Shipulski On Design

Archive for January, 2018

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

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

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

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

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

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

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

Image credit – Gabriel Rojas Hruska

Innovation is all about learning.  And if the objective of innovation is learning, why not start with learning objectives?

Here’s a recipe for learning: define what you want to learn, figure how you want to learn, define what you’ll measure, work the learning plan, define what you learned and repeat.

With innovation, the learning is usually around what customers/users want, what new things (or processes) must be created to satisfy their needs and how to deliver the useful novelty to them.  Seems pretty straightforward, until you realize the three elements interact vigorously.  Customers’ wants change after you show them the new things you created. The constraints around how you can deliver the useful novelty (new product or service) limit the novelty you can create.  And if the customers don’t like the novelty you can create, well, don’t bother delivering it because they won’t buy it.

And that’s why it’s almost impossible to develop a formal innovation process with a firm sequence of operations.  Turns out, in reality the actual process looks more like a fur ball than a flow chart. With incomplete knowledge of the customer, you’ve got to define the target customer, knowing full-well you don’t have it right. And at the same time, and, again, with incomplete knowledge, you’ve got to assume you understand their problems and figure out how to solve them.  And at the same time, you’ve got to understand the limitations of the commercialization engine and decide which parts can be reused and which parts must be blown up and replaced with something new.  All three explore their domains like the proverbial drunken sailor, bumping into lampposts, tripping over curbs and stumbling over each other.  And with each iteration, they become less drunk.

If you create an innovation process that defines all the if-then statements, it’s too complicated to be useful.  And, because the if-thens are rearward-looking, they don’t apply the current project because every innovation project is different. (If it’s the same as last time, it’s not innovation.)  And if you step up the ladder of abstraction and write the process at a high level, the process steps are vague, poorly-defined and less than useful.  What’s a drunken sailor to do?

Define the learning objectives, define the learning plan, define what you’ll measure, execute the learning plan, define what you learned and repeat.

When the objective is learning, start with the learning objectives.

Image credit Jean L.

Henry Ford famously said if he asked people what they wanted, they would have asked for faster horses.  And there’s a lot of truth to his statement. If you ask potential customers what they want next, they’ll give you an answer. And when you show them the prototype, they won’t like it.  Their intentions are good and their answers are truthful, but when you give them what they ask for and put the prototype in their hands, they will experience it in a way they did not expect.  It will be different than they thought.  Thinking how something will be is different than physically interacting with it.  That’s how it is with new things.

And just like with the horses, because they don’t know the emergent technologies and their radically different capabilities, they can’t ask for what’s possible.  They won’t ask for a combustion engine that eliminates their horses because all they know is horses.  They’ll ask for more horses, bigger horses or smaller ones, but they won’t ask for combustion cylinders.

The trick is to understand what people do and why they do it.  Like an anthropologist, spend time watching and understanding. And, if you can, understand what they don’t do and why they don’t do it.  The new and deeper understanding of their actions, along with the reasons for them, create an anchoring perspective from which to understand how emergent technologies can change their lives.

Technologies evolve along worn paths. And depending on the maturity of the technology, some worth paths are more preferential than others.  For example, if fuel economy is stagnant for the last ten years it means it’s likely time for a young technology to emerge that uses a different physical principle such as battery power.  Though technology’s evolutionary direction is not predictable in an exact sense, it is dispositional.  Like the meteorologist can’t pinpoint where the storm center will hit the coast or predict the maximum wind speed to within one or two miles per hour, she can say which states should hunker down and tell you if the wind will be strong enough to blow out your windows.  She cannot predict the specifics, but she knows there’s a storm on the horizon and she knows its character, disposition and tendencies.

Now, anchored in how people use the state-of-the-art technologies (ride horseback, ride in buggies, use a team of horses to pull a heavy wagon) look at what the new technologies want to become (horses to combustion engine) and image how people’s lives would be better (faster trips, longer pleasure rides, heavier payloads, no barns and cleaner streets.)  Now, using the new technology, build a prototype and show it to customers.  Put them in the driver’s seat and blow their minds.  Listen to the questions they ask so you can better understand the technology from their perspective because just as they don’t understand the technology, you don’t understand what the technology means to them, the people who will buy it.  Use their questions to improve the technology and the product.

Technologists know technology, technology knows what it wants to be when it grows up and customers know what they want after they see what could be.  And to create a new business, it takes all three working together.

Image credit — William Creswell

At first glance, it seems easy to run a good test, but nothing can be further from the truth.

The first step is to define the idea/concept you want to validate or invalidate.  The best way is to complete one of these two sentences: I want to learn that [type your idea here] is true. Or, I want to learn that [enter your idea here] is false.

Next, ask yourself this question: What information do I need to validate (or invalidate) [type your idea here]?  Write down the information you need.  In the engineering domain, this is straightforward: I need the temperature of this, the pressure of that, the force generated on part xyz or the time (in seconds) before the system catches fire.  But for people-related ideas, things aren’t so straightforward. Some things you may want to know are: how much will you pay for this new thing, how many will you buy, on a scale of 1 to 5 how much do you like it?

Now the tough part – how will you judge pass or fail?  What is the maximum acceptable temperature? What is the minimum pressure? What is the maximum force that can be tolerated? How many seconds must the system survive before catching fire?  And for people: What is the minimum price that can support a viable business? How many must they buy before the company can prosper? And if they like it at level 3, it’s a go. And here’s the most importance sentence of the entire post:

The decision criteria must be defined BEFORE running the test.

If you wait to define the go/no-go criteria until after you run the test and review the data, you’ll adjust the decision criteria so you make the decision you wanted to make before running the test. If you’re not going to define the decision criteria before running the test, don’t bother running the test and follow your gut.  Your decision will be a bad one, but at least you’ll save the time and money associated with the test.

And before running the test, define the test protocol.  Think recipe in a cookbook: a pinch of this, a quart of that, mix it together and bake at 350 degrees Fahrenheit for 40 minutes.  The best protocols are simple and clear and result in the same sequence of events regardless of who runs the test.  And make sure the measurement method is part of the protocol – use this thermocouple, use that pressure gauge, use the script to ask the questions about price and the number they’d buy.

And even with all this rigor, good judgement is still part of the equation.  But the judgment is limited to questions like: did we follow the protocol? Did the measurement system function properly? Do the initial assumptions still hold? Did anything change since we defined the learning objective and defined the test protocol?

To create formal learning objectives, to write well-defined test protocols and to formalize the decision criteria before running the test require rigor, discipline, time and money.  But, because the cost of making a bad decision is so high, the cost of running good tests is a bargain at twice the price.

Image credit – NASA Goddard Flight Center

If the work doesn’t stretch you, choose new work.  Don’t go overboard and make all your work stretch you and don’t choose work that will break you.  There’s a balance point somewhere between 0% and 100% stretch and that balance point is different for everyone and it changes over time.  Point is, seek your balance point.

To find the right balance point, start with an assessment of your stretch level. List the number of projects you have and sum the number of major deliverables you’ve got to deliver.  If you have more than three projects, you have too many.  And if you think you take on more than three because you’re superhuman, you’re wrong.  The data is clear – multitasking is a fallacy.  If you have four projects you have too many. And it’s the same with three, but you’d think I was crazy if I suggested you limit your projects to two.  The right balance point starts with reducing the number of projects you work on.

Now that you eliminated four or five projects and narrowed the portfolio down to the vital two or three, it’s time to list your major deliverables. Take a piece of paper and write them in a column down the left side of the page. And in a column next to the projects, categorize each of them as: -1 (done it before), 0 (done something similar), 1 (new to me), 2 (new to team), 3 (new to company), 5 (new to industry), 11 (new to world).

For the -1s, teach an entry level person how to do it and make sure they do it well. For the 0s, find someone who deserves a growth opportunity and let them have the work. And check in with them to make sure they do a good job.  The idea is to free yourself for the stretch work.

For the 1s, find the best person in the team who has done it before and ask them how to do it.  Then, do as they suggest but build on their work and take it to the next level.

For the 2s, find the best person in the company who has done it before and ask them how to do it. Then, build on their approach and make it your own.

For the 3s, do your research and find out who in your industry has done it before.  Figure out how they did it and improve on their work.

For the 5s, do your research and figure out who has done similar work in another industry.  Adapt their work to your application and twist it into something magical.

And for the 11s, they’re a special project category that live in rarified air and deserve a separate blog post of their own.

Start with where you are – evaluate your existing deliverables, cull them to a reasonable workload and assess your level of stretch.  And, where it makes sense, stop doing work you’ve done before and start doing work you haven’t done yet. Stretch yourself, but be reasonable.  It’s better to take one bite and swallow than take three and choke.

Image credit – filtran