1. Ch. 27: Standard data The reuse of previous times. Advantages
For example, predict cost of automotive repairs.
Advantages
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.
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2. Random vs constant errors
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3. 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.
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4. 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.
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5. 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.
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6. Developing the standard
Plan the work.
Classify the data.
Group the elements.
Analyze the job.
Develop the standard.
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7. 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.
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8. Statistical Concepts Least-squares equation Standard error
Coefficient of variation
Coefficient of determination
Coefficient of correlation
Residual
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9. Curve Shapes Y independent of X Y = A
Determine that Y is independent of X by looking at the SE. 10 8 6 4 2
[x]
[y]
y=4
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10. Curve Shapes Y depends on X, 1 variable Examples Others: (2) Y = AeBX
(1) Y = A + BX
(2) Y = AeBX
(3) Y = X / (A + BX)
Others:
Y = AXB
Y = A + BXn
Y = A + BX + CX2
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11. Curve Shapes Y depends on X, multiple variables Y = A + BX + CZ
Results in a family of curves
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12. Example: Walk normal times (min)
From table 27.2, pg. 526
5 m
10 m
15 m
20 m
.0553
.1105
.1654
.2205
.0590
.1170
.1751
.0550
.1660
.2090
.0521
.1045
.1680
.2200
.0541
.1080
.1625
.2080
.0595
.1200
.1800
.1980
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13. Walk Data Scatterplot
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14. Equations for Walk Data Set
Walk time h = D
r2 = .986 σ = h
Walk time h = – D –.00013D2
r2 = .989 σ = h
Walk time h = – loge D
r2 = .966 σ = .012 h
1/Walk time h = .24 – .96 (1/D)
r2 = .881 σ = .021 h-1
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