Data collection is a crucial and fundamental aspect of all problem solutions and process improvements. The first step to making things better is having a clear understanding of the problem. To learn the areas that need to be improved, it is necessary that we collect data about where one is currently positioned. Every organization that deals with customers have the desire to have an honest feedback of what the customers think about them, and if they are fully satisfied. A perfect example of data collection is the use of surveys, which allows an organization to evaluate their effectiveness.
Graphics tools is a key tool that is used for the sole purpose of the process improvement framework. When I am to choose the best graphics tool that is effective for the process improvement purpose, I would consider such tools as statistical process control, scatter diagrams, matrix analysis, control charts, bar chart, process mapping, or statistical process control. All these tools have proven to be very effective in providing the user with the output that they desire, thus helping them discern the areas that need improvement.
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The problem-solving process is systematic, and it requires the user to make use of at least one problem-solving strategy. The methods or strategies can be ad hoc or generic. Problem-solving is usually performed by computer systems that are artificially intelligent. There are also some computer programming algorithms referred to as evolutionary algorithms, which can evolve to have a clear understanding of the problems, incorporate them, and then come up with feasible and workable solutions for these problems. Some of these problem-solving strategies include abstraction where the problem solution is pre-performed in a problem model before being applied to the actual problem; Divide and conquer which involves division of the problem in smaller, manageable chunks; and lateral thinking which involves the creative use of indirect problem-solving methods.
References
Box, G. E., Hunter, J. S., & Hunter, W. G. (2005). Statistics for experimenters: design, innovation, and discovery (Vol. 2). New York: Wiley-Interscience.
