both have good business margins that are right for them and us. And we both make parts to help ultimately my business, which determines our success.

Accelerating Sourcing Workflows With aPriori’s Manufacturing Simulation Models

We also use aPriori in the strategic sourcing world. Now, strategic sourcing is a bit of a broad church, but there are many ways we can use the fact that aPriori provides simulations very, very quickly. So, for example, I can take one component and simulate it in 79 countries of the world. I can see that it’s very cheap over in India and Vietnam, as you’d expect to see, and very expensive in Switzerland and the Nordics; that’s not a surprise, it’s not news to me. But what is news is by how much? What is the actual difference? Because I could start to think about supplying through multiple zones to reduce risk, which is an important factor, especially today, and maintain a good cost, and identify the regions of the world where there’s inflation, from Spain to Portugal. And from Spain to Portugal is a land border of a big cost difference that I could exploit and plan my manufacturing strategy.

So, once we can start to understand this very quickly, which we need to be able to, ’cause we’ve gotta make these decisions very early on and very quickly. I can start to run comparisons. So, here’s a set of parts that I’ve run the same simulation for but in two different places. One in China where I could have my own manufacturing plants. So, this is now a make-buy decision, or I could be using a supplier in China versus Mexico as the other one. So, I’m looking at two manufacturing regions or actual suppliers. Over here the costs are about the same. So, if I really want to produce in China, ideally, well, the costs are about the same compared to Mexico, so I’ll keep those parts in China. However, the components over here are much more affordable in Mexico; they’re much cheaper. Of course, there’s a risk, and when the world changes, as we’ve already seen with the pandemic, Brexit, and US elections, all the other factors are going to have an impact: transportation, logistics, tax, and duty material brace risings. So, I can use aPriori to perhaps second guess some of that to look at different scenarios where these numbers go up. But also to look for the big gaps that will allow me to accommodate large changes in the world’s structure in global politics and economics.

And therefore, I can make decisions early on to say which are the best candidates with the lowest risk to put in those cost regions which are more affordable. So, there are many more ways that we can help strategic sourcing make decisions just like that. But what does it mean to us? Well, let’s suppose that half of my supply chain, that’s 74 million, is flexible. The stuff that just has to be made in certain places for all sorts of reasons, I can’t really mess around with that, but let’s say half of it I can really explore the options on, and I took 2% out the cost of that. That’s worth one and a half million per year. And over the life of the product, that could be worth about $7.5 million to us. That’s significant.

And the key to that is making strategic decisions as early on as possible ’cause these things are not easy to set up, and that’s why aPriori’s simulation with a digital twin very, very fast and precise and reliable allows us to make these kinds of decisions. Let’s move into the engineering world. So, a lot of companies today are innovating much more. Innovation is the key to what they do, and we would like more time to innovate, not fix things, and aPriori helps us do that. So, this is the simulation that’s performed on a piece part that gives guidance back to the design teams. And those guidance issues are things that we should consider now, like things that are impossible to manufacture or things we’ve not considered, like the draft angle that will ultimately result in engineering change orders or supplier discussions that just take away from the business of innovating.

So, if we know these early on, ’cause aPriori tells us them, we can address them before we commit to critical design stages, and we can avoid late-stage change, which impacts our ability to innovate. But where in a product like this do we make entire drills where if all these components are these issues? Where do we discover them? Well, aPriori is going to give us that through reporting. It’s gonna combine not just design from manufacture information but cost into the equation ’cause cost is gonna govern where the best opportunities lie. So, if I plot all my die castings here on a graph of cost along the Y axis versus mass or weight on the bottom axis, I can see a trend for die castings. Weight and cost are somewhat proportional, but this component is above the line. It’s more expensive than it should be, and the color of that bubble represents its manufacturability. Purple is bad. Therefore, in a sea of opportunities that all have DFM issues, these are the ones that I should actually focus my attention on and go and fix before the ECUs come along because they’re gonna really hurt me.

What does that mean to us? So, if we can spend more time on innovation, we can impact our margin. We could sell more products because they’re better, and a 5% increase in that margin or gross sales on a 43 million margin is worth $2.1 million to us. That could be conservative; sometimes, there’s a threshold, especially in the world of electrification and automation and automotive, for example, that making small steps in innovation can have huge increases in revenues. And we’ve seen that in many cases today. Design for excellence is a process that many organizations take on board, especially today, and it covers many factors, including manufacturability and cost. But sustainability is coming into the equation as well. Now aPriori is gonna provide us with more insight through these simulations. The same component here that’s being simulated will provide guidance issues that are also more around design for excellence, more cost-related, and manufacturer-related, which may not be problems from the manufacturer but have an impact on it.

For example, I’ve designed these blind holes into my component. They don’t go through the component, and aPriori tells me, well, it’s fine, but there is an impact on the tooling. We’re gonna have fragile core pins in the tool, which could lead to yield problems to maintenance problems that may limit my ability to design multi-cavity components with sliders. It just creates complexity we really want to avoid. In other words, if I made those through holes, life would be better. We also have issues around wall thickness as well as castings. Here, it tells me I’ve designed some small walls in my casting, and there are tools in aPriori to show me where they all are. But basically, I’ve breached good manufacturability practices. And this will have an impact on the yield, as well as the cycle time of this component, all the way through the design process is pointing me towards things that I should or could consider in the design process.

The other thing we can do from a design-for-excellence approach is think about absolutely everything. I can run the same component through different processes like additive machines and sand casting and compare different manufacturing approaches. High volume, low volume, different batch sizes, different manufacturing regions. In fact, we can create a matrix of costings that can look at all these possibilities that focus on the ultimate goal of excellence. Whatever the factors need to be considered in our company, it does this very quickly, of course, and it would need to because there are literally thousands of permutations that we could consider, and aPriori is gonna process them because the digital twin helps us with that automation. But our sub-component of design for excellence is design to cost.

Evaluating and Selecting Cost-Effective Production Processes

And aPriori gives us cost information as we design, as well as guidance to tell us what’s good and what’s bad. But here’s a couple of examples of where design to cost can be exploited with aPriori. This is a weldment, a series of sheet metal and plate parts welded together, and aPriori costs the part, and it costs the welding process as well to get me a should cost. But I could just as easily cast this and then start to design for castability. I can machine it from solid and all sorts. Here are three different processes. One on the left is the original process where we’re going to weld it ’cause that’s what we’ve always done. That’s what we do because of the low volumes we work on. However, supposedly, the volumes go up like they have in this case, then from a fully burdened cost perspective, you can see here we’ve gone from 3000 if we weld it down to 1100 if we machine from a casting.

There’s a 64% increase… Decrease rather in the cost if we cast rather than weld. And sometimes, these opportunities aren’t that obvious, but aPriori can help us find them. What it can also tell us is that if we go down a casting route, and casting becomes a risky process through the supply chain because of pandemics and Brexits and things, then well, I could machine from solid. That could mean my get-out-of-jail strategy and the cost of machining that from solid is a lot higher, of course, but I can plug that into my risk equation. Fabrication is much easier to do, and there’s less risk around it, but it requires tooling and fixturing. I can go straight to manufacturing from solid if the world changes and have a supply shortage. It gives me options.

We do the same, of course, with the electrical world for a wire harness or a printed circuit board. We have different design strategies we can do. I can take a bill of materials and put them into aPriori to see how much this board costs, including the process of making it as well. If I use lots of silicon, could I use more passives at a greater price? But I could de-risk my supply chain as we’ve seen today and perhaps avoid expensive ASICS and applications-specific intermediate circuits. I can compare one design version with the other, bring into the equation risk, and make decisions on which design is best from a manufacturability and cost perspective—most importantly, from a design-to-cost view, as aPriori analyzes everything. As we design stuff, we can analyze it and set targets that we follow.

When we get aPriori to give us our costs, and there are overruns, this part of the system here, the motor is overrun on cost. The blue bar shows me that I then focus my very finite and limited resources on solving that problem, and therefore, I can bring the cost under control. The simulations themselves allow us to track where we are going off base and then bring it back to when we need to in a timely fashion, of course. Designing into cost can be achieved in many ways, but what does it mean to us?

Let’s say 30% of my products, including my custom products, are new every year. So, $63 million of my COGS, my cost of goods sold, can be reduced if we avoid these issues. Let’s say we take a 5.2% cost reduction or cost avoidance, let’s say 5.2% because that’s what our customers get on average, that’s worth $3.3 million to my organization per year, over five years, of course, that’s worth 16.5. And if you look at this from a pure cost avoidance perspective, when I design something out, and it goes into production for five years, that is real. That’s a real cost saving or avoidance, which will carry on through the life of the new product. To get that cost back out later requires expensive value engineering and cost reduction that I don’t want to do. Remember, I want to focus on innovation, not trying to get costs out later on. I wanna do it today. And that’s the impact I can have.

I can also use aPriori to optimize my manufacturing throughput. So, we make our own stuff in our organization, some of it, but we also have suppliers who do the same thing. They’re trying to optimize their throughput. So how does aPriori help with that? Well, first of all, it gives us reports. So, here’s a list of a package of work for each machine in its capacity requirement in terms of hours. So, to make all this stuff, I need this much five access capacity, this much four access capacity. It knows this because it has a cycle time, a set of information, and other parameters that can add to the old overall requirements. So therefore, I can decide if this is possible to make internally or if I need to route it somewhere else. If I make it internally, I know how many machines I need.

If I don’t have the capacity for the five-axis, I could switch to four and get aPriori to re-run the simulation, and I know what the impact’s gonna be. It’ll tell me how much time I’m gonna need in the four-axis; it’ll be a bit more ’cause there might be some more setups involved. It might be less powerful machines, but I can see that output and make decisions and route my parts through my manufacturing facility based on that. My suppliers will do exactly the same. In fact, when I give my suppliers bid packages to work on, I’ll provide this with them, too, so they can make the best decisions that don’t hurt them or me later on.

Utilizing aPriori’s Manufacturing Simulation Tool to Reduce Raw Material Costs

aPriori does the same with material as well. But if we look at our internal costs, let’s say 32 million, and we take 3% out of this through better utilization, then that’s worth nearly a million to us just by optimizing our manufacturing facilities as smidge, just a wee bit better equals nearly a million dollars, which is huge. So that same principle, as I said, applies to material cost reduction through all the simulations going through a bid package. aPriori adds them all up and gives us a view of the material requirements. And when I say the material requirements, I mean absolutely everything, including all the material that’s going to be scrapped and bought back by the material provider. But this is what I need to purchase in terms of volume. It also calculates how much money is gonna be got back from the scrap, too.

So, there’s two things that come from this. One is we can plan better for supply and know which suppliers or materials we will be buying more or less of and then choose our regions and suppliers very carefully to get the best cost-effective prices for them. But it also allows us to inform the design to say use more of this material, use less of that material, so I’m not buying much. And therefore, feeding that back into the design process allows us to design for more cost-effective materials, reducing material spend. But it’s slightly complicated. Here’s an example of two design options that we were exploring. The left-hand component is the original component we planned to make in basic ABS. And the other one is one with a glass fill in it. Now, we chose ABS for the original design because it’s a cheap, cheap material.

The glass one is 11.54% more expensive. So, this sounds like it’s going in the wrong direction with the design-to-cost perspective because the material span is higher. But aPriori told me to go and look at that material and its design-to-cost guidance because the cycle time for that material is significantly lower. And the bottom line there for us is that I reduced the component cost by spending more money on material that’s counterintuitive. We need this kind of simulation to help us find these opportunities to get the cost down. Of course, if we look across our whole business, we may find more opportunities just like this. And if we focus our design effort on this other material grade that, for certain components, is better, then we can start to drive a better price for that material and get the best of both worlds. Using that same material here, I get a lower cycle time, of course. And I also get a lower material cost because I’m buying that material in more volume now. Therefore, my overall impact is 22.4% cheaper. That’s huge just by considering the material strategy and the design at the same time.

So, if I look at my spend on material, let’s say 59 million, I take 2% out of that by being smarter about decision-making. That’s a small amount really to consider, then I’m saving 1.2 million going forward. Another way of looking at that is if material prices are starting to rise, I can limit the damage by using this kind of analysis. And we see that today. Over five years, decisions that we make on materials in today’s money terms of our $6 million, but that saving can be much larger as material prices go up.

With respect to bid win rate and bid margin, all we do with aPriori is use exactly the same stuff we’ve just gone through. Understanding the costs for different manufacturing processes, we have in our facilities and our supply chain to know what the cost of something is as we bid against a specification. We start to design things, we see what the prices are, and we can put in a bid. What does that mean to us? Well, there’s two areas we can impact it. One is through winning more bids. Let’s say we want 10% more bids because we have better prices, we understand our margins, we can control that bit better. We can bid a little bit sharper to win more business. 10% is kind of achievable if we reduce our price a little bit, we can grab a much bigger market share ’cause our prices attract people. Certainly, avoid those ones that we don’t win because we’ve just over-egged the bid to make sure we don’t lose out on them.

So, that could be worth 0.3 million based on the actual margin improvement. Sorry, 1.1 and 0.3 if we give some of that margin back to ourselves, so total improvement could be worth 1.4 million a year. So, adding all these up, the total savings potential is a whopping 39.4 million per year. And there’s little asterisks because there are some caveats around that. Some of these caveats imply that this can be a lot larger, by the way. But finally, what happens if we automate all of this with CIG, with Cost Insight Generate? What’s the impact then? Well, let’s look at all those savings potential on a normal distribution. So, the number of parts versus the saving potential on parts on the left-hand side has no real savings, but some parts have very large savings. The trick is to find them and avoid the low saving potential. Traditional approaches to cost reduction will yield an average percentage improvement every year. With aPriori, of course, we expand that because we have greater speed and insight. We can look at a lot more, sanction it, and look at the best stuff.

But what about everything else? This is where our Cost Insight Generate automation can help us. We haven’t had time to look at all these components and assemblies before, but if we can, we can find the best opportunities. But unfortunately, we can’t do everything without significant investment in our company, which we could do if we wanted to. Without that, we have a finite team of people that can execute what aPriori provides through insight people to design and cost the procurement, cost…cost engineers as well. So therefore, what we really will do then is just move the focus onto the things that we can do. So that blue bar… Blue boundary, rather, represents what we can address. So, let’s shift our focus forward to that. We can measure what improvement that is. So, let’s say, for example, my business, I go from the ability to understand 30% of what I do to now 90% because automated simulation allows me to look at a much greater part of my business in the background.

That means we can shift the focus and the center of gravity over to the areas where we should be spending our valuable resources. So, if the conventional approach is 5% per year, that’s what we’re getting today with the highest savings potential of 10%. And we use automation to shift everything to the right, which would yield an extra 2.64% on everything based on those parameters. That’s real. That’s just mathematics using average as a normal distribution. And across all these different scenarios that I’ve talked about, there are greater or lesser numbers based on these parameters here. And if I run everything through based on automation, I get an extra $14 million. But let me be really clear; I’m getting that $14 million for doing nothing, zero. I’m doing exactly what I do today, but I’m doing it on the things that aPriori already tells me I should be doing it on because of automation. Now that whole number starts to get very large and perhaps even unrealistic for some people. That’s a very large number in a company that only turns over 350 million a year.

But the numbers don’t lie. They add up. But the question is, could I really get those percentage savings? Are they mutually exclusive? Just look at the material versus the design and manufacturing process. Even if everything only had a 1% improvement and everything I just talked about, we only found 1% in every scenario that’s still worth 15.3 million a year. And I don’t believe for a second there’s an organization out there that’s interested in making that kind of impact. And we can do that with insight and automation. So that’s how we solve real business problems with manufacturing simulation. Thank you for your time. For the aPriori customers out there, remember to leave a review in G2, Capterra, or TrustRadius for a chance to win some prizes, and I look forward to speaking to some of you very soon. Thank you very much. Bye-bye.