DOI 10.1007/s00170-003-1886-5
O R I G I N A L A R T I C L E
K.S. Chen · K.L. Chen · R.K. Li
Contract manufacturer selection by using the process incapability index C
pp
Received: 11 April 2003 / Accepted: 25 July 2003 / Published online: 10 August 2005
©Springer-Verlag London Limited 2005
Abstract In the competitive global business environment, enter-prises should effectively respond to and satisfy customer needs. Outsourcing manufacturing is an effective method of adjusting the flexibility of production capability. To ensure final prod-uct quality, evaluating and selecting a contract manufacturer is essential. Process capability indices are extensively adopted in manufacturing to determine whether a process meets capability requirements. This study attempts to determine the score index
Ri and then apply it to assess the process performance of
con-tract manufacturer by using the process incapability index Cpp.
The formulae for Cppand Riare simple to understand and to
ap-ply. The procedure developed in this paper is also an easy and convenient tool for practitioners to assess contract manufacturer quality performance and make more reliable decisions regarding contract manufacturer.
Keywords Contract manufacturer· Outsourcing manufacture · Process capability index
1 Introduction
In the competitive global business environment, customer-oriented economics is the major development trend in the 21st century. To satisfy changing customer demands, shorter prod-uct lifecycles and management challenges due to globalization, enterprises should apply supply chain management. The com-petitiveness of an enterprise can be increased by using strategic alliances to integrate supply chain parties and activities, includ-ing procurement, production, outsourcinclud-ing and distribution. Lee K.S. Chen (u)
Department of Industrial Engineering & Management, National Chin-Yi Institute of Technology,
Taichung, Taiwan, R.O.C. E-mail: kschen@ncit.edu.tw K.L. Chen · R.K. Li
Department of Industrial Engineering & Management, National Chiao-Tung University,
Hsinchu, Taiwan, R.O.C.
and Billington [1] proposed that the supply chain is a network co-ordinating various chain participants in value creating activities ranging from raw material procurement, through to its transform-ation, and eventual distribution to customers. Accordingly, the major parties in the supply chain, including suppliers, manufac-turers, contract manufacmanufac-turers, distributors and retailers, should be integrated and managed effectively to respond to customer needs.
Companies must frequently adjust their manufacturing cap-acity to react to a fluctuating and uncertain market. Compa-nies can expand their manufacturing capacity by: (1) expand-ing existexpand-ing plants, (2) buildexpand-ing new plants, (3) automation, and (4) outsourcing production to contract manufacturers [2]. Lee and Tang [3] suggested that contracting manufacturing should help companies form strategic alliances with other manufactur-ers with special expertise, enabling each company involved to focus on its core competence.
Outsourcing refers to a situation in which a company lets a contract manufacturer manufacture parts and components, then assemble these parts and components and deliver the final prod-uct to customers. Outsourcing boosts professionalism in areas of core competence and enhances logistics performance and market competitiveness. Moreover, outsourcing might help a company maintain a better competitive position in the industry as a whole. Kim et al. [4] demonstrated that manufacturers could reserve fu-ture capacity from contract manufacfu-turers but delay specifying the actual parts to be outsourced. Restated, outsourcing is an ef-fective method of adjusting organizational production flexibility. Based on the above argument, selecting suppliers and con-tract manufacturer is an important task in supply chain man-agement. Manufacturers purchase components from suppliers or hire contract manufacturers to produce necessary parts, then as-semble these parts to deliver the finished products to customers. Kim et al. [4] treat contract manufacturers as another type of supplier, and outsourcing manufacturing to a contract manufac-turer is similar to procuring from a supplier. The major con-siderations when choosing a contract manufacturer thus include quality, cost, goodwill, service, delivery, and so on. According to research conducted by Dickson [5], quality and delivery are two
of the items most demanded by component suppliers. Twenty-five years after Dickson’s research, Weber et al. [6] still consider quality of “extreme importance” and delivery of “considerable importance.” According to Weber’s research on the Just In Time (JIT) model, the importance of quality and delivery remains the same. Pearson [7] surveyed 210 members of the National Asso-ciation of Purchasing Management (NAPM) who were randomly selected from the listings of electronic firms in the two-digit SIC code 38 and indicated that quality is the most important crite-rion in the selection and evaluation suppliers for both small and large electronic firms surveyed. Moreover, according the survey of current and potential outsourcing end-users by the Outsourc-ing Institute [8], the top 10 factors in vendor selection are com-mitment to quality, price, reference/reputation, flexible contract terms, scope of resources, additional value-added capability, cul-tural match, existing relationship, location and other. Quality still is the most important factor of all. Thus, this study considers quality in determining a procedure for assessing and selecting contract manufacturers.
Quality has long been essential to corporate policy. Good quality is essential to maintaining corporate competitiveness and customer loyalty. However, the emergence of a supply chain means that enhancing product quality is no longer just the re-sponsibility of the manufacturer, but is also the rere-sponsibility of the contract manufacturers who produce the parts and com-ponents. The manufacturing capability and product quality of contract manufacturers determine the finished product quality, as well as customer satisfaction and loyalty. Therefore, manufac-turing capability and product quality are the key considerations when choosing contract manufacturers. Among the quality as-sessment methods discussed in many previous investigations, the process capability index is an effective and convenient tool for evaluating process effectiveness and quality performance. Many statisticians and quality engineers have investigated this area, including Kane [9], Chan et al. [10], Choi [11], Boyles [12], Pearn et al. [13], Boyles [14] and Chen [15]. Though the use of the process capability index to measure process performance is already mature, it is generally limited to evaluating the produc-tion process of manufacturers, but not contract manufacturers. Chou [16] suggested using the Cp index to evaluate both
pro-duction processes. However, in the real business environment, there are usually more than two contract manufacturers to choose from. Consequently, to assess contract manufacturers more ac-curately and reliably, this study applies the process incapability index Cppintroduced by Greenwich and Jahr-Schaffrath [17] to
develop an evaluation model. The index Cppis easy to apply, and
provides more process information than other process indices, such as process inaccuracy and process impression. The index thus allows practitioners to select contract manufacturers more effectively and easily, and to better understand the process situa-tions of the other partners involved in a strategic alliance. Finally, this study applies the novel evaluation process to the produc-tion of TFT-LCD, a segment of the high-tech industry with high contracting rates. Not surprisingly, decisions regarding contract manufacturer selection made using the novel procedure is more reliable than those made without the procedure.
2 Incapability index Cpp
Process capability indices are extensively adopted in manufac-turing, and provide numerical and unitless measures of whether a process is capable of producing items that meet the quality requirements of the product designer. Various process capabil-ity indices and approaches for measuring process capabilcapabil-ity have been developed by statisticians and quality engineers. Kane [9] discussed five popular process capability indices, namely Cp,
CPL, CPU, k and Cpk. Boyles [12] noted that Cp and Cpk are
yield-based indices and independent of the target T , which fails to account for process centering. Moreover, Chan et al. [6] de-veloped index Cpm to consider process entering (the departure
of the process meanµ from the target T). Additionally, Pearn et al. [13] proposed index Cpmk to deal with asymmetric
tol-erances. Finally, Kotz and Johnson [18] surveyed and briefly interpreted and commented on some 170 publications on process capability indices, which appeared in various sources from 1992 to 2000, and also assessed the most widespread process capabil-ity indices.
Greenwich and Jahr-Schaffrath [17] introduced a new index,
Cpp, which is simpler and more analytically convenient than
ex-isting indexes. Let D = d/3, then Cppis defined as follows:
Cpp= µ − T D 2 + σ D 2 ,
where T denotes the target value, d= (USL − LSL)/2, USL rep-resents the upper specification limit, LSL is the lower speci-fication limit, µ denotes the process mean, and σ represents the process standard deviation. A small value of Cpp
indi-cates that a process is more capable of meeting the required specifications than a process with a larger value of Cpp. In
fact, Cpp is a simple transformation of Cpm (Cpp= (1/Cpm)2)
and provides an uncontaminated separation between information concerning process accuracy and process precision, something not available with Cpm. As observed by Greenwich and
Jahr-Schaffrath [17], (µ − T )2/D2 is denoted by Cia (inaccuracy
index) andσ2/D2 is denoted by Ci p(imprecision index). Thus,
Cpp= Cia+ Ci p. Table 1 displays the application of the indices
Ciaand Ci p.
For a process with(LSL, T, USL) = (27, 30, 33), the differ-ent process means and standard deviation of the four processes A, B, C and D can be found, as listed in Table 1. However, the values of Cpmand Cppare equal for each A, B, C and D, and thus
Cpm and Cppfail to distinguish among the four processes.
How-Table 1. A comparison of process A, B, C and D
Process µ σ Cpm Cp p Cia Ci p
A 30.0 1.00 1.00 1.00 0.00 1.00
B 30.5 0.866 1.00 1.00 0.25 0.75
C 30.6 0.800 1.00 1.00 0.36 0.64
ever, from Table 1, the different values of Ciaand Ci pfor the four
processes help to simply and accurately distinguish and improve them.
In practice, the real process measurements µ and σ can-not be found; instead sample data must be collected to cal-culate the index value. If practitioners ignore the sampling errors and simply use the index value calculated from the sam-ple data as the basis for deciding whether the given process meets the capability requirements, then the decision will be unreliable. Chen [19] proposed the uniformly minimum vari-ance unbiased estimator (UMVUE) and the probability dens-ity function of Cpp. According to the same approximation
method used by Boyles [12] in obtaining confidence intervals for Cpm, Greenwich and Jahr-Schaffrath [17] proposed that
that ˆCpp/Cpp is approximately distributed as χv2/v, where χv2
is a chi-square distribution withv degrees of freedom and v =
n1+ [(µ − T )/σ]22 1+ 2 [(µ − T )/σ]2, and developed the 100(1 − α) confidence interval of Cpp. The confidence
in-terval is ˆv ˆCpp/χα/2;ˆv2 , ˆv ˆCpp/χ12−α/2;ˆv
, where α denotes the producer risk,χ(p,k)2 represents the upper p percentage point of
χ2distribution with k degrees of freedom andˆv is the maximum
likelihood estimator (MLE) ofv. Pearn et al. [20] also developed a statistical hypothesis testing method based on the incapabil-ity index Cppto determine whether or not a given process meets
quality requirements. Finally, Huang et al. [21] showed the re-lationship between process incapability index Cpp and process
yield. All of the above statistical approaches can increase relia-bility in decision making.
3 Evaluation of contract manufacturers process
capability
The process capability index Cppis a reliable tool for
determin-ing whether a process meets capability requirements. In fact, using the Cppindex to evaluate the process capabilities of
sup-pliers or contract manufacturers and help practitioners to select the best supplier or contract manufacturers is feasible. Impor-tantly, Cppis easy to apply and includes more process
informa-tion than other process indices, for example process inaccuracy and process impression. If a company lacks its own facilities for producing particular components, then it must outsource the manufacturing of these components. Apparently, contract manu-facturers are very closely linked to the company and outsourcing management is very important for practitioners, because pro-cess yield is closely correlated with the propro-cess quality level of the outsourcing manufacturer. Thus, objectively evaluating and selecting a suitable contract manufacturer is essential. Fur-thermore, Cpp can provide additional process information, thus
helping better understand the process situation of contract man-ufacturers and facilitating mutual cooperation to improve quality performance. Chou [16] developed a procedure using estima-tors of Cp, Cpu, and Cplto assess whether or not two processes
are equally capable. In real-world applications, more than two candidates can be selected. Furthermore, Cp is independent of
the target value with bilateral specifications and fails to meas-ure the process yield. This study used Cppto determine the score
index Ri for evaluating the process performance of individual
contract manufacturers. This approach can be used to evaluate more than two contract manufacturers and make more accu-rate selection decisions. Furthermore, the subindices Ciaand Ci p
can be used to understand process inaccuracy and impression of contract manufacturers, respectively, because possible contract manufacturers will become our strategic alliance in the future.
Suppose that some components need to be outsourced to con-tract manufacturers for manufacturing and the evaluation and selection involves h candidates. Since direct observation of the entire processes is impossible, products must be sampled from contract manufacturers and the sample data used to select the contract manufacturers capable of providing better product qual-ity. Let Xi1,Xi2, . . ., Xin, i= 1, 2, . . ., h, be h sets of random
samples of size n from each contract manufacturer. Moreover, each product of the contract manufacturers has the same product specification and target value.
Each process capability of the contract manufacturers can be understood based on each value in index ˆCpp, but we cannot
make a conclusion on which contract manufacturer is better. Be-cause sampling error can lead to inappropriate decisions if we just look at the index value calculated from the sample data. To avoid inappropriate decisions, the confidence interval approach is used here to enhance reliability. From the previous section, the confidence intervals of indices Cp pi and Cp p j are [CLi,
CUi] and [CLj, CUj], respectively. Moreover, the confidence
interval of the ith contract manufacturer is ˆvi ˆCp pi/χα/2;ˆv2 i,
ˆv ˆCp pi/χ12−α/2;ˆvi
, whereα denotes the producer risk, χ2 (p,k)
rep-resents the upper p percentage point ofχ2 distribution with k
degrees of freedom and ˆvi is the maximum likelihood estimator
(MLE) ofvi. Statistically, the comparison of these two indices
can be represented as:
1. If[CLi, CUi] ∩ [CLj, CUj] = φ, then Cp pi= Cp p j. In other
words, the process capabilities of contract manufacturers i and j are equivalent.
2. If CLj> CUi, then Cp pi< Cp p j. In other words, the process
capabilities of contract manufacturer i are better than those of contract manufacturer j.
3. If CUj< CLi, then concluded that Cp p j< Cp pi. In other
words, the process capabilities of contract manufacturer j are better than those of contract manufacturer i.
Nevertheless, the comparison is rather vague and indetermi-nate in rule one. In this rule, the indices are concluded to be equal regardless of the intersection size. For instance:
Illustration A: when[CLi, CUi] = [0.5, 1] and [CLj, CUj] =
[0.9, 1.5] then [CLi, CUi] ∩ [CLj, CUj] = [0.9, 1].
Accord-ing to rule one, we concluded that Cp pi = Cp p j. Similarly,
given[CLk, CUk] = [0.7, 1.2] then [CLi, CUi] ∩ [CLk, CUk] =
[0.7, 1], we concluded that Cp pi= Cp pk. Obviously, regardless
of the intersection size, the conclusion in the above context will be the same. The grade of index Cpp(for i, j and k) cannot be
Illustration B: when[CLi, CUi] = [0.5, 1] and [CLj, CUj] =
[0.9, 1.5] then [CLi, CUi] ∩ [CLj, CUj] = [0.9, 1]. According
to rule one, we concluded that Cp pi= Cp p j. Similarly, given
[CLr, CUr] = [1.1, 1.8] then [CLi, CUi] ∩ [CLr, CUr] = φ and
CLr > CUi, we concluded that Cp pi< Cp pr. However, given
[CLj, CUj]∩[CLr, CUr] = [1.1, 1.5], we concluded that Cp pi =
Cp pr. Apparently, the above conclusions are incompatible.
To avoid the ambiguous situations like illustrations A and B, we propose using score index Rito evaluate the process
perform-ance of each contract manufacturer. Ri is expressed as follows:
Ri= 1 −
mi− m
l+li ,
where, mi = (CLi+ CUi)/2, i = 1, 2, . . . , h, li = (CUi−
CLi)/2, and m = min{m1, m2, . . . , mh}. If the jth contract
man-ufacturer has a minimum value of all mi, then let m= mj,
l= (CUj− CLj)/2. Restated, li denotes the half length of the
confidence interval for contract manufacturer i and l represents the half length of the confidence interval for the contract man-ufacturer with the lowest confidence interval centering value among all the contract manufacturers. Table 2 summarizes the average, standard deviation, estimator ˆCp pand score index Ri.
From the score index Ri, the following information can be
obtained:
1. When Ri= 1, then mi= m. Contract manufacturer i has the
lowest value of miamong all contract manufacturers,
indicat-ing that the process performance of contract manufacturer i is better than other contractors. Clearly, in terms of process ca-pability, we can conclude that contract manufacturer i is the best and that products should be outsourced to this contract manufacturer.
2. When Ri< 1, then mi> m. The mivalue of contract
manu-facturer i is not the lowest among all the contract manufac-turers. Restated, some contract manufacturers definitely have better process performance than contract manufacturer i, and thus we can conclude that contract manufacturer i is not the best outsourcing manufacturer in terms of process capability. 3. The score index Rican be used to set priorities for a number
of contract manufacturers. The smaller the index Ri is, the
less capable the process would be. The score index Rican be
calculated for all h contract manufacturers. If the Rjvalue of
the jth contract manufacturer is one (Rj= 1), then the
pro-cess performance of that contract manufacturer is better than other contract manufacturers. Furthermore, if the Ri value
Sample Average Standard deviation Estimator Ri
X11,. . . ,X1n1 X1= Σn1j=1 X1 j n1 S1= Σn1j=1(X1 j −X1)2 n1 ˆCp p1= X1−T D 2 +S1 D 2 R1= 1 −m1−ml+l1 .. . ... ... ... ... Xi1,. . . ,Xini Xi= Σnij=1 Xij ni Si= Σjni=1(Xij −Xi )2 ni ˆCp pi= Xi −T D 2 +Si D 2 Ri= 1−mi −ml+li .. . ... ... ... ... Xh1,. . . , Xhnh Xh= Σjnh=1 Xh j nh Sh= Σjnh=1(Xh j −Xh)2 nh ˆCp ph= Xh−T D 2 +Sh D 2 Rh= 1 −mh −ml+lh
Table 2. Sample data mean, variance,
estimator ˆCp pand score index Ri
of the ith contract manufacturer is negative (Ri< 0), then
the lower confidence limit of the ith contract manufacturer is larger than the upper confidence limit of the jth contract manufacturer (CLi> CUj). The confidence intervals of two
contract manufacturers are completely disjoint and their in-tersection is empty. Restated, the process performance of contract manufacturer i is significantly worse than that of other contract manufacturers, and there is no need to waste further time in considering this contract manufacturer. Evaluating a group of contract manufacturers and selecting one or more as contract manufacturers is a complex task. Besides quality, numerous other factors must also be considered, such as cost, delivery, goodwill, service, conformity and so on. This study suggests that practitioners should use Ri to sort the
con-tract manufacturers in terms of quality, and then distinguish some contract manufacturers whose process capabilities meet our quality criteria. Secondly, cost, delivery, goodwill and other factors can be considered to select the better contract manu-facturers for outsourcing from other candidates. For instance, the index Ri of 0.5 can be set as a minimum standard (critical
value), and candidates with Ribelow 0.5 can then be eliminated.
This approach simplifies the task of contract manufacturer eval-uation and also makes decision-making more reliable. Figure 1 displays the process of simplifying the task of contract manu-facturer evaluation. In fact, using the score index Ri to assess
contract manufacturers is more reliable than other approaches and avoids problems of the sort demonstrated in illustrations A and B.
The complete procedure using score index Ri to rank
con-tract manufacturers is summarized as follows:
STEP 1 Determine the sample size ni for each contract
manu-facturer process and determine theα-risk (normally set to 0.05).
STEP 2 Take a random sample for each process and calculate the values Xi, Si, ˆvi and the estimator ˆCp p from the
sample, for i= 1, 2, . . ., h.
STEP 3 Calculate the values of confidence interval CLi, CUi, mi and li for all contract manufacturers. Set m=
min{m1, m2, . . ., mh} and calculate l = (CUj−CLj)/2
if the jth contract manufacturer have the minimum value of all mi. Then, use mi, li, m and l to determine
Fig. 1. The process of simplifying the task of contract manufacturer evaluation
STEP 4 Setting the priorities for all contract manufacturers based on the value of Ri. Process capability decreases
with Ri. Moreover, if Rj= 1 then the jth contract
man-ufacturer is the best in terms of quality performance, while if Ri< 0 then the process performance of
con-tract manufacturer i is conspicuously worse than other contract manufacturers and there is no need to waste further time on considering this contract manufacturer. Choices regarding contract manufacturers for outsourc-ing can then be made based on the priorities of contract manufacturers with a non-negative Ri index value, as
well as cost, delivery, service, goodwill, conformity and so on.
4 Application for selecting better contract
manufacturers
The TFT-LCD industry is another new leading industry after semiconductors. TFT-LCD is a flat, lightweight, low radiation appliance with lower electricity consumption than traditional monitors, and thus it is considered superior to CRT. In addition, TFT-LCD is increasing in size and therefore gradually replacing CRT. Notably, the TFT-LCD industry is capital and technology intensive and has a short sales cycle. The product must be assem-bled from many different parts. Therefore, vendors must respond
quickly to changing market demand as well as providing good production technology, high quality yield and timely delivery to fulfill the needs of manufacturers.
Here, the contract manufacturer evaluation procedure de-scribed above is applied to a TFT-LCD manufacturer. The sub-ject firm outsources the manufacturing of most of the parts re-quired to build its TFT-LCD display to contract manufacturers. Thus the first step to ensuring product quality is to evaluate and choose a good contract manufacturer whose process quality level is superior. Backlight module is a key component of TFT-LCD display, since the TFT-LCD panel does not emit light by itself and requires a light source. Consequently, the backlight module is a crucial optical component in TFT-LCD displays called the “sun in the TFT-LCD panel.”
Backlight module is composed of light guide panel, diffuser, reflection panel and illuminant. The manufacturing process of the backlight module is illustrated in Fig. 2. Notably, the major quality characteristics of backlight module include length, width, thickness, and brightness. Each of these quality characteristics has their own specification limits. Among these characteristics, width is crucial to subsequent assembly by the manufacturer. If component width deviates from the specifications, then final product assembly becomes very difficult.
Outsourcing the backlight module manufacturing to a con-tract manufacturer is the firm’s policy. Considering the width of the backlight module, the specification limits are 294.95 ±
Fig. 2. The manufacturing process of the backlight module Contract manufacturers A B C D E Xi 294.92 294.95 294.95 294.92 294.94 Si 0.098 0.069 0.060 0.070 0.056 ˆvi 50.54 50.00 50.001 50.80 50.06 ˆCp pi 2.42 1.07 0.81 1.25 0.74
Table 3. The value Xi, Si,ˆviand ˆCp p for five
con-tract manufacturers Contract manufacturers A B C D E CUi 3.734 1.653 1.256 1.929 1.136 CLi 1.699 0.749 0.569 0.880 0.515 mi 2.717 1.201 0.913 1.405 0.826 li 1.017 0.452 0.344 0.525 0.311 Ri -0.4236 0.5084 0.8672 0.3073 1.0000 Priority 5 3 2 4 1
Table 4. The confidence interval and the score index Rifor five contract manufactureres
0.2 mm, that is, the upper and lower specification limits are set to USL= 295.15 and LSL = 294.75, and the target value is set to T= 294.95. Apparently, this process is the nominal-the-best type. The process capability of contract manufacturers must be able to meet this quality level because of insoluble assembly problems if the backlight module width fails to fall within the tolerance (LSL, USL). The TFT-LCD producer considered here wants to re-evaluate their contract manufacturers. Five candidate contract manufacturers exist, two of which are old contract man-ufacturers that had previously cooperated and three of which are new. Moreover, 50 random samples are taken from the five can-didates and the incapability index and score index Riare used as
measures to compare process capability. The full procedure used to assess the five candidates is as follows:
STEP 1 Determine the sample size ni= 50, i = 1, 2, 3, 4, 5 for
each contract manufacturers process as well as the α-risk (generally set to 0.05).
STEP 2 Take a random sample from each process and calcu-late the values Xi, Si,ˆviand the estimator ˆCppfrom the
sample, for i= 1, 2, 3, 4, 5. Table 3 displays the values
Xi, Si,ˆvi and ˆCpp.
STEP 3 From Table 3, determine the confidence intervals CLi, CUi mi and li for all five contract manufacturers.
Moreover, set m= min{m1, m2, . . ., mh} and calculate l= (CUj− CLj)/2 if the jth contract manufacturer
have the minimum value of all mi. From the data, m= 0.826, l = 0.311. The mi, li, m and l can then be
used to determine the score index Ri. Table 4 lists the
confidence interval, and the values of mi, liand score
index Ri.
STEP 4 From Table 4, we can understand the priorities for these five contract manufacturers. Since RE= 1,
con-tract manufacturer E has the best quality performance, making it the best choice in terms of quality. More-over, contract manufacturer A can be set aside
imme-diately because the value of RAis negative. The
nega-tive RA reveals that the process performance of
con-tract manufacturer A is significantly lower than that of other contract manufacturers and there is no need to waste further time in considering this contract manu-facturer. Notably, the manufacturer set the quality cri-teria at Ri= 0.5, meaning that quality performances of
contract manufacturers E, C, B are satisfactory. While the quality performance of contract manufacturer E is better than other candidates, the outsourcing price of contract manufacturer E is also more expensive than the others. Besides quality, the second consideration of the manufacturer in evaluating contract manufactur-ers is cost. Outsourcing the parts or components for manufacturing to the contract manufacturer E involves the higher cost. Oppositely, not only the quality of contract manufacturer C satisfies the quality criteria, but also the outsourcing price of him is cheaper than contract manufacturer E. Furthermore, the outsourc-ing prices are not greatly different between the con-tract manufacturer B and C and according the index
Ri, the quality performance of contract manufacturer
C is better than contract manufacturer B. Thus,
con-tract manufacturer C is selected for outsourcing in this example. Obviously, adopting the above procedure to assess contract manufacturers can facilitate managers in making reliable decisions.
5 Conclusions
Process capability indices provide single-number assessments of ability to meet the quality requirements preset by product de-signers, and are widely used in manufacturing. Among process capability indices the Cpp index developed by Greenwich and
Jahr-Schaffrath [17] can not only evaluate the process capabil-ity, but also can easily distinguish different degrees of process inaccuracy and the process imprecision and provide additional process information. This study applies the Cpp index to
de-termine the score index Ri and apply it to assess the process
performance of contract manufacturers. Evaluating and selecting suitable contract manufacturers is a crucial task in supply chain management. The quality of parts and components that are out-sourced to contract manufacturers is closely related to final prod-uct quality, and thus influences customer satisfaction and loyalty. The procedure presented here provides an easy and convenient tool for practitioners to use for assessing contract manufacturer
quality performance. The proposed procedure can rank contract manufacturers in terms of quality, distinguish contract manufac-turers whose process capabilities fail to meet the required quality criteria, simplify complex evaluation tasks and increase the reli-ability of decisions regarding contract manufacturers. Of course, the procedure is efficient and reliable, and hence its adoption by manufacturers should be encouraged.
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