Shipulski On Design

Archive for the ‘Complexity’ Category

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.

Productivity generates profit.  No argument.  But it has two sides – it can be achieved through maximization by increasing output with constant resources (machines and people) or through minimization with constant output and decreasing machines and people.  And the main pillars of both flavors are data, tools, and process.

Data is used to understand how things are going so they can be made more productive.  Process output is measured, yields are measured, and process control charts are hung on the wall like priceless art.  Output goes up and costs go down.  And the two buckets of cost – people and machines – are poured out the door.  But data on its own doesn’t know how to improve anything.  The real heroes are the people that look at the data and use good judgment to make good decisions.

You can pull the people out of the process to reduce costs, but you can’t pull the judgment out productivity improvement work.  And here’s the difference – processes are made transactional and repetitive so people can be removed, and because judgment can’t be made into a transactional process, people are needed to do productivity improvement work.  People and their behavior – judgment – are the keys.

Tools are productivity’s golden children.  Better tools speed up the work so more can get done.  In the upswing, output increases to get more work done; in the downturn, people leave to reduce cost.  Tools can increase the quality (maximize) or reduce the caliber of the people needed to do the work (minimize).  But the tools aren’t the panacea, the real panacea are the people that run them.

Any analytical tool worth its salt requires judgment by the person that runs it.  And here’s where manufacturing’s productivity-through-process analogy is pushed where it doesn’t belong.  Companies break down the process to run the tools into 6000 to 7000 simple steps, stuff them into a 500 page color-coded binder, provide a week of training and declare standard work has saved the day because, now that the process has been simplified and standardized, everyone can run the tool at 100% efficiency.  But the tool isn’t the important part, neither is the process of using it. The important part is the judgment of the people running it.

Productivity of tools is not measured in the number of design cycles per person or the number of test cases run per day.  This manufacturing thinking must be banished to its home country – the production floor.  The productivity of analytical tools is defined by the goodness of the output when the time runs out.  And at the end of the day, measuring the level of goodness also requires judgment – judgment by the experts and super users.   With tools, it’s all about judgment and the people exercising it.

And now process.  When the process is made repetitive, repeatable, and transactional, it brings productivity.  This is especially true when the process lets itself to being made repetitive, repeatable, and transactional.  Here’s a good one – step 1, step 2, step 3, repeat for 8 hours.  Dial it in and watch the productivity jump.  But when it’s never been done before, people’s judgment governs productivity; and when the process has no right answer, the experts call the ball. When processes are complex, undefined, or the first of their kind, productivity and judgment are joined at the hip.

Processes, on their own, don’t rain productivity from the sky; the real rainmakers are the people that run them.

Today’s battle for productivity is overwhelmingly waged in the trenches of minimization, eliminating judgment skirmish by skirmish. And productivity’s “more-with-less” equation has been toppled too far toward “less”, minimizing judgment one process at a time.

Really, there’s only one pillar of productivity, and that’s people.  As everyone else looks to eliminate judgment at every turn, what would your business look like if you went the other way?  What if you focused on work that demanded more judgment?  I’m not sure what it would look like, other than you’d have little competition.

We strive to get everyone on the same page, to align the crew in a shared direction. The thinking goes – If we’re all pulling in the same direction, we’ll get there faster and more efficiently. Yes, the destination will come sooner, but what if it’s not there when we get there?

There’s implicit permanence to our go-forward travel plans. We look out three years and plan our destination as if today’s rules and fundamentals will still apply. We think – That imaginary tropical vacation spot will be beautiful in three years because it looks beautiful through the kalidascope of today’s success. But as the recent natural disasters have taught us, whole islands can be destroyed in an instant. But still, the impermenance of today’s tried-and-true business models is lost on us, and we see the unknowable future as statically as the unchangeable map of the continents.

Thing is, all around us there are weak indications the fundamental tradewinds have started to shift – weak signals of impermenance that may invalidate today’s course heading. But weak signals are difficult to hear – the white noise of yesterday’s success drowns out the forward-looking weak signals.  And more problematic, once heard, weak signals are easily dismissed because their song threatens the successful status quo.

You feel weak signals in your chest. It could be a weak signal when your experience tells you things should go one way and they actually go another. Martin Zwilling (Forbes) has some great examples. (Thanks to Deb Mills-Scofield [@dscofield] for retweeting the article.)

100% alignment reduces adaptability because it deadens us to weak signals, and that’s a problem in these times of great impermanence. To counter the negative elements of alignment, there must be a balancing injection of healthy misalignment.  This is an important and thankless task falls on the shoulders of a special breed – the radical fringe.  They’re the folks smart enough to knit disjointed whispers into coherent ideas that could unravel everything and brave enough to test them.

Disruptive movements and revolutions build momentum quietly and slowly.  But if you can recognize them early, there’s a chance you can get into position to ride their tsunami instead of being ambushed and scuttled by it.  But you’ve got to listen closely because these young movements are stealthy and all they leave in their wake are weak signals.

In a confusing way, the seemingly negative elements of novelty are actually tell-tale signs you’re doing it right.  Here are some examples:

No uncertainty, no upside.
No heresy, no game-changer.
No recipe, no worries.
No ambiguity, no new markets.
No effectiveness, no success.
No efficiency, no matter.
No disagreement, no gravity.
No answers, no problem.
No problem, no innovation.
No dispute, no dilemma.
No headache, no quandary.
No obstacle, no predicament.
No unrest, no virtue.
No failure, no creativity.
No discomfort, no relevance.
No agitation, no consequence.
No questions, no significance.
No best practice, no anxiety.
No downside, no upside.

For systems with high levels of complexity, such as organizations, business models, and cross-domain business processes, it’s characterize the current state, identify the future state, and figure out how to close the gap. That’s how I was trained. Simple, elegant, and no longer fits me.

The block diagram of the current state is neat and clean. Sure, there are interactions and feedback loops but, known inputs generate known outputs. But for me there are problems with the implicit assumptions. Implicit is the notion that the block diagram correctly represents current state; that uncontrollable environmental elements won’t change the block diagram; that a new box or two and new inputs (the changes to achieve the idealized future state) won’t cause the blocks to change their transfer functions or disconnect themselves from blocks or rewire themselves to others.

But what really tipped me over was the realization that the blocks aren’t blocks at all. The blocks are people (or people with a thin wrapper of process around), and it’s the same for the inputs. When blocks turn to people, the complexity of the current state becomes clear, and it becomes clear it’s impossible to predict how the system will response when it’s prodded and cajoled toward the idealized future state. People don’t respond the same way to the same input, never mind respond predictably and repeatably to new input. When new people move to the neighborhood, the neighborhood behaves differently. People break relationships and form others at will. For me, the implicit assumptions no longer hold water.

For me the only way to know how a complex system will respond to rewiring and new input is to make small changes and watch it respond. If the changes are desirable, do more of that. If the changes are undesirable, do less.

With this approach the work moves from postulation to experimentation and causation – many small changes running parallel with the ability to discern the implications. And the investigations are done in a way to capture causality and maintain system integrity. Generate learning but don’t break the system.

It’s a low risk way to go because before wide-scale implementation the changes have already been validated. Scaling will be beneficial, safe, and somewhat quantifiable. And the stuff that didn’t work will never see the light of day.

If someone has an idea, and it’s coherent, it should be tested. And instead of arguing over whose idea will be tested, it becomes a quest to reduce the cost of the experiments and test the most ideas.