- Email: [email protected]
Computer-aided statistical quality control learning
Computers ind. Engng Vol. 33, Nos 1-2, pp. 125-128, 1997
Pergamon PIh S0360-83$2(97)00056-9
O 1997 Elsevier Science Ltd Printed in Great Britain. All rights reserved 0360-8352197 $17.00 + 0.00
C O M P U T E R - M D E D STATISTICAL QUALITY CONTROL LEARNING
by Felipe Llaugel Science and Technology Center Universidad Dominicana O&M Santo Domingo, Dominican Republic.
e-mail:oym.dom2(~codetel.net.do and SilverioConfesor Industrial Engineering Department Instituto Tecnologico de Santo Dommgo Santo Domingo, Domimcan Republic. ABSTRACT Managing Quality Control is a natural function of the competitive companies of the current period, the 90's. Lcaming how to obtain quality from different processes of the manufacturing and service industries is needed in order to be part of the World Class Quality Workers. The teaching process about quality topics requires new concepts and tools for the teaching process itself to be efficient. One of the most difficult obstacles in the learning process about quality is the fact that the teaching classroom and the production plants are quite different and separated. Also, even when the students start practicing in a company or they get a job, they are affected for the lack of experience in conducting quality control and process improvement works. A virtual room is proposed as a productive tool in teaching quality control and quality improvement through combining theory and practice of different processes in an interactive program. 0 1997 E l s e v i e r S c i e n c e Ltd
KEYWORDS Computer-Based Training; SPC; Simulation; Quality Control; Virtual Factory.
INTRODUCTION The reliability of an instructional model in any field of instruction, depends on how well it fits reality. Naturally, a model that does not cover all possible situations of a problem will not present a valid reflection of that problem. Taking into account all the problem facts may result in a very complex model. However, complexity alone does not ensure validity. Validity also depends on the degree to which the model has behavioral basis. Models that merely extrapolate past actions without concern for the behaviors that influenced those actions are likely to be invalid representations of the learning situation. This paper discuss the use of computer aided instruction to give a hand-on experience to undergraduate students and bottom line workers, on the use of statistical tools to control an industrial operation. The advantages of using a simulated process are evident. The students can asses the results of their actions in situations similar o harder to those found in real facilities. The process simulated has been simplified to use the Shewhart X-chart to control the mean of a process operation. The program is intended to develop pattern recognition capabilities from the X-chart control chart. The students are provided with statistical tools and data collection possibilities in order that they can develop sampling methodologies according with their believes about process performance. The program is designed to be used in a competitive environment, that it is, students teams compete with each other, and tAlE 33: l/2-f
125
126
2Ist International Conference on Computers and Industrial Engineering
the grades are provided according with the relative performance of the teams. The fact that the program was written in Spanish makes easier to be applied in universities like those of the Dominican Republic and other Latin American countries. The program is capable of performing different processes situations according with instructor commands, and also of being modified when the process, obeying his natural variance, shows an out of control pattern in the X-chart or it is producing defective items. With the virtual class room, the student will be physically linked to the realism of the manufacturing and service process, and will be trained with the natural problems of quality control and quality improvement techniques. The program simulates the operation of a factory subject to almost all the more common constraints of a particular kind. The type of process perturbation is modeled by the instructor and is the students duty to do early recognition of such a perturbation to take actions to avoid further production defects. This paper explains our experience using the program with undergraduate industrial engineering students and bottom-line operators, and compares the skills developed by them with those of real practitioners. The program exposes the students to different type of situations even some that probably they only find under very hard conditions.
PROBLEM STATEMENT The simulated operation is the filling of medicine bottles. The content of the bottle must be kept between fixed limits (specification limits). The operation is controlled with a valve which may be opened or closed acx,ordmg with the student believe about the performance of the operation. If the student think that the content of the bottle is outside the specification limits, he can close or open the valve. To be sure about the process performance, the student is able to take random samples of ten bottles and measure the content of liquid. The student must keep the process under control because to over fill the bottle may cause leak in the filling line with a known cost. If the bottles are packed with a content under the specification limit, the customer may return the whole lot with a huge cost for the factory. Over filling and under filling have a cost; taking samples has other cost. The program compute a total cost that must be kept minimum. Due to the competition among teams, each team tries to make their best.
PROGRAM OPERATION The program interacts with the students, allowing them to take samples of the process at a variable interval time according with their believes about the process performance. Almost all the students exposed to the program have shown a fast adaptability to the keyboard and to the layout of the data shown in the screen, even when they have never used a computer before. The program was developed with Microsoft Quick C due to the facilities provided to build the graphics for the bottles animation and the control chart in DOS platform. The fact that the process only present variation in the process mean (it was developed to do so), makes not necessary to plot the standard deviation and range chart. At loading time, the instructor passes to the program a parameter indicating the kind of variation desired for the process run. A sequence of numbers in the parameter indicatesifthe deviation in the mean will be upward or downward. The deviations are produced randomly, so even the instructordoes not know exactly when the perturbation will take effect.The students teams must detect and correct the deviation before the consequences be catastrophic.
21st International Conference on Computers and Industrial Engineering
127
The program give a sio~'n_a_lof perturbation with an alarm indicating bottle overfilling or when a lot is returned by the customer. When the overfilling alarm is noticed, a lot of material have been wasted. Because the student ignores the sampling plan of the customer, they do not know how many undefiled bottles must be in a lot to be returned by the customer. To asses the performance of the operation the student presses a key that tells the program to take a random sample of ten bottles, then, they are shown m the screen with the animation of the ftlling process. The students see how the bottles are filled and the program depicts the content of each bottle as well as the average, the standard deviation and range of the sample. Immediately after that, the program plots the average content of the bottle on a X-chart. The student analyze the chart and decides if the valve must be opened or closed in order to adjust the process. The decision is based in the pattern shown by the points plot in the control chart. The students teams get their grades according with their performance to keep low the total cost and to keep high the production quality. Upon completion of training time, an average cost per lot is computed based on the number of good lots produced and the total production cost. Good lots is the difference between the total number of produced lots and the number of rejected lots. The team with the minimum cost per lot, gets the higher score. Those teams who has produced a number of lots over 80% of the production of the team with the higher number of good lots, qualify for scores, the others get not points. The costs to operate the process are assigned to show the ability of the students to control the operation, maximizing the production and the quality. The sampling cost is the lower cost, and the rejected lots are assigned the higher cost.
CONCLUSION Our experience using computer's software to teach quality control principles have been very successful. Almost all the students exposed to the program have shown a fast comprehension of the theory behind the use of control charts. The use of competition among student is the main incentive in order they to do their best in keeping the simulated process under control. Despite the progress acquired with the use of this virtual factory, the software may be improved to allowed to the students: verify the stability of the process variability, to view a wider window in the control chart (more than the 25 points plotted in the current version), to implement other statistical tests like CUSUM, Run test, process capability, etc. So far, in the current version of the program, pattern recognition is the only method to detect a shift or decrease in the process mean. Currently we are developing a new version of the program to integrate multimedia capabilities. The new version is been developed using Borland C++ for Windows. This new platform allows to use object oriented programming, to combine video and sound to the program, and to increase the number of operations incorporated to the program taking advantage of the previous work. The program may be enhanced including: variable sampling size, different quality characteristic, other control charts, analysis of greater data series, use of multimedia, more control tools, tracking of student actions, and the capability to allow the instructor to simulate more complicated industrial operations.
REFERENCES
Alessi, Stephen M, and TroUip, Stanley R. (1992). Computer-based Instruction: Methods and Development. Englewood Cliffs, NJ. Prentice-Hall, Inc. Barker, Philip and Yeates, Henry (1985). Introducing Computer Assisted Learning. London. Prentice-Hall International.
128
21st International Conferenceon Computersand Industrial Engineering
Cui, R.Q. and Reynolds, M.R., Jr. (1988). X-charts with Run Rules and Variable Sampling Intervals. Communications in Statistics-Simulation, 17,3, 1073-1093. Das, T.K. and Jam, V. (1994). Variable Sampling Intervals Policies for X-charts. Proceeding of the 3rd International IERC, pp. 600-604. Downes T (1993), Student-Teachers' Experiences m Using Computars During Teaching Practice. Journal of Computer Assisted Learning, Vol. 9 number l, March, pp 17-33 Maleyeff, John (1991). Videotape/Simulation Case Studies for SQC Training. 45th Annual Quality Congress, May 1991, Milwauk~ WI, pp. 580-584. Montgomery, D.C. (1992). Economic design of an X control chart. Journal of Quality technology. 14, 1 pp. 40-43. Page, E.S. (1955). Control charts with warning lines, Biometrics 42, pp. 243-257. Reynolds, M.R., Jr. (1989). Optimal Variable Sampling Intervals Control Chart. Sequential Analysis, 8, pp. 361-379. Roberts, S.W. 0958). Properties of Control Charts Zone Tests. The Bell System Technical Journal. 37, pp. 83-114.
Pergamon PIh S0360-83$2(97)00056-9
O 1997 Elsevier Science Ltd Printed in Great Britain. All rights reserved 0360-8352197 $17.00 + 0.00
C O M P U T E R - M D E D STATISTICAL QUALITY CONTROL LEARNING
by Felipe Llaugel Science and Technology Center Universidad Dominicana O&M Santo Domingo, Dominican Republic.
e-mail:oym.dom2(~codetel.net.do and SilverioConfesor Industrial Engineering Department Instituto Tecnologico de Santo Dommgo Santo Domingo, Domimcan Republic. ABSTRACT Managing Quality Control is a natural function of the competitive companies of the current period, the 90's. Lcaming how to obtain quality from different processes of the manufacturing and service industries is needed in order to be part of the World Class Quality Workers. The teaching process about quality topics requires new concepts and tools for the teaching process itself to be efficient. One of the most difficult obstacles in the learning process about quality is the fact that the teaching classroom and the production plants are quite different and separated. Also, even when the students start practicing in a company or they get a job, they are affected for the lack of experience in conducting quality control and process improvement works. A virtual room is proposed as a productive tool in teaching quality control and quality improvement through combining theory and practice of different processes in an interactive program. 0 1997 E l s e v i e r S c i e n c e Ltd
KEYWORDS Computer-Based Training; SPC; Simulation; Quality Control; Virtual Factory.
INTRODUCTION The reliability of an instructional model in any field of instruction, depends on how well it fits reality. Naturally, a model that does not cover all possible situations of a problem will not present a valid reflection of that problem. Taking into account all the problem facts may result in a very complex model. However, complexity alone does not ensure validity. Validity also depends on the degree to which the model has behavioral basis. Models that merely extrapolate past actions without concern for the behaviors that influenced those actions are likely to be invalid representations of the learning situation. This paper discuss the use of computer aided instruction to give a hand-on experience to undergraduate students and bottom line workers, on the use of statistical tools to control an industrial operation. The advantages of using a simulated process are evident. The students can asses the results of their actions in situations similar o harder to those found in real facilities. The process simulated has been simplified to use the Shewhart X-chart to control the mean of a process operation. The program is intended to develop pattern recognition capabilities from the X-chart control chart. The students are provided with statistical tools and data collection possibilities in order that they can develop sampling methodologies according with their believes about process performance. The program is designed to be used in a competitive environment, that it is, students teams compete with each other, and tAlE 33: l/2-f
125
126
2Ist International Conference on Computers and Industrial Engineering
the grades are provided according with the relative performance of the teams. The fact that the program was written in Spanish makes easier to be applied in universities like those of the Dominican Republic and other Latin American countries. The program is capable of performing different processes situations according with instructor commands, and also of being modified when the process, obeying his natural variance, shows an out of control pattern in the X-chart or it is producing defective items. With the virtual class room, the student will be physically linked to the realism of the manufacturing and service process, and will be trained with the natural problems of quality control and quality improvement techniques. The program simulates the operation of a factory subject to almost all the more common constraints of a particular kind. The type of process perturbation is modeled by the instructor and is the students duty to do early recognition of such a perturbation to take actions to avoid further production defects. This paper explains our experience using the program with undergraduate industrial engineering students and bottom-line operators, and compares the skills developed by them with those of real practitioners. The program exposes the students to different type of situations even some that probably they only find under very hard conditions.
PROBLEM STATEMENT The simulated operation is the filling of medicine bottles. The content of the bottle must be kept between fixed limits (specification limits). The operation is controlled with a valve which may be opened or closed acx,ordmg with the student believe about the performance of the operation. If the student think that the content of the bottle is outside the specification limits, he can close or open the valve. To be sure about the process performance, the student is able to take random samples of ten bottles and measure the content of liquid. The student must keep the process under control because to over fill the bottle may cause leak in the filling line with a known cost. If the bottles are packed with a content under the specification limit, the customer may return the whole lot with a huge cost for the factory. Over filling and under filling have a cost; taking samples has other cost. The program compute a total cost that must be kept minimum. Due to the competition among teams, each team tries to make their best.
PROGRAM OPERATION The program interacts with the students, allowing them to take samples of the process at a variable interval time according with their believes about the process performance. Almost all the students exposed to the program have shown a fast adaptability to the keyboard and to the layout of the data shown in the screen, even when they have never used a computer before. The program was developed with Microsoft Quick C due to the facilities provided to build the graphics for the bottles animation and the control chart in DOS platform. The fact that the process only present variation in the process mean (it was developed to do so), makes not necessary to plot the standard deviation and range chart. At loading time, the instructor passes to the program a parameter indicating the kind of variation desired for the process run. A sequence of numbers in the parameter indicatesifthe deviation in the mean will be upward or downward. The deviations are produced randomly, so even the instructordoes not know exactly when the perturbation will take effect.The students teams must detect and correct the deviation before the consequences be catastrophic.
21st International Conference on Computers and Industrial Engineering
127
The program give a sio~'n_a_lof perturbation with an alarm indicating bottle overfilling or when a lot is returned by the customer. When the overfilling alarm is noticed, a lot of material have been wasted. Because the student ignores the sampling plan of the customer, they do not know how many undefiled bottles must be in a lot to be returned by the customer. To asses the performance of the operation the student presses a key that tells the program to take a random sample of ten bottles, then, they are shown m the screen with the animation of the ftlling process. The students see how the bottles are filled and the program depicts the content of each bottle as well as the average, the standard deviation and range of the sample. Immediately after that, the program plots the average content of the bottle on a X-chart. The student analyze the chart and decides if the valve must be opened or closed in order to adjust the process. The decision is based in the pattern shown by the points plot in the control chart. The students teams get their grades according with their performance to keep low the total cost and to keep high the production quality. Upon completion of training time, an average cost per lot is computed based on the number of good lots produced and the total production cost. Good lots is the difference between the total number of produced lots and the number of rejected lots. The team with the minimum cost per lot, gets the higher score. Those teams who has produced a number of lots over 80% of the production of the team with the higher number of good lots, qualify for scores, the others get not points. The costs to operate the process are assigned to show the ability of the students to control the operation, maximizing the production and the quality. The sampling cost is the lower cost, and the rejected lots are assigned the higher cost.
CONCLUSION Our experience using computer's software to teach quality control principles have been very successful. Almost all the students exposed to the program have shown a fast comprehension of the theory behind the use of control charts. The use of competition among student is the main incentive in order they to do their best in keeping the simulated process under control. Despite the progress acquired with the use of this virtual factory, the software may be improved to allowed to the students: verify the stability of the process variability, to view a wider window in the control chart (more than the 25 points plotted in the current version), to implement other statistical tests like CUSUM, Run test, process capability, etc. So far, in the current version of the program, pattern recognition is the only method to detect a shift or decrease in the process mean. Currently we are developing a new version of the program to integrate multimedia capabilities. The new version is been developed using Borland C++ for Windows. This new platform allows to use object oriented programming, to combine video and sound to the program, and to increase the number of operations incorporated to the program taking advantage of the previous work. The program may be enhanced including: variable sampling size, different quality characteristic, other control charts, analysis of greater data series, use of multimedia, more control tools, tracking of student actions, and the capability to allow the instructor to simulate more complicated industrial operations.
REFERENCES
Alessi, Stephen M, and TroUip, Stanley R. (1992). Computer-based Instruction: Methods and Development. Englewood Cliffs, NJ. Prentice-Hall, Inc. Barker, Philip and Yeates, Henry (1985). Introducing Computer Assisted Learning. London. Prentice-Hall International.
128
21st International Conferenceon Computersand Industrial Engineering
Cui, R.Q. and Reynolds, M.R., Jr. (1988). X-charts with Run Rules and Variable Sampling Intervals. Communications in Statistics-Simulation, 17,3, 1073-1093. Das, T.K. and Jam, V. (1994). Variable Sampling Intervals Policies for X-charts. Proceeding of the 3rd International IERC, pp. 600-604. Downes T (1993), Student-Teachers' Experiences m Using Computars During Teaching Practice. Journal of Computer Assisted Learning, Vol. 9 number l, March, pp 17-33 Maleyeff, John (1991). Videotape/Simulation Case Studies for SQC Training. 45th Annual Quality Congress, May 1991, Milwauk~ WI, pp. 580-584. Montgomery, D.C. (1992). Economic design of an X control chart. Journal of Quality technology. 14, 1 pp. 40-43. Page, E.S. (1955). Control charts with warning lines, Biometrics 42, pp. 243-257. Reynolds, M.R., Jr. (1989). Optimal Variable Sampling Intervals Control Chart. Sequential Analysis, 8, pp. 361-379. Roberts, S.W. 0958). Properties of Control Charts Zone Tests. The Bell System Technical Journal. 37, pp. 83-114.