Shipulski On Design

Archive for the ‘Manufacturing Competitiveness’ Category

In today’s world of continuous improvement, everything is seen as an opportunity for improvement. The good news is things are improving. But the bad news is without governance and good judgement, things can flip from “lots of opportunity for improvement” to “nothing is good enough.”  And when that happens people would rather hang their heads than stick out their necks.

When there’s an improvement goal is propose like this “We’ve got to improve the throughput of process A by 12% over the next three months.” a company that respects their people should want (and expect) responses like these:

As you know, the team is already working to improve processes C, D, and E and we’re behind on those improvement projects. Is improvement of process A more important than the other three? If so, which project do you want to stop so we can start work on process A? If not, can we wait until we finish one of the existing projects before we start a new one?  If not, why are you overloading us when we’re making it clear we already have too much work?

Are we missing customer ship dates on process A? If so, shouldn’t we move resources to process A right now to work off the backlog? If we have no extra resources, let’s authorize some overtime so we can catch up. If not, why is it okay to tolerate late shipments to our customers? Are you saying you want us to do more improvement work AND increase production without overtime?

That’s a pretty specific improvement goal. What are the top three root causes for reduced throughput? Well, if the first part of the improvement is to define the root causes, how do you know we can achieve 12% improvement in 3 months? We learned in our training that Deming said all targets are artificial. Are you trying to impose an artificial improvement target and set us up for failure?

Continuous improvement is infinitely good, but resources are finite.  Like it or not, continuous improvement work WILL be bound by the resources on hand. Might as well ask for continuous improvement work in a way that’s in line with the reality of the team’s capacity.

And one thing to remember for all projects – there’s no partial credit.  When you’re 80% done on ten projects, zero projects are done.  It’s infinitely better to be 100% done on a single project.

Image credit – Gabriel Rojas Hruska

Additive Manufacturing (AM) is technology/product space with ever-increasing performance and an ever-increasing collection of products. There are many different physical principles used to add material and there are a range of part sizes that can be made ranging from micrometers to tens of meters.  And there is an ever-increasing collection of materials that can be deposited from water soluble plastics to exotic metals to specialty ceramics.

But AM tools and technologies don’t deliver value on their own.  In order to deliver value, companies must deploy AM to solve problems and implement solutions.  But where to start? What to do next? And how do you know when you’ve arrived?

To help with your AM journey, below a maturity model for AM.  There are eight categories, each with descriptions of increasing levels of maturity.  To start, baseline your company in the eight categories and then, once positioned, look to the higher levels of maturity for suggestions on how to move forward.

For a more refined calibration, a formal on-site assessment is available as well as a facilitated process to create and deploy an AM build-out plan.  For information on on-site assessment and AM deployment, send me a note at mike@shipulski.com.

Execution

1. Specify AM machine – There a many types of AM machines. Learn to choose the right machine.
2. Justify AM machine – Define the problem to be solved and the benefit of solving it.
3. Budget for AM machine – Find a budget and create a line item.
4. Pay for machine –  Choose the supplier and payment method – buy it, rent to own, credit card.
5. Install machine – Choose location, provide necessary inputs and connectivity
6. Create shapes/add material – Choose the right CAD system for the job, make the parts.
7. Create support/service systems – Administer the job queue, change the consumables, maintenance.
8. Security – Create a system for CAD files and part files to move securely throughout the organization.
9. Standardize – Once the first machines are installed, converge on a small set of standard machines.
10. Teach/Train – Create training material for running AM machine and creating shapes.

Solution

1. Copy/Replace – Download a shape from the web and make a copy or replace a broken part.
2. Adapt/Improve – Add a new feature or function, change color, improve performance.
3. Create/Learn – Create something new, show your team, show your customers.
4. Sell Products/Services – Sell high volume AM-produced products for a profit. (Stretch goal.)

Volume

1. Make one part – Make one part and be done with it.
2. Make five parts – Make a small number of parts and learn support material is a challenge.
3. Make fifty parts – Make more than a handful of parts. Filament runs out, machines clog and jam.
4. Make parts with a complete manufacturing system – This topic deserves a post all its own.

Complexity

1. Make a single piece – Make one part.
2. Make a multi-part assembly – Make multiple parts and fasten them together.
3. Make a building block assembly – Make blocks that join to form an assembly larger than the build area.
4. Consolidate – Redesign an assembly to consolidate multiple parts into fewer.
5. Simplify – Redesign the consolidated assembly to eliminate features and simplify it.

Material

1. Plastic – Low temperature plastic, multicolor plastics, high performance plastics.
2. Metal – Low melting temperature with low conductivity, higher melting temps, higher conductivity
3. Ceramics – common materials with standard binders, crazy materials with crazy binders.
4. Hybrid – multiple types of plastics in a single part, multiple metals in one part, custom metal alloy.
5. Incompatible materials – Think oil and water.

Scale

1. 50 mm – Not too large and not too small. Fits the build area of medium-sized machine.
2. 500 mm – Larger than the build area of medium-sized machine.
3. 5 m – Requires a large machine or joining multiple parts in a building block way.
4. 0.5 mm – Tiny parts, tiny machines, superior motion control and material control.

Organizational Breadth

1. Individuals – Early adopters operate in isolation.
2. Teams – Teams of early adopters gang together and spread the word.
3. Functions – Functional groups band together to advance their trade.
4. Supply Chain – Suppliers and customers work together to solve joint problems.
5. Business Units – Whole business units spread AM throughout the body of their work.
6. Company – Whole company adopts AM and deploys it broadly.

Strategic Importance

1. Novelty – Early adopters think it’s cool and learn what AM can do.
2. Point Solution – AM solves an important problem.
3. Speed – AM speeds up the work.
4. Profitability – AM improves profitability.
5. Initiative – AM becomes an initiative and benefits are broadly multiplied.
6. Competitive Advantage – AM generates growth and delivers on Vital Business Objectives (VBOs).

Image credit – Cheryl

For established companies, when times are good, it’s not the right time to try something new – the resources are there but the motivation is not; and when times are tough it’s also the wrong time to try something new – the motivation is there but the breathing room is not.  There are an infinite number of scenarios, but for the established company it’s never a good time to try something new.

For startup companies, when times are good, it’s the right time to try something new – the resources are there and so is the motivation; and when times are tough it’s also the right time to try something new – the motivation is there and breathing room is a sign of weakness.  Again, the scenarios are infinite, but for the startup is always a good time to try something new.

But this is not a binary world. To create new markets and new customers, established companies must be a little bit startup, and to scale, startups must ultimately be a little bit established. This ambidextrous company is good on paper, but in the trenches it gets challenging. (Read Ralph Ohr for an expert treatment.)  The establishment regime never wants to do anything new and the startup regime always wants to.  There’s no middle ground – both factions judge each other through jaded lenses of ROI and learning rate and mutual misunderstanding carries the day.  Trouble is, all companies need both – established companies need new markets and startups need to scale. But it’s more complicated than that.

As a company matures the balance of power should move from startup to established.  But this tricky because the one thing power doesn’t like to do is move from one camp to another. This is the reason for the “perpetual startup” and this is why it’s difficult to scale.  As the established company gets long in the tooth the balance of power should move from the establishment to the startup.  But, again, power doesn’t like to change teams, and established companies squelch their fledgling startup work. But it’s more complicated, still.

The competition is ever-improving, the economy is ever-changing and the planet is ever-warming.  New technologies come on-line, and new business models test the waters. Some work, some don’t.  Huge companies buy startups just to snuff them out and established companies go away.  The environment is ever-changing on all fronts.  And the impermanence pushes and pulls on the pendulum of power dynamics.

All companies want predictability, but they’ll never have it.  All growth models are built on rearward-looking fundamentals and forward-looking conjecture.  Companies will always have the comfort of their invalid models, but will never the predictability they so desperately want.  Instead of predictability, companies would be better served by a strong sense of how it wants to go about its business and overpowering genetics of adaptability.

For a strong definition of how to go about business, a simple declaration does nicely. “We want to spend 80% of our resources on established-company work and 20% on startup-company work.” (Or 90-10, or 95-5.)  And each quarter, the company measures itself against its charter, and small changes are made to keep things on track.  Unless, of course, if the environment changes or the business model runs out of gas.  And then the company adapts.  It changes its approach and it’s projects to achieve its declared 80-20 charter, or, changes the charter altogether.

A strong charter and adaptability don’t seem like good partners, but they are.  The charter brings focus and adaptability brings the change necessary to survive in an every-changing environment.  It’s not easy, but it’s effective.  As long as you have the right leaders.

Image credit – Rick Abraham1

In Star Trek, the Prime Directive is the over-arching principle for The United Federation of Planets.  The intent of the Prime Directive is to let a sentient species live in accordance with its normal cultural evolution.  And the rules are pretty simple – do whatever you want as long as you don’t violate the Prime Directive.   Even if Star Fleet personnel know the end is near for the sentient species, they can do nothing to save it from ruin.

But what does it mean to “live in accordance with the normal cultural evolution?” To me it means “preserve the status quo.”  In other words, the Prime Directive says – don’t do anything to challenge or change the status quo.

Though today’s business environment isn’t Star Trek and none of us work for Star Fleet, there is a Prime Directive of sorts.  Today’s Prime Directive deals not with sentient species and their cultures but with companies and their business models, and its intent is to let a company live in accordance with the normal evolution of its business model.   And the rules are pretty simple – do whatever you want as long as you don’t violate the Prime Directive.  Even if company leaders know the end is near for the business model, they can do nothing to save it from ruin.

Business models, and their decrepit value propositions propping them up, don’t evolve.  They stay just as they are.  From inside the company the business model and value proposition are the very things that provide sustenance (profitability).  They are known and they are safe – far safer than something new – and employees defend them as diligently as Captain Kirk defends his Prime Directive.  With regard to business models, “to live in accordance with its natural evolution” is to preserve the status quo until it goes belly up. Today’s Prime Directive is the same as Star Trek’s – don’t do anything to challenge or change the status quo.

Innovation brings to life things that are novel, useful, and successful.  And because novel is the same as different, innovation demands complete violation of today’s Prime Directive.  For innovators to be successful, they must blow up the very things the company holds dear – the declining business model and its long-in-the-tooth value proposition.

The best way to help innovators do their work is to provide them phasers so they can shoot those in the way of progress, but even the most progressive HR departments don’t yet sanction phasers, even when set to “stun”.  The next best way is to educate the company on why innovation is important.  Company leaders must clearly articulate that business models have a finite life expectancy (measured in years, not decades) and that it’s the company’s obligation to disrupt and displace it.them.

The Prime Directive has a valuable place in business because it preserves what works, but it needs to be amended for innovation.  And until an amendment is signed into law, company leaders must sanction purposeful violation of the Prime Directive and look the other way when they hear the shrill ring a phaser emanating  from the labs.

Image credit – svenwerk

When 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

There are three types of innovation: innovation that creates jobs, innovation that’s job neutral, and innovation that reduces jobs.

Innovation that reduces jobs is by far the most common. This innovation improves the efficiency of things that already exist – the mantra: do the same, but with less. No increase in sales, just fewer people employed.

Innovation that’s job neutral is less common. This innovation improves what you sell today so the customer will buy the new one instead of the old one. It’s a trade – instead of buying the old one they buy the new one. No increase in sales, same number of people employed.

Innovation that creates jobs is uncommon. This innovation radically changes what you sell today and moves it from expensive and complicated to affordable and accessible. Sell more, employ more.

Clay Christensen calls it Disruptive Innovation; Vijay Govindarajan calls it Reverse Innovation; and I call it Less-With-Far-Less.

The idea is the product that is sold to a relatively small customer base (due to its cost) is transformed into something new with far broader applicability (due to its hyper-low cost). Clay says to “look down” to see the new technologies that do less but have a super low cost structure which reduces the barrier to entry. And because more people can afford it, more people buy it. And these aren’t the folks that buy your existing products. They’re new customers.

Vijay says growth over the next decades will come from the developing world who today cannot afford the developed world’s product. But, when the price comes down (down by a factor of 10 then down by a factor of 100), you sell many more. And these folks, too, are new customers.

I say the design and marketing communities must get over their unnatural fascination with “more” thinking. To sell to new customers the best strategy is increase the number of people who can afford your product. And the best way to do that is to radically reduce the cost signature at the expense of features and function. If you can give ground a bit on the thing that makes your product successful, there is huge opportunity to reduce cost – think 80% less cost and 20% less function. Again, you sell new product to new customers.

Here’s a thought experiment to help put you in the right mental context: Create a plan to form a new business unit that cannot sell to your existing customers, must sell a product that does less (20%) and costs far less (80%), and must sell it in the developing world. Now, create a list of small projects to test new technologies with radically lower cost structures, likely from other industries. The constraint on the projects – you must be able to squeeze them into your existing workload and get them done with your existing budget and people. It doesn’t matter how long the projects take, but the investment must be below the radar.

The funny thing is, if you actually run a couple small projects (or even just one) to identify those new technologies, for short money you’ve started your journey to selling new products to new customers.

If 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.

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.

It’s difficult to do something for the first time. Whether it’s a new approach, a new technology, or a new campaign, the mass of the past pulls our behavior back toward itself.  And sadly, whether the past has been successful or not, its mass, and therefore it’s pull, are about the same.  The past keeps us along the track of sameness.

Trains have tracks to enable them to move efficiently (cost per mile), and when you want to go where the train is heading, it’s all good. But when the tracks are going to the wrong destination, all that efficiency comes at the expense of effectiveness.  Like we’re on rails, company history keeps us on track, even if it’s time for a new direction.

The best trains run on a ritualistic schedule.  People queue up at same time every morning to meet their same predictable behemoth, and take comfort in slinking into their regular seats and turning off their brains.  And this is the train’s trick. It uses its regularity to lull riders into a hazy state of non-thinking – get on, sit down, and I’ll get your there – to blind passengers from seeing its highly limited timetable and its extreme inflexibility.  The train doesn’t want us to recognize that it’s not really about where the train wants to go.

Trains are powerful in their own right, but their real muscle comes from the immense sunk cost of their infrastructure. Previous generations invested billions in train stations, repair facilities, tight integration with bus lines, and the tracks, and it takes extreme strength of character to propose a new direction that doesn’t make use of the old, tired infrastructure that’s already paid for. Any new direction that requires a whole new infrastructure is a tough sell, and that’s why the best new directions transcend infrastructure altogether.  But for those new directions that require new infrastructure, the only way to go is a modular approach that takes the right size bites.

Our worn tracks were laid in a bygone era, and the important destinations of yesteryear are no longer relevant. It’s no longer viable to go where the train wants; we must go where we want.

The natural mindset of innovation is more-centric. More throughput; more performance; more features and functions; more services; more sales regions and markets; more applications; more of what worked last time. With innovation, we naturally gravitate toward more.

There are two flavors of more, one better than the other. The better brother is more that does something for the first time. For example, the addition of the first airbags to automobiles – clearly an addition (previous vehicles had none) and clearly a meaningful innovation. More people survived car crashes because of the new airbags. This something-from-nothing more is magic, innovative, and scarce.

Most more work is of a lesser class – the more-of-what-is class. Where the first airbags were amazing, moving from eight airbags to nine – not so much. When the first safety razors replaced straight razors, they virtually eliminated fatal and almost fatal injuries, which was a big deal; but when the third and fourth blades were added, it was more trivial than magical. It was more for more’s sake; it was more because we didn’t know what else to do.

While more is more natural, less is more powerful. The Innovator’s Dilemma clearly called out the power of less. When the long-in-the-tooth S-curve flattens, Christensen says to look down, to look down and create technologies that do less. Actually, he tells us someone will give ground on the very thing that built the venerable S-curve to make possible a done-for-the-first-time innovation.  He goes on to say you might as well be the one to dismantle your S-curve before a somebody else beats you to it. Yes, a wonderful way to realize the juciest innovation is with a less-centric mindset.

The LED revolution was made possible with less-centric thinking. As the incandescent S-curve hit puberty, wattage climbed and more powerful lights became cost effective; and as it matured, output per unit cost increased. More on more.  And looking down from the graying S-curve was the lowly LED, whose output was far, far less.

But what the LED gave up in output it gained in less power draw and smaller size.  As it turned out, there was a need for light where there had been none – in highly mobile applications where less size and weight were prized. And in these new applications, there was just a wisp of available power, and incandesent’s power draw was too much.  If only there was a technology with less power draw.

But at the start, volumes for LEDs were far less than incandesent’s; profit margin was less; and most importantly, their output was far less than any self-respecting lightbulb.  From on high, LEDs weren’t real lights; they were toys that would never amount to anything.

You can break intellectual inertia around more, and good things will happen. New design space is created from thin air once you are forced from the familiar. But it takes force. Creative use of constraints can help.

Get a small team together and creatively construct constraints that outlaw the goodness that makes your product great. The incandescent group’s constraint could be: create a light source that must make far less light. The automotive group’s constraint: create a vehicle that must have less range – battery powered cars.  The smartphone group: create a smartphone with the fewest functions – wrist phone without Blutooth to something in your pocket , longer battery life, phone in the ear, phone in your eyeglasses.

Less is unnatural, and less is scary. The fear is your customers will get less and they won’t like it. But don’t be afraid because you’re going to sell to altogether different customers in altogether markets and applications. And fear not, because to those new customers you’ll sell more, not less. You’ll sell them something that’s the first of its kind, something that does more of what hasn’t been done before. It may do only a little bit of that something, but that’s far more than not being able to do it all.

Don’t tell anyone, but the next level of more will come from less.

Overall, our upward evolutionary spiral toward infinite productivity is a good thing. (More profit with less work – can’t argue with that.) And also good is our Darwinian desire to increase our chances of realizing profit by winding a thick cocoon of risk reduction around the work.

But with our productivity helix comes a little known illness that’s rarely diagnosed. It’s not a full-fledged disease, rather, it’s a syndrome. It’s called Transplant Syndrome, or TS.

Along with general flu-like symptoms, TS produces a burning and itching desire to transplant something that worked well in one area into another. On its own, not a bad thing; the dangerous part of TS is that scratching its itch feels so good. And it feels so good because the scratching fits with capitalism’s natural law – only the most productive species will survive.

In a brain suffering from TS, transplanting Region A’s successful business model into Region B makes perfect productivity sense (No new thinking, but plenty of new revenue.)  But that’s not the problem. The problem is the TS brain’s urge to transplant is insatiable and indiscriminate. With TS, along with Region B, it makes perfect sense to transplant into Region C, Region D, and Regions L, M, N, O, and P.  And with TS, it must happen in record time.  Like a parasite, TS feeds on our desire for productivity.

When you transplant your favorite flowering plant from one region of your yard to another, even the inexperienced gardener in us knows to question whether the new region will support the plant. Is the soil similar? Is there enough sun or too much? Will it be blocked from the wind like it is now? And because you know your yard (and because you asked the questions) you won’t transplant unless the viability threshold is met.

But what if you wanted to transplant your most precious flowering plant from your yard in North America to someone else’s yard in South America? At a high level, the viability questions are the same – sun, soil, and water, but the answers are hard to come by. Should you use google to get the answers? Should you get in an airplane and check the territory yourself? Should you talk to the local gardeners? (They don’t speak English.)

But digging deeper, there are many questions you don’t even know to ask. Some of the local bugs may eat your precious plant, so you better know the little crawlies by name and learn what they like to eat. But still, since the bugs have never seen your plant, you won’t know if they’ll eat it until they eat it. You can ask the local gardeners, but they won’t know.  (They, too, have never seen it.) Or worse, they may treat it as invasive species and pull it out of the ground after you leave for home.

Here’s an idea.  You could scout out local plants that look like yours and declare viability by similarity.  But be careful because over the years the local plants have built up tacit defenses you can’t see.

Transplant Syndrome not just a business model syndrome; it infects broadly.  In fact, there have been recent outbreaks reported in people that work with products, technologies, processes, and company cultures.

Unfortunately, there is no cure for TS. But, with the right prescription, symptoms can be managed.

Symptoms have been pushed into dormancy when companies hire the best, most experienced, local gardeners.  These special gardeners must have been born and raised in-region. And in clinical trials the best results have been achieved when the chief gardener, a well respected local gardener in their own right, has full responsibility for designing, viability testing, and implementing the transplant program.

There have been reported cases of TS symptoms flaring up mid program, but in all cases there was a single common risk factor:  no one listened to the gardeners on the receiving end of the transplant.