EBK PRODUCTION AND OPERATIONS ANALYSIS
7th Edition
ISBN: 8220102480681
Author: Olsen
Publisher: WAVELAND
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Question
Chapter 1.10, Problem 30P
Summary Introduction
To explain: Learning curve strategy and the role played by this technique in the success of automobile company F till the mid-1920 and the problems faced by company F after 1920.
Introduction:
Learning curve strategy refers to the graphical representation of the relationship between cost and output over a fixed period. Learning curve assumes that workers will become more familiar with the work and that greater efficiency is introduced in the production process.
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By selecting Cigna Accredo pharmacy that i identify in my resand compare the current feedback system against the “Characteristics of a Good Multiple Source Feedback Systems” described in section 8-3-3. What can be improved? As a consultant, what recommendations would you make?
Scenario
You have been given a task to create a demand forecast for the second year of sales of a premium outdoor grill.
Accurate forecasts are important for many reasons, including for the company to ensure they have the materials
they need to create the products required in a certain period of time. Your objective is to minimize the forecast
error, which will be measured using the Mean Absolute Percentage Error (MAPE) with a goal of being below 25%.
You have historical monthly sales data for the past year and access to software that provides forecasts based on
five different forecasting techniques (Naïve, 3-Month Moving Average, Exponential Smoothing for .2, Exponential
Smooth for .5, and Seasonal) to help determine the best forecast for that particular month. Based on the given
data, you will identify trends and patterns to create a more accurate forecast.
Approach
Consider the previous month's forecast to identify which technique is most effective. Use that to forecast the next…
Approach
Consider the previous month's forecast to identify which technique is most effective. Use that to forecast the next
month.
Remember to select the forecasting technique that produces the forecast error nearest to zero. For example:
a. Naïve Forecast is 230 and the Forecast Error is -15.
b. 3-Month Moving Forecast is 290 and the Forecast Error is -75.
c. Exponential Smoothing Forecast for .2 is 308 and the Forecast Error is -93.
d. Exponential Smoothing Forecast for .5 is 279 and the Forecast Error is -64.
e. Seasonal Forecast is 297 and the Forecast Error is -82.
The forecast for the next month would be 230 as the Naïve Forecast had the Forecast Error closest to zero with a
-15. This forecasting technique was the best performing technique for that month. You do not need to do any
external analysis-the forecast error for each strategy is already calculated for you in the tables below.
Naïve
Month Period
Actual
Demand
Naïve Forecast
Error
3-
Month
Moving
Forecast
3-
Month
Moving…
Chapter 1 Solutions
EBK PRODUCTION AND OPERATIONS ANALYSIS
Ch. 1.3 - Prob. 1PCh. 1.3 - Prob. 2PCh. 1.3 - Prob. 3PCh. 1.3 - Prob. 4PCh. 1.3 - Prob. 5PCh. 1.3 - Prob. 6PCh. 1.3 - Prob. 7PCh. 1.3 - Prob. 8PCh. 1.3 - Prob. 9PCh. 1.3 - Prob. 10P
Ch. 1.3 - Prob. 11PCh. 1.3 - Prob. 12PCh. 1.7 - Prob. 13PCh. 1.7 - Prob. 14PCh. 1.7 - Prob. 15PCh. 1.7 - Prob. 16PCh. 1.7 - Prob. 17PCh. 1.7 - Prob. 18PCh. 1.7 - Prob. 19PCh. 1.7 - Prob. 20PCh. 1.7 - Prob. 21PCh. 1.8 - Prob. 22PCh. 1.8 - Prob. 23PCh. 1.8 - Prob. 24PCh. 1.9 - Prob. 25PCh. 1.9 - Prob. 26PCh. 1.9 - Prob. 27PCh. 1.9 - Prob. 28PCh. 1.10 - Prob. 29PCh. 1.10 - Prob. 30PCh. 1.10 - Prob. 31PCh. 1.10 - Prob. 32PCh. 1.10 - Prob. 33PCh. 1.10 - Prob. 34PCh. 1.10 - Prob. 35PCh. 1.10 - Prob. 36PCh. 1.10 - Prob. 37PCh. 1.11 - Prob. 38PCh. 1.11 - Prob. 39PCh. 1.11 - Prob. 40PCh. 1.11 - Prob. 41PCh. 1.11 - Prob. 42PCh. 1 - Prob. 43APCh. 1 - Prob. 44APCh. 1 - Prob. 45APCh. 1 - Prob. 46APCh. 1 - Prob. 47APCh. 1 - Prob. 48APCh. 1 - Prob. 49APCh. 1 - Prob. 50AP
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