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Second Interim Report
Clair Brown, Editor

3.0 Results

Our modeling methodology (Bowen, 1992) is based on the premise that performance in a given work system is determined by a combination of factors occurring at four different levels; individual, group, organizational and external-enviromental.

The functional form of the model is



P is the Performance of the work group,

C1, is a constant representing the system upper limit to performance,

C2 is a constant representing the costs incurred as a result of imperfect KSA, and X is the level of KSA of the work group.

For the model, we make the following assumptions:

-the work system is attempting to produce as much as possible, i.e., that production is not purposefully restricted due to inadequate demand.

- the organizational structure is group-based rather than individual-,based.

- the group works within a work system and that there is some autonomy within the group, but there is a group boundary and limits to the autonomy of the group.

-the amount of formal training is a reasonable estimate or proxy for the amount of knowledge, skills and abilities of the group-members.

For the work group under investigation, a linear regression model using performance as the dependent variable and the inverse square of KSA as the independent variable produces an equation with the following coefficients.

Moves-per-week = 13,900 - 4.22 x 108 (KSA)-2 or

P = 13,900 - 4.22 x 108 (X)-2 (Equation 1)

This model of performance results in a coefficient of determination, R2, of 0.78 and F(l, 22) = 78.2, which is significant at the P<0.0000001 level. The standard error of the model is 740 moves-per week. The model and data are depicted graphically in Figures 3A and 3B. Figure 3A is restricted to the range of existing data, while Figure 3B shows the performance predicted by the model over a wider range of group KSA.

Because this study examines a single group, and in order to be unobtrusive in collecting data, little group process data were collected. For example, data concerning group cohesion or the emergence of informal leaders were not collected.

In spite of these limitations, there are still some factors that we can examine that may have an impact on group performance. To accomplish this, we first view a plot of the performance data versus group KSA, and then identify the data points by factors suspected of influencing performance. We then observe whether these points consistently fall above or below the performance predicted by the KSA only model.'

By this method, we find that the data contains interesting patterns concerning performance differences regarding addition of members to the group and between three and four day work weeks. We now present these findings.
The change in KSA over time is shown in Figure 1.

Performance is measured in 'moves', which is the successful processing of a wafer on a piece of equipment and its transport to the next processing step or to an appropriate inventory--holding area. The group's facilitator calculated the weekly average moves-per-shift as a routine data collection activity. The performance data used in this report encompasses the time interval from the be-inning of the introduction of the group structure, to the last complete month before the site visits occurred, a period of 23 weeks. This performance information is shown in Figure 2. Time for both Figure 1 and 2 is in calendar year weeks. Both performance and KSA show increasing trends, with performance fluctuating week to week and group KSA monotonically increasing.

By this method, we find that the data contains interesting patterns concerning performance differences regarding addition of members to the group and between three and four day work weeks. We now present these findings.


Four individuals were added to the work group during the period under study, in the 2nd, 4th 16th and 22nd weeks. From Figure 4, we see that the four weeks in which new members joined the group are all weeks that lie above the KSA versus performance curve predicted by the model. This result is interesting in that it is counter intuitive. One could naturally expect the addition of a new person would disrupt normal operations and consume the time of the facilitator and other group members required to orient and the newcomer, resulting in a reduction in performance which would last until the new employee 'comes up to speed.' Based on the data, this does not appear to be the case.

An alternative explanation is that the addition of a person to the group means an addition of KSA which is not captured by the model,as this 'innate' KSA is not the result of any of the formal training that is captured in the training database.. Because the KSA is not captured in the training data, while the increase in moves-per-shift is captured in the performance data, the addition of a person to the group could appear as a jump m group performance. This explanation must also be rejected because the jumps in performance are not sustained, i.e., they are always followed by a reduction in the following week's performance which is not explained by this 'uncaptured KSA' theory.

While the data is sufficient to discredit the above propositions, it is not sufficient to support or refute others. Further investigation is indicated, and relationships that could be explored include:

-increase or reduction in cleanroom presence of facilitator

-performance motivated by trying to impress newcomer or by mistrust of member whose allegiances have not been ascertained by the group,

-genuine excitement over the prospect of a fresh face in the group

-increased production on the fastest/easiest equipment as a newcomer is trained, which inflates the move-per-shift performance numbers, and requires later balancing when the harder/more time consuming- processes are required to catch-up.


The group alternates between working three and four day weeks. When the performance data is identified by three and four-day work weeks in a graph which shows the line predicted by the model (see Figure 5), it is seen that the three day work weeks are generally above the predicted performance while the four-day work weeks are generally below that predicted by the model. Performance occurring in the four day work weeks shows a steady rate of improvement over time. Performance in three day work weeks generally exhibits more variation from what the model predicts From the level of data analyzed for this study, it is not possible to determine the cause(s) of this difference between three and four-day work weeks. The data shows that people were added to the work group only on three day work weeks. Though this is likely a coincidence' rather than a conscious policy, it is reasonable to conclude that whatever is driving the higher than expected performance in the weeks when people are added to the group is also contributing to the disparity in performance between three and four day work weeks in the data set.

Some of the variation is likely due to day to day 'random' variation which is likely to appear more random when days are grouped together and averaged in smaller sets. However if the variation were due only to such random causes, the data should reflect this by being unbiased, i.e. performance in 3-day work weeks would be equally likely to be above or below the regression line, as would the performance in 4-day work weeks. For the data to appear as it does due to random variation is unlikely (p<0.005 by a sign test analysis), leading to the conclusion that some systematic causes produce the observed patterns of variation in work group performance.

Factors that could affect the performance differences between three and four-day work weeks include maintenance or training practices. For example, if maintenance or vendor training occurs only during four-day work weeks, this could affect performance. Similarly, if deliveries are always promised on the first and fifteenth of the month, then motivation could increase dramatically as deadlines approach and the likelihood of missing a shipment increases.

Some other potential determinants warranting consideration are; 'communication and hand-off differences' (especially WTP levels and equipment status) from the other shifts, 'group norms' as the extra day in a four day work week may lead to a more relaxed pace of production, and 'fatigue' since by the fourth consecutive day of 12 hour shifts group-members could be fatigued to the point of being unable to continue production at the higher pace.

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