Tires for You, Inc. Tires for You, Inc. (TFY), founded in 1987, is an automotive repair shop specializing in replacement tires. Located in Altoona, Pennsylvania, TFY has grown successfully over the past few years because of the addition of a new general manager, Katie McMullen. Since tire replacement is a major portion of TFY?s business (it also performs oil changes, small mechanical repairs, etc.), Katie was surprised at the lack of forecasts for tire consumption for the company. Her senior mechanic, Skip Grenoble, told her that they usually stocked for this year what they sold last year. He readily admitted that several times throughout the season stock outs occurred and customers had to go elsewhere for tires. Although many tire replacements were for defective or destroyed tires, most tires were installed on cars whose original tires had worn out. Most often, four tires were installed at the same time. Katie was determined to get a better idea of how many tires to hold in stock during the various months of the year. Listed below is a summary of last year?s individual tire sales by month: Month Tires Used January 510 February 383 March 1,403 April 1,913 May 1,148 June 893 July 829 August 638 September 2,168 October 1,530 November 701 December 636 Total 12,752 Case Questions: Katie has hired you to determine the best technique for forecasting TFY demand based on the given data. 1. Calculate a forecast using a simple three-month moving average. 2. Calculate a forecast using a three-period weighted moving average. Use weights of 0.60, 0.25, and 0.15 for the most recent period, the second most recent period, and the third most recent period, respectively. 6. Based on the various methods used to calculate a forecast for TFY, which method produced the best forecast? Why? How could you improve upon this forecast? * Attached is the excel spreadsheet

Simple Three Month Average Month Last Year January February March April May June July August September October November December Total Demand Avg Demand Avg Bias Abs Dev Mean Abs Dev 510 383 1403 1913 1148 893 829 638 2168 1530 701 636 12752 1062.67 3 period moving avg 3 period forecast Error 765.3 1233.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 765 1233 0 0 0 0 0 0 0 1147.67 -85.00 893.00 829.00 638.00 2168.00 1530.00 701.00 636.00 Total Bias 8457.67 Bias x 939.74 1233.67 137.07 Simple Average caluclation example (510+383+1403)/3 = 765.3 (March avg, April forecast) Weighted Three Month Average (0.6,0.25, 0.15) 0.6 Month 0.25 0.15 Last Year 3 period 3 period moving avg forecast 510 383 1403 1014.1 1913 1556.0 1014 1148 0.0 1556 893 0.0 0 829 0.0 0 638 0.0 0 2168 0.0 0 1530 0.0 0 701 0.0 0 636 0.0 0 12752 1062.67 Total Bias Bias x January February March April May June July August September October November December Total Demand Avg Demand Avg Bias Abs Dev Mean Abs Dev Weighted Average calculation example (.6*1403)+(.25*383)+(.15*510) = 1014.1 (March avg, April forecast) 1014.05 Error 899 -408 893 829 638 2168 1530 701 636 7885.95 876.22 -1752.05 -194.67 6. Which method produced the best forecast? To assess the accuracy you can compare the "Mean Abolute Deviation"; the smaller the value the more accurate the forecast. Based on the forecasts, we have: Simple Moving Average MAD: 137.07 3-Month Weighted Moving Average MAD: -194.67