See attached file.
Tires for You, Inc. (TFY), founded in 1987, is an automotive repair shop specializing in replacement tires. Located in Altoona, Peensylvania, 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 bsuiness (it also performs ooil 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 ususally 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 deective or destroyed tires, most tires were installed on cars whose original tires had worn out. Most ofter, 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.
Mth Tires Used
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.
Mth Year 3 Demand Year 2 Demand
Jan 501 526
Feb 376 394
Mar 1,377 1,446
April 1,878 1,972
May 1,127 1,183
June 876 920
July 814 854
Aug 626 657
Sept 2,128 2,235
Oct 1,502 1,578
Nov 689 723
Dec 626 658
TOTAL 12,520 13,146
6. Based on the various methods used to calculate a forecast for TFY, which method produces the best forecast? Why? How could you improve upon this forecast.
(I've done 3, 4, 5 - 10).© BrainMass Inc. brainmass.com October 17, 2018, 2:16 am ad1c9bdddf
The problem deals with forecasting output using a 3-month moving average.
Quantitative Analysis: Forecasting Techniques
Develop a sales volume forecast using the least squares method *and* one other forecasting method.
One forecast method must be using the least squares method, the second can use any other normal forecasting method.
Provide the two sales volume forecasts in excel format, each with data shown and a graph.
Smoothing constant for average 0.3 Smoothing constant for trend 0.4
3Q 2008 90,000
4Q 2008 95,000
1Q 2009 98,000
2Q 2009 96,000
3Q 2009 102,000
4Q 2009 99,000
1Q 2010 118,000
2Q 2010 109,000
3Q 2010 124,000.