Archive for the ‘Decisions’ Category

Step-Wise Learning

staircaseAt every meeting you have a chance to move things forward or hold them back.  When a new idea is first introduced it’s bare-naked.  In its prenatal state, it’s wobbly and can’t stand on its own and is vulnerable to attack. But since it’s not yet developed, it’s impressionable and willing to evolve into what it could be.  With the right help it can go either way – die a swift death or sprout into something magical.

Early in gestation, the most worthy ideas don’t look that way.  They’re ugly, ill-formed, angry or threatening.  Or, they’re playful, silly or absurd.  Depending on your outlook, they can be a member of either camp. And as your outlook changes, they can jump from one camp to the other.  Or, they can sit with one leg in each.  But none of that is about the idea, it’s all about you.  The idea isn’t a thing in itself, it’s a reflection of you. The idea is nothing until you attach your feelings to it.  Whether it lives or dies depends on you.

Are you looking for reasons to say yes or reasons to say no?

On the surface, everyone in the organization looks like they’re fully booked with more smart goals than they can digest and have more deliverables than they swallow, but that’s not the case.  Though it looks like there’s no room for new ideas, there’s plenty of capacity to chew on new ideas if the team decides they want to.  Every team can spare and hour or two a week for the right ideas.  The only real question is do they want to?

If someone shows interest and initiative, it’s important to support their idea.  The smallest acceptable investment is a follow-on question that positively reinforces the behavior.  “That’s interesting, tell me more.” sends the right message.  Next, “How do you think we should test the idea?” makes it clear you are willing to take the next step.  If they can’t think of a way to test it, help them come up with a small, resource-lite experiment.  And if they respond with a five year plan and multi-million dollar investment, suggest a small experiment to demonstrate worthiness of the idea.  Sometimes it’s a thought experiment, sometimes it’s a discussion with a customer and sometimes it’s a prototype, but it’s always small.  Regardless of the idea, there’s always room for a small experiment.

Like a staircase, a series of small experiments build on each other to create big learning.  Each step is manageable – each investment is tolerable and each misstep is survivable – and with each experiment the learning objective is the same: Is the new idea worthy of taking the next step?  It’s a step-wise set of decisions to allocate resources on the right work to increase learning.  And after starting in the basement, with step-by-step experimentation and flight-by-flight investment, you find yourself on the fifth floor.

This is about changing behavior and learning.  Behavior doesn’t change overnight, it changes day-by-day, step-by-step.  And it’s the same for learning – it builds on what was learned yesterday.  And as long at the experiment is small, there can be no missteps.  And it doesn’t matter what the first experiment is all about, as long as you take the first step.

Your team will recognize your new behavior because it respectful of their ideas.  And when you respect their ideas, you respect them.  Soon enough you will have a team that stands taller and runs small experiments on their own.  Their experiments will grow bolder and their learning will curve will steepen.  Then, you’ll struggle to keep up with them, and you’ll have them right where you want them.

image credit — Rob Warde

Is it novel?

Sarge are you sure this egg doesn't belong to anyone???Agree or not, companies think they have to grow to survive.  (I don’t believe it.)  For companies of all sizes and shapes, growth is the single most important forcing function.  Is has tidal wave power, and whether you’re a surfer, sailor or power-boater, it’s important to respect it.  More than that, when push comes to shove, it’s the only wave in town.

Companies’ recipe for growth is simple: make more, sell more. And some keep it simpler: sell more.

The best growth: sell new products or provide new services to new customers; next best: sell new to the same customers; next next best: sell more of the same to the same customers.  The last flavor is the easiest, right up until it isn’t. And once it isn’t, companies must come up with new things to sell.  That works for a while, until it doesn’t.  Then, and only then, after exhausting all other possibilities, companies must create real newness and try to sell it to strangers.

The model works well as long as everyone in the industry follows it.  But when an up-start outsider enters the market back-to-front, the wheels fall off.  When they develop useful newness before you and sell it to your customers (new customers for them), that’s not good. And that’s why it’s so important to start with different — right now.

To help your company do more work that’s different, start with an inventory of your novelty. Novel work is work that creates difference, and that difference can be defined only in comparison with the state-of-the-art (what is, or the baseline system).  Start with a functional analysis of your state-of-the-art. Create a block diagram of your business model, your most successful product and the service that defines your brand.  Take a look at your technology and new product development projects and flag the ones that will create things that aren’t on your three functional analyses.  (Improvement projects, because they improve what is, cannot be flagged as novel.)

Put all your novelty on one page and decide if you like it.  (No way around it, how you respond to the level and type of novelty in your quiver is a judgment call.) If you like what you see, keep going. If you don’t, stop some improvement projects and start some projects that create useful novelty.  The stopping will not come easy.  Existing projects have momentum and people have personal attachment to them. The only thing powerful enough to stop them is the all-powerful growth objective.  If company leaders learn the existing projects won’t meet the growth objective, the tidal wave will sweep away some lesser projects to make room for new ones.

There will be great internal pressure to add projects without stopping some, but that won’t work.  Everyone is fully booked and can’t deliver on additional projects even if you tell them to.  If you’re not willing to stop projects, you’re better off staying the course and waiting until you finish one before you start a project to increase your novelty score.

Novelty is good because there’s more upside potential, and improvement is good because there’s more certainty. One is not better than the other. You need both.

In the end, you’re going to have to judge if you’re happy with what you’ve got.  That’s a difficult task that no one can make easier for you.  But it is possible to use your judgment better.  If you can clearly call out what’s novel and what’s not, you’re on your way.

Image credit – s3aphotography (image cropped)

The People Business

the common vision bus


Things don’t happen on their own, people make them happen.

With all the new communication technologies and collaboration platforms it’s easy to forget that what really matters is people.  If people don’t trust each other, even the best collaboration platforms will fall flat, and if they don’t respect each other, they won’t communicate – even with the best technology.

Companies put stock in best practices like they’re the most important things, but they’re not.  Because of this unnatural love affair, we’re blinded to the fact people are what make best practices best.  People create them, people run them, and people improve them. Without people there can be no best practices, but on the flip-side, people can get along just fine without best practices.  (That says something, doesn’t it?) Best practices are fine when processes are transactional, but few processes are 100% transactional to the core, and the most important processes are judgement-based.  In a foot race between best practices and good judgement, I’ll take people and their judgment – every day.

Without a forcing function, there can be no progress, and people are the forcing function.  To be clear, people aren’t the object of the forcing function, they are the forcing function.  When people decide to commit to a cause, the cause becomes a reality.  The new reality is a result – a result of people choosing for themselves to invest their emotional energy.  People cannot be forced to apply their life force, they must choose for themselves.  Even with today’s “accountable to outcomes” culture, the power of personal choice is still carries the day, though sometimes it’s forced underground.  When pushed too hard, under the cover of best practice, people choose to work the rule until the clouds of accountability blow over.

When there’s something new to do, processes don’t do it – people do.  When it’s time for some magical innovation, best practices don’t save the day – people do.  Set the conditions for people to step up and they will; set the conditions for them to make a difference and they will.  Use best practices if you must, but hold onto the fact that whatever business you’re in, you’re in the people business.

Image credit – Vicki & Chuck Rogers

To make the right decision, use the right data.

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

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

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

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

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

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

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

Profit = (price – cost) x volume.

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

Image credit – alabaster crow photographic

Prototypes Are The Best Way To Innovate

Protype of first mouseIf you’re serious about innovation, you must learn, as second nature, to convert your ideas into prototypes.

Funny thing about ideas is they’re never fully formed – they morph and twist as you talk about them, and as long as you keep talking they keep changing. Evolution of your ideas is good, but in the conversation domain they never get defined well enough (down to the nuts-and-bolts level) for others (and you) to know what you’re really talking about. Converting your ideas into prototypes puts an end to all the nonsense.

Job 1 of the prototype is to help you flesh out your idea – to help you understand what it’s all about. Using whatever you have on hand, create a physical embodiment of your idea. The idea is to build until you can’t, to build until you identify a question you can’t answer. Then, with learning objective in hand, go figure out what you need to know, and then resume building. If you get to a place where your prototype fully captures the essence of your idea, it’s time to move to Job 2. To be clear, the prototype’s job is to communicate the idea – it’s symbolic of your idea – and it’s definitely not a fully functional prototype.

Job 2 of the prototype is to help others understand your idea. There’s a simple constraint in this phase – you cannot use words – you cannot speak – to describe your prototype. It must speak for itself. You can respond to questions, but that’s it. So with your rough and tumble prototype in hand, set up a meeting and simply plop the prototype in front of your critics (coworkers) and watch and listen. With your hand over your mouth, watch for how they interact with the prototype and listen to their questions. They won’t interact with it the way you expect, so learn from that. And, write down their questions and answer them if you can. Their questions help you see your idea from different perspectives, to see it more completely. And for the questions you cannot answer, they the next set of learning objectives. Go away, learn and modify your prototype accordingly (or build a different one altogether). Repeat the learning loop until the group has a common understanding of the idea and a list of questions that only a customer can answer.

Job 3 is to help customers understand your idea. At this stage it’s best if the prototype is at least partially functional, but it’s okay if it “represents” the idea in clear way. The requirement is prototype is complete enough for the customer can form an opinion. Job 3 is a lot like Job 2, except replace coworker with customer. Same constraint – no verbal explanation of the prototype, but you can certainly answer their direction questions (usually best answered with a clarifying question of your own such as “Why do you ask?”) Capture how they interact with the prototype and their questions (video is the best here). Take the data back to headquarters, and decide if you want to build 100 more prototypes to get a broader set of opinions; build 1000 more and do a small regional launch; or scrap it.

Building a prototype is the fastest, most effective way to communicate an idea. And it’s the best way to learn. The act of building forces you to make dozens of small decisions to questions you didn’t know you had to answer and the physical nature the prototype gives a three dimensional expression of the idea. There may be disagreement on the value of the idea the prototype stands for, but there will be no ambiguity about the idea.

If you’re not building prototypes early and often, you’re not doing innovation. It’s that simple.

How do you choose what to work on?

Eagle Nebula M16There are always too many things to do, too much to work on. And because of this, we must choose. Some have more choice than others, but we all have choice. And to choose, there are several lenses we look through.

What’s good enough? If it’s good enough, there’s no need to work on it. “Good enough” means it’s not a constraint; it’s not in the way of where you want to go.

What’s not good enough? If it’s not good enough, it’s important to work on it. “Not good enough” means it IS a constraint; it IS in the way; it’s blocking your destination.

What’s not happening? If it’s not happening and the vacancy is blocking you from your destination, work on it. Implicit in the three lenses is the assumption of an idealized future state, a well-defined endpoint.

It’s the known endpoint that’s used to judge if there’s a blocking constraint or something missing. And there are two schools of thought on idealized future states – the systems, environment, competition, and interactions are well understood and idealized future states are the way to go, or things are too complex to predict how things will go. If you’re a member of the idealized-future-state-is-the-way-to-go camp, you’re home free – just use your best judgment to choose the most important constraints and hit them hard. If you’re a believer in complexity and its power to scuttle your predictions, things are a bit more nuanced.

Where the future state folks look through the eyepiece of the telescope toward the chosen nebula, the complexity folks look through the other end of the telescope toward the atomic structure of where things are right now. Complexity thinkers think it’s best to understand where you are, how you got there, and the mindset that guided your journey. With that knowledge you can rough out the evolutionary potential of the future and use that to decide what to work on.

If you got here by holding on to what you had, it’s pretty clear you should try to do more of that, unless, of course, the rules have changed. And to figure out if the rules have changed? Well, you should run small experiments to test if the same rules apply in the same way. Then, do more of what worked and less of what didn’t. And if nothing works even on a small scale, you don’t have anything to hold onto and it’s time to try something altogether new.

If you got here with the hybrid approach – by holding on to what you had complimented with a healthy dose of doing new stuff (innovation), it’s clear you should try to do more of that, unless, of course, you’re trying to expand into new markets which have different needs, different customers, and different pocketbooks. To figure out what will work, runs small experiments, and do more of what worked and less of what didn’t. If nothing works, your next round of small experiments should be radically different. And again, more of what worked, less of what didn’t.

And if you’re a young company and have yet to arrive, you’re already running small experiments to see what will work, so keep going.

There’s a half-life to the things that got us here, and it’s difficult to predict their decay. That’s why it’s best to take small bets on a number of new fronts – small investment, broad investigation of markets, and fast learning. And there’s value in setting a rough course heading into the future, as long as we realize this type of celestial navigation must be informed by regular sextant sightings and course corrections they inform.

Image credit – Hubble Heritage.

The Complexity Conundrum

ConfusedIn school the problems you were given weren’t really problems at all. In school you opened the book to a specific page and there, right before you in paragraph form and numbered consecutively, was a neat row of “problems”. They were fully-defined, with known inputs, a formal equation that defined the system’s response, and one right answer. Nothing extra, nothing missing, nothing contradictory. Today’s problems are nothing like that.

Today’s problems don’t have a closed form solution; today’s problems don’t have a right answer. Three important factors come into play: companies and their systems are complex; the work, at some level, is always new; and people are always part of the equation.

It’s not that companies have a lot of moving parts (that makes them complicated); it’s that the parts can respond differently in different situations, can change over time (learn), and the parts can interact and change each others’ response (that’s complex). When you’re doing work you did last time, there’s a pretty good chance the system will perform like it did last time. But it’s a different story when the inputs are different, when the work is new.

When the work is new, there’s no precedent. The inputs are new and the response is newer. Perturb the system in a new way and you’re not sure how it will respond. New interactions between preciously unreactive parts make for exciting times. The seemingly unconnected parts ping each other through the ether, stiffen or slacken, and do their thing in a whole new way. Repeatability is out the window, and causal predictability is out of the question. New inputs (new work) slathers on layers of unknownness that must be handled differently.

Now for the real complexity culprit – people. Companies are nothing more than people systems in the shape of a company. And the work, well, that’s done by people. And people are well known to be complex. In a bad mood, we respond one way; confident and secure we respond in another. And people have memory. If something bad happened last time, next time we respond differently. And interactions among people are super complex – group think, seniority, trust, and social media.

Our problems swim with us in a hierarchical sea of complexity. That’s just how it is. Keep that in mind next time you put together your Gantt chart and next time you’re asked to guarantee the outcome of an innovation project.

Complexity is real, and there are real ways to handle it. But that’s for another time. Until then, I suggest you bone up on Dave Snowden’s work. When it comes to complexity, he’s the real deal.

Image credit – miguelb.

The Illusion of Planning

Pin The Tail on the Donkey

Planning is important work, but it’s non-value added work. Short and sweet – planning is waste.

Lean has taught us waste should be reduced, and the best way to reduce waste from planning is to spend less time planning. (I feel silly writing that.) Lean has taught us to reduce batch size, and the best way to reduce massive batch size of the annual planning marathon is to break it into smaller sessions. (I feel silly writing that too.)

Unreasonable time constraints increase creativity. To create next year’s plan, allocate just one for the whole thing. (Use a countdown timer.) And, because batch size must be reduced, repeat the process monthly. Twelve hours of the most productive planning ever, and countless planning hours converted into value added work.

Defining the future state and closing the gap is not the way to go. The way to go is to define the current state (where you are today) and define how to move forward. Use these two simple rules to guide you:

  • Do more of what worked.
  • Do less of what didn’t.

Here’s an example process:

The constraint – no new hires. (It’s most likely the case, so start there.)

Make a list of all the projects you’re working on. Decide which to stop right now (the STOP projects) and which you’ll finish by the end of the month (the COMPLETED projects). The remaining projects are the CONTINUE projects, and, since they’re aptly named, you should continue them next month. Then, count the number of STOP and COMPLETED projects – that’s the number of START projects you can start next month.

If the sum of STOP and COMPLETED is zero, ask if you can hire anyone this month. If the answer is no, see you next month.

If the sum is one, figure out what worked well, figure out how to build on it, and define the START project. Resources for the START project should be the same as the STOP or COMPLETED project.

If the sum is two, repeat.

Now ask if you can hire anyone this month. If the answer is no, you’re done. If the answer is yes, define how many you can hire.

With your number in hand, and building on what worked well, figure out the right START project. Resources must be limited by the number of new hires, and the project can’t start until the new hire is hired. (I feel silly writing that, but it must be written.) Or, if a START project can’t be started, use the new resource to pile on to an important CONTINUE project.

You’re done for the month, so send your updated plan to your boss and get back to work.

Next month, repeat.

The process will evolve nicely since you’ll refine it twelve times per year.

Ultimately, planning comes down to using your judgment to choose the next project based on the resources you’re given. The annual planning process is truly that simple, it’s just doesn’t look that way because it’s spread over so many months. So, if the company tells its leaders how many resources they have, and trusts them to use good judgment, yearly planning can be accomplished in twelve hours per year (literally). And since the plan is updated monthly, there’s no opportunity for emergency re-planning, and it will always be in line with reality.

Less waste and improved quality – isn’t that what lean taught us?

Summoning The Courage To Ask

Alone in the line.

I’ve had some great teachers in my life, and I’m grateful for them.  They taught me their hard-earned secrets, their simple secrets.  Though each had their own special gifts, they all gave them in the same way – they asked the simplest questions.

Today’s world is complex – everything interacts with everything else; and today’s pace is blistering – it’s tough to make time to understand what’s really going on.  To battle the complexity and pace, force yourself to come up with the simplest questions.  Here are some of my favorites:


For new products:

  • Who will buy it?
  • What must it do?
  • What should it cost?


For new technologies:

  • What problem are you trying to solve?
  • How will you know you solved it?
  • What work hasn’t been done before?


For new business models:

  • Why are you holding onto your decrepit business model?


For problems:

  • Can you draw a picture of it on one page?
  • Can you make it come and go?


For decisions:

  • What is the minimum viable test?
  • Why not test three or four options at the same time?


For people issues:

  • Are you okay?
  • How can I help you?


For most any situation:

  • Why?


These questions are powerful because they cut through the noise, but their power couples them to fear and embarrassment – fear that if you ask you’ll embarrass someone.  These questions have the power to make it clear that all the activity and hype is nothing more than a big cloud of dust heading off in the wrong direction. And because of that, it’s scary to ask these questions.

It doesn’t matter if you steal these questions directly (you have my permission), twist them to make them your own, or come up with new ones altogether.  What matters is you spend the time to make them simple and you summon the courage to ask.

Image credit — Montecruz Foto.

Can It Grow?

Retired SunflowerIf you’re working in a company you like, and you want it to be around in the future, you want to know if it will grow.  If you’re looking to move to a new company, you want to know if it has legs – you want to know if it will grow. If you own stock, you want to know if the company will grow, and it’s the same if you want to buy stock.  And it’s certainly the case if you want to buy the whole company – if it can grow, it’s worth more.

To grow, a company has to differentiate itself from its competitors.  In the past, continuous improvement (CI) was a differentiator, but today CI is the minimum expectation, the cost of doing business.  The differentiator for growth is discontinuous improvement (DI).

With DI, there’s an unhealthy fascination with idea generation.  While idea generation is important, companies aren’t short on ideas, they’re short on execution.  But the one DI differentiator is the flavor of the ideas.  To do DI a company needs ideas that are radically different than the ones they’re selling now.  If the ideas are slightly twisted variants of today’s products and business models, that’s a sure sign continuous improvement has infiltrated and polluted the growth engine. The gears of the DI engine are gummed up and there’s no way the company can sustain growth.  For objective evidence the company has the chops to generate the right ideas, look for a process that forces their thinking from the familiar, something like Jeffrey Baumgartner’s Anticonventional Thinking (ACT).

For DI-driven growth, the ability to execute is most important.  With execution, the first differentiator is how the company investigates radically new ideas.  There are three differentiators – a focus on speed, a “market first” approach, and the use of minimum viable tests (MVTs).  With new ideas, it’s all about how fast you can learn, so speed should come through loud and clear.  Without a market, the best idea is worthless, so look for “market first” thinking.  Idea evaluation starts with a hypothesis that a specific market exists (the market is clearly defined in the hypothesis) which is evaluated with a minimum viable test (MVT) to prove or disprove the market’s existence.  MVTs should error on the side of speed – small, localized testing.  The more familiar minimum viable product (MVP) is often an important part of the market evaluation work.  It’s all about learning about the market as fast as possible.

Now, with a validated market, the differentiator is how fast company can rally around the radically new idea and start the technology and product work.  The companies that can’t execute slot the new project at the end of their queue and get to it when they get to it.  The ones that can execute stop an existing (lower value) project and start the new project yesterday.  This stop-to-start behavior is a huge differentiator.

The company’s that can’t execute take a ready-fire-aim approach – they just start.  The companies that differentiate themselves use systems thinking to identify gaps in resources and capabilities and close them. They do the tough work of prioritizing one project over another and fully staff the important ones at the expense of the lesser projects.  Rather than starting three projects and finishing none, the companies that know how to do DI start one, finish one, and repeat.  They know with DI, there’s no partial credit for a project that’s half done.

All companies have growth plans, and at the highest level they all hang together, but some growth plans are better than others.  To judge the goodness of the growth plan takes a deeper look, a look into the work itself.  And once you know about the work, the real differentiator is whether the company has the chops to execute it.

Image credit – John Leach.

Slower Can Be Faster

Radar GunThere are two types of drivers – those that speed as a way of life and those that speed when they forget to pay attention.  But for both, speed kills.

For the everyday speeder, life is an opportunity to push limits and break rules.  Every highway is a Formula One course; every traffic light an opportunity to run the red light.  They know every speed trap and have honed their drag racing tactics, and, mostly, they don’t get caught.  And driving this way year-on-year, they no longer realize they’re speeding and no longer see it as dangerous behavior.  They see themselves as invincible and even take pride in their reckless behavior, and that’s dangerous.

If you’ve made the product before, or you’ve done something similar, you know all the traps and a fast business decision isn’t bad.  But fast all the time isn’t the answer. When you’re in country that drives on the wrong side of the road and you approach a round-about, slower is better.  When you don’t understand the road signs; when your left-right decisions are backward; and you don’t know how to negotiate the big circle of traffic, it’s pretty clear slower is better.  But if you’ve been successful with your habitual speeding, you’ll likely accelerate into the traffic circle, rear-end someone, and flip yourself end others into a deadly pileup.  And if you survive, likely curse those stupid drivers who didn’t know enough to get out of your way.

But there’s a simpler case that seals it.  When you don’t know where you’re going, clicking off miles on the odometer isn’t progress, it’s just activity (that burns fuel).  In these conditions, going fast in the wrong directions is worse than not driving at all.  When you’re lost, it doesn’t make sense to speed.

The conscientious speeder keeps two hands on the wheel and maintain safe separation distance at upwards of ten car lengths.  For them, every day is an opportunity to check the tire pressure and check the dipstick for oil.  They plan out the trip, check the road conditions, and pay attention. (Cell phones off for these folks.)

Today’s cars are quiet and smooth which makes for calm, comfortable driving.  But they’re also powerful, and, even with good intentions, a brief lapse in attention can generate breakneck speed.  The conscientious speeder backs off the accelerator as soon as attention returns, and the danger is low.  But, when a lapse in attention overlaps with a quick change in driving conditions (a deer runs across the highway, or the car in front jacks on the breaks), you can’t react quickly enough, and that’s dangerous.

If you’ve made the product before, or you’ve done something similar, and you checked the tire pressure, a lapse in attention once in a while isn’t bad. But doing the same drive every day and lulling yourself into a road trip stupor isn’t the answer.  When you’re cruising over the limit on a well-lit, dead-straight highway, in your serenity you can easily speed past your exit without knowing.  And the faster you’re going, the more exits you’ll miss until you realize it.

Sometimes, when the conditions are right, slower is faster.

Mike Shipulski Mike Shipulski
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