$\backslash$table$[[$Year$,$Month,TIRES USED,$3$ Month MA$,$Error,$3$ Period WMA,Error,Expo Smoothing $(=0\mathrm{.}4),$Error$],[2014,$October,$9,797,,,,,,],[,$November,$11,134,,,,,,],[,$December,$10,687,,,,,,],[2015,$January,$9,724,,,,,,],[,$February,$8,786,,,,,,],[,$March,$9,254,,,,,,],[,$April,$10,691,,,,,,],[,$May,$9,256,,,,,,],[,$June,$8,700,,,,,,],[,$July,$10,192,,,,,,],[,$August,$10,751,,,,,,],[,$September,$9,724,,,,,,],[,$October,$10,193,,,,,,],[,$November,$11,599,,,,,,],[,$December,$11,130,,,,,,]]$

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, lan Overbaugh. Since tire replacement is a major portion of TFY$\text{'}$s business $($it also performs oil changes, small mechanical repairs, etc.$),$ lan 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. lan 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 by month:

$\backslash$table$[[$PERIod$,$TIRES USED$],[2014,9,797],[$Oclober$,11,134],[$November$,10,687],[$December$,],[2015,9,724],[$January$,8,786],[$February$,9,254],[$March$,10,691],[$Aprill$,9,256],[$Maye$,8,700],[$July$,10,192],[$August$,10,751],[$September$,9,724],[$Noclober$,10,193]]$

Ian has hired you to determine the best technique for forecasting TFY demand based on the given data.

CASE QUESTIONS

Calculate a forecast using a simple three$-$month moving average.

Calculate a forecast using a three$-$period weighted moving average. Use weights of $0\mathrm{.}60,0\mathrm{.}30,$ and $0\mathrm{.}10$ for the most recent period, the second most recent period, and the third most recent period, respectively,

Calculate a forecast using the exponential smoothing method. Assume the forecast for period $1$ is $9,500.$ Use alpha $=0,40.$

Based an 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 thls forecast?

$\backslash$table$[[$Year$,$Month,TIRES USED,$3$ Month MA$,$Error,$3$ Period WMA,Error,Expo Smoothing $(=0\mathrm{.}4),$Error$],[2014,$October,$9,797,,,,,,],[,$November,$11,134,,,,,,],[,$December,$10,687,,,,,,],[2015,$January,$9,724,,,,,,],[,$February,$8,786,,,,,,],[,$March,$9,254,,,,,,],[,$April,$10,691,,,,,,],[,$May,$9,256,,,,,,],[,$June,$8,700,,,,,,],[,$July,$10,192,,,,,,],[,$August,$10,751,,,,,,],[,$September,$9,724,,,,,,],[,$October,$10,193,,,,,,],[,$November,$11,599,,,,,,],[,$December,$11,130,,,,,,]]$

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, lan Overbaugh. Since tire replacement is a major portion of TFY$\text{'}$s business $($it also performs oil changes, small mechanical repairs, etc.$),$ lan 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. lan 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 by month:

$\backslash$table$[[$PERIod$,$TIRES USED$],[2014,9,797],[$Oclober$,11,134],[$November$,10,687],[$December$,],[2015,9,724],[$January$,8,786],[$February$,9,254],[$March$,10,691],[$Aprill$,9,256],[$Maye$,8,700],[$July$,10,192],[$August$,10,751],[$September$,9,724],[$Noclober$,10,193]]$

Ian has hired you to determine the best technique for forecasting TFY demand based on the given data.

CASE QUESTIONS

Calculate a forecast using a simple three$-$month moving average.

Calculate a forecast using a three$-$period weighted moving average. Use weights of $0\mathrm{.}60,0\mathrm{.}30,$ and $0\mathrm{.}10$ for the most recent period, the second most recent period, and the third most recent period, respectively,

Calculate a forecast using the exponential smoothing method. Assume the forecast for period $1$ is $9,500.$ Use alpha $=0,40.$

Based a