Shipulski On Design

Archive for the ‘Innovation’ Category

How long will it take? The short answer – same as last time. How long do we want it to take? That’s a different question altogether.

If the last project took a year, so will the next one. Even if you want it to take six months, it will take a year. Unless, there’s a good reason it will be different. (And no, the simple fact you want it to take six months is not a good enough reason in itself.)

Some good reasons it will take longer than last time: more work, more newness, less reuse, more risk, and fewer resources. Some good reasons why it will go faster: less work, less newness, more reuse, less risk, more resources. Seems pretty tight and buttoned-up, but things aren’t that straight forward.

With resources, the core resources are usually under control.  It’s the shared resources that are the problem. With resources under their control (core resources) project teams typically do a good job – assign dedicated resources and get out of the way. Shared resources are named that way because they support multiple projects, and this is the problem. Shared resources create coupling among projects, and when one project runs long, resource backlogs ripple through the other projects. And it gets worse. The projects backlogged by the initial ripple splash back and reflect ripples back at each other. Understand the shared resources, and you understand a fundamental dynamic of all your projects.

Plain and simple – work content governs project timelines. And going forward I propose we never again ask “How long will it take?” and instead ask “How is the work content different than last time?” To estimate how long it will take, set up a short face-to-face meeting with the person who did it last time, and ask them how long it will take. Write it down, because that’s the best estimate of how long it will take.

It may be the best estimate, but it may not be a good one. The problem is uncertainty around newness. Two important questions to calibrate uncertainty: 1) How big of a stretch are you asking for? and 2) How much do you know about how you’ll get there? The first question drives focus, but it’s not always a good predictor of uncertainty.  Even seemingly small stretches can create huge problems. (A project that requires a 0.01% increase in the speed of light will be a long one.) What matters is if you can get there.

To start, use your best judgment to estimate the uncertainty, but as quickly as you can, put together a rude and crude experimental plan to reduce it. As fast as you can execute the experimental plan, and let the test results tell you if you can get there. If you can’t get there on the bench, you can’t get there, and you should work on a different project until you can.

The best way to understand how long a project will take is to understand the work content. And the most important work content to understand is the new work content. Choose several of your best people and ask them to run fast and focused experiments around the newness. Then, instead of asking them how long it will take, look at the test results and decide for yourself.

If you want to learn, to really learn, experiment.

But I’m not talking about elaborate experiments; I’m talking about crude ones. Not simple ones, crude ones.

We were taught the best experiments maximize learning, but that’s dead wrong. The best experiments are fast, and the best way to be fast is to minimize the investment.

In the name of speed, don’t maximize learning, minimize the investment.

Let’s get right to it. One of the best tricks to minimize investment is to minimize learning – learning per experiment, that is. Define learning narrowly, design the minimum experiment, and run the trial. Learning per trial is low, but learning per month skyrockets because the number of trials per month skyrockets. But it gets better. There’s an interesting learning exponential at work. The first trial informs the second which shapes the third. But instead of three units of learning, it’s cubic. And minimizing learning doesn’t just half the time to run a trial, it reduces it by 100 or more. It’s earning to the hundredth power.

Another way to minimize investment is to minimize resolution. Don’t think nanometers, think thumbs up, thumbs down. Design the trial so the coarsest measuring stick gives an immediate and unambiguous response. There’s no investment in expensive measurement gear and no time invested in interpretation of results. Think sledgehammer to the forehead.

A third way to minimize investment is to evaluate relative differences. The best example is the simple, yet powerful, A-B test . Run two configurations, decide which is better, and run quickly in the direction of goodness.  No need to fret about how much better, just sprint toward it.  The same goes for trial 1 versus trial 2 comparisons. Here’s the tricky algorithm: If trial 2 is better, do more of that.  And the good news applies here too –  the learning exponential is still in play. Better to the hundredth power, in record time.

I don’t care what norms you have to bend or what rules you have to break. If you do one thing, run more trials.

But don’t take my word for it. Dr. Seuss had it right:

And I would run them in a boat!
And I would run them with a goat…
And I will run them in the rain.
And in the dark. And on a train.
And in a car. And in a tree.
They are so good so good you see!

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.