HWAnswers 2 - Homework 2 PDF

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Homework 2...

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Answers: 1. (A)- By using the Naïve method and plotting each data set Using the data

provided and the excel sheet from the resources area. The data below shows that muffins orders are somewhat stable. The demand for cinnamon buns has a trend. By using the last three periods, I see the number increased by 2 so 33-31=2 and the last one is 33+2= 35. And the demand for cupcakes has apparent seasonal variations and peaks every five days. So on day 1=45, day 6=48 and day 11=47 so the next peak, which will fall on day 16, is going to equal to 50. 60 50 40

Day Blueberry_x000d_Muffi ns Cinnamon_x000d_Buns Cupcakes

30 20 10 0 1 2 3 4 5 6 7 8 9 101112131415

(B)- Using the sales data in place of the demand implies that the sales reflect demand. The demand equals sales if there are no shortages.

2. (A). Using the data provided and the excel sheet from the resources: I have plotted the following data.

Sales_x000d_(000 units) 25 20

Sales_x000d_(000 units)

15 10 5 0 Feb. Mar. Apr.

May

Jun.

Jul.

Aug.

(B) T 1 2 3 4 5 6 7 Sum=28

T^2 1 4 9 16 25 36 49 Sum of T^2= 140

Y 19 18 15 20 18 22 20 Sum= 132

TY 19 36 45 80 90 132 140 Sum= 542

N= 7 Using the following formula to calculate sales volume for September. x 2−¿ (∑ x ) n∑ ¿ (n ∑ xy−∑ x ∑ y )/ ¿ 2

y−b x a= ∑ n ∑

b= ((7*(542)-28(132))/(7(140)-28(28)) b= 0.50 plugging in and finding a a= (132-0.50(50))/(7)

a= 16.86 So then y= 16.86+0.50x Using this formula to find September y=0.50x+16.86 Which if plugged in for September, which is 8, comes after August 7. Y=0.50(8)+16.86 Y=20.86

Using formula Ft=Ft-1+a(At-1-Ft-1) Ft= Forecast for period t Ft-1=Forecast for the pervious period A= smoothing constant At-1) actual data from previous period

Now plugging in the provided data for August (.60), July (.30) and June (.10). Aug-F=18.34+0.20(22-18.34)=19.07 July –F = 18.34+0.20(18-18.43)=18.34 June –F= 18.34+0.20(20-18.04)=18.43 (C) The probability method trend seems the least appropriate because the data appears to vary around 18.86. (Using y=16.86+0.5(4)) (D) Sales data doesn’t take into account the demand, which could have been greater than the actual sales.

4. (A) Naïve method a. Using the excel template provided by plotting the given data. For period 6 the forecast will be 23. 25

20

15

10

5 0.00 0 0

5

10

15

20

25

Period

Actual

(B) For the moving average using the formula n

Ft =MA n=(∑ At −i)/ n i=1

= (22+18+21+22)/(4) =20.75 (C) For exponential smoothing using the formula Ft =α A t−1 +( 1−α ) Ft −1 α=.30

MAD=1.67, MSE=4.0 PERIOD 1 2 3 4 5 6

ACTUAL 20 22 18 21 22 20

FORCAST

ERROR

20 20.6 19.82 20.17 20.72

2 -2.6 1.18 1.82 -0.72

30

7

20.50

5. So given that Ft= 80+15t and F^t = Annual sales (000bottles) in t in years. (A) From the given formula above we can predict that the slope of the equation is 15 and this value shows us if the annual sales increase or decrease. Knowing that the sign value is positive and that is why the annual sale is going up by 15000 bottles per year. (B) Using the formula given I know that t= year. In this case t=6 so plugging that into our original formula Ft=80+15(6) = 17000 8. (A). It would be not suitable the averaging procedure for determining in this issue since it can't uncover the pattern of huge changes. In the event that we utilize the averaging strategy we simply utilize the most recent couple of long stretches of data to make the anticipating, however we have 18 weeks of traveler going in Mountain Airlines. So we will lose a lot of data to make an exact gauge. For this situation, it is smarter to apply a direct pattern to do the figure. 600 484.25

500 400 300 200 100 0 0

5

10

15 Period

Actual

20

25

30

(B). For Week 19, 20 and 21 the following results were obtained by using excel provided sheet to calculate the forecast error the following table was obtained.

Slope = 4.5934 So for the expected Intercept 396.9738562 = number of passengers for the Period Actual next 3 weeks 1 405 (week 19-21): are 410 484, 489, and 493 2 3 420 passengers are 415 expected to travel 4 5 412 with the airlines. 6 420 7 424 8 433 9 438 10 440 11 446 12 451 13 455 14 464 15 466 16 474 17 476 18 482 19 20 21 22 23

MAD = MSE =

2.91 15.51

Forecast 401.57 406.16 410.75 415.35 419.94 424.53 429.13 433.72 438.31 442.91 447.50 452.09 456.69 461.28 465.87 470.47 475.06 479.65 484.24 488.84 493.43 498.03 502.62

Error 3.43 3.84 9.25 -0.35 -7.94 -4.534 -5.13 -0.72 -0.31 -2.91 -1.50 -1.095 -1.69 2.72 0.13 3.53 0.94 2.34...