Shipulski On Design

Archive for the ‘Innovation’ Category

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.

What is to what isn’t.

What isn’t to what could.

What could to what should.

What should to what will.

What will to what is.

Repeat.

Short term is getting shorter, and long term is a thing of the past.

We want it now; no time for new; it’s instant gratification for us, but only if it doesn’t take too long.

A short time horizon drives minimization. Minimize waste; reduce labor hours; eliminate features and functions; drop the labor rate; cut headcount; skim off the top. Short term minimizes what is.

Short term works in the short term, but in the long term it’s asymptotic. Short term hits the wall when the effort to minimize overwhelms the benefit. And at this cusp, all that’s left is an emaciated shadow of what was. Then what? The natural extrapolation of minimization is scary – plain and simple, it’s a race to the bottom.

Where short term creates minimization, long term creates maximization. But, today, long term has mostly negative connotations – expensive, lots of resources, high risk, and low probability of success. At the personal level long term, is defined as a timeframe longer than we’re measured or longer than we’ll be in the role.

But, thankfully, there comes a time in our lives when it’s important for personal reasons to inject long term antibodies into the short term disease. But what to inject?

Before what, you must figure out why you want to swim against the current of minimization. If it’s money, don’t bother. Your why must have staying power, and money’s is too short. Some examples of whys that can endure: you want a personal challenge; you want to help society; your ego; you want to teach; or you want to help the universe hold off entropy for a while. But the best why is the work itself – where the work is inherently important to you.

With your why freshly tattooed on your shoulder, choose your what. It will be difficult to choose, but that’s the way it is with yet-to-be whats. (Here’s a rule: with whats that don’t yet exist, you don’t know they’re the right one until after you build them.) So just choose, and build.

Here are some words to describe worthwhile yet-to-be whats: barely believable, almost heretical, borderline silly, and on-the-edge, but not over it. These are the ones worth building.

Building (prototyping) can be expensive, but that’s not the type of building I’m talking about. Building is expensive when we try to get the most out of a prototype. Instead, to quickly and efficiently investigate, the mantra is: minimize the cost of the build. (The irony is not lost on me.) You’ll get less from the prototype, but not much. And most importantly, resource consumption will be ultra small – think under the radar. Take small, inexpensive bites; cover lots of ground; and build yourself toward the right what.

Working prototypes, even crude ones, are priceless because they make it real. And it’s the series of low cost, zig-zagging, leap-frogging prototypes that make up the valuable war chest needed to finance the long campaign against minimization.

Short term versus long term is a balancing act. Your prototype must pull well forward into the long term so, when the ether of minimization pulls back, it all slides back to the middle term, where it belongs.

When engineers see a big opportunity, we want desperately to move the company in the direction of our thinking, but find it difficult to change the behavior of others. Our method of choice is usually a full frontal assault, explaining to anyone that will listen the opportunity as we understand it. Our approach is straightforward and ineffective. Our descriptions are long, convoluted, complicated, we use confusing technical language all our own, and omit much needed context that we expect others should know. The result – no one understands what we’re talking about and we don’t get the behavior we’re looking for (immediate company realignment with what we know to be true).  Then, we get frustrated and shut down – opportunity lost.

To change the behavior of others, we must first change our own. As engineers we see problems which, when solved, result in opportunity. And if we’re to be successful, we must go back to the problem domain and set things straight. Here’s a sequence of new behaviors we as engineers can take to improve our chances of changing the behavior of others:

Step 1. Create a block diagram of the physical system using simple nouns (blocks) and verbs (arrows). Blue arrows are good (useful actions) and red arrows are bad (harmful actions). Here’s a link to a PowerPoint file with a live template to create your own.

Step 2. Reduce the system block diagram down to its essence to create a distilled block diagram of the problem, showing only the system elements (blocks) with the problem (red arrow).For a live template, see the second page of the linked file. [Note – if there are two red arrows in the system block diagram, there are two problems which must be solved separately. Break them into two and solve the first one first. For an example, see page three of the linked file.]

Step 3. Create a hand sketch, or cartoon, showing the two system elements (blocks) of the distilled block diagram from step 2. Zoom in so only the two elements are visible, and denote where they touch (where the problem is), in red. For an example, see page four of the linked file.

Step 4. Now that you understand the real problem, use Google to learn how others have solved it.

Step 5. Choose one of Google’s most promising solutions and prototype it. (Don’t ask anyone, just build it.)

Step 6. Show the results to your engineering friends. If the problem is solved, it’s now clear how the opportunity can be realized. (There’s a big difference between a crazy engineer with a radically new market opportunity and a crazy engineer with test results demonstrating a new technology that will create a whole new market.)

Step 7. If the problem is not solved, or you solved the wrong problem, go back to step 1 and refine the problem

With step 1 you’ll find you really don’t understand the physical system, you don’t know which elements of the system have the problem, and you can’t figure out what the problem is. (I’ve created complicated system block diagrams only to realize there was no problem.)

With step 2, you’ll continue to struggle to zoom in on the problem. And, likely, as you try to define the problem, you’ll go back to step 1 and refine the system block diagram. Then, you’ll struggle to distill the problem down to two blocks (system elements). You’ll want to retain the complexity (many blocks) because you still don’t understand the real problem.

If you’ve done step 2 correctly, step 3 is easy, though you’ll still want to complicate the cartoon (too many system elements) and you won’t zoom in close enough.

Step 4 is powerful. Google can quickly and inexpensively help you see how the world has already solved your problem.

Step 5 is more powerful still.

Step 6 shows Marketing what the future product will do so they can figure out how to create the new market.

Step 7 is how problems are really solved and opportunities actually realized.

When you solve the real problem, you create real opportunities.

We’ve been sufficiently polluted by lean and Six Sigma, and it’s time for them to go.

Masquerading as maximizers, these minimizers-in-sheep’s-clothing have done deep harm. Though Six Sigma is almost dead (it’s been irrelevant for some time now), it has made a lasting mark. Billed as a profit maximizer, it categorically rejects maximization. In truth, it’s a variation minimizer and difference reducer.  If it deviates, Six Sigma cuts its head off. Certainly this has a place in process control, but not in thinking control. But that’s exactly what’s happened. Six Sigma minimization has slithered off the manufacturing floor and created a culture of convergence. If your thinking is different, Six Sigma will clip it for you.

Lean is worse. All the buzz around lean is about maximizing throughput, but it doesn’t do that. It minimizes waste. But far worse is lean’s standard work. Minimize the difference among peoples’ work; make them do it the same; make the factory the same, regardless of the continent. All good on the factory floor, but lean’s minimization mania has spread like the plague and created a culture of convergence in its wake. And that’s the problem – lean’s minimization-standardization mantra has created a culture of convergence. If your thinking doesn’t fit in, lean will stomp it into place.

We need maximization at the expense of minimization, and divergence before convergence. We need creativity and innovation. But with Six Sigmaphiles and lean zealots running the show, maximization is little understood and divergence is a swear.

First we must educate on maximization. Maximization creates something that had not existed, while minimization reduces what is. Where Six Sigma minimization converges on the known right answer, creativity and innovation diverge to define a new question. The acid test: if you’re improving something you’re minimizing; if you’re inventing something you’re maximizing.

Like with He Who Shall Not Be Named, it’s not safe to say “diverge” out loud, because if you do, the lean Dementors will be called to suck out your soul. But, don’t despair – the talisman of guided divergence can save you.

With guided divergence, a team is given a creatively constructed set of constraints and very little time (hours) to come up with divergent ideas. The constraints guide the creativity (on target), and the tight timeline limits the risk – a small resource commitment. (Though counterintuitive, the tight timeline also creates remarkable innovation productivity.) Done in sets, several guided divergence sessions can cover a lot of ground in little time.

And the focused/constrained nature of guided divergence appeals to our minimization bias, and makes it okay to try a little divergence. We feel safe because we’re deviating only a little and only for a short time.

Lean and Six Sigma have served us well, and they still have their place. (Except for Six Sigma.) But they must be barred from creativity sessions and front end innovation, because here, divergence carries the day.

Whether it’s innovation, creativity, continuous improvement, or discontinuous improvement, it’s all about cultural change, and cultural change is about change in behavior.

With the police state approach, detailed processes are created and enforced; rules are created and monitored; and training is dealt out and attendance taken. Yes, behavior is changed, but it’s fleeting. Take your eye off the process, old behavior slips through the fence; look the other way from the rules, old behavior clips the barbed wire and climbs over the wall. To squelch old behavior with the police state approach, gulag energy must be consistently applied.

To squelch is one thing, but to create lasting behavior change is another altogether. But as different as they are, there’s a blurry line of justice that flips innocent to guilty. And to walk the line you’ve got to know where it is:

• Apply force, yes, but only enough to prevent backsliding – like a human ratchet. Push much harder and heels dig in.
• The only thing slower than going slow is going too fast. (Remember, you’re asking people to change the why of their behavior.) Go slow to go fast.
• Set direction and stay the course, unless there’s good reason to change. And when the team comes to you with a reason, deem it a good one, and the cornerstone of trust is laid. (This is a game of trust, not control.)

But there are some mantras to maximize:

• Over emphasize the positive and overlook the negative.
• Praise in public.
• Don’t talk, do.

The first two stand on their own, but the third deserves reinforcement.

This isn’t about your words, it’s about your behavior. And that’s good because you have full authority over your behavior. Demonstrate the new behavior so everyone knows what it looks like. Lead the way with your actions. Show them how it’s done. For lasting change, change your behavior.

Even if changing your behavior influences only one person, you’re on your way. The best prison riots start with a single punch.

Today’s answer to everything is process and tools. Define the desired outcome; create the process; create the tools.  Problem solved.

But if the desired outcome is lasting change, deterministic processes and static tools won’t get us there.  Lasting change comes from people and their behavior.

Going forward, instead of creating process, create an environment of trust so people will investigate the unfamiliar; and instead of creating tools, create time – time for people to prototype the unfamiliar.

Engineers solve technical problems so

Other engineers can create products so

Companies can manufacture them so

They can sell them for a profit and

Use the wealth to pay workers so

Workers can support their families and pay taxes so

Their countries have wealth for good schools to

Grow the next generation of engineers to

Solve the next generation of technical problems so…

I’ve been thinking about how to use video to train engineers.  The trouble with video is it takes time and money to create.  But what if you could create lessons using existing video?  That’s what the new TED-Ed platform can do.  With TED-Ed, any YouTube video can be “flipped” into a customized lesson.

Instead of trying to describe it, I used the new platform to create a video lesson.  Click the link below and give it a try. (The platform is still in beta version, so I’m not sure how will go. But that’s how it is with experiments.)

Video lesson: Innovation, Caveman-Style

When answering the questions, it may ask you to sign up for an account.  Click the X in the upper right of the message to make it go away, and keep going.  If the video does not work at all, poke around the TED-Ed website.

Either way, so we can accelerate our learning and get out in front, please post a comment or two.

We’re afraid of technology development because it’s risky. And figuring out where to go is the risky part. To figure out where to go companies use several strategies: advance multiple technologies in parallel; ask the customer; or leave it to company leader’s edict. Each comes with its strengths and weaknesses.

I think the best way to figure out where to go is to figure out where you are. And the best way to do that is data-driven S-curve analysis.

To collect data, look to your most recent product launches, say five, and characterize them using a goodness-to-cost ratio. (Think miles per gallon for your technology.) Then plot them chronologically and see how the goodness ratio has evolved – flat, slow growth, steep growth, or decline. The shape of the curve positions your technology within the stages of the S-curve and its location triangulated with contextual clues. You know where you are so you can figure out where to go.

Here’s what the stages feel like and what to do when you’re in them:

Stage 1: Infancy – New physics are used to deliver a known function, but it’s not ready for commercialization. This is like early days of the gasoline-electric hybrid vehicles, where the physics of internal combustion was combined with the physics of batteries. In Stage 1 the elements of the overall system are established, like when Honda developed its first generation Honda Insight and GM its EV1. Prototypes are under test, and they work okay, but not great. In Stage 1, goodness-to-cost is lower than existing technologies (and holding), but the bet is when they mature goodness-to-cost will be best on the planet.

If your previous products were Stage 4 (Maturity) or Stage 5 (Decline), your new project should be in Stage 1. If your existing project is in Stage 1, focus on commercialization. If all your previous projects were (are) in Stage 1, you should focus on commercializing one (moving to Stage 2) at the expense of starting a new one.

Stage 2: Transitional – A product is launched in the market and there is intense competition with existing technologies. In Stage 2, several versions of new technology are introduced (Prius, Prius pluggable, GM’s Volt, Nissan Leaf), and they fight it out. Goodness-to-cost is still less than existing technologies, but there’s some element of the technology that’s attractive. For electric vehicles, think emissions.

If your previous products were Stage 4 (Maturity) or Stage 5 (Decline) and your current project just transitioned from Stage 1, you’re in the right place. In Stage 2, fill gaps in functionality; increase controllability – better controls to improve battery performance; and develop support infrastructure -electric fueling stations.

Stage 3: Growth – Goodness-to-cost increases rapidly, and so do sales. (I think most important for an electric vehicle is miles per charge.)

If you’re in Stage 3, it’s time to find new applications – e.g., electric motorcycles, or shorten energy flow paths – small electric motors at the wheels.

Stage 4: Maturity – The product hits physical limits – flat miles per gallon; hits limits in resources – fossil fuels; hits economic limits – costly carbon fiber body panels to reduce weight; or there’s rapid growth in harmful factors – air pollution.

If you’re in Stage 4, in the short term add auxiliary functions – entertainment systems, mobile hotspot, heated steering wheel, heated washer fluid; or improve aesthetics – like the rise of the good looking small coupe. In the long term, start a Stage 1 project to move to new physics – hydrogen fuel cells.

Stage 5: Decline – New and more effective systems have entered their growth stage – Nissan Leaf outsells Ford pickup trucks.

If you’re in Stage 5, long ago you should have started at Stage 1 project – new physics. If you haven’t, it may be too late.

S-curve analysis guides, but doesn’t provide all the answers. That said, it’s far more powerful than rock-paper-scissors.

(This thinking was blatantly stolen from Victor Fey’s training on Advanced S-Curve Analysis. Thank you, Victor.)

WHY? To grow sales in existing markets and create sales in new markets.

WHAT? Create innovative technologies and design products with more function and less cost.

HOW? Educate the engineering engine.

This is easier said than done, because for years we’ve set one-sided expectations – new products must work and timelines must be met – and driven risk tolerance out of our engineering engine. Now it’s time to inject it back in.

The message – Our thinking must change.  We must take more risk, but do it safely by reducing negative consequences of risk.

To reduce negative consequences of risk, we must learn to localize risk through the narrowest and deepest problem definition, and learn to secure the launch so it’s safe to try new things.

We must do more up-front technology work, but learn to do it far more narrowly and deeply. We must learn to hold ourselves accountable to rigorous problem definition, and we must put our best people on technology projects.

To focus creativity we must learn to set seemingly unrealistic time constraints; to focus our actions we can look to a powerful mantra – spend a little, learn a lot.

The trouble with new thinking is it takes new thinking. If you don’t have it, go get it. If you already have it, figure out why you haven’t used it.