Forecasting is the process of using data from previous intervals to determine future data. Meteorologists use data from previous weather events to predict future weather patterns. In a similar way, sales can help to predict future inventory stocking needs by accumulated data from previous years. The first step in the process is to create an index for each month by dividing the current month by the index (or first) month. For example, month one of the first year is equal to 55,200. Month one of the second year is equal to 39,800. Dividing the second year by the first year gives an index result of 0.721014. An index number smaller than one indicates a decrease in the number ...view middle of the document...
By using linear regression, the plot yields a solvable slope intercept formula to determine future inventory needs.
The resulting formula, y = 0.203x + 0.403, can be extrapolated to find the index for the fourth year using x = 4. The result of this equation is 1.215, which means that the next number in the series will be greater than the first number or index number. The following below shows the indices for each month with the addition of Year 5.
Index Y2 Index Y3 Index Y4 Index Y5
January 0.721014 0.582971 1.128623 1.215
February 1.117698 0.67306 1.159547 1.021
March 3.090909 1.624675 2.038961 1.199
April 1.554152 1.851986 1.31769 1.339
May 1 .836449 1.485514 0.785047 0.32
June 0.602339 1.818713 1.105263 1.676
July 2.505556 3.322222 1.972222 2.069
August 2.3 1.552525 2.588384 2.401
September 1.407643 1.378545 1.268657 2.995
October 0.771455 1.378545 1.268657 1.634
November 0.552885 0.723558 0.81851 0.96
December 0.573388 0.757202 0.838134 0.986
After the extrapolation of the indices, the researcher can multiply the index number by the first year data to determine the forecast for year five....