How to Use aPriori to Overcome Procurement Challenges

Evan Roux: In today’s session, we will cover an array of different strategies where aPriori can be deployed to help overcome critical procurement challenges through deployment of various reports, allowing aPriori users to visualize data in more creative ways. My name is Evan Rue. I’m a Senior Expert Services Consultant at aPriori.

My background prior to aPriori was focused on the digital transformation and supply chain spaces within the manufacturing industry. In today’s session, we will cover an assortment of critical risks that aPriori can support. I’ll begin with a brief overview of aPriori’s digital factories, what data we have that can be leveraged through procurement analyses, and why that data is so important. I’ll walk through common strategies for regional variation investigation, trying to determine where in the world and of what you should make something.

The Procurement Process

From a volume and batch size perspective, I will talk through capabilities and process decisions that can be driven based on your volume and batch size analyses. I will look at some material risk analysis to determine where your optimal materials may be, what purchase volumes you can work with, where there’s opportunities for significant scrap production, as well as where there’s opportunities to identify materials that may be substituted.

Finally, I will cover RFQ capacity breakdown. This includes understanding how much capacity you’re requiring, what kind of processes are the best and most optimal for the components you’re making. And I will do a deep dive into a report in aP Analytics to show some of this demonstration and activity in the tool. Finally, we’ll wrap up with some question and answer, and hopefully everyone enjoys. As we all know, the procurement risk in our modern world is widespread and appears in every facet of a product lifecycle. We have seen invasive supply chain disruption from every direction, including global conflicts, product shortages, and lingering response to the COVID-19 pandemic. Within all of our supply chains, we’ve seen sporadic and immediate appearance of labor and material shortages.

This is constantly changing how we are getting our products, where we are getting them from, and really how we forecast the future of our companies. With the digitalization of the modern world, supplier and buyer collaboration becomes a critical piece of the procurement puzzle, enabling us to continue to share healthy and functional margins across businesses. As we look more into the near future, global ESG goals will become more and more prevalent within our supply base, and attacking these from a procurement perspective will show opportunity and risk within your supply chain. And finally, intertwined into all these examples, process and material optimization is something that will continue to be an opportunity, but poses a large risk if you are not on top of things and your decision-making is not in the optimal sense.

Defining Your Procurement Strategy

Before I go too far, let me review aPriori’s digital factory and what level of detailed data we provide that make these analyses simple. I’m sure over the past few years, each of us have been presented with a similar problem. You have a labor shortage in your supply base. You need to re-evaluate your options. Is it unique to my supplier, this labor shortage? Is it only in my region? Do I need to find a new source? If I do, what are the wider economic conditions that are going to impact my material costs, and how will that impact the parts I need to move? What about the availability of the material in those regions of the world? Is labor the cost driver, and/or am I willing to pay overtime and keep my parts where they are for a minimal cost increase?

All of these questions can be difficult to answer, but with aPriori data, they can be available at a few clicks of the mouse. Within aPriori’s Regional Data Libraries, we have a complex data that helps identify the impact of these economic factors I’ve talked about a moment ago and guide you to the best possible solutions. I may make this part that I’m looking at in India, and due to supply chain disruption or any other factors that may limit me, I may want to evaluate reshoring this component back to the United States. aPriori has a regionalized labor rates to help evaluate this cost impact. You can understand the significant hourly increase of labor in these 2 regions and see how that overall increases the cost of your component. I may have a steel component in Mexico that I want to move to a strategic supplier in Europe.

aPriori’s regional material rates can help you evaluate the impact of those changes. Is the steel a significant cost driver within my component? If so, perhaps the additional logistics costs to get the component from Western Europe to my home base in the United States may be of benefit, whereas if material isn’t a big contributor, maybe I keep it in Mexico for simplicity of my supply chain. Even down to the granularity of something like the gas rate in a certain region, this will be evaluated in aPriori to help understand the costs on a part-by-part level and how that slight change can really impact things. And aPriori digital factories contain all this level of granularity to help you make these smart and strategic data-driven decisions.

Regional Supply Analysis and Disruptions

One of the most common analyses done utilizing aPriori is regional supply analysis, determining where in the world is optimal for part production based on specific criteria. In our economic climate over the past few years, reshoring, nearshoring have become common themes amongst industry. The advantage of avoiding shipping lead times and skirting disruption can eliminate late delivery. Regulations and restrictions of workforces create uncertainty. Evaluating new suppliers with these criteria in mind becomes a mainstay in risk avoidance. The goal of all our companies is to not only identify suppliers, but truly find collaborative partners within your supply base that align with your strategic goals. And intertwined with all the decisions, the future need for ESG clarity, clean energy sources, it all creates another threat of decision-making, which is weight against cost.

So how can aPriori help you evaluate all sorts of these risks that are appearing in our model? With aPriori’s wide range of Regional Data Libraries, you have the data at your fingertips to identify optimal and plausible options for alternative sources. In this report, I’ve costed a single component across around 20 of our regional data libraries. We have the regional data libraries along the bottom here, and each of the individual part costs for that component in each of the regions is listed on the y-axis. In this scenario, you may have a part where you are currently making it in Denmark. It’s highlighted in yellow here, where labor costs are increasing and may be driving you up to 60% above your target cost, which I’ve established at 30 cents on this component.

Rather than enduring those rising costs, you can analyze a number of regions and understand how the variations and modifications here will impact the underlying cost. Within one report, you can begin to whittle down the feasible regions for further analysis. As your original supply base was in Denmark, you can start to ask yourself, “Do I want to keep my component in Europe for simplicity’s sake?” If so, some of the green bars here, the United Kingdom or Czech Republic may be feasible alternatives. They’re below your target cost. Perhaps you have strategic suppliers in those regions that can be leveraged and get a cost closer to the target cost you desire. Alternatively, perhaps you’re a US-based company and moving it closer to your location would be beneficial for you.

Using Data To Set Procurement Strategy

You know, aPriori Mexico there, we see the data will bring you a cost benefit compared to your target cost and your current supplier. And it can really just unlock the idea of where I can take this part by simply running a matrix cost across all of these different regions on a single component. Within a few minutes, you can have the granularity of the data you need to decide, “What are my feasible regions?” Eliminate regions that may not be an opportunity for you and then contribute this analysis into all of the other decision-making processes you as a procurement professional need to make. Once you get past that first initial step, there would be a second step to really determine beyond that single group of parts. Maybe you’re looking at a larger group of components and you’ve already identified five regions which may be great opportunities for you based on your supply base and your current processing environment.

In this example, I’ve taken 10 or so components and costed them across five different regions, China, India, Mexico, United States, and Western Europe. In this evaluation, I’ve been able to see how cost and the region impacts across each of the different regions as we fluctuate them. All the way to the left side of the screen, you’ll see 2 components where perhaps you’re a US-based company again and you’re currently making these parts in Western Europe. You can see in this chart that the Western Europe region is significantly more expensive than China, India, and Mexico. But as a US-based company, Mexico may be your most viable alternative. And you may elect, because it’s so much cheaper than the United States, let’s see if we can bring this part into Mexico for a 50 or 40 percent cost reduction while keeping it in a reasonable place for our company.

As we move across the screen, you can see there’s a couple of components that I’ve pointed to, number 46 and 159, that are good candidates for repatriation. The costs in Western Europe and the costs in the United States are reasonably close to one another. So labor is likely not a big contributor to cost here. Therefore, these components may be good candidates for you to take a deeper dive at and look at some of your closer range suppliers to join in. Finally, all the way to the right, you can quickly identify parts that would be your last to repatriate. You can see the components here are significantly more expensive than Western Europe, where you’re currently making these parts. China and India are slightly cheaper, but perhaps you don’t have as robust of a supply base there, there’s some unpredictability in those regions, and you’re not exactly sure you want to go that direction.

And Mexico is fairly like the Western European price. So, in these components, you may elect to keep them where they are, they may be slightly more expensive, but they would be your last opportunity to evaluate further as it compares to the rest of the parts on the screen. So fairly quickly, by just taking our data, identifying a couple of regions from analysis, and then being able to get a little bit more granular into each of the components in an RFQ list, you can really start to understand what the best path forward is with each of these components. Is it keeping it where it is? Is it moving it to the closest to the destination you want it to be? Or is there a viable alternative that meets somewhere in the middle where cost, location, and all the other factors come into play?

Streamlining Inventory Management & Supply Chain

Another key aPriori tool is evaluating how volume and batch sizes impact the cost and optimal process for a component. Within the decisions and forecasting behind volume and batch sizing, there are numerous complex factors at play. You may want to optimize your batch size for lead times and economic ordering quantities to streamline your inventory management and supply chain. As your product line grows and changes, the optimal process may change with it, but these can be difficult to see just on surface-level data. You may have labor-intensive processes that you want to evaluate alternative ways of making, and that would ultimately reduce the risk you would have when you’re working in complex labor climates. And finally, as upfront tooling investment can be a critical decision point, evaluation of those choices presents an opportunity to optimize and forecast changes.

You can really make the correct decisions of what processes, what tools to use, and at what volumes in which they become feasible and unfeasible. Within aPriori Excel-based reports, our team has developed the capability to easily view the impact batch sizing has on part cost. By leveraging our batch set-up calculations, you can customize the batch sizes you want to evaluate and quickly graph a trend that they have on impact of cost. So in this example, I’ve costed a single component and a single scenario and ran this part out. The batch sizes you see on the left side of the screen, I fill those out myself, knowing my volumes and knowing my potential batch sizes. And based on those selections, aPriori has recalculated amortized batch set-up cost and ultimately recalculated piece part cost.

Based on these factors, we’ve been able to graphically represent, as we increase our batch size, how the piece part cost is impacted by that. And with a graph and a trend such as this, you can quickly identify maybe what range is your optimal order and quantity in. In this case, maybe somewhere around 2 to 300 units. You can quickly understand at what point increasing your batch size has less of an impact, and on the other end, what low batch sizes that you may be ordering at now based on… Just-in-time. And other factors you may include, how much that truly is impacting your ultimate piece part cost. So, with a report such as this, just running one scenario in aPriori, you can really start to understand and dig into some of the data of, “If I’m in an ideal world and I want to create a batch size, what should that be and how does that impact my cost?”

Evaluation and Decision-Making

Another frequently yet hidden, yet powerful evaluation when you’re working with tooling decisions, specifically in plastic molding, is the evaluation of cold versus hot runner systems. This is a common conversation in a lot of our customer engagements. And in the example here, we just ran a simple matrix cost on the same component. One scenario had a cold runner, one scenario had a hot runner, and across this range of volumes, we’ve been able to plot the fully burdened cost over volume. And what this graph represents is showing where the additional investment in hot runner becomes a cheaper, more feasible option than cold runner. Perhaps today you’re running this part at 10,000 in annual volume, but based on long-term forecasts, maybe you expect the kind of final production value is around 30,000 components.

Perhaps today you may be paying a little bit more up front right now, but long-term you would see a cost benefit of investing in hot runner system. This is a very easy report to run and a very little detail within a costing event, but even across a single component can have a dollar difference in your final production costs, which over the course of many of these plastic molding components can represent hundreds of thousands, if not millions of dollars’ worth of benefit. So even a granular piece of data like this is something that aPriori can tweak. You can quickly set up your report and quickly analyze the data to understand where your inflection point exists and when one process becomes more viable than the other.

One of the biggest drives across our hard tool processes, such as castings and plastics, is the proper cavitation decisions. I know this is dependent a lot on part geometry and feasibility here, but in this example, I’ve set up a single component and run it under four separate scenarios. A one cavity mold, a 2-cavity mold, a 4-cavity mold, and an 8-cavity mold. I have then matrix costed each of these scenarios across a wide range of annual volumes that I may look at. And even understanding where I am today versus where I want to be in the future, I can start to make the most strategic decisions and get the best cost opportunity for my tooling investment. And as each of these lines cross one another, we can see right here where the 2-cavity mold crosses the one cavity mold. We can understand where the 2-cavity becomes a cheaper investment than the one if we’re weighing those two against each other.

Maybe we operate at slightly higher volumes, and we have the geometry that can withstand it. Maybe we can go from a 2-cavity to a 4-cavity. And once we get around 17,000 units a year, we can see the 2 cavity becomes a cheaper investment than four. And finally, if we had the opportunity to go even bigger and our volumes were high enough, we can start to say one and 8-cavity may be cheaper than a 4-cavity. Now we all know this is all tied to investment from a total cost, and that’s why the initial scenarios of low volumes are so highly impacted by the additional cost impact of the tooling. But the other thing that comes out is the processing advantage, the cycle-time reduction, maybe even some more efficiency within your machine capabilities that come out of utilizing these larger molds.

Process-Based Optimization

So really understanding and using the aPriori data to identify the capacity optimization can help you not only get the best tooling investment you can possibly get for the long-term of a component, but you can also reduce the risk that you have or the cycle time within a supplier and understand how your decisions can impact that. The final example I have of volume analysis is process-based. This is very common, and as product lines and volumes grow, going from a soft tool to a hard tool, the sheet metal process becomes an opportunity for cost and risk reduction. In the chart here, I’ve plotted cost versus annual volume again. I’ve taken 2 scenarios of the same exact component. One costed in laser cut, one costed in progressive die, and I’ve costed them over an assortment of different volume levels.

Here you can see the black line is the laser cut. It’s not very responsive to volume increases. This is due to the small amount of investment that’s needed for a soft tool process such as laser cutting, whereas progressive die significantly starts off more expensive early on because you do have that initial tooling investment required. But with a graph like this and a couple of scenarios, you can start to understand at what volumes does progressive die become a feasible alternative to product die. And you can compare those 2 data points to one another to understand if I’m at a 40,000-unit volume for a little bit more cost, I may be able to open my supply base a little bit more to progressive die. I may be able to move this part to another strategic supplier that I have that has progressive die capabilities. All these things come out with just running a couple of scenarios within aPriori.

One of the biggest risks we’ve seen in the last few years is clearly tied to the unpredictability of the materials risk, everyone has been impacted by this. Within our roles, we are constantly battling material risk at every level of the organization. This may be material rate inflation or shortages that drive paying top dollar for any raw material and even driving you to evaluate alternative material types. Scrap can be a major factor as well, in the cost for certain components. Understand what materials and parts are critical to establish buyback programs or recycling programs and what parts you need to pay attention to from a scrap perspective. Across an entire product line, the materials can be completely wide ranging, so understanding this and establishing what optimal materials are and eliminate low usage materials early on in a product life cycle can help the purchasing team just be more strategic in the long term.

Alternatives in Procurement Management

Finally, while material is such a driver, how can we work closely with design to explore candidates for redesign process changes to reduce our total scrap and total material footprint? Depending on if you’re using sheet metal, casting, a machining component, there may be alternatives there that can be creative solutions to problems that you don’t even really see exist yet, poor utilization in a machining process may indicate the capability of being able to go to something like a die casting process that can get the same product performance. So, with aP Analytics we can set up quick reports to look at a group of components and really understand our total material usage and really in turn review the risk and opportunity that may exist on this subset of components. So, for a report like this, I’ve taken a group of components across a couple of different steels and calculated out the annual material usage based on rough mass and annual volumes.

Very quickly I can see Steel 1020 here is my biggest usage material. So that’s one that I have to prioritize from a negotiation standpoint to get the best pricing I can possibly get on the market and be very aware that specific material I’m going to need to keep a very close eye on the material fluctuation. On the single component basis, you can also see parts that are heavily impacted by material fluxes compared to the others. So 319, 17 and 18 here are our biggest runners from a material volume perspective by a lot, if the price of 1020 increases by 10%, our total costs on those parts may increase exorbitantly and that’s something to be very aware and very in-depth on. Additionally, you can investigate what similar materials are there and can you modify them. In this example we have a 1020, the 1020 we buy so much of, can we transition those 1010 parts into 1020.

They’re very similar materials that might show the opportunity to be able to bring those across. And we can consolidate our total volume of purchase here on these materials into almost a million kilograms a year and hopefully get better purchasing power than we would splitting those apart. And the final thing you can take away is find low usage specialized materials that exist within your part base. This may be a design choice for whatever reason there may be functional performance based on it. So, you can start to ask yourself this part here made of galvanized steel, does it have to be galvanized steel or can I just move it to a standard steel, if it does have to be galvanized for performance, could I make it out of standard steel and do some part finishing to get the same quality level I could before? There may be additional performance capabilities and cost put into that because of additional processing, but you’ve eliminated yourself from buying a very, very low volume of a specialized material over the course of a year.

In addition to the annual material, annual scrap is a huge opportunity to dig into these components, you can start to see by running a report where we’ve calculated annual scrap based on aPriori in-depth analysis, you can really start to understand what parts you are scrapping a lot of material on and make some strategic decisions based off that. Knowing that you scrap a lot of the 2 highlighted materials here, can you run through those components specifically and investigate if you have a good scrap buyback program on that material type with your suppliers. That’s something you would want to prioritize. Alternatively, review those parts and look for opportunities where there may be VAVE projects. Can you switch the materials? Can you redesign them to eliminate scrap costs? Can you modify your processes to find the opportunity to eliminate some of your scrap material?

All these things can just be impacted and identified by simply running a report such as this with the data that’s already created by aPriori. Finally, another graphical representation of a material part. In this case, I’ve taken 2 different book charts here and 2 pieces of data where I’ve looked at annual material usage, the dark blue chart and annual scrap material, the light blue chart. In this example I’m able to quickly hone in on our materials that have a lot of annual volume and scrap. This would indicate a significant waste and a significant cost driver, so in the part I’ve highlighted here 158, that part has very, very poor utilization compared to its peers and therefore we are scrapping a lot of material every year, which in turn is a lot of wasted dollars that we’re throwing into these components. This would be a perfect component to really take a dive into, are there opportunities for those redesigns to maybe eliminate or split it apart and do some welding on a component?

Are there process alternatives you could do? Maybe it’s a machine part and you want to cast it and then machine it out or forge it and machine it out and get better utilization there. And finally, is there a cheaper material? Depending on how expensive this material is, you may be able to find something where you’re scrapping a similar amount of material, but you’ve chosen something that’s cheaper and the cost impact of that scrap is a little bit less. So just by running through the data that’s already here and looking at it in a little bit of a different way, you can start to understand really where opportunity arises in your components. The final risk area I want to cover is understanding capacity requirements of your parts and how that data can highlight risks and opportunities across your supply chain. As you work with suppliers and purchase parts, a key driver with so much of the relationship is driven by your capacity requirements.

Identifying The Right Suppliers

aPriori can help you understand based on your required capacity, how much of your supplier’s business you represent, how important you are to them and how critical it is that you get the optimal processes there, because they may be just putting you on a suboptimal process because that’s where there’s availability. You can explore alternative processes that may help alleviate any capacity issues that your suppliers are having. So go from a machining operation to another machining operation that they may have more availability on those machines. And a critical decision point in this all is sourcing the right parts to the right suppliers, at the right time. Knowing that they have the capability, the expertise, and the availability to make the parts competitively is the most important question you have to ask yourself. Just recently I was on a call with a customer and working with their supplier.

We reviewed a handful of parts and 2 of the parts that we reviewed due to the simplicity of them, the supplier indicated that these weren’t the typical parts they make, but they were included in the initial RFQ. So they accepted making them. aPriori saw them as an opportunity, when we analyze their quote versus aPriori cost, the supplier admitted these aren’t parts we typically like to make and that just led us to the, we need to look at these parts and bring them to a different supplier. And that’s really the, the key to that question is aPriori being able to identify that parts that might not be at the right supplier and take that data and bring it to the right supplier. Finally, you might want to reduce your energy intensive product mix with CO2 in mind and all the goals around that.

Understanding what processes, you have on what energy intensive products will really help you drive some of those decisions. Are there alternatives we can take or are there other processes that may be available that require less energy from a capacity perspective? So, in this example I’ve ran five different components through three core aPriori machining processes, 3-Axis Mill, a 4-Axis Mill, and a 5-Axis Mill. Each of these components have a different response to the different costs related to those individual processes and it can help you lead to some strategic decision making in your components. So, this first part here in the list, each of the parts are comparable on cost, but the 3-Axis Mill here would be your last resort, it’s the most expensive of the three. So, if you had decisions and were conversing with your supplier based on availability you may be flexible between a five and a four, but you really would want to resist moving to that 3-Axis Mill. On the second part here, 3 and 4-Axis Mill are very similar in cost. The 5-Axis Mill is more expensive that really eliminates it as an option within the facility and within your supply base. So that would be something you’d want to prioritize. On the third part, it’s very clear 3-Axis Mill is the cheapest of the options by far.

And if you’re evaluating this with a supplier, you need to be certain that this part is being made on a 3-Axis Mill if they’re not making it on a four or five due to some sort of capacity restraint. And finally, all the way to the right you see a part where all three bars are pretty much the same, it’s a very flexible part. This might be a candidate where if they have high capacity on their 4-Axis Mill, but you know a lot of open on their 3-Axis Mill, you might say I might pay a little bit more to go to the 3-Axis Mill just so I know I can get my parts on time. I might not pay as much of a premium for that machine as I would for the 4-Axis Milling machine.

So those conversations can happen with your suppliers, and it really will show what parts are flexible, what parts have a mandatory process and what parts kind of have a hierarchy of what processes would be preferred. Finally, I will do a quick demonstration of a capacity report directly in aP Analytics. Think through this following scenario, you’re including a group of sheet metal components in RFQ for a new product. How can aPriori help you understand the capacity needs, risks, areas, and outlier parts that may need to be addressed? So now I’ll jump into our aP Analytics report. So, this report is one that we create, and it is unique and can give you some really interesting insights beyond just simple costs or manufacturing insights that a typical report would give you. So, I wanted to do some additional digging and kind of show you how you can build a report like this with some complex view of some data.

So, what we’re displaying here is about 20 scenarios within aPriori that I ran across a group of components. In this example they’re all part of the same RFQ package. For each of these components, I’ve exposed the types of processes they use as well as the specific machine type that they used. Based on those 2 values, I am also calculating annual capacity utilized. So, I’m taking the cycle time and I’m taking the batch setup time and utilizing those 2 to calculate the amount of time needed for each of these components on each of these machines over the course of an entire year. So fairly quickly what you can determine is what parts in your RFQ package demand a very, very high volume of a specific machine type, you can view what types of processes really require a lot of processing based on this RFQ and you can review some of the more niche processes that you have based on some of your components that you’ve sent in this RFQ and perhaps those parts aren’t the best candidates to be a part of your specific group of components you’ve sent out here.

So, for this example, we have a bend brake, a fiber laser, some prog die, turret press, water jet, and you can start to see all of the data that we’ve been calculating with our annual capacity. So let me start with laser cut as an example here. So, in the laser cut process we have almost 3000 hours of total cycle time across six different components. That’s a pretty significant chunk of time based on what aPriori annual machine hours and kind of the total hours in a year may be. So, if you start to understand that when you’re sending out these as an RFQ, you will need to know your supplier has a lot of capability to be able to withstand that hit of components into their processes. Additionally, you can hone in and see what machines are being utilized. Specifically for the laser cut, we’re just using one single 2000 watt, it’s not spread across multiple different processes.

Overcoming Procurement Challenges

So in this case we would want to make sure that our supplier had the right amount of capability on a 2000 watt laser cutting machine and are they responding to our RFQ with some on a two and some on a four based on capacity restraints and you pay more for the 4000 watt laser cutter and therefore you’re ultimately at exposure to be paying a little bit of a premium on those components. Additionally, a machine-like laser cut where we have almost 4000 total hours of production volume on this component, we can look at the machines and there might be 2 different machines utilized. There’s one on a 10,000 kilowatt and three or so on a 6,000 kilowatt. So, you make the decision of here’s where I need to know, my supplier has a lot of availability on their six-kilowatt machine, but I also need to understand that they have the capability and the machine size to do a 10 kilowatt. If not, this specific component may not be the best fit within the supplier, you may want to go look for another supplier who can do a little bit of something else.

Additionally, if we look at like the turret press, we only have a hundred hours on one specific part in here. It’s a very simple part. It doesn’t seem to have a lot of process overlap. It seems to have low volume just based on the total cycle time required across the year. So, this would be another candidate to say, you know, am I going to get the best pricing because of the low volume requirements here? Or perhaps I want to find an RFQ or a group of components or supplier where I’m already making a lot of parts with a turret press and send this to them because then I have more capacity than I have within that facility, and I can get better dollar for my money on making this component in a turret press. So, a very simple report like this where we’ve been able to use the underlying aPriori data to calculate something a little bit different, we’re able to really analyze our RFQ, understand what parts fit with another part and you know if they don’t, what might our action process be?

We can also see parts that are at exposure to a high risk because of the need of capacity we are acquiring from our suppliers. So, we can work with them to understand, hey, we have all these parts we need them made, there’s a lot of capacity here. How are we going to get this done and am I getting the optimal process on those components? And a very simple report like this can have you make all those insights.

And that concludes my presentation on procurement risk and how aPriori can help you analyze it. Thank you very much for attending and reviewing my data.