Posts Tagged ‘Product Development Engine’
Hands-on versus hands-off – as a leader it’s a fundamental choice. And for me the single most important guiding principle is – do what it takes to maintain or strengthen the team’s personal ownership of the work.
If things are going well, keep your hands off. This reinforces the team’s ownership and your trust in them. But it’s not hands-off in and ignore them sense; it’s hands-off in a don’t tell them what to do sense. Walk around, touch base and check in to show interest in the work and avoid interrogation-based methods that undermine your confidence in them. This is not to say a hands-off leader only superficially knows what’s going on, it should only look like the leader has a superficial understanding.
The hands-off approach requires a deep understanding of the work and the people doing it. The hands-off leader must make the time to know the GPS coordinates of the project and then do reconnaissance work to identify the positions of the quagmires and quicksand that lay ahead. The hands-off leader waits patiently just in front of the obstacles and makes no course correction if the team can successfully navigate the gauntlet. But when the team is about to sink to their waists, leader gently nudges so they skirt the dangerous territory.
Unless, of course, the team needs some learning. And in that case, the leader lets the team march it’s project into the mud. If they need just a bit of learning the leader lets them get a little muddy; and if the team needs deep learning, the leader lets them sink to their necks. Either way, the leader is waiting under cover as they approach the impending snafu and is right beside them to pull them out. But to the team, the hands-off leader is not out in front scouting the new territory. To them, the hands-off leader doesn’t pay all that much attention. To the team, it’s just a coincidence the leader happens to attend the project meeting at a pivotal time and they don’t even recognize when the leader subtly plants the idea that lets the team pull themselves out of the mud.
If after three or four near-drowning incidents the team does not learn or change it’s behavior, it’s time for the hands-off approach to look and feel more hands-on. The leader calls a special meeting where the team presents the status of the project and grounds the project in the now. Then, with everyone on the same page the leader facilitates a process where the next bit of work is defined in excruciating detail. What is the next learning objective? What is the test plan? What will be measured? How will it be measured? How will the data be presented? If the tests go as planned, what will you know? What won’t you know? How will you use the knowledge to inform the next experiments? When will we get together to review the test results and your go-forward recommendations?
By intent, this tightening down does not go unnoticed. The next bit of work is well defined and everyone is clear how and when the work will be completed and when the team will report back with the results. The leader reverts back to hands-off until the band gets back together to review the results where it’s back to hands-on. It’s the leader’s judgement on how many rounds of hands-on roulette the team needs, but the fun continues until the team’s behavior changes or the project ends in success.
For me, leadership is always hands-on, but it’s hands-on that looks like hands-off. This way the team gets the right guidance and maintains ownership. And as long as things are going well this is a good way to go. But sometimes the team needs to know you are right there in the trenches with them, and then it’s time for hands-on to look like hands-on. Either way, its vital the team knows they own the project.
There are no schools that teach this. The only way to learn is to jump in with both feet and take an active role in the most important projects.
Image credit – Kerri Lee Smith
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
For clarity on the innovative new product, here’s what the CEO needs.
Valuable Customer Outcomes – how the new product will be used. This is done with a one page, hand sketched document that shows the user using the new product in the new way. The tool of choice is a fat black permanent marker on an 81/2 x 11 sheet of paper in landscape orientation. The fat marker prohibits all but essential details and promotes clarity. The new features/functions/finish are sketched with a fat red marker. If it’s red, it’s new; and if you can’t sketch it, you don’t have it. That’s clarity.
The new value proposition – how the product will be sold. The marketing leader creates a one page sales sheet. If it can’t be sold with one page, there’s nothing worth selling. And if it can’t be drawn, there’s nothing there.
Customer classification – who will buy and use the new product. Using a two column table on a single page, these are their attributes to define: Where the customer calls home; their ability to pay; minimum performance threshold; infrastructure gaps; literacy/capability; sustainability concerns; regulatory concerns; culture/tastes.
Market classification – how will it fit in the market. Using a four column table on a single page, define: At Whose Expense (AWE) your success will come; why they’ll be angry; what the customer will throw way, recycle or replace; market classification – market share, grow the market, disrupt a market, create a new market.
For clarity on the creative work, here’s what the CEO needs: For each novel concept generated by the Innovation Burst Event (IBE), a single PowerPoint slide with a picture of its thinking prototype and a word description (limited to 12 words).
For clarity on the problems to be solved the CEO needs a one page, image-based definition of the problem, where the problem is shown to occur between only two elements, where the problem’s spacial location is defined, along with when the problem occurs.
For clarity on the viability of the new technology, the CEO needs to see performance data for the functional prototypes, with each performance parameter expressed as a bar graph on a single page along with a hyperlink to the robustness surrogate (test rig), test protocol, and images of the tested hardware.
For clarity on commercialization, the CEO should see the project in three phases – a front, a middle, and end. The front is defined by a one page project timeline, one page sales sheet, and one page sales goals. The middle is defined by performance data (bar graphs) for the alpha units which are hyperlinked to test protocols and tested hardware. For the end it’s the same as the middle, except for beta units, and includes process capability data and capacity readiness.
It’s not easy to put things on one page, but when it’s done well clarity skyrockets. And with improved clarity the right concepts are created, the right problems are solved, the right data is generated, and the right new product is launched.
And when clarity extends all the way to the CEO, resources are aligned, organizational confusion dissipates, and all elements of innovation work happen more smoothly.
Image credit – Kristina Alexanderson
Funny thing about ideas is they’re never fully formed – they morph and twist as you talk about them, and as long as you keep talking they keep changing. Evolution of your ideas is good, but in the conversation domain they never get defined well enough (down to the nuts-and-bolts level) for others (and you) to know what you’re really talking about. Converting your ideas into prototypes puts an end to all the nonsense.
Job 1 of the prototype is to help you flesh out your idea – to help you understand what it’s all about. Using whatever you have on hand, create a physical embodiment of your idea. The idea is to build until you can’t, to build until you identify a question you can’t answer. Then, with learning objective in hand, go figure out what you need to know, and then resume building. If you get to a place where your prototype fully captures the essence of your idea, it’s time to move to Job 2. To be clear, the prototype’s job is to communicate the idea – it’s symbolic of your idea – and it’s definitely not a fully functional prototype.
Job 2 of the prototype is to help others understand your idea. There’s a simple constraint in this phase – you cannot use words – you cannot speak – to describe your prototype. It must speak for itself. You can respond to questions, but that’s it. So with your rough and tumble prototype in hand, set up a meeting and simply plop the prototype in front of your critics (coworkers) and watch and listen. With your hand over your mouth, watch for how they interact with the prototype and listen to their questions. They won’t interact with it the way you expect, so learn from that. And, write down their questions and answer them if you can. Their questions help you see your idea from different perspectives, to see it more completely. And for the questions you cannot answer, they the next set of learning objectives. Go away, learn and modify your prototype accordingly (or build a different one altogether). Repeat the learning loop until the group has a common understanding of the idea and a list of questions that only a customer can answer.
Job 3 is to help customers understand your idea. At this stage it’s best if the prototype is at least partially functional, but it’s okay if it “represents” the idea in clear way. The requirement is prototype is complete enough for the customer can form an opinion. Job 3 is a lot like Job 2, except replace coworker with customer. Same constraint – no verbal explanation of the prototype, but you can certainly answer their direction questions (usually best answered with a clarifying question of your own such as “Why do you ask?”) Capture how they interact with the prototype and their questions (video is the best here). Take the data back to headquarters, and decide if you want to build 100 more prototypes to get a broader set of opinions; build 1000 more and do a small regional launch; or scrap it.
Building a prototype is the fastest, most effective way to communicate an idea. And it’s the best way to learn. The act of building forces you to make dozens of small decisions to questions you didn’t know you had to answer and the physical nature the prototype gives a three dimensional expression of the idea. There may be disagreement on the value of the idea the prototype stands for, but there will be no ambiguity about the idea.
If you’re not building prototypes early and often, you’re not doing innovation. It’s that simple.
From the outside it’s unclear how things happen; but from the inside it’s clear as day. No, it’s not your bulletproof processes; it’s not your top down strategy; and it’s not your operating plans. It’s your people.
At some level everything happens like this:
An idea comes to you that makes little sense, so you drop it. But it comes again, and then again. It visits regularly over the months and each time reveals a bit of its true self. But still, it’s incomplete. So you walk around with it and it eats at you; like a parasite, it gets stronger at your expense. Then, it matures and grows its voice – and it talks to you. It talks all the time; it won’t let you sleep; it pollutes you; it gets in the way; it colors you; and finally you become the human embodiment of the idea.
And then it tips you. With one last push, it creates enough discomfort to roll over the fear of acknowledging its existence, and you set up the meeting.
You call the band and let them know it’s time again to tour. You’ve been through it before and you all know deal. You know your instruments and you know how to harmonize. You know what they can do (because they’ve done it before) and you trust them. You sing them the song of your idea and they listen. Then you ask them to improvise and sing it back, and you listen. The mutual listening moves the idea forward, and you agree to take a run at it.
You ask how it should go. The lead vocalist tells you how it should be sung; the lead guitar works out the fingering; the drummer beats out the rhythm; and the keyboardist grins and says this will be fun. You all know the sheet music and you head back to your silos to make it happen.
In record time, the work gets done and you get back together to review the results. As a group you decide if the track is good enough play in public. If it is, you set up the meeting with a broader audience to let them hear your new music. If it’s not, you head back to the recording studio to amplify what worked and dampen what didn’t. You keep re-recording until your symphony is ready for the critics.
Things happen because artists who want to make a difference band together and make a difference. With no complicated Gantt chart, no master plan, no request for approval, and no additional resources, they make beautiful music where there had been none. As if from thin air, they create something from nothing. But it’s not from thin air; it’s from passion, dedication, trust, and mutual respect.
The business books over-complicate it. Things happen because people make them happen – it’s that simple.
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.
At the start of projects, no one knows what to do. Engineering complains the specification isn’t fully defined so they cannot start, and marketing returns fire with their complaint – they don’t yet fully understand the customer needs, can’t lock down the product requirements, and need more time. Marketing wants to keep things flexible and engineering wants to lock things down; and the result is a lot of thrashing and flailing and not nearly enough starting.
Both camps are right – the spec is only partially formed and customer needs are only partially understood – but the project must start anyway. But the situation isn’t as bad as it seems. At the start of a project fully wrung out specs and fully validated customer needs aren’t needed. What’s needed is definition of product attributes that set its character, definition of how those attributes will be measured, and definition of the competitive products. The actual values of the performance attributes aren’t needed, just their name, their relative magnitude expressed as percent improvement, and how they’ll be measured.
And to do this the project manager asks the engineering and marketing groups to work together to create simple bar charts for the most important product attributes and then schedules the meeting where the group jointly presents their single set of bar charts.
This little trick is more powerful than it seems. In order to choose competitive products, a high level characterization of the product must be roughed out; and once chosen they paint a picture of the landscape and set the context for the new product. And in order to choose the most important performance (or design) attributes, there must be convergence on why customers will buy it; and once chosen they set the context for the required design work.
Here’s an example. Audi wants to start developing a new car. The marketing-engineering team is tasked to identify the competitive products. If the competitive products are BMW 7 series, Mercedes S class, and the new monster Hyundai, the character of the new car and the character of the project are pretty clear. If the competitive products are Ford Focus, Fiat F500, and Mini Cooper, that’s a different project altogether. For both projects the team doesn’t know every specification, but it knows enough to start. And once the competitive products are defined, the key performance attributes can be selected rather easily.
But the last part is the hardest – to define how the performance characteristics will be measured, right down to the test protocols and test equipment. For the new Audi fuel economy will be measured using both the European and North American drive cycles and measured in liters per 100 kilometer and miles per gallon (using a pre-defined fuel with an 89 octane rating); interior noise will be measured in six defined locations using sound meter XYZ and expressed in decibels; and overall performance will be measured by the lap time around the Nuremburg Ring under full daylight, dry conditions, and 25 Centigrade ambient temperature, measured in minutes.
Bar charts are created with the names of the competitive vehicles (and the new Audi) below each bar and performance attribute (and units, e.g., miles per gallon) on the right. Side-by-side, it’s pretty clear how the new car must perform. Though the exact number is not know, there’s enough to get started.
At the start of a project the objective is to make sure you’re focusing on the most performance attributes and to create clarity on how the attributes (and therefore the product) will be measured. There’s nothing worse than spending engineering resources in the wrong area. And it’s doubly bad if your misplaced efforts actually create constraints that limit or reduce performance of the most important attributes. And that’s what’s to be avoided.
As the project progresses, marketing converges on a detailed understanding of customer needs, and engineering converges on a complete set of specifications. But at the start, everything is incomplete and no part of the project is completely nailed down.
The trick is to define the most important things as clearly as possible, and start.
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.
If the last project took a year, so will the next one. Even if you want it to take six months, it will take a year. Unless, there’s a good reason it will be different. (And no, the simple fact you want it to take six months is not a good enough reason in itself.)
Some good reasons it will take longer than last time: more work, more newness, less reuse, more risk, and fewer resources. Some good reasons why it will go faster: less work, less newness, more reuse, less risk, more resources. Seems pretty tight and buttoned-up, but things aren’t that straight forward.
With resources, the core resources are usually under control. It’s the shared resources that are the problem. With resources under their control (core resources) project teams typically do a good job – assign dedicated resources and get out of the way. Shared resources are named that way because they support multiple projects, and this is the problem. Shared resources create coupling among projects, and when one project runs long, resource backlogs ripple through the other projects. And it gets worse. The projects backlogged by the initial ripple splash back and reflect ripples back at each other. Understand the shared resources, and you understand a fundamental dynamic of all your projects.
Plain and simple – work content governs project timelines. And going forward I propose we never again ask “How long will it take?” and instead ask “How is the work content different than last time?” To estimate how long it will take, set up a short face-to-face meeting with the person who did it last time, and ask them how long it will take. Write it down, because that’s the best estimate of how long it will take.
It may be the best estimate, but it may not be a good one. The problem is uncertainty around newness. Two important questions to calibrate uncertainty: 1) How big of a stretch are you asking for? and 2) How much do you know about how you’ll get there? The first question drives focus, but it’s not always a good predictor of uncertainty. Even seemingly small stretches can create huge problems. (A project that requires a 0.01% increase in the speed of light will be a long one.) What matters is if you can get there.
To start, use your best judgment to estimate the uncertainty, but as quickly as you can, put together a rude and crude experimental plan to reduce it. As fast as you can execute the experimental plan, and let the test results tell you if you can get there. If you can’t get there on the bench, you can’t get there, and you should work on a different project until you can.
The best way to understand how long a project will take is to understand the work content. And the most important work content to understand is the new work content. Choose several of your best people and ask them to run fast and focused experiments around the newness. Then, instead of asking them how long it will take, look at the test results and decide for yourself.
If I could choose my competitive advantage, it would be an unreasonably strong engineering team.
Ideas have no value unless they’re morphed into winning products, and that’s what engineering does. Technology has no value unless it’s twisted into killer products. Guess who does that?
We have fully built out methodologies for marketing, finance, and general management, each with all the necessary logic and matching toolsets, and manufacturing has lean. But there is no such thing for engineering. Stress analysis or thermal modeling? Built a prototype or do more thinking? Plastic or aluminum? Use an existing technology or invent a new one? What new technology should be invented? Launch the new product as it stands or improve product robustness? How is product robustness improved? Will the new product meet the specification? How will you know? Will it hit the cost target? Will it be manufacturable? Good luck scripting all that.
A comprehensive, step-by-step program for engineering is not possible.
Lean says process drives process, but that’s not right. The product dictates to the factory, and engineers dictate the product. The factory looks as it does because the product demands it, and the product looks as it does because engineers said so.
I’d rather have a product that is difficult to make but works great rather than one that jumps together but works poorly.
And what of innovation? The rhetoric says everyone innovates, but that’s just a nice story that helps everyone feel good. Some innovations are more equal than others. The most important innovations create the killer products, and the most important innovators are the ones that create them – the engineers.
Engineering as a cost center is a race to the bottom; engineering as a market creator will set you free.
The only question: How are you going to create a magical engineering team that changes the game?
The new product development process creates more value than any other process. And because of this it’s a logical target for improvement. But it’s also the most complicated business process. No other process cuts across an organization like new product development. Improvement is difficult.
The CEO throws out the challenge – “Fix new product development.” Great idea, but not actionable. Can’t put a plan together. Don’t know the problem. Stepping back, who will lead the charge? Whose problem is it?
The goal of all projects is to solve problems. And it’s no different when fixing product development – work is informed by problems. No problem, no fix. Sure you can put together one hell of a big improvement project, but there’s no value without the right problem. There’s nothing worse than spending lots of time on the wrong problem. And it’s doubly bad with product development because while fixing the wrong problem engineers are not working on the new products. Yikes.
Problems are informed by outcomes. Make a short list of desired outcomes and show the CEO. Your list won’t be right, but it will facilitate a meaningful discussion. Listen to the input, go back and refine the list, and meet again with the CEO. There will be immense pressure to start the improvement work, but resist. Any improvement work done now will be wrong and will create momentum in the wrong direction. Don’t move until outcomes are defined.
With outcomes in hand, get the band back together. You know who they are. You’ve worked with them over the years. They’re influential and seasoned. You trust them and so does the organization. In an off-site location show them the outcomes and ask them for the problems. (To get their best thinking spend money on great food and a relaxing environment.) If they’re the right folks, they’ll say they don’t know. Then, they’ll craft the work to figure it out – to collect and analyze the data. (The first part of problem definition is problem definition.) There will be immense pressure to start the improvement work, but resist. Any work done now will be wrong. Don’t move until problems are defined.
With outcomes and problems in hand, meet with the CEO. Listen. If outcomes change, get the band back together and repeat the previous paragraph. Then set up another meeting with the CEO. Review outcomes and problems. Listen. If there’s agreement, it’s time to put a plan together. If there’s disagreement, stop. Don’t move until there’s agreement. This is where it gets sticky. It’s a battle to balance everyone’s thoughts and feelings, but that’s your challenge. No words of wisdom on than – don’t move until outcomes and problems are defined.
There’s a lot of emotion around the product development process. We argue about the right way to fix it – the right tools, training, and philosophies. But there’s no place for argument. Analyze your process and define outcomes and problems. The result will be a well informed improvement plan and alignment across the company.