Appunti methods and tools for systematic innovation - Parte B

Politecnico di Milano - School of Industrial and Information Engineering

MSc in Mechanical Engineering 2021-2022 Academic Year

Methods and tools for systematic innovation

Module B - Lecture notes

Summary

• Idea generation for innovation: obstacles
  • Problem solving process
  • Psychological tools
• System thinking
  • Conceptual maps
• Functional modelling
  • EMS model
  • TRIZ model
• Design cognitive processes and analogies
  • Design by analogy
• Types of problem solving strategies
  • How to deliver the desired function
  • How to improve an insufficient useful function
  • How to avoid undesired/harmful functions
  • How to avoid an excessive consumption of resources
• Bio-inspired design
  • How to perform a bio-inspired design approach
• Life cycle assessment in decision-making for design
• TRIZ: theory of inventive problem solving
  • Law of engineering systems evolution
  • Contradictions
• Contradiction modelling and logic of ARIZ
  • Separation strategies

Idea generation for innovation: obstacles

There are some practical procedures that can be exploited in order to overcome some of the most typical obstacles that can arise when dealing with the process of idea generation. We can see that it is possible to identify three main types of obstacles: the first one is called psychological inertia. Normally, there are many design choices that we take for granted, but that could be changed. There are many scenarios that could be considered, but they are ignored due to psychological inertia. Humans tend to create mental patterns that should make life easier in order to avoid spending time making decisions for actions that are made on a daily basis.

The more someone becomes an expert in a specific field, the more they become efficient, but on the other hand, they also become more exposed to the problem of psychological inertia. The question that someone should ask when dealing with mental inertia is: “Am I solving the right problem?” To avoid mental inertia, many techniques try to think of a solution in all possible directions. This method can work, but it is not efficient, as it requires too much time to consider and elaborate on all possible ideas.

The second obstacle that can appear during idea generation is the lack of a structured approach. Which approach should we adopt to solve the problem? Is there a model to follow for the solution? In some situations, it is possible to perform experimental tests or to create prototypes to better understand how to solve the problem; nevertheless, in many other situations, this is not possible, so it is necessary to find another way. It is better to analyze the problem through an abstract representation because then it will be possible to apply some general tools: we want to do the same with the innovation task, so find a way to generally represent the problem and then apply general procedures for the solution generation. The main concept that must be formulated when dealing with a task is: what is the most desirable result?

The third obstacle is design conflicts. It could frequently happen that during the solution process, some conflicts may emerge: a particular choice could improve some results but damage others. When we are in this situation, it is important to understand which is the most desirable result, considering the available resources. The optimal solution is the best configuration of the design parameters of the problem. In other words, it can be seen as the best compromise. However, it must be considered that often the best solution is not to accept a compromise, but to propose something that goes beyond: something that completely changes the rules of the game. In other words, introducing and changing the parameters of the problem.

Obstacles for idea generation

• Psychological inertia
• Lack of structured approach
• Design conflicts

Problem solving process

Typically, we start from a certain design task, and the first thing to do is to select the proper problem to analyze. It can be useful to split the main problem into several smaller problems that will be considered separately. For every specific task, there are different alternative options to look at. It should be noticed that the design activity is a continuous decision problem. Every time a new problem comes out, it must be evaluated the best possible way to solve it. A complex design situation does not only require generating new ideas but also making decisions.

Once we have selected one or more problems, we should find a way to model them. We need a language through which we can abstractly model the problem. After we have found this language, we can use a general method to solve the problem, so to find a model of the solution.

Psychological tools

Brainstorming is the first psychological tool that we are going to examine. It requires a group of people to approach a problem and generate ideas. Then, it will be asked to a second group to analyze those ideas. The generation process should be absolutely free and consider also the ideas which are less achievable or can look stupid. One of the key points is that the ideas are reported anonymously, so that the decision won’t be influenced by who made the idea. During brainstorming, it is important to be open to any proposal and listen to anything that comes out.

An evolution of brainstorming is the 6-3-5 brain writing, where six participants come out with an idea on a particular problem, and then they should come out with new ideas, starting from the ones previously made by others. By doing so, it is possible to obtain a long list of new ideas.

Synectic: the term means “joining together different and apparently irrelevant elements.” A synectic group is a group made by people with different occupations, that join together to solve a problem, by combining elements that are not superimposable. Through synectic, it is possible to analyze the problem under different points of view. Also in this procedure, it is important to look at any ideas, even if they can look stupid or not achievable, since the ultimate goal is to look beyond our standard thinking.

Forced analogy: in forced analogy, participants generate a list of random things and write them on cards. For each item, participants write qualities and attributes. The cards are then distributed randomly among the participants, who then use the cards to develop analogies with the problem that must be solved. “How is the problem similar to [random object]?” Even if a lot of comparisons could look stupid at first, a lot of ideas could come out. The main goal is to find properties of the random object that can be considered also in the problem of the analysis.

System thinking

In the process we would like to follow, as previously mentioned, we want to define an abstract language that generalizes the problem. After doing that, what we want to do is apply general rules that permit us to solve the problem. This means that the process that we want to follow starts from the particular case, gets generalized in order to apply general solutions, and then comes back to the particular case. What we are going to see now is how to select a specific problem from a general design task.

If we have to consider a word, and we are required to associate other words with it, we can associate words which are a subsystem of the word, or instead words related to “supersystems” which contain that first word. Second, it is possible to make associations related to the use that is possible to do with that word. When dealing with a design task, it is important to look both at the details and at the global environment of the task.

It is also important to look at the time dimension: look at the evolution of the task, at its past and at its probable future, in order to understand what the dynamic of the process is. System thinking is the process of understanding which are the mutual influences of elements in a given environment. While designing in a complex situation, having a system thinking approach is essential in order to avoid undesired situations.

Systems Thinking has been defined as an approach to problem solving by viewing "problems" as parts of an overall system, rather than seeing them just as a specific part, reacting to specific part outcomes, or events, and potentially contributing to further development of unintended consequences. Time can have three different interpretations, based on the situation, we can choose the most suitable one.

• Technical evolution: This meaning is referred to historical time; it means analyzing what principle was used in the past, what principle is currently used, and finally, what principle might be used in the future to perform the same task. By comparing past solutions with current ones, it is possible to understand the general tendencies of a given technology.
• Phases of a process: Analyze the phases that have preceded the process under analysis and try to identify the next ones. Before analyzing a process, we should ask ourselves: “Which were the previous phases of this process?” Sometimes, before solving the problem, it might be necessary to do something before or after that could make it easier to solve the problem. Another important question to ask is “Can we solve the problem in a different phase?” It is essential to identify the phases during which it is easier to solve the problem.
• Cause and effect chains: We have to consider which element or event has caused the current situation. In other situations, we may have to understand the consequences of a particular event (for example, a failure). Typically, a way to solve problems is prevention. Another indirect way to solve them is to mitigate/compensate the consequences of the problem. In many situations, working on these aspects could be more efficient than directly facing the problem. It is important to understand what it means “preventing” or “compensating” for that particular task.

We call system operator a multi-view of the system, which considers past and present, supersystems and subsystems. While looking for resources, the System Operator helps focus attention on every relevant aspect of the system and its environment by analyzing any time stage at any detail level with a systematic approach. It can be useful to also add indicators to better understand the cause-effect relationships of the task. After doing that, we can represent the problem through a conceptual map, which can be useful to better visualize the problem under a global point of view.

Conceptual maps

Concept maps are graphical tools for organizing and representing knowledge. The aim of a conceptual map is to represent the most important concepts and the relationships that exist between them; in fact, the two main elements of a conceptual map are nodes and links. When we want to create a conceptual map, the first thing to do is to identify the problem. What is a problem? A problem is whatever we are not comfortable with, whatever objective or condition we would like to achieve.

After we have identified the problem, this is the first thing that we should put in it. Second, come the partial solutions, which partially address the problem or mitigate it. Then there are the information needed (questions to experts), which are all the information that could be useful in order to find a solution or better understand the problem. In a real scenario, information can come from the company, from partners, or from knowledge sources (patents, handbooks, standards, etc.). The last type of elements are the constraints, which are all the elements of the problem that cannot be modified. It is important to notice that constraints are different from problems. Constraints can come from clients, be represented by physical laws, contracts or specifications, standards, and rules, etc. It is important to not confuse constraints with problems; however, we must always check if a particular constraint can be turned into a problem, and so, it is possible to find a solution to change it.

After all the elements of the map have been identified, it is necessary to connect them, by properly considering the relationships between them. When we connect the elements through the links, it is important to show the relations between the elements. The elements can also be connected by logical operators (and, or). In the end, the phases that should be performed are the following:

1. Identify all the problems, the partial solutions, the questions to experts, and the constraints.
2. Identify the relationships between all the elements.
3. For each problem, look for further problems and possible questions to experts.
4. For each problem, apply the knowledge to identify a solution by using problem-solving strategies.
5. Return to step 3, until all the problems have been solved.
6. Identify contradictions and solve them through a classical ARIZ-like approach.

Functional modelling

The purpose of this model is to define an ontology of design, which has the object of defining the possible levels of the design process. The function of a technical system is its reason for existing; it’s the reason why we have created the system. In order to fulfill its function, the system should have some specific elements. These elements could be physical parts, but also software instructions, etc.

The behavior is defined as the sequential changing of states that describes how the system delivers its function. It is important to notice that different behaviors can produce the same function, and that different structures, which are the physical elements that have the duty of delivering the function, can be characterized by the same behavior. In principle, we might have different functions that adopt the same behavior (example: bottle and boiler, they have different functions, but they adopt the same behavior: contain a liquid). The process through which we solve a problem starts from a function, which represents our need. The second thing that comes after is thinking about the most suitable physical principle that can be used in order to solve that problem, so, how it should behave.