Shipulski On Design

Archive for the ‘Manufacturing Competitiveness’ Category

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.

We’ve been conditioned to ask for direction; to ask for a plan; and ask for permission. But those ways no longer apply. Today that old behavior puts you at the front of the peloton in the great race to the bottom.

The old ways are gone.

Today’s new ways: propose a direction (better yet, test one out on a small scale); create and present a radical plan of your own (or better, on the smallest of scales test the novel aspects and present your learning); and demonstrate you deserve permission by initiating activity on something that will obsolete the very thing responsible for your success.

People that wait for someone to give them direction are now a commodity, and with commodities it always ends in the death spiral of low cost providers putting each other out of business.  As businesses are waist deep in proposals to double-down on what hasn’t worked and are choking on their flattened S-curves, there’s a huge opportunity for people that have the courage to try new things on their own. Today, if you initiate you’ll differentiate.

[This is where you say to yourself – I’ve already got too much on my plate, and I don’t have the time or budget to do more (and unsanctioned) work. And this is where I tell you your old job is already gone, and you might as well try something innovative. It’s time to grab the defibrillator and jolt your company out of its flatline. ]

It’s time to respect your gut and run a low cost, micro-experiment to test your laughable idea. (And because you’ll keep the cost low, no one will know when it doesn’t go as you thought. [They never do.]) It’s time for an underground meeting with your trusted band of dissidents to plan and run your pico-experiment that could turn your industry upside down. It’s time to channel your inner kindergartener and micro-test the impossible.

It’s time to choose yourself.

Our threshold for uncertainty is too low.

Early in projects, even before the first prototype is up and running, you know what the product must do, what it will cost, and, most problematic, when you’ll be done. Independent of work content, level of newness, and workloads, there’s no uncertainty in your launch date. It’s etched in stone and the consequences are devastating.

A zero tolerance policy on uncertainty forces irrational behavior. As soon as possible, engineering gets something running in the lab, and then doesn’t want to change it because there’s no time. The prototype is almost impossible to build and is hypersensitive to normal process variation, but these issues are not addressed because there’s no time.  Everyone agrees it’s important to fix it, and agrees to fix it after launch, but that never happens because the next project is already late before it starts. And the death cycle repeats project after project.

The root cause of this mess is the mistaken porting of manufacturing’s zero uncertainly mindset into design. The thinking goes like this – lean and Six Sigma have achieved magical success in manufacturing by eliminating uncertainty, so let’s do it in product design and achieve similar results. This is a fundamental mistake as the domains are fundamentally different.

In manufacturing the same product is made day-in and day-out – no uncertainty; in product design no two product development efforts are the same and there’s lots of stuff that’s done for the first time – uncertainty by definition. In manufacturing there’s a revision controlled engineering drawing that defines the right answer (the geometry and the material) – make it like the picture and it’s all good; in product design the material is chosen from many candidates and the geometry is created from scratch – the picture is created from nothing. By definition there’s more inherent uncertainty in product design, and to tighten the screws and fix the launch date at the start is inappropriate.

Design engineers must feel like there’s enough time to try new things because new products that provide new functionality require new technologies, new materials, and new geometries. With new comes inherent uncertainty, but there are ways to manage it.

To hold the timeline, give on the specification and cost. Design as fast as you can until you run out of time then launch. The product won’t work as well as you’d like and it will cost more than you’d like, but you’ll hit the schedule. A good way to do this is to de-feature a subassembly to reduce design time, and possibly reduce cost. Or, reuse a proven subassembly to reduce design time – take a hit in cost, but hit the timeline. The general idea – hold schedule but flex on performance and cost.

It feels like sacrilege to admit that something’s got to give, but it’s the truth. You’ve seen how it goes when you edict (in no uncertain terms) that the timeline will be met and there’ll be no give on performance and cost. It hasn’t worked, and it won’t – the inherent uncertainty of product design won’t let it.

Accept the uncertainty; be one with it; and manage it. It’s the only way.

With websites, e-books, old fashioned books, Twitter, LinkedIn, Facebook, and blogs, there’s a seemingly limitless flood of information on every facet of business. There are heaps on innovation, new product development, lean, sales and marketing, manufacturing, and strategy; and within each there are elements and sub elements that fan out with multiple approaches.

With today’s search engines and bots to automatically scan the horizon, it’s pretty easy to find what you’re looking for especially as you go narrow and deep. If you want to find best practices for reducing time-to-market for products designed in the US and manufactured in China, ask Google and she’ll tell you instantly. If you’re looking to improve marketing of healthcare products for the 20 to 40 year old demographic of the developing world, just ask Siri.

It’s now easy to separate the good stuff from the chaff and focus narrowly on your agenda. It’s like you have the capability dig into a box of a thousand puzzle pieces and pull out the very one you’re looking for. Finding the right puzzle piece is no longer the problem, the problem now is figuring out how they all fit together.

What holds the pieces together? What’s the common thread that winds through innovation, sales, marketing, and manufacturing? What is the backplane behind all this business stuff?

The backplane, and first fundamental, is product.

Every group has their unique work, and it’s all important – and product cuts across all of it. You innovate on product; sell product; manufacture product; service product. The shared context is the product. And I think there’s opportunity to use the shared context, this product lens, to open up design space of all our disciplines. For example how can the product change to make possible new and better marketing? How can the product change to radically simplify manufacturing? How can the product change so sales can tell the story they always wanted to tell? What innovation work must be done to create the product we all want?

In-discipline improvements have been good, but it’s time to take a step back and figure out how to create disruptive in-discipline innovations; to eliminate big discontinuities that cut across disciplines; and to establish multidisciplinary linkages and alignment to power the next evolution of our businesses. New design space is needed, and the product backplane can help.

Use the product lens to look along the backplane and see how changes in the product can bridge discontinuities across sales, marketing, and engineering. Use the common context of product to link revolutionary factory simplification to changes in the product. Use new sensors in the product to enable a new business model based on predictive maintenance. Let your imagination guide you.

It’s time to see the product for more than what it does and what it looks like. It’s time to see it as Superman’s kryptonite that constrains and limits all we do that can become Popeye’s spinach that can strengthen us to overpower all obstacles.

There’s a natural tendency to simplify, to reduce, to narrow. In the name of problem solving, it’s narrow the scope, break it into small bites, and don’t worry about the subtle complexities. And for a lot of situations that works. But after years of fixing things one bite at a time, there are fewer and fewer situations that fit the divide and conquer approach. (Actually, they’re still there, but their return on investment is super low.) And after years of serial discretization, what are left are situations that cannot be broken up, that cut across interfaces, that make up a continuum. What are left are big problems and big situations that have huge payoff if solved, but are interconnected.

Whether it’s cross-discipline, cross-organization, cross-cultural, or cross-best practice, the fundamental of these big kahunas is they cross interfaces. And that’s why they’ve never been attacked, and that’s why they’ve never been solved. But with payoffs so big, it’s time to take on connectedness.

For me, the most severe example of connectedness is woven around the product. To commercialize a product there are countless business process that cut across almost every interface. Here are a few: innovation, technology development, product development, robustness testing, product documentation, manufacturing engineering, marketing, sales, and service. Each of these processes is led by one organization and cuts across many; each cut across expertise-specialization interfaces; each requires information and knowledge from the other; and each new product development project must cooperate with all the others. They cannot be separated or broken into bits. Change one with intent and change the others with unintended consequences. No doubt – they’re connected.

Green thinking is much overdue, but with it comes connectedness squared. With pre-green product commercialization, the product flowed to the end user and that was about it. But with environmental movement there’s a whole new return path of interconnected business processes. Green thinking has turned the product life cycle into the circle of life – the product leaves, it lives it’s life, and it always comes back home.

And with this return path of connectedness, how the product goes together in manufacturing must be defined in conjunction with how it will be disassembled and recycled. Stress analysis must be coordinated with packaging design, regulations of banned substances, and material reuse of retired product. Marketing literature must be co-produced with regulatory strategy and recycling technologies. It’s connected more than ever.

But the bad news is the good news. Yes, things are more interwoven and the spider web is more tangled. But the upside – companies that can manage the complexity will have a significant advantage. Those that can navigate within connectedness will win.

The first step is to admit there’s a problem, and before connectedness can be managed, it must be recognized. And before it can become competitive advantage, it must be embraced.