How do I reduce the cost of materials?

In 1899, for example, when Henry Ford founded his own car company, he didn’t just toil away on his big problem — how to make automobiles affordable for everyone. He broke the problem down into subproblems:

How do I reduce the cost of labor?

While history would like you to believe breakthroughs are the result of genius inspiration, or divine intervention, the truth is far more prosaic. Whether it’s the invention of basketball or an organization-wide system for learning and innovation, our greatest minds have arrived at their Eureka! moments by way of clever choosing. That is, they identified their big problem, broke it down into several subproblems, searched to find options for how each subproblem had been previously solved, and combined those options in unique ways to arrive at a novel solution.

I call this method the Think Bigger method. I first began teaching it to my students at Columbia Business School, but quickly realized how much value it had beyond the classroom. For instance, organizations that have dedicated innovation committees have been shown to outperform those who lack such a committee. But these results are not guaranteed, and I believe even these firms are leaving revenue on the table, based on society’s collective poor understanding of how innovation really happens. Research suggests we can do even better, if we infuse the science of choice into the processes that produce world-changing solutions.

The Power of Constraints

Ever since the late 1990s, social psychologists have known a surprising truth: If you want to maximize someone’s satisfaction with a choice, don’t give them unlimited options. Instead, as my colleagues and I have shown, you should give them some choice but with clear constraints. This added structure is crucial for picking a desired option with confidence.

Almost 30 years later, I’ve been applying my research on choice to understand how innovation happens. To create a solution, you must start by defining a problem. The caveat here is that each big problem is secretly made up of several subproblems in disguise, so you must search for options that solve for those various subproblems. It turns out, choice is the active ingredient for innovation. Every great innovator in history has solved their problem by way of these subproblems, whether consciously or unconsciously.

How do I reduce production time?

He found options to solve those subproblems in things that already existed. Oldsmobile had developed an assembly line that reduced the number of workers needed to assemble a single car. Ford was also inspired by a “disassembly line” at a Chicago slaughterhouse in which animal carcasses were broken down by specialty trained workers. He realized he could reduce production time by moving the product, not the workers, along the line. He was able to reduce the cost of materials by using a black lacquer paint that dried quickly on metal and could produce a brilliant sheen in smaller amounts than typical automotive paint. From the combination of these solutions to his subproblems, the price of Ford’s car went from more than $1,000 in 1900 to $265 in 1924.

Structuring your problem-solving in this way adds helpful constraints to your process. It allows you to choose between novel solutions, particularly in ways that traditional brainstorming doesn’t allow.

Brainstorming is useful when you or your team members have all the necessary information to make a decision. But it’s not so helpful when information is missing. As a result, brainstorm sessions usually produce fewer, lower-quality ideas that don’t solve big problems.

To illustrate the power of constraints, let’s look at an example: the invention of basketball. In 1891, James Naismith was a physical education teacher in Springfield, Massachusetts. He wanted to offer his students a game they could play indoors during the harsh New England winters. That was his big problem. Like Henry Ford, Naismith didn’t just rack his brain dreaming up new activities from scratch. He broke his problem down into four subproblems:

It had to fit in an indoor room, not on a vast field outdoors.

It needed the speed, effort, skill, and complexity of a field sport in order to keep the students in shape physically and mentally.

It couldn’t be rough, because the players would fall down on a hard floor, not soft earth.

It had to be a team activity that involved lots of students at once in the confined space.

These constraints limited Naismith’s menu of options in ways that helped him solve his problem. He couldn’t reinvent football because it was too rough when played on the gym’s hardwood floor. He couldn’t reinvent baseball because the gym was too small.

Ultimately, Naismith realized he could combine elements from lacrosse, football and rugby, soccer, and an element from a game Naismith played as a child, called “duck on a rock”— passing a ball between teammates (lacrosse and soccer), penalty shots for excessive roughness (soccer), speed and complexity (football and rugby) and throwing at a target (“duck on a rock”) — to solve his subproblems. And solving them all is what allowed him to invent a game that would last for more than 130 years and become an industry worth billions.

Creating a “Choice Map”

Choice mapping is an exercise based on the science of choice that helps you produce as many possible solutions as your imagination can come up with. You can use the below template to create a choice map. Here’s how it works.

Start by identifying your big problem and breaking it down into a list of subproblems. Let’s say you work for a hospital and your problem is: What is the best method for transporting donated organs safely? Your subproblems might be:

How can we ensure the organs are transported in sanitary conditions?

How can we keep the organs preserved in the right climate?

What are the greatest risks associated with improper transport?

Once you list these subproblems on your Choice Map, you can start searching for what I call “precedents,” or past solutions that either come from the same world as your problem (in-domain) or from other worlds (out-of-domain).

For the subproblems of organ transportation we just listed, under “in-domain” you might research what other hospitals have done in the past and list those in your Choice Map. Under “out-of-domain,” you might go looking for answers to questions like the below:

How do food companies keep food fresh in transit?

What is the fastest way to ship glass sculptures?

What is the best way to travel with a newborn?

How do bakers transport wedding cakes to the reception?

Seeing all these options in front of you should spark your thinking so that you can start mixing and matching solutions to your subproblems. For instance, you may borrow a solution from food service that keeps food at safe temperatures, or the cradling system used by a car seat manufacturer to keep it secure in transit.

By combining options for each subproblem in the right way, you may end up with a solution like “Heart in a Box,” an FDA-approved device developed by TransMedics that allows for organ donation after the donor has already died. It does this through a technique called perfusion, which replicates the body’s warm climate to extend the organ’s shelf life, rather than simply freezing it. Through Heart in a Box and similar technologies, doctors can now access a wider donor pool and save more patients in need of transplants.

To be sure, the Choice Map can’t help you determine how your solution satisfies your various stakeholders, such as investors and customers. Nor does it give you a sense of whether other people view the problem in the same ways you do. The main utility of the Choice Map is to help you create multiple possible solutions to choose from. In fact, a five-by-five Choice Map can generate up to 3,125 potential solutions. As we’ve seen, it does this by allowing you to constrain your thinking in ways that decades of research have proven will put you in the Goldilocks zone of decision making. You aren’t brainstorming for just any wild idea. You’ve collected verified solutions from different worlds to give yourself a range of choices, which you can then combine and recombine to find a solution that is greater than the sum of its parts. In the process, you are more empowered to innovate because you freed yourself from the burden of coming up with something wholly original. Which, as Mark Twain wisely observed more than a century ago, is impossible anyway.

“There is no such thing as a new idea,” he said. “We simply take a lot of old ideas and put them into a sort of mental kaleidoscope. We give them a turn and they make new and curious combinations. We keep on turning and making new combinations indefinitely, but they are the same old pieces of colored glass that have been in use through all the ages.” Check out the original article: https://hbr.org/2023/08/how-the-science-of-choice-can-boost-innovation