1ISE 311 - Ch. 30

Ch. 30: Standard Data

Means the reuse of previous times. For example, predict cost of automotive repairs.

2ISE 311 - Ch. 30

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.

3ISE 311 - Ch. 30

Random and Constant Errors

4ISE 311 - Ch. 30

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.

5ISE 311 - Ch. 30

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.

6ISE 311 - Ch. 30

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.

7ISE 311 - Ch. 30

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

8ISE 311 - Ch. 30

Curve Fitting To analyze experimental data:

1. Plot the data.

2. Guess several approximate curve shapes.

3. Use a computer to determine the constants for the shapes.

4. Select which equation you want to use.

9ISE 311 - Ch. 30

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

10ISE 311 - Ch. 30

Curve Shapes

Y independent of XY = ADetermine that Y is independent of X by

looking at the SE.

0 2 4 6 8 10

10

8

6

4

2

[x]

[y]y=4

11ISE 311 - Ch. 30

Curve Shapes

Y depends on X, 1 variable Examples

Others:

12ISE 311 - Ch. 30

Curve Shapes

Y depends on X, multiple variables Y = A + BX + CZ Results in a family of curves

13ISE 311 - Ch. 30

Example Application: Walk Normal Times (min)

5 m 10 m 15 m 20 m

.0553 .1105 .1654 .2205

.0590 .1170 .1751 .2205

.0550 .1105 .1660 .2090

.0521 .1045 .1680 .2200

.0541 .1080 .1625 .2080

.0595 .1200 .1800 .1980

14ISE 311 - Ch. 30

Walk Data Scatterplot

0

0.05

0.1

0.15

0.2

0.25

0 5 10 15 20 25

15ISE 311 - Ch. 30

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