Suppose that simple exponential smoothing with w=0.4 is used to forecast monthly wine sales at a...

Suppose that simple exponential smoothing with w=0.4 is used to forecast monthly wine sales at a liquor store. After April's Demand is observed, the forecasted Demand for May is 4500 bottles of wine.

a. At the beginning of May, What is the forecast of July's Wine Sales?

b. Suppose that actual Demands during May and June are as follows: May - 5000 bottles of wine and June - 4000 bottles of wine. After observing June's Demand, What is the forecast for July's Demand?

c. Based on the data of part b., The demands during May and June averages to 4500 bottles of wine. This is the same as the forecast of the monthly sales before we observed the May and June Data. Yet, after we we observe the May and June demands for wine, our forecast for July's Demand has decreased from what it was at the end of April. Why did this happen?

The process which leads to the answer is needed, not the guidance. Thanks a lot

Solutions

Expert Solution

Answer

a) FORECAST FOR JULY'S WINE SALES AT THE BEGINNING OF MAY:

Given the simple exponential smoothing with . After the April's demand, the forecasted demand for May is 4500 bottles of wine. At the beginning of May, the forecast of July's wine sales is 4500. This is beacause for a stationary series, when we use simple exponential smoothing , the forecast for each future period remains constant until additional information is available.

b GIVEN THAT:

Forecasted demand for May=4500 bottles of wine

Actual demand for May=5000 bottles of wine

Actual demand for June=4000 bottles of wine

The formula to calculate forecasted value is where Y_t is actual value of previous time and F_t is forecasted value of previous time (either year, month or week).

FORECAST FOR JUNE:

The formula is where Y_t is actual value of May's demand and F_t is forecasted value of May's demand.

Forecasted value of June

FORECAST FOR JULY:

The formula is where Y_t is actual value of June's demand and F_t is forecasted value of June's demand.

Forecasted value of July

Thus forecasted value for July is 4420 bottles of wine

c) after we we observe the May and June demands for wine, our forecast for July's Demand has decreased from what it was at the end of April. was increased .

orchestra answered 1 year ago

Next > < Previous

Related Solutions

Use the information below to produce the Simple Exponential Smoothing forecast assuming Alpha=0.3

Use the information below to produce the Simple Exponential Smoothing forecast assuming Alpha=0.3. Enter period 4's forecasted value below. The initialization value for period 1 is given as the actual value for period 1. Round your answer to two decimal places (e.g., 1.23).Alpha=0.3PeriodXSES11.41.427.931.54?

Use the information below to produce the Simple Exponential Smoothing forecast assuming Alpha=0.1.

Use the information below to produce the Simple Exponential Smoothing forecast assuming Alpha=0.1. Enter period 4's forecasted value below. The initialization value for period 1 is given as the actual value for period 1. Round your answer to two decimal places (e.g., 1.23).Alpha=0.1PeriodXSES19.29.228.338.14?

What is the formula for a two step ahead exponential smoothing forecast?

Explain the difference in Simple and Souble Exponential Smoothing methods.

Using exponential smoothing with α of 0.1 and the given forecast for year 1

As you can see in the following table, demand for heart transplant surgery at Washington General Hospital has increased steadily in the past few years: Year12345Heart Transplants46.048.055.057.057.0The director of medical services predicted 6 years ago that demand in year 1 would be 42.0 surgeries a) Using exponential smoothing with α of 0.1 and the given forecast for year 1, the forecasts for years 2 through 6 are (round your responses to one decimal place): Year123456Forecast42.0For the forecast made using exponential smoothing with α...

Use an exponential smoothing method with a starting forecast of 21 for month 1 and a...

Use an exponential smoothing method with a starting forecast of 21 for month 1 and a smoothing constant α = 0.5 to calculate month-in-advance forecasts for months 4–12 and forecast for the ﬁrst month of next year. Calculate the MAD.

Generate one step ahead forecast using simple exponential smoothing(SES), using alfa= 0.80. Calculate RMSE, MAE, and...

Generate one step ahead forecast using simple exponential smoothing(SES), using alfa= 0.80. Calculate RMSE, MAE, and MAPE. Please explain how to do this in excel. Notes: In the case of SES: a. uses the actual value of day one (18,085.45) as initial of value (starting value) of the forecast for 9/9/16 and calculate forecasts for day 2 to day 20. b. In process of calculating RMSE, MAE, and MAPE (after you generate forecast for day 2 to day 20) ignore...

What is this year's forecast using exponential smoothing with alpha = .4, if last year's smoothed forecast was 2600?

The dean of a school of business is forecasting total student enrollment for this year's summer session classes based on the following historical data:Four years ago 2000There years ago 2200Two years ago 2800Last year 3000What is this year's forecast using exponential smoothing with alpha = .4, if last year's smoothed forecast was 2600?A. 2,600B. 2,760C. 2,800D. 3,840E. 3,000

a. Show the naive forecast, an exponential smoothing forecasts using α = 0.2, and a 3-month moving average forecast.

Month137244335450534630750829936103511411245a. Show the naive forecast, an exponential smoothing forecasts using α = 0.2, and a 3-month moving average forecast.b. Compare the MFE, MSE, and MAPE on the modelsc. Make a conclusion on which model to use.d. Find the alpha (smoothing constant) that minimizes the MSE.

Question: Use the first-order exponential smoothing model with α = 0.2 to forecast for these data...

Question: Use the first-order exponential smoothing model with α = 0.2 to forecast for these data and for the first six months of the third year. Compute MSD/E, MAD, MAPE, and Bias. Month (t) Demand A(t) 1 148 2 125 3 78 4 53 5 25 6 29 7 9 8 68 9 84 10 110 11 147 12 120 13 147 14 109 15 96 16 70 17 42 18 36 19 34 20 28 21 71 22 102...

Subjects