Enterprise Resource Planning Systems - Coursework Example

Enterprise Resource Planning Systems By The of the The of the School The Introduction The Nature’s Cream Company produces creams and medicines for health and healing. The marketing team led by Quality Control manager is currently working on Dermi-cure cream, a product that is known to heal and sooth wounds. In this case, there is determination of the final process that could produce the final cream. The determination of ingredients has already been done but the problem is producing the required cream quality with the required pH level of 6.5 since the current process produces cream with too high or too low pH which never function appropriately. Therefore, the conceptual model was designed in order to determine the best process to be used in producing cream with expected quality. In order to produce a new cream referred to as Dermi-cure that is able to aid in soothing and healing the wounds, there were three critical factors considered in the production. They include temperature(X1), process time(X2) and pressure(X3).These three factors are to be evaluated to ascertain their relative importance on the yield (The PH of the product should be constant; 6.5 +/- 0.05).The major problem the company faces is that the pH of the product is ever high or low and this does not aid in wound healing and soothing. Therefore, the objective is to determine the optimal settings of these three factors that will result to production of the required cream. Temperature, process time and pressure could be continuously varied along their corresponding scale, that is, from low to high settings. When there is progressive change on these factors, there is a smooth variation of the pH of the cream. Factor settings level table High (+1), Low (-1) and standard (0) settings for Dermi-cure cream production Low(-) Standard High(+1) Units Temperature 100 115 130 oF Process time 3 5 10 min Pressure 120 135 150 psi Factor Combination Various combinations of the settings were tried in order to establish the optimum settings of production of the required cream. Eight different ways can be used to combine high and low settings of temperature process time and pressure and they have been illustrated as below; X1 X2 X3, X1X2, X1X3, X2X3, X1X2X3 Factor setting in a table format   X1 X2 X3 1 -1 -1 -1 2 +1 -1 -1 3 -1 +1 -1 4 +1 +1 -1 5 -1 -1 +1 6 +1 -1 +1 7 -1 +1 +1 8 +1 +1 +1 To run the whole design for at least once is seen to be much easier in terms of analysis since there is an average (of response) obtained for every run. This also includes idea related to response dispersion such as consistency and variability at that given settings. The analysis assumption indicates that there is uniform dispersion across the space of the experiment and this is referred to as homogeneity (NIST, 2006). Factor setting with replication in a standard order   Temp Time Pressure Y 1 -2.608 -2.608 -2.608 -2.608 2 10.385 -10.385 -10.385 10.385 3 -10.433 10.433 -10.433 10.433 4 7.39 7.39 -7.39 7.39 5 -2.588 -2.588 2.588 -2.588 6 10.407 -10.407 10.407 10.407 7 -10.394 10.394 10.394 10.394 8 7.402 7.402 7.402 7.402 Determination of the significant effect on the response In order for this to be determined, the main effects of the variables are required to be calculated. In this case, the mean responses for the variables is determined at high level then subtracted from the mean response at the low level. According to simulation, below is the result (main effects) Temp Time Pressure 4.988 5.006 0.004 According to the table above, it is clearly evident that the significant of each variable is given by the main effects (NIST, 2006). Basing on the values provided, it is observed that the largest effect is for time then followed by temperature which is too close then lastly pressure that is too low when compared to the other two variables. Positive sign on the effect value also provides useful information. For instance, a positive sign or positive effects implies that the response is optimal at a high setting. Hence, the pH of the cream is optimal at the high process time setting of 5.006. Also, the pH of the cream is optimum at a high temperature and pressure setting. The main objective is to determine the main effects and interaction effects of these factors after developing a design matrix indicating the setting levels (-1, +1) for both interaction and main effects as shown below. A main effect is considered to be an outcome which is a consistent difference between factor levels. On the other hand, interaction effect is important because the interaction effects between factors can be examined. There is an interaction effect when there is dependent of one factor on the other Main effects   Temp Time Press 1 -1 -1 -1 2 +1 -1 -1 3 -1 +1 -1 4 +1 +1 -1 5 -1 -1 +1 6 +1 -1 +1 7 -1 +1 +1 8 +1 +1 +1 Basing on main effect results, the largest effect should have a greater value. For instance, the largest effect can be for time then followed by temperature which is too close then lastly pressure that is too low when compared to the other two variables. A positive sign or positive effects implies that the response is optimal at a high setting. Main Effect Plots The main effect plot provides another way of comparing the variable effects and the data with responses can be used to calculate the main effect as well as generating the main effect plot as shown below. In this case, each column is ordered from lowest to highest. The resultant table will have -1 (low) setting and +1 setting of the responses. The average of the responses will then be determined at each setting. Lastly, the main effect plot can be generated to compare the 2 averages Main Effect Plot In terms of interpretation of the graph above, it can be noted that a factor that has the biggest difference between low and high values is considered to be the most significant. For instance, basing on the above main effect plot, it is observed that process time and temperature are the most significant then followed by pressure which has the least and lowest. These observations are based on the steepness of the slopes as shown above. Furthermore, the slope of pressure is seen to be almost flat Interaction Effects The interactions that exist between variables can as well be calculated. In this case, coefficients of interaction can be got through multiplication of the corresponding variables coefficient columns as shown below .The interaction effect is later determined through multiplying the coefficient columns by the response column, each column is summed up then divided by half the number of runs of the experiment. Coefficient of interaction Coeficient of interaction 2.608 2.608 2.608 -2.608 -10.385 -10.385 10.385 10.385 -10.433 10.433 -10.433 10.433 7.39 -7.39 -7.39 -7.39 2.588 -2.588 -2.588 2.588 -10.407 10.407 -10.407 -10.407 -10.394 -10.394 10.394 -10.394 7.402 7.402 7.402 7.402 -0.6759 0.002906 -0.0009 0.000281 Basing on the coefficient of interaction results, it may be observed that the largest interaction effect is X1X2 which represents temperature(X1) and process time(X2). How the effort will Improve Quality The design is considered to consist of various crucial features. Firstly, there is high flexibility for enhancing the treatment in the study. In situations where there is interest in treatment variations examination, factorial designs are considered to be the most appropriate (NIST, 2006). Secondly, the design is considered to be more efficient. In this case, several studies can be combined into one instead of undertaking a series of studies that are independent. Lastly, interaction effects can only and effectively be examined by the designs Reference NIST (2006).Engineering Statistics Handbook retrieved on 3/2/2012, from http://www.itl.nist.gov/div898/handbook/pri/section3/pri333.htm Rhiannon Meyers. (2014).Chevron Investigates bad premium gas at area pumps; Retrieved from http://www.houstonchronicle.com/business/energy/article/Chevron-investigates-bad-premium-gas-at-Houston-5613765.php NIST (2006).Engineering Statistics Handbook retrieved on 3/2/2012, from http://www.itl.nist.gov/div898/handbook/pri/section3/pri333.htm Read More