Question

In: Operations Management

What is the MSE, MAPE, and CFE using additive seasonal forecasting? hipments Fasteners Forecasted trend of...

What is the MSE, MAPE, and CFE using additive seasonal forecasting?

hipments Fasteners Forecasted trend of fasteners
17-Jan 335798 335798
17-Feb 297853 297853
17-Mar 318399 319454.75
17-Apr 311730 322836.875
17-May 363876 320098.5
17-Jun 296832 318664.5
17-Jul 297513 314066.375
17-Aug 321144 311873.375
17-Sep 317677 312383.25
17-Oct 325487 303703.5
17-Nov 272937 300316
17-Dec 276282 300664.625
18-Jan 335439 315645.125
18-Feb 310514 342483.125
18-Mar 407754 353704.375
18-Apr 356169 357625.125
18-May 345322 352223.625
18-Jun 331997 343400.25
18-Jul 343059 332649.125
18-Aug 350277 329801.375
18-Sep 265205 332895.125
18-Oct 389332 323591
18-Nov 310474 323797.8125
18-Dec 308429

Solutions

Expert Solution

Answer:

Formulas used is as under:

Shipments

Fasteners

Forecasted trend of fasteners

Demand

Forecast

Error

Running Sales Forecast Error (RSFE) or CFE (Cumulative Forecast Error)

Absolute Error

Errors2

Absolute
% Error

At

Ft

et = At-Ft

CFE (Sum of all Error)

|et|

|et|2

|At-Ft|/At

At is Demand

Ft is Forecast

et = At-Ft is Error or Bias

|et| is Absolute Error

|et|2 is Square of Absolute Error

MSE = Average of |et|2

MAPE = Average of |At-Ft|/At

CFE = Cumulative Forecast Error = RSFE Running Sales Forecast Error = Sum of all Error

Data of 18-Dec is not considered in calculation as only the value of “Fastners” is mentioned and not its Forecasted trend of fasteners

Based in the formulas, following is the calculation:


Related Solutions

What is the forecast and MSE using Additive Seasonal Forecasting? 2019 is the holdout sample. Shipments...
What is the forecast and MSE using Additive Seasonal Forecasting? 2019 is the holdout sample. Shipments Fasteners Jan-17 335798 Feb-17 297853 Mar-17 318399 Apr-17 311730 May-17 363876 Jun-17 296832 Jul-17 297513 Aug-17 321144 Sep-17 317677 Oct-17 325487 Nov-17 272937 Dec-17 276282 Jan-18 335439 Feb-18 310514 Mar-18 407754 Apr-18 356169 May-18 345322 Jun-18 331997 Jul-18 343059 Aug-18 350277 Sep-18 265205 Oct-18 389332 Nov-18 310474 Dec-18 308429 Jan-19 385807 Feb-19 332529 Mar-19 407606 Apr-19 361946 May-19 453432 Jun-19 412892 Jul-19 447359 Aug-19...
3. Chapter 3: Using the table below, calculate the following: MAD, MSE, MAPE. What do you...
3. Chapter 3: Using the table below, calculate the following: MAD, MSE, MAPE. What do you conclude? Period Demand Predicted 1 129 124 2 194 200 3 156 150 4 91 94 5 85 80 6 132 140 7 126 128
Two different forecasting techniques, Linear Regression and Trend-Seasonal, were used to forecast demand for cases of...
Two different forecasting techniques, Linear Regression and Trend-Seasonal, were used to forecast demand for cases of bottled water. Actual demand and the two sets of forecasts are as follows. Predicted Demand Period Sales Regression Linear regression Trend-Seasonal 1 282 200 290 2 255 210 230 3 262 220 270 4 290 230 210 5 230 240 250 A) Compute the MAD and Bias for each technique. B) Construct a sentence to describe what the MAD and Bias tell the average...
Describe the differences between Holt-Winters additive seasonal models and multiplicative seasonal models. Under what circumstances would...
Describe the differences between Holt-Winters additive seasonal models and multiplicative seasonal models. Under what circumstances would you employ each? How is the modeling different if trend and seasonality are both multiplicative
How can u solve the following question by using the method: Computing trend and seasonal factor...
How can u solve the following question by using the method: Computing trend and seasonal factor from linear regression line (without excel) Question: The following table shows the past two years of quarterly sales information. Assume that there are both trend and seasonal factors and that the seasonal cycle is one year. sales per quarter: 215, 240, 205, 190, 160, 195, 150, 140
What is the forecast and MSE using regression? 2019 is the holdout sample and "car sales"...
What is the forecast and MSE using regression? 2019 is the holdout sample and "car sales" is the independent variable. Shipments Car Sales Fasteners Jan-17 17680000 335798 Feb-17 17650000 297853 Mar-17 17130000 318399 Apr-17 17230000 311730 May-17 17200000 363876 Jun-17 17200000 296832 Jul-17 17180000 297513 Aug-17 17020000 321144 Sep-17 18380000 317677 Oct-17 18200000 325487 Nov-17 17860000 272937 Dec-17 17700000 276282 Jan-18 17550000 335439 Feb-18 17560000 310514 Mar-18 17690000 407754 Apr-18 17770000 356169 May-18 17780000 345322 Jun-18 17700000 331997 Jul-18 17380000...
What is a business problem in business management that could be solved using a forecasting solution?...
What is a business problem in business management that could be solved using a forecasting solution? State what the issue is and how forecasting could aid in solving this problem, and the type of data that would be need to collect to solve this problem. Would there possibly be seasonal or trend factors in forecasts?
When using Time Series forecasting, what is true about Double Exponential Smoothing? Group of answer choices...
When using Time Series forecasting, what is true about Double Exponential Smoothing? Group of answer choices The double exponential smoothing technique should not be used for predicting seasonal data. Double Exponential Smoothing is able to predict both a level effect and a trend effect in the data. It is good for predicting cyclical data. When Alpha (level) coefficient and Gamma (trend) coefficient are closer to .1 (rather than closer to .9), this tends to smooth the prediction. All of the...
ADVERTISEMENT
ADVERTISEMENT
ADVERTISEMENT