In the previous module, we defined innovation as a decision-making process that leads to the identification of a problem and of a solution that addresses a major want or need that people have, something they know they want or need, or that they will want or need once we provide it. Such customer want or need might refer to, or translate into, minor improvements in some of the features of a given product or service: process betterments that allow to reduce a given product or service cost and price, or brand new - at times revolutionary - products, processes and services that improve people's life. Whatever the type of innovation we refer to, they all share a common characteristic. They identify a problem, solving which is valuable from the customer perspective, and address it with effective, efficient, helpful solutions. Therefore, the innovation process is - in essence - a problem-solving process. A process of discovery of problem-solution pairings that creates value for existing or potential customers. Sometimes solutions come first because of chance, luck, serendipity or the autonomous self-driving push of scientific research and technological change make new technologies available. Consider a home appliance we use in our everyday life, the microwave oven. Percy Spencer was an engineer working for Raytheon, where he built magnetrons for radars. One day in 1945, Spencer was standing in front of an active radar set when he noticed that the candy bar he had in his pocket had melted. Spencer started to investigate it, and decided to experiment using food, including popcorn kernels, eggs, and other things. The technology had met a customer need. Quickly heat or cook food, becoming an innovation that revolutionized our everyday life. Another example is plastics, whose use is so common in today's world. They were made available in the second half of the last century, when scientific research discovered that plastics consist of very large molecules comprised of long chains of smaller molecules. After Karl Ziegler developed a method for creating these molecular chains using catalysts, substances that hasten the chemical process without affecting the end products, Julian Arthur developed it further. In 1955, he discovered a catalyst that formed molecular chains with their parts oriented in certain directions. This made it possible to produce rubbery and textile-like materials, opening up a variety of potential markets and industrial applications, and creating the conditions for the birth and development of a number of firms and industries. In other cases, problems and solutions originate together and co-evolve. For example, this happens when lead customers or users try to solve their own problems. Eric von Hippel at MIT has shown that innovation in social networks, sports like mountain bikes, kitesurfing, skateboarding, and other industries are often developed by users. Selective compression socks, also known as boosters, are a typical example of a problem-solution pairing, born and developed in parallel thanks to use leaders. In 1989, Michael Prufer, multiple world champion and speed skiing champion at the Albertville Winter Olympics, together with another physician, invented a new and revolutionary concept of compression support socks, designed specifically for athletes, the Booster Veins Sport System. The boosters are the result of exertion specific manufacturing technology. They encourage the vertical movement of the leg muscles and soleus by reducing oscillatory movements, the source of fatigue and muscular injuries. Moreover, they reduce muscular vibration, and improve the contraction and toning of muscles. This idea was patented. A full new company, BV Sport, was built around it. And boosters have since then become a must-have for professional and amateur athletes of all sports all over the world. In many cases, however, it is problems that come first, as they define and make explicit the performance gap to be filled, which constitutes the basis of the innovation opportunity. Now, sometimes problems are evident, predefined, or easily identifiable. They might be difficult to solve, but they are clear, known. Solving these problems is basically an optimization problem. There might be some uncertainty with regard to the solution. And in some cases, there might not be an optimal solution. However, through algorithms, experiments, and data, they can be solved, thus, allowing to improve operational efficiency, as defined in the previous modules. But in other cases, they are not clearly defined. Maybe they exist but have not been articulated yet. Or maybe, they are hard to elicit. Problems might be even unknown, so that they have to be discovered, invented themselves. In this case, the innovation process does not start with the search for a solution. But with the search for a problem, which then has to be solved. One interesting case of innovation based on a customer problem is Geox. A winemaker by ancestry and training, Mario Moretti Polegato stumbled upon his destiny as inventor and entrepreneur in 1992, during a wine convention in Reno, Nevada. To relax between conference sessions, he went hiking on the Rockies, during which time his feet began to sweat and burn. So, in order to get relief, he took out of his pocket a knife and cut holes in each of the soles of his sneakers to let the heat out. Back in Italy, he realized that no one had successfully solved the problem of how to design shoes that both kept out water and ventilated air. He spent three years studying, experimenting and doing research and development, eventually creating an innovative type of shoe: the breathable shoe, which combines a perforated rubber sole, with a special microporous membrane that absorbs and expels sweat without letting water in. Moretti Polegato patented it and knocked on the doors of the big footwear manufacturers, Nike, Adidas, Timberland and others. They all turned him down. Instead of giving up, Moretti Polegato took on five employees and started production on his own. In less than a decade, Geox has become one of the world's largest manufacturer of brown shoes, outperforming the industry in terms of profitability and business growth. We were saying that in some cases problems are not known or visible, but rather wicked and ambiguous. The innovation process, then, starts with the search for an unknown unknown, so that its level of uncertainty is particularly high. This type of innovation process is particularly complex, because the limits of human cognition, and especially the use of heuristics to economize on bounded rationality with the associated biases might hinder or even impede the process of problem discovery and identification. Identifying and solving these problems allow to improve strategic efficiency, as defined in the previous modules. In all the above-described cases, the process of problem identification and solution, which underlies innovation, can be supported by two different types of thought processes, analytical processes and synthetic processes. Analytical processes are actionable routines, sequences of steps that individuals or organizations might take to facilitate the problem identification and solution, especially under conditions of low to moderate uncertainty. For instance, the sets of conceptual tools included in management practices like Total Quality Management, Lean Production, and Six Sigma are typical analytic processes that help problem solving, allowing to innovate products, services, and processes. The famous 5-WHY TECHNIQUE used in the Lean Startup Method to identify the root cause of problems, is a method originally pioneered by Toyota, and today it is widely used to improve innovation, engineering and production processes across firms and industries around the world. It is a typical example of analytic process, because it provides a behavioral routine, a series of steps to make sure that the problem is correctly framed and that the countermeasure solutions that will be developed will likely solve it, improving some performance dimensions, like quality, cost and others - that are known and are controversial - that the customers will appreciate. The typical result of analytic processes, therefore, is value creation through incremental innovation of an existing business model. Analytic processes mostly rely on deductive and inductive thinking. Synthetic processes are, instead, actionable routines, sequences of steps that individuals or organizations might take to facilitate problem identification and solution, and especially under conditions of high uncertainty. While analytic processes disassemble and decompose, synthetic processes are designed to actively combine and integrate. For instance, the sets of conceptual tools included in management practices like Design Thinking, TRIZ, and Lateral Thinking are typical synthetic processes that help the problem identification process, and specifically problem identification, allowing to come up with new products, services and processes. For instance, the creativity practice of the Six Thinking Hats, which has been extensively used around the world as a means for groups to think creatively, is a typical example of a synthetic process, as it focuses on asking novel, catalytic questions in response to ambiguity. Even creating ambiguity. Synthetic processes often use abduction and analogy as a way of reasoning, and may generate a wide range of alternative problems and potential solutions, that managers and entrepreneurs must evaluate and select. Personal experience, direct observation and the associated emotions and intuitions are often the triggering factor of abduction. Ideas like Airbnb and Uber, are examples of how abductive and analogical reasoning can help generating and framing new problems. When problems are large and significant enough, the typical result of synthetic processes, therefore, is value creation through radical innovation and, possibly, a new business model. While analytic and synthetic processes are both instrumental to problem identification and problem solving, analytic processes are comparatively more important under conditions of low to moderate uncertainty. While synthetic processes are comparatively more important under conditions of high uncertainty. Furthermore, while both analytic and synthetic processes are grounded on reality via facts and data, and through deductive, inductive, and abductive reasoning, analytic processes rely more on data, deductive, and inductive reasoning, while synthetic processes rely more on facts and abductive reasoning. Finally, synthetic processes focus more on logic, theory, and modelling, something unknown, while analytic processes focus more on empirics, evidence and optimizing for something known.
