OPER MGMT IN SUPPLY CHAIN ACCESS CARD
7th Edition
ISBN: 9781260943184
Author: SCHROEDER
Publisher: INTER MCG
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Textbook Question
Chapter 9, Problem 1DQ
Why did statistical quality control ideas catch on in the 1940s?
Expert Solution & Answer
Summary Introduction
To explain: Why did the statistical control ideas catch on in the 1940s.
Explanation of Solution
Reasons why statistical control ideas catch on in the 1940s:
The statistical control ideas catch on only in the 1940s because it was during World War II. World War II created a massive demand for the mass developed goods. It was not feasible to inspect all these goods manually for the quality.
They turned to an alternative mean to inspect the quality of these goods. It gave birth to the use of the statistical measure to analyze the quality of the mass developed goods. This led to the growth in the use of the statistical measure throughout the industry.
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Chapter 9 Solutions
OPER MGMT IN SUPPLY CHAIN ACCESS CARD
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Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, operations-management and related others by exploring similar questions and additional content below.Similar questions
- Under what conditions might a firm use multiple forecasting methods?arrow_forwardThe 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?arrow_forwardThe contribution of application of statistical techniques?arrow_forward
- what are the Descriptive Statistics, Ordinal Scale and Dichotomous Variable? What are the differences among them?arrow_forwardExplain what is the connection between forecasting and quality control?arrow_forwardDiscuss how the advantages of using simple regression could benefit the organization.arrow_forward
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