Answered: Actual Period Demand 123 456 1 200 2…

Business

Operations Management

Actual Period Demand 123 456 1 200 2 212 214 222 236 221 7 240 66 8 244 9 250 10 266

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter2: Introduction To Spreadsheet Modeling

Section: Chapter Questions

Problem 20P: Julie James is opening a lemonade stand. She believes the fixed cost per week of running the stand...

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The worksheet Hudson Demand Case Data in MindTap provides the number of visits over one year from January to December (52 weeks). Chart the data and explain the characteristics of the time series. How would you forecast future demand for customer visits? What criteria will you use to determine a “good” forecast? What methods would you use, and why? What is your final recommendation with respect to a forecasting method? Note:- Do not provide handwritten solution. Maintain accuracy and quality in your answer. Take care of plagiarism. Answer completely. You will get up vote for sure.
Forecasting with exponential smoothing has been compared to driving a car while gazing in the rearview mirror. What circumstances must exist for driving a car that are equivalent to the assumptions made while applying exponential smoothing.
The following table shows the actual demand observed over the last 11 years: Year 1 2 3 4 5 6 7 8 9 10 11 Demand 7 9 6 10 12 7 12 12 9 9 8 Part 2 Using exponential smoothing with α = 0.30 and a forecast for year 1 of 6.0, provide the forecast from periods 2 through 12 (round your responses to one decimal place). Part 3 Provide the forecast from periods 2 through 12 using the naive approach (enter your responses as whole numbers).
Use exponential smoothing with trend adjustment to forecast deliveries for period 5. Let α = 0.1, β = 0.2, and let the initial trend value be 4 and the initial forecast be 200 (both are given in the table). (a) Complete the table below and to predict the demand for Period 5 (See image). Show the calculations for the demand for Period 2. (b) Based on the actual demands of the first 4 periods forecast the demand for period 5 using the linear regression method. (c) Which method performs better based on the actual demand for period 5? Why do you think so?
Amazon finds that sales in July are much greater relative to other months because of the Amazon Prime days in that month. Assuming that an exponential smoothing model is used for forecasting sales, what should the company best do to make the forecast for the next month (Aug) more reflective of reality? Please pick from the provided options. F t+1 = αDt + (1-α) Ft increase value of alpha decrease value of alpha stay with the same alpha decrease the expected forecast for the current month (Ft) On hearing our forecast, our clients probe - how sure are we about the forecast? Which measure of errror (among those stated below) should we best use to respond? a. Root MSE (RMSE) b. Mean absolute percentage error (MAPE) c. Mean forecast error (Avg. error) d. Cumulative forecast error (CFE)
The number of cases of merlot wine sold by the Connor Owen winery in an eight-year period is as follows: CASES OF MERLOT WINE 276 362 YEAR 2005 2006 2007 2008 2009 2010 2011 2012 404 462 364 506 416 382 Using an exponential smoothing model with an alpha value of 0.30, estimate the smoothed value calculated as of the end of 2012. Use the average demand for 2005 through 2007 as your initial forecast for 2008, and then smooth the forecast forward to 2012. (Round your intermediate calculations and final answer to the nearest whole number.) Answer is complete but not entirely correct. Forecast for 2012 372
In a retail store, the actual sales of a particular product (in thousands of units) over the past few months are as follows: Month Sales 1 16 2 22 3 18 4 20 5 23 Using exponential smoothing method with α (smoothing constant) of 0.75 and the given forecast for month 1 equal to 10, what is the forecast for month 6?
Elna inc is a microwave manufacturing and distribution company based in Jakarta. The company needs to estimate production demand using the historical sales data of the last 24 months (periods).A logistics manager of the company then uses the moving average, exponential smoothing, and simple linear regression methods to determine sales forecasts for the next 12 months (Graphically illustrate the results). Provide an analysis and the results of bias, MAPE, MSE, and MAD for the forecasting decisions.Moving average is 4 month, and alfa is 0.05
If the forecast and the actual demand in the year 2021 was 600 and 700 respectively, what is the forecast for the year 2022 according to the naive forecasting method? 600 700 650 800
A company wants to generate a forecast for unit demand for year 2017 using exponential smoothing. The actual demand in year 2016 was 120. The forecast demand in year 2016 was 110. Using this data and a smoothing constant alpha of 0.1, which of the following is the resulting year 2017 forecast value?
The actual number of patients at Providence Emergency Medical Clinic for the first six weeks of this year follows: Week Actual No. of Patients 1 65 68 77 59 66 75 23456 2 5 6 Clinic administrator Dana Schniederjans wants you to forecast patient numbers at the clinic for week 7 by using this data. You decide to use a weighted moving average method to find this forecast. Your method uses four actual demand levels, with weights of 0.500 on the present period, 0.250 one period ago, 0.125 two periods ago, and 0.125 three periods ago. a) What is the value of your forecast? The value of the forecast is patients (round your response to two decimal places). b) If instead the weights were 40, 20, 10, and 10, respectively, how would the forecast change? OA. The value of the forecast will decrease. B. The value of the forecast will remain the same. C. The value of the forecast will increase. c) What if the weights were 0.40, 0.30, 0.20, and 0.10, respectively? Now what is the forecast for week 7?…
Harlen Industries has a simple forecasting model: Take the actual demand for the same month last year and divide that by the number of fractional weeks in that month. This gives the average weekly demand for that month. This weekly average is used as the weekly forecast for the same month this year. This technique was used to forecast eight weeks for this year, which are shown in the following tables along with the actual demand that occurred. The following eight weeks show the forecast (based on last year) and the demand that actually occurred (picture attached): Calculate the MAD and tracking signal for each week.