Abstract:
A method of generating quality matrices indicating a relationship between critical to quality characteristics and key control parameters for levels of a process. A plurality of rows of a first matrix are designated as critical to quality characteristics and a plurality of columns of the first matrix are designated as key control parameters. Each critical to quality characteristic is assigned a critical to quality weight. An interaction weight is assigned between at least one critical to quality characteristic and at least one key control parameter. A score is then generated for at least one key control parameter in response to said critical to quality weight and said interaction weight.

Description:
BACKGROUND OF THE INVENTION 
     The invention relates to a method for identifying critical to quality (CTQ) dependencies in quality function deployment. Quality function deployment (QFD) is a methodology for documenting and breaking down customer requirements into manageable and actionable details. The concept of “houses of quality” has been used to represent the decomposition of higher level requirements such as critical to quality characteristics or CTQ&#39;s (also referred to as Y&#39;s) into lower level characteristics such as key control parameters or KCP&#39;s (also referred to as X&#39;s). FIG. 1 depicts a conventional house of quality hierarchy in which high level requirements such as customer requirements are decomposed into lower level characteristics such as key manufacturing processes and key process variables within the manufacturing processes. 
     Each house of quality corresponds to a stage or level of the process of designing a product. At the highest level, represented as house of quality #1, customer requirements are associated with functional requirements of the product. At the next level of the design process, represented as house of quality #2, the functional requirements of the product are associated with part characteristics. At the next level of the design process, represented as house of quality #3, the part characteristics are associated with manufacturing processes. At the next level of the design process, represented as house of quality #4, the manufacturing processes are associated with manufacturing process variables. While the house of quality design process is useful, it is understood that improvements to this process are needed. 
     BRIEF SUMMARY OF THE INVENTION 
     An exemplary embodiment of the invention is directed to a method of generating quality matrices indicating a relationship between critical to quality characteristics and key control parameters for levels of a process. A plurality of rows of a first matrix are designated as critical to quality characteristics and a plurality of columns of the first matrix are designated as key control parameters. Each critical to quality characteristic is assigned a critical to quality weight. An interaction weight is assigned between at least one critical to quality characteristic and at least one key control parameter. A score is then generated for at least one key control parameter in response to said critical to quality weight and said interaction weight. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     Referring now to the drawings wherein like elements are numbered alike in the several Figures: 
     FIG. 1 illustrates the houses of quality in a conventional multi-level design process; 
     FIG. 2 is a quality matrix in an exemplary embodiment of the invention; 
     FIG. 3 is a graphical representation of scores for key control parameters; 
     FIG. 4 is a flowchart of a process for generating a quality matrix; 
     FIG. 5 depicts a hierarchy of quality matrices; and 
     FIG. 6 is a flowchart of the process of tracking critical to quality characteristics or key control parameters. 
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     FIG. 2 depicts a quality matrix shown generally at  100 . A quality matrix may be created for one or more levels of a multi-level design process. The quality matrices provide for tracking and assessing the relationship between critical to quality characteristics and key control parameters within a level and between levels of the design process. Critical to quality characteristics  110  are labeled y 1 -y 6  and are arranged in rows. Key control parameters  112  are labeled x 1 -x 8  and are arranged in columns. In the example shown in FIG. 2, the quality matrix is based on market data and the critical to quality characteristics  110  are customer expectations. The key control parameters  112  are product requirements. It is understood that other critical to quality characteristics and key control parameters may be arranged in a matrix as described below. Each critical to quality characteristic  110  and key control parameter  112  may be associated with stored data providing information for each entry (e.g., source of the data, assumptions, ranges, exceptions, etc.). The matrix  100  may be expanded by adding critical to quality characteristics and/or key control parameters. 
     Each critical to quality characteristic is assigned an importance or weight as shown in column  114 . The critical to quality weights range from 1 to 5 (with 5 being the highest) depending on how important each critical to quality characteristic is to customer expectation. It is understood that different weights may be used. For each critical to quality characteristic  110  and each key control parameter  112 , an interaction weight  116  is assigned representing the effect that a key control parameter  112  has on a critical to quality characteristic  110 . The interaction weights shown in FIG. 2 are h, m, and l representing high, medium and low respectively. For example, at the intersection of critical to quality characteristic y 1  and key control parameters x 1 , h indicates that key control parameters x 1  has a high effect on critical to quality characteristic y 1 . 
     A total score is generated for each key control parameter as shown in row  118 . Each interaction weight  116  may be assigned a numerical value. In the example shown in FIG. 2, low has a value of 1, medium has a value of 3 and high has a value of 9. To generate the total score for each key control parameter  112 , the interaction weights  116  are multiplied by the critical to quality weights  114  along a column of the matrix and these products are summed. If a key control parameter is not assigned an interaction weight, its interaction weight is zero. For example, key control parameter x 1  has a high interaction weight with critical to quality characteristic y 1  and a low interaction weight with critical to quality characteristic y 6 . The total score for key control parameter x 1  is (9·5)+(1·1)=46. The total score indicates the key control parameters that contribute the most to the critical to quality characteristics. A total column  120  contains a total score for each critical to quality characteristic  110  which may serve as a consistency check. Critical to quality characteristics having similar critical to quality weights  114  should have similar scores in total column  120 . If critical to quality characteristics  110  having similar critical to quality weights  114  but significantly different values in total column  120 , this indicates that a critical to quality characteristic  110  should be a key control parameter  112  or that some key control parameters are missing. 
     FIG. 3 depicts a pareto graph of the total score for each key control parameter. The present invention may be implemented on a general purpose computer. The user may create matrix  100 , enter the critical to quality weights  114  and the interaction weights  116  through a user interface. The process then computes the total score row  118 . FIG. 3 depicts a pareto graph of the total score values that may be presented to a user to facilitate identification of key control parameters having the greatest effect on critical to quality characteristics. 
     FIG. 4 is a flowchart depicting the steps for creating the quality matrix shown in FIG.  2 . The process begins at step  10  where the user enters the critical to quality characteristics y 1 -yn. Flow proceeds to step  12  where the user enters the key control parameters x 1 -xn. At step  14 , the critical to quality weights  114  are entered and at step  16 , the interaction weights  116  are entered for each combination of critical to quality characteristics and key control parameters. At step  18 , the total score  118  for each key control parameter is determined as described above. At step  20 , the total scores for each key control parameter are presented to the user either numerically as shown in FIG. 2 or graphically as shown in FIG.  3 . 
     The process of creating a quality matrix may be performed for multiple levels of the design process. FIG. 5 depicts a plurality of quality matrices each corresponding to one level of a hierarchical design process. Matrix  100  in FIG. 5 is similar to matrix  100  in FIG.  2  and is based on market data. Matrix  200  uses the key control parameters  112  from matrix  100  as the critical to quality characteristics  210 . Matrix  200  is directed to product design and associates the product requirements represented by critical to quality characteristics  210  with deign features represented by key control parameters  212 . Matrix  200  includes critical to quality weights  214  and interaction weights  216  similar to those described above with reference to FIG. 2. A total score  218  is determined for each key control parameter  212  as described above. 
     When there exists a higher level matrix, step  14  of entering the critical to quality weights  216  may be performed automatically by the system based on the total scores  118  from prior matrix  100 . The total score  118  from matrix  100  indicates the importance of the critical to quality characteristics  210 . To determine critical to quality weights  214 , the total score  118  for each critical to quality characteristic  210  may be compared to a first threshold and a second threshold. Scores equal to or exceeding the second threshold are assigned an importance of 5. Scores equal to or below the first threshold are assigned an importance of 1. Scores between the first and second threshold are assigned an importance of 3. In addition, the scores  118  may be normalized by dividing each score by the maximum score. 
     Quality matrix  300  is directed to product manufacturing and associates the design features represented by critical to quality characteristics  310  with manufacturing processes represented by key control parameters  312 . Matrix  300  uses the key control parameters  212  from matrix  200  as the critical to quality characteristics  310 . Matrix  300  includes critical to quality characteristic weights  314  and interaction weights  316  similar to those described above with reference to FIG. 2. A total score  318  is determined for each key control parameter as described above. The critical to quality characteristic weights  316  may be automatically determined based on the total scores  218  as described above with reference to matrix  200 . 
     The hierarchical relationship between the quality matrices and the use of key control parameters from a prior matrix as the critical to quality characteristics in a subsequent matrix allows for tracking of critical to quality characteristics or key control parameters having a specified level of interaction. FIG. 6 is a flow chart of an exemplary process for tracking critical to quality characteristics or key control parameters having a specified level of interaction. At step  20 , the user is queried whether they wish to track critical to quality characteristics (CTQ&#39;s) or key control parameters (KCP&#39;s). If the user selects critical to quality characteristics, flow proceeds to step  22  where the user defines the level of interaction weight to be tracked. For example, the user may desire locating all critical to quality characteristics having high interaction weights with key control parameters. The user may also designate a single critical to quality characteristic (e.g., y 1 ) and track key control parameters having the desired interaction weight. At step  24 , the key control parameters having the desired interaction weight (e.g., high) are detected and displayed to the user as described herein. At step  26 , the process determines if there exists any successor levels. If not, the process ends. If so, at step  28  the process examines the successor matrix and selects critical to quality characteristics corresponding to the key control parameters having the specified level of interaction in the previous matrix. As described above, the key control parameters from a predecessor level are used as the critical to quality characteristics in a successor level. At step  30 , the critical to quality characteristics selected at step  28  are examined for key control parameters having the desired interaction weight. Flow proceeds to step  26  and the process continues until all the levels of matrices have been processed. 
     If at step  20 , the user selects to track key control parameters, flow proceeds to step  32 . The process for tracking key control parameters is similar to that of tracking critical to quality characteristics but it is performed from low levels to high levels. At step  32 , the user defines the level of interaction weight to be tracked. For example, the user may desire locating all key control parameters having high interaction weights with critical to quality characteristics. At step  34 , the critical to quality characteristics having the desired interaction weight (e.g., high) are detected and displayed to the user as described herein. At step  36 , the process determines if there exists any predecessor levels. If not, the process ends. If so, at step  38  the process examines the predecessor matrix and selects key control parameters corresponding to the critical to quality characteristics having the specified level of interaction in the previous matrix. As described above, the critical to quality characteristics from a successor level are the key control parameters in a predecessor level. At step  40 , the key control parameters selected at step  38  are examined for critical to quality characteristics having the desired interaction weight. Flow proceeds to step  36  and the process continues until all the levels of matrices have been processed. 
     FIG. 5 illustrates the process of tracking critical to quality characteristics having a high interaction weight with key control parameter y 2 . As shown in the market data matrix, critical to quality characteristic y 2  has a high interaction weight with key control parameter x 8 . Both row y 2  and column x 8  are highlighted in color as shown by the cross-hatching in FIG.  5 . Moving to the successor design matrix, critical to quality characteristic x 8  is examined and key control parameters df 4 , df 5 , and df 6  are found to have a high interaction weight with critical to quality characteristic x 8 . Row x 8  and columns df 4 , df 5 , and df 6  are highlighted in color as shown by the cross-hatching. Moving to the design matrix, critical to quality characteristics df 4 , df 5  and df 6  are examined and key control parameters mp 1  and mp 3  are found to have a high interaction weight with critical to quality characteristics df 4 , df 5 , and df 6 . Rows df 4 , df 5  and df 6  and columns mp 1  and mp 3  are highlighted in color as shown by cross-hatching. By highlighting rows and columns where the desired interaction weight is found, the user is presented with an easily readable format for determining the relationship between critical to quality characteristics and key control parameters across multiple levels. The information from the highlighted rows and columns may then be extracted to a partitioned QFD to highlight the significant aspects of the QFD. The relative scores may change in performing the extraction because some interactions will be omitted. 
     The present invention can be embodied in the form of computer implemented processes and apparatuses for practicing those processes. The present invention can also be embodied in the form of computer program code, for example, whether stored in a storage medium, loaded into and/or executed by a computer, or transmitted over some transmission medium, such as over electrical wiring or cabling, through fiber optics, or via electromagnetic radiation, wherein, when the computer program code is loaded into and executed by a computer, the computer becomes an apparatus for practicing the invention. When implemented on a general-purpose microprocessor, the computer program code segments configure the microprocessor to create specific logic circuits. 
     While the invention has been described with reference to an exemplary embodiment, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed as an exemplary mode contemplated for carrying out this invention, but that the invention will include all embodiments falling within the scope of the appended claims.