Learning, Measurement, and Management

The Learning Model

Learning Purpose

Purpose:

To demonstrate a clear linkage between Learning and making Money in Manufacturing Companies,

Purpose:

To demonstrate a clear linkage between Learning and Measurement in Factories,

also:

To present management levers to increase the rate of making Money,

and:

To provide a theoretical and emperical underpinning which rationalizes (and makes manageable) the above,

by using:

The Learning Curve as the central theme.

A Brief History

In 1988, Bohn suggested "manageable learning:"   Cost(t)=Cost(0) * e^{-K * q / ( E *N )}

Manageable Learning

Cost(t)=Cost(0) * e^{-K * q / ( E *N )}

where:

K is the product of the factors influenced by the firm

q is the square root of the cumulative number of experiments, Q

E is the quality of the Experimental design

N is the total of all of the sources of Noise

note:

E can be metricized by the average number of experiments it takes to reveal what was being sought in each experiment.

q, K, and N can be further broken down.

Number of Cumulative Experiments

q² = Q = P * D * ( t / C )

where:

q is the square root of the number of cumulative experiments, Q

P is the degree of Parallelism ( # of coincident experiments)

D is the experimental Duty cycle ( weekly hours experimenting / 168 )

C is the experimental Cycle time ( hours between successive experiments )

t  is the elapsed time ( the same t as the parameter in the Cost function )

The Learning Agenda

Factors Manageable by the Firm

K = T * V * W * S * F

T is the sufficiency of the Technical knowledge and know-how

V is the requisite Variety of possibilities ( the probability that the answer is already in the "community" )

W is the receptivity of the Working environment to learning

S is the cohesiveness of the learning team’s Spirit

F is the Fluidity of the learning environment ( the ease with which learners can use tools )

Starter Kit - Hard Factors

C

Shorten experimental Cycle times

Install programmable drive systems

N

Reduce process and experimental Noise

Calibrate and upgrade equipment used in tests

E

Improve Experimental design

Use "herringbone analysis" and/or Toyota’s "5 Whys"

D

Increase experimenting Duty cycle

Snapshot events on all shifts and weekends

P

Increase Parallelism

Multiplex experimental platform

Starter Kit - Soft Factors

T

Enhance Technical knowledge

Study Fourier and Nyquest

V

Increase Variety of possibilities

Go outside your regular community

W

Cultivate conducive Work climate

Inform and include those who are affected

S

Develop cohesive team Spirit

Bond for Trust, Respect, and Joy

F

Increase learning Fluidity

Make user friendly interfaces and 24x7 access

The Du Pont Case

Measurement in the Factory

Photo © 1995 by National Instruments Corporation

Performance of the Factory

Place

Camden Nylon Factory

Instance

Break Reductions

Unifying Principle

Minimize Experimental Cycle time

Reconfiguration

Work on "floor," not in offices

Performance Parameter

Breaks per pound

Gain in Performance

3:1

Cost Reduction

> $25,000 / day

Payoff