In: Operations Management
Month |
Jan |
Feb |
Mar |
Apr |
May |
Jun |
Jul |
Aug |
Sept |
Oct |
Nov |
Dec |
Sales |
19 |
23 |
15 |
14 |
13 |
16 |
15 |
17 |
19 |
20 |
20 |
23 |
Using a method of trend projection, the forecast for the next month (Jan) =
nothing
sales (round your response to two decimal places).
Month period(X) sales(Y) XY X^2(square of X)
Jan. 1 19 19 1
Feb 2 23 46 4
Mar 3 15 45 9
Apr 4 14. 56. 16
May. 5. 13. 65. 25
June. 6. 16. 96. 36
July. 7. 15. 105. 49
Aug. 8. 17. 136. 64
Sept. 9. 19. 171. 81
Oct. 10. 20. 200. 100
Nov. 11. 20. 220. 121
Dec. 12. 23. 276. 144
X = 1+2+3+4+5+6+7+8+9+10+11+12 =
78
Y = 19+23+15+14+13+16+15+17+19+20+20+23 =
214
XY =
19+46+45+56+65+96+105+136+171+200+220+276 = 1435
X^2 = 1+4+9+16+25+36+49+64+81+100+121+144 =
650
Number of periods = n = 12
X-bar =
X/n = 78/12 = 6.5
Y-bar =
Y/n = 214/12 = 17.83
b = [
XY - (n. X-bar. Y-bar)] / [
X^2 - (n. Square of X-bar)]
= [1435 - (12 x 6.5 x 17.83333333)] / [650 - (12 x 6.5 x 6.5)]
= (1435 - 1391)/(650-507)
= 44/143
= 0.31
a = Y-bar - (b. X-bar) = 17.83333333 - (0.31 x 6.5) = 17.83333333-2.015 = 15.82
So the regression equation is Y = a+bx => Y = 15.82+0.31x
Where x = sequential number of the year.
January is period 13. So for January,value of x = 13.so the forecast for January = 15.82+0.31x = 15.82+(0.31x13) = 15.82+4.03 = 19.85