Shipulski On Design

Archive for the ‘Top Line Growth’ 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

Innovation starts with recognition of a big, meaningful problem. It can come from the strategic planning process; from an ongoing technology project that isn’t going well; an ongoing product development project that’s stuck in the trenches; or a competitor’s unforeseen action. But where it comes from isn’t the point. What matters is it’s recognized by someone important enough to allocate resources to make the problem go away. (If it’s recognized by someone who can’t muster the resources, it creates frustration, not progress.)

Once recognized, the importance of the problem is communicated to the organization. Usually, a problem is important because it blocks growth, e.g., a missing element of the new business model, technology that falls short of the distinctive value proposition (DVP), or products that can’t deliver on your promises. But whether something’s in the way or missing, the problem’s importance is best linked to a growth objective.

Company leaders then communicate to the organization, using one page. Here’s an example:

WHY – we have a problem. The company’s stock price cannot grow without meeting the growth goals, and currently we cannot meet them. Here’s what’s needed.
WHAT – grow sales by 30%.
WHERE – in emerging markets.
WHEN – in two years.
HOW – develop a new line of products for the developing world.

Along with recognition of importance, there must be recognition that old ways won’t cut it and new thinking is required. That way the company knows it’s okay to try new things.

Company leaders pull together a small group and charters them to spend a bit of time to develop concepts for the new product line and come back and report their go-forward reccommendations. But before any of the work is done, resources are set aside to work on the best ones, otherwise no one will work on them and everyone will know the company is not serious about innovation.

To create new concepts, the small group plans an Innovation Burst Event (IBE). On one page they define the DVP for the new product line, which describes how the new customers will use the new products in new ways. They use the one page DVP to select the right team for the IBE and to define fertile design space to investigate. To force new thinking, the planning group creates creative constraints and design challenges to guide divergence toward new design space.

The off-site location is selected; the good food is ordered; the IBE is scheduled; and the team is invited. The company leader who recognized the problem kicks off the IBE with a short description of the problem and its importance, and tells the team she can’t wait to hear their recoomendations at the report-out at the end of the day.

With too little time, the IBE team steps through the design challenges, creates new concepts, and builds thinking prototypes. The prototypes are the center of attention at the report-out.

At the report-out, company leaders allocate IP resources to file patents on the best concepts and commission a team of marketers, technologists, and IP staff to learn if viable technologies are possible, if they’re patentable, and if the DVP is viable.(Will it work, can we patent it, and will they buy it.)

The marketer-technologist-IP team builds prototypes and tests them in the market. The prototypes are barely functional, if at all, and their job is to learn if the DVP resonates. (Think minimum viable prototype.) It’s all about build-test-learn, and the learning loops are fast and furious at the expese of statistical significance. (Judgement carries the day in this phase.)

With viable technology, patentable ideas, and DVP in hand, the tri-lobed team reports out to company leaders who sanctioned their work. And, like with the IBE, the leaders allocate more IP resources to file more patents and commission the commercialization team.

The commercialization team is the tried-and-true group that launches products. Design engineering makes it reliable; manufacturing makes it repeatable; marketing makes it irresistible; sales makes it successful. At the design reviews more patents are filed and at manufacturing readiness reviews it’s all about process capability and throughput.

Because the work is driven by problems that limit growth, the result of the innovation work is exactly what’s needed to fuel growth – in this case a successful product line for the developing world. Start with the right problem and end up with the right solution. (Always a good idea.)

With innovation programs, all the talk is about tools and methods, but the two things that really make the difference are lightning fast learning loops and resources to do the innovation work. And there’s an important philosophical chasm to cross – because patents are usually left out of the innovation equation – like an afterthought chasing a quota – innovation should become the umbrella over patents and technology. But because IP reports into finance and technology into engineering, it will be a tough chasm to bridge.

It’s clear fast learning loops are important for fast learning, but they’re also important for building culture. At the end of a cycle, the teams report back to leadership, and each report-out is an opportunity to shape the innovation culture. Praise the good stuff and ignore the rest, and the innovation culture moves toward the praise.

There’s a natural progression of the work. Start – do one project; spread – use the learning to do the next ones; systematize – embed the new behaviors into existing business processes; sustain – praise the best performers and promote them.

When innovation starts with business objectives, the objectives are met; when innovation starts with company leadership, resources are allocated and the work gets done; and when the work shapes the culture, the work accelerates. Anything less isn’t innovation.

Image credit – Jaybird

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.

The Holy Grail of marketing is to identify emerging customer needs before anyone else and satisfy them to create new markets. It has been a long and fruitless slog as emerging needs have proven themselves elusive. And once candidates are identified, it’s a challenge to agree which are the game-changers and which are the ghosts. There are too many opinions and too few facts. But there’s treasure at the end of the rainbow and the quest continues.

Emerging things are just coming to be, just starting, so they appy to just a small subset of customers; and emerging things are new and different, so they’re unfamiliar. Unfamiliar plus small same size equals elusive.

I don’t believe in emerging customer needs, I believe in emergent customer behavior.

Emergent behavior is based on actions taken (past tense) and is objectively verifiable. Yes or no, did the customer use the product in a new way? Yes or no, did the customer make the product do something it wasn’t supposed to? Did they use it in a new industry? Did they modify the product on their own? Did they combine it with something altogether unrelated? No argument.

When you ask a customer how to improve your product, their answers aren’t all that important to them. But when a customer takes initiative and action, when they do something new and different with your product, it’s important to them. And even when just a few rouge customers take similar action, it’s worth understanding why they did it – there’s a good chance there’s treasure at the end of that rainbow.

With traditional VOC methods, it has been cost prohibitive to visit enough customers to learn about a handful at the fringes doing the same crazy new thing with your product. Also, with traditional VOCs, these “outliers” are thrown out because they’re, well, they’re outliers. But emergent behavior comes from these very outliers. New information streams and new ways to visualize them are needed to meet these challenges.

For these new information streams, think VOC without the travel; VOC without leading the witness; VOC where the cost of capturing their stories is so low there are so many stories captured that it’s possible to collect a handful of outliers doing what could be the seed for the next new market.

To reduce the cost of acquisition, stories are entered using an app on a smart phone; to let emergent themes emerge, customers code their own stories with a common, non-biasing set of attributes; and to see patterns and outliers, the coded stories are displayed visually.

In the past, the mechanisms to collect and process these information streams did not exist. But they do now.

I hope you haven’t given up on the possibility of understanding what your customers will want in the near future, because it’s now possible.

I urge you to check out SenseMaker.

It’s harder than ever to innovate, and getting harder.

The focus on growth can be empowering, but when coupled with signed-in-blood accountability, empowering turns to puckering.  It’s an unfair double-bind. Damned if you try something new and it doesn’t work, and damned if you stay the course and don’t hit the numbers.  The most popular approach seems to be to do more of what worked.  A good approach, but not as good as it’s made out to be.

Doing more of what worked is good, and it works.  But it can’t stand on its own.  With today’s unreasonable workloads, every resource is fully booked and before doing more of anything, you’ve got to do less of something else.  ‘More of what worked’ must walk hand-in-hand with ‘Stop what didn’t work.’  Without stopping, without freeing up resources, ‘more of what worked’ is insufficient and unsustainable.

But even the two together are insufficient, and there’s a much needed third leg to stabilize the stool – ‘starting new work.’  Resources freed by stopping are allocated to starting new work, and this work, also known as innovation, is the major source of growth.

‘More of what worked’ is all about productivity – doing more with the same resources; and so is ‘stopping what didn’t work’ – reclaiming and reallocating ineffective resources. Both are important, but more importantly – they’re not innovation.

As you’re well aware, the rules are changing faster than ever, and at some point what worked last year won’t work this year. The only way to stay ahead of a catastrophe is to make small bets in unproven areas.  If the bets are successful, they turn into profitable innovation and growth. But the real value is the resiliency that comes from the ritualistic testing/learning cycles.

Going all-in on what worked last year is one of the riskiest bets you can make.

What’s a new market worth without a new technology to capture it? The same as a new technology without a new market – not much. Technology and market are a matched set, but not like peanut butter and jelly, (With enough milk, a peanut butter sandwich isn’t bad.) rather, more like H₂ and O: whether it’s H₂ without O or O without H₂ – there’s no water. With technology and market, there’s no partial credit – there’s nothing without both.

You’d think with such a tight coupling, market and technology would be highly coordinated, but that’s not the case. There’s a deep organizational chasm between them. But worse, each has their own language, tools, and processes. Plain and simple, the two organizations don’t know how to talk to each other, and the result is the wrong technology for the right market (if you’re a marketer) or the right technology for the wrong market (if you’re a technologist.) Both ways, customers suffer and so do business results.

The biggest difference, however, is around customers. Where marketers pull, technologists push – can’t be more different than that. But neither is right, both are. There’s no sense arguing which is more important, which is right, or which worked better last time because you need both. No partial credit.

If you speak only French and have a meeting with someone who speaks only Portuguese, well, that’s like meeting between marketers and technologists. Both are busy as can be, and neither knows what the other is doing. There’s a huge need for translators – marketers that speak technologist and technologists that talk marketing. But how to develop them?

The first step is to develop a common understanding of why. Why do you want to develop the new market? Why hasn’t anyone been able to create the new market? Why can’t we develop a new technology to make it happen? It’s a good start when both sides have a common understanding of the whys.

To transcend the language barrier, don’t use words, use video. To help technologists understand unmet customer needs, show them a video of a real customer in action, a real customer with a real problem. No words, no sales pitch, just show the video. (Put your hand over your mouth if you have to.) Show them how the work is done, and straight away they’ll scurry to the lab and create the right new technologies to help you crack the new market. Technologists don’t believe marketers; technologists believe their own eyes, so let them.

To help marketers understand technology, don’t use words, use live demos. Technologists – set up a live demo to show what the technology can do. Put the marketer in front of the technology and let them drive, but you can’t tell them how to drive. You too must put your hand over your mouth. Let them understand it the way they want to understand it, the way a customer would understand it. They won’t use it the way you think they should, they’ll use it like a customer. Marketers don’t understand technology, they understand their own eyes, so let them.

And after the videos and the live demos, it’s time to agree on a customer surrogate. The customer surrogate usually takes the form of a fully defined test protocol and fully defined test results. And when done well, the surrogate generates test results that correlate with goodness needed to crack the new market. As the surrogate’s test results increase, so does goodness (as the customer defines it.) Instead of using words to agree on what the new technology must do, agreement is grounded in a well defined test protocol and a clear, repeatable set of test results. Everyone can use their eyes to watch the actual hardware being tested and see the actual test results. No words.

To close the loop and determine if everyone is on the same page, ask the marketers and technologist to co-create the marketing brochure for the new product. Since the brochure is written for the customer, it forces the team use plain language which can be understood by all. No marketing jargon or engineering speak – just plain language.

And now, with the marketing brochure written, it’s time to start creating the right new market and the right new technology.

Photo credit – TORLEY.

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.

More-with-less is our mantra for innovation. But these three simple words are dangerous because they push us almost exclusively toward efficiency. On the surface, efficiency innovations sound good, and they can be, but more often than not efficiency innovations are about less and fewer. When you create a new technology that does more and costs less the cost reduction comes from fewer hours by fewer people. And if the cash created by the efficiency finances more efficiency, there are fewer jobs. When you create an innovative process that enables a move from machining to forming, hard tooling and molding machines reduce cost by reducing labor hours. And if the profits fund more efficiency, there are fewer jobs. When you create an innovative new material that does things better and costs less, the reduced costs come from fewer labor hours to process the material. And if more efficiency is funded, there are less people with jobs. (The cost reduction could also come from lower cost natural resources, but their costs are low partly because digging them up is done with fewer labor hours, or more efficiently.)

But more-with-less and the resulting efficiency improvements are helpful when their profits are used to fund disruptive innovation. With disruptive innovation the keywords are still less and fewer, but instead of less cost, the product’s output is less; and instead of fewer labor hours, the product does fewer things and satisfies fewer people.

It takes courage to run innovation projects that create products that do less, but that’s what has to happen. When disruptive technologies are young they don’t perform as well as established technologies, but they come with hidden benefits that ultimatley spawn new markets, and that’s what makes them special. But in order to see these translucent benefits you must have confidence in yourself, openness, and a deep personal desire to make a difference. But that’s not enough because disruptive innovations threaten the very thing that made you successful – the products you sell today and the people that made it happen. And that gets to the fundamental difference between efficiency innovations and disruptive innovations.

Efficiency innovations are about doing the familiar in a better way – same basic stuff, similar product functionality, and sold the same way to the same people. Disruptive innovations are about doing less than before, doing it with a less favorable cost signature, and doing it for different (and far fewer) people. Where efficiency innovation is familiar, disruptive innovation is contradictory. And this difference sets the the pace of the two innovations. Where efficiency innovation is governed by the speed of the technology, disruptive innovation is governed by the speed of people.

With efficiency innovations, when the technology is ready it jumps into the product and the product jumps into the market. With disruptive innovations, when the technology is ready it goes nowhere because people don’t think it’s ready – it doesn’t do enough. With efficiency innovations, the new technology serves existing customers so it launches; with disruptive, technology readiness is insufficient because people see no existing market and no existing customers so they make it languish in the corner. With efficiency, it launches when ready because margins are better than before; with disruptive, it’s blocked because people don’t see how the new technology will ultimately mature to overtake and replace the tired mainstream products (or maybe because they do.)

Done poorly efficiency innovation is a race to the bottom; done well it funds disruptive innovation and the race to the top. When coordinated the two play together nicely, but they are altogether different. One is about doing the familiar in a more efficient way, and the other is about disrupting and displacing the very thing (and people) that made you successful.

Most importantly, efficiency innovation moves at the speed of technology while disruptive innovation moves at the speed of people.

Product costs, without product thinking, drop 2% per year. With product thinking, product costs fall by 50%, and while your competitors’ profit margins drift downward, yours are too high to track by conventional methods. And your company is known for unending increases in stock price and long term investment in all the things that secure the future.

The supply chain, without product thinking, improves 3% per year. With product thinking, longest lead processes are eliminated, poorest yield processes are a thing of the past, problem suppliers are gone, and your distributers associate your brand with uninterrupted supply and on time delivery.

Product robustness, without product thinking, is the same year-on-year. Re-injecting long forgotten product thinking to simplify the product, product robustness jumps to unattainable levels and warranty costs plummet. And your brand is known for products that simply don’t break.

Rolled throughput yield is stalled at 90%. With product thinking, the product is simplified, opportunities for defects are reduced, and throughput skyrockets due to improved RTY. And your brand is known as a good value – providing good, repeatable functionality at a good price.

Lean, without product thinking has delivered wonderful results, but the low hanging fruit is gone and lean is moving into the back office. With product thinking, the design is changed and value-added work is eliminated along with its associated non-value added work (which is about 8 times bigger); manufacturing monuments with their long changeover times are ripped out and sold to your competitors; work from two factories is consolidated into one; new work is taken on to fill the emptied factories; and profit per square foot triples. And your brand is known for best-in-class quality, unbeatable on time delivery, world class performance, and pioneering the next generation of lean.

The sales argument is low price and good payment terms. With product thinking, the argument starts with product performance and ends with product reliability. The sales team is energized, and your brand is linked with solid products that just plain work.

The marketing approach is stickers and new packaging. With product thinking, it’s based on competitive advantage explained in terms of head-to-head performance data and a richer feature set. And your brand stands for winning technology and killer products.

Product thinking isn’t for everyone. But for those that try – your brand will thank you.

When engineers see a big opportunity, we want desperately to move the company in the direction of our thinking, but find it difficult to change the behavior of others. Our method of choice is usually a full frontal assault, explaining to anyone that will listen the opportunity as we understand it. Our approach is straightforward and ineffective. Our descriptions are long, convoluted, complicated, we use confusing technical language all our own, and omit much needed context that we expect others should know. The result – no one understands what we’re talking about and we don’t get the behavior we’re looking for (immediate company realignment with what we know to be true).  Then, we get frustrated and shut down – opportunity lost.

To change the behavior of others, we must first change our own. As engineers we see problems which, when solved, result in opportunity. And if we’re to be successful, we must go back to the problem domain and set things straight. Here’s a sequence of new behaviors we as engineers can take to improve our chances of changing the behavior of others:

Step 1. Create a block diagram of the physical system using simple nouns (blocks) and verbs (arrows). Blue arrows are good (useful actions) and red arrows are bad (harmful actions). Here’s a link to a PowerPoint file with a live template to create your own.

Step 2. Reduce the system block diagram down to its essence to create a distilled block diagram of the problem, showing only the system elements (blocks) with the problem (red arrow).For a live template, see the second page of the linked file. [Note – if there are two red arrows in the system block diagram, there are two problems which must be solved separately. Break them into two and solve the first one first. For an example, see page three of the linked file.]

Step 3. Create a hand sketch, or cartoon, showing the two system elements (blocks) of the distilled block diagram from step 2. Zoom in so only the two elements are visible, and denote where they touch (where the problem is), in red. For an example, see page four of the linked file.

Step 4. Now that you understand the real problem, use Google to learn how others have solved it.

Step 5. Choose one of Google’s most promising solutions and prototype it. (Don’t ask anyone, just build it.)

Step 6. Show the results to your engineering friends. If the problem is solved, it’s now clear how the opportunity can be realized. (There’s a big difference between a crazy engineer with a radically new market opportunity and a crazy engineer with test results demonstrating a new technology that will create a whole new market.)

Step 7. If the problem is not solved, or you solved the wrong problem, go back to step 1 and refine the problem

With step 1 you’ll find you really don’t understand the physical system, you don’t know which elements of the system have the problem, and you can’t figure out what the problem is. (I’ve created complicated system block diagrams only to realize there was no problem.)

With step 2, you’ll continue to struggle to zoom in on the problem. And, likely, as you try to define the problem, you’ll go back to step 1 and refine the system block diagram. Then, you’ll struggle to distill the problem down to two blocks (system elements). You’ll want to retain the complexity (many blocks) because you still don’t understand the real problem.

If you’ve done step 2 correctly, step 3 is easy, though you’ll still want to complicate the cartoon (too many system elements) and you won’t zoom in close enough.

Step 4 is powerful. Google can quickly and inexpensively help you see how the world has already solved your problem.

Step 5 is more powerful still.

Step 6 shows Marketing what the future product will do so they can figure out how to create the new market.

Step 7 is how problems are really solved and opportunities actually realized.

When you solve the real problem, you create real opportunities.

We’ve been sufficiently polluted by lean and Six Sigma, and it’s time for them to go.

Masquerading as maximizers, these minimizers-in-sheep’s-clothing have done deep harm. Though Six Sigma is almost dead (it’s been irrelevant for some time now), it has made a lasting mark. Billed as a profit maximizer, it categorically rejects maximization. In truth, it’s a variation minimizer and difference reducer.  If it deviates, Six Sigma cuts its head off. Certainly this has a place in process control, but not in thinking control. But that’s exactly what’s happened. Six Sigma minimization has slithered off the manufacturing floor and created a culture of convergence. If your thinking is different, Six Sigma will clip it for you.

Lean is worse. All the buzz around lean is about maximizing throughput, but it doesn’t do that. It minimizes waste. But far worse is lean’s standard work. Minimize the difference among peoples’ work; make them do it the same; make the factory the same, regardless of the continent. All good on the factory floor, but lean’s minimization mania has spread like the plague and created a culture of convergence in its wake. And that’s the problem – lean’s minimization-standardization mantra has created a culture of convergence. If your thinking doesn’t fit in, lean will stomp it into place.

We need maximization at the expense of minimization, and divergence before convergence. We need creativity and innovation. But with Six Sigmaphiles and lean zealots running the show, maximization is little understood and divergence is a swear.

First we must educate on maximization. Maximization creates something that had not existed, while minimization reduces what is. Where Six Sigma minimization converges on the known right answer, creativity and innovation diverge to define a new question. The acid test: if you’re improving something you’re minimizing; if you’re inventing something you’re maximizing.

Like with He Who Shall Not Be Named, it’s not safe to say “diverge” out loud, because if you do, the lean Dementors will be called to suck out your soul. But, don’t despair – the talisman of guided divergence can save you.

With guided divergence, a team is given a creatively constructed set of constraints and very little time (hours) to come up with divergent ideas. The constraints guide the creativity (on target), and the tight timeline limits the risk – a small resource commitment. (Though counterintuitive, the tight timeline also creates remarkable innovation productivity.) Done in sets, several guided divergence sessions can cover a lot of ground in little time.

And the focused/constrained nature of guided divergence appeals to our minimization bias, and makes it okay to try a little divergence. We feel safe because we’re deviating only a little and only for a short time.

Lean and Six Sigma have served us well, and they still have their place. (Except for Six Sigma.) But they must be barred from creativity sessions and front end innovation, because here, divergence carries the day.