Shipulski On Design

Archive for the ‘Uncertainty’ Category

Innovation isn’t a thing in itself, and it’s not something to bolster for the sake of bolstering.

Innovation creates things (products, services, business models) that are novel, useful and successful.  It’s important to know which flavor to go after, but before that it’s imperative to formalize the business objective.  Like lean or Six Sigma, innovation is a business methodology whose sole intention is to deliver on the business objective.  The business objective is usually a revenue or profit goal, and success is defined by meeting the objective.  Successful is all about meeting the business objective and successful is all about execution.

There are a lot of things that must come together for an innovation to be successful. For an innovative product here are the questions to answer:  Can you make it, certify it, market it, sell it, distribute it, service it, reclaim it?  As it happens, these are the same questions to answer for any new product.  In that way, innovative products are not different.  But because innovation starts with novel, with innovative products the answers can be different.  For an innovative product there are more “no’s” and for each no there’s a reason that starts with a C: constraint, capacity, capability, competitor, cooperation, capital.  And the business objective cannot be achieved with closing the gaps.

After successful, there’s useful.   Like any work based on a solid marketing methodology, innovation must deliver usefulness to the customer.  Innovation or not, strong marketing is strong marketing and strong marketing defines who the customer is, how they’ll use the new service, and how they’ll benefit – the valuable customer outcome (VCO.)  But with an innovative service it’s more difficult to know who the customer is, how they’ll use the service and if they’ll pay for it.  (That’s the price of novelty.)  But in most other ways, an innovative service is no different than any other service.  Both are successful because they deliver usefulness customers, those customers pay money for the usefulness and the money surpasses the business objective.

Innovation is different because of novelty, but only in degree.  Continuous improvement projects have novelty.  Usually, it’s many small changes consistently applied that add up to meaningful results, for example waste reduction, improved throughput and product quality.  These projects have novelty, but the novelty is the sum of small steps, all of which stay close to known territory.

The next rung on the novelty ladder is discontinuous improvement which creates a large step change in goodness provided to the customer.  (Think 3X improvement.)  The high degree of novelty creates broader uncertainty.  Will the customer be able to realize the goodness?  Will the novelty be appealing to a set of yet-to-be-discovered customers?  Will they pay for it?  It is worth doing all that execution work?  Will it cannibalize other products?  The novelty is a strong divergence from the familiar and with it comes the upside of new customer goodness and the downside of the uncertainty.

The highest form of novelty is no-to-yes.  No other product on the planet could do it before, but the new innovative one can.   It has the potential to create new markets, but also has the potential to obsolete the business model.  The sales team doesn’t know how to sell it, the marketers don’t know how to market it and the factory doesn’t know how to make it.  There new technology is not as robust as it should be and the cost structure may never become viable.  There’s no way to predict how competitors will respond, there’s no telling if it will pass the regulatory requirements.  And to top it off, no one is sure who the customer is or if anyone will it.  But, if it all comes together, this innovation will be a game-changer.

Innovation is the same as all the other work, except there’s more novelty. And with that novelty comes more upside and more uncertainty.  With novelty, too much of a good thing isn’t wonderful.  Sufficient novelty must be ingested to meet the business objective, and a bit more for the long term to stay out in front.

Be clear about business objectives, deliver usefulness to customers and use novelty to make it happen.  And call it whatever you want.

Image credit – Agustin Rafael Reyes 

Even after playing lots of tricks to reduce its energy consumption, our brains still consume a large portion of the calories we eat.  Like today’s smartphones it’s computing power is too big for it’s battery so its algorithms conserve every chance they get.  One of its go-to conservation strategies is to make mental models.  The models capture the essence of a system’s behavior without the overhead of retaining all the details of the system.

And as the brain goes about its day it tries to fit what it sees to its portfolio of mental models.  Because mental models are so efficient, to save juice the brain is pretty loose with how it decides if a model fits the situation.  In fact the brain doesn’t do a best fit, it does a first fit. Once a model is close enough, the model is applied, even if there’s a better one in the archives.

Overall, the brain does a good job.  It looks at a system and matches it with a model of a similar system it experienced in the past.  But behind it all the brain is making a dangerous assumption.  The brain assumes all systems are static.  And that makes for mental models that are static.  And because all systems change over time (the only thing we can argue about is the rate of change) the brain’s mental models are always out of date.

Over the years your brain as made a mental model of how your business works – customers do this, competitors do that, and markets do the other.  But by definition that mental model is outdated.  There needs to be a forcing function that causes us to refute our mental models so we can continually refine them. [A good mantra could be – all mental models are out of fashion until proven otherwise.]  But worse than not having a mechanism to refute them, we have a formal business process the demands we converge on our tired mental models year-on-year.  And the name of that wicked process – strategic planning.

It goes something like this. Take a little time from your regular job (though you still have to do all that regular work) and figure out how you’re going to grow your business by a large (and arbitrary) percentage. The plan must be achievable (no pie in the sky stuff), it should be tightly defined (even though everyone knows things are dynamic and the plan will change throughout the year), you must do everything you did last year and more and you have fewer resources than last year.  Any brain in it’s right will fit the old models to the new normal and put the plan together in the (insufficient) time allotted. The planning process reinforces the re-use of old models.

Because the brain believes everything is static, it’s thinking goes like this – a plan based on anything other than the tried-and-true mental models cannot have certainty or predictability in time or resources.  And it’s thinking is right, in part.  But because all mental models are out of date, even plans based on existing models don’t have certainty and predictability.  And that’s where the wheels fall off.

To inject a bit more reality into strategic planning, ignore the tired old information streams that reinforce existing thinking and find new ones that provide information that contradicts existing mental models.  Dig deeply into the mismatch between the new information and the old mental models.  What is behind the difference?  Is the difference limited to a specific region or product line? Is the mismatch new or has it always been there?  The intent of this knee-deep dissection is not to invalidate the old models but to test and refine.

There is infinite detail in the world.  Take a look at a tree and there’s a trunk and canopy. Look at the canopy and see the leaves. Look deeper to see a leaf and its veins.  In order to effectively handle all this detail our brains create patterns and abstractions to reduce the amount of information needed to make it through the day.

In the case of the tree, the word “tree” is used to capture the whole thing – roots and all.  And at a higher level, “tree” can represent almost any type of tree at almost any stage in its life.  The abstraction is powerful because it reduces the complexity, as long as everyone’s clear which tree is which.

The message is this. Our brain takes shortcuts with its chunking of the world into mental models that go out style. And our brain uses different levels of abstraction for the same word to mean different things. Care must be taken to overtly question our mental models and overtly question the level of abstraction used when statements of facts are made.

Knowing what isn’t said is almost important as what is said. To maintain this level of clarity requires calm, centered awareness which today’s pace makes difficult.

There’s no pure cure for the syndrome. The best we can do is to be well-rested and aware. And to do that requires professional confidence and personal disciple.

Slowing down just a bit can be faster, and testing the assumptions behind our business models can be even faster.  Last year’s mental models and business models should be thought of as guilty until proven relevant.  And for that you need to make the time to think.

In today’s world we confuse activity with progress. But really, in today’s dynamic world thinking is progress.

Image credit – eyeliam.

Hands-on versus hands-off – as a leader it’s a fundamental choice.  And for me the single most important guiding principle is – do what it takes to maintain or strengthen the team’s personal ownership of the work.

If things are going well, keep your hands off.  This reinforces the team’s ownership and your trust in them.  But it’s not hands-off in and ignore them sense; it’s hands-off in a don’t tell them what to do sense. Walk around, touch base and check in to show interest in the work and avoid interrogation-based methods that undermine your confidence in them.  This is not to say a hands-off leader only superficially knows what’s going on, it should only look like the leader has a superficial understanding.

The hands-off approach requires a deep understanding of the work and the people doing it.  The hands-off leader must make the time to know the GPS coordinates of the project and then do reconnaissance work to identify the positions of the quagmires and quicksand that lay ahead.  The hands-off leader waits patiently just in front of the obstacles and makes no course correction if the team can successfully navigate the gauntlet.  But when the team is about to sink to their waists, leader gently nudges so they skirt the dangerous territory.

Unless, of course, the team needs some learning.  And in that case, the leader lets the team march it’s project into the mud.  If they need just a bit of learning the leader lets them get a little muddy; and if the team needs deep learning, the leader lets them sink to their necks.  Either way, the leader is waiting under cover as they approach the impending snafu and is right beside them to pull them out.  But to the team, the hands-off leader is not out in front scouting the new territory.  To them, the hands-off leader doesn’t pay all that much attention.  To the team, it’s just a coincidence the leader happens to attend the project meeting at a pivotal time and they don’t even recognize when the leader subtly plants the idea that lets the team pull themselves out of the mud.

If after three or four near-drowning incidents the team does not learn or change it’s behavior, it’s time for the hands-off approach to look and feel more hands-on.  The leader calls a special meeting where the team presents the status of the project and grounds the project in the now.  Then, with everyone on the same page the leader facilitates a process where the next bit of work is defined in excruciating detail.  What is the next learning objective? What is the test plan?  What will be measured? How will it be measured?  How will the data be presented?  If the tests go as planned, what will you know?  What won’t you know?  How will you use the knowledge to inform the next experiments? When will we get together to review the test results and your go-forward recommendations?

By intent, this tightening down does not go unnoticed.  The next bit of work is well defined and everyone is clear how and when the work will be completed and when the team will report back with the results.  The leader reverts back to hands-off until the band gets back together to review the results where it’s back to hands-on.  It’s the leader’s judgement on how many rounds of hands-on roulette the team needs, but the fun continues until the team’s behavior changes or the project ends in success.

For me, leadership is always hands-on, but it’s hands-on that looks like hands-off.  This way the team gets the right guidance and maintains ownership.  And as long as things are going well this is a good way to go.  But sometimes the team needs to know you are right there in the trenches with them, and then it’s time for hands-on to look like hands-on.  Either way, its vital the team knows they own the project.

There are no schools that teach this.  The only way to learn is to jump in with both feet and take an active role in the most important projects.

Image credit – Kerri Lee Smith

Work is never perfect, family life is never perfect and neither is the interaction between them.  Regardless of your expectations or control strategies, things go as they go.  That’s just what they do.

We have far less control than we think.  In the pure domain of physics the equations govern predictively – perturb the system with a known input in a controlled way and the output is predictable.  When the process is followed, the experimental results repeat, and that’s the acid test.  From a control standpoint this is as good as it gets. But even this level of control is more limited than it appears.

Physical laws have bounded applicability – change the inputs a little and the equation may not apply in the same way, if at all. Same goes for the environment. What at the surface looks controllable and predictable, may not be.  When the inputs change, all bets are off – the experimental results from one test condition may not be predictive in another, even for the simplest systems, In the cold, unemotional world of physical principles, prediction requires judgement, even in lab conditions.

The domains of business and life are nothing like controlled lab conditions.  And they’re and not governed by physical laws.  These domains are a collection of complex people systems which are governed by emotional laws.  Where physics systems delivers predictable outputs for known inputs, people systems do not.  Scenario 1. Your group’s best performer is overworked, tired, and hasn’t exercised in four weeks.  With no warning you ask them to take on an urgent and important task for the CEO.  Scenario 2. Your group’s best performer has a reasonable workload (and even a little discretionary time), is well rested and maintains a regular exercise schedule, and you ask for the same deliverable in the same way.  The inputs are the same (the urgent request for the CEO), the outputs are far different.

At the level of the individual – the building block level – people systems are complex and adaptive,  The first time you ask a person to do a task, their response is unpredictable.  The next day, when you ask them to do a different task, they adapt their response based on yesterday’s request-response interaction, which results in a thicker layer of unpredictability.  Like pushing on a bag of water, their response is squishy and it’s difficult to capture the nuance of the interaction.  And it’s worse because it takes a while for them to dampen the reactionary waves within them.

One person interacts with another and groups react to other groups.  Push on them and there’s really no telling how things will go.  One cylo competes with another for shared resources and complexity is further confounded.  The culture of a customer smashes against your standard operating procedures and the seismic pressure changes the already unpredictable transfer functions of both companies.  And what about the customer that’s also your competitor?  And what about the big customer you both share?  Can you really predict how things will go? Do you really have control?

What does all this complexity, ambiguity, unpredictability and general lack of control mean when you’re trying to build a culture of accountability?  If people are accountable for executing well, that’s fine.  But if they’re held accountable for the results of those actions, they will fail and your culture of accountability will turn into a culture of avoiding responsibility and finding another place to work.

People know uncertainty is always part of the equation, and they know it results in unpredictability.  And when you demand predictability in a system that’s uncertain by it’s nature, as a  leader you lose credibility and trust.

As we swim together in the storm of complexity, trust is the life boat.  Trust brings people together and makes it easier to row in the same direction.  And after a hard day of mistakenly rowing in the wrong direction, trust helps everyone get back in the boat the next day and pull hard in the new direction you point them.

Image credit – NASA