# Problem-solving process

**The AIPS-2015 algorithm is based on the process of obtaining a new knowledge.**

As much as I would like to tell you a sort of "magic word" that would help you to immediately solve any problem, or to give you some "magic wand", unfortunately, it is impossible. Solving a problem, eliminating a disadvantage is a normal work process that requires some effort. Of course, the process of solving an inventive problem has its unique features. Of great importance here is guessing and creativity. However, of no less importance is the right sequence of actions, the process that dictates actions to be performed at each step and shows what result should be obtained. It will also suggest the way it can be achieved.

The AIPS-2015 algorithm is based on the following idea. Solving an inventive problem means getting a new knowledge about how to fix something in a machine, equipment of concern or in their environment, how to eliminate the weak point that troubles us. In science, this is done through the process of obtaining a new knowledge, which includes the following steps:

- Observation of the object under study
- Analysis of the observation results
- Proposing of hypotheses
- Creating conditions for testing the hypotheses
- Testing the hypotheses and formulating a new knowledge.

Actions similar to the process of obtaining scientific knowledge can be conveniently used for solving inventive problems. Here the following sequence is beginning to take shape.

Initially, we observe some phenomenon, such as the operation of a technical tool of concern, accumulate information about its structure and operation and about the problem associated with this phenomenon. Then we perform the analysis itself trying to find out the essence of the problem - where, when, under what conditions and for what reasons the conflict interaction occurs. The next step is proposing hypotheses on how to eliminate the conflict in the system. Further, it is necessary to arrange testing of the proposed hypotheses, and before that we should think about how exactly to do it in this particular situation.

The description of the complete algorithm can be found in the additional information. Here we will use the algorithm adapted for initial distance learning. It only includes two stages: the analysis of a problem situation and the solution of a selected problem. The third stage, the analysis of an improved situation, is important when working with real-life problems; it can be omitted when solving training problems.

The problem-solving process can be divided into two stages: analysis of the problem situation and synthesis, solution of a selected problem.

The first stage is analytical - we gain insight into the problem situation, try to understand why an undesirable effect arises and under what conditions it would not arise. At the second stage, we solve the problem, i.e. we find the way to change the system under study so that no undesirable effect appears.

**Analysis of problem situation**

The purpose of the analytical stage is clarifying the problem statement. We need to obtain the most complete and accurate information about the problem-solving purpose, interacting objects, time, place and peculiarities of their interaction. The analysis starts with the clarification of the problem situation and ends with the formulation of the problem statement. This stage includes the study of the system that generated the undesirable effect, identification of the problem place in the system and conflict interaction; proposing of hypotheses about the ways to eliminate the conflict; problem formulation.

The analysis of a problem-generating situation is a constant gaining of deeper insight into the situation, from the known to the unknown; from the basic understanding of a situation to its technical details, from studying the operational principle of a machine to detecting its conflicting components and understanding the causes of the conflict and ways to eliminate them.

The problem situation is always associated with the production of a useful product, but contains some undesirable effect. Speaking generally, we lose money. This may be due to the low quality of a useful product, its high manufacturing cost, the emergence of some harmful phenomena, such as an adverse environmental impact.

The manufacture of a useful product is impossible without the functioning of technical equipment, personnel, software which may be collectively called a "machine". This machine either does not perform its function well enough or requires heavy functioning expenses, or produces some harmful product, etc.

First of all, it is necessary to understand how the machine operates. This will help to find the problem-causing operation, identify the problem area of the machine, and understand what exactly happens there. In the problem area, a conflict occurs between the system components: their interaction produces a negative result. It is essential to understand the root causes of the conflict and determine what opportunities we have in principle for resolving it. To conclude the analytical stage, we need to propose assumptions and hypotheses as to how the causes of the conflict can be eliminated. The hypothesis predetermines the way we can achieve our ultimate goal - the elimination of the undesirable effect.

It often happens that clarifying the method immediately makes obvious the means, i.e. the actions we can take. If the conflict-eliminating means are not obvious, then it is necessary to clarify the conflict-eliminating method and to bring it in accordance with the means (resources) available in the problem.

In this case, we formulate specific problems to be solved at the second stage of the algorithm (the stage of solving a selected task).

**Solving a problem selected from a problem situation**

When solving a problem, we need to find a system which makes it possible to create the conditions described in the hypothesis. In order to achieve this purpose, it is necessary to proceed as follows.

The problem statement is transformed into the problem model, i.e. the property that is most important in terms of problem solving is selected for consideration. In fact, we define the problem-solving goal, formulate the result we need and clarify the obstacles to gaining such a result. Typical TRIZ models such as different models of contradiction, a su-field model, a model of smart little creatures, etc. are well suited for building the model of the problem.

Next, it is necessary to find a basic idea of how to achieve the required result, the action you need to take. Here typical transformation models developed in TRIZ will be helpful such as Altshuller's principles, standard solutions, etc.

The next step is filling the abstract solution model with a specific content. To do this, we need to materialize it, i.e. to build a technical system that is free of the original disadvantage. To do that, it is necessary to understand what resources we need and how to use them for solving the problem. The main purpose of solving the problem is determining the required resource (X-element) as well as the place, time and peculiarities of its action. The entire solution course is aimed at determining the requirements for the X-element as fully and accurately as possible and finding among the available resources an object or a set of objects which most fully meet those requirements.

This sequence of actions ("what is required - how to achieve it - by what means - a solution variant") sometimes has to be repeated several times, defining with increasing precision what we want to obtain and what prevents us from doing that. Actually, at the second stage, we have to specify a goal, find a way to gain it, and determine means to implement this method. At the same time, it is necessary to coordinate the method and means for solving the problem, which requires several problem-solving iterations.

For this purpose, the problem is studied from different angles and different models are built. For each model, it is necessary to find a basic transformation that eliminates its identified disadvantage. Then the method of implementing this transformation in a specific technical system is determined - the solution idea.

In doing so, it is necessary to take into account the requirement of ideality, one of the key ones in TRIZ-based problem solving. According to it, first of all, we should use as an X-element those substances and fields which are available in the operational zone and in its close proximity.

**About the work with TRIZ-trainer**

АThe AIPS-2015 algorithm shows the entire problem-solving path – from a vague problem situation (problem) to obtaining a solution idea.

The series of problem-solving actions prescribed by the AIPS-2015 algorithm is represented by the template that should be used when solving the problems from the "Problems" section. In addition, the same series of actions is also contained in the case studies given in the respective section.