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# Ch. 30: Standard Data

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1. Ch. 30: Standard Data • Means the reuse of previous times. • For example, predict cost of automotive repairs.

2. Advantages of Using Standard Data • Ahead of Production • The operation does not have to be observed. • Allows estimates to be made for bids, method decisions, and scheduling. • Cost • Time study is expensive. • Standard data allows you to use a table or an equation. • Consistency • Values come from a bigger database. • Random errors tend to cancel over many studies. • Consistency is more important than accuracy.

3. Random and Constant Errors

4. Disadvantages of Standard Data • Imagining the Task • The analyst must be very familiar with the task. • Analysts may forget rarely done elements. • Database Cost • Developing the database costs money. • There are training and maintenance costs.

5. Motions vs. Elements • Decision is about level of detail. • MTM times are at motion level. • An element system has a collection of individual motions. • Elements can come from an analysis, time studies, curve fitting, or a combination.

6. Constant vs. Variable • Each element can be considered either constant or variable. • Constant elements either occur or don’t occur. • Constant elements tend to have large random error. • Variable elements depend on specifics of the situation. • Variable elements have smaller random error.

7. Developing the Standard • Plan the work. • Classify the data. • Group the elements. • Analyze the job. • Develop the standard.

8. Curve Fitting • To analyze experimental data: • Plot the data. • Guess several approximate curve shapes. • Use a computer to determine the constants for the shapes. • Select which equation you want to use.

9. Statistical Concepts • Least-squares equation • Standard error • Coefficient of variation • Coefficient of determination • Coefficient of correlation • Residual

10. 10 8 6 4 2 [y] y=4 0 2 4 6 8 10 [x] Curve Shapes Y independent of X • Y = A • Determine that Y is independent of X by looking at the SE.

11. Curve Shapes Y depends on X, 1 variable • Examples • Others:

12. Curve Shapes Y depends on X, multiple variables • Y = A + BX + CZ • Results in a family of curves

13. Example Application: Walk Normal Times (min)

14. Walk Data Scatterplot

15. Equations for Walk Data Set • Walk time h =.0054 + .01D r2 = .986 σ = .0073 h • Walk time h = –.01 + .014D –.00013D2 r2 = .989 σ = .0067 h • Walk time h = –.13 + .11 (loge Distance, m) r2 = .966 σ = .012 h • 1/Walk time h = .24 – .96 (1/D) r2 = .881 σ = .021 h-1