Design for Manufacturing Examples: Real-Life Engineering Case Studies
I need to optimize the cost of a part. Where do I start?
A substantial majority of a part’s cost (70% or more) is effectively locked in once a design is finalized. Once a design is complete, colleagues in Manufacturing or Sourcing have drastically limited options for optimizing part costs. That’s why effective design for manufacturing cost modeling is an essential foundation for truly optimizing parts costs. Engineers need the ability to cost-out design alternatives while a project is still on the drawing board.
But how can an engineer truly predict the most important cost drivers of a particular part’s design while it’s still on the drawing board?
The short answer is, the better tools an engineer has to estimate the manufacturability of design alternatives, the more effectively they make cost-effective engineering decisions. Spreadsheets and other tools that rely on historical estimates are a solid start. Meanwhile, simulated “digital factories” like aPriori provides a much more detailed, agile approach to linking design decisions to cost outcomes.
In this blog, we dive into the question above in much more detail, examining some real-life examples to illustrate a key design for manufacturability principles in action.
The Imperative for Design for Manufacturability Insight
What if an engineer had access to precise, design-level guidance on key cost drivers for their design? Guidance on how cost is being driven by raw materials, conversion (the cost of turning that raw material into a part), routing, and other manufacturability issues?
This capability would enable engineers to understand precisely how parts can be re-designed for superior cost efficiency. Even better, it would enable cost-efficient design choices when a new part is designed for the first time.
To really understand the transformative impact of advanced manufacturing cost modeling, it can be helpful to consider the full breadth of factors that contribute to a part’s final cost. The list below isn’t comprehensive but is intended to demonstrate just how many factors need to be considered for a truly robust cost model.
We break down a few key categories of part cost below. The specifics may vary greatly, but these basic cost categories apply whether the part in question is a sheet metal part or plastic, cast or machined.
Overview of Important Cost Categories
Direct + Variable Costs:
Both of these categories describe costs associated with the marginal cost of producing each additional part.
Key Drivers of Materials Costs
- Material Type
- Material Stock Size (standard or non-standard)
- Material utilization
- Special grain orientations (e.g., tight bends on a part may only allow manufacturing to orient the part in one direction when cutting it on the sheet)
Key Drivers of Labor and Overhead Cost
- Cycle time to make the part. Note that more than one machine may be used to make a part
- Number of times that the part must be set up – whether in one machine or multiple machines
- Type and size of machine(s) that will be used to make the part
- Any secondary processes – Paint, heat treatment, etc.
These costs matter for overall profitability but aren’t necessarily immediately impacted by marginal production changes. For instance, a factory will have some base level of maintenance costs regardless of how many parts are being made in a given period. For analysis, these costs must be associated with specific supporting functions and spread across all parts produced.
Key Factory-Related Cost Drivers
- Energy costs
- Heating and cooling the plant
- Cleaning and maintenance
- Purchasing, manufacturing, engineering, shipping and receiving, and other supporting business functions
Administrative Cost Drivers
- General management costs
- Sales, marketing, and business development expenditure
- Technology support e.g., IT staff or services
Capital Expenditures (CAPEX) and/or Non-Recurring Costs
- Examples include initial investments in productive capital like molds, stamping dies, machining fixtures, weld fixtures, etc.
- The cost impact of capital expenditures will vary depending on the complexity of the part, number of cavities, number of parts over the life of the tool, etc.
How can an engineer realistically consider this diverse array of factors when designing or redesigning a part?
Generating a model for a part’s cost is an essential first step to optimizing these costs. But with traditional tools, generating a robust estimate is a genuine challenge. Metrics like cost-to-weight ratio provide sanity checks for inefficient design choices, but they do not offer real insight into the thread linking design to the complex tangle of factors listed above.
To tackle this challenge, many companies develop tools like spreadsheets or custom software to record knowledge gained from past estimation efforts and apply them to new part designs. These tools provide a reasonable cost estimate that can be used in the design cycle.
Best practices for this estimation require a consistent, reliable method for considering manufacturability. Many companies have dedicated Manufacturing Engineers that create cost estimates to share with design engineer(s). At a bare minimum, this estimate may simply be a top-level cost target. Ideally, however, this estimate will go much deeper.
A more detailed breakdown gives a design engineer much more useful information for actually designing-to-cost.
It is especially easy to see the importance of the powerful interaction of each and every design choice within the entire Direct/Variable cost category. While engineering decisions may affect period costs over the long term, we’ll focus on direct costs in this blog, as this is typically where you will have the most dramatic impact.
We take a deep dive into how design for manufacturability can have a real impact on supporting new product pipeline and driving the revenue generation engine at your company here.
A Detailed Look at Real Success Stories: Using Digital Manufacturing Simulation to Pinpoint Cost Inefficiencies
We look at some more specific examples below. While these examples are generated using aPriori’s “Digital Factory” approach, employing simulated production based on modeling of a part’s “digital twin,” the overall process also applies to traditional estimation tools like spreadsheets as well.
Estimates don’t have to be perfect, but they do need to be reliable. In the design stage, you don’t need the absolute value of the estimate to be exact; a good approximation is fine. You need to know, for example, that 20% of the cost of this part is in Material, and 65% is in conversion cost. While these amounts may vary for final production, they provide a useful guidepost for prioritizing cost optimization projects. This practice will help you save time by not chasing changes that will have very little impact on cost.
A full-fledged digital manufacturing simulation like aPriori affords engineers the opportunity to work much faster. The software can automatically determine the most efficient manufacturing method to make a part while providing near-instant cost estimates for new design alternatives.
Material Cost Example One: Sheet Metal Fan Cover for Truck
Let’s begin with a real-life example of a Sheet Metal part, a fan cover for a truck. Note in the screenshot below that the cost estimate for this part shows that 88% of the cost is in material. To attack material cost, you can either:
- Change material to something cheaper (but changes must still reflect functional load requirements).
- Use less material, either by making the part thinner and adding ribbed forms to strengthen it, or by improving material utilization to reduce waste.
The engineer that re-designed this part realized that there was so much cost buried in the material choices that he could afford to increase the manufacturing cost of the part (conversion cost) to achieve even greater material savings. He went to work reducing the footprint of the part without changing the size of the opening or the mating points of the part. The next screenshot below shows his ultimate solution.
Note that while labor and overhead cost increased from $0.49 cents to $0.53 cents, the material cost dropped from $7.51 to $5.63, a $1.88 drop! This improvement was well worth the effort for a part that is used in tens of thousands of trucks.
This is a great example of how a reliable cost estimate is so useful for prioritizing redesign work. A good cost vector (is cost going up or down, by a little or a by lot?) is sufficient. For example, what would the engineer have done differently if the “conversion cost” had increased to $0.65 cents instead? Or if, in reality, the cost of material ends up dropping only $1.50 instead of $1.88. The overall result is the same: a successful re-design.
Material Cost Example Two: Plastic Seat
The manufacturer of the seat below makes about 200,000 of these seats per year. The digital manufacturing cost model revealed the cost of this part to be mostly material, 67%.
The engineer re-designing this part had two options:
- Use a cheaper material. Note: had the “Conversion Cost” been most of the cost, you may have wanted a material that cools faster, thereby decreasing the cycle time and cost of making the part.
- Find a way to use less material without compromising the integrity of the seat.
The engineer tried several alternative designs, here are a couple of examples:
First, she tapered the thickness of the plastic, both from the top edge of the back to about 2/3 down and from the edge of the seat to about ½ across the seat portion of the part. This change reduced the average thickness from 0.18” to 0.15”. Note that material, labor, and overhead cost went down as well, because, the thinner the part, the faster it cools, with this double benefit resulting in a decrease of $0.95 cents on a $5 part.
The second design change involved making the hole in the back slightly larger from its original 5” – 6” in height. Note that this change only resulted in a few cents reduction in cost: not worth risking potential quality issues or increased customer discomfort. The value of having real-time cost feedback “at the speed of design” gives you the ability to catch these false starts far earlier in the process.
Other Materials Cost Examples and Tips
- Watch the tightness of the bend radius on a Sheetmetal part. One customer had a practice of setting the bend radii of all bends to the same value, no matter the part. For some of the parts, this radius was less than ½ the thickness of the part. I asked the manufacturing engineer if he had trouble meeting that requirement. He said, “No, we just make the bend radius larger, we know the intent of the design.”
This approach worked satisfactorily until their factory became overwhelmed and started buying parts or sending them to another internal factory across the country. The parts became much more expensive because they needed to orient the parts perpendicular to the bend, which limits the nesting flexibility of the part so more material is used. Simulated production (like aPriori) will automatically tell you that a bend is too tight and recommend a minimum bend angle.
- Sometimes, sizing your parts just outside of standard raw material sizes can cost you a bundle.
A Fortune 100 customer told this story. They were designing a new flywheel for a larger version of a machine. Flywheels are simple in design and they expected the new cost to be proportional to the size increase. But the cost came in much larger, nearly 100% rather than the 30% increase they expected.
They suspected an unscrupulous bid from a supplier, but upon review, they found that to meet their drawing requirements, the supplier had to buy a special forging, or start with the next size up standard bar. Either way, the cost was going to be impacted disproportionately. A diameter reduction of just a few mm fixed the issue and the final design still had plenty of inertia margin.
Conversion Cost Example
Let’s now move into Conversion. Design Engineers make choices that affect a large range of conversion costs.
Just a few illustrative examples include:
- Labor cost: proportional to cycle time, also, the skill necessary to run the machine affects the wages of the operator. A 5-axis CNC machinist makes more than a 3-Axis mill operator, for example.
- Set-up cost: number of machines that need to be set up and number of times the part needs to be set up. Volume plays a large role in determining the per-product impact of set-up costs.
- Direct Overhead: this is again proportional to cycle time and the type and size of the machine.
An engineer was assigned to reduce cost for a part like the one below (this scenario is “based on a true story”). A quick review of the cost showed that there was about a 40/60 split between material cost and conversion cost. This implies that there may be opportunities on both sides of this split.
The engineer also noted that because this is a relatively lower volume part (300 units per year), it was being purchased as a machined part. And while not very complex in nature, the multiple slants on the surfaces were forcing this part to a 5 axis mill (rather than a comparatively cheap 3 axis mill).
His choices were:
- Redesign the part to reduce complexity so it can be made on a cheaper machine
- Investigate further what is driving machining costs, and how these costs can be addressed in the design.
- Investigate alternate manufacturing processes for the part if that shows promise
Using simulated manufacturing to analyze costs, the engineer found that the material utilization was only 11%, meaning nearly 9 lbs. out of every 10 would be thrown away. He investigated in more detail and found that, as expected, most of the cost of making the part was in Machining, but from roughing operations, not finishing the part. This demonstrated that getting the part to near net shape was costing a lot in both Material and Manufacturing cost (see figures below).
This part had been designated as a “machined” part because of the relatively low volume production of 300 units per year. But based on this evidence, the engineer decided to investigate sand casting the part. To see if it would be worth redoing design and fatigue analysis to turn this into a casting, he created a cost estimate for sand casting the part.
After this analysis of cost difference – about $190 per part on 300 parts, amounting to a potential annual savings of $57,000, the part was redesigned and purchased as a casting for significant savings.
Alternatively, imagine that this part was not a candidate for a casting process due to load and fatigue requirements, as is the part below. The process for reducing costs for the part is similar except that you need to explore machining costs (some parts may be extruded as well).
In this case we will look at potential manufacturability issues that may be costing us dearly. A digital simulation tool like aPriori looks at actual production methods that will be used in making a part, and in doing so, it catches features that are difficult to manufacture. In a plastic or die casting, it may be lack of draft angles, or areas that are too thick or too thin, features that cause a side action, a hole to close to an edge, etc. In a Machine part that may be tight corners, obstructed surfaces, curved surfaces that can only be ball-milled, etc.
Taking a look at this part below, we notice a similar ratio of material to conversion cost, but this time we will dig into the features that make it difficult to make, as casting or extruding it is not an option.
In the interest of time, we will limit ourselves to resolving as many of these L/D ratios as possible. The engineer realizes that the corner radius of those pockets is small, requiring a small tool diameter selection that violates customary L/D ratios and causes slower finishing times. He has the liberty to make those bigger, and since the material consumed is the same, he is happy to do so. See figure below for the redesigned part.
Larger corner radii allow for larger diameter selection which increases the ability for the tool to reach further down without shaking. Cycle time drops and cost goes down. While less than the $200 savings derived from going to a casting, a 17% savings is certainly worth the effort of the re-design.
Other Potential Methods for Cost-Effective Design for Manufacturability
You can affect machine size with your design. Say you are working on a part that is made in China, so the labor cost is low, but the overhead cost is high because it is made on a large, expensive machine. Consider if there are features that can affect machine selection.
The die cast part below has a web in the middle that really is not functionally necessary. This web is causing the part to require 2 side cores, one on each side. If the web were removed, only one core should be needed, the mold base size goes down, and the machine size (tonnage) should go down, causing a reduction in tooling and piece part cost with a smaller machine/lower overhead rate. Additionally, you may be able to have more cavities now, which is a big plus if this is a high-volume part.
The number of set-ups can dramatically affect the cost of a low-volume part. A hole that can’t be accessed from an already available set-up direction (aPriori can show you those) can cause an extra set-up.
Too many of these will require a more expensive machine, for example forcing a move from a 3-axis to a 4-axis or 5-axis. And, did you know that if your sheet metal part has an acute angle bend and an obtuse angle bend on the same part, then 2 bend breaks will have to be set up to make it? This may have minimal cost impact if the part is made in large volumes but, if this is a low-volume part, it could create serious cost inefficiencies.
If you’re interested in learning more about using Digital Manufacturing Simulation for dramatically enhanced design for manufacturability capabilities, we recommend watching our webinar: The Exhausted Engineer.
In this webinar, you’ll learn how aPriori’s capabilities for simulating production to estimate costs also include sophisticated automation that can save engineers substantial amounts of time. aP Generate, a revolutionary new digital manufacturing simulation technology from aPriori, works silently in the background, analyzing your digital twin CAD models every time they are checked into your PLM system to proactively recommend potential cost optimizations.
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