Demand Forecasting Free Essays

DEMAND FORCASTING Founded as a single store in 1960, Domino’s Pizza today stands as the recognized world leader in pizza delivery. From the beginning, we have been dedicated to the best of service, quality products and delivery excellence. They currently have over 9000 stores worldwide, all dedicated to providing great-tasting pizza delivered directly to your door or available for carryout. We will write a custom essay sample on Demand Forecasting or any similar topic only for you Order Now They have pioneered the pizza delivery business, and sell more than 400 million pizzas worldwide every year. Domino’s Pizza is recognized as a Megabrand by Advertising Age magazine, and has been named â€Å"Chain of the Year† by Pizza Today, the leading publication of the pizza industry, three times (Dominos). Metuchen is a very small town in Middlesex County, NJ. It is located right in the middle of Edison, NJ. It is only 2. 76 square miles with a population of over 13000. The median income is around $90000. There is approximately 5300 household consisting of 2. 56 people. And 30% of the population is under 18 years old (US Census). This can be interpreted as there are mostly families with kids in this community. Currently, there are no fast food pizza stores or any other fast food restaurants in the town. Edison does have many restaurants including two Dominos, one in South Edison and one in North Edison. However, neither of them delivers to Metuchen. The only direct competitor in the area is Pizza Hut. Metuchenites often get to know their local merchants and get personalized service they find missing at large chain stores in the nearby shopping malls Metuchen). Based on the Metuchen demographic information, I chose the following variables: households, income, and price of complimentary goods. I believe households to be more relevant than population based on the fact that the town consists of families with one or more children. A household will consume one or more pies per visit. And people with children are more likely to buy fast food. I chose to use income because income along with the fluctuation of price is a major factor for the demand of pizza. Also we used the variable for the price of the complimentary good Soda. People almost always buy soda or some other drink with their pizza. Although, there are some family owned competitors, I did not include them in this analysis because they are a ifferent type of pizza store. Dominos specializes in fast food delivery. And many of the family owned does not. VARIABLES Year| Qd| price/pie| soda/ liter| population| households| Income| 2010| 125000| 5| 1| 13,574| 5,249| 88,241| 2011| 127000| 5. 49| 1. 25| 13,648| 5,376| 91365| 2012| 129000| 5. 99| 1. 5| 13795| 5491| 94,410| Elasticity refers to the magnitude and the direction q uantity demanded changes in response to a percentage change in the variable. Based on the information collected, we determined that all of the variables are inelastic. The price elasticity is 0. 163 and the price elasticity of soda is 0. 64. This means that the change in price will result in a lesser percentage change in quantity. Basically this shows that the fluctuation in price will not affect the demand too much. This is also true for the income. The income elasticity is 0. 452. This shows that if income increases, the demand for pizza will increase at a lower rate also. When price goes up, the Qd will go down based on the disposable income of the families. The income is high enough to withstand the price increase. This is also true for the number of households. Household elasticity is 0. 661. ELASTICITY rice/pie| soda/ liter| population| households| Income| 0. 163| 0. 064| 2. 935| 0. 661| 0. 452| 0. 173| 0. 079| 1. 462| 0. 736| 0. 473| Inelastic| Inelastic| Elastic| Inelastic| Inelastic| Smoothing techniques assumes that a repetitive underlying pattern can be found in the historical values of the variable being forecasted. The moving average is calculated by taking an average of past observations. The more observations included, the greater the smoothing effect. It gives the same weight to all the observations. The exponential model allows you to determine the weight of the observation between 0 and 1. Below we used four different smoothing techniques to forecasts the demand for pizza for years 2013-2015. The four models show the demand to be between 127500 and 128500. DEMAND FORECAST Year| Actual Demand| 5 year Moving Average| 3 year Moving Average| Exponential Smoothing w=0. 9| Exponential Smoothing w=0. 3| 2007| 126000|   |   | 126000| 126000| 2008| 129000|   |   | 126000| 126000| 2009| 131000|   |   | 128700| 126900| 2010| 125000|   | 128667| 130770| 128130| 2011| 127000|   | 128333| 125577| 127191| 2012| 129000| 127600| 127667| 126858| 127134| 2013| 128300| 128200| 127000| 128786| 127694| 014| 128100| 128060| 128100| 128349| 127876| 2015|   | 127480| 128467| 128125| 127943| The root mean square error is used to evaluate the accuracy of the forecasting model. The lower RMSE means the estimate is more accurate. RMSE RMSE|   | 811| 1933| 2790| 2377| Based on the RMSE for each of the estimates, I would estimate the 3 year moving average to be the best estimate. Although the 5 year moving average has a lower RMSE, I don’t think it is an accurate estimate because there are not enough years to get an accurate estimate. Based on the information collected, we could open a Dominos in the community and expect to do well. The forecast for demand of pizza seems to fluctuate between 127500 and 128500. Although the forecasts seems pretty stable, it doesn’t look like there will be too much growth unless the number of households and income have a larger increase that previously. The inelasticity of variables shows me that as price goes up, there will be a smaller decrease in quantity demanded. However, the income and the number of households are also inelastic. As incomes and households grow, so will the quantity demanded by a smaller increase. The price elasticity is smaller than the income and household elasticity. So overall the quantity demanded should increase. WORKS CITED United States Census Bureau, US department of commerce, retrieved October 22, 2012 from http://quickfacts. census. gov/qfd/states/34/3445690. html Income Tax List, retrieved October 27, 2012 from http://www. incometaxlist. com/new-jersey-income-tax-by-zip-code-33. htm Domino’s Pizza, retrieved October 27, 2012 from http://phx. corporate-ir. net/phoenix. zhtml? c=135383p=irol-homeprofile Borough of Metuchen, retrieved October 23, 2012 from http://www. metuchennj. org/brochure1999. pdf How to cite Demand Forecasting, Essay examples