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Journal of Operations Management 29 (2011) 677–691

Contents lists available at ScienceDirect

Journal of Operations Management

journa l homepage: www.e lsev ier .com/ locate / jom

roduct portfolio architectural complexity and operational performance:ncorporating the roles of learning and fixed assets

ark A. Jacobsa,∗, Morgan Swinkb,c

Department of Operations Management, College of Business, University of Dayton, 300 College Park, Dayton, OH 45469, United StatesDepartment of Information Systems and Supply Chain Management, Neeley School of Business, Texas Christian University, TCU Box 298530,orth Worth, TX 76129, United StatesKorea University Business School, Republic of Korea

r t i c l e i n f o

rticle history:eceived 11 March 2010eceived in revised form 11 March 2011ccepted 25 March 2011vailable online 13 April 2011

eywords:arketing/operations interfacearketing/manufacturing interface

a b s t r a c t

Managers struggle to cope with complexity in their product portfolios. However, research into diversi-fication, product platforms, and other issues related to product portfolio complexity has often producedinconsistent guidance. This situation is at least partially attributable to an incomplete definition of portfo-lio complexity, and to corresponding limitations of theories applied to date. To address these limitations,we define product portfolio complexity as a design state manifested by the multiplicity, diversity, andinterrelatedness of products within the portfolio. We conceptually establish the three-dimensional natureof complexity and present a model to provide insights into how each dimension impacts operational per-formance. As an extension to prior theoretical perspectives, the model explicitly addresses the roles of

roduct developmentheory developmentocused factoryroduct portfolio complexityearning

organizational learning and the character of fixed assets (utilization and flexibility) as mediator andmoderator of product portfolio architectural complexity’s effects, respectively. We also incorporate theprinciple of diminishing returns to address potential non-linearities in the proposed relationships. Priortheories and research studies have neglected these issues. We conclude by discussing useful perspectiveswith which to view the model, and by presenting measures of portfolio complexity and approaches fortesting the propositions developed herein.

. Introduction

Businesses are developing products that are increasingly dif-erentiated and aimed at more and more narrowly defined marketegments (Berman and Korsten, 2010). As a case in point, executivesn one study reported that their firms added on average 1.7 newroducts for each product retired (Hoole, 2006) with the resultingrowth in the complexity of product portfolios creating significanthallenges and concerns (Berman and Korsten, 2010). In particular,majority of executives expressed concerns that these rising lev-

ls of complexity threaten to undermine operational efficienciesnd to consume profits (Berman and Korsten, 2010; Hoole, 2006).uch concerns are likely to be well founded. Firms with higher lev-ls of complexity in their portfolios reported profit margins thatere on average 3 percent lower than other firms (Hoole, 2006). On

he other hand, researchers suggest that added product complexityan create greater profits if the complexity is managed effectively

∗ Corresponding author. Tel.: +1 937 229 2204; fax: +1 937 229 1030.E-mail addresses: majacobs@udayton.edu (M.A. Jacobs), m.swink@tcu.edu

M. Swink).

272-6963/$ – see front matter © 2011 Elsevier B.V. All rights reserved.oi:10.1016/j.jom.2011.03.002

© 2011 Elsevier B.V. All rights reserved.

(Desai et al., 2001; Kekre and Srinivasan, 1990; Meeker et al., 2009;Meyer and Mugge, 2001).

It is the effective management of complexity that poses the dif-ficulty. Case studies confirm that companies struggle with productcomplexity decisions due to myriad related organizational andtechnological issues, including knowledgeable customers push-ing for unique applications, modular architectures that encouragethe pursuit of narrower niches, limited and inaccurate cost data,and limited understanding of customer needs (Closs et al., 2008;Meeker et al., 2009) Few business managers feel that their firmsare competent at managing these challenges (IBM, 2010). Someresearchers suggest that the reason for this insufficiency is a lackof understanding of the concept of product portfolio architecturalcomplexity (PPAC) itself, as well as the mechanisms by which itaffects operational performance (Fisher et al., 1995; Fisher andIttner, 1999; Ishii et al., 1995; Ramdas, 2003). Researchers frommany disciplines have employed a number of different theoret-ical perspectives to study PPAC (Table 1 provides a sample list),yet limitations to these perspectives have produced incomplete

and sometimes contradictory findings, thus creating the need fora more comprehensive explanation that integrates, compliments,or replaces existing explanations (Mahoney and Sanchez, 2004;Schmenner et al., 2009).

678 M.A. Jacobs, M. Swink / Journal of Operations Management 29 (2011) 677–691

Table 1Prior research on product portfolio complexity.

Study Year Purpose Theory Dimension Results

Rutenberg and Shaftel 1971 Determine cost implications ofcomponent standardization inmodular architectures

Inventory (PT) M Provides a model for a static marketand deterministic portfolio thatminimizes cost of modules

Kekre and Srinivasan 1990 Determine the impact of broaderproduct lines on market share andcost

Scope/scaleeconomies (TCE)

M Broader product lines increasemarket share and profit

Swaminathan and Tayur 1998 Determine the impact of delayeddifferentiation on cost

PT M Delayed differentiation leads tolower cost as variance increases andthe cost function is convex withrespect to capacity

Bayus and Putsis 1999 Determined the demand, cost, andstrategic implications of broaderproduct lines

Scope/scaleeconomies (TCE)

M The costs associated with broaderlines dominate potential demandincreases

Bajari and Tadelis 2001 Determine the linkage betweenproject complexity and procurementcontract form

TCE M, D Increased levels of complexitysuggest cost plus contract forms

Meyer and Mugge 2001 Define product platform and revealhow platforms drive business growth

Inventory (PT) M Reduced cost and time to market areattributed to the commonalityarising from use of platforms

Loch and Kavadias 2002 Find a way to determine optimalresource allocations for new productportfolios

PT M Decision rules are derived thatconsider market payoffs,interactions, and changinginvestment returns

Kaski and Heikkila 2002 Develop a method to guide productstructure development andquantitatively compare designalternatives

None explicit M Design metrics are presented thatare predictive of a new structure’simplication for cost efficiency

Ramdas et al. 2003 Determine a method for choosingwhich sets of components should beretained to support a definedfinished portfolio

TCE M Shows benefit of coordinatedprojects approach and howorganization and informationstructures are limitations

Wan and Hoskisson 2003 To explain the paradoxicalrelationship between product andinternational diversificationstrategies and firm performance indissimilar home countryenvironments

Institutionaleconomics

M Product diversification is negativelyrelated to performance in moremunificent environments, butpositively related less munificentenvironments

Lu and Beamish 2004 What is the relationship betweengeographic diversification andperformance

Scope/scaleeconomies (TCE)

M Costs and benefits of diversificationdepend on degree ofinternationalization and aremoderated by asset advantages thatincrease with geographical scope

Jiao and Zhang 2005 Determine the optimal mix ofproduct attributes to maximizemarket coverage at the leastengineering development cost

Inventory (PT) M Using market inputs with detailedcost information a model isconstructed that shows tradeoffsbetween benefit of variety andengineering cost

Otivson and Fry 2006 Determine how to handlediminishing returns on variety tobalance cost and benefit of portfoliovariety

None explicit M A process for value driven variety ispresented

da Cunha et al. 2007 Find the right mix of modules andproper stocking level

None explicit M Heuristic model shows significantsavings

Berger et al. 2007 Determine the impact of portfoliovariety on customer choice

None explicit M, D Larger numbers of product variantswere associated with perception ofhigher quality for the line

Chao and Kavadias 2008 Find the right balance betweenradical and incremental innovation

PT D Quantifies role of time andenvironment in striking appropriatebalance

Wiersema and Bowen 2008 Determine how industryglobalization, foreign competition inthe domestic market, and extent ofproduct diversification influenceinternational diversificationstrategies

Scope economies(TCE)

M Industries characterized by opennesswill have more internationaldiversification. Productdiversification negatively moderatesthe effect that rising globalizationhas on international diversification

Closs et al. 2008 Determine the drivers of portfoliocomplexity and how they can bemanaged

Socio-technical M, I Three competencies identified formanaging complexity and a modeldescribing profit impact is proposed

Kumar 2009 Determine how growth in theproduct and internationaldimensions are inter-related in theshort run

Scope economies(TCE)

M While opportunities to capture scopeeconomies through diversificationmay exist, firms are limited byvarious constraints from capturingthem

M = Multiplicity, D = Diversity, I = Interrelatedness, TCE = Transaction Cost Economics, PT = Portfolio theory.

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more functionally complex. Similarly, a portfolio containing fourpens that are identical except for ink color (a unique feature) is morecomplex than the portfolio containing only a blue ink pen. Hence

M.A. Jacobs, M. Swink / Journal of Op

Our purpose is to provide a theory-based explanation that clar-fies definitional ambiguities, and also potentially accounts for the

ixed results that businesses experience when they change levelsf complexity in their product portfolios. We do this by address-ng organizational capabilities and constraints largely overlookedn prior research. We focus on explaining PPAC’s effects on sales vol-me (unit quantities) and on operational performance. We use theerm “operational performance” to encapsulate the conventionalimensions of cost efficiency, quality, and delivery addressed in theperations management literature (Skinner, 1966; Schonberger,982; Hayes and Wheelwright, 1984; Miller and Roth, 1994). Wexpect all the effects we discuss to have the same direction andharacter for each of these sub-dimensions of operational perfor-ance. However, for simplicity we address operations performance

ingularly in terms of operational costs, since we are focused onhe efficient and productive use of operating resources associatedith managing and delivering a product portfolio. There may beuances that can be added to our propositions for specific opera-ional outcomes, e.g., quality. However, we leave such extensions touture research. We also ignore the effects of PPAC on sales revenue,ocusing instead only on unit sales volume and volatility.

Addressing this topic is important since the ramifications ofroduct portfolio architectural complexity (PPAC) touch almostll firm functions (Abbegglen and Stalk, 1985; Fisher et al., 1995;ubben, 1988). As the marketing function drives additions andhanges to the portfolio, engineering must perform additionalesign work, accounting must create an infrastructure to track theew product, sales agents must determine how to change prod-ct presentations, R&D may need to refine technology to make itore robust, the factory must determine how to integrate the new

roduct into the existing mix, and supply managers must incor-orate new purchases into the supply base. Hence the challengesresented by PPAC are pervasive and significant to organizations.

We begin our discussion of PPAC by offering a robust definitionn order to develop a common understanding of the construct. Next

e review two theories that have been used to describe how PPACmpacts operational performance, identify their limitations, andropose an extended model that more explicitly incorporates theffects of organizational learning and the character of fixed assets.e discuss propositions derived from the model, and then con-

lude by discussing ways to operationalize and test the associatedropositions.

. Definitions

The study of PPAC is hampered by the lack of a precise defini-ion. For example, past researchers have confounded the conceptf complexity with novelty, uncertainty, ambiguity, difficulty, andther concepts which are potentially related to, but distinct from,omplexity itself (Duncan, 1972; Eglese et al., 2005; Mintzberg,979; Novak and Eppinger, 2001; Sommer et al., 2009; Tung, 1979;achon and Klassen, 2002; Wilding, 1998). Our goal is to establishbasis for consensus beginning with a formal and robust definitionf product portfolio architectural complexity, rooted in the conceptu-lizations of complexity found in varied disciplines in the naturalnd social sciences (see Table 2). Though the conceptualizationsf complexity given in Table 2 apply to different objects, they arell similar in that they refer to complexity in terms of multiplic-ty, diversity, and interrelatedness. A system or object (tangible orntangible) can therefore be deemed to be complex if it is made upf a multiplicity of diverse, interrelated elements.

What are the ‘elements’ in product portfolio architecture? First,roduct architecture is the design system that links product func-ions to physical components (Fixson, 2005; Ulrich, 1995; Ulrichnd Eppinger, 2000) Second, consistent with marketing and prod-

ns Management 29 (2011) 677–691 679

uct design literatures, we consider a product to be a physicallydiscrete system sold as a single unit (Lefkoff-Hagius and Mason,1993; Ulrich, 1995). A product ‘portfolio’ is then the collection ofproducts offered for sale by the organization in question.1 Usingthese definitions, we can identify the product portfolio elementsas the sum of all product functions and physical components (e.g.,modules, parts) represented in the portfolio (Griffin, 1997a,b; Kaskiand Heikkila, 2002; Mac Cormack and Rusnak, 2006; Rutenberg andShaftel, 1971). Therefore, an increase in PPAC entails an increasein one or more of the three attributes of the product portfolio’selements: multiplicity, diversity, and interrelatedness. Multiplicitymeans a larger number of elements, including redundant and repli-cated elements. Diversity is reflected in the degree of differencesacross elements in terms of their attributes, e.g., shapes, materi-als, components, states. Interrelatedness refers to the common orinteracting functions embodied in portfolio elements.

For our purposes, we concentrate on the differences inoperational processing requirements in organizations causedby complexities embodied in product portfolios. For example,two diverse products are likely to have different organizationalrequirements for manufacturing, quality assurance, informationmanagement, sales support, etc. These differences in requirementsmean that associated resources are less likely to be sharable acrossthe products. Likewise, interdependencies among elements maycreate requirements for interrelated operational processes andhave implications for the patterns of demands placed upon opera-tional processes.

For conceptual clarity we stress that we do not consider theoperational processing requirements associated with a product tobe an element of the product. Hence we maintain a sharp distinctionbetween PPAC and the operational processes required to manageand deliver the portfolio. Maintaining this distinction allows us toestablish a criterion free definition of PPAC, thus defining com-plexity without regard to its outcomes. This approach avoids thetautological issues associated with defining complexity in termsof novelty, difficulty, etc. that have characterized some of thepast research. In fact, our primary thesis is that while productportfolio characteristics and operational processing requirementsare related, options for processing methods almost always existsuch that the relationship between product and process designs isnot fully constrained or deterministic. Thus, we choose to defineproduct portfolio architectural complexity in terms of its inherentcharacteristics, as opposed to defining it in terms of its potentialimpacts. Using these foundational precepts, we define PPAC as adesign state manifested by the multiplicity, diversity, and functionalinterrelatedness of products within the portfolio.

In order to apply this definition, each complexity dimensionmust be understood, especially in light of other related terminologyused in the literature. Consider a simple example involving a prod-uct portfolio comprised of pens and pencils. A pencil contains twowood slats, a cylinder of graphite, glue to join the slats, and paint.A ball point pen contains a ball and ink well assembly, ink, spring,lower and upper barrel, a clip, and a button assembly. The pen andpencil each have only a single function; writing. However, a secondfunction (erasing) can be added to the pencil with the addition of ametal crimp and eraser.

PPAC is increased with greater multiplicity (Gupta and Krishnan,1999). For example, one could argue that because the pencil hasmore functions (write and erase) than the pen (write only), it is

1 We confine our focus to portfolios of tangible products.

680 M.A. Jacobs, M. Swink / Journal of Operations Management 29 (2011) 677–691

Table 2Definitions of complexity.

Discipline Source Definition Dimension

Product Design Baldwin and Clark (2000) Complexity is proportional to the total number ofdesign decisions

M

Griffin (1997a,b) Complexity is represented by the number of functionsdesigned into a product

M

Kaski and Heikkila (2002) Complexity is represented by the number of physicalmodules and also by the degree of interrelatedness

M I

Gupta and Krishnan (1999)and Ramdas (2003)

Complexity is represented by the number ofcomponents in the product

M

Tatikonda and Stock (2003) Complexity is proportional to the interdependence oftechnologies

I

Organizational Design Blau and Shoenherr (1971) Complexity is manifested by the number of structuralcomponents that are formally distinguished

M

Price and Mueller (1986) Complexity is manifested by the degree of formalstructural differentiation

D

Daft (1983) Complexity is proportional to the number of activitiesor subsystems across levels or geographies

M

Scott (1992) Complexity is proportional to the number of elementsthat must be addressed simultaneously

M

Organizational Behavior Payne (1976) Complexity is proportional to the number of choicespresented a worker

M

Complex Systems Simon (1962) Complexity is manifested in a system comprised of alarge number of parts that interact in a non-simple way

M I

Klir (1985) Complexity is manifested in a system containingdifferentiation and connectivity

D I

ManagementInformation Systems

Meyer and Curley (1991) Complexity is proportional to the depth and scope oftechnical activities required for a process

M

Project Management Baccarini (1996) Complexity is proportional to the extent of variedinterrelated activities within a project

D I

Chemistry Whitten and Gailey (1984)and Kotz and Treichel(1996)

Complexity is revealed in chemical complexes whereinvalence electrons of certain transition metals areshared with one or more anions

I

Physics and Biology Mazzocchi (2008) Complexity is proportional to the degree of coupling orinteractions among the elements within a system

I

Kauffman (1993) Complexity is a function of the number of peptides andthe number of connections between the them

M I

Operations Research Eglese et al. (2005) Complexity is a synonym for difficulty which isproportional the number of constraints applied topossible solutions to a problem

M

Information ProcessingTheory

Wood (1986) Complexity is proportional to the number ofinformation cues which must be processed

M

Campbell (1988) Complexity is a function of the diversity of informationand the rate at which information changes.

D

Organizational Theory Child (1972) Complexity is manifested by the heterogeneity andrange of an organization’s activities

M D

Whetten and Aldrich(1979)

Complexity is represented by the number ofoccupational specialties and services offered, andbreadth of services offered

M D

Aldrich (1979) Complexity is represented on a continuum of homo toheterogeneous and concentration to dispersion

D

Supply Chain OperationsManagement

Choi and Krause (2006) Complexity is manifested in the varied number oftypes of suppliers and their interactions

M I

Bozarth et al. (2009) Complexity is proportional to the number of parts andthe degree of unpredictability in supply and demand

M

Fisher et al. (1999) Complexity is manifested by number of systems andthe rate at which products in the portfolio are replaced

M

Novak and Eppinger (2001) Complexity is represented the number of componentswithin a product, extent of interactions, and degree ofproduct novelty

M

Rutenberg and Shaftel Complexity is represented by the number of productles and

M

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roduct portfolio multiplicity is manifested by numbers of func-ions, which in turn create product variants. It is important to notehat some differences among product “variants” may be so slighthat the products are almost identical and essentially redundant.or example, identical products with small differences in packag-ng, or identical products with different part numbers due to distri-

ution through different channels both represent slight variants.

PPAC also increases with increasing diversity across products inhe portfolio (Price and Mueller, 1986). For example, if a fountainen is added to the portfolio of pens discussed prior, it represents an

markets

increase in complexity since the ink delivery system is a fundamen-tally different technology than that of the ball point pen. A productportfolio made up of a fountain pen, ball point pen, and pencil ismore complex than a portfolio of three ball point pens of differ-ing color because there is greater diversity of design architectures,technologies, and components represented, even though the num-

ber (multiplicity) of different products in each portfolio is the same.

Product architectural complexity also increases with the degreeof functional interrelatedness among products (Tatikonda andStock, 2003). Interrelatedness occurs by virtue of common func-

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M.A. Jacobs, M. Swink / Journal of Op

ions or functional interactions. First, two products are interrelatedf they offer a common functional feature, even if the function iselivered using diverse components or technologies. For example,onsider again the case of a pen and pencil. Because these two prod-cts offer consumers the same basic functionality, we would expectemand for the products to be positively correlated even thoughhey use diverse technologies and contain no shared components.unctional interactions also create interdependencies among prod-cts. For example, a product portfolio could contain both pencilsnd separate erasers. Demands for these two separate products areikely to be positively correlated, not because they offer the sameunctionalities, but because the function of the pencil creates theeed for the function of the eraser. Such functional interactionsan also lead to the creation of products with shared components.or example, the pencil and eraser might be sold separately or in aack. Similar functional interdependencies exist between variouslectronic components in audio systems, computer hardware andoftware, boats and paddles, bicycles and helmets, and so on.

The foregoing examples illustrate how multiplicity, diversity,nd interrelatedness can be manifested in the products of a port-olio. Also evident are basic relationships across the dimensions ofomplexity. Multiplicity is a prerequisite for both interrelatednessnd diversity, yet it is not perfectly correlated with either dimen-ion. For example, it is possible to have two product portfoliosith the same levels of diversity yet different levels of multiplicity,

nd vice versa. The same is true for multiplicity and interrelated-ess. These relationships, along with potential confusion regarding

nter-product versus intra-product elements, make PPAC itself aultifaceted and complicated concept. As we will establish later,

ach dimension of PPAC can affect operational performance. Thus,n discussions of PPAC it is important to clearly identify and isolatehe effects of one dimension of complexity from the effects of thether two potentially correlated dimensions. It is also important toistinguish between the complexity embodied in product architec-ural elements and the complexity of resources needed to processhese elements. Few prior studies have made these distinctions.onsider the following research streams.

Much research addressing product portfolio complexity haseen couched as the study of related and unrelated diversifica-ion (Kumar, 2009; Lu and Beamish, 2004; Tanriverdi and Chi-Hyon,008; Wan and Hoskisson, 2003; Wiersema and Bowen, 2008). Thisork has offered significant insights, but its ability to illuminate

he topic of PPAC is limited by how diversification is defined andeasured (Robins and Wiersema, 2003). For example, Rummelt

1974) characterizes diversification as “moves into new marketshat require an appreciable increase in managerial competence.”his conceptualization confounds both product and process. Addi-ionally, measures of diversification such as the specialization ratioWrigley, 1970), percentage of revenue (Geringer et al., 1989),r entropy (Jacquemin and Berry, 1979; Kim et al., 1989) are allevenue based, which confounds PPAC with an outcome (marketuccess measured in dollars). Other measures based on SIC or NAICSodes are an indication of the industries in which a firm operates,ut not the characteristics of the product portfolio. For example,ees and Kraft are both classified in food and kindred products,et Kraft’s product portfolio is substantially more diverse since itncludes a portfolio of chocolates (Marabou) equivalent to that ofees, but also refrigerated packaged meals, beverages, deli meats,ry goods, and dairy products.

The marketing literature uses the terms horizontal and verticalifferentiation to describe different levels of diversity in productortfolios. Horizontal differentiation indicates product variations

ithin a category (Draganska and Jain, 2005; Randall et al., 1998).

hese variations reflect minor differences such as flavors of iceream. Vertical differentiation indicates changes across categoriesr changes in features (Draganska and Jain, 2005; Randall et al.,

ns Management 29 (2011) 677–691 681

1998). These concepts are somewhat analogous, but not perfectlyaligned to our conceptualization of multiplicity (horizontal) anddiversity (vertical). However, both marketing concepts deal specif-ically with diversity, ignoring the unique effects of multiplicity andinterrelatedness. Moreover, the lines between categories can beblurry, often confounding product categories based on technologywith market categories defined by customer demographics.

Another research stream contains studies of product designstrategies for managing complexity found in engineering, mar-keting, and operations management literatures. This researchaddresses three primary design strategies: product plat-forms/modular architectures, component standardization (designreuse), and product/component consolidation. A product platformis the integrating mechanism whereby a number of differentfeatures, assemblies, or modules are attached to create productvariants. It is comprised of shared common components, productstructure, and production assets for a given product family (Huanget al., 2005). A platform strategy seeks to retain or increase thescope of end product variants offered while reducing the variety oftheir unique constituent components by standardizing and reusingcore subsystems and engineering designs (Meyer and Mugge,2001). However, component standardization and design reuse canalso be applied in the absence of a platform strategy (Fisher et al.,1999; Ramdas et al., 2003), and beyond the confines of a givenproduct family. For example, devices, fasteners, and electroniccomponents might be standardized across a wide array of differentproducts. Finally, a consolidation strategy seeks to replace mul-tiple components or multiple products with a single componentor product, by combining functions into one physical item (Ettlie,1997). For example, at the product portfolio level designers maydecide to replace separate phone, fax, and copying machines witha multifunction device that provides all three functions.

Like the other aforementioned literatures, the literatureaddressing design strategies tends to focus on diversity directly,only indirectly addressing multiplicity and largely ignoring func-tional interrelatedness as dimensions of PPAC. Although researchhas shown that the benefits from the deployment of a platformbased architecture include reduced procurement costs (Hillier,2002), improved operational performance (Desai et al., 2001;Gaur et al., 2005), and reduced engineering expense (Krishnanand Gupta, 2001), the extent to which these benefits are relatedto respective changes in multiplicity, diversity, and interrelated-ness has not been established. Furthermore, because the platformconcept can include both product and production assets, it mayconfound PPAC with complexity in operational processes.

3. Theoretical perspectives describing complexity’s effectson operational performance

In this section we provide a brief overview of two well estab-lished theoretical perspectives that have been used to describe theeffects of PPAC on performance: portfolio theory (PT) and Trans-action Cost Economics (TCE). Table 1 lists articles that have eitherexplicitly addressed PT or TCE, or have employed arguments implic-itly derived from PT or TCE (e.g., inventory theory). Rather thanprovide an exhaustive coverage, our purpose is to highlight some ofthe limitations that arise from applying these perspectives, therebyillustrating the need for additional insight.

3.1. Portfolio theory

A primary contention of portfolio theory (PT) is that volatility offinancial returns can be reduced through the pooling of imperfectlyor negatively correlated returns associated with individual itemswithin the portfolio (Markowitz, 1959). For example, the return

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82 M.A. Jacobs, M. Swink / Journal of Op

f a portfolio of stocks will be less volatile if the stocks representhe totality of the S&P 500 rather than just a collection of technol-gy companies. The principles of PT are based on the relationshipsetween volatility, uncertainty, and risk. Unknown sources ofolatility create uncertainties regarding investment decisions, thusutting invested capital at risk. However, if volatilities are not per-ectly correlated, the risks associated with individual investmentsan be pooled such that the aggregate portfolio risk is less than theimple sum of individual risks. These fundamental relationshipsold true regardless of whether capital is invested in stocks or inther types of assets (e.g., product designs, equipment, facilities,nd systems).

While PT was birthed in the context of financial instruments, aortfolio of products can be conceptualized similarly, i.e., as a bas-et of assets held for varying lengths of time generating varyingeturns. Hence, if the sales volumes for products within a productortfolio are taken to be analogous to financial returns on stocks,hen PT predicts that an increase in product portfolio diversity couldtabilize total sales volume by buffering the firm from sources ofarket volatility, e.g., the product lifecycle, seasonality, consumer

ckleness, or the entrance of alternative products. For example, aarm equipment manufacturer may choose to offer products target-ng the home gardener since the volatility of the home gardeningnd commercial agriculture markets are not highly correlated.

To the extent that production volume is positively correlatedith sales volume,2 and to the extent that the capacities of a com-on set of productive assets can be applied to multiple product

ines, i.e., shared, smoothing aggregate sales volume through prod-ct diversification can mitigate costly and disruptive changes toperations. As volume volatilities are reduced, more even flows ofaterials, information, and resources lead to improved operational

erformance (Schmenner and Swink, 1998). More even flows arelso more predictable. Combining demands from various productsools associated demand uncertainties such that aggregate uncer-ainty and risk are reduced (Baker et al., 1986; Collier, 1981, 1982;erchak et al., 1988; McClain et al., 1984). As a result, forecastingccuracies are improved, capacity planning becomes easier, and theisk of underutilized assets is lessened.

Inventory theory provides a special case of portfolio theory.nventory theory indicates that the total amount of inventoryequired to maintain a consistent service level will increase withhe number of stock keeping units (SKUs), at a decreasing rate. Con-ersely, if inventory assets can be shared, that is, if one SKU canubstitute for another to satisfy demand, then aggregate inventoryevels can be lowered through both safety stock and cycle stockeductions (Baker et al., 1986; Collier, 1981, 1982; Gerchak et al.,988; McClain et al., 1984). Safety stocks are reduced by poolingroduct demand volatilities and associated stockout risks. Cycletocks are reduced by pooling replenishment orders such that asso-iated fixed ordering costs are more fully absorbed (i.e., assets areore fully utilized).These examples illustrate the risk pooling and asset pooling

otential of product portfolio diversification indicated by portfo-io theory. Such effects can serve to both smooth and increaseggregate demand. If sales and associated production volatili-ies are smoothed through offering products with uncorrelated or

egatively correlated (complementary) demands, then planningecomes more certain and product demands can be more accu-ately matched to the capacities of productive assets. In addition,

2 We assume that sales volume and production volume are highly correlated.ver the intermediate term, this relationship must be true if the firm decides to

ulfill increasing demand internally (i.e., it does not outsource or forgo additionalales). Also note that we use the word ‘production’ in the economic sense, such thatt refers to any processing activity, e.g., manufacturing, transportation, service, etc.

ns Management 29 (2011) 677–691

if such a portfolio expansion increases aggregate sales and asso-ciated production volumes, then the utilization of the supportingorganizational assets is improved, thereby more fully absorbingfixed operating costs. Thus, the portfolio perspective suggests thatincreasing product portfolio diversity can create scope benefits thatresult from a more stable and uniform total load on the organiza-tion, as well as scale benefits that result from increased sales andproduction volumes. Again, it is important to note that an organiza-tion will realize such benefits only when the capacities of its assetscan be shared across products.

3.2. Transaction Cost Economics

Transaction Cost Economics (TCE) (Coase, 1937; Williamson,1975, 1985, 1996) is generally used to explain the governance oforganizations, and why they choose certain business transactionsover others. However Williamson (1991) indicates that this per-spective is also useful for explaining organizational structures andactions within a firm. Since a product portfolio manifests structuralproperties, TCE can be used to explain how the structure (i.e., com-plexity) of a product portfolio may affect the transactions needed tomanage and deliver it. According to Williamson, the determinantsof transaction costs are frequency, specificity, uncertainty, limitedrationality, and opportunistic behavior. Prior literature addressingPPAC has mostly ignored the behavioral aspects of limited rational-ity and opportunistic behavior. Instead researchers have focused onthe frequency and specificity of transactions necessitated by com-plexity. In many cases they have addressed these factors in termsof how scale and scope economies may apply to governance andresource changeover costs (e.g., Kekre and Srinivasan, 1990; Lu andBeamish, 2004; Wiersema and Bowen, 2008).

TCE suggests that portfolios with greater levels of multiplicitywill be more costly due to the number of transactions required tosupport them. For example, organizations will have higher costs asproduct portfolio multiplicity grows since there are larger numbersof managers, regulations, customers, interfaces, etc. to consider andmanage (Rothaermel et al., 2006; Sanchez and Mahoney, 1996). Theincrease in transaction cost arises from several factors, includingthe potential inability to recognize savings from common productarchitectures (Fisher et al., 1999; Gerchak and He, 2003; Gerchaket al., 1988; Krishnan and Gupta, 2001) and the increase in infor-mation processing loads (Rothaermel et al., 2006).

Quality assurance provides an example of the operational trans-action costs associated with PPAC. The quality costs of inspectionare volume dependent (Grant and Leavenworth, 1980). Givena fixed average outgoing quality target, as production volumedecreases the size and associated cost of required samples alsodecreases, but not at a constant rate. In fact, the sampling rateincreases as volume decreases. Therefore, as portfolios mani-fest increasing multiplicity and production volume per productaccordingly decreases, sampling and the associated costs will growexponentially.

TCE also suggests that increasing levels of diversity will increasethe cost of controlling the organization (Conner, 1991). Gover-nance costs will increase with diversified portfolios, which call forgreater specificity in operational assets required to support them.Indeed, costs may grow until they exceed diversification’s bene-fits (Bergh and Lawless, 1998; Hitt et al., 1997; Hoskisson and Hitt,1987; Tallman and Li, 1996). Additionally, coordination of opera-tions across disparate sources of supply may lead to diseconomiesas operations grow larger (Ghoshal and Bartlett, 1990). TCE also

suggests that diversity in the operations needed to support diverseproduct portfolios increases information processing demands ona firm’s managers and administrative systems (Hitt et al., 1997).Hence, increased levels of diversity drive additional costs.

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.3. Limitations of PT and TCE

As noted earlier, conceptual elements of PT and TCE have helpedrame the discussion of complexity’s effects on operational perfor-

ance. However, there remains a lack of clarity about the specificmpacts on operational performance by each dimension of PPAC.either PT nor TCE addresses all dimensions of PPAC, perhapsxplaining the somewhat mixed results of studies of product diver-ification (Hoskisson and Hitt, 1990; Ramanujam and Varadarajan,989).

A primary limitation is that neither PT nor TCE addresseshe full complement of mechanisms through which PPAC affectsperational outcomes. PT focuses on variance pooling effects andgnores transactions. TCE focuses on the presence or absencef transactional costs associated with multiplicity without fullyddressing diversity. Neither theory explicitly addresses non-inearities in operational cost functions, though they haveeen empirically established (Jacobs, 2008; Narasimhan andim, 2002; Tallman and Li, 1996). Furthermore, TCE does notxplicitly address changes to the fixed asset base which canhange the organization’s cost structure. For example, installa-ion of an electronic procure-to-pay system represents an assethange that, when accompanied with changes to policies androcedures, may reduce transaction costs. Today, billions of dol-

ars of purchases are executed automatically, whereas in theimes of Coase (1937) and Williamson (1975) such purchasesequired costly and large procurement and payables organiza-ions.

A second limitation is that PT and TCE tend to confoundultiplicity and diversity, while ignoring interrelatedness. Most

tudies of portfolio diversification based on portfolio and trans-ction cost theories have measured diversity without controllingor multiplicity. For example, studies of diversification use mea-ures of entropy (Jacquemin and Berry, 1979) to characterizeiversification, e.g., Hitt et al. (1994, 1997). Through its sum-ation property the entropy measure confounds diversity with

he multiplicity dimension. Similarly, studies of related versusnrelated diversification consider the number of products falling

nto various SIC classifications.3 Hence, these studies do not holdonstant the multiplicity dimension and as such are not ableo parse the impacts of diversity relative to multiplicity. Whilehese foregoing works have made significant contributions, therere nevertheless opportunities to add insight by distinguishinghe dimensions of complexity and investigating their differentialmpacts.

In failing to account for divergent effects, a given perspectiveometimes leads to conflicting predictions. For example, PT sug-ests on one hand that diversity is beneficial because throughemand pooling it provides the opportunity to manage volatility inhe total load placed on the system. Operational improvements areealized from the resultant smoothing of production flow, improve-ents such as increased consistency of quality and reduced effort

pent managing materials. However, research shows that in prac-ice the benefits of pooling are often not realized (Amit and Livnat,988; Narasimhan and Kim, 2002; Porter, 1987; Sirmon et al.,007). Moreover, PT also suggests that diversity can impede oper-tional performance, particularly for non-aggregatable operationalctivities where diversity increases costs and reduces opportunitiesor asset sharing or substitution. Hence PT and the pooling principle

o not provide complete insights into the operational performancef the organization.

3 Note that ‘relatedness’ in these studies is a descriptor indicating a low level ofiversity

ns Management 29 (2011) 677–691 683

3.4. Organizational learning

A third important limitation of the PT and TCE perspectives isthat they fail to consider the organization’s abilities to mitigatethe costs of complexity through learning and associated copingmechanisms. PPAC potentially determines the scope of learningopportunities available to an organization, and thus the opportu-nities to develop both efficiencies and unique capabilities.

Traditional learning theory states that learning increases withexperience (repetition) at a decreasing rate (Smith, 2000; Wright,1936; Yelle, 1979). One way PPAC affects the potential for learn-ing is through its effects on sales volume per product. Empiricalresearch reveals that increases in sales volume due to productdiversification are marginally decreasing and approaching zero(Aribarg and Arora, 2008; Baumol et al., 1982; Hoole, 2006; Jacobs,2008; Moorthy, 1984; Sievanen et al., 2004; Sloot et al., 2006).This means that as the product portfolio grows, there is less salesand production volume per product if overall sales increases arenot sufficient to offset the division of sales volume across increas-ing numbers of products. Ceteris paribus, a more diverse portfolioshould result in reduced operational performance (e.g., more man-ufacturing defects, poorer fill rates, reduced worker productivity,higher per unit costs) because fewer opportunities for learningthrough repetition are present. For example, scrap, rework, andwarranty claims are expected to be higher for a less experiencedworker since the worker is more likely to create defective products(Wright, 1936). The production manager may not route the prod-ucts through the factory as effectively as possible since she hasless knowledge about the nuances of the product–process inter-relationship. Production planners may hedge against shortages byraising inventories of materials which in turn leads to higher stor-age, spoilage, and damage costs (Schonberger, 1982).

More recently, researchers have proposed an alternative organi-zational learning model that suggests diversity may actually fosterlearning (Argote, 1999; Cohen and Levinthal, 1990; Schilling et al.,2003). Diversity in the portfolio drives diversity in the underly-ing systems, and greater levels of it can increase the number ofinteractions and the subsequent sharing of knowledge throughsocialization mechanisms (Grant, 1996; Nonaka, 1994; Tyre andVon Hippel, 1997), but seemingly only up to a point (Narayananet al., 2009). For example, design engineers may learn more frominteractions with engineers of other disciplines or of other productsthan they would in the absence of such opportunities. Cross-discipline interactions enable such engineers to combine theirrespective capabilities (Kogut and Zander, 1992). Further, someresearchers argue that the ability to learn is founded upon priorknowledge (Cohen and Levinthal, 1990), e.g., workers can buildknowledge using lessons learned from product #2 to improve prod-uct #1 (Carlile and Rebentisch, 2003).

Thus, the organizational learning perspective suggests thatchanges in PPAC produce multiple distinct effects. Increases inmultiplicity will increase repetitive learning opportunities, whileincreases in product portfolio diversity may at the same timereduce opportunities for learning from repetition and increaseopportunities for learning from socialization and cross-applicationsof knowledge. Presumably, any of these mechanisms could producethe greater impact on operational performance, depending on thesituation. Few research studies have considered an organization’sabilities to apply both types of learning in order to cope with, orleverage the effects of, PPAC.

3.5. The utilization and flexibility of fixed assets

A fourth shortcoming of PT and TCE is that they do not explicitlyaccount for either the state or the character of an organization’sfixed assets. In the same way that prior theories have neglected to

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ccount for an organization’s internal complexity coping mecha-isms (i.e., learning), they have also not considered how differences

n a given organization’s operating state might either amplify orampen complexity’s effects. We posit that two attributes are

mportant: fixed asset utilization and flexibility.Asset utilization is defined as the extent to which the capacity

f the asset is consumed by current production demands. Trans-ctions necessitated by increases in PPAC consume resources, thusdding to overall costs. If, however, substantial slack exists in theseesources, then the real cost of such transactions will be lower thantherwise, even to the extent that in some circumstances trans-ctions are virtually “free” (Goldratt and Cox, 1992). Such is thease if transactions consume underutilized assets that are alreadyaid for because of their fixed cost structures. Conversely, trans-ctions requiring the use of assets that are already highly utilizedill create larger real costs to the operation, especially when low

ost options for extending (e.g., overtime work) or replicating (e.g.,ew hires, new equipment) the assets are not available, i.e., whenssets are “fixed.” The phenomenon we are describing is akin tohe well known “queuing effect” present in all operating systemsConway et al., 1988; Lovejoy and Sethuraman, 2000). Because ofnteractions among tasks, the lead times (and associated costs)equired to process jobs in a system will increase exponentiallyith both the number (utilization) and variability (diversity) of

asks required. Utilization effects create increasing and decreasingeturns to scale in many of the relationships we have discussed thusar, including pooling and transaction cost effects. Non-linear rela-ionships such as these have been largely ignored in prior researchf PPAC.

In addition to utilization, which is a function of an operation’sapacity, we consider flexibility, which is a function of an opera-ion’s capability, to be an important fixed asset characteristic. Givenhe wide variety of processing options available for most prod-cts, we should not expect that dimensions of PPAC should affectll organizations to the same degrees. Technology choices regard-ng equipment, labor, and other inputs affect the flexibility of theperation. Koste and Malhotra (1999) define various dimensionsf flexibility in terms of an operation’s ability to remain efficientnder a range of operating states, where the elements of range areumber and heterogeneity of states. Using Koste and Malhotra’s1999) framework, two dimensions of flexibility are thought toe important in the management of PPAC. First, volume flexibilityi.e., scalability) is an operation’s ability to produce a wide rangef aggregate outputs with few transition penalties or changes inerformance outcomes (Koste and Malhotra, 1999). Assuming, ase have argued, that PPAC changes create changes in sales androduction volumes, we consider the technological ability of anrganization to accommodate such changes to be important. Sec-nd, mix flexibility is an operation’s ability to produce a wide rangef products with few transition penalties or changes in performanceutcomes (Koste and Malhotra, 1999). Mix flexibility is, therefore,n important capability for accommodating increases in productiversity.

Because fixed assets are not easy to replace or change (i.e.,hey are “fixed”), an organization’s volume and mix flexibilities areetermined in large part by the characteristics of the fixed assets itossesses. Aggregate capacity constraints embodied in fixed assetsreate upper bounds on volume flexibility, and the fixed cost struc-ures of fixed assets create lower bounds on the volumes that cane produced profitably. Similarly, the technological scope of fixedssets (e.g., general purpose versus special purpose equipment) cre-tes constraints on an organization’s mix flexibility. As we noted

arlier, the extent to which shared asset capacities can be usedo process multiple products is an important determinant of therganization’s abilities to realize pooling effects suggested by PTnd transaction cost effects suggested by TCE. Prior studies of PPAC

ns Management 29 (2011) 677–691

have not factored in the moderating effects of these fixed assetcharacteristics.

4. A more complete model of the effects of PPAC onoperational performance

In this section, we build upon the foregoing discussion todevelop a model of testable propositions that address the causalmeans by which the respective dimensions of PPAC affect oper-ational outcomes. Fig. 1 presents the model, which addresses theaforementioned limitations of prior theories. It illustrates how eachdimension of PPAC is proposed to affect sales volume and volatil-ities directly, and operational performance indirectly via resultantutilization and learning effects governed by the flexibility of thefixed asset base. This model of PPAC effects is itself complex.However, it can be summarized by two sets of effects: (1) PPACdetermines the nature of the demand load placed on the operatingsystem, and (2) the effects of this load on operational performanceare mediated and moderated by the constraints and capabilities ofthe organization. By explicating the impacts of PPAC specific dimen-sions on the demand load, and by incorporating the mediating andmoderating effects of organizational capabilities, our model inte-grates and extends the explanations offered by prior theories. Thefollowing propositions express these relationships in detail. All ofthe propositions are directional; Fig. 1 shows plus and minus signsindicating the nature of the hypothesized relationships.

As we have discussed, asset utilization plays a central role as anoperational constraint that influences the effects of PPAC on opera-tional performance. We treat production volume and utilization ashighly correlated terms, given that we assume that asset capacitiesare fixed. Researchers have consistently associated the utilizationof an organization’s fixed asset infrastructure with cost (Corradoand Mattey, 1997; Klein, 1960), quality (De Vany, 1976), and leadtimes (Conway et al., 1988). Production volume and associated uti-lization increases produce several potentially positive effects onoperational performance as a result of economies of scale and scope.Scale economies include the ability to capture quantity discounts,and to more fully absorb the fixed costs associated with trans-portation and other processes utilizing fixed assets. Related to thisis the potential for pooling as proportionately fewer setups maybe needed since common components can be produced in largerbatches.

The effects of production volume and utilization are not con-stant, however. If current asset capacity utilization is low, thenthe benefits of added volume are more likely to be seen than ifcapacity utilization is high. In fact, when utilization is very high,added volume may actually produce diseconomies in operationalperformance as processes become clogged with work, queues growexponentially and little time is available for equipment mainte-nance, training, and other support activities (Conway et al., 1988;Lovejoy and Sethuraman, 2000). These non-linear effects suggestthat additional volume will increase operational performance ata decreasing rate (Klein, 1960; Lovejoy and Sethuraman, 2000;Schmenner and Swink, 1998).

Proposition 1. Fixed asset utilization improves operational perfor-mance at a decreasing rate.

Now turning to the volume effects of PPAC, marketing researchindicates that organizations that offer more product functionaloptions or combinations often have larger overall sales volumes, forseveral reasons (Baumol et al., 1982; Bettis, 1981; Desai et al., 2001;Hoole, 2006; Jacobs, 2008; Kekre and Srinivasan, 1990; Lancaster,

1979; Moorthy, 1984; Quelch and Kenny, 1994; Sievanen et al.,2004). First, increases in product multiplicity through the cre-ation of bundled products may induce consumers to purchasebundles containing more items than they would have otherwise

M.A. Jacobs, M. Swink / Journal of Operations Management 29 (2011) 677–691 685

Multiplicity

Interrelatedness

Diversity

OrganizationLearning

OperationalPerformance

Fixed AssetFlexibility

Product Portfolio

Fixed AssetUtilization

SalesVolume

Volume Volatility

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urchased. Second, products that are differentiated in a minor wayrelated or horizontal differentiation) may induce additional salesue to providing closer matches with specific customer desires oreeds. Third, products that are distinctly different from one anotherunrelated or vertical differentiation) may increase sales becausedditional, new customer needs are met. Finally, creating function-lly interrelated products increases overall sales, as one product inhe portfolio stimulates demand for its complementary products. Its also fairly well established that increases to sales resulting fromuch product portfolio changes are subject to diminishing returns,ecause of product cannibalism, market saturation, and customeronfusion (Desai et al., 2001; Kalish, 1983; Meeker et al., 2009;aylor, 1986).

As explained earlier, we make the reasonable assumption thatales volumes and production volumes are positively correlatedver the interim term. Therefore, assuming that the capacity ofxed assets is unchanging, changes to sales volume will produceroportional changes in fixed asset utilization. Taken collectively,hese arguments define the mediating role of sales volume changess a generative means by which dimensions of PPAC affect assettilization. In the case of each of the three dimensions of PPAC,n increase (decrease) in complexity is expected to produce anncrease (decrease) in aggregate sales volume, which raises (low-rs) asset utilization. Stated formally:

roposition 2a–c. Aggregate sales volume mediates the effects ofa) multiplicity, (b) interrelatedness, and (c) diversity on fixed assettilization.

Propositions 1 and 2a–c chart a causal path where increases inPAC create increases in sales volume and asset utilization, whichn turn lead to improvements in operational performance, at aiminishing rate. This path captures some of the potentially pos-

tive effects of increases in PPAC not explicitly addressed in prioriterature.

There may be direct scale economies associated with increasedales volume that go beyond those accounted for by utilizationPropositions 2a–c) and learning (discussed later in Propositionsa–c). For example, other scale economies include quantity dis-

ounts in purchases from suppliers, product quality benefitsssociated with longer production runs, and justification for pur-hases of more specialized equipment. In accordance with classicalconomic theory, we expect that these scale benefits are achieved

and Constraints

al model.

as certain thresholds of volume are achieved, with diminishingreturns to scale (Keynes, 1936; Leontif, 1941; Pareto, 1906; vonBohm-Bawerk, 1889). Hence, in addition to the indirect effects ofsales volume on operational performance through utilization andlearning, we offer the following proposition to account for directsales volume effects.

Proposition 3a–c. Aggregate sales volume mediates the effects of(a) multiplicity, (b) interrelatedness, and (c) diversity on operationalperformance at a decreasing rate.

We hasten to note the potential for a feedback relationshipbetween sales volume and operational performance. Research hasclearly shown that operations performance can affect sales volume(Kaynak and Hartley, 2008; Schmenner and Vastag, 2006; Vickeryet al., 1994). Better operational outcomes (cost, quality, delivery)create more attractive value propositions for consumers, leading tosales growth. Thus, sales volume and operational performance havea potentially endogenous relationship, creating a virtuous cycle.While recognizing this relationship makes our explanation morecomplete, the existence of the feedback effect has no direct bearingon the other relationships specified in our model.

In addition to the effects of sales volume on asset utilization andoperational performance, we suggest that the volatility of aggregatesales volume will also have a bearing. As discussed earlier, volatil-ity usually creates greater uncertainty, risk, and underutilization ofassets. When sales are volatile, production managers must choosebetween buffering strategies based on either inventory (make-to-stock) or slack capacity (make-to-order). Either approach hascosts. Make-to-stock costs include inventory financing, spoilage,shrinkage, and stranding, whereas make-to-order costs includeequipment financing and ramp-up/ramp-down costs. In eithercase, volatility degrades operational performance by increasingquality failure costs, lengthening work queues, and increasingresource idle time (Schonberger, 1982).

If product diversification reduces aggregate demand volatil-ity, operations may enjoy direct scope-related benefits. To theextent that product diversity creates uncorrelated or countercycli-cal demands, aggregate sales and associated production volumes

can be smoothed (Gorman and Brannon, 2000; Kim et al., 1989). Bylayering complementary production schedules, operations man-agers can create more uniform loads on fixed assets. Thesesmoothed flows lead to improved asset utilization and the scale

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enefits described by portfolio theory (Gorman and Brannon, 2000;chmenner and Swink, 1998). In addition, reduced uncertainty andisk create opportunities for leaner operations with tighter capac-ty constraints. In these ways, reduced volatilities produced byncreases in product portfolio diversity can lead to improved oper-tional performance.

roposition 4a and b. Aggregate sales volatility mediates the effectsf diversity on (a) fixed asset utilization and (b) operational perfor-ance.

The next set of relationships we discuss pertains to orga-izational learning. As we noted earlier, increased productionolumes increase the potential for classical production learn-ng, more recently known as “learning through repetition” (Kekrend Srinivasan, 1990; Philippatos and Wilson, 1972; Varadarajan,990). The effects of learning through repetition on defect rates,ssembly time, and manufacturing costs are well established in theiterature (Alchian, 1963; Asher, 1956; Barkai and Levhari, 1973;arr et al., 1995; Epple et al., 1991; Gruber, 1994; Hirschmann,986; Lieberman, 1984; Rapping, 1965; Wright, 1936; Yelle, 1979).uch benefits are likely to be particularly salient for task repetitionsriven by increases in multiplicity and in sales volume. Processing

tems that are nearly alike gives operations managers and employ-es more observations from which to derive greater understandingf process constraints and sources of improvement. Added vol-me per transaction type will be translated into improvements

n variable transformation costs as workers become more skillednd procedures become more refined (Smith, 2000; Wright, 1936;elle, 1979). Additionally, growing volumes may justify process

mprovement investments to drive better cost and quality.Research suggests that exposing workers to greater prod-

ct variety may also improve or accelerate learning (Carlile andebentisch, 2003; Cohen and Levinthal, 1990; Schilling et al., 2003;pton, 1997). Diversity in the product portfolio drives diversity inroduction and product support systems (Rebentisch, 1995), andlso increases the number of required interactions among differ-ng functional representatives. These interactions foster the sharingnd cross-applications of knowledge through socialization mech-nisms (Grant, 1996; Nonaka, 1994; Tyre and Von Hippel, 1997).ence increased portfolio diversity might foster learning throughross-applications of knowledge in cases where organizational orersonal knowledge in one product area can be transferred to otherroduct areas (Kogut and Zander, 1992; Schilling et al., 2003). Forxample, the cost and throughput for aflatoxin testing in almondsas substantially improved by incorporating process design prin-

iples deployed in the determination of the absorbency of diapersWolf, 2010). This example also highlights the fact that learn-ng through repetition and diversity are not limited to tangibleesources; benefits may accrue from a more complete utilization orharing of intangible assets or inputs such as technological knowl-dge (Lu and Beamish, 2004).

In sum, increases in multiplicity, sales volume, and diversitympact the operating system by creating opportunities for learn-ng which can be applied to operational policies, procedures, andractices to take advantage of the capabilities conferred by exist-

ng assets (Caves, 1996; Helfat and Eisenhardt, 2004; Macher andoerner, 2006). When such learning takes place, it breeds capabil-

ties that extend operational performance outcomes (Rosenzweignd Roth, 2004; Schmenner, 1997; Teece et al., 1997). In these ways,ncreases in PPAC may lead to increased learning, which in turn

eads to improved performance. Learning is therefore a mediatorf PPAC’s effects on operational performance. Consistent with clas-ical learning theory, we expect that learning occurs at a decreasingate (Smith, 2000; Wright, 1936; Yelle, 1979).

ns Management 29 (2011) 677–691

Proposition 5a–c. Organizational learning mediates the effects of(a) multiplicity, (b) sales volume, and (c) diversity on operational per-formance at a decreasing rate.

Propositions 3c, 4b, and 5c express the expectation for indi-rect positive effects of diversity on operational performance viaits impacts on sales volume, sales volatility, and learning, respec-tively. Prior studies of product diversity have tended to focus on itsdirect effects, which are uniformly viewed as negative. However,empirical support of the detrimental effects on operational perfor-mance has been mixed (Geringer et al., 1989), possibly because theeffects of diversity on sales volume, volatility, and learning havenot been included in the models tested. We posit that accountingfor these effects will make the direct negative effect of diversity onoperational performance more clearly evident.

Two TCE concepts, transaction frequency and asset specificity,make a strong case for the negative direct effects of product diver-sity on operational performance. An increase in the number ofdifferent products in a product portfolio directly increases the num-ber of transactions that must be performed in operations. Suchtransactions occur throughout the supply chain, including sourcingtransactions, sales transactions, record keeping, inventory track-ing, and production resource changeovers. All of these transactionsadd costs by consuming resources, but also by interrupting pro-duction runs and material flows. Furthermore, a greater degree ofdifference across products increases the requirements for specificassets (both tangible and intangible) needed to handle the pecu-liar processing requirements of various products. Evidence of sucheffects is reported by researchers who have noted the increasedgovernance costs associated with product diversification, espe-cially “unrelated” diversification (Hitt et al., 1997; Hoskisson andHitt, 1987). Given this evidence, we offer Proposition 6.

Proposition 6. Diversity directly decreases operational perfor-mance.

One of the important factors neglected in many prior studies ofPPAC is the flexibility of the organization’s fixed assets. Flexibilityis an organizational capability that determines its ability to copewith the diversity and volatility of the demand load placed upon it.Earlier we identified volume flexibility and mix flexibility as poten-tially important elements. Both of these flexibilities are productsof infrastructure/structure choices regarding labor, operating poli-cies, equipment, information technology, and facilities (Hayes andWheelwright, 1984). The latter three structural elements consti-tute the organization’s fixed asset base. In the short to intermediateterm, the operating characteristics of these assets determine therange of operating conditions that the organization can handle effi-ciently. Accordingly, any estimation of PPAC’s effects on operationalperformance must factor in the potentially constraining or enablingcapabilities of the operation’s fixed asset base (Boyer et al., 1997;Holtz-Eakin and Lovely, 1996; Swink and Nair, 2007).

Manufacturing infrastructures characterized by high volumeand mix flexibilities (e.g., those containing general-purpose equip-ment, cellular layouts, etc.) can be used to mitigate congestioneffects, transaction costs, and other penalties associated withincreases in product volume and diversity (Jacobs et al., 2011). Flex-ible assets provide a wider range of task functionalities, greatermobility (speed) in transitioning from one processing state toanother, and greater uniformity in outputs over the range of tasksthey perform (Koste et al., 2004; Upton, 1997). Because of theseattributes, the capacity of a flexible asset can be applied to moreproducts, thus enabling the pooling benefits indicated by PT. In

addition, mobility and uniformity serve to mitigate the transactioncosts associated with diversity and volatility. Using fixed assets thatare more flexible, cost improvements can be achieved through fixedcost amortization; delivery performance can improve from faster

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hroughput; and quality can improve through fewer changeovers,educed inventories, and greater process capability (Schonberger,982). We summarize these effects in our final set of propositions:

roposition 7a and b. Fixed asset flexibility moderates (dampens)he effects of (a) diversity and (b) sales volatility on operational per-ormance.

. Implications for future research

Given that prior explanations of PPAC’s impact on operationalerformance are incomplete, we were compelled to seek otherxplanations that may be appropriate (Van de Ven, 1989). Byelineating the effects of various PPAC dimensions on sales char-cteristics and on organizational capabilities, and by clarifying theoderating role of fixed asset flexibilities, we have proposed a more

omplete picture of PPAC’s effects on operational performance.hile we built our model by drawing on several theories, including

lassical economics, PT, TCE, and organizational learning we sug-est that other perspectives may also be useful to future researcherss they seek to integrate and interpret the proposed effects. Weffer two suggestions here.

First, the Theory of Performance Frontiers (TPF) (Clark, 1996;ayes and Pisano, 1996; Schmenner and Swink, 1998; Skinner,966) may be useful as a holistic perspective that graphically inte-rates the effects illustrated in our model. The TPF originatedn the neoclassical school of economics, which holds that eco-omic growth arises from technological progress (Meade, 1962).t the operational level, such progress results from organizational

earning and associated resource investments. In accordance withconomic theory, the TPF asserts that such investments are subjecto diminishing returns, and are ultimately bounded by the capa-ilities of an organization’s fixed assets. Asset utilization is defined

n the TPF by the position of an organization’s current output tonput performance relative to its ultimate potential as defined byts asset frontier Schmenner and Swink (1998). While sales vol-me and volatility are not explicitly addressed by the TPF, thiserspective incorporates all of the other effects in our model. Itay therefore be useful as a means for explaining PPACs effects on

perational performance vis-à-vis an organization’s current oper-ting position relative to its asset frontier – its current utilization –nd by the ultimate flatness of its asset frontier – its fixed asset flex-bility. Recasting the effects of changes in PPAC in this way couldead to new insights and possibly new operationalizations of the

odel constructs. For example, applying the TPF would facilitateesting our model using a data-envelopment-analysis approach.

A second potentially useful perspective is the resource basediew (RBV) and related dynamic capabilities. The RBV indicateshat strategic advantage accrues by virtue of an organization’s valu-ble, rare, inimitable and nonsubstitutable assets (Barney, 1991;enrose, 1958; Wernerfelt, 1984). Such assets can be tangible orntangible. Researchers have questioned whether asset utilizationroduces competitive advantage, or if instead advantage derivesrom distinctive processes and the path of changes (i.e., dynamicapabilities) that led to the organization’s current asset configu-ation (Eisenhardt and Martin, 2000; Helfat, 1997; Teece et al.,997). If one considers the architecture of an organization’s productortfolio as a knowledge “asset,” the utilization and organizational

earning components of our model could be interpreted as theeans by which the organization exploits PPAC, according to its

dynamic capabilities.” The RBV/dynamic capabilities could be use-ul in predicting which path, utilization or learning, explains the

ost variance in PPAC’s effects on operational performance. Inddition, interpreting our model in this way might lead researcherso identify other organizational capabilities and constraints thaterve to mediate or moderate PPAC’s effects.

ns Management 29 (2011) 677–691 687

5.1. Testing the model

Empirical tests of our propositions represent important researchopportunities, albeit challenging ones. We have presented adynamic model wherein changes in PPAC will produce changes insales volume and volatility, which in turn will bring about changesin performance that are mediated by utilization and learning, andmoderated by flexibility. As such, the ideal method for testing themodel entails an analysis of time series data gathered from multi-ple companies. We submit that our arguments and supporting logicshould be applicable to several units of analysis, including the cor-poration, the strategic business unit, the manufacturing businessunit (as defined by Miller and Roth, 1994), and the manufacturingplant. Each of these organizational levels deals with a “portfolio” ofproducts that are produced on a dedicated set of assets characteri-zable in terms of utilization and flexibility.

A single company study could be executed initially to developmeasures and identify potential confounds. However, a multi-company, single-industry time series study would provide greateropportunity for controls and causal testing. Then, cross-sectional,multi-industry studies would aid in establishing the generalizabil-ity of the proposed relationships. In conducting such studies, it willbe important to include measures of all three dimensions of PPAC.As aforementioned, they are likely to be correlated and thereforeneed to be controlled either statistically or through sampling. Forexample, the propositions could be tested using data describingorganizations with similarly sized product portfolios targeted atthe same market but differing in levels of diversity. Alternatively,another useful setting would include organizations offering similardegrees of product diversity with differing numbers of products.These scenarios are present in many industries including elec-tronics (e.g., servers or medical devices), food (e.g., ice cream orsalty snacks), and chemicals (e.g., hydrocarbon refining or specialtychemicals).

Initial steps for constructing these studies include identifyingboth potential measures and sources of supporting data. The emer-gence of enterprise resource planning (ERP) and product lifecyclemanagement (PLM) systems is a tremendous aid to prosecutingresearch in this area. ERP systems typically contain financial andoperational performance data. PLM systems contain product designand bills of material information that can be helpful in quantifyinglevels of multiplicity, diversity, and interrelatedness. Informationsuch as number of product variants, percentage of common com-ponents, percentage of products comprised of new designs, dates ofdesigns and product launches, could be used as direct indicators orproxies for each of the three complexity elements. Less direct indi-cators could be constructed by borrowing approaches from otherfields. For example, Rodan and Galunic (2004) develop a knowledgeheterogeneity index using distances computed from the presenceor absence of technological attributes. A similar index could be con-structed for products based on whether or not they are comprised oftechnologies such as electrical, mechanical, and hydraulic systems.Harrison and Klein (2007) discuss interesting operationalizationsof organizational diversity that could be ported to a product port-folio setting. Table 3 presents several examples of these and otherpotential operationalizations of PPAC.

The most challenging constructs to measure are undoubtedlyorganizational learning and fixed asset flexibility. Because learningis a latent variable that is difficult to directly observe, researchersare often forced to rely on manager-reported perceptions of learn-ing (Dewey, 1938; Edmondson, 1999; Kolb, 1983; Lankau andScandura, 2002). An alternative would be to omit direct measures of

learning from the model, and infer learning from changes in opera-tional performance (e.g., Ittner et al., 2001). However, this approachwould preclude any ability to separate the effects of learning fromother utilization based economies of scale and scope resulting from

688 M.A. Jacobs, M. Swink / Journal of Operations Management 29 (2011) 677–691

Table 3Potential operationalizations of portfolio complexity.

Dimension Name Measure Source

Multiplicity Portfolio size # of SKU’s Novak and Eppinger (2001)Multiplicity Products per function # products/# functions Fixson (2005)Relatedness Commonality index # unique/# total Martin and Ishii (1996)Relatedness Dependency index # changing/# possible changes Kaski and Heikkila (2002)Relatedness Density # ties/# max possible ties Burt (2000)Diversity Age entropy � (% total age) × Ln (1/% total age) Jacobs (2008)

uniqnitud

icsli

fiafdarksonmrkBplda

6

cricetdtef

eaaicepeptd

ps

Diversity Newness # new/# totalDiversity Knowledge heterogeneity � � × distance ×Diversity Gini coefficient � distance mag

ncreased volume and diversity, respectively. Fixed asset flexibilityould also be assessed using manager-reported perceptions thaterve as reflective indicators (Koste et al., 2004). Proxies such asabor intensity and extent of advanced manufacturing technologymplementation could also serve as useful measures.

There are opportunities to apply our new perspective to diversi-cation research as well as to research into the effects of modularitynd product platforms. Future studies should consider the dif-erential impacts of each dimension of complexity (multiplicity,iversity, and interrelatedness). In addition to clarifying whichspects of diversification are beneficial or detrimental, futureesearch should include the role of organizational learning. To ournowledge, learning has not been a consideration in existing diver-ification research. Studies could investigate how changes in levelf diversification lead to changes in policies and associated orga-izational benefits or detriments. Similarly, studies investigatingodularity or platforms could investigate how changes in inter-

elatedness and standardization influence delivery performance,nowledge management systems, and decision-making processes.y implication, future studies in these areas should control forroduction volume changes, asset flexibility, and organizational

earning. The net effect will be a clearer understanding of howiversification impacts the exploitation of organizational resourcesnd a more reliable prediction of operational performance.

. Conclusion and limitations

By incorporating greater precision and additional theoreticalonsiderations, our discussion provides greater depth of insightegarding the effects of product portfolio architectural complex-ty on operational performance. First, we more precisely definedomplexity by narrowly defining its three dimensions. Second, wextended prior theories by explicating the roles of learning, utiliza-ion, and fixed asset flexibility while incorporating the principle ofiminishing returns. We submit that these factors and their interac-ions with dimensions of PPAC will upon proper testing be found toxplain more variance in operational performance than the limitedactors identified in previous theories.

Our discussion is limited to portfolios of tangible products. How-ver, it is not difficult to foresee that our arguments would applyt the product level as well. The concepts of multiplicity, diversity,nd interrelatedness could easily be applied to components embod-ed in a single product’s architecture. Moreover, the literatures onomponent commonality and modularization offer a number ofstablished operationalizations of complexity dimensions at theroduct level. For example, Opitz codes (Opitz, 1970) have beenstablished as a means for quantifying the complexity of workieces in terms of features, the number of unique features, andhe degree of difference between them. A similar system could be

eveloped to quantify product differences in a portfolio.

Given our sharp distinction between product architecture androcess structure, it may be difficult to apply our approach to pureervice ‘products’ since for many services the process is the product.

Jacobs (2008)ueness/size Rodan and Galunic (2004)e/2 × avg distance × (number of items)2 Martin and Gray (1971)

However, it may be possible to define the service product portfolioin terms of the elements of functional value delivered to customers,with each combination of functional elements defining a ‘product.’Then one could assess the multiplicity, diversity, and interrelated-ness of functional elements contained in various service portfolios.We leave it to future research to explore this approach.

We also limit our discussion to PPAC’s effects on sales volumeand operational performance. Future researchers should con-sider market effects including demand functions and competitiveresponses to product portfolio changes. We have considered theimplications of unit sales volume and volatility changes resultingfrom changes in the three dimensions of complexity. Yet our focushas mostly been internal, neglecting potential costs and benefitsof complexity changes related to pricing and market responses.Ultimately, these effects would need to be considered in order todevelop a profit maximization model.

A final consideration concerns the role of different asset classesin defining asset constraints for an operation. We have consideredlimiting assets to be fixed, thus establishing a hard constraint on theultimate productivity of an organization’s operating system. How-ever, in some cases asset constraints may be changed more easilyand quickly. In these cases, it will be important for researchersto isolate the performance effects of complexity changes on uti-lization from changes to performance resulting purely from assetimprovements or replacements.

Acknowledgements

We are indebted to Roger Schmenner, an anonymous associateeditor, and three anonymous reviewers for their constructive cri-tiques of this paper.

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