In manufacturing industry there is growing interest in measures of process capability under multivariate setting. Although there are many statistical packages to assess univariate capability, a current problem with the multivariate measures of capability is the shortage of user friendly software. In this article a Visual Basic program has been developed to realize an Excel spreadsheet that may be used to compute two multivariate measures of capability. The aim of this article is to provide a useful tool for practitioners dealing with multivariate capability assessment problems. The features of the program include easy data entry and clear report format

Scagliarini, Michele

Vermiglio, Raffaele

Digital Commons@Wayne State University

Journal of Modern Applied StatisticalMethodsVolume 7 | Issue 1 Article 305-1-2008Computing Multivariate Process Capability Indices(Excel)Michele ScagliariniUniversity of Bologna, michele.scagliarini@unibo.itRaffaele VermiglioTelecom Italia Mobile, raffaelevermiglio@tim.itFollow this and additional works at: http://digitalcommons.wayne.edu/jmasmPart of the Applied Statistics Commons, Social and Behavioral Sciences Commons, and theStatistical Theory CommonsThis Statistical Software Applications and Review is brought to you for free and open access by the Open Access Journals atDigitalCommons@WayneState. It has been accepted for inclusion in Journal of Modern Applied Statistical Methods by an authorized administrator ofDigitalCommons@WayneState.Recommended CitationScagliarini, Michele and Vermiglio, Raffaele (2008) "Computing Multivariate Process Capability Indices (Excel)," Journal of ModernApplied Statistical Methods: Vol. 7: Iss. 1, Article 30.Available at: http://digitalcommons.wayne.edu/jmasm/vol7/iss1/30Journal of Modern Applied Statistical Methods   Copyright © 2008 JMASM, Inc. May, 2008, Vol. 7, No. 1, 346-357                                                                                                                            1538 – 9472/08/$95.00 346 Computing Multivariate Process Capability Indices (Excel)   Michele Scagliarini    Raffaele Vermiglio University of Bologna                 Telecom Italia Mobile   In manufacturing industry there is growing interest in measures of process capability under multivariate setting. Although there are many statistical packages to assess univariate capability, a current problem with the multivariate measures of capability is the shortage of user friendly software. In this article a Visual Basic program has been developed to realize an Excel spreadsheet that may be used to compute two multivariate measures of capability. The aim of this article is to provide a useful tool for practitioners dealing with multivariate capability assessment problems. The features of the program include easy data entry and clear report format.  Key words: Multivariate process capability indices, statistical quality control, Visual Basic, Excel   Introduction  Process capability indices have been widely used in the manufacturing industry providing numerical measures on process performances. Juran et al. (1974) first introduced the idea of capability indices (the original name was capability ratios). The first indices were univariate, measured the process capability with regard to a single quality measure and focused on the percentage of non-conforming Kotz and Lovelace (1998). In recent years multivariate capability indices were developed as a natural extension to the univariate concept. Multivariate capability indices appeared in the literature during the early 1990s. Most of them assumed multivariate normal data, a stable process, and were generalizations of their univariate counterparts.   Michele Scagliarini is Associate Professor of Statistics. Research interests include statistical methods for quality improvement and environmental statistics. Email: michele.scagliarini@unibo.it. Raffaele Vermiglio’s research interests include statistical methods for quality control and statistical software. Email: raffaelevermiglio @tim.it.    Wang et al. (2000) compared three multivariate indices: the multivariate capability vector, Shahriari et al. (1995); the multivariate capability index MCpm, Taam et al. (1993); the multivariate capability index for process potential MCp, Chen (1994). Although there are many statistical packages for running univariate capability analysis, a current problem with the multivariate measures of capability is the shortage of user friendly software. Recently, an interesting contribution is the work by Phnadnis et al. (2005). The authors proposed a Visual Basic program to perform bivariate capability analysis using the MCpm index with Excel. In this article, a set of Visual Basic macros are developed that may be used to compute multivariate measures of capability using Excel. Since Excel is often used by engineers, or generally by non statisticians, our purpose is to provide a user-friendly tool to help practitioners in performing multivariate capability analysis. Two different multivariate capability measures are examined: the multivariate capability vector, Shahriari et al. (1995) and the multivariate capability index MCpm, Taam et al. (1993). The two multivariate capability measures are described, and the software is described.   Two multivariate capability indices Assume that a process can be described by a v-dimensional vector of measurements x and we further assume that the joint probability SCAGLIARINI & VERMIGLIO  347  distribution of the v quality characteristics is the multivariate normal distribution                                 ( ),Nx μ Σ                       (1)  The multivariate capability vector  The multivariate capability vector was proposed by Shahriari et al. (1995) based on the original work of Hubele et al. (1991). The vector consists of three components and appears as  ( ), ,pMC PV LI   The first component of the vector, CpM, is a ratio of areas or volumes, analogous to the ratio of lengths of the univariate Cp index. The numerator is the area (two-dimensional case) or the volume (three or more dimensions) defined by the engineering tolerance region, while the denominator is the area or volume of a modified process region, defined as the smallest region similar in shape to the engineering tolerance region, circumscribed about a specified probability contour:      1Volume of engineering tolerance regionVolume of modified process regionvpMC =    (2)  where v is the number of characteristics of the process. The volume of the engineering tolerance region is                         ( )1i iiUSL LSLν=−∏                   (3)  where USLi and LSLi are the upper and lower limits respectively, relative to the characteristic i (i=1,2,...,v). To compute the volume of the modified process region it is worth reminding that under the hypothesis of multivariate normality the statistic              ( ) ( ) ( )g′− − =x μ Σ x μ x        (4)  follows a 2χ  distribution with v degree of freedom. Therefore, the borders of the process region UPLi, the upper process limit, and LPLi, the lower process limit (i=1,2,...,v) are determined by solving the systems of equations of first derivative, with respect to each xi of the quadratic form                 ( ) ( ) 2( , )ν αχ′− − =x μ Σ x μ      (5)  where 2( , )ν αχ  is the upper 100(α) of a 2χ  distribution with v degrees of freedom associated with the probability contour. Usually, in analogy with the “6σ“ in the denominator of the univariate indices, α=0.0027. The solutions (two for each dimension i) of equation (5) are Wang et al. (2000):               ( )( )2 1( , )1detdetii iU PLν αχμ−−= +ΣΣ   (6)               ( )( )2 1( , )1detdetii iL P Lν αχμ−−= −ΣΣ   (7)  where i=1,2,...,v and ( )1det i−Σ  is the determinant of a matrix obtained from Σ-1 by deleting the ith row and column. Thus, the volume of the modified process region is                              ( )1i iiUPL LPLν=−∏             (8)  In practice μ and Σ are unknown and their sample estimates x  and S can be used:                               ==niin 11 xx                 (9)                ( )( )'111ni iin == − −−S x x x x (10) Values of CpM higher than one indicate that the modified process region is smaller than the engineering tolerance region, therefore we have high probability that the produced items will be classified as conform. The second component of the vector is defined as the significance level of a Hotelling’s T2 statistic computed under the assumption that the center of the engineering specifications is COMPUTING MULTIVARIATE PROCESS CAPABILITY INDICES 348  considered to be the true underlying mean of the process:              ( )( )2,1Pr (ν n ν)v n -PV T Fn - ν − = >             (11)  where                     ( ) ( )μxSμx −′−= −12  nT             (12)  and F(v,n-v) is the F distribution with v and n-v degrees of freedom. Values of PV close to zero indicate that the center of the process is far from the engineering target value. The third component of the vector summarizes a comparison of the location of the modified process region and the tolerance region. It indicates whether any part of the modified process region falls outside the engineering specifications. It has a value of 1 if the entire modified process region is contained within the tolerance region and, otherwise, a value of 0:     1 if modified process region is contained within the tolerance region 0  otherwiseLI=         (13)  The multivariate capability index MCpm The index MCpm was proposed by Taam et al. (1993) and is defined as a ratio of two volumes. The numerator is the volume of the modified tolerance region R1 and the denominator is the volume of the scaled 99.73 percent process region R2. Under the multinormality hypothesis we have an elliptical process region, while the modified tolerance region is the largest ellipsoid that is centered at the target completely within the original tolerance region. In the general case of v characteristics R1 is an hyperellipsoid and the volume is given by Kendall (1961)           ( )/ 21122V o l.( )v viivaRvπ==Γ∏        (14) where the ia  (i=1,2,...v) are the lengths of the semi-axes. Then the multivariate capability index is written as     ( ) ( )11V o l.( )V o l. ( )p mTRM CK v−=′ − − ≤  x μ Σ x μ    (15)  where x is the vector (v×1) of measurements from a multivariate normal distribution with mean vector μ and covariance matrix Σ, ( )( )T E ′ = − −  Σ x T x T  is the mean square error matrix from the process, T is a vector of target values, and K(v) is a 99.73th percentile of a 2χ  with v degrees of freedom. The denominator of MCpm can be also expressed as a product of two terms:   ( ) ( ) 1/ 21/ 22Vol.( ) ( ) / 2 1vR K v vπ − = Γ + × Σ  ( ) ( )1/ 211 − ′× + − − =  μ T Σ μ T          ( ) ( ) ( )1/ 213Vol. 1R− ′= × + − −  μ T Σ μ T   (16)  where R3 is the region in which 99.73% of the process values fall within. Therefore MCpm can be rewritten as:  pmMC =           ( ) ( )11/ 213Vol.( ) 1Vol.( ) 1pCRR D−= ′+ − −  μ T Σ μ T . (17)  The MCpm index is a function of two components: Cp which represents the process variability relative to the modified tolerance region; D which detects the process deviation from the target. Given a random sample of n measurements, x1, x2, ...,xn each of dimension v, the estimator for MCpm is given by       SCAGLIARINI & VERMIGLIO  349  ˆpmMC =  ( ) ( )11/ 21/ 2Vol.( )/ 2 1vRK vπ−× Γ + S ( ) ( )1/ 21111nn−= ′+ − − − x T S x T                                 ˆˆpCD                           (18)  When the process mean vector equals the target vector, and the index has the value 1, then 99.73% of the process values lie within the modified tolerance region. The numerator Cp is analogous to the univariate Cp, that is, a value greater than 1 implies that the process has smaller variation than allowed by the specification limits with a certain confidence level; a value less than 1 implies more variation. Also, ˆ0 1 1D< <  measures the closeness between the process mean and the target; a larger ˆ1 D  indicates that the mean is close to target.  Software Description The macros are stored in the MultiCap.xls file. The user can directly open this file to perform the capability analysis. To illustrate the software consider a simulated example. We generate a sample of 100 observation from a multivariate normal process of dimension v=3 with mean vector and covariance matrix given by  [ ]40 60 15′ =μ   and  1.100 0.483 0.3080.483 0.4 0.1850.308 0.185 0.600  =    Σ   respectively.  The target values coincide with the means and the specification limits are reported in Table 1. The user interface is the worksheet “INPUT” (Figure 1) where the main parameters of the analysis can be specified. Clicking the button “PARAMETERS” the form “ANALYSIS PARAMETERS” will appear, as shown in Figure 2, thus can be specified: the number v of quality characteristics i.e. the dimensions of the process (for this version the maximum number is 5); the α value to define the size of the tolerance region (usually α =0.0027); the sample size. Therefore, these values will be displayed in the Table “GENERAL PARAMETERS” and the Tables “SPECIFICATIONS LIMITS AND TARGET” and “SAMPLE DATA” will be automatically modified on the basis of the values of v and n. Clicking the button “LIMITS-TARGET” the corresponding form (Figure 3) will appear, thus for each quality characteristic can be entered: the specification limits; the target value. Clicking the button “SAMPLE DATA” the form “INPUT SAMPLE DATA” (Figure 4) will appear, in this way the sampling observations can be inserted. If the sampling observations are already available, then the data can be directly pasted in the Table “SAMPLE DATA”. Using the simulated data the worksheet appears as shown in Figure 5. The calculations can be performed clicking the button “COMPUTE”. The procedure is splitted in two separate steps. The first step consists in the estimation of the mean vector and covariance matrix. The results of the computations are displayed in the worksheet “MEANS AND COVARIANCES” as shown in Figure 6. In the second step the user can choose between the two capability measures (Figure 7). The results will be displayed, together with brief report, in a suitable form (Figures 8 and 9). In the report box are comments on the results. The messages reported here are only examples and can be modified if necessary.   COMPUTING MULTIVARIATE PROCESS CAPABILITY INDICES 350  Table 1. Specification limits  Characteristic LSLi LSLi 1 33 47 2 52 68 3 12 18       Figure 1. The worksheet INPUT       Figure 2. User form “ANALYSIS PARAMETERS”   SCAGLIARINI & VERMIGLIO  351        Figure 3. User form “LIMITS AND TARGET”    Figure 4. User form “INPUT SAMPLE DATA”    Figure 5. A portion of the worksheet “INPUT” with the example's data    COMPUTING MULTIVARIATE PROCESS CAPABILITY INDICES 352                          Figure 6. Worksheet MEANS AND COVARIANCES          Figure 7. Index choice    SCAGLIARINI & VERMIGLIO  353                  Figure 8. Results and report      Figure 9. Results and report    COMPUTING MULTIVARIATE PROCESS CAPABILITY INDICES 354                Figure 10. The upper portion of the worksheet “VECTOR”        Figure 11. The lower portion of the worksheet “VECTOR”    SCAGLIARINI & VERMIGLIO  355                  Figure 12. The upper portion of the worksheet “INDEX MCpm”     Figure 13. The lower portion of the worksheet “INDEX MCpm”   COMPUTING MULTIVARIATE PROCESS CAPABILITY INDICES 356         In the worksheet “VECTOR” (Figure 10 and Figure 11) are reported the details of the calculations for the multivariate capability vector. Similarly, in the worksheet “INDEX MCpm” (Figure 12 and 13) are reported the details of the calculations for the MCpm index. Results concerning both capability measures are summarized in the worksheet “OUTPUT” as shown in Figure 14.  Some details The Visual Basic code for the calculation management is stored in the Macro CALCOLO1. The inversion of the matrices and the computation of the determinants are performed using the Excel functions MInverse and MDeterm respectively. Moreover, we used the function INV.CHI() to calculate the quantiles of the chi-square distributions in the computations of the process regions and the function DISTRIB.F() to compute the PV component of the multivariate vector. As         pointed out by Knusel (1998) and McCullog and Wilson (2002), it is important to take into  account the accuracy problems when using these Excel functions. Keeping this caution in mind, in agreement with the purposes of the present work, we retain the degree of precision acceptable.  Conclusion  An Excel spreadsheet is presented which can be used to calculate two multivariate capability measures: the multivariate capability vector Shahriari et al. (1995) and the multivariate capability index MCpm Taam et al. (1993). The proposed software requires no installation, since the user can directly open the .xls file. The spreadsheet interface is easy to use, moreover a set of instructions can be visualized clicking the button “INSTRUCTIONS”. Because a problem with the multivariate measures of capability is the shortage of user-friendly programs we hope that this tool can help practitioners in performing multivariate capability analyses. The software   Figure 14. Worksheet “OUTPUT”  SCAGLIARINI & VERMIGLIO  357  has been validated using several data set. However, the user should understand that there may be undetected bugs and problems and will be grateful for any feedback with relevant comments and suggestions for improvements. The program mentioned in this article is available from the author writing to: michele.scagliarini@unibo.it.  Acknowledgments  This work has been partly funded by a 2006 MIUR (Italian Ministry for University and Research) grant.  References  Chen, H. (1994). A multivariate process Capability index over a rectangular solid zone. Statistica Sinica, 4, 749-758. Hubele, N. F. & Shahriari, H. & Cheng, C.S. (1991). A bivariate process Capability vector. In: J.B. Keats, and D.C. Montgomery (Eds.), Statistical Process Control in Manufacturing, Marcel Dekker, New York, 299-310. Juran, J. M. & Gryna, F.M. & Bingham, R. S. Jr (1974). Quality Control Handbook, McGraw-Hill, New York. Kendall, M. (1961) A course in the Geometry of n-dimensions. Charles Griffin and Company limited, London.                   Knusel, L. (1998). On the accuracy of statistical distributions in Microsoft Excel 97. Computational Statistics and Data Analysis, 26, 375-377. Kotz, S. & Lovelace, C. (1998). Process Capability Indices in Theory and Practice. Arnold, London. McCullough, B. D. & Wilson, B. (2002). On the accuracy of statistical procedures in Microsoft Excel 2000 and Excel XP. Computational Statistics and Data Analysis, 40, 713-721. Phandnis, M. A. & Elam, M. E. & Fonseca, D.J. & Batson, R.G. & Adams B.M. (2005). A Prototype DSS for PCI Selection, Calculation, and Interpretation. Proceedings of the 14th Industrial Engineering Research Conference, http://www.bama.ua.edu/~melam/ pdss.ht Shahriari, H. & Hubele, N. F. & Lawrence, F.P. (1995). A multivariate process capability vector. Proceedings of the 4th Industrial Engineering Research Conference, Institute of Industrial Engineers, 304-309. Taam, W. & Subbaiah, P. & Liddy, W. J. (1993). A note on multivariate capability indices. Journal of Applied Statistics, 20(3) 339-351. Wang, F. K, & Hubele, N.F. & Lawrence, F.P. & Miskulin, J.D. & Shahriari, H. (2000). Comparison of three multivariate process capability indices. Journal of Quality Technology, 32(3), 263-275.                   

Computing Multivariate Process Capability Indices (Excel)

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Computing Multivariate Process Capability Indices (Excel)

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