Previous analysis has shown that Company X needs a substantial improvement of its information management strategy. The changes in the way, in which data is processed in the company, as well as the ways, in which the staff members interact, is bound to have a significant effect on the quality of its performance. Therefore, the tools used for data analysis and the output is delivered by the staff members when adopting each of these devices can be considered the primary variables to be analyzed (Jones, 2014).
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To carry out the analysis, one will have to identify the performance rates for each of the data management tools, locate the outcome, and create a table that will contain the key results. As the table provided above shows, the usage of the tools needs to be correlated with the average speed of the staff’s performance, the number of defective items produced, and the speed of data processed before the production process commences. As a result, the ultimate strategy that permits the most detailed data processing and the smallest number of errors made can be selected (Pyzdek & Keller, 2014).
Table 1. Errors, Performance Speed, and Data Processing.
|Number of Defective Items||Number of Items per Hour||Amount of Information Processed per Hour (essential pieces)|
A brief analysis of the above information will require locating whether the number of defective items is linked to the number of items per hour produced, whether the number of defective items is linked with the amount of information processed per hour, and whether the number of items produced per hour has something to do with the speed of data processing. Setting the level of significance at α=0.05 and rejecting the null hypothesis in case it is higher than the P-value, one will be able to perform the test and identify possible links between the variables mentioned above. It should be borne in mind that the p-value should be calculated for all three of the possible combinations; thus, the connection between the variables can be identified (Castaneda-Mendez, 2015).
As the ANOVA test has shown, there is a strong correlation between all of the variables mentioned above. Therefore, it can be assumed that the appropriate strategy should be chosen on the basis of the number of items produced, the speed of data analysis, and the error rates. Seeing that the p-value equals 0.00013, the null hypothesis can be rejected; in other words, there is a very strong correlation between the choice of the information management approach and the number of defects produced in the course of the company’s operations (Agustiady, 2013).
Similarly, the main effects plot indicates that the strategy chosen to carry out the information management process, i.e., the amount of data processed, affects the output retrieved during the production process significantly. The plot shows quite graphically that there is a direct correlation between the above variables. More importantly, the graph also indicates that there is a link between the number of defective items and the speed of data processing (i.e., the choice of the information management approach). Particularly, the graph indicates that the three actors listed above are in direct proportion to each other. Hence, it is strongly suggested that the information management principle based on the principles of sharing and the adoption of the latest IT tools should be considered as the essential step in facilitating informational security of the entrepreneurship.
Agustiady, T. (2013). Communication for continuous improvement projects. Boca Raton, FL: CRC Press.
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Castaneda-Mendez, K. (2015). Understanding statistics and statistical myths: How to become a profound learner. Boca Raton, CA: CRC Press.
Jones, E. (2014). Quality management for organizations using lean Six Sigma techniques. Boca Raton, CA: CRC Press.
Pyzdek, T., & Keller, P. (2014). Process behavior charts. In The Six Sigma handbook (4th ed.) (pp. 293-392). New York, NY: McGraw-Hill.