Problem 3-28 (Algo) The following are historical demand data: АCTUAL DEMAND 206 YEAR SEASON Spring Summer 2 years ago 135 Fall winter Spring Summer 373 583 last year 474 275 Fall winter 694 952 Use regression analysis and seasonal indexes to forecast this summer's demand. (Do not round Intermedlate calculations. Round your answer to the nearest whole number.) Forecast for this summer's demand
Q: b. Discuss how forecasting accuracy relates to the practical application of aggregate planning.
A: Aggregate planning is the process that involves the development, analysis and other operational…
Q: (Round your answers to 0 decimal place,e.g.,200.) Season Forecast Fall Winter Spring Summer
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Q: The average demand for January has been 100 and the average annual demand has been 1000. Calculate…
A: Given - Demand for January = 100 units Average annual demand = 1000 units
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A: The formulas used for calculating the sum are: EOQ = 2 x Annual Demand x Ordering CostCarrying…
Q: Please show work
A:
Q: The Bango Toy Company produces several types of toys to seasonal demand. The forecast for the next…
A: : Since we only answer up to 3 sub-parts, we’ll answer the first 3. Please resubmit the question and…
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Q: The historical data for 4 periods demand are 65, 60, 80, and 70 respectively. Calculate the weighted…
A: The answers would be as follows:
Q: Given the following demand data, Period Demand 57 1 2 55 3 59 4 56 5 60 a. Compute a weighted…
A: Forecasting is the process of estimation in which future demand is determined using the previous and…
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A: Forecasting future demand uses historical evidence, patterns, seasonality, and expert opinion to…
Q: The following table shows the actual demand observed over the last 11 years: year 1 2…
A: We can calculate a three-year forecast by using the formula- Three-year moving average method ( Year…
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A: THE ANSWER IS AS BELOW:
Q: The average demand for January has been 80, and the average annual demandhas been 1800. Calculate…
A: Forecasting in operations management is a process by which predictions are made for the production.…
Q: The following table shows the actual demand observed over the last 11 years: Year 1 2 3 4 6. 7 8 10…
A:
Q: Given the following forecast and cost information, Regular time cost $ 40.00 per unit determine the…
A: Given data is Month Forecast 1 570 2 600 3 630 4 650 5 670 6 690 Regular…
Q: ACTUAL DEMAND 2011 Spring 203 Summer 144
A: YEAR SEASONACTUAL DEMAND 2011 Spring 203 Summer 144 Fall 382 Winter 565…
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A: The forecast for period 5 can be computed as follows:
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A: Two years ago Units Last Year Unit This Year Units I 4805 I 3505 I 3195 II 3505 II 2705 II…
Q: 45 & 46
A: Note: Answered the first question. Kindly post another question separatelyDetermine slope and…
Q: 1 demand of cotton(intones) by Westham textile S.C are shown below 1 2 3 4 7 8 year Actual 10 11 13…
A: Note: Since you have posted multiple parts in the same questions, we will be answering the first…
Q: The following are historical demand data: ACTUAL YEAR SEASON DEILAND 204 2011 Spring Summer 150 Fall…
A: given,
Q: Please show how you got part C
A: Year Demand Workings Forecasted Demand 4 10 0.20×7+0.25×9+0.55×5 6.4 5 13 0.20×9+0.25×5+0.55×10…
Q: A company has the following actual and forecasted demand history for eight periods: Period Actual 26…
A: Solution Tracking signal is calculated as the ratio of cumulative error divided by the mean absolute…
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A: Find the Given details below: Month Jan Feb Mar Apr May Jun Total Demand 700 600 400 650 850…
Q: Problem 3-24 (Algo) Zeus Computer Chips, Inc., used to have major contracts to produce the…
A: Given data is
Q: PROBLEM 2:The manager of a large manufacturer of industrial pumps prepare forecasts for a six- month…
A: Formula:
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A: seasonal forecast is obtained by multiplying the forecast with the relative values fo the periods.
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A: Formula:
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A: YEAR PERIOD DEMAND FORECAST 2017 SEP 9400 OCT 10300 NOV 11200 DEC…
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A: Given data is
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A: Given data is
Q: a. Using linear regression analysis, what would you estimate demand to be for each month next year?…
A: THE ANSWER IS AS BELOW:
Q: Demand of Hairdryer from January to July Month Demand January 2800 February 2870 March…
A: a) 3 months simple moving average Given data: Month Demand 3 months simple moving average…
Q: Find forecasting error from the following table by using Tracking Signal. Period Actual…
A: n = 6
Q: Calculate four periods moving average forecast from the following last six periods Period Demand 1…
A: Period Demand 1 38 2 40 3 42 4 40 5 44 6 38
Q: March demand was pridicted at 590 units of gear cycles of trevaa ltd. But the actual demand was 400…
A: Given: March demand forecasted (Ft-1) = 590 Units March actual demand (At-1) = 400 Units Alpha =…
Q: 2G MTN Zambia ll 78% 3:30 PM < OperationsManagement_HW1.. 4.12 Consider the following actual and…
A: Given Data, Day Actual demand Forecast Demand Monday 88 88 Tuesday 72 88…
Q: 1 demand of cotton(intones) by Westham textile S.C are shown below year 1 2 3 4 6 7 8 Actual demand…
A: Given information, Year Actual Demand 1 10 2 11 3 13 4 15 5 14 6 16 7 18 8 20
Q: The following table shows the actual demand observed over the last 11 years:…
A: Given Information:
Q: A manager would like to know the total cost of a chase strategy that matches the forecast belowusing…
A:
Q: Week Demand of beef 1 500 2 550 3 600 4 720 5 780 6 800 Corresponding weights are…
A: An accuracy of the forecasting method can decide by identifying the MAD value of the each method.…
Q: Year 1 Demand Year 2 Demand Year 3 Demand 1 12 1 16 1 14 45 25 32 3 76 52 3 71 84 4 4 62 4 47 a.…
A: Since we only answer up to 3 sub-parts, we’ll answer the first 3. Please resubmit the question and…
Q: Determine the total cost for this plan given the following forecast:Month 1 2 3 4 5 6Forecast 380…
A: Following is the brief of production schedule: While forecast for month 1 is 380, actual production…
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- The file P13_42.xlsx contains monthly data on consumer revolving credit (in millions of dollars) through credit unions. a. Use these data to forecast consumer revolving credit through credit unions for the next 12 months. Do it in two ways. First, fit an exponential trend to the series. Second, use Holts method with optimized smoothing constants. b. Which of these two methods appears to provide the best forecasts? Answer by comparing their MAPE values.The Baker Company wants to develop a budget to predict how overhead costs vary with activity levels. Management is trying to decide whether direct labor hours (DLH) or units produced is the better measure of activity for the firm. Monthly data for the preceding 24 months appear in the file P13_40.xlsx. Use regression analysis to determine which measure, DLH or Units (or both), should be used for the budget. How would the regression equation be used to obtain the budget for the firms overhead costs?The file P13_22.xlsx contains total monthly U.S. retail sales data. While holding out the final six months of observations for validation purposes, use the method of moving averages with a carefully chosen span to forecast U.S. retail sales in the next year. Comment on the performance of your model. What makes this time series more challenging to forecast?
- The owner of a restaurant in Bloomington, Indiana, has recorded sales data for the past 19 years. He has also recorded data on potentially relevant variables. The data are listed in the file P13_17.xlsx. a. Estimate a simple regression equation involving annual sales (the dependent variable) and the size of the population residing within 10 miles of the restaurant (the explanatory variable). Interpret R-square for this regression. b. Add another explanatory variableannual advertising expendituresto the regression equation in part a. Estimate and interpret this expanded equation. How does the R-square value for this multiple regression equation compare to that of the simple regression equation estimated in part a? Explain any difference between the two R-square values. How can you use the adjusted R-squares for a comparison of the two equations? c. Add one more explanatory variable to the multiple regression equation estimated in part b. In particular, estimate and interpret the coefficients of a multiple regression equation that includes the previous years advertising expenditure. How does the inclusion of this third explanatory variable affect the R-square, compared to the corresponding values for the equation of part b? Explain any changes in this value. What does the adjusted R-square for the new equation tell you?The file P13_26.xlsx contains the monthly number of airline tickets sold by the CareFree Travel Agency. a. Create a time series chart of the data. Based on what you see, which of the exponential smoothing models do you think will provide the best forecasting model? Why? b. Use simple exponential smoothing to forecast these data, using a smoothing constant of 0.1. c. Repeat part b, but search for the smoothing constant that makes RMSE as small as possible. Does it make much of an improvement over the model in part b?Under what conditions might a firm use multiple forecasting methods?
- The file P13_02.xlsx contains five years of monthly data on sales (number of units sold) for a particular company. The company suspects that except for random noise, its sales are growing by a constant percentage each month and will continue to do so for at least the near future. a. Explain briefly whether the plot of the series visually supports the companys suspicion. b. By what percentage are sales increasing each month? c. What is the MAPE for the forecast model in part b? In words, what does it measure? Considering its magnitude, does the model seem to be doing a good job? d. In words, how does the model make forecasts for future months? Specifically, given the forecast value for the last month in the data set, what simple arithmetic could you use to obtain forecasts for the next few months?The file P13_29.xlsx contains monthly time series data for total U.S. retail sales of building materials (which includes retail sales of building materials, hardware and garden supply stores, and mobile home dealers). a. Is seasonality present in these data? If so, characterize the seasonality pattern. b. Use Winters method to forecast this series with smoothing constants = = 0.1 and = 0.3. Does the forecast series seem to track the seasonal pattern well? What are your forecasts for the next 12 months?The file P13_28.xlsx contains monthly retail sales of U.S. liquor stores. a. Is seasonality present in these data? If so, characterize the seasonality pattern. b. Use Winters method to forecast this series with smoothing constants = = 0.1 and = 0.3. Does the forecast series seem to track the seasonal pattern well? What are your forecasts for the next 12 months?
- Do the sales prices of houses in a given community vary systematically with their sizes (as measured in square feet)? Answer this question by estimating a simple regression equation where the sales price of the house is the dependent variable, and the size of the house is the explanatory variable. Use the sample data given in P13_06.xlsx. Interpret your estimated equation, the associated R-square value, and the associated standard error of estimate.Suppose that a regional express delivery service company wants to estimate the cost of shipping a package (Y) as a function of cargo type, where cargo type includes the following possibilities: fragile, semifragile, and durable. Costs for 15 randomly chosen packages of approximately the same weight and same distance shipped, but of different cargo types, are provided in the file P13_16.xlsx. a. Estimate a regression equation using the given sample data, and interpret the estimated regression coefficients. b. According to the estimated regression equation, which cargo type is the most costly to ship? Which cargo type is the least costly to ship? c. How well does the estimated equation fit the given sample data? How might the fit be improved? d. Given the estimated regression equation, predict the cost of shipping a package with semifragile cargo.A small computer chip manufacturer wants to forecast monthly ozperating costs as a function of the number of units produced during a month. The company has collected the 16 months of data in the file P13_34.xlsx. a. Determine an equation that can be used to predict monthly production costs from units produced. Are there any outliers? b. How could the regression line obtained in part a be used to determine whether the company was efficient or inefficient during any particular month?