QUESTION ONE
The organization seems to be fully established and already has the required resources as well as references. What they need to do is only checking whether everything is working as expected. In this case, the organization will make use of the two-factor experimental design. This type of experimental design is useful in the event that a need to control the operation of an already established and existing system arises (Chen & Lee, 2014). It makes every useful resource in the knowledge of the user readily accessible.
QUESTION TWO
The data can be evaluated using two treatment methods. An experiment with variables can be used to evaluate operators ' data and to compare their call-up time based on different factors. In order to know the value of the data, the criteria rules and conditions can be evaluated. SPC graph can be used to map the effects, and history can be used to analyze the effect of the operator-based call time (Chen & Lee, 2014). The multi-factor experiment is useful in the assessment and testing of a set of multiple factors at multiple levels (Imai et al., 2012). This also serves to assess the efficacy and effects of operators-based call times.
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QUESTION THREE
The assumption, in this case, is that the change of personnel type causes the service time variation. It is therefore definite that the 'personnel type' is the factor of this particular experimental design. A 'factor' means any attribute or variable that is capable of assuming features of levels in the case of an attribute and values in the case of a variable. In the present case, personnel type happens to be a qualitative factor (Imai et al., 2012). This means it is an attribute that assumes three levels, which are the three individual service personnel. It is worth noting that these levels are of one particular factor, which makes the set-up of this experiment suitable for a one-factor design (Ibanez & Velasco, 2012). The experimental design would have been a multi-factor if there had been different factors other than the personnel that influences the service time, such as the condition of the work environment like humidity, temperature, disturbance, shift timing, et cetera.
Given the same working conditions, the service time varies according to the service person. One factor design experiment will be conducted to define if the service time varies between individuals (Ibanez & Velasco, 2012). The only independent variable is the service person. Single-factor design studies impact the independent variable on the dependent variable. In other words, the number of tires installed in the same shift is defined by the service time of each individual. One factor design experiment can be conducted using ANOVA (an analysis of the variance) (Ibanez & Velasco, 2012). The dealer will map the ideal time to install the tire and then measure the time taken by all three individuals.
The variance from ideal time mapped at ten different times during the week will show the variance of each individual against the ideal time. The F ratio will define if the model is significant (Imai et al., 2012). The p-value helps to define the probability of service time being influenced by the service personnel (noise factor). People are generally considered noise factors that are uncontrollable and cause variability under normal working conditions. To conclude one-factor design can define if the service personnel is influencing the service time.
References
Chen, C., & Lee, C. (2014). A two-factor authentication scheme with anonymity for multi-server environments. Security And Communication Networks, 8(8), 1608-1625. https://doi.org/10.1002/sec.1109
Ibanez, A., & Velasco, C. (2012). One-Factor Based Exercise Strategies for American Options in Multi-Factor Models. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2151387
Imai, K., Tingley, D., & Yamamoto, T. (2012). Experimental designs for identifying causal mechanisms. Journal Of The Royal Statistical Society: Series A (Statistics In Society), 176(1), 5-51. https://doi.org/10.1111/j.1467-985x.2012.01032.x
