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EVALUATING THE IMPACT OFCOLLABORATIVE PRODUCT COMMERCE ON THE
PRODUCT DEVELOPMENT LIFECYCLE
RAJIV D. BANKER INDRANIL R. BARDHAN
Center for Practice and Research in Software Management (PRISM)
School of Management
The University of Texas at Dallas
Richardson, TX 75083
This white paper is also available from the Working Paper series of the PRISM Center at theUniversity of Texas at Dallas (www.utdallas.edu/prism). An earlier version was presented atthe Workshop on Information Systems and Economics (WISE) in December 2001 in NewOrleans, LA. Please do not quote without written permission from the authors. Allcorrespondence may be directed to [email protected] .
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Abstract
New collaboration-based information technologies have enabled companies to compete more efficiently
in a global networked economy by enhancing the interactions and information transfer in the supply
chain associated with the product design and development lifecycle. In this research, we empirically
investigate the relationships between investment in collaborative product commerce (CPC) and product
development process variables such as product quality, complexity, development cycle time, and user
satisfaction. Our findings indicate that collaboration in product design and development, resulting from
implementation of a CPC solution, had a significant and positive impact on product quality, product
time-to-market, and user satisfaction. This research also provides insights into the role of business
process maturity in moderating the impact of CPC software on the outcomes of product development.
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1.0 Introduction
The accelerating rate of technologicalchange, coupled with growing demand for customized products has dramatically
reduced product lifecycles. There isincreasing reliance on the use of informationtechnology to manage the productdevelopment lifecycle. Newer collaboration- based information technologies haveenabled faster and more accurate productdevelopment cycles (Carroll, 2001;Bhambri, 2000; Johnson, 2000). The visionof such collaborative software is to harnessthe speed and efficiency of Web-basedtechnologies to optimize supply, design,
manufacturing and distribution channelsacross the extended enterprise by enablingfaster and more accurate exchange of information across business processes(Smith and Reinertsen, 1998). In other words, collaboration technologies providethe enabling platform for companies tocollaborate with their customers, suppliersand partners in a global networkedeconomy.
Considerable attention has been paid insupply chain management and informationsystems research to study the impact of key processes and technologies on the supplychain lifecycle. However, little attention has been given to the economic impact of
information systems on product lifecycle
management . In their recent survey articleon product development research, Krishnanand Ulrich (2001) conclude that “… the
benefit of new tools to manage product
knowledge and support development decision making within the extended
enterprise needs to be explored in greater
detail …” Considering that improvements in product design and development costs havea significant impact on overall product costand time-to-market, more attention needs to be paid to studying the impact of investment
in software that improves the efficiency of the product development lifecycle.
Collaborative Product Commerce (CPC)
is a relatively new technology that has beenintroduced to streamline productdevelopment processes that are not wellstructured or require significant manualintervention. CPC is a class of software andservices that use Internet technology to permit individuals to collaboratively shareintellectual data, improving thedevelopment, manufacture, and managementof products throughout the entire lifecycle(Carroll, 2001). This sharing of intellectual
data related to the delivery of a productrequires an ability to encapsulate a business process and extend that process across theentire supply chain. Specific business processes that can be facilitated throughcollaboration include product design,sourcing, change request management,channel management, and distribution.
The basic premise of investment in CPCsoftware is that improvement in cycle time,cost and quality can be simultaneouslyattained by improving the effectiveness of the product development process. Theseimprovements are thought to arise fromreduction in product development cycle timeand reduced rework associated with mature business processes and investments in CPCtools.
In this white paper, we summarize theresults of our empirical research toinvestigate the relationships betweeninvestment in CPC software and productdevelopment process variables such as product quality, complexity, developmentcycle time, and overall productivity. Dataon CPC implementations in thirty-fivecompanies were studied to evaluate researchhypotheses about factors that influence the benefits from investment in CPC. Thisresearch also provides insights into the
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moderating role of business process maturityon the product development lifecycle, andhow process maturity improves the overallefficiency of product development.
2.0 Collaborative Product Commerce
As shown in Figure 1, CollaborativeProduct Commerce (CPC) involvesmanagement of all product data, appliedacross the extended enterprise includingsuppliers and customers and using theInternet (or other Internet-basedtechnologies). The collaborative focus of CPC is the emphasis on sharing engineering
information with suppliers and customers.
Figure 1: Collaborative Product
Commerce
CPC encompasses management and
sharing of product design and developmentdata that is generated in each phase of the product development lifecycle. The sixmajor phases that comprise the productdevelopment lifecycle, include:
SProduct Concept and Initiation that involves conceptualizing the productrequirements
SProduct Development Proposal that involves developing a preliminary project plan and product specifications.
SResearch and Development thatcomprises concept review, preliminary billof materials (BOM) and finalizing productdesign specifications.
SProduct Development and
Manufacturing Design that consists of prototype verification, final BOM, andcapital approval for product development
SProduct Design Verification and
Manufacturing Development that consistsof output design and design verificationtesting
SPilot Production and Product
Introduction which involves productmarketing plans and customer approval of pilot samples
The six-phase product developmentlifecycle consists of several sub-processesand activities as described in Figure 2.
Figure 2: Product Design and
Development Processes
The information, that supports thevarious tasks comprising the productdevelopment lifecycle, resides on manydifferent systems and in multiple locations.Systems that organize and control thisinformation are called Collaborative ProductCommerce (CPC) systems.
Several articles in the popular presshave touted the perceived benefits of CPC
(Carroll, 2001; Welty & Becerra-Fernandez,2001) such as:
• Faster cycle time for new designsand engineering changes
• Increased engineering productivity° Less time spent searching for
data and chasing approvals
° Reduction in overlapping or inconsistent designs
Conceptdocument
ProductRequirement
s
Project Plan Design Inputs
ProductStrategy
Prelim. productspecifications
Prelim. test
Plan
Prototypeverification tests
Customer approval of prototype
Certified Design
Final BOM
ManufacturingPlans
Mfg. processPlans
Capital approval
Design outputs Designverificationtesting
Productionmaterial on order Operator instructions
Pilot runproductionprocess
Productionverification &
validation testing
ProductConcept &
Initiation
ProductDevelopment
Proposal
Research &Developmen
t
ProductDevelopment&Manufacturing
Design
ProductDesignVerification &Manufacturing
Development
PilotProduction &Product
Introduction
ConceptReview
Prelim. BOM
Prelim. supplier selection
Prelim.manufacturingprocess plan
Final Eng. TestPlan
PrototypeControl Plan
Final productspecification
Marketing pimplementat
Quality Contsystemevaluation
Prelim. proccapability st
End of line a
Preventivemaintenanc Customer approval of
samples
Suppliers Company Customers
Partners
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° Re-use of existing parts anddesign know-how
• Fewer new parts introduced, and lesscost per part
• Significant improvement in thequality of product data
• Improved ability to share productdata with suppliers, to seek design input,solicit quotes, and discuss changes
However, these perceived benefits are based on anecdotal evidence and have not been supported by field-based researchstudies. Our white paper is a result of thefirst research effort aimed at studying theinter-relationships between the variables thatcomprise the product design anddevelopment lifecycle. Our research alsoindicates that it is not enough to realize these benefits by implementing the CPC softwarein a stand-alone manner. Rather, companieswhich reported significant benefits fromCPC also reengineered their business processes in a manner that facilitated theexchange of intellectual capital and business process logic for improving the design anddevelopment of existing and future products.
3.0 Prior Research
Prior research investigating theeconomic impact of information technology(IT) has been focused at two different levels.Several studies have looked at the economicimpact of IT and computer investments onoverall productivity and firm output (Baruaet al., 1995; Brynjolfsson and Hitt, 1993;Brynjolfsson and Yang, 1996; Chircu andKauffman, 2000). While the early work on
small samples did not find a productivityimpact (Loveman, 1994), more recent work has consistently found a positive correlation between computers and productivity andfirm output.1
1 A small number of studies have focused on the productivity impact of specific informationtechnologies. For instance, Banker, Kauffman, and
In the area of collaborative product planning, several reports have touted thenumerous benefits of collaboration bymultiple partners within the supply chain(Carroll, 2001). Some of these benefits are
faster cycle times for new designs andengineering changes, increased engineering productivity, significant improvements inthe quality of product data. These benefitsresult in reduced inventory and less rework and the improved ability to share productdata with suppliers, and to seek design input,solicit quotes, and discuss changes.However, IT researchers have notinvestigated the productivity impact of CPCtechnologies on the product development
lifecycle.In software engineering research, thelifecycle cost impact of quality in software products was examined by Krishnan et al.(2000). They found that improvedconformance quality in system software products led to significant improvement inlife-cycle productivity. Further studies byHarter et al. (2000) have investigated therelationship between process maturity, cycletime and quality on overall cost of softwaredevelopment. Their findings, based on datafor thirty software products at a largesoftware development company, reveal thatimprovements in process maturity lead tohigher quality which, in turn, leads toreduced cycle time and softwaredevelopment cost. These findings provideempirical support in the context of software production for existing theories of cycletime and cost benefits of improved qualityderived from process improvement. Thequality, cost, process and cycle time trade-offs associated with software development isinherent in other forms of new productdevelopment as well. For instance, Bohn
Morey (1990) found that deployment of a new cashregister point-of-sale and order coordinationtechnology at a large fast-food restaurant chainreduced materials waste and improved operationalefficiency.
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(1995) reported a field study that providedempirical evidence for the significance of process effectiveness in enhancing processyield and product quality in semiconductor manufacturing.
In collaborative product development, animportant issue is whether investment inCPC tools pays off in terms of reduced cycledevelopment time, higher product quality,and lower cost. Given the growingimportance of CPC software and serviceswithin the domain of supply chainmanagement, it is important to provideempirical evidence to substantiate the benefits (if any) of investment in CPCsoftware and services. The objective of this
research is to develop and test our hypotheses regarding investment in CPCsoftware and its impact on productdevelopment cycle time, quality, designcomplexity, and cost. We will alsoinvestigate the moderating effect of processmaturity and its role in determining themagnitude of impact from investment inCPC software.
4.0 Research Hypotheses
Based on prior research where therelationships between quality, cost, cycletime and process maturity have been studiedin a software engineering environment(Harter et al., 2000, Krishnan et al., 2000),we hypothesize several key relationships asshown below:
Figure 3: CPC Model Framework
In Figure 3, we specify a model thatintegrates four equations to representoutcomes impacted by investments in CPCsoftware - product quality, productdevelopment cycle time, productdevelopment cost, and user satisfaction. A basic premise is that companies who investin CPC software will experience significantimprovement in collaboration and learningacross product design and developmentteams, and the impact of the CPC software
on process outcomes is mediated byincreased collaboration activity.
4.1 Product QualityThe first equation relates quality of the
product development process to investmentin CPC and process maturity, controlling for the design complexity of the productdevelopment process.
Product Quality = f (Collaboration, Process maturity, Product design
complexity) (1)
Our definition of product quality is based on the number of engineering changeorders for the designed product and totalnumber of product errors. Number of engineering change orders is defined as the
Collaboration
& Learning
ProductDevelopment
Cost
Process
Maturity
ProductDesign
Complexity
ProductQuality
Time to
Market
(-)
(-)
(+)
(+)
(-)(-)
(+)
(+)
(+)
(-)
(-)
(+)
Investment
In CPC
(+)
(+) User Satis-
faction
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number of requests for design changes thatare fed into the product design process
Process maturity provides a measure of the overall effectiveness of the process, based on the dynamics of the specific
company and industry’s environment (suchas those driven by customers, markets,competition and regulatory demands).Process maturity was measured based on amodification of the Capability MaturityModel (CMM) framework to account for the process dynamics of the industry. TheCMM practices aid in reducing producterrors and in early identification of defects.As a result, the number of product errorsshould be lower for products that are
designed with mature business processes(Harter et al., 2000). This implies:
Hypothesis 1 (Product Quality andCollaboration):
Collaboration in product design anddevelopment is associated withimprovement in product design quality(fewer defects and rework).
In equation (1), we control for the effectof product design complexity. Prior work has shown that the more complex the product design, the higher the likelihood thaterrors will be introduced into the productdevelopment process (Munson, 1996).
4.2 Time-to-Market
The second equation relates time-to-market to collaboration, process maturity,and product quality, controlling for the product design complexity.
Time-to-Market = f (Collaboration,
Process maturity, Quality, Designcomplexity) (2)
Time-to-market is the overall timeelapsed from product conceptualization untilits final launch and acceptance by the user.The relationship between time-to-market,
product quality and process maturity has been viewed from two perspectives. Oneview is that time-to-market must be tradedoff in terms of improvements in quality.However, a contrasting view is that these
variables are complementary and thatimprovements in process and quality canlead to improvements in the time-to-market(Harter et al., 2000). Our next hypothesis isstated as follows:
Hypothesis 2 (Time-to-Market andCollaboration):
Collaboration in product design anddevelopment is associated with reducedtime-to-market for the product being
developed.
4.3 Product Development Cost
The third equation relates the productdevelopment cost to product quality andtime-to-market, controlling for the productdesign complexity.
Product Development Cost = f (Time-to-
market, Product quality, Design complexity)
(3)
Product development cost refers to theoverall cost incurred in product developmentand is analogous to the effort required todevelop the product. The conventionalschool of thought asserts that there must betrade-offs between product developmentcost, quality and time-to-market. However,as argued in section 4.2, these can beconsidered complementary variables and weargue that improved quality and shorter time-to-market are associated with lower overall product development cost. Hence,our hypothesis is:
Hypothesis 3 (Product DevelopmentCost):
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Time-to-market and product quality areassociated with lower overall productdevelopment costs.
4.4 User SatisfactionThe fourth equation relates user satisfaction to collaboration, product quality,time-to-market, and product developmentcost.
User satisfaction = f
(Collaboration, Time-to-market, Product quality, Product development
cost )
(4)
User satisfaction refers to thesatisfaction of the design engineers and product development teams who areintimately involved in using the CPCsoftware for product design anddevelopment activities. Since one of thegoals of collaboration software is to improvecommunication and learning across productdevelopment teams, we posit thatcollaboration should have a positive impacton user satisfaction.
Hypotheses 4 (User Satisfaction andCollaboration):
Collaboration in product design anddevelopment is associated with greater user satisfaction.
Prior research in software productdevelopment has shown that the effects of process maturity and design complexity ontime-to-market, quality and developmenteffort are not linear. (Banker and Kemerer,1989; Banker and Slaughter, 1997). Thus ageneric multiplicative specification of our models is adopted through logarithmictransformation of the variables (Davidsonand Mackinnon, 1985). The estimationmodel is represented by the followingsystem of equations:
ln(Product Quality) = α 0 + α 1 * ln(Process
Maturity) + α 2 * ln(Collaboration) +
α 3 * ln(Design Complexity) + α 4 * ln(Process
Maturity) * ln(Collaboration) + ε 1 (5) (5) (5) (5)
ln(Time-to-Market) = β 0 + β 1 * ln(Process
Maturity) + β 2 * ln(Collaboration) + β 3 *
ln(Design Complexity) + β 4 * ln(Product
Quality) + β 5 * ln(Process Maturity) *
ln(Collaboration) + ε 2 (6) (6) (6) (6)
ln(Product Development Cost) = δ 0 + δ 1 *ln(Time-to-Market) + δ 2 * ln(Design
Complexity) + δ 3 * ln(Product Quality) + ε 3
(7) (7) (7) (7)
ln(User Satisfaction) = γ 0 + γ 1 * ln(Time-to-
Market) + γ 2 * ln(Product Development Cost) +γ 3 * ln(Product Quality) + γ 4 *ln(Collaboration) + ε 4 (8) (8) (8) (8)
Our model is specified as a simultaneoussystem of equations represented in equations(5), (6), (7) and (8). This is a recursivesystem of equations that can be estimatedefficiently using ordinary least squares(OLS) if the errors across equations are
uncorrelated. However, because eachobservation in any equation is related tocorresponding observations that correspondto the same company in the other equations,it may be possible that the error terms in theregressions are correlated. Therefore, for consistent and efficient estimation, weestimated the system of equations usingseemingly unrelated regressions (SUR) thatallows for correlation of disturbances acrossequations (Lahiri and Schmidt, 1978;
Greene, 1997).
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5.0 Research Data
A cross-sectional survey methodologywas employed for data collection. Productdesign and development managers of 55
companies, who use CPC software as the basic engine for product designcollaboration and engineering, werecontacted for this research project. Thesurvey questionnaire was initially testedusing a small sample of potentialrespondents (companies). Based on theinitial test, modifications were made and thefinal version of the questionnaire was mailedout. Of the initial sample of 55 companies,12 did not respond with relevant data within
the project data collection time frame. Eightcompanies provided incomplete data. Atotal of 35 companies responded with data tothe entire questionnaire. An industry profileof the study participants is shown in Table 1.
Table 1: Distribution of Study
Participants by Industry
Industry Category Number of
Participants
Industrial Products 9Automotive 13
Medical 4
Aerospace / Defense 5
Hi-tech / Electronics 10
Other 2
TOTAL 43
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All of the respondents had recentlyimplemented CPC software as the basicengine for collaboration involving productdesign, engineering, manufacturing and for end-to-end coordination of the product
development process, which involvescustomers and suppliers in many cases.The data was collected by a combination
of archival data retrieval and a survey of managers who were very familiar with the product development process at their companies. Product design engineers andmanagers were interviewed to obtain keydetails of the product development processand identify the bottlenecks in thedevelopment cycle. The survey guidelines
required that the project manager was withthe project from beginning to end, hadinteraction with both senior managementand project personnel, and had a significanttechnical understanding of the product.These guidelines assured that the projectmanager had a broad view of the project thatcrossed functional boundaries and could provide data on the survey questionnaire atdifferent points in time (before and after CPC) in the project effort.
To assess the significance of the relativeimpact of CPC, we collected data for eachvariable before and after implementation of CPC software. Respondents were asked to provide their responses to each question on anumerical Likert scale with values rangingfrom 1 (unsatisfactory / negative impact) to7 (very satisfactory / positive impact).
Data on the following variables werecollected:
SInvestment in CPC (which includeshardware, software, integration, deployment,training and support costs)
SCollaboration solutions and their impact on collaboration across productdevelopment teams
SProduct time-to-market (measured asreduction in cycle time)
SProduct development cost (measuredas reduction in cost)
SProduct quality (measured asimprovement in product defects)
SProcess maturitySProduct design complexitySUser satisfaction
The Process Maturity construct wasdeveloped using the guidelines described inthe Capability Maturity Model (CMM)-Integrated Product Design and Development(IPDD) framework. The construct ismeasured based on the following variables: process and design concurrency, quantitative project management, product integrationmanagement, and project requirementsmanagement. A single factor was obtainedwith high loadings on each of the four
variables that comprise the process maturityconstruct.The Product Design Complexity
construct was developed using amodification of a similar constructdeveloped by Novak and Eppinger (2001) intheir product development research withautomotive companies. The construct wasdeveloped based on the following variables:number of product components, number of new design features, product componentinter-connectedness, and degree of component design re-use. A single factor was obtained with high loadings for each of the four variables.
6.0 Impact of CPC
All respondents to the research surveyreported significant savings in time and cost.Table 2 provides a summary of the time andcost savings reported across each phase of the product development lifecycle. For instance, companies reported that timesavings in the product concept and initiation phase was 10% on average, while annualcost savings in the first full year of operationwas between $50,000 and $1 Million (thewide range is explained by the size of thecompany and scope of CPC implementation)with an average cost savings of $100,000.
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Table 2: Reported Time and Cost
Savings across the Product Development
Lifecycle
Time SavingsCost Savings
BusinessProcessBenefits Range Average Range Average
ProductConcept &initiation 5 - 50% 10%
$50K -$1M $100K
ProductDevelopmentProposal 10-50% 15%
$50K -$1M $100K
Research andDevelopment 10-20% 10%
$50K -$200K $150K
Product
Development&ManufacturingDesign 10-50% 15%
$50K -$1M $200K
Product DesignVerification &ManufacturingDevelopment 10-50% 20%
$20K -$750K $100K
PilotProduction 10-50% 15%
$20K -$100K $50K
We conducted a similar analysis of reported time and cost savings for each of
the eleven functional areas that interfacewith the product design and development processes. Table 3 provides a summary of the savings reported across each functionalarea. For instance, companies reported thattime savings in product data managementwas 20% on average, while annual costsavings in the first full year of operation was between $100,000 and $2 million (the widerange is explained by the size of thecompany and scope of CPC implementation)
with an average cost savings of $500,000.
Table 3: Reported Time and Cost
Savings across Functional Areas
Time Savings Cost Savings
FunctionalBenefits Range Average Range Avera
Product DataManagement
10 -50% 20%
$100K -$2M $500
Product DesignManagement
10-30% 20%
$50K -$1M $150
ProductDevelopment
10-35% 15%
$50K -$1M $200
New Productintroduction 5-20% 10%
$20K -$50K $25K
ECOEvaluation
10-50% 20%
$20K -$150K $50K
ECOImplementation
10-25% 15%
$20K -$1.5M $200
Product
Reworks
10-
15% 10% N/A N/A
Design Re-use10-15% 10%
$100K -$5M $500
InventoryManagement N/A N/A N/A N/AInternalCollaboration
10-25% 15%
$100K -$3M $250
ExternalCollaboration
10-35% 20% N/A N/A
We analyzed reported changes in thecost structure of product development before
and after implementation of CPC. For instance, as observed in Table 4, the left-hand column indicates the reported percentage of cost expended before CPCimplementation in each of the six phases of the product development lifecycle. Theright-hand column indicates the reported percentage of cost expended after CPCimplementation in each of the six phases of the product development lifecycle. Table 4indicates that the reported overall cost after
CPC implementation is only 75-80% of thecost before CPC implementation. Thisreported reduction in cost can be attributedto cost savings observed primarily in three phases: Research and Development, Product
Development & Manufacturing Design, and
Product Design Verification &Manufacturing Development . This analysis provides some managerial insight on the
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impact of CPC on the costs incurred for each phase of the product development lifecycle.
Table 4: Change in Product
Development Cost Structure before and
after CPC
Change inCost Structure
Benefits by phase Before CPC After CPC
Product Concept &initiation 5% 3-5%ProductDevelopmentProposal 5% 3-5%Research andDevelopment 10-15% 5-10%Product
Development &ManufacturingDesign 40-50% 40-45%Product DesignVerification &ManufacturingDevelopment 15-20% 10-15%Pilot Production andProduct Introduction 10-15% 10-15%
100% 75-80%
7.0 Estimation Model Results
Descriptive statistics on the modelvariables are summarized in Table 5. Thevalues represent the scores on a numericalLikert scale with a range from 1 (very low /negative impact) to 7 (very high / positiveimpact). Our basic premise that investmentin CPC software results in significantimprovement in collaboration activity wassupported by the data.
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Table 5: Descriptive Statistics
Variables Before MatrixOne
Solution
After MatrixOne
Solution
Mean Median StandardDeviation
Mean Median StandardDeviation
ProductQuality
3.7 4.0 0.6 5.0 5.0 0.8
ProductDevelopmentCost
3.4 4.0 0.7 4.9 5.0 1.0
Product Time-to-Market
3.5 4.0 0.7 5.3 6.0 1.0
Collaboration 3.4 4.0 0.7 5.5 6.0 0.9
User satisfaction
3.9 4.0 1.1 5.7 6.0 0.6
ProductDesignComplexity
3.8 4.7 0.6 4.7 4.8 0.7
ProcessMaturity
4.2 4.0 1.3 5.3 5.3 0.8
The average collaboration scoreincreased from 3.4 (before CPC) to 5.5(after CPC) on a seven- point Likert scale,significant at a 1% level2, as shown in Table5. Several companies also reported
substantial improvements in processmaturity, which was aided by processreengineering efforts implemented inconjunction with implementation of theMatrixOne CPC solution. Changes in thevariables that define the productdevelopment process, before and after implementation of the MatrixOne solution,is shown graphically in Figure 4.
2 The difference in means was statistically significantat the 1% level using a student’s t-test.
Figure 4: Product Development
variables before and after
CPC implementation
3
3.4
3.8
4.2
4.6
5
5.4
5.86.2
Before CPC After CPC
Product Quality
Product Dev. Cost
Product Time toMarket
Collaboration
User Satisfaction
Process Maturity
The regression model, described in
section 4, was estimated using the SUR procedure. All model variables are
represented by the differences (∆) in their values before and after implementation of the Matrixone CPC solution. In other words, we seek to estimate the relationships between collaboration and outcomes of the product development process based on their
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change in relative magnitude before andafter implementation of CPC.
7.1 Estimation results for product
quality
Results of estimating the SUR modelindicate that collaboration in product designand development, resulting fromimplementation of CPC, had a significantand positive effect on product quality.However, the improvement in productquality due to collaboration is statisticallysignificant only in the presence of higher levels of process maturity. As shown in
Table 6, the coefficient for the interactionterm ∆ (process maturity * collaboration) isstatistically significant at the 10% level.The value of 0.369 for this regressioncoefficient means that a 1% improvement inthe interaction term will result in a 0.369%increase in product quality. From amanagerial perspective, the results indicatethat companies should reengineer their business processes to ensure that they aresufficiently mature to support the benefits of
design collaboration.Design complexity also had a positiveimpact on product quality, indicating thathigher levels of design re-use andcomponent integration (coupling) leads toless product defects, rework, andengineering change orders, which leads toimprovement in product quality.
Table 6: SUR Estimation Results for
Product Quality3
Variable Parameter t-statistic p-value
Intercept -0.503 -1.97 0.058
∆∆∆∆(Process
Maturity) *
∆∆∆∆(Collaboration)
0.369 ** 1.88 0.071
∆∆∆∆(Collaboration) -0.208 -0.78 0.439
∆∆∆∆(Design
Complexity)
0.397 * 2.17 0.026
∆∆∆∆(Process
Maturity)
-0.074 -0.38 0.708
R-square 0.37
7.2 Estimation results for product
time-to-market
Collaboration in product design anddevelopment due to CPC implementationhas a positive effect on reduction in product
time-to-market. As evidenced by thestatistically significant coefficient of the
interaction term ∆ (Process maturity *Collaboration), higher process maturity hasa positive and significant impact on reducingtime-to-market. From a managerial perspective, this implies companies shouldfirst re-engineer their business processes tomaximize the business benefits of collaboration.
3 An asterisk (*) indicates statistical significance atthe 5% level. A double asterisk (**) indicatesstatistical significance at the 10% level.
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Table 7: SUR Estimation Results for
Product Time-to-Market
Variable Parameter t-statistic p-value
Intercept -0.211 -1.05 0.303
∆∆∆∆(ProcessMaturity)*
∆∆∆∆(Collaboration)
0.290** 1.90 0.061
∆∆∆∆(Collaboration) 0.464 * 2.20 2.340
∆∆∆∆(Design
Complexity)
0.223 1.62 0.116
∆∆∆∆(Product
Quality)
0.002 0.01 0.99
∆∆∆∆(Process
Maturity)
-0.087 -0.60 0.556
R-square 0.37
7.3 Estimation results for product
development cost
The primary driver of productdevelopment cost is product time-to-market.Reduction in product time-to-market has a positive and statistically significant effect inreducing product development cost.
Neither product quality nor productdesign complexity play a significant role indetermining product development cost.These results are consistent with our hypotheses and prior research in softwaredevelopment. From a managerial perspective, the results indicate that designengineers use collaboration software toshare designs electronically, store designdocumentation, and speed up the designreview process, which reduces the product
development cycle time which, in turn,reduces product development costs.
Table 8: SUR Estimation Results for
Product Development Cost
Variable Parameter t-statistic p-value
Intercept -4.72 ** -1.72 0.096
∆∆∆∆(Time-to-
Market)
1.63** 1.79 0.083
∆∆∆∆(Design
Complexity)
-0.282 -1.46 0.155
∆∆∆∆(Product
Quality)
1.36 0.97 0.341
R-square 0.30
7.4 Estimation results for user
satisfaction
The primary drivers of user satisfactionare product quality and collaboration. Theresults show that collaboration in productdesign and development has a significantand positive impact in reducing producttime-to-market and improving qualitywhich, in turn, has a positive and significantimpact on user satisfaction. In other words,collaboration software enables design
engineers to improve communication, sharedata, and design products faster and moreeasily, which reduces the productdevelopment cycle time and productdefects/errors, which in turn has a positiveand significant impact on user satisfaction.Furthermore, our results also indicate thatreduction in product development cost doesnot have a significant impact on user satisfaction. This is consistent with prior research in product development (Adler et
al., 1995).
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Table 9: Estimation Results for User
Satisfaction
Variable Parameter t-statistic p-value
Intercept -0.014 -0.06 953
∆∆∆∆(Collaboration) 0.482 ** 1.72 095
∆∆∆∆(Time-to-
Market)
0.002 0.01 991
∆∆∆∆(Product
Development
Cost)
0.075 0.43 668
∆∆∆∆(Product
Quality)
0.364 * 2.30 029
R-square 0.28
8.0 Conclusions
We have empirically studied the impactof investment in collaborative productcommerce software on the productdevelopment lifecycle, using data collectedfrom 35 CPC implementations at severalcompanies. We have evaluated the
relationships between collaboration usingCPC and performance of the productdevelopment process, as measured by four primary outcomes – product time-to-market, product quality, overall productdevelopment cost, and user satisfaction.
The impact of collaboration on productquality was significantly enhanced in the presence of higher levels of processmaturity. In other words, companies wereable to realize greater improvement in product quality if they also undertook business process improvements prior toimplementation of CPC. Our results alsoindicate that design collaboration has asignificantly positive impact on user satisfaction, which is primarily driven byreduction in product time-to-market andquality. The overall impacts of collaboration on the product developmentlifecycle are summarized in Figure 5.
The ability to collaborate effectively andefficiently across inter-organizational boundaries becomes critical as companiesconduct a significant number of transactionsthrough collaborative entities such as e-
Markets and other types of “internetmarketplaces.” CPC solutions provide thetransparency and visibility necessary for companies to share vital supply chaininformation with their partners, suppliersand customers in an effective manner.
Figure 5: Impact of CPC on the Product
Development Lifecycle
The development of new informationtechnologies appears to be revolutionizingcommerce generally and productdevelopment to a considerable degree(Krishnan and Ulrich, 2001), and this is thefirst academic research in evaluating a newtype of information technology, namelycollaborative product commerce, andstudying its impact on the productdevelopment lifecycle. Our results areconsistent with prior IT research in the
software development area and operationsmanagement research in the productdevelopment literature (Adler et al., 1995).Considering the importance of a globallynetworked economy and the fact thatcompanies are increasingly collaboratingwith customers, suppliers and even with potential competitors, the importance of CPC cannot be overlooked as a technology
Time-to-Market: 10-20% Faster
Product Development Cost: 10-
20% lower
Quality: 10-25%% higher
Investment
in CPC
User
SatisfactioCost
Reduction
Quality
Rapid Time
To Market
Positive
Cash
Faster product
development cycle time
Improved
Product Quality
Increased
Profitability
Sustained user
satisfaction
Time
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that enables companies to collaborateefficiently across the value chain (Welty andBecerra-Fernandez, 2001).
Companies who have implemented theCPC solution from MatrixOne have realized
the tangible and intangible benefits of collaboration, as evidenced by significantimprovement in the outcomes of the productdevelopment process. Our research wasfurther supported by anecdotal quotes byseveral companies, such as:
Director of PDM, Fortune 500
Industrial Products conglomerate – “MatrixOne has reduced cycle time
to find the product data dramatically. It hasalso forced us to improve the quality of our
data …” – “MatrixOne has reduced productdesign management time for some tasks by afactor of 60. For example, processing achange order used to take 60 days, now wecan do it in a day. On the low end of reduction of cycle time (for other tasks), ithas reduced it by about 10 to one …”
Director, Engineering, Large OEM
Automotive supplier – “MatrixOne’s e-Matrix platform for
integrating all the applications is truly Web-enabled. They traditionally grew up throughPDM, but the Web-enabling aspects of PDMis something that MatrixOne started long before others did … MatrixOne helps to bring greater visibility into the ProductDesign Management process, almost adesign encyclopedia or knowledgebase tothe laptops of all those engineers …”
9.0 Future ResearchThis research opens the door for future
research to explore several new possibilities.A new direction is to study the impact of design collaboration on inter-organizationallearning and knowledge management andstudy its impact on productivityimprovement. Another possibility includesstudying the impact of collaboration outside
the intra-organizational boundaries toaddress question such as: What is theimpact of collaboration on customers’ andsuppliers’ performance? How does valuechain collaboration impact the performance
of the “value network” or “value net” over time? These new research areas provideinteresting directions for extending our current research.
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ACKNOWLEDGEMENTS
The authors gratefully acknowledge the research support provided by MatrixOne for thisresearch project and their help in facilitating the data collection from several companies. Theauthors also acknowledge comments on an earlier version of this research from Lorin Hitt, Eric
Clemons, Thomas Davenport, and participants at the Workshop on Information Systems andEconomics (WISE) held in New Orleans in December 2001, as well as feedback received from MarkO’Connell, John Donovan, Lori Webber, Frank Kang, and senior executives of MatrixOne, Inc.
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AUTHORS
Rajiv D. Banker is the Ashbel Smith Chair in Accounting and Information Management and theDirector of Accounting and Information Management Programs at the University of Texas atDallas. Prior to joining the University of Texas at Dallas he served as a Professor of
Management at Carnegie Mellon University and as the Arthur Andersen Chair in Accountingand Information Systems at the University of Minnesota.Dr. Banker is internationally recognized as a leader in interdisciplinary research in
management information systems and software engineering economics. He has receivednumerous awards for his research. He has published more than 100 articles in prestigiousresearch journals including Management Science, MIS Quarterly, Information Systems Research,
Communications of ACM, IEEE Transactions in Software Engineering, Journal of MIS, Information Technology and Management, Information Economics and Policy, Journal of
Organizational Computing, Operations Research, Accounting Review, Journal of Accounting
and Economics, Journal of Accounting Research, Academy of Management Journal, StrategicManagement Journal, and Econometrica. Dr. Banker has co-edited a book on Strategic
Information Technology Management . He is the Department Editor of the Information Systemssection for Management Science and a Senior Editor for Manufacturing and Service OperationsManagement . He has also co-edited special issues on Economics of Operations Management and on Software Technology Management . His research articles are cited frequently by other researchers in a wide range of disciplines.
Dr. Banker is an expert in the analysis of complex and emerging strategic problems in theinformation age. He specializes in information based competitive strategy, performancemeasurement and incentive compensation, productivity and quality metrics, and management of software development and maintenance. He is the originator of object points and reuse leveragemetrics for software cost estimation. His research has been supported by the National ScienceFoundation, the Financial Executives Research Foundation, the Institute of ManagementAccountants, and several leading corporations. He has consulted extensively with manyorganizations and has been invited to lecture to executives and academics at leading institutions.
Indranil R. Bardhan is Assistant Professor of Accounting and Information Management in theSchool of Management at the University of Texas at Dallas. He is also the Co-Director of theCenter for Practice and Research in Software Management (PRISM) at the University of Texasat Dallas. Prior to joining UT-Dallas, he was a Principal in the Information Technology Strategy practice of PricewaterhouseCoopers Consulting where he advised senior management of Fortune500 companies in the area of information technology strategy.
Dr. Bardhan’s current research interests are in the areas of software economics andmanagement and information technology strategy. He is currently working on several research projects in these areas, including evaluation of productivity gains from e-business technologiesand their effect on manufacturing performance, and evaluation of the impact of different types of software development and maintenance practices on organizational performance. His researchinterests also span other areas such as business process outsourcing and development of financialmodels for prioritization of information technology projects.
He has several publications in leading journals including Operations Research, European Journal of Operational Research, Annals of Operations Research, Journal of Productivity
Analysis, and has also published articles in two books on Operations Research.