Information Leaders in Product Development Organizational Networks: Social Network Analysis of the Design Structure Matrix

570 IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, VOL. 53, NO. 4, NOVEMBER 2006

Information Leaders in Product DevelopmentOrganizational Networks: Social Network Analysis

of the Design Structure MatrixDiego Andres Batallas and Ali A. Yassine

Abstract—Many models of Product Development (PD) areconcerned with managing the decomposition and integrationof tasks, teams and subsystems transforming a conceptual ideainto a finished product. Specifically, a PD process is formed ofcross-functional teams continuously exchanging information onspecified tasks to integrate the product’s final structure. Recently,it has been shown that large PD networks (e.g., tasks, teams, orcomponents) follow a Scale Free structure. That is, each PD net-work included hubs that control information flow. Nevertheless,there is no literature on the implications of these findings on PDmanagement. As a consequence, the objective of this paper istwo-folded. First, we examine a set of mathematical measuressuch as centrality and brokerage used in Social Networks Analysis(SNA) to identify critical players in PD networks. Second, welink these findings to insights and recommendations for the man-agement of complex PD organizational networks; in particular,detection and role designation of information leaders based on thegiven PD network structure.

Index Terms—Centrality and brokerage measures, designstructure matrix, information flow, product development, socialnetworks.

I. INTRODUCTION

F IRMS rely heavily on new product development (PD) tosucceed in increasingly competitive global markets. Com-

petition forces these firms to launch more innovative productsin shorter periods of time. However, the development of com-plex products can be a challenging task that requires the coordi-nation of hundreds and even thousands of specialists [38]. Forexample, the number of people involved in the development ofthe Volkswagen New Beattle Automobile reached around 1600people, while for the Boeing 777 Airplane reached approxi-mately 16 800 [33]. Capturing and understanding the interactionpatterns within a large PD network is essential for devising ef-fective communication and coordination strategies resulting inan improved PD process [1], [19], [22], [20], [26], [28].

Communication is commonly accepted as one of the most im-portant factors in new PD [1]. For instance, Leenders et al. as-

Manuscript received September 1, 2005; revised March 1, 2005, June 1, 2005,and January 1, 2006. Review of this manuscript was arranged by DepartmentEditor J. K. Pinho.

The authors are with the Product Development Research Laboratory, Depart-ment of Industrial and Enterprise Systems Engineering (IESE), University ofIllinois at Urbana-Champaign, Urbana, IL 61801 USA (e-mail: [email protected]).

Color versions of Figs. 1–6 and Tables II–IV are available online at http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TEM.2006.883706

serted that the productivity of PD teams depends to a large extenton the ability of its members to tap into an appropriate networkof information and knowledge flows [19]. Sosa et al. pointedout that workgroups with higher levels of communication aremore successful in the creativity process of innovation [28]. Infact, they suggested that tasks’ dependencies between groupsmust be recognized beforehand in order to stimulate informa-tion exchange among them [28]. Smith [26] also asserted that in-novation requires high levels of communication between units,so managers must focus on creating an organizational structurethat facilitates information exchange. Finally, Moenaert et al.[20] notes that the creation of a “core team,” which other teamsfind as a connection point, enhances project’s information ex-change.1 In a nutshell, all these studies echo the same messagethat improving PD intercommunication can make a differencein PD execution as well as in product innovation.

Consequently, this paper is concerned with the modelingand analysis of information flows within PD organizations,and specifically, the identification and role designation ofinformation leaders in complex PD environments. We explainhow to detect central teams in a PD organizational structurebased on Social Network Analysis (SNA) techniques, andsuggest that information exchange, system integration andinnovation throughout PD can be greatly enhanced by creatinga mega-team of these core central groups called “Informa-tion Leaders Team (ILT).” These findings are explained in areal-world PD case (based on previous research of Sosa etal. [27], [29]–[31]) where the company could benefit fromILT members, which due to their strategic position in theorganizational network, would better manage communication,coordination and innovation among multiple-tasks’ teams.

The paper is organized as follows. In the next section, wepresent an overview of the DSM method which has been widelyused in PD environments to understand complex informationflows. In Section III, the concepts of centrality and brokerageused in SNA are introduced and a detailed explanation of cen-trality and brokerage measures as applied to PD networks is pro-vided. Section IV presents a real PD case study based on theprevious work of Sosa et al. [27], [29]–[31], where high centraland broker teams are identified. Numerical results are examinedfirst and their managerial implications discussed later. In Sec-tion V, the concept of Information Leaders Teams (ILTs) in PDnetworks is developed, and it is showed how these teams allowfor both system integration and diffusion of innovation. Finally,

1A core team is defined as a limited group of people responsible for the inter-functional management and coordination of project activities.

0018-9391/$20.00 © 2006 IEEE

BATALLAS AND YASSINE: SOCIAL NETWORK ANALYSIS OF THE DESIGN STRUCTURE MATRIX 571

Fig. 1. Example of a DSM.

Section VI covers the main conclusions of the paper and futureresearch directions.

II. DESIGN STRUCTURE MATRIX (DSM) IN PD ANALYSIS

The DSM was first introduced by Steward as a systemsanalysis tool [32]. As Steward showed, a DSM decomposes asystem into parts and relationships to study the aggregates ofits complexity. In terms of PD systems, new product projectsare formed of cross-functional teams continuously exchanginginformation on specified tasks to integrate the product’s finalstructure. That is, organizations deal with product decomposi-tion into components, their design and development, and theirfinal integration into the new product [8]. This process requireshigh information exchange between development units to guar-antee a functional innovative design. In fact, this informationflow is not necessarily sequential or parallel and thereforenot appropriately modeled/managed using PERT and CPMmethods. On the contrary, PD relies on concurrent engineering:countless feedback loops and interactions among workgroups[13]. Consequently, PD management requires new tools thatgo beyond PERT and CPM such as the design structure matrix(DSM). A DSM allows analysts to visualize and streamlineinformation exchanges between components of a concurrentPD process [36].

A DSM is a square matrix. Labels along rows and columnsare the same and correspond to the components of the analyzedsystem. Matrix entry equals 1 if there is an interaction be-tween parts and and 0 otherwise. Reading along rows captureinformation inflow while reading along columns captures infor-mation outflow [36]. An example of a DSM is shown in Fig. 1.This system contains 7 subsystems or components: A, B, C, D,E, F, and G. In this figure, subsystem A depends on (or receivesinput from) B, C, and E while it influences (or sends informa-tion to) C and D.

A DSM can be used to model components, teams, tasks anddesign parameters [8]. These four categories are assigned intotwo groups: static and time based models. Static DSMs corre-spond to elements coexisting at the same time. For instance,product components and organizational workgroups coexistsimultaneously thus they are modeled using static DSMs. On

the other hand, the order of rows and columns in time-basedDSMs determine the sequence in which information travelsalong time. For instance, project tasks and design parameterschange with time according to a schedule so they are modeledusing time based DSMs [8]. In addition, static and time-basedDSM categories require different optimization algorithms.Static DSMs use clustering algorithms [25]. The objective is tocreate groups of highly interactive clusters, modules, or teamsand minimize the interaction between them. Time based DSMsuse partitioning algorithms in order to obtain a lower-triangularmatrix such that tasks can be performed sequentially or inparallel with no feedback [36].

Although DSM models proved helpful in managing infor-mation flows in complex PD processes (compared to classicalproject management techniques such as CPM and PERT), theydo not allow for characterization of the importance of each nodein the network (but relative ordering of nodes instead). In doingso, we would be able to focus management attention on specificnodes for improving the PD process. In this regard, social net-work analysis (SNA) techniques provide a wealth of analyticalmeasures that describe the statistical properties of networks andcan be readily used in the analysis of information flows in PDenvironments, as explained next.

III. SOCIAL NETWORK ANALYSIS: CENTRALITY AND

BROKERAGE MEASURES

Recent research by Braha and Bar-Yam has shown that PDnetworks follow scale free properties [7]. Namely, they haveshown that PD networks follow a power of law degree distribu-tion which suggests organizational connectivity is not equallydistributed among all nodes, but contrastingly, a few very welllinked nodes control it.2 They illustrate that PD structures areformed by a major percentage of nodes with few relations anda considerable number of nodes with a large amount of linkstowards them [7]. Therefore, similar to many natural and man-made scale free networks [4], [14], [17], [21], [23], [24], [34],they conjecture that the topology of PD networks is best repre-sented by the presence of highly connected nodes or hubs [3].In particular, their insight implies that certain teams (i.e., nodes)may control information flow within a PD network. These nodesbecome critical to the network structure and functionality be-cause of their strategic location and relevant relations to othernodes. For example, central/broker actors in social networkshave a larger number of colleagues that communicate with, thusbecoming an influential member in the community. In addition,peripheral nodes rely on central ones to reduce the length ofcommunication paths with other people. As a consequence, theidentification and measurement of centrality becomes crucial inunderstanding networks [6].

There are several indexes used to localize the most signifi-cant nodes and quantify how important they are relative to othernodes. These formulas are known as centrality and brokeragemeasures and will be described next [35], [15].

2For an exploration of various possible reasons to support the formation ofsale free networks in PD projects, the reader is referred to the following: prefer-ential attachment [4], local optimization [34], and modularity and encapsulation[2].


A. Degree Centrality

A central node is one that relates to a large quantity of othernodes in the network. In that case, centrality can be measuredas the number of out-links connecting a node to its neighbors oras the number of in-links that a certain node is receiving fromadjacent nodes. Distinction between out-degree and in-degreecentrality is particularly important in some cases since their in-terpretation could not be the same. For instance, if we think of aPD process where tasks are nodes and information flow are linksthen some task releasing information is not the same as a taskreceiving information. On the other hand, in a product develop-ment organization (PDOs), where teams are modeled as nodesand communication requirements as links, there is no mean-ingful distinction between in and out degree centrality since twoconnected teams communicate regardless which team sends orwhich one receives information. In fact, this assumption is sup-ported by empirical evidence showing that two-way communi-cation is more frequent in PDOs than one-way information flow[22].

The most straightforward measure of centrality is a count ofthe number of links that a node possesses. By definition, the de-gree of a node is the number of lines incident to it and is repre-sented as [35]. Consequently, when there is no distinctionbetween in-degree and out-degree (as in PDOs) centrality of anode is calculated as follows [35]:

(1)

where

Degree centrality of actor .

Degree of node .

if is indicent toif is not indicent to

.

Clearly, this magnitude depends on the size of the network(specifically, the number of actors) and it becomes complicatedto use when comparing different networks. Therefore, a way tostandardize degree centrality is to divide (1) by the maximumnumber of actors that a node can be connected to [35]. Since thenumber of nodes in a network is then a node can be connectedat maximum to all other actors but itself ( ).

(2)

B. Closeness Centrality

There is an inconvenience in just measuring degree centralityto understand the most important actors in a network. Degreecentrality just show how many nodes are directly joined to a cen-tral node, nevertheless it does not consider indirect ties by whichan actor can reach others using paths available in the network.A node that is central-close can reach other actors though shortdistance paths. As a consequence, the notion of closeness-cen-trality is related to the inverse of distance between actors [35]

(e.g., the higher the distance, the less central-close). In socialnetworks literature, a shortest path between two nodes is definedas a geodesic [35]. As a result, a closeness index has to accountfor the geodesics that a given node has to all other nodes in thenetwork.

To obtain the closeness centrality index of node ( ) first itis necessary to calculate the geodesics between and all othernodes in the network (assuming the graph is connected). Indi-vidual geodesics are added up and finally the inverse of thisvalue measures how central-close is . The standardized formulaof a node’s closeness centrality is [35]3

(3)

where

Standardized closeness centrality of node .

Geodesic between and .

There is a major drawback of this index. It needs a connectedgraph in order to have a useful meaning. That is, there mustbe a path from every node to every other node. Otherwise iftwo nodes are not reachable its geodesic will be infinite and thecentrality index zero. As a consequence, this index is not veryuseful for directed graphs (e.g., PD process) unless all nodeshave correspondence between them (e.g., PDOs).

Close-centrality index plays a significant role in understatingand improving a network structure. For example, transmittingthe information through high central-close nodes can optimizeknowledge diffusion on a constrained resource network. Notonly news will become available faster, but also the amount ofresources used will be at minimum. Additionally, creating newnodes or enhancing existing ones in positions where higher cen-tral-close is needed can improve communication channels. As aresult, close-central indexes facilitate network structure devel-opment.

C. Betweenness Centrality

Another way of measuring centrality is by focusing on nodesthat lie in the path between other actors. In fact, these nodeshave control over knowledge flow since information must travelthrough them. In that sense, central nodes become powerfulgatekeepers that regulate the amount of information transmittedin a network [35]. Measuring betweenness centrality becomesrelevant since high between-central actors are repositories ofpower and knowledge in a structure although they are not nec-essarily high directly connected to other colleagues.

In order to calculate an index that measures betweenness cen-trality several considerations are taken into account. First, it isassumed that an actor who wants to reach another uses prefer-ably shortest paths available between them. This assumptionmakes sense in the real world since each actor wants to min-imize the number of colleagues that it must travel through in

3The index can be standardized taking into consideration that a given nodewill be maximum close when it is adjacent to all other nodes in the network.Since geodesics of length 1 to all other nodes but itself will count up to (n�1).


order to reach its destiny. Second, if two or more geodesics areavailable then the actor can choose between them with equallylikely probability. Third, it’s also understood that if A commu-nicates with B then B must communicate with A. Finally, addingup the proportion that a node is between others gives the stan-dardized betweenness centrality index as follows [35]4:

(4)

where,

Standardized betweenness centrality of node .

Number of geodesics linking and thatcontains in between.

Total number of geodesics linking and .

D. Brokerage Measures

Another way to understand centrality in a social network isto identify actors which intermediate relationships. Specifically,a broker is a player that lies between two others, who do nothave direct communication, and acts as a channel by whichthey can relate [15]. Due to this strategic position in the net-work, a broker facilitates information exchange when accuratelyand constantly transfers data between nonfrequently communi-cating parties and complicates otherwise. In addition, a brokernotion can be extended from intra-group to intergroup relation-ships. In fact, Gould and Fernandez point out that communica-tion patterns are not the same within than between groups [15],thus allowing a broker to be a potential workgroups’ integrator.In consequence, a broker has the opportunity to enhance thecommunication between two nondirectly/nonfrequently relatedactors that belong to same or different groups.

To calculate brokerage first it is necessary to partition nodesinto mutually exclusive groups [15]. This partition must bemeaningful in accordance to the context of the studied network.For example, PDO techniques identify mega-teams formed ofhighly interacting teams using clustering algorithms on a DSM[8]. As a result, mega-teams in PDO can be treated as mutuallyexclusive groups. Next, it is essential to identify triads in thenetwork, that is, groups of three nodes where two of themare connected through an intermediary. For instance, considernodes A, B and C where A is connected to B, B is connectedto C but not to A. That is, B mediates the relationship betweenA and C [5]. Gould and Fernandez [15] identified five differentbrokerage categories5 according to whether nodes formingtriads belong to the same or different subgroups as shown inFig. 2. In Fig. 2, brokers (B) control the information flow. Thatis, they either help or complicate information exchange by lim-iting the information received or transmitted in the organization

4It is also possible to standardize this index. The maximum value betweennesscan take is when node i is between all pairs of nodes, which is quantified as thetotal number of pairs in the network not including actor i.

5Gould and Fernandez [15] considered directed relationships. Nevertheless,we assume that if team A communicates with team B then B must communicatewith A. Thus, their original model has been adjusted to satisfy PD organizationrequirements.

Fig. 2. Brokerage types [15].

through to the established communication channels. In thatview, it is possible to identify five different types of brokerage:internal and external coordinator, gatekeeper, representativeand liaison.

1) Internal Coordinator: A, B, and C belong to the samegroup. B mediates the relationship between A and C.

2) External Coordinator: A and C belong to the same group.B belongs to a different team and mediates the relationshipbetween A and C.

3) Gatekeeper and Representative: B and C belong to thesame group. A belongs to a different group. B mediates therelationship between A and C. In case that C sends infor-mation to B, then B acts as a representative when it sendsinformation to A. On the other hand, when A sends infor-mation to B, then B acts as a gatekeeper when it sends in-formation to C.

4) Liaison: A, B, and C belong to different groups. B mediatesthe relationship between A and C.

Table I shows the formulas used to obtain brokerage indexesaccording to the five categories defined before. Each formulacounts the number actor brokers into a relationship due to thebrokerage category analyzed. Actor ’s total brokerage score( ) is the sum of the individual brokerage categories, whichin turn is the total number of times actor brokers a relation-ship. These formulas have been slightly modified from Gouldand Fernandez initial model to fit PDO conditions. Liaison andinternal and external coordinators’ original formulas are dividedby 2 to account for the bi-directional nature of PDO commu-nication assumption. In addition, representative and gatekeeperformulas output the same number, since a representative is a


TABLE IFORMULAS TO OBTAIN BROKERAGE INDEXES [15]

gatekeeper and vice versa depending on the analyst’s point ofview.

IV. CASE STUDY: CENTRALITY AND BROKERAGE

APPLICATION TO PDO NETWORKS

In this section, a large PD case study is examined in detailbased on a previous work by Sosa et al. [27], [29]–[31].They presented a study for a large commercial aircraft enginedeveloped at Pratt & Whitney Aircraft Company. This is acomplex product composed of the following six modular chunksor subsystems: A) Fan; B) Low Pressure Compressor (LPC); C)High Pressure Compressor (HPC); D) Combustion Chamber(CC); E) High Pressure Turbine (HPT); F) Low PressureTurbine (LPT), and two integral subsystems: G) MechanicalComponents and H) Externals and Controls. Each chunk isdivided into its major components (54 in total) as shown inFig. 3. The organization behind the development of this engineis based on 54 cross-functional teams working simultaneouslyon the design of each component and 6 teams working on systemintegration of the final engine assembly. The analysis in thispaper is based on the interactions of the 54 teams working onthe components’ design. Even though the analysis does not takeinto consideration the six remaining system integration teams,our results present managerial insights for all 60 teams. The

main reason for not including the six system integrative teamsin the current analysis is because they already perform centraland brokerage functions due to explicit managerial assignment.As shown in Fig. 3, design teams are confronted with complexinformation exchange patterns. Modular mega-teams (A, B,C, D, E, and F) exchange information mostly internally, whileintegrative mega-teams (G, H) exchange information with allworkgroups [30], [37].

The Organizational DSM (Team Interaction Matrix) is formedof 54 teams (nodes) and their respective communication needs(links). Each entry contains either a 1 if there is an information ex-changeor0otherwise.The54teamsarearrangedin8mega-teamsaccording to the product architecture (A through H). A specificmechanical component of the final product is assigned to eachmega-team. The original matrix in [30] is asymmetric since therelationships are based on the information needs of the person in-terviewed. However, we assumed reciprocal interactions and thematrix shown here is symmetric. The reason behind this processis the early assumption that if team A communicates with team Bthen team B must communicate with A (two-way information ex-change).Asaconsequenceof thesymmetrization,33%newcom-munication links have been added. Nevertheless, the DSM fol-lows thesamepatternsof informationexchange. Inaddition,Sosaet al. research [27], [29]–[31] includes the analysis of the Design


Fig. 3. Team interaction matrix – Large commercial aircraft engine [30].

Interface Matrix (productarchitecture), and thealignmentofboththe Team Interaction Matrix (which is studied in this paper) andthe Design Interface Matrix (which is not studied in this paper).It is important to note that even though they report some mis-alignment between these DSMs, their study also found conclu-sive evidence to suggest that both matrices are largely aligned[31]. Therefore, the Organizational DSM will practically main-tain its core structure even though some missing communicationlinks will be revealed and covered throughout the project.

A. Computational Results

The information in Fig. 3 allows computation of standardizedcentrality and brokerage measures since the symmetric DSMis a bi-directional one-component graph. The resulting graph ispresented in Fig. 4 which pictorially shows the significant teams(from an information centrality and brokerage viewpoint): G1,H10, H3, D2, E5, D1, G2, B7, and H8. These teams consti-tute the “Information Leaders Team” (ILT), a concept that isexplained in Section V.

The computational results are shown in Table II.6 As Table IIpoints out, certain nodes have more information power andcontrol than others in the network. As a consequence, theybecome specifically relevant to the success of the project. Thereare nine teams (i.e., nodes) that exhibit higher scores in allmeasures: Main Shaft Mechanical Components (G1), ElectricalControls (H10), Air Systems Externals and Controls (H3),Combustion Chamber Diffuser (D2), High Pressure TurbineCase and Blade Outer Air Seal (E5), Combustion ChamberBurner (D1), Gearbox Mechanical Components (G2), LowPressure Compressor Intermediate Case (B7) and Sensor Con-trols (H8). Indeed, Table III shows high correlation coefficientsbetween centrality and brokerage indexes. It is worth notingthat this correlation is just the outcome of this case study andin other cases the measures might be uncorrelated. That is, ingeneral all measures are necessary but in this case study allmeasures point into the same direction.7

6Computations were performed using Ucinet software [5].7The authors would like to thank one of the anonymous reviewers for pointing

this to our attention.


Fig. 4. Team interaction graph (bi-directional): large commercial aircraft engine.

To decide which teams (i.e., nodes) are the most significantin terms of information flow control, we sort the scores of eachnode, based on the seven centrality and brokerage measures, indecreasing order. Then, the top scoring nodes in each categorywill be selected to facilitate the various roles required for com-munication and brokerage. In case of correlation between thevarious SNA measures, some nodes may appear on the top ofmultiple categories. In this case, if an already selected node ap-pears at top of another category, then the next highest scoringnode is selected to represent that category. Alternatively, in caseof high correlation (similar to this case where the same nodesappear in the top 10 positions of every category), we propose asimple procedure as follows. First, highlight the top 10 nodesin each category, and then measure the frequency of occurrenceeach highlighted node has in the top 10 of each category.8 Indoing so, we obtained the results shown in Table IV, where G1,G2, and H10 occurred 7 times in top 10 positions. Finally, if ateam appears frequently (e.g., say 50%) in the top 10 of eachcategory, then this team will be considered significant.9 Thisprocess resulted in the selection of the following nodes: G1, G2,H10, D1, D2, E5, H3, B7, and H8.

It is also important to note that high central and brokergroups do not belong only to integrative subsystems as one

8This is consistent with the literature on concurrent engineering teams thatadvocate teams to contain no more than 10 members (Salomone 1995, Smithand Reinertsen 1995, Haddad 1996, and Smith 1998).

9The 50% cutoff value could be adjusted (i.e., higher or lower) to change (i.e.,diminish or enlarge) the set of significant nodes.

might expect. In fact, out of these nine high scoring teams,56% belong to integrative subsystems (G1, G2, H10, H3,and H8) and 44% to modular chunks (D1, D2, E5, and B7).Finally, Table II shows that internal and external coordinatorsdo not play a relevant role. Only 7% of the total brokeragescore corresponds to these categories. The reason for this lowpercentage is that mega-teams are highly internally connected,so there is no need for a third party between two members ofthe same mega-team. On the other hand, 50% of total brokeragecorresponds to gatekeepers/representatives and 43% to liaisons.Clearly these two categories play a relevant role in coordinatingnonregularly communicating teams in the project.

B. Interpretation of Results

As discussed earlier, the idea behind centrality and brokeragecalculations is to identify main players in the network. In viewof the fact that PD deals with information exchange within andbetween groups, then some teams are dominant in terms of in-formation control and flow. In the “Commercial Aircraft En-gine” project, design teams G1, H3, H10, D2, E5, D1, G2, B7,and H8 are key players because of their location in the net-work as well as their links to other teams. It is important to notethat this special arrangement is mainly driven by the configura-tion of the product developed (i.e., product architecture) [31].Thus,these teams control more information because of final as-sembly requirements and not because of explicit managerial as-signment. Therefore, it is necessary to understand the meaning


TABLE IICENTRALITY AND BROKERAGE MEASURES—LARGE COMMERCIAL AIRCRAFT ENGINE: CENTRALITY AND BROKERAGE ARE SORTED IN DESCENDING ORDER

behind these central and brokerage scores in order to devise suc-cessful managerial strategies and plans. Accordingly, this sec-tion presents an investigation of the numerical results presentedearlier and analyzes the roles and challenges faced by these cen-tral and broker design teams.

First, consider teams G1, H3, H10, and D2. These teams scorehigh on degree-centrality which means they receive as well asthey distribute information to a large number of teams. Con-sequently, these groups spend large amounts of time communi-cating (e.g., meetings). In terms of knowledge in-flow, they must

bring together efforts to assimilate and process the infor-mationdirectly received. In terms of information out-flow, they have tospend time guarantying that the data transferred is as accurateas possible because a great number of teams depend on it. In ad-dition, these teams play the role of integrators, that is, they ex-change information indirectly between two nonregularly com-municating workgroups. They are advantageous because theyrepresent knowledge repositories. Nevertheless, their main dis-advantage is the large amount of time used in coordination ef-forts to understand and distribute information.


TABLE IIICENTRALITY AND BROKERAGE PEARSON CORRELATION COEFFICIENTS

TABLE IVFREQUENCY OF OCCURRENCE IN TOP 10 POSITIONS FOR VARIOUS SNA

MEASURES

Next, these teams (G1, H3, H10, and D2) also score high oncloseness-centrality. This means that they acquire informationfrom many other teams rapidly through the use of intermedi-aries. In fact, their main advantage is the connection to all otherworkgroups through short communication paths [11]. Since weassume that indirect channels do not lose much information,then they are not only information repositories of adjacent rela-tionships but also the overall project. In addition, they have moreinformation about what happens in the project on a constantbasis in less amount of time than any other team. This is truedue to the formal communication methods established in the or-ganization (e.g., collocation [1], meetings [33], synchronizationsessions [12], and telephone and email [28]). On each encounterinformation is exchanged, they receive overall project status in-formation quickly through intermediaries and short communi-cation paths. As a result, constant knowledge diffusion can beoptimized by transmitting it through these workgroups.

Finally, G1, D2, H10, and D1 score high on betweenness cen-trality. This implies that they lie in many of the shortest com-munication paths connecting two workgroups. In effect, theyhave control over whether to share or not crucial information re-garding the project. This characterizes them as information gate-keepers. On worst case scenario, these groups represent infor-mation bottlenecks because they can limit the amount of infor-mation exchanged between nonregularly communicating teams[11]. On the other hand, they become natural integrators. If theyknow how to share information accurately and decide to do so,

then two noncommunicating teams benefit from their knowl-edge.

In summary, G1, G2, H10, D1, D2, E5, H3, B7, and H8 re-ceive and share large amount of information through adjacentteams and shortest communication paths. They do so quickerthan other teams and on a constant basis through the establishedcommunication channels in the organization. These characteris-tics allow them to become natural system integrators and knowl-edge repositories whose careful management can smooth andenhance project execution.

In terms of brokerage, core teams have certain control overthe relationships between nonregular communicating teams.Teams G1, H3, H10, and D2 score high as gatekeepers/ repre-sentatives, while G1 and D2 as liaisons (as mentioned earlier,internal and external coordinators do not play a relevant roledue to their low scores). Since these teams are well positionedin several subnetworks, their control takes the form of passingor restricting information among units [16].10 As a conse-quence, these teams can ease/facilitate or impede/complicateinformation exchange depending on whether they play sourceand receiver roles correctly. Additionally, when these groupsplay their role properly they become projects’ communicationintegrators. For example, these teams not only share informa-tion, but because of their strategic position they are able topinpoint nonregular communication members that will benefitfrom discussion and information exchange between them [19].To sum up, high brokerage teams (G1, G2, H10, D1, D2, E5,H3, B7, and H8) have the potential or risk of being projectintegrators or des-integrators depending on whether their com-munication roles are performed adequately.

V. INFORMATION LEADERS TEAM (ILT)

Based on our findings in the previous section, we suggestthat information exchange, system integration and innovationthroughout PD could be greatly enhanced by creating a mega-

10Moenaert et al.[20] points out that effective communication requires boththe source and the receiver sending and receiving information correctly. For in-stance, the source must be willing to share information which can be constrainedby three factors: 1) not able to transmit, 2) not willing to transmit, 3) thinks theinformation is not relevant to be transmitted. In addition, this information musthave an impact on the receiver, which also takes place in three categories: 1)increment knowledge, 2) modify attitude, 3) change on behavior [20].


team of core nodes (high central and broker) called “Informa-tion Leaders Team ” (ILT). Since ILTs deal with information ex-change in PD projects, it is important to understand which kindof communication they impact. The three main communicationcategories in PD organizations are as follows [1], [22], [28].

1) Coordination-type: refers to technical information ex-change (e.g., parameters) necessary to integrate finalproduct’s assembly.

2) Knowledge-type (innovation): refers to new knowledgecreated and shared during a new PD process.

3) Affirmative: team members and managers communicate formotivation and inspiration needs.

It is worth noting that the case study data as represented inthe DSM of Fig. 3 contains only information regarding the firstcommunication category [27]. Furthermore, since ILT is formedof highest scoring nodes on centrality and brokerage measures,then ILT inherits their properties. Therefore, a member of an ILTdo the following.

1) Receive/distribute information to a large number of teams.2) Acquire/send information from/to many other teams

rapidly if they decide to use their available intermediaries.3) Spend large amounts of time communicating and coordi-

nating.4) Possibly identify teams that will benefit from discussion

and information exchange (for instance, identify and covermisalignments between product architecture and organiza-tional structure [31]).

5) Control over the relationships between nonregular commu-nicating teams.

6) Ease or complicate information exchange depending onwhether they play source and receiver roles correctly.Therefore, they have the potential or risk to be a system’sintegrator or disintegrator.

7) Possibly become information bottlenecks.8) Possibly become information repositories, and conse-

quently, a source of knowledge and innovation.These characteristics show ILT as a group with challenges

and opportunities. Its main challenges are to deal with highamounts of information, play correctly their integrator/commu-nicator role, and be at the same time engineers with specific de-sign tasks. On the other hand, ILT has the opportunity of beingknowledge repositories and a potential source for innovation.

The major challenge faced by ILT is to distribute its time onsolving assigned engineering tasks and coordinating the effortsof others. Although ILT has a strategic position to increase com-munication and coordination in a PD project it also needs todedicate time to design components, innovate and solve engi-neering problems. Other authors have recognized this issue. Forinstance, Kazanjian et al. [18] speculated that cross-team inter-dependencies, which require coordination and integration withone or more subteams, result in constraints on the creative de-sign process. The combination of added constraints and coordi-nation requirements would reduce the individuals’ engagementin the creativity process, as creative options are fewer and moretime must be spent coordinating instead of designing or inno-vating. In addition, since design parameters change constantlyduring the PD process, ILT will spend time resolving conflictsand getting agreements among teams [18]. Moreover, some lit-

erature suggests that information centrality harms the innova-tion process. For instance, Leenders et al. [19] showed empiricalevidence that high interaction levels divert engineer’s attention,which in the end damages the creativity process. Therefore, eventhough ILT members possess a strategic network position to ac-cess information regarding the overall project, this might over-load their work, and potentially disrupt their design-innovationtasks as well as will harm their role as system’s integrator.

Managers must face this challenge. The first step is to realizethese conflicting tasks. That is, understand that ILTs will notonly become overloaded due to engineering design work, butalso as system integrators. Second, it is important to take advan-tage of ILT’s network position and exploit their role as projectintegrators. Browning suggested that PD organizations can beimproved by designating and appropriately locating “zone en-gineers” or “liaisons” [8]. Thus, we suggest creating a parallelstructure to the engineering design team on each of the ILTmembers as shown in Fig. 5. The figure shows how zone engi-neers act as liaisons in PD projects. As proposed by our model,zone engineers must be located in high central and broker teams.In addition, they will form part of the ILT. In that case, ILTs willtake advantage of their strategic location to manage communi-cation, information exchange and coordinate tasks adequately.

On the opportunities side, the ILT is a potential source for in-novation. As shown earlier, central and broker actors in a PDOnetwork are information leaders (ILs). They obtain and ex-change more data with less effort due to their strategic locationin the network structure. Hence, creating an ILT will guaranteenot only information centralization and quick distribution, butalso opportunities to create and share new knowledge that canbe incorporated in the new product design. In fact, Kazanjianet al. stated that members involved in interfunctional rela-tionships have more opportunities to participate in product’sinnovation [18]. Burt stated that ideas tend to homogenizewithin workgroups [9]. For example, due to the fact that teamsworking on the same component meet constantly, they getused to sharing the same information over and over again. Inaddition, their access to others teams’ information is limitedby the established communication channels. Hence, intra-teamroutinization diminishes their capability to come up with newways of thinking. On the contrary, people working across teamsattain information from different-heterogeneous sources whichchallenge their current beliefs and increase their creativity [9],[10]. As Burt [9] pointed out: “Information arbitrage is theiradvantage. They are able to see early, see more broadly, andtranslate information across groups.” ILTs have the advantageof having information of all teams in the network on a constantbasis. As a consequence, they are in a better position to inno-vate. The cycle in Fig. 6 illustrates the innovation processesfollowed by the proposed ILT. In fact, Burt recognizes severaladvantages of ILs as shown in Fig. 6.

1) Observe and analyze groups practices: because of theirprivileged position, IL can draw analogies and differencesbetween teams.

2) Synthesize information: IL has a higher probability ofseeing new practices.

3) Create new ideas: combine the information synthesizedinto new ideas.


Fig. 5. Improving high central-broker teams performance.

Fig. 6. ILT innovation cycle.

4) Transfer best practices: IL can make workgroups aware ofnew improved methodologies learned on other teams.

Indeed, research suggests that innovation is a process ofideas’ interconnection rather than one time bright creation[19]. Hargadon and Sutton [16] indicated that new ideas arebuilt of previous unrelated ones. Thus, they suggested that a“technology broker” benefits from its network position as wellas from organizational procedures to access, acquire, retainand retrieve new information. For all these reasons, an ILT hasthe advantage of being a great source of innovation for the PDprocess.

VI. CONCLUSION, LIMITATIONS AND FUTURE RESEARCH

In this paper, we have suggested that PDOs are composed ofteams which play a critical role in information exchange dueto their strategic location in the network. That is, complex PDprojects include teams with the challenge of being system bot-tlenecks but at the same time with opportunities of becomingsystem integrators and potential innovation diffusers.

First, we propose the use of centrality and brokerage indexesused in SNA to identify core teams in PDO. These measure-ments allow for central teams’ identification that is not possiblejust by visual inspection or analysis of the DSM. In addition,we suggest that PD managers employ different strategies to fa-cilitate the work of various teams depending on their role in thePD process as reflected by their corresponding centrality and

brokerage scores. In the particular case study described in thispaper, we have shown that if a team scores high on any centralityor brokerage measures, then this team will also score high on theremaining indexes. As a consequence, we recommended em-ploying a unified strategy to manage central and broker teams.In general, we suggest using all centrality and brokerage indexespresented (degree, closeness, betweenness, internal coordinator,external coordinator, gatekeeper and representative, liaison, andtotal brokerage) in order to identify ILT members. We do sosince it is not possible to generalize the high correlation coef-ficients found in this case study to all complex PD projects. Infact, there might be cases where degree centrality is homoge-neous among all groups but the other indexes point out that cer-tain teams control information flow.

Second, we propose the creation of a mega-team formedof high central teams called Information Leaders Team (ILT).ILT’s main issue is to deal with high amounts of informa-tion which can create a system bottleneck. In addition, ILTmembers face the challenge of being engineering designersand overall PD coordinators at the same time. As a solutionto these issues, we propose the location of zone engineersin each of the ILT teams. Zone engineers will work mainlyon project coordination, communication and overall systemintegration in conjunction with management assigned systemintegration teams. Finally, we point out the great opportunityof ILT members to play the role of information repositoriesand sources of innovation. To sum up, in this paper we arguedthat PD information exchange can be greatly enhanced byidentification of high central teams, grouping them as an ILTand assigning them specific roles as system integrators andinformation diffusers for potential innovation.

The results presented in this paper depend on two assump-tions which might limit the application of our model. First, wesuppose that the original DSM composition (e.g., Fig. 3) pre-vails throughout project execution as the main structure for in-formation exchange. Even though empirical evidence supportsthis hypothesis [22], there is no conclusive evidence to suggestthat DSM’s structure remains fixed over time. Second, we as-sume that information is not only transmitted directly betweentwo communicating teams but also indirectly through the use of


intermediaries. As a result, there is some information accuracyloss each time it passes along a third party.

Another limitation of our research constitutes the accuratecollection of data to construct the DSM. For instance, Sosa etal. [27], [29]–[31] work on the case study relies on interviews tokey players on each of the design teams about communicationfrequency and criticality. First, this information is not easy tocollect and takes plenty of time to do so. In addition, responsesmay not be as precise as one might expect due to several reasonssuch as changes in the project, people reassignment, and expe-rience of people interviewed and so on. As a consequence, it isimportant to note that it may be not easy to identify high centraland broker teams due to data compilation difficulties.

Our study of PD networks using SNA opens many opportu-nities for future research. First, it is essential to verify our theo-retical insights by conducting a field study to identify the frac-tion of time core teams spend on performing engineering (e.g.,design) work versus the time spent communicating (or coordi-nating) with other teams. During this field study, our model as-sumptions can be verified as well (e.g., real utilization of all pos-sible communication links). It will also be interesting to verifythe innovation potential (e.g., amount of ideas generated) by ILTmembers compared to other teams.

Second, a more rigorous statistical procedure could be devel-oped for determining what nodes have statistically significanthigher scores to include in ILTs. In this regard, Snijders and Bor-gatti [35] proposed the use of re-sampling methods to constructmany artificial data sets out of the observed data set (by deletingone node at a time and recalculating the network statistics) andusing the variability between these artificial data sets for sta-tistical inference. Alternatively, a similar method would be torandomly generate networks with similar characteristics (suchas similar density, degree distribution) and from that constructthe probability distribution of centrality scores of networks thatare comparable to our PD network [13].

Finally, an alternative SNA approach for finding ILTs is touse the “key player problem” proposed by Borgatti [8], [9] orthe use of group centrality measures instead of a single nodemeasures as illustrated in this paper [17].

ACKNOWLEDGMENT

The authors would like to thank Prof. M. E. Sosa (INSEAD,France) for his help in sharing the case study data presented inthis paper and for his feedback and reviews of an earlier versionof this manuscript. They would also like to acknowledge the in-sightful comments of the associate editor and three anonymousreviewers.

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Diego Andres Batallas received the B.S. degree inindustrial engineering in 1999 from Universidad SanFrancisco de Quito, Quito, Ecuador. He received theM.S. degree in industrial engineering from the Uni-versity of Illinois at Urbana-Champaign, in 2004.

His work experience includes serving as QualityDirector at Citibank Ecuador and Assistant Professorat the Department of Industrial Engineering at Uni-versidad San Francisco de Quito. He has served asSenior Evaluator for the Ecuadorian National QualityAward. Currently, he is serving as Cash Operations

Head at Citibank Ecuador and part time Professor at Universidad San Franciscode Quito.

Ali A. Yassine received the B.E. degree in mechan-ical engineering from the American University ofBeirut, Beirut, Lebanon, in 1988. He received theM.S. and Ph.D. degrees in industrial and manufac-turing engineering from Wayne State University,Detroit, MI, in 1989 and 1994, respectively

He is an Assistant Professor in the newly formedDepartment of Industrial and Enterprise SystemsEngineering (IESE) at the University of Illinois(UIUC), Urbana-Champaign and the Director ofthe Product Development Research laboratory. He

teaches a graduate class in Systems and Entrepreneurial Engineering and anundergraduate class in Operations Research. His research involves managingthe development process of complex engineering systems, design processmodeling, and IT-enabled concurrent engineering.

Dr. Yassine is a member of INFORMS, ASME, and PDMA.

Documents

Information Leaders in Product Development Organizational Networks: Social Network Analysis of the Design Structure Matrix