# Quality statistical process control at Cherry Textron.

Quality Statistical Process Control At Cherry Textron

Competitive pressures have forced many U.S. firms to improve the quality of their products. One quality improvement method being introduced in the production process by Cherry Textron is statistical process control (SPC). The objective of SPC is to manufacture all parts to specifications the first time, eliminating costly rework, scrap, and unnecessary 100 percent inspection. This is accomplished by having each machine operator regularly measure parts they are producing against statistically pre-established quality standards and charting their results. When parts fall outside of the statistically acceptable range, production is immediately stopped and the problem is identified and corrected. Such an approach prevents discovery of faulty components after completion of an entire production run. Responsibility rests with the machine operators for producing acceptable parts only, thus they become in essence their own inspectors.

Cherry Textron (Cherry) produces and sells blind rivet fasteners to the aerospace and automotive industries. Frequently, customers within these industries demand that vendors implement and maintain evidence that their manufacturing processes are within statistical control. In response, Cherry started an extensive program to train 350 employees in SPC methods. SPC will give production personnel the tools to measure their own quality, and when out of the control, a means to systematically analyze the causes.

The concept of x-bar and R control charts is based on statistical hypothesis testing including computation of mean and standard deviation under a normal distribution curve. Upper and lower control limits are typically calculated [plus or minus] 3 standard deviations, thus providing a 99.7 percent assurance that a type 1 error will not occur (rejection of a null hypothesis that is true).

The prime use of the control chart is to detect "assignable causes" of process variation. Process variations are attributable to two kinds of causes: random - due solely to chance, and assignable - due to specific "findable" causes. Ideally, only random causes should be present; therefore, they represent the minimum possible variation. A process which is operating without assignable causes of variation is said to be "in a state of statistical control." This occurs when samples selected and charted on the x-bar and R control charts fall within the control limits. Assignable causes exist when the actual variation exceeds the control limits. After giving consideration to cost/benefit analysis, the process is investigated, causes are identified and corrected, and finally the process is remeasured to determine if it is now in control.

The x-bar and R control chart concepts are used repeatedly to control quality in the following order:

Gauge Capability Study. Cherry determines that the gauges used for measuring critical part specifications are in control.

Process Capability Study. Production processes are measured for control to pre-established tolerances.

Process Control Procedures. These constitute the day-to-day surveillance to ensure that the production process remains in control. After a short explanation of x-bar and R control charts, we discuss each of the above functions.

An example of a typical x-bar and R control chart data sheet is Figure 1 (next page).

Twenty-five subgroups of five samples each were selected. For each subgroup, Cherry calculated the average (x-bar), the range(R) (the difference between the highest and lowest measure), and a grand total average (shown in Figure 1 as an "x" with a double-bar above) and range (shown in Figure 1 as an "R" with a single bar above) for the 25 averages (x-bar) and ranges (R). Upper and lower control limits were also computed for x-bar and R. The mean and range for each subgroup are plotted on graphs. Since all plot points in Figure 1 fall within the upper and lower limits, the process is in statistical control.

The chart of x-bar values tells when a change has occurred in central tendency. This may be due to such factors as tool wear, a gradual increase in temperature, a new batch of material of greater toughness, or a different machine setting used by the night operator. Eventually, Quality and Productivity Improvement teams will compile a list of potential reasons for lack of control that operators can check against.

The R chart indicates when a significant gain or loss in uniformity has taken place. Since processes have inherent variability (dispersion), some deviation from the mean is expected. The chart's upper and lower limits reflect the range allowable for uniform process dispersion. A loss of uniformity indicates a machine malfunction (necessitating repair) or, more likely, a lack of operator skill or concentration. The R chart should always be read first because a lack of control appearing on it will normally show on the x-bar chart, whereas the opposite does not usually occur - lack of control on the x-bar chart may not appear on the R chart because machines are normally able to stay within their inherent variability limits (R).

The gauge and process capabilities studies are the two primary steps for implementing SPC on any process. They ensure the inspection gauges are sound and the process is capable of meeting stated requirements. Hence, the first step in SPC implementation is to perform a gauge capability study. An analysis of a process cannot be meaningful unless the measuring gauges used to collect data are both accurate and reliable. Accuracy of the gauges is the responsibility of quality engineers so accuracy will not be tested as part of the gauge study, but the firm will test for statistical error determined by repeated measurements with the gauges.

For example, to measure rivet specifications, Cherry uses comparators and micrometers. A comparator is a microscope which projects the image of the part onto a screen. The screen has two axes for measuring: (y) up and down, and (x) right and left. One edge of the part specification to be quantified is placed on the appropriate axis then moved across the screen to the other edge. An electronic counter measures the distance moved is .0001 of an inch. The comparator is used to determine critical rivet specifications that cannot be read by a hand held micrometer, i.e., distances between angles of a part. The hand held micrometer, which also measures diameters to .0001 of an inch, is operated by turning a series of dials until the fingers of the micrometer barely pinches the surface of the part.

Reliability is affected by two variations: (1) Repeatability variation occurs when one operator uses the same gauge for measuring the identical characteristics of the same parts. It can be affected when the gauge needs maintenance or redesign. (2) Reproducibility variation results from variations in the average of amounts sampled made by different operators using the same gauge for measuring identical characteristics of the parts. This variation can occur when the operator has been improperly trained to read and use the gauge. To help minimize it, as well as standardizing and reducing reproducibility variation, classes are being conducted in proper measuring techniques.

Cherry's gauge capability study is conducted using three operators and 10 parts, numbered 1 through 10. Using the gauge, each operator randomly selects 10 parts, records their values, and the quality engineer calculates repeatability and reproducibility.

A percent tolerance analysis determines if the gauges are capable of repeating statistically acceptable measurement. The combined percentage calculated (in our example, 60.21 percent) represents the amount of total part tolerance variation that is consumed by gauge repeatability and reproducibility measuring variations. The balance (29.79 percent) results from the process. Generally, the percent tolerance criteria for acceptance of gauge repeatability and reproducibility (combined, not separate) are:

* Under 10 percent error - acceptable.

* Ten to 30 percent error - may be acceptable based upon the importance of the application, gauge and repairs costs, etc.

* Over 30 percent error - generally not acceptable.

The quality engineer should make effort to identify and correct the problem. In our example, the combined tolerance percentage would not be acceptable as 60.21 percent is substantially in excess of 30 percent. The individual calculations for percent tolerance of repeatability (E.V. = 45 percent) and reproducibility (A.V. = 40 percent) provide information as to which of these variations contributes the most to the combined variation (60.21 percent) and can be used as a starting point for investigation.

Once the gauges are in a state of control, process capability studies can be performed. Results of these studies are used to determine center line and control limits for ongoing real-time process control charts. To be effective, studies should be conducted under normal operating conditions. The operator is allowed to make only normal operating adjustments - the date and time of each must be recorded in the events log. For most studies, Cherry's operators will collect five consecutive parts every half hour until 10 groups have been collected. Parts must be collected for the same raw material batch, shift, machine, and operator. More than one characteristic can be validated for the same part. The operator will measure and prepare x-bar and R charts for each characteristic, such as diameter or length of part.

The R chart is evaluated first for control. If all data fall between the upper and lower limits, then the R chart is in control. If points fall outside of the acceptable range, the parts are remeasured to determine if a measuring error occurred and if they are still outside the limits, the uniformity of the process is in question. A search is conducted to find out what person, machine, or material is affecting the variability. After corrective action, the study is repeated.

Next, the x-bar and R charts are used to determine if the process is in control in relation to three characteristics: variability, stability, and centering of the process. Variability is validated by the capability ratio being compared to the part tolerance range. The capability ratio formula is based on six standard deviations and is shown in the Appendix.

The stability of the process is concerned with the process average. If the x-bar chart is in control, the process average is considered stable, that is, acceptable.

The centering of the process concerns where the process average is with respect to the part's nominal specification. For instance, a part specification may be .4040 units [plus or minus] .007 units. The nominal specification would be .4040 units, while the tolerance range for the capability ratio would be .0014 units. If the specification nominal is within the control limits for the x-bar chart, then the process average is considered not significantly different from the specification nominal (requirement).

The criteria for whether a process capability study is acceptable, marginal or unacceptable are shown in Figure 3. Variability (Cr), stability (X...) and centering (Specification nominal...) are each evaluated. When results are marginal management must decide if they are acceptable, as unacceptable results require assignable causes (previously discussed) to be uncovered and corrected. Figure 4 is an example of how variability, stability and centering are determined. This example is based on material in Figure 1, Figure 2, and Figure 3. [Graphical Data Omitted]

Table : Figure 2 X and R Control Chart Factors and Formulas for Computing Control Limits

Subgroup or

[Mathematical Expression Omitted]

Table : Figure 3 Evaluation Criteria for Process Capability Studies Involving Variable Data

A process can be in statistical control, but when compared to tolerances set by the engineers, the process may not be acceptable. The process has no "assignable" causes of variability to correct. To meet tolerances, the company must make a fundamental change in the process (buy a new machine), change the tolerances, or perform 100 percent inspection.

The opposite result can also occur; a process may be able to meet tolerances, but it may not be in statistical control. Many tolerances are set wider than necessary to allow for a safety factor because the engineer knows that the process has great degrees of variability. Since the process is not in statistical control (meaning that at some point variation could be outside of tolerance limits), the firm must eliminate the "assignable" causes to bring the process into control or perform 100 percent inspection.

Processes in control can now be charted by machine operators for day-to-day surveillance. For most processes, a sample of five consecutive parts are measured and charted every half hour for each machine by the operator. Since Cherry's machines are highly mechanized, the sample frequency does not place a burden on the operator. A daily events log of all process changes (material, tools, person, etc.) is also maintained. Initially, the calculation of the averages (x-bar) and ranges (R) and the posting to the charts will be performed manually, but eventually these functions will be performed by computers located throughout the plant. Charts are visibly posted next to the machines for review by the operators, inspectors, and supervisors.

When the charting reveals that processes are out of control, the machine operator must stop the process and investigate the causes starting with the daily events log. If the R chart is out of control, the error may be machine malfunctions of setup procedures. Tooling wear, change of material, and machine setup would explain x-bar chart control problems.

Processes can also fall out of control even though the data fall within the control limits. This can be spotted when the points fall into a definite pattern. As a rule of thumb, seven points in a pattern indicate an out-of-control situation. One pattern, called a shift, appears as seven points in a row above or below the center line. The cause could be a change in material, method, or worker. A trend consists of seven points in a row upward or downward without a change of direction. Such a pattern could result from deterioration of equipment or tools or worker fatigue. Another pattern is the recurring cycle, which could be caused by shift changes, recurring preventative maintenance, worker fatigue, or temperature changes.

Statistical process control provides the machine operators with the measuring methods to maintain the quality. They become their own inspectors and are accountable for what they produce. Supervisors and inspectors must review control charts periodically throughout the day, observe workers performing their tasks, and offer assistance when needed. Quality engineers or inspectors must also perform periodic surprise process audits to test the validity of the chart data. Management needs to impress upon the workers that falsification of records can result in dismissal. However, the intent of SPC is not to be a negative performance monitor but should be viewed as a positive system for maintaining quality.

SPC training of 350 employees by outside consultants will take approximately one year. The goals of the SPC training are: 1) To teach fundamental SPC and problemsolving skills so that manufacturing personnel can effectively communicate and solve production quality problems. 2) To establish Quality and Productivity Improvement teams and steering committees for providing direction, improving processes, and eliminating barriers to higher quality and productivity. 3) To develop a team oriented environment which places a high value on solving problems and achieving results. SPC terminology and concepts cannot be understood or communicated unless the workers can understand English; therefore, training began by teaching English to 34 targeted production workers. Next, 278 machine operators, machine analysts, and inspectors will be taught basic mathematics, blueprint reading, and measuring skills. This is intended to improve mathematical accuracy and reduce measuring errors. Then, this same group will be taught SPC concepts and applications in the classroom and through on-the-job applications. The 278 production workers will receive 122 hours of training spread over 19 weeks. The Quality and Productivity Improvement teams will also be taught SPC concepts and applications plus problem solving and quality planning skills. This group includes 72 employees composed of production managers, supervisors, lead persons, engineers, design engineers and maintenance machinists. Each of these employees will receive 340 hours of training spread over 21 weeks.

The total cost of SPC training will be in excess of $1 million - approximately 50 percent for outside consultants, and 50 percent as the cost of the lost productivity (95 percent) and out-of-pocket costs (5 percent). The direct cost portion is substantially reimbursed by California under a "Cost Sharing" system. Reimbursement is made only after training is complete and if the employee remains on the job for 90 days.

Implementation of SPC is expected to change the entire manufacturing atmosphere at Cherry. Employees' attitudes, language, and actions should be directed more toward improving quality and productivity. Machine operators' morale and motivation should improve as they develop a greater sense of ownership over what they produce. Workers are expected to spend less time on rework. Machine capacity should increase, machine lead time should drop off, and less scrap produced. Customers' satisfaction should improve as past due shipments decrease. Because ultimately, the success of any program is dependent upon the backing of top management, Cherry Textron's management has made a firm commitment to the system.

PHOTO : Figure 1 Examples of X and R Control Charts

Competitive pressures have forced many U.S. firms to improve the quality of their products. One quality improvement method being introduced in the production process by Cherry Textron is statistical process control (SPC). The objective of SPC is to manufacture all parts to specifications the first time, eliminating costly rework, scrap, and unnecessary 100 percent inspection. This is accomplished by having each machine operator regularly measure parts they are producing against statistically pre-established quality standards and charting their results. When parts fall outside of the statistically acceptable range, production is immediately stopped and the problem is identified and corrected. Such an approach prevents discovery of faulty components after completion of an entire production run. Responsibility rests with the machine operators for producing acceptable parts only, thus they become in essence their own inspectors.

Cherry Textron (Cherry) produces and sells blind rivet fasteners to the aerospace and automotive industries. Frequently, customers within these industries demand that vendors implement and maintain evidence that their manufacturing processes are within statistical control. In response, Cherry started an extensive program to train 350 employees in SPC methods. SPC will give production personnel the tools to measure their own quality, and when out of the control, a means to systematically analyze the causes.

The concept of x-bar and R control charts is based on statistical hypothesis testing including computation of mean and standard deviation under a normal distribution curve. Upper and lower control limits are typically calculated [plus or minus] 3 standard deviations, thus providing a 99.7 percent assurance that a type 1 error will not occur (rejection of a null hypothesis that is true).

The prime use of the control chart is to detect "assignable causes" of process variation. Process variations are attributable to two kinds of causes: random - due solely to chance, and assignable - due to specific "findable" causes. Ideally, only random causes should be present; therefore, they represent the minimum possible variation. A process which is operating without assignable causes of variation is said to be "in a state of statistical control." This occurs when samples selected and charted on the x-bar and R control charts fall within the control limits. Assignable causes exist when the actual variation exceeds the control limits. After giving consideration to cost/benefit analysis, the process is investigated, causes are identified and corrected, and finally the process is remeasured to determine if it is now in control.

The x-bar and R control chart concepts are used repeatedly to control quality in the following order:

Gauge Capability Study. Cherry determines that the gauges used for measuring critical part specifications are in control.

Process Capability Study. Production processes are measured for control to pre-established tolerances.

Process Control Procedures. These constitute the day-to-day surveillance to ensure that the production process remains in control. After a short explanation of x-bar and R control charts, we discuss each of the above functions.

An example of a typical x-bar and R control chart data sheet is Figure 1 (next page).

Twenty-five subgroups of five samples each were selected. For each subgroup, Cherry calculated the average (x-bar), the range(R) (the difference between the highest and lowest measure), and a grand total average (shown in Figure 1 as an "x" with a double-bar above) and range (shown in Figure 1 as an "R" with a single bar above) for the 25 averages (x-bar) and ranges (R). Upper and lower control limits were also computed for x-bar and R. The mean and range for each subgroup are plotted on graphs. Since all plot points in Figure 1 fall within the upper and lower limits, the process is in statistical control.

The chart of x-bar values tells when a change has occurred in central tendency. This may be due to such factors as tool wear, a gradual increase in temperature, a new batch of material of greater toughness, or a different machine setting used by the night operator. Eventually, Quality and Productivity Improvement teams will compile a list of potential reasons for lack of control that operators can check against.

The R chart indicates when a significant gain or loss in uniformity has taken place. Since processes have inherent variability (dispersion), some deviation from the mean is expected. The chart's upper and lower limits reflect the range allowable for uniform process dispersion. A loss of uniformity indicates a machine malfunction (necessitating repair) or, more likely, a lack of operator skill or concentration. The R chart should always be read first because a lack of control appearing on it will normally show on the x-bar chart, whereas the opposite does not usually occur - lack of control on the x-bar chart may not appear on the R chart because machines are normally able to stay within their inherent variability limits (R).

The gauge and process capabilities studies are the two primary steps for implementing SPC on any process. They ensure the inspection gauges are sound and the process is capable of meeting stated requirements. Hence, the first step in SPC implementation is to perform a gauge capability study. An analysis of a process cannot be meaningful unless the measuring gauges used to collect data are both accurate and reliable. Accuracy of the gauges is the responsibility of quality engineers so accuracy will not be tested as part of the gauge study, but the firm will test for statistical error determined by repeated measurements with the gauges.

For example, to measure rivet specifications, Cherry uses comparators and micrometers. A comparator is a microscope which projects the image of the part onto a screen. The screen has two axes for measuring: (y) up and down, and (x) right and left. One edge of the part specification to be quantified is placed on the appropriate axis then moved across the screen to the other edge. An electronic counter measures the distance moved is .0001 of an inch. The comparator is used to determine critical rivet specifications that cannot be read by a hand held micrometer, i.e., distances between angles of a part. The hand held micrometer, which also measures diameters to .0001 of an inch, is operated by turning a series of dials until the fingers of the micrometer barely pinches the surface of the part.

Reliability is affected by two variations: (1) Repeatability variation occurs when one operator uses the same gauge for measuring the identical characteristics of the same parts. It can be affected when the gauge needs maintenance or redesign. (2) Reproducibility variation results from variations in the average of amounts sampled made by different operators using the same gauge for measuring identical characteristics of the parts. This variation can occur when the operator has been improperly trained to read and use the gauge. To help minimize it, as well as standardizing and reducing reproducibility variation, classes are being conducted in proper measuring techniques.

Cherry's gauge capability study is conducted using three operators and 10 parts, numbered 1 through 10. Using the gauge, each operator randomly selects 10 parts, records their values, and the quality engineer calculates repeatability and reproducibility.

A percent tolerance analysis determines if the gauges are capable of repeating statistically acceptable measurement. The combined percentage calculated (in our example, 60.21 percent) represents the amount of total part tolerance variation that is consumed by gauge repeatability and reproducibility measuring variations. The balance (29.79 percent) results from the process. Generally, the percent tolerance criteria for acceptance of gauge repeatability and reproducibility (combined, not separate) are:

* Under 10 percent error - acceptable.

* Ten to 30 percent error - may be acceptable based upon the importance of the application, gauge and repairs costs, etc.

* Over 30 percent error - generally not acceptable.

The quality engineer should make effort to identify and correct the problem. In our example, the combined tolerance percentage would not be acceptable as 60.21 percent is substantially in excess of 30 percent. The individual calculations for percent tolerance of repeatability (E.V. = 45 percent) and reproducibility (A.V. = 40 percent) provide information as to which of these variations contributes the most to the combined variation (60.21 percent) and can be used as a starting point for investigation.

Once the gauges are in a state of control, process capability studies can be performed. Results of these studies are used to determine center line and control limits for ongoing real-time process control charts. To be effective, studies should be conducted under normal operating conditions. The operator is allowed to make only normal operating adjustments - the date and time of each must be recorded in the events log. For most studies, Cherry's operators will collect five consecutive parts every half hour until 10 groups have been collected. Parts must be collected for the same raw material batch, shift, machine, and operator. More than one characteristic can be validated for the same part. The operator will measure and prepare x-bar and R charts for each characteristic, such as diameter or length of part.

The R chart is evaluated first for control. If all data fall between the upper and lower limits, then the R chart is in control. If points fall outside of the acceptable range, the parts are remeasured to determine if a measuring error occurred and if they are still outside the limits, the uniformity of the process is in question. A search is conducted to find out what person, machine, or material is affecting the variability. After corrective action, the study is repeated.

Next, the x-bar and R charts are used to determine if the process is in control in relation to three characteristics: variability, stability, and centering of the process. Variability is validated by the capability ratio being compared to the part tolerance range. The capability ratio formula is based on six standard deviations and is shown in the Appendix.

The stability of the process is concerned with the process average. If the x-bar chart is in control, the process average is considered stable, that is, acceptable.

The centering of the process concerns where the process average is with respect to the part's nominal specification. For instance, a part specification may be .4040 units [plus or minus] .007 units. The nominal specification would be .4040 units, while the tolerance range for the capability ratio would be .0014 units. If the specification nominal is within the control limits for the x-bar chart, then the process average is considered not significantly different from the specification nominal (requirement).

The criteria for whether a process capability study is acceptable, marginal or unacceptable are shown in Figure 3. Variability (Cr), stability (X...) and centering (Specification nominal...) are each evaluated. When results are marginal management must decide if they are acceptable, as unacceptable results require assignable causes (previously discussed) to be uncovered and corrected. Figure 4 is an example of how variability, stability and centering are determined. This example is based on material in Figure 1, Figure 2, and Figure 3. [Graphical Data Omitted]

Table : Figure 2 X and R Control Chart Factors and Formulas for Computing Control Limits

Subgroup or

Sample Size Average Range n A2 D3 D4 d2 2 1.880 0 3.268 1.128 3 1.023 0 2.574 1.693 4 0.729 0 2.282 2.059 5 0.577 0 2.114 2.326 6 0.483 0 2.004 2.534 7 0.419 0.076 1.924 2.704 8 0.373 0.135 1.854 2.847 9 0.337 0.184 1.816 2.970 10 0.308 0.223 1.777 3.078 11 0.285 0.256 1.744 3.173 12 0.266 0.284 1.717 3.258 13 0.249 0.308 1.692 3.336 14 0.235 0.329 1.671 3.407 15 0.223 0.348 1.652 3.472

[Mathematical Expression Omitted]

Table : Figure 3 Evaluation Criteria for Process Capability Studies Involving Variable Data

Specification nominal within control limits on Cr X chart in control? X chart? Decision 75% or less Yes Yes Acceptable Yes No Marginal No Yes Marginal No No Unacceptable 75-100% Yes Yes Marginal Yes No Marginal No Yes Marginal No No Unacceptable 100% or more Not applicable Not applicable Unacceptable

A process can be in statistical control, but when compared to tolerances set by the engineers, the process may not be acceptable. The process has no "assignable" causes of variability to correct. To meet tolerances, the company must make a fundamental change in the process (buy a new machine), change the tolerances, or perform 100 percent inspection.

The opposite result can also occur; a process may be able to meet tolerances, but it may not be in statistical control. Many tolerances are set wider than necessary to allow for a safety factor because the engineer knows that the process has great degrees of variability. Since the process is not in statistical control (meaning that at some point variation could be outside of tolerance limits), the firm must eliminate the "assignable" causes to bring the process into control or perform 100 percent inspection.

Processes in control can now be charted by machine operators for day-to-day surveillance. For most processes, a sample of five consecutive parts are measured and charted every half hour for each machine by the operator. Since Cherry's machines are highly mechanized, the sample frequency does not place a burden on the operator. A daily events log of all process changes (material, tools, person, etc.) is also maintained. Initially, the calculation of the averages (x-bar) and ranges (R) and the posting to the charts will be performed manually, but eventually these functions will be performed by computers located throughout the plant. Charts are visibly posted next to the machines for review by the operators, inspectors, and supervisors.

When the charting reveals that processes are out of control, the machine operator must stop the process and investigate the causes starting with the daily events log. If the R chart is out of control, the error may be machine malfunctions of setup procedures. Tooling wear, change of material, and machine setup would explain x-bar chart control problems.

Processes can also fall out of control even though the data fall within the control limits. This can be spotted when the points fall into a definite pattern. As a rule of thumb, seven points in a pattern indicate an out-of-control situation. One pattern, called a shift, appears as seven points in a row above or below the center line. The cause could be a change in material, method, or worker. A trend consists of seven points in a row upward or downward without a change of direction. Such a pattern could result from deterioration of equipment or tools or worker fatigue. Another pattern is the recurring cycle, which could be caused by shift changes, recurring preventative maintenance, worker fatigue, or temperature changes.

Statistical process control provides the machine operators with the measuring methods to maintain the quality. They become their own inspectors and are accountable for what they produce. Supervisors and inspectors must review control charts periodically throughout the day, observe workers performing their tasks, and offer assistance when needed. Quality engineers or inspectors must also perform periodic surprise process audits to test the validity of the chart data. Management needs to impress upon the workers that falsification of records can result in dismissal. However, the intent of SPC is not to be a negative performance monitor but should be viewed as a positive system for maintaining quality.

SPC training of 350 employees by outside consultants will take approximately one year. The goals of the SPC training are: 1) To teach fundamental SPC and problemsolving skills so that manufacturing personnel can effectively communicate and solve production quality problems. 2) To establish Quality and Productivity Improvement teams and steering committees for providing direction, improving processes, and eliminating barriers to higher quality and productivity. 3) To develop a team oriented environment which places a high value on solving problems and achieving results. SPC terminology and concepts cannot be understood or communicated unless the workers can understand English; therefore, training began by teaching English to 34 targeted production workers. Next, 278 machine operators, machine analysts, and inspectors will be taught basic mathematics, blueprint reading, and measuring skills. This is intended to improve mathematical accuracy and reduce measuring errors. Then, this same group will be taught SPC concepts and applications in the classroom and through on-the-job applications. The 278 production workers will receive 122 hours of training spread over 19 weeks. The Quality and Productivity Improvement teams will also be taught SPC concepts and applications plus problem solving and quality planning skills. This group includes 72 employees composed of production managers, supervisors, lead persons, engineers, design engineers and maintenance machinists. Each of these employees will receive 340 hours of training spread over 21 weeks.

The total cost of SPC training will be in excess of $1 million - approximately 50 percent for outside consultants, and 50 percent as the cost of the lost productivity (95 percent) and out-of-pocket costs (5 percent). The direct cost portion is substantially reimbursed by California under a "Cost Sharing" system. Reimbursement is made only after training is complete and if the employee remains on the job for 90 days.

Implementation of SPC is expected to change the entire manufacturing atmosphere at Cherry. Employees' attitudes, language, and actions should be directed more toward improving quality and productivity. Machine operators' morale and motivation should improve as they develop a greater sense of ownership over what they produce. Workers are expected to spend less time on rework. Machine capacity should increase, machine lead time should drop off, and less scrap produced. Customers' satisfaction should improve as past due shipments decrease. Because ultimately, the success of any program is dependent upon the backing of top management, Cherry Textron's management has made a firm commitment to the system.

PHOTO : Figure 1 Examples of X and R Control Charts

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Author: | Heinricks, Jan; Fleming, Mary M.K. |
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Publication: | Industrial Management |

Date: | May 1, 1991 |

Words: | 2983 |

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