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THE COMPETITIVE SEMICONDUCTOR MANUFACTURING HUMAN RESOURCES PROJECT:

Second Interim Report
CSM-32
Clair Brown, Editor

14. Statistical Tools for Industry Data
Linda Sattler

14.5 Pursuing Interesting Leads

After viewing the two original "overall picture" graphs, some of the more interesting aspects may be pursued:

1. What are the differences between the high PIRK and low PIRK fabs?
2. What are the differences between the high performing fabs and the low performing fabs?
3. Are there detectable differences in any of the above that are not confounded?

Five hundred factors are still too many variables to go through without some statistical help. Several methods are proposed to help pinpoint delineate patterns.

Percent Alike Graphs

One would like to find the interesting areas of an organization that reflect the differences found in the fabs overall. For example, if one is interested in finding the organizational areas of fabs in group A that differ from fabs in group B, these areas should have the following characteristics:

  • The group A fabs should be similar in this area

  • The group A fabs should be different from group B fabs in this area

The similarity measure is used, but instead of using it on all the organizational factors, each area of an organization is measured separately. The organizational factors are grouped into the following 11 categories:

1. Com: Communications. Factors involving meetings, problem notification, work instructions and shift hand-offs.
2. DT-A: Direct Teams - Approach. How teams involving direct labor (operators and technicians) are organized and set up.
3. DT-F: Direct Teams - Function. How teams involving direct labor (operators and technicians) function - responsibilities, training, meetings and member descriptions.
4. Hind: Hindrances/Desired Changes. Problems employees may have or changes they would like to see.
5. Hist: Historical Changes. Changes that occurred in the past such as work force reductions, new hours introduced, demand dropping, etc.
6. IT-A: Indirect Teams - Approach. How teams involving indirect labor (engineers and management) are organized and set up.
7. IT-F: Indirect Teams - Function. How teams involving indirect labor (engineers and management) function - responsibilities, training, meetings and member descriptions.
8. Knowledge & Skills. Education requirements and training.
9. Org: Organization. Shifts worked, work organization, job grades and categories.
10. Perf: Performance. Perceived job priorities and performance evaluations.
11. Task: Tasks Performed. Job category tasks, leadership roles and how time is spent.

The groups analyzed are:

  • The high PIRK fabs versus the low PIRK fabs

  • The high performing fabs (*** and **) versus the low performing fabs (-- and -)

To create the "Percent Alike Graphs" the data of similarity scores need to be acquired. The techniques used to obtain these scores are outlined in the appendix. The following sections display and explain the graphs.

High PIRK versus Low PIRK

The similarity measures of high PIRK fabs with each other (H) are in the left bars of the bar pairs. The similarity measures of high PIRK fabs with low PIRK fabs (L) are in the right bars. It is interesting here to note that over half of the high PIRK fabs are also Japanese fabs (thus separating the strictly Japanese characteristics with the PIRK characteristics). In this figure, the interesting areas include:

  • Com: Communications

  • K&S: Knowledge & Skills

  • Perf: Performance
    and, possibly, with less of a contrast:

  • DT-A: Direct Teams - Approach

  • DT-F: Direct Teams - Function

  • IT-F: Indirect Teams - Function


The top three interesting areas are not too surprising since these areas relate directly to one or more of Power, Information, Rewards, and/or Knowledge. The possible interesting areas related to direct teams probably display more of a Japanese/non-Asian difference than a High/Low PIRK difference, since the minimum is so low in these measures. It is especially interesting to see the high similarity in communication with high PIRK fabs since this does not seem to be a Japanese similarity. The factors will be explored in greater detail with the Common Value Analysis.

High Performance versus Low Performance

In Figure 14-11 it is shown how similar high performance fabs are with each other in the left bar and how similar high performance fabs are with low performance fabs in the right bar. In this figure there are two areas of interest:

  • DT-F: Direct Teams - Function

  • K&S: Knowledge & Skills

As discussed previously, performance is not solely a result of organizational characteristics, and Figure 14-11 certainly seems to reflect this. However, the graph reflects only the interesting areas of an organization where all the high performing fabs do very similar things. If each high performing fab was on its own "road to success," the areas may be too dissimilar to be reflected in the graph. Within the CSM study, high performing fabs did tend to stress the importance of the direct employee. High performance fabs tended to have their operators and technicians do more complicated/self-managed tasks (DT-F), thus acquiring (or requiring) a more advanced set of skills (K&S).

It may be of interest to see if there is anything the low performing fabs have in common. Is there, perhaps, one road to mediocrity? To study this, another Percent Alike Graph (Figure 14-12) was created which displays the similarity measures the low performing fabs have with each other versus the similarity measures with the high performing fabs.

There are few striking contrasts. The biggest differences seem to be in the areas of:

  • Hind: Hindrances/Desired Changes

  • K&S: Knowledge & Skills

  • Perf: Performance


Even these areas do not reflect the extreme differences found in the other analyses. However, employees of low performing fabs in the CSM study did tend to view their job priorities in terms of "whatever my boss tells me to do" (Perf). They also had hindrances (Hind) which reflected a negative view of management rather than, for example, problems with equipment. Training (K&S) was not emphasized as much in low performing fabs. These differences will be explored further in the next section.

Common Value Analysis

Description of Technique

To explore the individual components, a method of Common Value Analysis is proposed. This analysis finds the individual factors of a "Group A" that greatly differ from those in "Group B." The technique used is described in the appendix.
The Common Value Analysis was used on the two interesting group differences:

  • High PIRK vs. Low PIRK

  • High Performance vs. Low Performance


High PIRK versus Low PIRK

In the Common Value Analysis of high PIRK versus low PIRK fabs, many factors were found that were reflected in only the Japanese companies. This is expected because of the high correlation between the two. What is interesting to find are factors or themes in high PIRK fabs that are not necessarily "Japanese" qualities. These are highlighted these as follows:

  • Meetings are more common in high PIRK fabs, especially employee-supervisor meetings and with peers in shift hand-offs.

  • There is a theme in high PIRK fabs of the employees knowing how performance is. This can be through formal evaluations, supervisory meetings, or simply a good knowledge of SPC to know when a machine is not performing correctly.

  • On-the-job training is a PIRK theme. As opposed to classroom training, employees learn by doing, usually at the hands of their supervisors or team leaders. Extensive training in Japanese companies is well documented in the literature ([2],[7] and [11] for example) and is beginning to show itself in high participative companies outside Japan.

It may be possible that a fab could become a high involvement fab by focusing on one of the PIRK aspects. For example, one high PIRK fab concentrates its efforts on information. Monitors are everywhere in the fab, giving detailed information on each piece of equipment. Communication is encouraged between all employees, further spreading information. By placing a high emphasis on information to the lowest levels, management may be implicitly stating that operators are important; thereby eventually giving more power, knowledge and rewards to this lowest level.

High Performance versus Low Performance

As discovered earlier, a theme in high performing versus low performing fabs is difficult to detect when so many other factors are involved in high performance. The list of interesting factors in the Common Value Analysis is short, but a few points can be mentioned:

  • Pre-employment screening for operators and technicians tends to be common in high performing fabs.

  • Low performing fabs seem to have disgruntled employees who mention production problems, inadequate training, and communication problems as some of their job hindrances.

Summary

Using multivariate analysis and exploratory methods some interesting and intriguing possibilities were discovered. Using the tools outlined in this chapter, the following industry characteristics were brought to light:

  • Mapping organizational practices in fabs relative to each other resembles a map of the world. The progression from Asia to Europe is easily distinguished in this graph (Figure 14-1).

  • Significant biases in the methodology were found, mostly in the type of data that were coded rather than in the coding itself. It was also discovered that much of the bias can be removed by eliminating variables that are coded only in a particular year.

  • Once biases were removed, interesting patterns appeared in the data. PIRK and fab performance increased as "self-management" increased and "uncertainty" decreased.

  • The differences between high and low PIRK fabs seemed to lie in the specific areas of communication, knowledge and skills, and performance.

  • There are many roads to high performance (and low performance as well). High performing fabs are similar in the way their direct teams function and requiring (or allowing employees to acquire) more advanced knowledge and skills.

The tools developed in this chapter help a researcher delve into a large data set to underscore interesting characteristics for further research. The observations highlighted in this chapter may be used as focal points for concentrated research efforts and/or theories to test with new data.

End of Chapter 14

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