Forecasting Is the Process of Using Data

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 from the first year to the second, and an index number greater than one shows an increase from one year to the next. The following chart shows the resulting index for each month.

Index Year 2 Index Year 3 Index Year

January

February

March

April

May 1 .836449

June

July

August

September 1.407643 1.378545 1.268657

October 0.771455 1.378545 1.268657

November 0.552885 0.723558 0.81851

December 0.573388 0.757202 0.838134

The next step in the forecasting process is to plot each of the monthly indices onto a scatter plot and to use the trend line to determine the function for finding the next month's indices. 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.

Indices

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. Multiplying the index for the first month of year five times the first month of the first year, 1.215 x 55,200, yields a forecast of 67,068. The following table includes the forecast for each month of year five.

Winter Inventory Data

Month Year 1 Year 2-Year 3-Year 4 Forecast Y5

January 55,200 39,800 32,180 62,300 67068

February 57,350 64,100 38,600 66,500 58554

March 15,400 47,600 25,020 31,400 18465

April 27,700 43,050 51,300 36,500 37090

May 21,400 39,300 31,790 16,800 6848

June 17,100 10,300 31,100 18,900 28660

July 18,000 45,100 59,800 35,500 37242

August 19,800 46,530 30,740 51,250 47540

September 15,700 22,100 47,800 34,400 47022

October 53,600 41,350 73,890 68,000 87582

November 83,200 46,000 60,200 68,100 79872

December 72,900 41,800 55,200 61,100 71879