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Sales of product X each quarter for the last 3 years have been as follows (in thousands of units). Trend values, found by a moving average method, are shown in brackets.
Year1:
Q1: 18 Q2: 30 Q3: 20(18.75)
Q4: 6 (19.375)
Year 2:
Q1: 20(20) Q2: 33(20.5)
Q3: 22 (21) Q4: 8(21.5)
Year 3:
Q1: 22 (22.125) ,Q2:33( 22.75)
Q3: 25 Q4: 10
Average seasonal variations for quarters 1-4 are -0.1, +12.4, +1.1, -13.4 respectively.
Required:
Use the trend line and estimates of seasonal variations to forecast sales in each quarter of year 4.
Solution :
It says to calculate by subtracting 18.75 from 22.75
(22.75 – 18.75)÷7
So I guessed that maybe because these were the most highest and lowest of trends that’s why they were chosen for calculation. Please correct me if I’m wrong because I really don’t know.
Secondly, I am confused as to why they divided it by 7. This is from Bpp text. I saw similar case in Kaplan as well.Thank you Sir. 🙂
You are correct in saying the 22.75 and 18.75 have been taken because they are the highest and lowest trend values.
Therefore between quarter 3 of Year 1 and quarter 2 of year 3, the trend has increased by the difference of 4.00.
If you count up the number of quarters of increases (Q3 of year 1 to Q4 of year 1 is one quarters increase; Q4 of year 1 to Q1 of year 2 is a second quarters increase, and so on), then you will find that between Q3 of year 1 and Q2 of year 3 there are 7 quarters of increase.
Since the total increase is 4.00, the average increase in the trend per quarter is 4.00/7.
Thank you so much Sir. I was badly stuck at this problem in both the texts. It’s so easy now.
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