(2008) thun & miling

PRODUCTION AND OPERATIONS MANAGEMENTVol. 17, No. 3, May–June 2008, pp. 373–384issn 1059-1478 �eissn 1937-5956 �08 �1703 �0373

POMSdoi 10.3401/poms.1080.0023

©2008 Production and Operations Management Society

System Dynamics as a Structural Theory inOperations Management

Andreas GrößlerInstitute for Management Research, Radboud University Nijmegen, 6500 HK Nijmegen, The Netherlands,

[email protected]

Jörn-Henrik Thun, Peter M. MillingIndustrieseminar der Universität Mannheim, 68131 Mannheim, Germany

{[email protected], [email protected]}

The purpose of the paper is to demonstrate the usefulness of (1) system dynamics as a structural theoryfor operations management and (2) system dynamics models as content theories in operations manage-

ment. The key findings are that, although feedback loops, accumulation processes, and delays exist and arewidespread in operations management, often these phenomena are ignored completely or not considered appro-priately. Hence, it is reasoned why system dynamics is well suited as an approach for many operations man-agement studies, and it is shown how system dynamics theory can be used to explain, analyze, and understandsuch phenomena in operations management. The discussion is based on a literature review and on conceptualconsiderations, with examples of operations management studies based on system dynamics. Implications ofusing this theory include the necessary re-framing of some operations management issues and the extensionof empirical studies by dynamic modeling and simulation. The value of the paper lies in the conceptualiza-tion of the link between system dynamics and operations management, which is discussed on the level oftheory.

Key words : system dynamics; structural theory; modeling; simulation; operations strategyHistory : Received: January 2006; Revised: August 2006 and February 2007; Accepted: March 2007 by Roger

Schroeder.

System dynamics is a method to depict, model, andsimulate dynamic systems—for instance, the opera-tions of industrial or service firms. In the course ofthis paper, we will discuss system dynamics as astructural theory about how dynamic social systemsare constructed. Furthermore, we propose that for-mal models, which are built using system dynam-ics, are content theories. The purpose of this paperis to demonstrate how system dynamics is able toexplain a substantial subset of those operations man-agement (OM) issues that are difficult to understandbased on traditional OM methods, like queuing the-ory or linear programming. Thus, from the theoret-ical understanding of operations management as afield characterized by feedback and resource accumu-lation, general insights into the design of the oper-ations function are derived with the help of systemdynamics analyses. The focus of the issues discussedis on the strategic side, i.e., when it comes to decisionsthat are long term and that have a direct influence onthe competitive position of the firm.

The structure of the paper is as follows. In theintroductory section, the discussion focuses on the

characteristics of system dynamics that qualify it as astructural theory. Feedback loops, accumulations, anddelays are identified as ubiquitous characteristics ofoperations management, which is a strong argumentfor using system dynamics as a theory of the struc-ture of OM systems. In addition, we show that systemdynamics models are content theories of the real-world systems that they represent. System dynamicsas theory and method is discussed on a rather abstractlevel in the second section. Here, system dynamics isdescribed in more detail with emphasis on the theoryaspect, but the discussion also includes some remarkson how system dynamics is applied. The third sec-tion gives an overview of OM literature that appliessystem dynamics and contains two examples (orga-nizational improvement programs and supply chainmanagement) that demonstrate the value of systemdynamics as a structural theory for operations man-agement issues. The paper closes with a short sec-tion about the combination of system dynamics withother methods for theory generation in operationsmanagement.

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Größler, Thun, and Milling: System Dynamics as a Structural Theory in Operations Management374 Production and Operations Management 17(3), pp. 373–384, © 2008 Production and Operations Management Society

1. System Dynamics as StructuralTheory and System DynamicsModels as Content Theories

1.1. System Dynamics as TheoryWithout trying to define the term “theory” in a generaland comprehensive manner, we adopt Amundson’s(1998, p. 345; loosely going back to Brunswick’s “lensmodel”; 1952, 1956) metaphor that theories are likelenses that influence the perception of phenomena.Theories are sets of hypotheses that provide a cog-nitive frame for describing, explaining, understand-ing, predicting, and controlling real systems. This lastaspect of theories relates to Lewin’s (1945, p. 129)famous saying, “[N]othing is so practical as a goodtheory” (see also Van de Ven 1989), which underpinsthe managerial usefulness of theories. This perspectiveis also emphasized by Filippini (1997), who states thattheories support decision making under uncertaintyand variability.

Theories can be classified along many dimensions.Two dimensions that proved to be appropriate whendiscussing system dynamics and operations manage-ment are (1) whether a theory is about the con-tent or the structure of a social system (Lane 2000a)and (2) the range of phenomena a theory claims tocover (Homans 1978, p. 59, speaks of the “power”of a theory). Content theories of social systems con-tain hypotheses about the number and nature of ele-ments (of which humans are the most importantones) within a system, their relationships, the pro-cesses going on, and the effects of these processes—allof which depend on certain contingencies. Structuraltheories of social systems make statements about howelements in a system can be configured and how theycausally relate to each other. Frequently, when talk-ing about theories, only content theories are consid-ered; the consideration of structural theories seemsto be rather unfamiliar to OM researchers. Examplesfor content theories from operations management (orwith a major influence on OM) are microeconomictheory (Pindyck and Rubinfeld 2005) or the theory ofperformance frontiers (Schmenner and Swink 1998).An example of a structural theory is systems theory(von Bertalanffy 1968, Luhmann 1993).

The range of phenomena explained by a theory hasso-called “grand theories” of social systems on theone extreme. Grand theories should be able to explainall elements and processes in social systems (Skinner1985). However, the idea of a content grand theoryhas been rejected because of the complexity of socialsystems (Mills 1959, but cf. Swamidass 1991). On theother extreme of this dimension are minor theoriesexplaining specific systems from a specific perspec-tive. Theories in OM are necessarily restricted to thenature of the systems they deal with, which is the

operations function in organizations. In addition, OMcontent theories do not try to explain operations man-agement as such but specific elements of OM. Thus,OM deals more with theories that are closer to the“minor theory” extreme on the range dimension.

System dynamics is a structural theory of dynamicsystems (Lane 1999); it is based on the main hypoth-esis that the structure of social systems is gener-ally characterized by feedback loops, accumulationprocesses, and delays between cause and effect. Theadjective “structural” should emphasize that systemdynamics does not offer a content theory about theelements and processes in social systems. In addition,system dynamics as such does not provide a con-tent theory of specific social systems, such as man-ufacturing firms in the automotive industry. Rather,as a structural theory, system dynamics makes state-ments about the principal interdependencies of ele-ments in social systems: system dynamics postulatesthat dynamic processes in social systems function infeedback loops and that the history of systems accu-mulates in state variables. In addition, the accumu-lated history influences the future development ofa system—a process that is often affected by timedelays.

The “products” of applying system dynamics the-ory and method to the analysis of social systems aremodels. System dynamics models are content theo-ries of the system they represent: all variables in sys-tem dynamics models represent real-world objects;a model contains linkages between the objects as theyare hypothesized to exist in reality. In other words,“good” system dynamics models are assumed validrepresentations of specific real-world problems—forinstance, the material ordering mechanisms in anautomotive plant. However, system dynamics mod-els always possess a certain perspective and do notepitomize the system in total and with all details.Thus, a system dynamics model of a real-world set-ting is a specific content theory of this setting (Barlasand Carpenter 1990, Clarkson and Simon 1960; seealso McKelvey 1999, Sutton and Staw 1995, Meredith1993 for the relationship between models and theo-ries). It offers a lens to observe and to understandthe represented system, but not a description of thetotal system or of all social systems. Thus, systemdynamics models are understood to represent minoror midrange content theories. Figure 1 summarizesour view on system dynamics as a structural theoryand on system dynamics models as content theoriesby locating these concepts in a matrix.

1.2. System Dynamics and OperationsManagement

When accepting system dynamics as a structuraltheory, the question arises whether and how it

Größler, Thun, and Milling: System Dynamics as a Structural Theory in Operations ManagementProduction and Operations Management 17(3), pp. 373–384, © 2008 Production and Operations Management Society 375

Figure 1 System Dynamics and System Dynamics Models as Theories

Range oftheory

Goal oftheory

Content Structure

Explaining…

Grandtheory

Midrangetheory

Minortheory

Systemdynamics

Systemdynamicsmodels

can be usefully transferred to operations manage-ment. Amundson (1998, p. 353) proposes apply-ing four criteria to assess whether a theory shouldbe imported to operations management: (1) match-ing issues, (2) meaningful concepts, (3) explanatorypower, and (4) matching underlying assumptions.Although these criteria are originally related to con-tent theories (Amundson uses transaction cost theoryand the resource-based view as examples), they canbe applied to evaluate system dynamics (a structuraltheory) as well.

1.2.1. Matching Issues. The phenomena studiedin system dynamics are complex and dynamic sys-tems, in particular, socioeconomic systems. This char-acterization fits to OM settings. Operations can beregarded as the “nexus of systems, people, processesand procedures” (Hill et al. 1999, p. 148), where valueis generated in either the organization or the supplychain. Within this setting, OM has to deal with chang-ing requirements and resources. Accordingly, systemdynamics fulfills this first criterion.

1.2.2. Meaningful Concepts. The main conceptsbeing used in system dynamics are (a) feedback loops,(b) accumulation processes, and (c) delays betweencause and effect. These concepts seem to be rele-vant in many operations management situations. Forinstance, the implementation of an improvement pro-gram might be characterized by a feedback loop thathas an effect on worker motivation and, ultimately, onthe improvement program when worker motivationdecreases because of fear of being made redundantwith a successful program in place (a). An examplefor accumulations is the dynamics of growing anddecreasing inventories within a supply chain (b). Theswitch in manufacturing strategy from being a nicheproducer to becoming a volume manufacturer is char-acterized by a substantial delay caused, for instance,by the time it takes to build up capacity and customerreputation (c). This criterion correspondingly is ful-filled by system dynamics as well.

1.2.3. Explanatory Power. The concepts of feed-back, accumulation, and delay—when incorporatedinto system dynamics models—have the power toexplain various operations management phenomena,as very briefly indicated in the examples above.More examples are provided in §3. Generally, systemdynamics models provide explanations because theyrepresent a set of causal hypotheses between systemelements. Hence, system dynamics also satisfies thisrequirement.

1.2.4. Matching Underlying Assumptions. Un-derlying assumptions of system dynamics are theideas of systems and causality. Both do usually alsounderlie OM as axiomatic concepts. For instance,operations is often represented as a subsystem ofthe higher order system “company,” thus applyingsystem theoretic terminology and ideas. In addition,OM studies are frequently based on a nomotheticview of cause and effect—when, for example, theeffects of a quality program regarding manufacturingperformance are investigated. Accordingly, systemdynamics—with its focus on causal relationships—meets this last claim, too.

In summary, system dynamics is a structural the-ory and offers a lens on operations managementissues. The system dynamics lens intensifies the per-ception of feedback, accumulation, and delays. Inthis way, it offers new and additional insights intowell-known issues of operations management, likeinventory management or lead-time reduction. Forinstance, Gonçalves et al. (2003) show how the con-sideration of a feedback relation between productavailability and customer demand renders inappro-priate conventional prescriptions of lean inventoryand fast-responsive capacity utilization. As anotherexample, a study by Anderson et al. (2003) demon-strates that lead-time reduction does not necessarilyresult in a dampening of the bullwhip effect, but—onthe contrary—can amplify it, if it is not coordinatedwith capacity policies.

Despite the usefulness of system dynamics for OMissues and the successful examples of applying sys-tem dynamics in this domain, system dynamics, likeall theories, has “blind spots.” Characteristics of real-ity exist on which system dynamics does not focus,for instance, discrete events or the behavior of indi-vidual agents within a system. These are not in thecenter of interest when applying the system dynam-ics lens to operations management issues. Systemdynamics provides a continuous perspective on sys-tems, which are characterized by relatively stable poli-cies and processes, not by a sequence of discreteevents. In addition, system dynamics does not allowfor the analysis of the behavior of single agents orobjects (microbehavior). Rather, it groups similar enti-ties together, investigating the behavior of such stocks


of entities (macrobehavior). These features of sys-tem dynamics are also the reason why it is usuallyapplied to strategic issues—when the emphasis is onunderstanding the general dynamics of a situationand where it is not necessary or possible to spec-ify the behavior of all individual agents and objects.For instance, if the focus of the analysis is on track-ing specific units of an unfinished product througha production line, discrete event simulation is moreappropriate and powerful; however, if the object ofinvestigation is the amount of work-in-progress circu-lating in a production line or the variation of inven-tory, system dynamics is more suitable. In general, ifevents or the analysis of individual entities is crucialfor describing and managing a system, other mod-eling approaches might be more appropriate to beused (for example, discrete event simulation, queu-ing theory, or agent-based simulation, which are notdiscussed in this article). However, when addressingstrategic issues it is regularly “possible to constructaggregate relationships that describe, in a simplifiedbut adequate manner, the overall behavior of an initself highly complex operations system without mod-eling this complex system in detail” (Akkermans andBertrand 1997, p. 953). This is why system dynam-ics frequently is an appropriate method and structuraltheory to be used.

By no means does this imply the principal supe-riority of one modeling approach (and the structuraltheory it manifests) over others. In practice, sys-tem dynamics uses tools and concepts from otherapproaches. For instance, system dynamics proposesthat most real-world systems with considerable com-plexity are not open for optimization; thus, sys-tem dynamics does not have the goal of optimizingsystems (in the sense of finding the one best solu-tion). Nevertheless, modern system dynamics appli-cations make use of optimization algorithms forfinding robust solutions by parameter and policyvariation (Coyle 1985, Graham and Ariza 2003). Fur-thermore, while system dynamics postulates the prin-cipal nonlinear nature of systems (caused by theinterconnection of feedback loops and accumulation),linearization is acknowledged as an important heuris-tic to control systems (Özveren and Sterman 1989).

2. Feedback, Accumulation, andDelays as Major Characteristics ofSystem Dynamics

2.1. Basics of System DynamicsAs argued in the prior section, system dynamicsis a theory about the structure (and the resultingbehavior) of social systems; however, it also pro-vides a method to represent this structure, in the

form of diagrams and mathematical equations. Origi-nally, system dynamics was crafted by Forrester (1961,1958) for the analysis of industrial enterprises. Today,system dynamics is applied to many kinds of systemsthat change over time, in particular to socioeconomicsystems (Morecroft 2007, Sterman 2000; for the historyof system dynamics, see Lane 2007, 1999; Forrester1989). System dynamics is an extension of servo-mechanism theory (Richardson 1991). Usually, systemdynamics projects comprise two phases: conceptual-ization/modeling and simulation/experiments.

Although one can have a theory perspective on sys-tem dynamics, in most applications the focus is on themethods and tools it offers for the analysis of dynamicsystems, i.e., methods to design formal models andto generate their behavior over time. To representdynamic systems, a graphical syntax exists in whichflow (rate) and state (level) variables can be distin-guished and combined to level-rate diagrams (Lane2000b; Forrester 1968a, Chapter 5). The graphical rep-resentation of systems using this syntax proves tobe a valuable tool for understanding complex issues.There are some commercially available software pack-ages to support the process of modeling and simula-tion (for instance, Vensim, Powersim, or iThink). Byquantifying variables and linkages between variables,a system of differential equations is created that canbe simulated by numerical algorithms (Sterman 2000,p. 903n). A variety of tests exists to support the valid-ity of models, which are always relative to the pur-pose of the modeling project (Sterman 2000, p. 843n;Forrester and Senge 1980; Barlas and Carpenter 1990;Barlas 1989). In addition, model libraries are availablethat contain components for re-use (Wolstenholme2004, Hines 2000, Lane and Smart 1996). Finally, pro-totypical process models support the procedure whenconducting a system dynamics study (Maani andCavana 2000, Forrester 1994a, Richardson and Pugh1983); many authors emphasize the importance of themodeling process (not only the resulting model) forgaining insights into the problem (Lane 1995, Sterman1988, Forrester 1985).

In the following, the main characteristics of sys-tem dynamics are presented, with connection to thetheory perspective and the method view on systemdynamics. The fundamentals of system dynamics as astructural theory—feedback loops, accumulation, anddelays—result in nonlinear behavior of systems. Themore method-related features of system dynamics—quantification of all relevant variables, simulation, thelinkage to mental models, and policy design—are fur-ther discussed.

2.2. Feedback LoopsThe central notion of system dynamics is that feed-back loops are the building blocks of all social sys-tems (Forrester 1961). This means that any activity


ultimately has an effect back on that element of thesystem that undertook the activity in the first place.Corollaries of this definition are that action cannotoccur without reaction, that side effects are inevitablein social systems, and that decisions trigger effectsthat are remote in time and space. Feedback loops areconstituted by causal links between elements of real-ity. System dynamics models offer a causal depictionof a system’s structure (which comprises feedbackloops) and of the behavior resulting from this struc-ture (which is particularly influenced by the feedbackloops). The focus is on giving causal explanations,in line with Van de Ven’s (1989) requirements fortheories.

Two types of feedback loops exist: negative (goalseeking) and positive (reinforcing). Examples fromoperations management for the existence of nega-tive feedback loops are inventory or capacity adjust-ment systems: whenever actual inventory or capacityfalls short of a certain target, adjustments are made,such as putting items in stock or investing in addi-tional machines and personnel. An example for areinforcing feedback loop is when an improvementprogram implemented in manufacturing results inincreased profits that lead to the possibility of con-ducting further improvement programs that lead tofurther increased profits and so on. Of course, theseexamples are simplified; in real-world systems—aswell as in system dynamics models—usually morethan just one feedback loop exists. For instance, forthe positive feedback loop described above, we mustassume that limits to this otherwise indefinite growthprocess exist (Meadows 1982), which could be totalmarket demand or capacity restraints of underlyingresources.

The concept of feedback is so prominent in sys-tem dynamics that it is also used to define the bor-ders of any issues analyzed: all feedback loops thathave a substantial impact on the behavior of a sys-tem must be included in a system dynamics study(Forrester 1968a, Chapter 4). Like more general sys-tems perspectives, system dynamics asks for a holisticand connected view of reality (Senge 1990) and triesto incorporate all factors that are relevant to a specificproblem (cf. Gonçalves et al. 2003).

2.3. Accumulation and DelaysIn “pure” system dynamics tradition, feedback loopsare seen as the major structural element of social sys-tems, such that the other two characteristics consti-tuting it as a structural theory—accumulation anddelays—are considered just derivations of the exis-tence of feedback loops. Nevertheless, some authorshave recently argued that the concept of accumula-tion is even more important for system dynamics thanfeedback loops (Warren 2008, 2002). Every feedback

loop contains at least one level variable that conservesthe state of the system and that can be used as aninitial value for calculating the simulated behavior.These level (or stock) variables are defined indepen-dently from the chosen measurement interval, andthey represent the path of a system through time:they incorporate the history of a system and deter-mine the future of a system. Flow (or rate) variables,which contain the mechanisms for state changes, andlevel variables represent the two main types of vari-ables in system dynamics (Forrester 1968a, Chap-ter 4). Examples from OM for the widespread occur-rence of accumulation processes are the accumula-tion of raw materials, modules, and finished goodsin sequences of production stages and inventories(Slack et al. 2004, Chapter 12); accumulative strate-gic capabilities in production (Ferdows and De Meyer1990); and the accumulation of production experience(Henderson 1984).

Closely connected to the concepts of feedback loopsand accumulating levels is the idea that delays areubiquitous in social systems. No business process canbe conducted in zero time. Decisions lead to effectsthat are distant in time and space. With the help ofsimulation, system dynamics compresses such delaysin order to analyze their effects within an accept-able time frame (Kim and Senge 1994). Examplesfrom operations management are the delays con-nected with the effectiveness of improvement pro-grams, the time it takes until new employees are fullyproductive, or the delays that are inherent in orga-nizational reporting and controlling processes. Theresulting behavior modes of delayed processes areoften cycles, which frequently have been analyzed indifferent forms with the help of system dynamics, forexample, business cycles (Meadows 1970, Forrester1976, Lorenz 1992, Liehr et al. 2001, Randers andGöluke 2007) and Kontradieff cycles (Sterman 1985,Ryzhenkov 2000).

2.4. Computer SimulationAlthough single feedback loops or delays can con-stitute linear systems as in classic control theory(Bubnicki 2005) or cybernetics (Wiener 1948, Ashby1956), the combination of various feedback loops,accumulation processes, and delays frequently resultsin nonlinear behavior of systems. The behavior modesof such systems are hardly understandable by intu-ition (Booth Sweeny and Sterman 2000, Dörner 1996,Reason 1990, Sterman 1989). Furthermore, analyti-cal solutions are usually not available to describethese systems. Thus, simulation serves as a methodto observe a system’s behavior. Simulations requirea quantification of all elements and the linkagesbetween the elements of the system to be analyzed.If no mathematical function can be formulated, it is


graphically and ad hoc presented by so-called tablefunctions (Sterman 2000, p. 551n). Regularly this is thecase when fuzzy or hard-to-quantify variables havea significant effect on a real-world issue and there-fore need to be incorporated into a system dynam-ics model. Such variables are considered no matterhow difficult it is to measure and to quantify thembecause neglecting them would certainly lead to anerroneous model and simulation (in this way, systemdynamics connects to the research in behavioral OM;see Bendoly and Hur 2007, Amundson 1998, Powelland Johnson 1980). Examples are the influence thatorganizational factors (like work satisfaction, stress,and motivation) have on worker productivity in amodel of skill formation in new production systems(Diawati et al. 1994) or perceived delivery delays thataffect product sales in a model of corporate growthand capacity restrictions (Forrester 1968b). The effectsof such variables are often described with the helpof estimations that are formulated by competent indi-viduals, in the form of “educated guesses” (Fordand Sterman 1998a). Estimated parameters and func-tions indicate starting points for additional empiricalresearch when they demonstrate an effect on systemsbehavior in the simulation runs (which can be testedby sensitivity analyses).

Simulation is a feature of system dynamics that dis-tinguishes it from other forms of systems thinking(and general systems theory). In system dynamics, amodel-based analysis is incomplete without simula-tion (Forrester 1994a). A reason for this is the diffi-culty people have to induce behavior from reasonablycomplex system structures (Dörner 1996, Frensch andFunke 1995, Sterman 1994). The effects of delays,feedback loops, or exponential growth or decay arebehavior modes that are hardly possible for humansto comprehend completely and correctly (Sterman1989, Dörner 1980). By way of simulation, such effectscan be explored and analyzed; dangerous, unethical,impossible, costly, or just very creative decisions canbe tested to determine their effects on a system. Simu-lations can be started as often as demanded and witharbitrary initial settings (Pidd 2004).

2.5. Results of System Dynamics StudiesStarting and ending points of system dynamics mod-eling projects are the mental models of the indi-viduals involved (Doyle and Ford 1998). They arestarting points because the mental models of the peo-ple are the most important source of informationwhen modeling social systems because many relevantvariables are not or even cannot be measured in for-mal measurement systems (Forrester 1994b). Mentalmodels are also the ending points of system dynam-ics projects because improvement—that is, the genera-tion of insights into the relationship between structure

and behavior—is the ultimate goal of system dynam-ics (Sterman 2000, p. 34n). System dynamics projectsare meant to initiate learning processes by providinga content theory of a complex situation that typicallycannot be understood intuitively.

However, in organizational contexts, system dy-namics projects usually do not only aim to make cog-nitive improvements. Based on insights gained aboutthe issue, policies and structures of the real-worldsystem should be changed, with the goal of improv-ing its performance (Forrester 1994a). The assump-tion behind this goal is that decisions and policiesfollow implicit or explicit rules and are implementedin fixed structures (March 1994). Most policies areinformal and heuristic (i.e., they are not optimal)and are influenced by bounded rationality, organiza-tional norms, traditions, and politics (Conlisk 1996,Eisenhardt and Zbaracki 1992, Simon 1972, Marchand Simon 1958). Information that is used in poli-cies is filtered in a multistage process (Morecroft1988). System dynamics claims to depict policies andstructures as realistically as possible, to test potentialimprovements with models and simulations, and toimplement improvements in the real system (Snabeand Größler 2006). The actual implementation of apotential improvement is considered more importantthan the search for an abstract optimal system state(Sterman 1988).

3. Exemplary OM Studies Based onSystem Dynamics Theory

System dynamics has been used in a variety of oper-ations management studies. Bertrand and Fransoo(2002) distinguish model-based articles in opera-tions management along two dimensions: “empir-ical” versus “axiomatic” and “descriptive” versus“normative.” Concerning this classification of mod-eling approaches in operations management, sys-tem dynamics studies can be classified as empirical.In contrast to axiomatic uses of modeling, systemdynamics studies are driven by empirical evidence,not by abstract concepts. With regard to the otherdimension, the focus of system dynamics studies isnot on deriving at an analytically solvable modelbut on investigating a system with its relevant com-plexity (Akkermans 1993). System dynamics modelsare more descriptive than normative. Nevertheless,the improvement of real systems is a goal in systemdynamics even though it does not pursue an optimalsolution. Optimality can only be achieved in eitherlow-complex artificial situations or when agents pos-sess perfect rationality. Both assumptions do not holdfor common system dynamics projects and in typicalOM settings, where the aim usually is “to compromiserather than optimise” (Hill et al. 1999, p. 142).


Five major areas of application of system dynamicsin operations management can be identified:

(1) Articles dealing with production flow and sup-ply chain management issues (e.g., Forrester 1958,Towill 1982, Morecroft 1983, Sterman 1989, Guptaand Gupta 1989, Wikner et al. 1991, Towill 1996,Fowler 1999, Anderson et al. 2000, Akkermans andVos 2003, Spengler and Schröter 2003, Akkermans andDellaert 2005),

(2) Papers dealing with improvement programs inoperations (e.g., Sterman et al. 1997, Burgess 1998,Keating et al. 1999, Jambekar 2000, Repenning andSterman 2001, Salge and Milling 2006),

(3) Authors that address project managementissues (e.g., Abdel-Hamid and Madnick 1991, Fordand Sterman 1998b, Ford and Sterman 2003, Lyneisand Ford 2007),

(4) Papers about new product development, inno-vation, and diffusion (e.g., Milling 1996, Maier 1998,Repenning 2002, Anderson and Parker 2002), and

(5) Studies about the effects of different productiontechnologies (e.g., Ebrahimpour and Fathi 1984; Zahnet al. 1987, 1998).However, these papers do not concentrate on the the-oretical basis that system dynamics offers in an opera-tions management context. Rather system dynamics is“just” used as a suitable method for operations man-agement research (cf., Bertrand and Fransoo 2002).In this section, we want to present two operationsmanagement studies that were conducted with sys-tem dynamics. We focus on the question of wheresystem dynamics as a structural theory had an effecton the results of the studies. The two applicationsare from the area of improvement programs and fromsupply chain management. However, as the refer-ences and examples above underpin, system dynam-ics has also been used to analyze issues in operationsmanagement in addition to the two subjects wepresent next.

3.1. Understanding the Dynamics of MaintenanceThe example in this section deals with the ques-tion of how system dynamics can help to understandthe dynamics of maintenance. On the one hand, sys-tem dynamics is discussed as an approach to cap-ture qualitatively the primary maintenance problemas it often can be observed in manufacturing compa-nies (Sterman 2000, Sterman et al. 1997). On the otherhand, system dynamics is introduced as a simulationmethod to analyze the dynamic implications of totalproductive maintenance (TPM).

In traditional maintenance systems, actions formaintaining equipment are not undertaken before amachine finally breaks down (Nakajima 1988). Thisapproach is commonly referred to as “breakdownmaintenance” because such a reactive fire-fightingstrategy results in a situation with many unexpected

Figure 2 “Shifting the Burden” Archetype in a Maintenance System

–

Equipmentdefects

“Repairseat up PM ”

Time forpreventive

maintenanceRepairs

+

+

+

“ReactiveMaintenance ”

“PlannedMaintenance ”

–

–

Maintenancedepartments

time

machine breakdowns (Jambekar 2000). Consequencesof breakdown maintenance can be explained by the“shifting the burden” system archetype described bySenge (1990). In Figure 2, arrows indicate causal link-ages. Feedback loops are easily observable; positiveand negative loop polarities are shown. In addition,polarities of individual connections between variablesare given, with a plus sign indicating the develop-ment of two variables in the same direction and aminus sign showing development in different direc-tions (Senge 1990).

The archetype consists of two balancing and onereinforcing loop with a side effect moving a systemin an unintended direction. There are two possiblereactions to the observation of a problem’s symptom,which in this example is the high amount of equip-ment defects on the shop floor: a symptomatic solu-tion and a fundamental solution, whereby the lattertakes more time to show a positive effect because of adelay. The symptomatic solution is similar to repairingmachines reactively. The fundamental solution equalspreventive maintenance and is a planned activity.

In terms of maintenance, both approaches copewith the problem differently because the symptomaticsolution tries to achieve an immediate effect by in-stant machine repairs, whereas the fundamental solu-tion seeks for a long-term solution of the real causeof the problem, which is the unsuitable maintenancesystem. As analyzing the system archetype shows,the scenario is problematic because both ways tosolve the maintenance problem are connected, dueto the limited capacity of the maintenance depart-ment. By disregarding the fundamental solution andconcentrating on an instant symptomatic solution,machine breakdowns will “eat up” the maintenancedepartment’s time repairing machines instead ofaccomplishing preventive maintenance activities orimproving the maintenance system. This unintendedside effect means that preventive maintenance tasksare abandoned in the end. Accordingly, the funda-mental problem is not solved and the symptom will


return, leading to an inferior situation. This is the typ-ical behavior of a manufacturing system with an over-loaded maintenance department.

By clearly visualizing the underlying system pat-tern, system dynamics improves the management ofmaintenance and gives insights for better understand-ing the dynamic behavior of a maintenance system.By recognizing the side effect (“Repairs eat up PM”),typical mistakes can be avoided; ignoring the funda-mental solution is impossible. Symptomatic solutionsare used for the compensation of short-term problemsonly.

As a second step in this analysis, a system dynam-ics simulation is used to analyze the dynamic behav-ior of implementing TPM (see also Mashayekhi 1996,Crespo Márquez and Usano 1994). Simulations showthat the components of TPM have different impactson the overall equipment effectiveness (Thun 2006;see Figure 3 for the results of exemplary simulationruns). By preventive maintenance, the maintenancesystem will be improved but with the side effectof an overloaded maintenance department because ithas to fulfill additional maintenance tasks. To dis-burden the maintenance department, simple main-tenance tasks are assigned to machine operators interms of autonomous maintenance. Therefore, themachine operators have to be trained by the main-tenance department. Autonomous maintenance andtraining takes time and can lead to a “worse beforebetter”-effect (Repenning and Sterman 2001), whichmeans that the overall performance of the system willinitially suffer before it improves in the long run.

Furthermore, maintenance prevention has dynamicimplications as well. Through maintenance preven-tion, the system will be improved in the end. Forinstance, such improvements are achieved by a bet-ter design in the machine development stage lead-ing to maintenance free machines or at least tomachines with higher maintenance ease. Becauseeffective maintenance prevention is mainly based onsuggestions and experience, it takes time to unfold itsfull potential.

Figure 3 Simulation Runs for Different TPM Components

Ove

rall

equi

pmen

t effe

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Time

Reactive maintenance

Maintenance prevention

Preventive maintenance

Total productive maintenance

3

4

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Finally, simulations allow testing the timing ofmaintenance activities. It seems to be reasonable tofocus on short-term activities like preventive main-tenance first and then turn to maintenance preven-tion, which will be effective in the end. To choosethe wrong implementation strategy in terms of theright timing might lead to a failure of total productivemaintenance. Here, system dynamics helps as a toolto understand the dynamics behind the maintenanceactivities, their implications for better maintenancemanagement, and their impact on the overall produc-tion system by simulating such a complex system.

The simulation model is a content theory of thedependencies and causalities when implementingTPM. In addition, system dynamics proved to beuseful as a structural theory and supported the under-standing of dynamic issues connected to the imple-mentation of improvement programs. These issuescomprise the effects of feedback loops, accumulations,and delays. For instance, successful improvement pro-grams can increase the fear of workers of being madeobsolete, thus reducing the success of these programs;unnoticed failures in machinery accumulate until acertain level is reached when these failures lead tobreakdowns; and training of employees often causes amomentary decline of productivity, and it takes sometime until this investment pays off.

3.2. Understanding the Dynamics ofSupply Chains

One major field of operations management is supplychain management. Following Handfield and Nichols(1999), the supply chain “� � �encompasses all activitiesassociated with the flow and transformation of goodsfrom the raw materials stage, through to the end user,as well as the associated information flows” (p. 2).One of the first published works in the field of sup-ply chain management is Forrester’s article “Indus-trial dynamics: A Major Breakthrough for DecisionMakers” (Forrester 1958), which also marks the foun-dation of system dynamics. In his model, Forresterdescribes the flow of goods and orders between fourdifferent delivery stages. Investigating the informa-tion feedbacks, Forrester observed a dynamic behav-ior of the system, nowadays commonly referred to asthe bullwhip effect.

The bullwhip effect describes a situation in whicha delivery system shows increasing amplifications inits different stages. Reasons for the bullwhip effectare identified by Lee et al. (1997a, b, inspired byForrester’s fundamental work; Sterman 1989) as, forinstance, order batching or insufficient forecasts. Thebasic structure of the supply chain as described byForrester (1961, 1958) and the corresponding behaviorof the system in the particular stages are depicted inFigure 4.


Figure 4 The Forrester Supply Chain and the Bullwhip Effect

Factory warehouseDistributer

Factory

Retailer

Inventory

Factory Distributer Retailer

Customer orders

Deliveries

Flow of material

Flow of information

50 700 10 20 30 40 60 80

Weeks

Factorywarehouse

The system dynamics-based simulation shows thatinventories in the different stages amplify when mov-ing down the supply chain. The reason for thisbehavior is a supply chain structure characterizedby delays. If these delays are not considered whenmaking order decisions, the orders within the sys-tem will accumulate, leading to extensive inventories.Thus, system dynamics gives a structural explana-tion for how delayed information and material flows,arranged in a feedback fashion, cause the systembehavior to show oscillations.

Like in the case of improvement programs, sys-tem dynamics models offer content theories of supplychain issues. For instance, the supply chain depictedin Figure 4 represents a theory of a four-tier, sequen-tial logistics chain, with different kinds of companieson the different tiers and a certain flow of materialand information upstream and downstream the chain.Although parameterization allows for various con-crete instantiations of a supply chain, it will alwaysbe a four-tier and sequential one. Because of the lim-ited scope in this case, we speak of a system dynamicsmodel as a minor content theory.

However, system dynamics theory also offers astructural lens on how to perceive and manage supplychains, which focuses on the existence of feedbacks,accumulations, and delays. Examples of dynamic phe-nomena in supply chains are reverse logistics, whenproducts are sent back by customers (feedback); themeshed flow of information upstream and of prod-ucts downstream in a supply chain (feedback); thebuilding up of inventories within supply chains (accu-mulation); the effects of bottlenecks in supply chains(accumulation); production and shipment lead times(delays); and the time it takes until an upstream pro-ducer realizes changing consumer patterns (delays).

4. A New Perspective on Modelingand Simulation in OperationsManagement

In the operations management field, modeling andsimulation are often viewed as a “rationalist” and

purely “deductive” way of generating new knowl-edge (Meredith 1998, Swamidass 1991). The basisfor this perspective is grounded in the operationsresearch type models that are used and theories thatresult from them (for example, inventory theory). AsSwamidass (1991) states, fuzzy and messy concepts(like manufacturing strategy) are unsuitable for suchdeductive research (see also Hill et al. 1999). Thecurrent paper argues that this conclusion does notimply that generally modeling and simulation can-not be applied to strategic issues. For instance, sys-tem dynamics is a modeling and simulation theoryand method where the models that have been builtare grounded in empirical observations. Furthermore,system dynamics models are descriptive rather thannormative. They are used to analyze complex andmessy concepts (also in the strategic area). However,they have the drawback that some mathematical ele-gance is sacrificed (the analytical derivation of opti-mal solutions) for the attainability and relevance of“just” robust solutions.

The validity of system dynamics models is shownby the transferability of insights generated by themodel to reality, thus fulfilling a requirement of Hillet al. (1999) for the usefulness of models in OM. Start-ing with Forrester’s Industrial Dynamics model, resultsfrom system dynamics studies have been imple-mented in real systems and have led to improve-ments in these systems. The information bases ofsystem dynamics modeling relies on empirical stud-ies like surveys (Gupta et al. 2006, Rungtusanathamet al. 2003, Scudder and Hill 1998). In addition,more qualitative methods of knowledge generation—like case studies and interviews (Hill et al. 1999,Meredith 1998)—are appropriate and useful for build-ing simulation models following a grounded theoryapproach (Glaser and Strauss 1967, Yin 1994). Olivaand Sterman’s (2001) combination of a case studywith a system dynamics model to describe undesiredeffects of total quality management provides a proto-typical example for this kind of work.


To achieve a “deductive-inductive balance,” systemdynamics studies stop neither with empirical obser-vations nor with abstract models. Rather, in a cyclicway, empirical work and modeling work take turns:empirical observations lead to a model of the situ-ation, which shows the need for more and specificempirical data, etc. In addition to empirical tests ofthe model (and of the related theory), simulations canbe understood as experiments and serve—togetherwith field studies—as theory testing devices (Phelanand Wigan 1995).

To summarize, system dynamics is a structural the-ory of social systems and can be fruitfully used in theOM context to explain a variety of phenomena thatare otherwise difficult to get under control. Its maincharacteristic as a structural theory is a focus on feed-back loops, accumulation processes, and delays. Sys-tem dynamics models are minor or midrange contenttheories of social settings, for instance of issues fromoperations management. The development of suchmodels (and, thus, theory generation) is a process inwhich modeling and empirical work take turns, pro-viding a deductive-inductive balance. Model simu-lations are a form of experimentation, in which thetheory (represented by the model) is evaluated whenreal-world testing is not feasible or appropriate.

AcknowledgmentsThe constructive “feedback” of two anonymous refereesand of the special issue editors is gratefully acknowledged.Furthermore, the authors thank David Lane, London Schoolof Economics and Political Science, for his shared insightson the linkage between theory and system dynamics.

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