Question

CASE 7.1 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, Ian Overbaugh. Since tire replacement is a major portion of TFY's business (it also performs oil changes, small mechanical repairs, etc.), Ian was surprised at the lack of forecasts for tire consumption for the company. His senior mechanic, Skip Grenoble, told him that they usually stocked for this year what they sold last year. He readily admitted that several times throughout the season stockouts 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. Ian 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 individual tire sales month: PERIOD TIRES USED 2014 9,797 October 11,134 November 10,687 December 2015 9,724 January February 8,786 March 9,254 April 10,691 Ma 9,256 8,700 June 10,192 10,751 August September 9,724 October 10,193 November 11,599 December 11,130 Ian has hired you to determine the best technique for forecasting TFY demand based on the given data. CASE QUESTIONS 1. Calculate a forecast using 2. Calculate a forecast using a three-period weighted moving average. Use weights of 0.60, 0.30, and 0.10 for the most recent period, the second most recent period, and the third most recent period, respectively. 3. Calculate a forecast using the exponential smoothing method. Assume the forecast for period 1 is 9,500. Use alpha 0.40 4. Once you have calculated the forecasts based on the above data, determine the error terms by comparing them to the actual sales for 2012 given below: a simple three-month moving average. PERIOD TIRES USED 2016 January 10,696 February 9,665 10,179 March 11,760 April 9,150 May 9,571 June July 8,375 11,826 August 10,696 September 11,212 October 9,750 November December 9,380 5. Based on the three methods used to calculate a forecast for TFY, which method produced the best forecast? Why? What measures of forecast error did you use? How could you improve upon this forecast? Q2. Weighted Moving Average Demand 3PMA 3PF Abs Error MAPE Error alpha Oct 9,797 0.6 11,134 beta Nov 0.3 0.1 Dec 10,687 10,732 gama Jan 9,72410,154 Feb 8,786 9,258 9,254 9,161 Mar Apr 10,69110,069 May 9,256 9,686 8,700 9,066 10,1929,651 Jun Jul Aug 10,751 10,378 Sep 9,72410,079 Oct 10,19310,108 Nov 11,59910,990 Dec 11,13011,177 Total Demand Avg. Demand Total Bias Ave. Bias Absolute Dev Mean Abs Dev MAPE

Answer 1

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Parcentap T500