Shipulski On Design

The data-driven people in our lives tell us that you can’t improve what you can’t measure.  I believe that. And it’s no different with product cost. Before improving product cost, before designing it out, you have to know where it is. However, it can be difficult to know what really creates cost.  Not all parts and features are created equal; some create more cost than others, and it’s often unclear which are the heavy hitters. Sometimes the heavy hitters don’t look heavy, and often are buried deeply within the hidden factory.

Measure, measure, measure.  That’s what the black belts say.  However, it’s difficult to do well with product cost since our costing methods are hosed up and our measurement systems are limited. What do I mean? Consider fasteners (e.g., nuts, bolts, screws, and washers), the product’s most basic life form. Because fasteners are not on the BOM, they’re not part of product cost. Here’s the party line: it’s overhead to be shared evenly across all the products in a socialist way.  That’s not a big deal, right?  Wrong.  Although fasteners don’t cost much in ones and twos, they do add up. 300-500 pieces per unit times the number of units per year makes for a lot of unallocated and untracked cost.  However, a more significant issue with those little buggers is they take a lot of time attach to the product.  For example, using standard time data from DFMA software, assembly of a 1/4″ nut with a bolt, locktite, a lockwasher, and cleanup takes 50 seconds.  That’s a lot of time. You should be asking yourself what that translates to in your product. To figure it out, multiply the number nut/bolt/washer groupings by 50 seconds and multiply the result by the number of units per year. Actually, never mind.  You can’t do the calculation because you don’t know the number of nut/bolt/washer combinations that are in your product. You could try to query your BOMs, but the information is likely not there.  Remember, fasteners are overhead and not allocated to product. Have you ever tried to do a cost reduction project on overhead?  It’s impossible.  Because overhead inflicts pain evenly to all, no one is responsible to reduce it.

With fasteners, it’s like death by a thousand cuts.

The time to attach them can be as much as 20-50% of labor. That’s right, up to 50%.  That’s like paying 20-50% of your folks to attach fasteners all day. That should make you sick.  But it’s actually worse than that.  From Line Design 101, the number of assembly stations is proportional to demand times labor time. Since fasteners inflate labor time, they also inflate the number of assembly stations, which, in turn, inflates the factory floor space needed to meet demand. Would you rather design out fasteners or add 15% to your floor space?  I know you can get good deals on factory floor space due to the recession, but I’d still rather design out fasteners.

Even with the amount of assembly labor consumed by fasteners, our thinking and computer systems are blind to them and the associated follow-on costs. And because of our vision problems, the design community cannot be held accountable to design out those costs.  We’ve given them the opportunity to play dumb and say things like, “Those fastener things are free. I’m not going to spend time worrying about that.  It’s not part of the product cost.”  Clearly not an enlightened statement, but it’s difficult to overcome without cost allocation data for the fasteners.

The work-around for our ailing thinking and computer-based cost tracking systems is simple: get the design engineers out to the production floor to build the product.  Have them experience first hand how much waste is in the product.  They’ll come back with a deep-in-the-gut understanding of how things really are. Then, have them use DFMA software to score the existing design, part-by-part, feature-by-feature.  I guarantee everyone will know where the cost is after that. And once they know where the cost is, it will be easy for them to design it out.

I have data to support my assertion that fasteners can make up 20-50% of labor time, but don’t take my word for it. Go out to the factory floor, shut your eyes and listen.  You’ll likely hear the never ending song of the nut runners. With each chirp, another nut is fastened to its bolt and washer, and another small bit of labor and factory floor space is consumed by the lowly fastener.

Lean is all about parts. Don’t think so? What do your manufacturing processes make? Parts. What do your suppliers ship you? Parts. What do you put into inventory? Parts. What do your shelves hold? Parts. What is your supply chain all about? Parts.

Still not convinced parts are the key? Take a look at the seven wastes and add “of parts” to the end of each one. Here is what it looks like:

1. Waste of overproduction (of parts)
2. Waste of time on hand – waiting (for parts)
3. Waste in transportation (of parts)
4. Waste of processing itself (of parts)
5. Waste of stock on hand – inventory (of parts)
6. Waste of movement (from parts)
7. Waste of making defective products (made of parts)

And look at Suzaki’s cartoons. (Click them to enlarge.) What do you see? Parts.

Take out the parts and the waste is not reduced, it’s eliminated. Let’s do a thought experiment, and pretend your product had 50% fewer parts. (I know it’s a stretch.) What would your factory look like? How about your supply chain? There would be: fewer parts to ship, fewer to receive, fewer to move, fewer to store, fewer to handle, fewer opportunities to wait for late parts, and fewer opportunities for incorrect assembly. Loosen your thinking a bit more, and the benefits broaden: fewer suppliers, fewer supplier qualifications, fewer late payments; fewer supplier quality issues, and fewer expensive black belt projects. Most importantly, however, may be the reduction in the transactions, e.g., work in process tracking, labor reporting, material cost tracking, inventory control and valuation, BOMs, routings, backflushing, work orders, and engineering changes.

However, there is a big problem with the thought experiment — there is no one to design out the parts. Since company leadership does not thrust greatness on the design community, design engineers do not have to participate in lean. No one makes them do DFA-driven part count reduction to compliment lean. Don’t think you need the design community? Ask your best manufacturing engineer to write an engineering change to eliminates parts, and see where it goes — nowhere. No design engineer, no design change. No design change, no part elimination.

It’s staggering to think of the savings that would be achieved with the powerful pairing of DFA and lean. It would go like this: The design community would create a low waste design on which the lean community would squeeze out the remaining waste. It’s like the thought experiment; a new product with 50% fewer parts is given to the lean folks, and they lean out the low waste value stream from there. DFA and lean make such a powerful one-two punch because they hit both sides of the waste equation.

DFA eliminates parts, and lean reduces waste from the ones that remain.

There are no technical reasons that prevent DFA and lean from being done together, but there are real failure modes that get in the way. The failure modes are emotional, organizational, and cultural in nature, and are all about people. For example, shared responsibility for design and manufacturing typically resides in the organizational stratosphere – above the VP or Senior VP levels. And because of the failure modes’ nature (organizational, cultural), the countermeasures are largely company-specific.

What’s in the way of your company making the DFA/lean thought experiment a reality?

I can’t believe everyone isn’t doing Design for Assembly (DFA), especially in these tough economic times. It’s almost like CEOs really don’t want to grow stock price. DFA, where the product design is changed to reduce the cost of putting things together, routinely achieves savings of 20-50% in material cost, and the same for labor cost. And the beauty of the material savings is that it falls right to the bottom line. For a product that costs $1000 with 60% material cost ($600) and 10% profit margin ($100), a 10% reduction in material cost increases bottom line contribution by 60% (from $100 to $160). That sounds pretty good to me. But, remember, DFA can reduce material cost by 50%. Do that math and, when you get up off the floor, read on.

Unfortunately for DFA, the savings are a problem – they’re too big to be believed. That’s right, I said too big. Here’s how it goes. An engineer (usually an older one who doesn’t mind getting fired, or a young one who doesn’t know any better) brings up DFA in a meeting and says something like, “There’s this crazy guy on the web writing about DFA who says we can design out 20-50% of our material cost. That’s just what we need.” A pained silence floods the room. One of the leaders says something like, “Listen, kid, the only part you got right is calling that guy crazy. We’re the world leaders in our field. Don’t you think we would have done that already if it was possible? We struggle to take out 2-3% material cost per year. Don’t talk about 20-50% because is not possible.” DFA is down for the count.

Also unfortunate is the name – DFA. You’ve got to admit DFA doesn’t roll off the tongue like six sigma which also happens to sound like sex sigma, where DFA does not. I think we should follow the lean sigma trend and glom some letters onto DFA so it can ride the coat tails of the better known methodologies. Here are some letters that could help:

Lean DFA; DFA Lean; Six Sigma DFA; Six DFA Sigma (this one doesn’t work for me); Lean DFA Sigma

Its pedigree is also a problem – it’s not from Toyota, so it can’t be worth a damn. Maybe we should make up a story that Deming brought it to Japan because no one in the west would listen to him, and it’s the real secret behind Toyota’s success. Or, we can call it Toyota DFA. That may work.

Though there is some truth to the previous paragraphs, the main reason no one is doing DFA is simple:

No one is asking the design community to do DFA.

Here is the rationalization: The design community is busy and behind schedule (late product launches). If we bother them with DFA, they may rebel and the product will never launch. If we leave them alone and cross our fingers, maybe things will be all right. That is a decision made in fear, which, by definition, is a mistake.

The design community needs greatness thrust upon them. It’s the only way.

Just as the manufacturing community was given no choice about doing six sigma and lean, so should the design community be given no choice about doing DFA.

No way around it, the first DFA effort is a leap of faith. The only way to get it off the ground is for a leader in the organization to stand up and say “I want to do DFA.” and then rally the troops to make it happen.

I urge you to think about DFA in the same light as six sigma or lean: If your company had a lean or six sigma project that would save you 20-50% on your product cost, would you do it? I think so.

Who in your organization is going to stand up and make it happen?

There is a healthy tension between level of improvement, or level of innovation, and time to market. Marketing wants radical improvement, infinitely short project schedules, and no change to the product. Engineers want to sign up for the minimum level of improvement, project schedules sufficiently long to study everything to death, and want to change everything about the new product. It’s healthy because there is balance – both are pulling equally hard in opposite directions and things end up somewhere in the middle. It’s not a stress-free environment, but it’s not too bad. But, sometimes the tension is unhealthy.

There are two flavors of unhealthy tension. First is when engineering has too much pull; they (we) sandbag on product performance and project timelines and change the design willy-nilly simply because they can (and it’s fun). The results are long project timelines, highly innovative designs that don’t work well, a lack of product robustness, and a boatload of new parts and assemblies. (Product complexity.) Second is when Marketing has too much pull; they ask for radical improvement in product functionality with project timelines too short for the level of innovation, and tightly constrain product changes such that solutions are not within the constraints. The results are long project timelines and un-innovative designs that don’t meet product specifications. (The solutions are outside the constraints.) Both sides are at fault in both scenarios. There are no clean hands.

What are the fundamentals behind all this gamesmanship? For engineering it’s technical risk; for marketing it’s schedule risk. Engineering minimizes what it signs up for in order to reduce technical risk and petitions for long project timelines to reduce it. Marketing minimizes product changes (constraints) to reduce schedule risk and petitions for short project timelines to reduce it. (Product development teams work harder with short schedules.) Something’s got to change. Read the rest of this entry »

There is no question that Six Sigma helps companies make money. So much so that everyone in the manufacturing community knows the five hallowed letters: DMAIC (Define, Measure, Analyze, Improve, Control). It’s straightforward and fully wrung-out. But that’s not the case for the wicked step sister Design for Six Sigma (DFSS). She’s fundamentally different and more complicated. To start, it’s an alphabet soup out there. Here are some of the letters: DMADV, DMADOV, IDOV, and DMEDI, and there are likely more. Does everyone know these letters and what they stand for? Not me. But here is the fundamental difference: with DMAIC the thing to be improved already exists and with DFSS the thing to be created does not. In essence, there is no formalized problem to solve. So what you say?

With DMAIC it’s all about reducing variation relative to the specification; with DFSS there is no specification. In fact, there is no product yet a process on which we can measure variation. First the product itself must be created and its functional performance must be defined over a range of parameters. Only then is manufacturing variation measured relative to the range functional parameters (DMAIC). But I got ahead of myself.

Before creating the thing that does not exist and make sure it meets the functional specification, some mind reading of customer needs is required, an even lesser defined thing. So, there is a round of reading customers minds followed by round of creating something that does not exist to satisfy the customer needs define in the mind reading sessions. Oh yea, then the tolerances must be defined so the product always functions the way it’s supposed to. All this before we turn the DMAIC crank.

My point with all this is to help set expectations when dealing with product design/DFSS. It is wrong to expect the predictability and standardization of DMAIC when doing product design/DFSS.  It’s different.  Product design/DFSS is not the same turn-the-crank kind of operation. That is not to say there is zero predictability and standard work or that predictability is not something to strive for. It’s just different. With product design the problems are unknown at the start and sometime even the fundamental physics are unknown. Please keep this in mind when your product development projects are late relative to hyper aggressive, non-work-content-based schedules or when new products don’t meet the arbitrary cost targets.

Improving product robustness is straightforward and difficult. Here’s how to do it.

Identify specific failure modes, prioritize them, and go after the biggest ones first. Failure modes can be identified through multiple sources. Warranty data is sometimes coded by failure mode (more precisely, symptom type), so start there. The number one failure mode in this type of data is typically “no problem found”, so be ready for it. Analysis of the actual products that come back is another good way. Returned product is routed to the appropriate engineer who analyzes it and enters the failure mode into a database. A formal design FMEA generates a list of prioritized failure modes through the risk priority number (RPN), where larger is more important. To do this, engineers are hauled into a room and a facilitator helps them come up with potential failure modes. One caution – the process can generate many failure modes, more than you can fix, so make the top five or ten go away and don’t argue the bottom fifty. It makes no sense to even talk about number eleven if you haven’t fixed the top ten. But the best way I have found to identify failure modes (problems) that are meaningful to the customer is to ask the technical services group for their top five things to fix. They will give you the right answer because they interact daily with customers who have broken product. They won’t expect you to listen to them (you never listened before), so surprise them by fixing one or two on their list. They will be grateful you listened (they’ll likely want to buy you coffee for the rest of your career) and your customers will notice.

Once failure modes are identified, define the physics of failure – why the product breaks. This is tough work and requires focused thought and analysis. If, when you break the product, it “looks like” the ones coming back from the field, you have defined the physics of failure. This is the same thing as replicating the problem in the lab. Once that’s defined, create an automated test rig or experimental setup that breaks the product in a way that captures the physics of failure. I call this test rig a robustness surrogate because it stands in for the actual failure mode seen in the field. The robustness surrogate should break the product as fast as possible while retaining the physics of failure so you can break it and fix it many times before product launch. The robustness surrogate should be designed to break the product within minutes, not hours or days – the faster the better.

To know if product robustness is improved, the baseline (or existing) design is broken on the robustness surrogate. The new design must survive longer on the robustness surrogate than the baseline design. The result is A/B data (baseline design/ new design) that is presented at the design review using a simple bar graph format which I call big-bar-little-bar. Keep improving robustness of the new design even if it outperforms the baseline design by a factor of ten – that’s not good enough for your customers.

Don’t stop improving robustness until you run out of time, and don’t stop if you meet the arbitrary MTBF specification. Customers like improved robustness, and in this case too much of a good thing is wonderful.

Using this method, I reduced warranty cost per unit by 75% over a five year period. It worked.

In a previous post I defined the term brand-damaging threshold and said I’d talk about how to improve product robustness. So, here goes.

Every company is at a different stage in their formalized product robustness efforts, so it’s challenging to talk meaningfully to everyone. But there are two especially meaningful principles that have served me well through the years.

I had the privilege of working with Don Clausing – Total Quality Design, The House of Quality, Enhanced QFD, and Robust Quality. I vividly remember the conversation where Don shared one of his secrets. As we watched a robustness test run, Don, in his terse way, barked out a guiding principle of improving product robustness. He said:

“Improve robustness at the expense of predicting it.”

I asked Don what the hell he meant (he liked to make his students work for it), and after some prodding, he went on to explain why it’s so important. He said people spend far too much time running tests to predict robustness and then spend even more time calculating mean time between failures (MTBF). If that’s not enough, then they spend time arguing about MTBFs and the confidence intervals. He said companies should dedicate all their time and energy improving robustness. “That’s what matters to the customer,” he said. And then he continued with something like: “Predicting robustness is worse than a simple waste of time.” (He wasn’t that polite.) But I still didn’t get it. What’s the big deal about predicting robustness? Read the rest of this entry »

There are many definitions of product robustness and just as many formally trained specialists willing to argue about them. I get confused by all that complexity, I don’t like to argue, and I am not a specialist, I am a generalist. I like simplicity so I use operational definitions every chance I get. Here’s one for product robustness:

A customer walks up to your product, turns it on, and it works without breaking or getting in its own way.

Bad product robustness is bad for your brand. Very bad. Customers do not like when they pay money for a product and it doesn’t work, especially when they rely on those products to make money for themselves. And they remember the experience in a visceral way.

You can’t fix bad product robustness with great marketing; you can’t fix it with spin selling; you can’t tell customers you fixed it when you didn’t (since they use your product, they know the truth); and you can’t hide it because customers talk (so do competitors). There is no quick fix – it takes tools, time, training, and new thinking to improve product robustness. And when you do manage to fix it, customers won’t believe you until the see it for themselves. They don’t want to get burned again.

No product is infinitely robust, nor should it be. It doesn’t make financial sense. The product would be infinitely expensive and would take an infinite amount time to develop. But how much robustness is enough? An easier, and possibly more important, question to answer is – how much is too little? Or, stated another way, what is the minimum level of product robustness?

The specialists won’t agree with my assertion that there is a minimum threshold for product robustness, but I don’t care. I think there is one. I call this minimum value the brand-damaging threshold. Here’s an operational definition of product robustness that’s below the brand-damaging threshold:

Customers don’t buy your product because they know it breaks or gets in its own way and they go out of their way to tell others about it.

It is difficult to know when customers don’t buy, never mind know why they don’t. But there are some tell-tale signs that product robustness is below the brand-damaging threshold. Here are a few.

The CEO takes enough direct calls about products that don’t work to feel obligated to send you a thoughtfully-crafted, four word email saying something like “Fix that @#&% thing!” Customers have to be really pissed off to call the CEO directly, so the situation is bad. It’s also bad for a reason that’s closer to home – the CEO sent the email to you.

You get a little sick to your stomach when sales increase. You know you should be happy, but you’re not. Deep down you know you’ll see many of those products again because they’ll be sent back by angry customers, in pieces.

The volume of returns is so significant you create a refurbishment department. Or you create a new group to scavenge the reusable stuff off the piles of returned product. Not good signs.

Your product’s lack of robustness is the headline message in your customers’ marketing literature.

Now that the brand-damaging threshold is defined, the next logical topic is how to improve product robustness so it’s above the threshold. But that’s for another post.

Lean has been beneficial for many companies, helping improve competitiveness and profitability. But, lean has not been nearly as effective as it can be because there is a missing ingredient – product design. Where lean can reduce the waste of making and moving parts, product design can eliminate the parts altogether; where lean can reduce setup times for big machines, product design can change the parts so they no longer need the big machines; where lean can reduce inventory, product design can eliminate it by designing out parts; where lean can make the supply chain more efficient, product design can radically shorten it by designing out the long lead time elements.

The power of product design is even more evident when considering the breakdown of product cost. Here is some data from Nick Dewhurst taken from multiple-hundred DFMA analyses showing the typical cost breakdown of products.

Of the three buckets of cost, material cost is by far the largest 74%, and this is where product development shines. Product design can eliminate 40 to 50% of material cost resulting in radical cost savings. Lean cannot. I will go a bit further and say that material cost reductions are largely off limits to the lean folks since it requires fundamental product changes.

Side note – Probably most surprising about cost breakdown data is labor cost is only 4%. Why we move our manufacturing to “low cost countires” to chase 50% labor reductions to net a whopping 2% cost reduction is beyond me, but that’s for a different post.

Let’s face it – material cost reduction is where it’s at, and lean does not have the toolbox to reduce material cost. There’s no mystery here. What is mysterious, however, is that companies looking to survive at all costs are not pulling the biggest lever at their disposal – product design. Here is a bit of old data from Ford showing that Product Design has the biggest lever on cost. We’ve know this for a long time, but we still don’t do it.

Clearly, the best approach of is to combine the power of product design with lean. It goes like this: the engineers design a low cost, low waste product that is introduced to the production line, and the lean folks improve efficiency and reduce cost from there. We’ve got the lean part down, but not the product design part.

There are two things in the way of designing low cost, low waste products in a way that helps take lean to the next level. First, product development teams don’t know how to do the work. To overcome this, train them in DFMA. Second, and most important, company leaders don’t give the product development teams the tools, time, and training to do the work. Company leaders won’t take the time to do the work because they think it will delay product launches. Also, they don’t want to invest in the tools and training because the cost is too high, even though a little math shows the investment is more than paid back with the first product launch. To fix that, educate them on the methods, the resource needs, and the savings.

Good luck.

Congratulations. You’ve managed to keep your product development engine running. Good work.  But now the hard part. Marketing and sales know new products are a key to profitability, and so does the CEO. So they’ve all asked for more new products, and now you have more active product development projects in the pipeline. The product development folks will do whatever they can to crank out the products. But can they get it done?

When does the product pipeline become too much for your product development engine to handle? We all know you can’t keep adding more new product development projects without adding capacity or improving productivity. Sure you can ask your product development engine to do more (and more), and it will try; but at some point it will run out of gas. So, ask yourself: Has your product development engine run out of gas? How can you tell? If it hasn’t, do you know how many miles are left in the tank?

If you don’t measure it you can’t improve it, that’s what the black belts say. But what to measure? What are the right metrics to tell you if your product development engine is out of gas? One of the best books on the subject is Managing the Design Factory, by Don Reinertsen. The rest of the post is strongly shaped by Don’s book, if not taken directly from it. Remember, genius steals.

The best metrics are simple, relevant to the objective, and are leading indicators. Simple so they’re easy to interpret; relevant so they move you toward the objective, in this case launching more new products; and are leading indicators, in that they are predictors of outcomes, so you can take action before catastrophic outcomes occur.  Here are three good ones. Read the rest of this entry »

It’s time to write, but, again, no topic.  This writing-once-a-week thing is tough.  I drop my son off at the hockey rink and walk back to the parking lot to write in my car (I’m telling you, this is a good place to write). Before I get to my car, my cell phone rings. It’s a teacher friend of mine. He’s the guy at the high school who helps kids work out issues with substance use/abuse and related topics. He’s a real pro – every high school should have a person of his caliber. Without introducing himself, he says, “You want to go for a hike tomorrow?” “I have to work,” I say. “It’s Veteran’s Day,” he says. “Yeah, I know, and I have to work,” I reply. “Oh ya, I forgot about that,” he says with a chuckle.

My mind clicks and I remember a discussion we had the previous week while on a walk.  I ask, “Do you remember talking about that trick to break intellectual inertia?” “Ya, we talked about how I used it to help a kid work himself out of some destructive behavior. Make it worse and do the opposite,” he says. “I love it; it works great,” he says. I now have my topic. We talk for a while and he helps my thinking converge. This one is a joint effort.

Here’s the problem: problems are stressful. We have a physiological reaction to problems; adrenaline rushes through our veins; our blood pressure increases; our heart rate increases; we get flushed. This is real. It’s run or attack, flight or fight. Our mental processing is all about survival. And there is real reason for concern; there are real consequences to not solving a problem – your reputation, your authority, your job. Read the rest of this entry »