aPriori End to End: Unlocking Business Value from Design to Source

Evan Roux:

My goal today is to take us through a lot of what we’ve heard, both from a product perspective and a customer perspective, and look at aPriori end to end, helping you see where aPriori can be leveraged from design to source.

The data we generate is the valuable piece of this information. How can we use that data to drive faster, better decisions in your organizations?

Let’s say we’re a server rack manufacturer. We just won a new RFQ for some new work. Maybe the volume is a little bit higher than we’re typically used to working with. There are new specs, so we have to do some redesigning. There are internal capacity constraints based on our organization and our industry’s boom within the marketplace, and general inconsistencies across the board in what we see in quoting.

These are challenges you’re traditionally used to seeing within your organizations, and I want to show, from an aPriori product perspective, how we can attack them. Given that scenario and the challenges I just talked about, I’ll walk through the three different personas we’ve spent some time mentioning today.

Product design: taking those new requirements and iterating quickly and seamlessly to track toward target costs. Manufacturing: making data-driven decisions. What products should you make? How should you make them? Where should you make them? And procurement: driving fact-based negotiation to get the right parts to the right suppliers at the right cost.

Let’s start with the product designer. Nowadays, they’re expected to deliver highly innovative, manufacturable, cost-conscious parts faster than ever before. With aPriori alongside them in the development process, what we can do for designers is give them continuous cost tracking across every concept and every iteration going through their organization.

We can give them the ability to take cost out on demand. This has been a theme of a lot of the conversation today. Do this while they’re designing, not after they’ve gone through critical design review. It’s too late in the process.

Most importantly, how can we give them time back? Time not spent working on ECOs or cost-out activities later, but actually working on the designs they’re deploying within the organization.

As that designer develops products, they’re doing their work as usual, checking parts into PLM and developing different iterations. aP Generate, our automation platform, will automatically analyze each one of these components, proactively beginning to generate this data we’ve been talking about and helping set the stage for future-stage development projects and delivering the best work possible.

As aP Generate develops that data, aPriori meets the engineer where they are, bubbling up the critical data they need to do the best job they can. Every day, they can come in and see: What are the parts I worked on yesterday? What are the costs associated with them? What are the design risks associated with those components?

That way, they can focus on where there’s opportunity, where there’s risk, and, most importantly, what’s on target and what they can just forget about and move on from.

As they see products with manufacturing risk, they can access aP Design directly and begin to respond immediately, addressing design changes or manufacturability risks. There might be something like bend radii. We’ll give you the idea of: This is where you are, and this is where you need to be to eliminate that risk.

That allows the engineer to get the feedback they need without having to wait for SMEs to get involved in the conversation. They can then take that part directly into CAD and do their job, make some slight changes, drive live updates, and push that directly back into aPriori to evaluate a couple of things: Did my new design satisfy the requirements that I had? Did I do that without impacting cost, weight, or whatever other metrics I’m measuring?

But we are a company designing products. At the product level, we can start to look across our entire design, from components being generated to all the design risks and all the costs associated with those components. We can look at having it fully analyzed to the point of understanding what our manufacturing costs from an assembly level will look like and have that fully rolled-up BOM to guide us in where we’re tracking today.

We can understand, as this BOM gets developed and all the parts get deployed: Where are we tracking? Are we over cost? Are we under cost? Are we on target? Do we need to pull additional cost out of this product?

This allows program managers to understand very quickly: We’re 8 percent over cost. We need to do something about it. We can then add more categorical data around your organization: These are the subassemblies or subsystems that are driving costs. This is where you need to pay attention.

These are traditional ways you look at things, but now you’re putting aPriori data at your fingertips to make those decisions. Then go one step further and look at aPriori data from not just knowing a product is over cost, but actually understanding which products are right for us to cost out.

Using outlier graphs like this, we can say: Big purple bubbles, poorly designed components, a lot of spend in them. That’s where we want to focus our engineering time on pulling cost out of the programs we’re running.

Then I can start digging into those components as a value engineer or an engineer. I have a $53 sheet metal part that’s 80 percent material cost. I’ve got a couple of levers I can pull. Can I help with utilization? Can I change the material? Maybe not. Maybe it needs to be steel for the performance.

Then the last piece is stock. Maybe I can thin this material out. It’s a door panel, not a structural component. What if I used 20-gauge steel instead of 18-gauge steel?

These are very quick questions aPriori can help you answer. We can drive that analysis back to your engineers and say, “This will satisfy the requirements of your component while driving cost out of the products you’re developing.”

All the while, we’re tracking every iteration and every cost that’s being generated within your organization and your design environment for this component. I can have a historical track of: Here’s where I started. Here’s where I designed cost into my product. Here’s where I designed cost out. I can understand what my optimum iteration, optimum inputs, and optimum analysis would be.

Then take that one step further and actually put cost to the work your engineers are doing. How many dollars did I take out of my product? How much money does that represent in a year? We can really put some metrics against the design work your team is doing.

All of this bubbles up into our Design Value Dashboard, which we’ve deployed and you’ve heard about. You can create projects, put all the components we’re working on in this server rack into a project, and track how we’ve performed across all the iterations of this part as it’s deployed.

You can say: This program has pulled out this amount of cost through cost avoidance, design activities, and iterations over time. That really allows your program managers and internal experts to understand the progress that’s been made throughout the design cycle.

Everything comes back to being able to understand where I’m tracking. Getting that aPriori data elsewhere allows you to understand: Am I on pace? Am I on target? Is there additional work to be done? And it saves you from doing that extra work to pull cost out downstream.

In this case, maybe we’ve hit our target cost. Now we’re at a point of using aPriori Workspaces to help designers clearly hand off projects. When should manufacturing get involved? When should procurement get involved? We can hand off and notify: This design has been established. It’s a new design. This is the volume. Manufacturing, can we make this? Procurement, should we source this? We can ask those questions and do it within our environment itself.

From the product design perspective, aPriori data provides continuous cost tracking during development. We can allow engineers to do on-demand engineering as they’re working. Know when costs are high. Be aware of how to attack them with the aPriori data we provide. Then allow them to understand how to impact that and what the quantifiable value of their effort is. And, as we’ve said, give them more time to engineer and more time to design.

From a manufacturing standpoint, aPriori data can help solve a few of their challenges. Given our ability to create digital factories, we can allow true make-versus-buy decisions. Understand early in the process: What will this look like to make? What will it look like to buy?

We can help them understand cost versus risk. What do my materials look like? What process decisions and product decisions can I make that can help avoid risk as we get to production? And we can help them optimize internal capabilities by understanding the impact of using certain machines and certain processes for certain parts.

For those of you who’ve used aPriori, graphs like this allow you to say, at the design stage, all this data has been generated. How much cycle time, machine time, and process time is required for all of the components in this BOM?

That’s a really good trigger for manufacturing to know: We need this much capacity for these components. How are we going to do this? We can go beyond just processes and get down to the granular data points of how much cycle time is required for every single machine that may exist in there. You can get down to that level of granularity to start making some decisions.

In aPriori Professional, we can do a very quick analysis. Look at all the different routings that we ran this component through. Maybe a turret press is a feasible opportunity. It’s 20 percent more expensive, but we have capacity there. Maybe that’s the direction we want to go, and we can look at moving our parts as a manufacturing engineer to those sorts of processes.

Or maybe we want to go one step further and say, “aPriori always picks the optimal machine. What if we did something suboptimal?” Take that and put it on a 10-kilowatt laser, rerun the analysis, and very quickly understand the impact on our organization. If we have open capacity, let’s use the suboptimal machine, be aware of that, but capitalize on the ability to actually deploy this product.

From a material perspective, when we have processes honed in, manufacturing can look beyond just understanding the risks and requirements to make all these parts. With aPriori’s ability to calculate rough mass and understand the annual quantities required, you can start to make more strategic preparations as you get into purchasing that material.

We can give you an understanding of what products and materials are high-purchase items. Do we have established material rates that we’ve discussed with our supply base? Are there things that we need to proactively make sure we’re sourcing strategically to pull cost out of the products and make sure we’re not as exposed to variable material prices in the market?

From a manufacturing perspective, our data can give early visibility to make complex make-versus-buy decisions using our digital factories, allowing for all different scenarios to be evaluated, whether that’s processes or machines. We can put our data next to your reality, help you optimize those true internal capabilities, and give manufacturing teams the ability to understand cost versus risk early in the process, when they actually have the ability to make those decisions.

Lastly, we’ll look at things from a procurement and sourcing lens. We can help optimize a lot of the procurement strategies teams have talked about already today. Things like where, how, and with whom should I buy all the components I’m going to source?

We can help identify areas where opportunities exist to get cost out. Once we do, through aiSource, we can help prepare your buyers and support the necessary collaboration with their own internal teams to help get to the cost we need to deploy this in the most optimal fashion.

One thing I wanted to start with is that, in order to determine what kind of supplier can make a part, aPriori can help you understand what those parts look like and what types of suppliers fit.

Traditionally, it might be: Send quotes out to a whole bunch of suppliers and get some data back. There are going to be some no-quotes and some quotes that are way too high. You’re wasting your supplier’s time because they shouldn’t be making that part.

With aPriori, a lot of our teams will begin by actually looking at the components. How is it made? What is it made out of? How big is it? How complex is it? How tightly toleranced is it?

Some of these metrics allow you to bucket your parts and understand: This is what this part looks like. What kind of suppliers can make it? Using that information, start to map some of this data. Have your supplier capabilities mapped out with what they can handle. Have your parts mapped with an understanding of what capabilities are required, and start to match those in a more efficient way.

As you quote parts, you’re getting fewer frivolous no-quotes from suppliers that aren’t a fit, less time internally working through quotes that aren’t optimized, and really speeding up that process of going from RFQ to PO.

Once we know what kind of suppliers we’re looking for, we want to look at the parts and where we should source them. Obviously, part of aPriori is running across a whole bunch of regions and understanding, with a matrix cost analysis, what my different cost targets are in each of these regions. That gives me referenceable benchmark pricing.

But more importantly, as people have discussed already today, there are logistics, tariffs, and other pieces of information. Once we can get this information into the hands of your teams, we can start to overlay some of that. Now you have a fully rolled-up landed-cost aPriori analysis, all the different regions and all the different impacts, and can start to make those decisions in a more robust manner.

Let’s say we have an established supplier in Poland. We might be exploring a new supplier in Türkiye as a low-cost alternative. Traditional procurement decisions are made through this lens from aPriori, saying, “Let’s look at low-cost countries. What low-cost countries are out there? How can we execute that?”

What we can do is take that a little bit further. We can run an analysis for all of these components in Poland, run another analysis for all of these in Türkiye, and start to stack-rank these based on which parts we should move.

Parts on the right, the costs are pretty comparable. Probably not a big labor difference, or labor impact, on those components. Parts on the left are the ones we want to focus on moving.

It’s all about understanding where we want to focus and where the biggest opportunity lies. aPriori’s underlying data can help drive some of those decisions so your teams can focus on the best parts to move, the biggest benefit to moving them, and understand the risks associated with things like setting up a new supplier and the quality of that supplier.

How can we start to manage some of those risks and make sure we’re executing on the benefit as well?

Beyond the who, aPriori data can help you optimize how we buy. Things like understanding the differences between order quantities and what processes are required at different volumes that may exist out in the market.

We can help your team strategize around optimizing those decision points and putting data behind those decisions. How can we balance inventory costs versus sourcing costs versus batch-size costs? That’s a really significant and challenging problem that aPriori can help put data behind for your teams.

The data aPriori generates can also be really invaluable inside procurement tools. We’ve talked about some of those that are out in the market today. We can begin to identify regions and batch sizes. We can put benchmark costs against all of your internal procurement tools and expected costs as quotes come in, putting that information in the places where your procurement teams actually work.

We can give that instant visibility to understand: Here’s my BOM cost. Here are all the subcomponent costs. Here are the parts driving cost. Which ones should I focus on as I go forward?

As quotes begin to come in, let’s put aPriori data against those quotes in your systems. Give your procurement team, in their native environment, the ability to say, “Here’s my cost. Here’s where my quotes are coming in in comparison. Here’s where I need to address some concerns,” getting down to even the deepest levels of cost drivers.

But beyond just a single component, do all of this at a very high level. As your quotes come in, let’s push that data back into aPriori and run these outlier analyses we’ve talked about.

Where’s your opportunity? Parts on the left-hand side show a ton of annual opportunity. Maybe it’s a small percentage difference. It’s going to be hard to get that. Allow your procurement teams to focus on the big gaps and the big opportunities out there and drive those into the organization.

Really just find the low-hanging fruit that exists in all the parts you’re already sourcing, already getting quotes in for, and already delivering should-cost with aPriori.

At this point, once you have your quotes, a should-cost, and opportunities against that should-cost, the next step is negotiating. That’s where AI Source comes in. Put your quote in. Allow your aiSource chat to guide you and help build that negotiation strategy. Turn your most junior, non-technical buyers into seasoned professionals, preparing them for negotiations with aiSource.

Our goal is to be that intelligence layer, putting all the relevant data across your organization, whether that be ERP, PLM, CAD, or the granular aPriori data you’re already leveraging. That data is ripe to pull into those systems and deploy throughout the organization.