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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 Please do not quote without written permission from the authors.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] .

http://www.utdallas.edu/prism


mailto:[email protected]






Banker and Bardhan, 2002

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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


Maturity)

-0.074 -0.38 0.708

R-square 0.37


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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Product Time-to-Market


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


Maturity)

-0.087 -0.60 0.556

R-square 0.37


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.


Product Development Cost


Intercept -4.72 ** -1.72 0.096

∆∆∆∆(Time-to-

Market)

1.63** 1.79 0.083

∆∆∆∆(Design

Complexity)

-0.282 -1.46 0.155


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


Intercept -0.014 -0.06 953

∆∆∆∆(Collaboration) 0.482 ** 1.72 095

∆∆∆∆(Time-to-

Market)

0.002 0.01 991


Development

Cost)

0.075 0.43 668


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




17

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.




18

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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.




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.

collaboration roi

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