Report Analysis for Jakes
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Added on 2020-05-11
Report Analysis for Jakes
Added on 2020-05-11
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1. Report analysis for jakesThe optimal value of the objective function is 1,756,000.the solution further shows that theoptimal number of SUV produced for quarter 1, quarter 2, quarter 3 and quarter 4 is 200, 300,100 and 400 respectively. The optimal number of cars produced in quarter 1, quarter2, quarter 3and quarter 4 is 400, 0, 700 and 0 respectively.The sensitivity report also shows the objective coefficient with which we can be able to deducethe maximization problem which isP=1800X1+1700X2+1720Xalignl¿3¿¿+1700X4+50X5+20X7¿. The allowable increase and decrease allows us to have the region of optimality in the in thevariable cells. Under all circumstances the variables can increase to infinity but the allowabledecrease is constrained to zero, for example the number of SUR produced in quarter one canincrease to infinity but can never go below 0.The shadow price shows how the objective value changes as you obtain an additional unit of oneconstrain while all other constrains remain constant. In this case a change in one unit of thenumber of SUV produced in quarter one would change the optimal value of the objectivefunction by 1800.however this is only effective at an allowable increas of 177.78 units and an allowable decrease of 200 units. Values not within this range wouldlead to a change not equal to 1800.Change of one unit of the number of SUV produced in quarter 2 would change the optimal valueby 1700 at an allowable increase of 255.56 and an allowable decrease of 300 when all otherconstrains remain constant. In quarter 3 change of one unit of the number of SUV producedwould change the optimal value of the objective function by 1720 at an allowable increase of144.44 and an allowable decrease of 100 while in quarter 4 a unit change of the number of SUVproduced would change the optimal value of the objective function by 1700 at an allowableincrease of 155.56 and an allowable decrease of 400.When all other constrains remain constant, change in one unit of the number of cars allowed forquarter one will change the optimal value of the objective function by 50 at an allowable increaseof 400 and an allowable decrease of 400 while one unit of number of cars allowed in quarter 3will change the optimal value of the objective function by 20 at an allowable increase of 325 and
an allowable decrease of 700. Change in one unit of the number of cars allowed in quarter 2 andquarter 4 has no effect on the optimal value of the objective function.The company should consider persuading the regional government to relax the mpg requirementas this will be beneficial since more SUV can be produced in all quarter and more cars allowedin quarter 1 and 3 since a change in one unit has a significant change in the optimal value of theobjective function.Fall in the profit contribution of SUV would heavily affect the production plan since it’s themain contributor to the net profit.2.Forecasting method mainly depend on what data are available. The main types of forecast arequalitative and quantitative forecasting. In the absence of past observation qualitative method offorecasting must be used. Quantitative method on the other hand is used when past numericalinformation is available and under the reasonable assumption that past patterns will continue inthe future.In this data time series data shall be used since the observation are collected over a regularinterval of time. Time series forecast is useful when forecasting something that is changing overtime for example the stock market, monthly rainfall, quarterly sales of companies and profit.The aim of forecasting is time series data is to estimate how the sequence of the visitors willcontinue into the future. We shall only use the information on the variable to be forecasted whichis visitors but shall not explain the factors that cause the behavior.The main linear model used includes the autoregressive process (AR), the moving average (MA)and the autoregressive moving average (ARMA). The ARMA model is a statistical techniquethat uses the series data to predict the future parameters used. An ARMA model takes care oftrend, seasonality, errors and non-stationary aspect of data when forecasting. In order to applythe models, any deterministic trends or cycles must be removed. The data provided should firstbe checked if it is stationary or non-stationary. If stationary differencing should be used This can
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