Computers ind. Engng Vol. 19, Nos I-4, pp. 351-355, 1990 0360-8352/90 $3.00+0.00 Printed in Great Britain. All rights reserved Copyright © 1990 Pergamon Press plc

The Impacts of visual Display units used for Highly Cognitive Tasks

on Learning Curve Models

Catherine Sparks and Robert Yearout

Department of Management University of North Carolina at Asheville

ABSTRACT

This paper compares progress (learning) curves for a task that requires high manual dexterity where information is obtained from a video display unit (VDU) to a manual assembly line task. Sixteen volunteer subjects either assembled peg-boards or performed a task on a VDU. Subjects were randomly assigned to a task or control group for each task. Each subject performed their assigned task for approximately two hours. The peg-board assembly simulated a very low cognitive but highly manual task, and the VDU task simulated a high technological industrial operation. There were no statistical differences in learning between the control and test group for either task. The progress curve rates were 95 percent for the peg- board and 91 percent for the VDU. Steady states for the peg-board and VDU tasks were reached at the 25th and 30th iterations respectively. Subjects then were tested four weeks later. During this 28 day period the test group refrained from participating in any activity that would approximate their assigned task. The control group maintained proficiency by practicing 45 minutes per week. There was no difference between the control and test groups for the peg-board task. Thus, there was no significant forgetting. However, there was a significant difference between the control and test groups for the video task. For the control group (VDU) the cycle time for the first iteration was reduced by 68.0 percent. The group's progress curve rate was 97.4 percent and steady state was reached at the 23rd iteration. Though the test group did improve their cycle time for the first iteration, it was only 33 percent of the control group's improvement. The test group's progress curve rate was 94.5 percent and steady state was reached at the 33rd iteration. This significant difference between the two curves is

explained by forgetting. The forgetting rate decrease in learning for the VDU was 3.2 percent. This rate gradually decreased until the 45th iteration where there was no significant differences in cycle time between the two groups. Thus, forgetting occurs in the earlier iterations. To maintain its competitive edge in the world marketplace, industry has begun to implement many processes that rely heavily on VDUS. Inherent in these processes are shorter production runs and multiple set-ups. Traditional curves developed from low cognitive tasks do not reflect the forgetting that occurs in more typical modern industrial tasks. This impacts on industrial engineers and production managers whose operations require information obtained from VDUs. To set standard times for these type tasks, more consideration should be given to forgetting rather than relying totally on the more traditional models.

BACKGROUND

Learning curves, progress curves, cost reduction curves, manufacturing progress functions, experience curves, etc. are based on the theory that if a quantity on a given lot size is increased, the labor required is reduced in some proportion to the increase in quantity. They are a function of the complexity of the part, labor involved, tooling, equipment used, design changes during production and the ratio of the total quantity to the average lot run (McCampbell and McQueen, 1956). These curves relate the direct-labor hours required to perform a given task to the number of times the task has been performed. This can be expressed as a cycle time for a very low level task to the completion of a major end item such as an aircraft. Since most manufacturing operations combine manual and mechanical elements, learning can be limited by an

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352 Proceedings of the 12th Annual Conference on Computers & Industrial Engineering

automated operation. Thus the cycle. Some forgetting curves will learning function may be dependent show a rapid initial decrease in upon the ratio between manual and performance followed by a gradual automated portion of the operation leveling off as a function of the (Titleman, 1957). Learning curves interruption interval period. The differ in the time required to rate and amount of forgetting complete the first iteration of a decreases as an increased number of task and the curves slope which units are completed before the defines the learning function. In interruption occurs. Thus the amount the initial iterations, improvement is quite evident. However, as the operator becomes familiar with the task there are fewer fumbles, delays and hesitations. Thus the time required to complete an iteration becomes constrained by the physical characteristics of the task. Any further reduction in assembly times become so small that it appears that the curve is static (Conley, 1970). This condition is described as steady state. Though many models have been developed to describe the learning function, one of the most universally accepted is the negative exponential model as shown in equation 1 (Evans, et. al. 1984).

y = ax -b (i)

Where y is the time required to complete the task for a given iteration, a is the time required to complete the task for the first iteration, x is the number for a given iteration and b is the learning function. This equation is shown in equation 2.

of the forgetting and the corresponding level of performance is a function of both the performance at the time the project was interrupted (or total amount learned) and the time of interruption (Carlson and Rowe, 1976). Therefore, 100% retention is reflected by no change in time per unit (i.e., cycle time).

METHOD

Introduction

This study was conducted to determine the differences in the forgetting functions between very low cognitive, highly manual and directly observed tasks and very high cognitive tasks where information was obtained from a VDU. The manual dexterity required for both tasks was of the same degree of difficulty. The experiment was conducted in two parts and as follows; part 1 was the initial testing to determine the learning rate and part 2 was the follow-up testing at the end of 28 days to determine the forgetting rate.

b = (- in p / in 2) (2) Task

where p is the learning rate as defined by the ratio of manual to automated work. As a rule of thumb, if the ratio of manual to automated work is 3 to 1 (three-fourths manual), then 80 percent is a good value; if the ratio is 1 to 3, then 90 percent is often used. An even split of manual to automated work produces an 85 percent curve (Evans, et. al. 1984). (Konz, 1987) describes several learning rates and the number of iterations required for an operator to reach that rate. Some typical examples are as follows: machining and fitting a small casting yields a 74 percent rate after 50 iterations and a punch press operation yields a 95 percent rate after i0,000 iterations. Regardless of the manual dexterity required to perform these tasks, the operator gained task information by direct observation.

Subjects performed one of two simulated industrial tasks. The peg- board assembly was selected because of its close approximation of a typical highly repetitive assembly line task (Konz, 1987). This task consisted of an 18 X 18 inch wooden block with 30 chamfered 3/8 inch diameter holes. Pegs were 3 1/2 inches long X 3/8 inches in diameter and had one end sharpened. Both hands were used during the simulated assembly. Operators assembled the board using a rowing motion and placed the pegs into the board with the sharpened end down. The starting point was the two center columns on the top row. The second task was to complete the first screen of the video game, Popeye (Parker Brothers, Inc., 1983), on a Commodore 64 computer. A WICO Command Controller, model number 2456559730 Joystick was used. The controller was manipulated with one hand while resting on the table in front of the VDU. Required

The forgetting function or reach and grasp motions were amount of decay in the speed of activated by a punch trigger on the performing the task depends on how top of the controller. Directional much has been learned or where the movements were manipulated by the task is interrupted in the learning controller. Subjects performed

Sparks and Yearout: Learning Curve Models 353

either task at 15 min intervals for a total of 120 min. Scheduled breaks were required to compensate for any fatigue bias. Between each interval subjects were required to take a 3 minute standing break. There also was a i0 minute break at the end of the fourth test interval. Prior to collecting data, subject's received an orientation and were allowed 2 practice trials of their assigned task. Total experimental time was 3 hours.

Subjects:

Sixteen subjects were selected from a group of volunteers from the Asheville area. The 8 female and 8 male subjects were not familiar with either the peg-board or the video game. The subjects age ranged between 18 to 62 years (mean = 28.7 years).

Design:

Subjects were divided into 2 groups of 4 males and 4 females each. One group would assemble peg-boards and the other complete the video task. Each of these groups were further sub-divided into a control group and a test group. Subjects were then randomly assigned a time for their initial testing. Each subject was then given a follow-up test 28 days from their initial test date. The test groups were instructed to refrain from participating in any activity that could approximate their assigned task. The control groups were required to perform their assigned tasks a minimum of 30 min per week for 4 weeks. All testing was done at the same workstation under the same conditions.

Procedure:

RESULTS

Initial test:

Steady state was determined by the following procedure. A graph of the cycle time means for each task was plotted against its respective iteration number. The starting point for the analysis was at the point where the curve appeared to become flat. Pairwise comparisons for data groupings of i0, 5 and 1 cycle time means were used. Due to unequal variances, Satterthwaite's approximation was used (Milliken and Johnson, 1984). For a significance level of 0.05, steady state for the peg-board and video tasks were reached at 25 and 30 iterations respectively.

A regression analysis was performed on the data. The resulting model for the peg-board is equation 3. The learning rate was 95 percent which reflects the reported learning rate for that task (Konz, 1984). This model's mean square error and multiple correlation coefficient was 9.669 and 0.98 respectively.

Y = 27.25x -'074 (3)

Females were significantly faster than male subjects. Table 1 shows the differences in means. Older, greater than 29 years, were faster than the younger subjects. Comparison of means is shown in Table 2. There were no significant interactions between sex and age. Equation 3 is depicted as model 3 in figure i.

Table 1. Sex differences for the peg-board task

All tests were performed in the MEANS UNCA Owen laboratory. Test time was 3 hours. This included a 15 min SEX (Initial) (Follow-up) GROUP subject briefing, a 5 min period for FEMALE 21.82 20.98 A testing procedure familiarization, eight 15 min data collection periods MALE 23.58 22.05 B and a debriefing at the completion of data collection. To insure a uniform degree of difficulty only data Means with different letters are collected from the first screen of significantly different. the video game was used. Alpha = 0.05

Criteria:

The criteria for both tasks was cycle time and its corresponding iteration number. Cycle time for the peg-board was the time it took an operator to assemble a complete board. Cycle time for the video game was computed by dividing the time per iteration by the point score for that iteration.

354 Proceedings of the 12th Annual Conference on Computers & Industrial Engineering

Table 2. peg-board

Age differences for the

MEANS

AGE (Initial) (YEARS) (Test) (Follow-up) GROUP

29 > 21.57 21.02 A

< 29 23.82 22.44 B

Means with different letters are significantly different. Alpha = 0.05

25

20

15

~C

t~t30£L 5

5 15 10

25 35 45 55 20 30 40 50 60

~RA~ON MA~£R

Figure i. Peg-board learning curves

The model for the VDU is equation 4. Its learning rate was 91 percent. The model's mean square error and multiple correlation coefficient was 0.04573 and 0.31 respectively.

y = 0.046x -'136 (4)

Model 4 in Figure 2 is the VDU task learning curve (equation 4).

0.02 I " - - "~

O'010IODEL 6

5 15 10

25 55 45 55 20 50 40 50 60 ITERATION NUMBER

Figure 2. VDU learning curves

Follow-up Test:

For the peg-board, steady state was reached immediately. There were no significant differences between iteration groupings. Also, there were no significant differences detected between control and test groups. The resulting model was equation 5. The learning curve rate was 99.15 percent. This model's mean square error and multiple correlation coefficient was 0.2896 and 0.96 respectively.

Y = 21.44x -'012 (5)

Model 5, equation 5, in Figure 1 shows the curve after 28 days. The significant differences between sex and age for the subjects remained. Means are shown in Tables 1 and 2.

For the VDU, steady state was reached at the 23rd and 33rd iteration for the control (practice) and test (no practice) groups respectively. There were significant differences between the cycle times of groups. Means are shown in Table 3.

Table 3: test

Cycle times for follow-up

GROUP MEAN GROUPING CONTROL 0.0126 A

TEST 0.0180 B

The model for the control group is equation 6. The learning rate was 97.4 percent. Its mean square error and multiple correlation coefficient was 0.0293 and 0.5107 respectively. The resulting model for the test group was equation 7. The mean square error and multiple correlation coefficient was 0.0590 and 0.5655 respectively. The learning rate was 94.5 percent.

y = 0.013x -0"038 (6)

y = 0.032x -0"082 (7)

Model 6, equation 6, and model 7, equation 7, in Figure 3 shows these curves after 28 days. There were no significant differences between sex and age for the subjects.

Sparks and Yearout: Learning Curve Models 355

0045

0.04

QOZ5

003

Q025

Q02

0015

001

0005

TEST -

5 15 25 ~6 45 55 10 20 30 40 5(3 60

ITG~.AllON

Figure 3. VDU cycle time means for test and control group operators (follow-up test)

with increased technology, shorter production runs, the introduction of cellular manufacturing and increased use of VDUs in the production process, attention must be directed towards forgetting rates. The forgetting function's impact will be observed in processes where shorter production runs are becoming the norm. Thus the loss of production time for the earlier iterations becomes more significant. No longer should traditional learning curves developed in the 1950's to early 1960's be used by production managers for setting standard times. Managers and engineers should develop their own specific progress curves based on empirical data gathered from observing their most experienced and proficient operators.

The cycle times for both groups were no longer significantly different at approximately the 45th iteration. Thus any loss in time to forgetting was recovered by the 45th iteration. A plot of the actual data, figure 3 illustrates this convergence of the cycle time means for the two groups.

REFERENCES

Carlson, J., and Towe, A., "How Much Does Forgetting Cost?", Industrial Engineering, vol. 8, No. 9, pp. 22- 25, 1976.

Conley, P., "Experience Curves as a Planning Tool", IEEE Spectrum, pp. 63-68, June 1970.

CONCLUSIONS

Once peg-board steady state was reached, operators experienced very little forgetting. Practice had no significant effect. It could be inferred from the data that older female operators have fewer fumbles, delays and hesitations on highly repetitive manual directly observed tasks. Since differences remained consistent throughout this test group and the learning rate remained the same, it would appear that this difference may be a result of the random error in subject selection.

Although operators did not reach steady state for the VDU control group until the 23rd iteration, there was a 72 percent decrease in the first iteration time. The learning rate increased by 7 percent. For the test group, steady state was not reached until the 33rd iteration. This is not significantly different from that in the initial test. Thus without practice steady state must be reestablished. There was a 30 percent decrease in the time for the first iteration. The learning rate only reflected a 3.8 percent increase. The differences of 0.019 seconds/polnt and 3.2 percent describe the forgetting function and are depicted by the area between the model 6 and model 7 curves in figure 2.

Evans, E., Anderson, D., Sweeney, D. and Williams, T., Applied Production and Operations Management, pp. 140- 144, West Publishing Company, St. Paul, MN 1984.

Konz, S., work Design: Industrial Ergonomics Second Edition, pp. 519- 535, Publishing Horizons, Inc., Columbus, Ohio, 1987.

McCampbell, E. and McQueen, C., "Cost Estimating From the Learning Curve", Aero Digest, pp. 36-39, October 1956.

Milliken, G. and Johnson, D., Analysis of Messy Data, Vol. i, Designed Experiments, Lifetime Learning Publications, Belmont, CA 1984.

Titleman, M., "Learning Curves - Key to Better Labor Estimates", Product En@ineerlng, pp. 36-38, Nov. 1957.