Problems and tasks are sometimes used interchangeably in everyday terms, but they can also be clearly used as terms within a company.
The problem is the gap between what it should be and what it really is. Defective products or machine failures may occur.
On the other hand, the task is the gap between what we want to be in the future and what we really want to be. It will be the realization of new business.
However, as a solution to the problem, you may want to start a new business, so you may think of the same thing as a problem or a task. From experience, it is often easier to think of it as a problem.
At the heart of problem solving and task achievement is what is happening and what is about to happen. As summarized on the Data Science page for problem solving and task achievement , data science can play an important role in grasping the current situation and analyzing factors in advancing them.
It seems that there are many images in the world of "a project of about 3 months to 1 year" for problem solving and task achievement.
I think this is because the people who publish explanations about problem solving and task achievement are often in the position of consultants. It seems that the reality is that when people from outside the company participate from the standpoint of a consultant, only such a form can be taken.
Although it is within the scope of my experience, there are only a few themes in the company where a sense of speed (delivery time) of "about 3 months to 1 year" is appropriate.
Especially when it comes to problem solving, there are many themes that need to be solved in some way, such as "immediately", "today", "next week", and "this month". Supplementally, the shorter the period, the more. Based on my experience, I tried to summarize it with a feeling like "Is this a number ...". The number of themes currently underway at one factory is shown in the figure below.
If you're thinking about launching a project, using problem-solving and task-achieving procedures , or using data science, you'll find this sense of speed. It may be quite difficult.
On the other hand, rushing to a solution does not mean that any solution is OK. After all, the rationale is necessary.
It seems to be a contradiction, but to get the rationale, you need problem-solving and task-achieving procedures , and data science . By prioritizing, you can reduce the accuracy of completeness and completeness and proceed efficiently.
The more speed is required, the more efficiency is emphasized.
At first, you will be checking the procedure, so I think it's a good idea to try a few themes that are okay over a period of 3 months to 6 months.
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