Question

The monthly sales for Yazici​ Batteries, Inc., were as​ follows:

                                                                                                                                                                                             

Month	Sept	Oct	Nov	Dec
Sales	19	20	23	24

This exercise contains only parts b and c.

b) Forecast January sales using each of the following methods.

​i) Compute the January sales forecast using naive method.

The January sales forecast using the naive method = _____ sales. ​(Enter your response as a whole​ number.)

​ii) Compute the January sales forecast using a​ 3-month moving average.

The January sales forecast using a​ 3-month moving average

approach = ____ sales. ​(Round your response to two decimal​ places.)

​iii) Compute the January sales forecast using a​ 3-month weighted average with weights of

0.10​, 0.30, and 0.60 with the heaviest weights applied to the most recent months.

The January sales forecast using a​ 3-month weighted average = ___ sales. ​(Round your response to two decimal​ places.)

​iv) Compute the January sales forecast using exponential smoothing with

α= 0.40 and a starting forecast for September being 21.

The January sales forecast using exponential smoothing = ___ sales ​(Round your response to two decimal​ places.)

​v) Compute the January sales forecast using a trend projection.

Using a method of trend​ projection, the forecast for January sales = _____ sales. ​(Round your response to two decimal​ places.)

c) The method that can be used for making a forecast for the month of March is either (choose one)

-a 3-month moving average

-a 6-month weighted moving average

-exponential smoothing

-the naive method

-a trend projection

Answer 1

1. NAIVE FORECAST = DEMAND IN PREVIOUS PERIOD = 24

2. FORECAST = SIGMA(PREVIOUS N DEMANDS) / N
WHERE N = 3

FORECAST 5 = (20 + 23 + 24) / 3 = 22.33

3. FORECAST = SIGMA(WEIGHT FOR PERIOD * DEMAND PER PERIOD) / SUM OF THE WEIGHTS
WHERE LARGEST WEIGHTS ARE MULTIPLIED BY THE MOST RECENT DEMANDS

FORECAST 5 = ((24 * 0.6) + (23 * 0.3) + (20 * 0.1)) / 1 = 23.3

4. FORECAST = FORECAST + (ALPHA * (ACTUAL DEMAND - FORECAST))

FORECAST 2 = 21 + (0.4 * (19 - 21) = 20.2
FORECAST 3 = 20.2 + (0.4 * (20 - 20.2) = 20.12
FORECAST 4 = 20.12 + (0.4 * (23 - 20.12) = 21.27
FORECAST 5 = 21.27 + (0.4 * (24 - 21.27) = 22.36

5.
PERIOD (X)
DEMAND (Y)
X
Y
X * Y
X^2
1
19
1
19
19
1
2
20
2
20
40
4
3
23
3
23
69
9
4
24
4
24
96
16
SIGMA

10
86
224
30

INTERCEPT = (SIGMA(Y) * SIGMA(X^2) - SIGMA(X) * SIGMA(XY)) / (N * SIGMA(X^2) - SIGMA(X)^2)
INTERCEPT = (86 * 30) - (10 * 224) / ((4 * 30) - 10^2) = 17

SLOPE = ((N * SIGMA(XY)) - (SIGMA(X) * SIGMA(Y))) - (N * SIGMA(X^2) - SIGMA(X)^2)
SLOPE = ((4 * 224) - (10 * 86) / ((4 * 30) - 10^2) = 1.8

Y = A + B(x), WHERE A IS THE INTERCEPT, B IS THE SLOPE, x IS THE PERIOD = 17 + (1.8 * X)
FOR THE VALUE OF X = 5 FORECAST = 17 + (1.8 * 5) = 26

5. THE DATA SHOWS A CONSTANT TREND GOING UPWARDS AND THEREFORE, TREND PROJECTION WOULD BE THE MOST APPROPRIATE FORECASTING TECHNIQUE FOR THIS DATASET.