netrashetty
Netra Shetty
Dillard's (NYSE: DDS) also known as Dillard's, Inc., based in Little Rock, Arkansas, is a department store chain in the United States, with 330 stores in 29 states.[1] Its locations are concentrated in Texas and Florida; with a major presence in other states including Arizona, Iowa, Colorado, Wyoming, Kansas, Missouri, Alabama, Georgia, Tennessee, Oklahoma, Mississippi, Louisiana, Nebraska, New Mexico, Nevada, Utah, North Carolina, Virginia, Idaho, South Carolina, Kentucky, Indiana, Ohio, and Illinois. Dillard's also maintains a minor footprint in California, and Montana.
Dillard's carries brands such as Chanel, Clinique, DKNY, Dior, Dolce & Gabbana, Jones New York, Levi's, and Ralph Lauren.
unterparts. Quantitative forecasts can be time-series forecasts (i.e., a projection of the past into the future) or forecasts based on associative models (i.e., based on one or more explanatory variables). Time-series data may have underlying behaviors that need to be identified by the forecaster. In addition, the forecast may need to identify the causes of the behavior. Some of these behaviors may be patterns or simply random variations. Among the patterns are:
Trends, which are long-term movements (up or down) in the data.
Seasonality, which produces short-term variations that are usually related to the time of year, month, or even a particular day, as witnessed by retail sales at Christmas or the spikes in banking activity on the first of the month and on Fridays.
Cycles, which are wavelike variations lasting more than a year that are usually tied to economic or political conditions.
Irregular variations that do not reflect typical behavior, such as a period of extreme weather or a union strike.
Random variations, which encompass all non-typical behaviors not accounted for by the other classifications.
Among the time-series models, the simplest is the naïve forecast. A naïve forecast simply uses the actual demand for the past period as the forecasted demand for the next period. This, of course, makes the assumption that the past will repeat. It also assumes that any trends, seasonality, or cycles are either reflected in the previous period's demand or do not exist. An example of naïve forecasting is presented in Table 1.
Table 1
Naïve Forecasting
Period Actual Demand (000's) Forecast (000's)
January 45
February 60 45
March 72 60
April 58 72
May 40 58
June 40
Another simple technique is the use of averaging. To make a forecast using averaging, one simply takes the average of some number of periods of past data by summing each period and dividing the result by the number of periods. This technique has been found to be very effective for short-range forecasting.
Variations of averaging include the moving average, the weighted average, and the weighted moving average. A moving average takes a predetermined number of periods, sums their actual demand, and divides by the number of periods to reach a forecast. For each subsequent period, the oldest period of data drops off and the latest period is added. Assuming a three-month moving average and using the data from Table 1, one would simply add 45 (January), 60 (February), and 72 (March) and divide by three to arrive at a forecast for April:
45 + 60 + 72 = 177 ÷ 3 = 59
To arrive at a forecast for May, one would drop January's demand from the equation and add the demand from April. Table 2 presents an example of a three-month moving average forecast.
Table 2
Three Month Moving Average Forecast
Period Actual Demand (000's) Forecast (000's)
January 45
February 60
March 72
April 58 59
May 40 63
June 57
A weighted average applies a predetermined weight to each month of past data, sums the past data from each period, and divides by the total of the weights. If the forecaster adjusts the weights so that their sum is equal to 1, then the weights are multiplied by the actual demand of each applicable period. The results are then summed to achieve a weighted forecast. Generally, the more recent the data the higher the weight, and the older the data the smaller the weight. Using the demand example, a weighted average using weights of .4, .3, .2, and .1 would yield the forecast for June as:
60(.1) + 72(.2) + 58(.3) + 40(.4) = 53.8
Forecasters may also use a combination of the weighted average and moving average forecasts. A weighted moving average forecast assigns weights to a predetermined number of periods of actual data and computes the forecast the same way as described above. As with all moving forecasts, as each new period is added, the data from the oldest period is discarded. Table 3 shows a three-month weighted moving average forecast utilizing the weights .5, .3, and .2.
Table 3
Three–Month Weighted Moving Average Forecast
Period Actual Demand (000's) Forecast (000's)
January 45
February 60
March 72
April 58 55
May 40 63
June 61
A more complex form of weighted moving average is exponential smoothing, so named because the weight falls off exponentially as the data ages. Exponential smoothing takes the previous period's forecast and adjusts it by a predetermined smoothing constant, ά (called alpha; the value for alpha is less than one) multiplied by the difference in the previous forecast and the demand that actually occurred during the previously forecasted period (called forecast error). Exponential smoothing is expressed formulaically as such:
New forecast = previous forecast + alpha (actual demand − previous forecast)
F = F + ά(A − F)
Exponential smoothing requires the forecaster to begin the forecast in a past period and work forward to the period for which a current forecast is needed. A substantial amount of past data and a beginning or initial forecast are also necessary. The initial forecast can be an actual forecast from a previous period, the actual demand from a previous period, or it can be estimated by averaging all or part of the past data. Some heuristics exist for computing an initial forecast. For example, the heuristic N = (2 ÷ ά) − 1 and an alpha of .5 would yield an N of 3, indicating the user would average the first three periods of data to get an initial forecast. However, the accuracy of the initial forecast is not critical if one is using large amounts of data, since exponential smoothing is "self-correcting." Given enough periods of past data, exponential smoothing will eventually make enough corrections to compensate for a reasonably inaccurate initial forecast. Using the data used in other examples, an initial forecast of 50, and an alpha of .7, a forecast for February is computed as such:
New forecast (February) = 50 + .7(45 − 50) = 41.5
Next, the forecast for March:
New forecast (March) = 41.5 + .7(60 − 41.5) = 54.45
This process continues until the forecaster reaches the desired period. In Table 4 this would be for the month of June, since the actual demand for June is not known.
Table 4
Period Actual Demand (000's) Forecast (000's)
January 45 50
February 60 41.5
March 72 54.45
April 58 66.74
May 40 60.62
June 46.19
An extension of exponential smoothing can be used when time-series data exhibits a linear trend. This method is known by several names: double smoothing; trend-adjusted exponential smoothing; forecast including trend (FIT); and Holt's Model. Without adjustment, simple exponential smoothing results will lag the trend, that is, the forecast will always be low if the trend is increasing, or high if the trend is decreasing. With this model there are two smoothing constants, ά and β with β representing the trend component.
An extension of Holt's Model, called Holt-Winter's Method, takes into account both trend and seasonality. There are two versions, multiplicative and additive, with the multiplicative being the most widely used. In the additive model, seasonality is expressed as a quantity to be added to or subtracted from the series average. The multiplicative model expresses seasonality as a percentage—known as seasonal relatives or seasonal indexes—of the average (or trend). These are then multiplied times values in order to incorporate seasonality. A relative of 0.8 would indicate demand that is 80 percent of the average, while 1.10 would indicate demand that is 10 percent above the average. Detailed information regarding this method can be found in most operations management textbooks or one of a number of books on forecasting.
For the latter, the advantages of the emerging markets are the expansion of middle class to excel, the relatively unsaturated markets, the urbanized and highly-populated cities, the growing youth market, the free-trade zones, the relatively friendly business laws, liberalized markets and transitioning economies and the growing demand for western goods and services. This suggests that franchising companies are that companies should target the countries having the greatest growth in their industry, and their initial international expansion should probably be in nations that are nearby or culturally similar. The latter market characteristic is important for minimizing the adaptations needed and, therefore, preserving the core concept of the franchise (Preble and Hoffman, 2002).
Welsh et al (2006) also found out that the new symbiotic relationships are achieved because of the expanded reach and improved efficiencies that are associated with internationalization. To wit, franchising involves network of franchisees that are governed by the parent firm or the franchisor. Well-established franchisors achieve greater efficiencies through incorporating smaller franchisees in emerging markets into international franchise networks. Whitney (2007) maintains that franchise exhibitions can be an efficient way to discover lots of business opportunities fast, and that potential franchisees should be prepared. There are specific areas to consider such as working out on how you can invest in a franchise as well as looking for suitable exhibitors. Specifically, potential franchisees should call target companies well in advance of the exhibition. If possible, make appointments to talk to franchisors.
Dillard's carries brands such as Chanel, Clinique, DKNY, Dior, Dolce & Gabbana, Jones New York, Levi's, and Ralph Lauren.
unterparts. Quantitative forecasts can be time-series forecasts (i.e., a projection of the past into the future) or forecasts based on associative models (i.e., based on one or more explanatory variables). Time-series data may have underlying behaviors that need to be identified by the forecaster. In addition, the forecast may need to identify the causes of the behavior. Some of these behaviors may be patterns or simply random variations. Among the patterns are:
Trends, which are long-term movements (up or down) in the data.
Seasonality, which produces short-term variations that are usually related to the time of year, month, or even a particular day, as witnessed by retail sales at Christmas or the spikes in banking activity on the first of the month and on Fridays.
Cycles, which are wavelike variations lasting more than a year that are usually tied to economic or political conditions.
Irregular variations that do not reflect typical behavior, such as a period of extreme weather or a union strike.
Random variations, which encompass all non-typical behaviors not accounted for by the other classifications.
Among the time-series models, the simplest is the naïve forecast. A naïve forecast simply uses the actual demand for the past period as the forecasted demand for the next period. This, of course, makes the assumption that the past will repeat. It also assumes that any trends, seasonality, or cycles are either reflected in the previous period's demand or do not exist. An example of naïve forecasting is presented in Table 1.
Table 1
Naïve Forecasting
Period Actual Demand (000's) Forecast (000's)
January 45
February 60 45
March 72 60
April 58 72
May 40 58
June 40
Another simple technique is the use of averaging. To make a forecast using averaging, one simply takes the average of some number of periods of past data by summing each period and dividing the result by the number of periods. This technique has been found to be very effective for short-range forecasting.
Variations of averaging include the moving average, the weighted average, and the weighted moving average. A moving average takes a predetermined number of periods, sums their actual demand, and divides by the number of periods to reach a forecast. For each subsequent period, the oldest period of data drops off and the latest period is added. Assuming a three-month moving average and using the data from Table 1, one would simply add 45 (January), 60 (February), and 72 (March) and divide by three to arrive at a forecast for April:
45 + 60 + 72 = 177 ÷ 3 = 59
To arrive at a forecast for May, one would drop January's demand from the equation and add the demand from April. Table 2 presents an example of a three-month moving average forecast.
Table 2
Three Month Moving Average Forecast
Period Actual Demand (000's) Forecast (000's)
January 45
February 60
March 72
April 58 59
May 40 63
June 57
A weighted average applies a predetermined weight to each month of past data, sums the past data from each period, and divides by the total of the weights. If the forecaster adjusts the weights so that their sum is equal to 1, then the weights are multiplied by the actual demand of each applicable period. The results are then summed to achieve a weighted forecast. Generally, the more recent the data the higher the weight, and the older the data the smaller the weight. Using the demand example, a weighted average using weights of .4, .3, .2, and .1 would yield the forecast for June as:
60(.1) + 72(.2) + 58(.3) + 40(.4) = 53.8
Forecasters may also use a combination of the weighted average and moving average forecasts. A weighted moving average forecast assigns weights to a predetermined number of periods of actual data and computes the forecast the same way as described above. As with all moving forecasts, as each new period is added, the data from the oldest period is discarded. Table 3 shows a three-month weighted moving average forecast utilizing the weights .5, .3, and .2.
Table 3
Three–Month Weighted Moving Average Forecast
Period Actual Demand (000's) Forecast (000's)
January 45
February 60
March 72
April 58 55
May 40 63
June 61
A more complex form of weighted moving average is exponential smoothing, so named because the weight falls off exponentially as the data ages. Exponential smoothing takes the previous period's forecast and adjusts it by a predetermined smoothing constant, ά (called alpha; the value for alpha is less than one) multiplied by the difference in the previous forecast and the demand that actually occurred during the previously forecasted period (called forecast error). Exponential smoothing is expressed formulaically as such:
New forecast = previous forecast + alpha (actual demand − previous forecast)
F = F + ά(A − F)
Exponential smoothing requires the forecaster to begin the forecast in a past period and work forward to the period for which a current forecast is needed. A substantial amount of past data and a beginning or initial forecast are also necessary. The initial forecast can be an actual forecast from a previous period, the actual demand from a previous period, or it can be estimated by averaging all or part of the past data. Some heuristics exist for computing an initial forecast. For example, the heuristic N = (2 ÷ ά) − 1 and an alpha of .5 would yield an N of 3, indicating the user would average the first three periods of data to get an initial forecast. However, the accuracy of the initial forecast is not critical if one is using large amounts of data, since exponential smoothing is "self-correcting." Given enough periods of past data, exponential smoothing will eventually make enough corrections to compensate for a reasonably inaccurate initial forecast. Using the data used in other examples, an initial forecast of 50, and an alpha of .7, a forecast for February is computed as such:
New forecast (February) = 50 + .7(45 − 50) = 41.5
Next, the forecast for March:
New forecast (March) = 41.5 + .7(60 − 41.5) = 54.45
This process continues until the forecaster reaches the desired period. In Table 4 this would be for the month of June, since the actual demand for June is not known.
Table 4
Period Actual Demand (000's) Forecast (000's)
January 45 50
February 60 41.5
March 72 54.45
April 58 66.74
May 40 60.62
June 46.19
An extension of exponential smoothing can be used when time-series data exhibits a linear trend. This method is known by several names: double smoothing; trend-adjusted exponential smoothing; forecast including trend (FIT); and Holt's Model. Without adjustment, simple exponential smoothing results will lag the trend, that is, the forecast will always be low if the trend is increasing, or high if the trend is decreasing. With this model there are two smoothing constants, ά and β with β representing the trend component.
An extension of Holt's Model, called Holt-Winter's Method, takes into account both trend and seasonality. There are two versions, multiplicative and additive, with the multiplicative being the most widely used. In the additive model, seasonality is expressed as a quantity to be added to or subtracted from the series average. The multiplicative model expresses seasonality as a percentage—known as seasonal relatives or seasonal indexes—of the average (or trend). These are then multiplied times values in order to incorporate seasonality. A relative of 0.8 would indicate demand that is 80 percent of the average, while 1.10 would indicate demand that is 10 percent above the average. Detailed information regarding this method can be found in most operations management textbooks or one of a number of books on forecasting.
For the latter, the advantages of the emerging markets are the expansion of middle class to excel, the relatively unsaturated markets, the urbanized and highly-populated cities, the growing youth market, the free-trade zones, the relatively friendly business laws, liberalized markets and transitioning economies and the growing demand for western goods and services. This suggests that franchising companies are that companies should target the countries having the greatest growth in their industry, and their initial international expansion should probably be in nations that are nearby or culturally similar. The latter market characteristic is important for minimizing the adaptations needed and, therefore, preserving the core concept of the franchise (Preble and Hoffman, 2002).
Welsh et al (2006) also found out that the new symbiotic relationships are achieved because of the expanded reach and improved efficiencies that are associated with internationalization. To wit, franchising involves network of franchisees that are governed by the parent firm or the franchisor. Well-established franchisors achieve greater efficiencies through incorporating smaller franchisees in emerging markets into international franchise networks. Whitney (2007) maintains that franchise exhibitions can be an efficient way to discover lots of business opportunities fast, and that potential franchisees should be prepared. There are specific areas to consider such as working out on how you can invest in a franchise as well as looking for suitable exhibitors. Specifically, potential franchisees should call target companies well in advance of the exhibition. If possible, make appointments to talk to franchisors.