Shipulski On Design

Posts Tagged ‘Cost Reduction’

When it’s time for a tough decision, it’s time to use data.  The idea is the data removes biases and opinions so the decision is grounded in the fundamentals.  But using the right data the right way takes a lot of disciple and care.

The most straightforward decision is a decision between two things – an either or – and here’s how it goes.

The first step is to agree on the test protocols and measure systems used to create the data.  To eliminate biases, this is done before any testing.  The test protocols are the actual procedural steps to run the tests and are revision controlled documents.  The measurement systems are also fully defined.  This includes the make and model of the machine/hardware, full definition of the fixtures and supporting equipment, and a measurement protocol (the steps to do the measurements).

The next step is to create the charts and graphs used to present the data. (Again, this is done before any testing.) The simplest and best is the bar chart – with one bar for A and one bar for B.  But for all formats, the axes are labeled (including units), the test protocol is referenced (with its document number and revision letter), and the title is created.  The title defines the type of test, important shared elements of the tested configurations and important input conditions.   The title helps make sure the tested configurations are the same in the ways they should be.  And to be doubly sure they’re the same, once the graph is populated with the actual test data, a small image of the tested configurations can be added next to each bar.

The configurations under test change over time, and it’s important to maintain linkage between the test data and the tested configuration.  This can be accomplished with descriptive titles and formal revision numbers of the test configurations.  When you choose design concept A over concept B but unknowingly use data from the wrong revisions it’s still a data-driven decision, it’s just wrong one.

But the most important problem to guard against is a mismatch between the tested configuration and the configuration used to create the cost estimate.  To increase profit, test results want to increase and costs wants to decrease, and this natural pressure can create divergence between the tested and costed configurations. Test results predict how the configuration under test will perform in the field.  The cost estimate predicts how much the costed configuration will cost.  Though there’s strong desire to have the performance of one configuration and the cost of another, things don’t work that way.  When you launch you’ll get the performance of AND cost of the configuration you launched.  You might as well choose the configuration to launch using performance data and cost as a matched pair.

All this detail may feel like overkill, but it’s not because the consequences of getting it wrong can decimate profitability. Here’s why:

Profit = (price – cost) x volume.

Test results predict goodness, and goodness defines what the customer will pay (price) and how many they’ll buy (volume).  And cost is cost.  And when it comes to profit, if you make the right decision with the wrong data, the wheels fall off.

Image credit – alabaster crow photographic

Rumor has it, manufacturing is back. Yes, manufacturing jobs are coming back, but they’re coming back in dribbles. (They left in a geyser, so we still have much to do.) What we need is a fire hose of new manufacturing jobs.

Manufacturing jobs are trickling back from low cost countries because companies now realize the promised labor savings are not there and neither is product quality. But a trickle isn’t good enough; we need to turn the tide; we need the Mississippi river.

For flow like that we need a fundamental change. We need labor costs so low our focus becomes good quality; labor costs so low our focus becomes speed to market; labor costs so low our focus becomes speed to customer. But the secret is not labor rate. In fact, the secret isn’t even in the factory.

The secret is a secret because we’ve mistakenly mapped manufacturing solely to making (to factories). We’ve forgotten manufacturing is about designing and making. And that’s the secret: designing – adding product thinking to the mix. Design out the labor.

There are many names for designing and making done together. Most commonly it’s called concurrent engineering. Though seemingly innocuous, taken together, those words have over a thousand meanings layered with even more nuances. (Ask someone for a simple description of concurrent engineering. You’ll see.) It’s time to take a step back and demystify designing and making done together. We can do this with two simple questions:

• What behavior do we want?
• How do we get it?

What’s the behavior we want? We want design engineers to understand what drives cost in the factory (and suppliers’ factories) and design out cost. In short, we want to connect the engineer to the factory.

Great idea. But what if the factory and engineer are separated by geography? How do we get the behavior we want? We need to create a stand-in for the factory, a factory surrogate, and connect the engineer to the surrogate. And that surrogate is cost. (Cost is realized in the factory.) We get the desired behavior when we connect the engineer to cost.

When we make engineering responsible for cost (connect them to cost), they must figure out where the cost is so they can design it out. And when they figure out where the cost is, they’re effectively connected to the factory.

But the engineers don’t need to understand the whole factory (or supply chain), they only need to understand places that create cost (where the cost is.) To understand where cost is, they must look to the baseline product – the one you’re making today. To help them understand supply chain costs, ask for a Pareto chart of cost by part number for purchased parts. (The engineers will use cost to connect to suppliers’ factories.) The new design will focus on the big bars on the left of the Pareto – where the supply chain cost is.

To help them understand your factory’s cost, they must make two more Paretos. The first one is a Pareto of part count by major subassembly. Factory costs are high where the parts are – time to put them together. The second is a Pareto chart of process times. Factory costs are high where the time is – machine capacity, machine operators, and floor space.

To make it stick, use design reviews. At the first design review – where their design approach is defined – ask engineering for the three Paretos for the baseline product. Use the Pareto data to set a cost reduction goal of 50% (It will be easily achieved, but not easily believed.) and part count reduction goal of 50%. (Easily achieved.) Here’s a hint for the design review – their design approach should be strongly shaped by the Paretos.

Going forward, at every design review, ask engineering to present the three Paretos (for the new design) and cost and part count data (for the new design.) Engineering must present the data themselves; otherwise they’ll disconnect themselves from the factory.

To seal the deal, just before full production, engineering should present the go-to-production Paretos, cost, and part count data.

What I’ve described may not be concurrent engineering, but it’s the most profitable activity you’ll ever do. And, as a nice side benefit, you’ll help turn around the economy one company at a time.

Your product costs are twice what they should be. That’s right. Twice.

You don’t believe me. But why? Here’s why:

If 50% cost reduction is possible, that would mean you’ve left a whole shitpot of money on the table year-on-year and that would be embarrassing. But for that kind of money don’t you think you could work through it?

If 50% cost reduction is possible, a successful company like yours would have already done it. No. In fact, it’s your success that’s in the way. It’s your success that’s kept you from looking critically at your product costs. It’s your success that’s allowed you to avoid the hard work of helping the design engineering community change its thinking. But for that kind of money don’t you think you could work through it?

Even if you don’t believe 50% cost reduction is possible, for that kind of money don’t you think it’s worth a try?

I have been watching the news and listening to the pundits, and, apparently, we are steaming out of the great recession and the manufacturing flywheel is nearing full speed.

As we all know, that’s a bunch of crap.  Many manufactures are still in survival mode where cost cutting has crossed into the ridiculous; where the best talent has been cut; and where the product development flywheel is motionless.  We are far from coming out of this thing, and the bad stuff we had to do to survive will take time to undo.

However, some companies are considering options to accelerate themselves out of the soup.  They are asking the big question – what is the fastest way out?

To me, the fastest way out is all about three things: product, product, product — do you have the right products coming to market?  Or, if not, how can you get your product development flywheel moving so the right products hit the market as quickly as possible?  But, what are the attributes of the “right product”?

I think there are two components of the right product: the top line component and the bottom line component.  The top line component (which drives top line growth) is all about function and features.  More function equals increased sales through market share and price.  The bottom line component (bottom line growth) is all about cost.  Pretty basic.  But, if your resources are limited (like most of us) and can improve only one, which should you improve?

Bottom line cost reduction is not glamorous, but the balance sheet improvment is surprisingly good.  Let me give an example.  Product A is an existing product that sells for $1000 and it costs you $800 to produce, providing $200 profit per unit.  You spend your product development resources on a bottom line effort and reduce product cost by 20%.  Still selling for $1000 but with a cost of $640 (0.8 * $800), profit dollars increase by 80% ($360 vs. $200).  Not bad especially since sales have not increased.

Top line growth has a strong emotional component which energizes people, and the upside potential is huge.  Here is an example using the same product as above.  Product A still sells for $1000, costs you $800, and you make $200 per unit.  You spend your product development resources on a top line project to add better functionality and more features.  Because you don’t have time to address the bottom line component, your costs go up 10% (to $880).  But, you do get the function and features you wanted, and the market can support a 10% price increase to $1100.  Profit per unit is up 10% t0 $220 ($1100 – $880).  Your engineering really came through and the market likes your new product and sales increase by 20%.  With all that, profit dollars increase by 32% ($220*1.2 = $264 vs. $200).

Clearly the examples are contrived to illustrate a point: bottom line cost reduction is powerful and so are top line sales growth and price increase.  And the best answer is not to choose between top line and bottom line components.  It makes a lot of sense to do a little of both, because it’s the fastest way out of the soup.

Part Cutters — Design News

The engineers at Hypertherm Inc., a maker of plasma cutting systems,know a thing or two about cutting metals. They also know how to cut cost. A lot of cost. While redesigning one of the company’s best-selling plasma cutting systems, they managed to reduce parts’ count from more than 1,000 components to fewer than 500. System assembly time fell from 20 hours to less than five. And the output from the company’s existing assembly operation quadrupled — without any additional floor space or an expensive second shift. Bottom line: the redesign saved the company about $5 million in assembly costs over the past 24 months alone, according to Engineering Manager Mike Shipulski. Read the rest of this entry »