Posts Tagged ‘Product Design’

To improve innovation, improve clarity.

Looking through binocularsIf I was CEO of a company that wanted to do innovation, the one thing I’d strive for is clarity.

For clarity on the innovative new product, here’s what the CEO needs.

Valuable Customer Outcomes – how the new product will be used.  This is done with a one page, hand sketched document that shows the user using the new product in the new way.  The tool of choice is a fat black permanent marker on an 81/2 x 11 sheet of paper in landscape orientation. The fat marker prohibits all but essential details and promotes clarity.  The new features/functions/finish are sketched with a fat red marker.  If it’s red, it’s new; and if you can’t sketch it, you don’t have it. That’s clarity.

The new value proposition – how the product will be sold. The marketing leader creates a one page sales sheet.  If it can’t be sold with one page, there’s nothing worth selling.  And if it can’t be drawn, there’s nothing there.

Customer classification – who will buy and use the new product.  Using a two column table on a single page, these are their attributes to define: Where the customer calls home; their ability to pay; minimum performance threshold; infrastructure gaps; literacy/capability; sustainability concerns; regulatory concerns; culture/tastes.

Market classification – how will it fit in the market.  Using  a four column table on a single page, define: At Whose Expense (AWE) your success will come; why they’ll be angry; what the customer will throw way, recycle or replace; market classification – market share, grow the market, disrupt a market, create a new market.

For clarity on the creative work, here’s what the CEO needs: For each novel concept generated by the Innovation Burst Event (IBE), a single PowerPoint slide with a picture of its thinking prototype and a word description (limited to 12 words).

For clarity on the problems to be solved the CEO needs a one page, image-based definition of the problem, where the problem is shown to occur between only two elements, where the problem’s spacial location is defined, along with when the problem occurs.

For clarity on the viability of the new technology, the CEO needs to see performance data for the functional prototypes, with each performance parameter expressed as a bar graph on a single page along with a hyperlink to the robustness surrogate (test rig), test protocol, and images of the tested hardware.

For clarity on commercialization, the CEO should see the project in three phases – a front, a middle, and end.  The front is defined by a one page project timeline, one page sales sheet, and one page sales goals. The middle is defined by performance data (bar graphs) for the alpha units which are  hyperlinked to test protocols and tested hardware.  For the end it’s the same as the middle, except for beta units, and includes process capability data and capacity readiness.

It’s not easy to put things on one page, but when it’s done well clarity skyrockets.  And with improved clarity the right concepts are created, the right problems are solved, the right data is generated, and the right new product is launched.

And when clarity extends all the way to the CEO, resources are aligned, organizational confusion dissipates, and all elements of innovation work happen more smoothly.

Image credit – Kristina Alexanderson

The Threshold Of Uncertainty

Limbo under the threshold of uncertaintyOur threshold for uncertainty is too low.

Early in projects, even before the first prototype is up and running, you know what the product must do, what it will cost, and, most problematic, when you’ll be done. Independent of work content, level of newness, and workloads, there’s no uncertainty in your launch date. It’s etched in stone and the consequences are devastating.

A zero tolerance policy on uncertainty forces irrational behavior. As soon as possible, engineering gets something running in the lab, and then doesn’t want to change it because there’s no time. The prototype is almost impossible to build and is hypersensitive to normal process variation, but these issues are not addressed because there’s no time.  Everyone agrees it’s important to fix it, and agrees to fix it after launch, but that never happens because the next project is already late before it starts. And the death cycle repeats project after project.

The root cause of this mess is the mistaken porting of manufacturing’s zero uncertainly mindset into design. The thinking goes like this – lean and Six Sigma have achieved magical success in manufacturing by eliminating uncertainty, so let’s do it in product design and achieve similar results. This is a fundamental mistake as the domains are fundamentally different.

In manufacturing the same product is made day-in and day-out – no uncertainty; in product design no two product development efforts are the same and there’s lots of stuff that’s done for the first time – uncertainty by definition. In manufacturing there’s a revision controlled engineering drawing that defines the right answer (the geometry and the material) – make it like the picture and it’s all good; in product design the material is chosen from many candidates and the geometry is created from scratch – the picture is created from nothing. By definition there’s more inherent uncertainty in product design, and to tighten the screws and fix the launch date at the start is inappropriate.

Design engineers must feel like there’s enough time to try new things because new products that provide new functionality require new technologies, new materials, and new geometries. With new comes inherent uncertainty, but there are ways to manage it.

To hold the timeline, give on the specification and cost. Design as fast as you can until you run out of time then launch. The product won’t work as well as you’d like and it will cost more than you’d like, but you’ll hit the schedule. A good way to do this is to de-feature a subassembly to reduce design time, and possibly reduce cost. Or, reuse a proven subassembly to reduce design time – take a hit in cost, but hit the timeline. The general idea – hold schedule but flex on performance and cost.

It feels like sacrilege to admit that something’s got to give, but it’s the truth. You’ve seen how it goes when you edict (in no uncertain terms) that the timeline will be met and there’ll be no give on performance and cost. It hasn’t worked, and it won’t – the inherent uncertainty of product design won’t let it.

Accept the uncertainty; be one with it; and manage it. It’s the only way.

Innovation Eats Itself

We all want more innovation, though sometimes we’re not sure why. Turns out, the why important.

We want to be more innovative. That’s a good vision statement, but it’s not actionable. There are lots of ways to be innovative, and it’s vitally important to figure out the best flavor. Why do you want to be more innovative?

We want to be more innovative to grow sales. Okay, that’s a step closer, but not actionable. There are many ways to grow sales. For example, the best and fastest way to sell more units is to reduce the price by half. Is that what you want? Why do you want to grow sales?

We want to be more innovative to grow sales so we can grow profits. Closer than ever, but we’ve got to dig in and create a plan.

First, let’s begin with the end in mind. We’ve got to decide how we’ll judge success. How much do we want to grow profits? Double, you say? Good – that’s clear and measurable. I like it. When will we double profits? In four years, you say? Another good answer – clear and measureable. How much money can we spend to hit the goal? $5 million over four years. And does that incremental spending count against the profit target? Yes, year five must double this year’s profits plus $5 million.

Now that we know the what, let’s put together the how. Let’s start with geography. Will we focus on increasing profits in our existing first world markets? Will we build out our fledgling developing markets? Will we create new third world markets? Each market has different tastes, cultures, languages, infrastructure requirements, and ability to pay. And because of this, each requires markedly different innovations, skill sets, and working relationships. This decision must be made now if we’re to put together the right innovation team and organizational structure.

Now that we’ve decided on geography, will we do product innovation or business model innovation? If we do product innovation, do we want to extend existing product lines, supplement them with new product lines, or replace them altogether with new ones? Based on our geography decision, do we want to improve existing functionality, create new functionality, or reduce cost by 80% of while retaining 80% of existing functionality?

If we want to do business model innovation, that’s big medicine. It will require we throw away some of the stuff that has made us successful. And it will touch almost everyone. If we’re going to take that on, the CEO must take a heavy hand.

For simplicity, I described a straightforward, linear process where the whys are clearly defined and measurable and there’s sequential flow into a step-wise process to define the how. But it practice, there’s nothing simple or linear about the process. At best there’s overwhelming ambiguity around why, what, and when, and at worst, there’s visceral disagreement. And worse, with 0% clarity and an absent definition of success, there are several passionate factions with fully built-out plans that they know will work.

In truth, figuring out what innovation means and making it happen is a clustered-jumbled path where whats inform whys, whys transfigure hows, which, in turn, boomerang back to morph the whats. It’s circular, recursive, and difficult.

Innovation creates things that are novel, useful, and successful.  Novel means different, different means change, and change is scary. Useful is contextual – useful to whom and how will they use it? – and requires judgment. (Innovations don’t yet exist, so innovation efforts must move forward on predicted usefulness.) And successful is toughest of all because on top of predicted usefulness sit many other facets of newness that must come together in a predicted way, all of which can be verified only after the fact.

Innovation is a different animal altogether, almost like it eats itself. Just think – the most successful innovations come at the expense of what’s been successful.

Small Is Good, And Powerful

If lean has taught us anything, it’s smaller is better. Smaller machines, smaller factories, smaller teams, smaller everything.

The famous Speaker of the House, Tip O’Neill, said all politics are local. He meant all action happens at the lowest levels (in the districts and neighborhoods), where everyone knows everyone, where the issues are well understood, and the fundamentals are not just talked about, they’re lived. It’s the same with manufacturing. But I’m not talking about local in the geography sense; I’m talking about the neighborhood sense. When manufacturing is neighborhood-local, it’s small, tight, focused and knowledgeable.

We mistakenly think about manufacturing strictly as the process of making things—it’s far more. In the broadest sense, manufacturing is everything: innovation, design, making and service. It’s this broad-sense manufacturing that will deliver the next economic revolution.

Previously, I described how big companies break themselves into smaller operating units. They recognize lean favors small, and they break themselves up for competitive advantage. They want to become a collective of small companies with the upside of small without of the downside of big. Yet with small companies, there’s an urge to be big.

Lean says smaller is better and more profitable. Lean says small companies have an advantage because they’re already small. Lean says small companies should stay small (neighborhood small) and be more of what they are.

Small companies have a size advantage. Their smaller scope improves focus and alignment. It’s easier to define the mission, communicate it, and work toward it. It’s easier to mobilize the neighborhood. It’s clearer when things go off track and easier to get things back on track. At the lowest level, smaller companies zero in on problems and fix them. At the highest, they align themselves with their mission. These are important advantages, but not the most important.

The real advantage is deep process knowledge. Smaller companies have less breadth and more depth, which allows them to focus energy on the work and develop deep process knowledge. Many large manufacturers have lost process knowledge over the years. Small companies tend to develop and retain more of it. We’ve forgotten the value of deep process knowledge, but as companies look for competitive advantage, its stock is rising.

Lean wants small companies to build on that strength. To take it to the next level, lean wants companies to think about manufacturing in the more-than-making sense and use that deep process knowledge to influence the product itself. Lean wants suppliers to inject their process knowledge into their customers’ product development process to radically reduce material cost and help the product sprint through the factory.

The ultimate advantage of deep process knowledge is realized when small companies use it to design products. It’s realized when people who know the process fundamentals work respectfully with their neighbors who design the product. The result is deeper process knowledge and a far more profitable product. Big companies like to work with smaller companies who can design and make.

Tip O’Neill and lean agree. All manufacturing is local. And this local nature drives a focus on the fundamentals and details. Being neighborhood-local is easier for small companies because their scope is smaller, which helps them develop and retain deep process knowledge.

Lean wants companies to be small—neighborhood-small. When small companies build on a foundation of deep process knowledge, sales grow. Lean wants sales growth, but it also wants companies to reduce their size in the neighborhood sense.

Fix The Economy – Connect The Engineer To The Factory

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.

Radically Simplify Your Value Stream – Change Your Design

The next level of factory simplification won’t come from your factory.  It will come from outside your factory.  The next level of simplification will come from upstream savings – your suppliers’ factories – and downstream savings – your distribution system.  And this next level of simplification will create radically shorter value streams (from raw materials to customer.)

To reinvent your value stream, traditional lean techniques – reduction of non-value added (NVA) time through process change – aren’t the best way.  The best way is to eliminate value added (VA) time through product redesign – product change.  Reduction of VA time generates a massive NVA savings multiple. (Value streams are mostly NVA with a little VA sprinkled in.) At first this seems like backward thinking (It is bit since lean focuses exclusively on NVA.), but NVA time exists only to enable VA time (VA work).  No VA time, no associated NVA time.

Value streams are all about parts (making them, counting them, measuring them, boxing them, moving them, and un-boxing them) and products (making, boxing, moving.)  The making – touch time, spindle time – is VA time and everything else is VA time.  Design out the parts themselves (VA time) and NVA time is designed out.  Massive multiple achieved.

But the design community is the only group that can design out the parts. How to get them involved? Not all parts are created equal. How to choose the ones that matter? Value streams cut across departments and companies. How to get everyone pulling together?

Watch the video: link to video.  (And embedded below.)

How To Create a Sea of Manufacturing Jobs

It’s been a long slide from greatness for US manufacturing.  It’s been downhill since the 70s – a multi-decade slide.  Lately there’s a lot of hype about a manufacturing renaissance in the US – re-shoring, on-shoring, right-shoring.  But the celebration misguided.  A real, sustainable return to greatness will take decades, decades of single-minded focus, coordination, alignment and hard work – industry, government, and academia in it together for the long haul.

To return to greatness, the number of new manufacturing jobs to be created is distressing. 100,000 new manufacturing jobs is paltry. And today there is a severe skills gap.  Today there are unfilled manufacturing jobs because there’s no one to do the work. No one has the skills. With so many without jobs it sad.  No, it’s a shame.  And the manufacturing talent pipeline is dry – priming before filling.  Creating a sea of new manufacturing jobs will be hard, but filling them will be harder.  What can we do?

The first thing to do is make list of all the open manufacturing jobs and categorize them. Sort them by themes: by discipline, skills, experience, tools.  Use the themes to create training programs, train people, and fill the open jobs. (Demonstrate coordinated work of government, industry, and academia.)  Then, using the learning, repeat.  Define themes of open manufacturing jobs, create training programs, train, and fill the jobs.  After doing this several times there will be sufficient knowledge to predict needed skills and proactive training can begin.  This cycle should continue for decades.

Now the tough parts – transcending our short time horizon and finding the money.  Our time horizon is limited to the presidential election cycle – four years, but the manufacturing rebirth will take decades. Our four year time horizon prevents success. There needs to be a guiding force that maintains consistency of purpose – manufacturing resurgence – a consistency of purpose for decades.  And the resurgence cannot require additional money. (There isn’t any.)  So who has a long time horizon and money?

The DoD has both – the long term view (the military is not elected or appointed) and the money.  (They buy a lot of stuff.) Before you call me a war hawk, this is simply a marriage of convenience.  I wish there was, but there is no better option.

The DoD should pull together their biggest contractors (industry) and decree that the stuff they buy will have radically reduced cost signatures and teach them and their sub-tier folks how to get it done.  No cost reduction, no contract.  (There’s no reason military stuff should cost what it does, other than the DoD contractors don’t know how design things cost effectively.) The DoD should educate their contractors how to design products to reduce material cost, assembly time, supply chain complexity, and time to market and demand the suppliers.  Then, demand they demonstrate the learning by designing the next generation stuff.  (We mistakenly limit manufacturing to making, when, in fact, radical improvement is realized when we see manufacturing as designing and making.)

The DoD should increase its applied research at the expense of its basic research.  They should fund applied research that solves real problems that result in reduced cost signatures, reduce total cost of ownership, and improved performance.  Likely, they should fund technologies to improve engineering tools, technologies that make themselves energy independent and new materials.  Once used in production-grade systems, the new technologies will spill into non-DoD world (broad industry application) and create new generation products and a sea of manufacturing jobs.

I think this is approach has a balanced time horizon – fill manufacturing jobs now and do the long term work to create millions of manufacturing jobs in the future.

Yes, the DoD is at the center of the approach. Yes, some have a problem with that.  Yes, it’s a marriage of convenience. Yes, it requires coordination among DoD, industry, and academia.  Yes, that’s almost impossible to imagine. Yes, it requires consistency of purpose over decades. And, yes, it’s the best way I know.

What is Design for Manufacturing and Assembly?

Design for Manufacturing (DFM) is all about reducing the cost of piece-parts. Design for Assembly is all about reducing the cost of putting things together (assembly).  What’s often forgotten is that function comes first.  Change the design to reduce part cost, but make sure the product functions well.  Change the parts (eliminate them) to reduce assembly cost, but make sure the product functions well.

Paradoxically, DFM and DFA are all about function.

Here’s a link to a short video that explains DFM and DFA: link to video. (and embedded below)

 

We must broaden “Design”

Design is typically limited to function – what it does – and is done by engineering (red team).  Manufacturing is all about how to make it and is done by manufacturing (blue team).  Working separately there is local optimization.  We must broad to design to include both – red and blue. Working across red-blue boundaries creates magic.  This magic can only be done by the purple team.

Below is my first video post.  I hope to do more.  Let me know what you think.

 

 

Engineering’s Contribution to the Profit Equation

We all want to increase profits, but sometimes we get caught in the details and miss the big picture:
a
Profit = (Price – Cost) x Volume.

It’s a simple formula, but it provides a framework to focus on fundamentals. While all parts of the organization contribute to profit in their own way, engineering’s work has a surprisingly broad impact on the equation.

The market sets price, but engineering creates function, and improved function increases the price the market will pay. Design the product to do more, and do it better, and customers will pay more. What’s missing for engineering is an objective measure of what is good to the customer.

To read the complete article, click this link.

Secret Sauce that Doubles Profits

Last month a group of engineers met secretly to reinvent the US economy one company at a time.  Here are some of the players, maybe you’ve heard of them:

Alcoa, BAE, Boeing, Bose, Covidien, EMC, GE Medical, GE Transportation, Grundfos, ITT, Medrad, Medtronic, Microsoft, Motorola, Pratt & Whitney, Raytheon, Samsung, Schneider Electric, Siemens, United Technologies, Westinghouse, Whirlpool.

Presenter after presenter the themes were the same: double profits, faster time to market, and better products – the triple crown of product development. Magic in a bottle, and still the best kept secret of the product development community. (No sense sharing the secret sauce when you can have it all for yourself.)

Microsoft used the secret sauce to increase profits of their hardware business by $75 million; Boeing recently elevated the secret methodology to the level of lean. Yet it’s still a secret.

What is this sauce that doubles profits without increasing sales?  (That’s right, doubles.) What is this magic that decreases time to market? That reduces engineering documentation? That reduces design work itself? What is this growth strategy?

When trying to spread it on your company there are some obstacles, but the benefits should be enough to carry the day.  First off, the secret sauce isn’t new, but double the profits should be enough to take a first bite.  Second, its name doesn’t roll off the tongue (there’s no sizzle), but decreased time to market should justify a taste test. Last, design engineering must change its behavior (we don’t like to do that), but improved product functionality should be enough to convince engineering to swallow.

There are also two mapping problems: First, the sauce has been mapped to the wrong organization – instead of engineering it’s mapped to manufacturing, a group that, by definition, cannot do the work. (Only engineering can change the design.) Second, the sauce is mapped to the wrong word – instead of profit it’s mapped to cost.  Engineering is praised for increased profits (higher function generates higher profits) and manufacturing is responsible for cost – those are the rules.

With double profits, reduced time to market, and improved product function, the name shouldn’t matter. But if you must know, its name is Design for Manufacturing and Assembly (DFMA), though I prefer to call it the secret sauce that doubles profits, reduces time to market, and improves product function.

Mike Shipulski Mike Shipulski
Subscribe via Email

Enter your email address:

Delivered by FeedBurner