Production and Operations Analysis, Seventh Edition
Production and Operations Analysis, Seventh Edition
7th Edition
ISBN: 9781478623069
Author: Steven Nahmias, Tava Lennon Olsen
Publisher: Waveland Press, Inc.
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Chapter 2.7, Problem 19P
Summary Introduction

To determine: The MAD for the obtainedforecasts and the method of forecasts that can provide accurate results.

Introduction: Forecasting is the main function of predicting the future using the information available for decision making. It is a mechanism for planning decisions based on the predicted information.

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The problem is based on the following data given. Observations of the demand for a certain part stocked at a parts supply depot during the calendar year 2013 were ( as shown ). Compute the one-step-ahead three-month and six-month moving-average forecasts for July through December. What effect does increasing N from 3 to 6 have on the forecasts?
A pharmacist has been monitoring sales of a certain over-the-counter pain reliever. Daily sales during the last 15 days were   a. Assume the data refer to demand rather than sales. Using trend-adjusted exponential smoothing with an initial forecast of 46 for Day 8, an initial trend estimate of 2, and α = β = .3, develop demand forecasts for Days 9 through 16. Then compute the resultant MSE using the error values from Days 8 through 15. (Round your intermediate period-by-period forecast and error values to 3 decimal places. Round your final MSE answer to 3 decimal places.) MSE = Day: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Number sold: 31 32 35 38 38 42 42 43 41 44 46 46 49 55 52
Aarthi Medicals, a fictitious company, has been monitoringthe sales of the health drink for diabetics. As the demand for the health drink has been steadily increasing, theowner of the store, Nicole Carter, wants to develop goodforecasts for this product to determine how many casesof this drink to order every week form the manufacturer.Nicole has compiled the demand data shown in theaccompanying table for the past 12 weeks. Nicole wantsto evaluate forecasts using the exponential smoothing method using smoothing constants values of α1 = 0.2and α2 = 0.40.Week Demandin Cases1 482 523 494 355 476 537 488 469 5510 5411 5812 5713a Assuming a forecast for Week 2 of 48 cases(f2 = 48), generate forecasts for Weeks 13 usingexponential smoothing for both values of the smoothing constant (α1 = 0.20 and α2 = 0.40).b Compute the forecast error measures of MAD and MSEand determine which value of the smoothing constant provides more accurate forecasts.
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