od goes digital

8/12/2019 Od Goes Digital

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at the organizational level, change is ubiquitous in contemporary organizations(By, 2007; Karoly, 2007). Organizations must adapt to their present conditionsthrough change management interventions in order to maintain effective levels

of performance, and the military is no exception (Barlow and Batteau, 2000).The military is faced with the current demands of the Global War on Terror(GWOT) which is taxing its ability to operate effectively. The high operationstempo characteristic of the last decade has been shown to affect soldiers well-being and commitment (Huffman et al., 2005). Complexity in demands must bemet with complexity in organizational design (Galbraith, 2002). Yet, this iseasier said than done. Recent estimates posit that the majority of change initiativesfail to reach the goals they set out to accomplish (Pellettiere, 2006). This fact isexemplified in the following quote from a prominent organizational change prac-titioner, The typical twentieth-century organization has not operated well in a

rapidly changing environment. Structure, systems, practices, and culture oftenhave been more of a drag on change than a facilitator. If environmental volatilitycontinues to increase, as most people now predict, the standard organization of thetwentieth century will likely become a dinosaur. (Kotter, 1996, p. 161). Research-ers and practitioners alike must seek novel techniques for use in organizationalchange initiatives. This article discusses organizational simulation methods as amechanism to evaluate and support organizational change alternatives. Further,the article briefly discusses the use of simulation in two US Air Force organiz-ational change scenarios.

The underlying causes for the shortcoming of organizational change initiativesgo well beyond the scope of this article, however what is pertinent is that organ-izational psychologists use all the tools available to them to help ensure that futureorganizational change initiatives do not suffer a similar fate. Although this articlefocuses on how simulation technologies can be applied to the organizationalchange context, the authors provide more in-depth coverage of other factors(such as change readiness, employee participation, leadership) that impactchange initiatives in the latter sections of the article. Organizational development(OD) efforts and or work redesign programs can have a positive impact on organ-izational outcomes (Hackman and Oldham, 1976; Porras and Berg, 1978; Klingerand Klein, 1999). Classic organizational development literature discusses OD as aresponse to mismatches between organizational and environmental factors (Porras

and Silvers, 1991). To address this mismatch, one can focus on influencing theorganizational vision or elements of the work environment (Porras and Silvers,1991); the present research focuses on the latter, specifically, organizationaldesign issues. The article discusses the application of simulation technologiesthat allow researchers and practitioners to experiment with different organiz-ational designs to foster effective organizational changes within the US Depart-ment of Defense (DoD).

Organizational Design

Organizational designs are imperative to the success of any organization becausethey specify how the organization functions at various levels, thus enabling indi-viduals to traverse individual outputs and allowing a collective output to emerge

208 J.B. Lyonset al.


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Researchers consider factors such as structure, process, rewards, personnel andculture as the key elements of organizational design (Galbraith, 1974, 2002).Others have referred to similar constructs in organizational design such as

people, process, technology and governance (i.e. structure and policies; Garudet al., 2006). Although traditionally thought of as division of labor, contemporarytheorists suggest that organizations can gain strategic advantages by designingthemselves in such a way as to foster their internal organizational capabilities(Galbraith, 2002). There are multiple facets that contribute to an effective organ-izational design, however structure and process are two fundamental aspects oforganizations that can be used as inputs for simulation technologies.

Ultimately, and unfortunately for organizational consultants, there are no pana-ceas to prescribe the optimal organizational design for every organization. Rather,each organizational change initiative has a unique set of demands, constraints and,

very importantly, different leadership. The latter is an important point becausedifferent leaders will have unique goals to accomplish through the organizationaldesign, as was the case in the following two examples. In the first scenario, organ-izational leaders were focused on establishing a baseline process model whichcould be used to evaluate the impact of a wide range of factors on the productivityof an engine repair center. This scenario was very high-level and was used primar-ily as an exploratory tool for leadership in strategic planning. The second scenariowas intended to provide decision support to organizational leaders in evaluatingtwo very specific organizational structures. This scenario was more detailed andwas used for tactical planning for an actual structural change. The authors usedthese two scenarios to highlight the breadth of how simulation can be used inorganizational change initiatives. Again, however, the authors must emphasizethat simulation alone is insufficient to execute organizational changes. Rather, acomprehensive approach must be taken to incorporate various elements oforganizational change, with simulation representing but one.

Organizational Simulation

Modern organizations are constantly changing and, if they are to be successful,they need to be able to evolve in a seamless fashion that does not disrupt theirongoing productivity (Garud et al., 2006). This may require an evolutionary

approach to organizational change (By, 2007). These complexities of design(i.e. structure) and the evolutions that accompany them can be captured indigital representations of organizational design, whereas the activities (i.e. pro-cesses) of the organization can be animated through organizational simulationtechnologies. In addition, in the realm of organizational change, there is a signifi-cant need to provide return on investment (ROI) estimates for organizationalchange initiatives (Cascio, 1995). Such estimates can support decision-makingamong senior leaders and provide a reality check for different organizationaldesign alternatives to help ensure that change is being engaged for the rightreasons.

ROI estimates for organizational change initiatives may be particularly impor-tant for government organizations because government organizational leaderstend to have shorter tenures relative to their industry counterparts (Ostroff

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2006). As a result, the potential exists for two scenarios which could both havedeleterious effects on government organizations. First, organizational leadersmay plan organizational change initiatives but not remain in the organization

long enough to see the plans through to completion. This could lead to reducedsupport for change initiatives as well as a tendency to declare victory prematurely,both of which can stifle the success of an organizational change program (Winumet al.1997). Second, with a short tenure may come the desire to leave ones markon the organization. What more assured way to leave a signature than to haveplanned the design of a new representation of ones organization? The conse-quences of engaging in change initiatives for the wrong reasons include:wasting resources, increased formalization and centralization, as well as inhibitedemployee motivation (By et al., 2008). Therefore, it is imperative that organiz-ational leaders have the best information at their disposal when planning and con-

ducting organizational change initiatives. Organizational simulations may be onemechanism to help foster increased awareness of the costs and benefits of variousorganizational change alternatives and these simulations may focus on eitherprocess or structural issues, or both.

Simulation As an Organization Modeling Tool

Researchers can examine process or structural changes by experimenting with thereal organization or with a mathematical model of the organization, the latterbeing preferred to minimize disruptions to operations. Linear programming,network models, queuing theory and simulation provide mathematically soundsolutions for organizational development because they foster real-time experimen-tation with hypothetical scenarios. Although the former three techniques are math-ematically superior to the latter, their fabrication and implementation are quitecomplicated and require skill sets well beyond those typically found in OD pro-fessionals. In addition, the simplifying assumptions needed to fit these types ofmodels to organizations can diminish their value. So, although there are numerousways to model an organization or system that are beneficial for decision-makingthroughout organizational change initiatives, simulation remains a practicalmethod for OD consultants.

Simulation is a multidisciplinary term used in the fields of physics, chemistry,biology, economics, engineering and the social sciences. A computer simulationmay be static or dynamic, that is, a repeated depiction of a system at one pointin time or over time, respectively. Systems are broken down into discrete and con-tinuous types and are typically a mix of both. A discrete system changes at definitepoints over time, whereas a continuous system changes continuously over time. Asimulation is further categorized into stochastic and deterministic. A stochasticmodel incorporates a number of random input variables to produce an overallrandom output, this being a statistical estimate of the true output of the system.A deterministic model contains constant parameters such as a system of differen-

tial equations (Law and Kelton, 2000). Most organizations can be classified asdynamic, discrete and stochastic, thus most can be modeled using discrete eventsimulation (DES)



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Discrete Event Simulation

Organizations engaged in organizational design initiatives often neglect processissues and proceed straight to structural changes; however, this neglects the

details of how information flows and how decisions are made within the boundsof the organization (Neilson et al., 2008). Discrete event simulation is useful inrepresenting an organization for the purpose of studying and analyzing itsprocess and information flows. Organizations are a conglomeration of stochasticrelationships so the simplifying assumptions needed to build a mathematicalmodel of a complex organization typically minimizes the value of the analysis.A simulation approach, however, allows great flexibility in representing the realsystem, and allows the testing of different policies, varying parameters, alternatedesigns and contingency scenarios. In addition to the benefits of a discrete eventsimulation tool, an organization that goes through the simulation building process

will gain new understanding into the true operation and function of their organiz-ation. This alone can provide tremendous value to an organization because it maypromote communication about organizational changes and their desired impactwhile facilitating detailed analyses of key relationships or processes within theorganization. Critically attempting to model an organization as a discrete eventsimulation requires creativity and an open mind. These lead to an increasedknowledge of the true system and consequently more creative solutions to theorganizations problems.

Many options exist for creating a discrete event simulation. They can be con-structed by hand or programmed into a general purpose computer language

such as FORTRAN or JAVA, which is typically very time-consuming. Special-purpose simulation languages are designed to provide a framework for specifictypes of simulation applications, but also require significant time investments.High-level simulators provide an easier method for building models but sacrificethe flexibility of using computer programming languages. A common softwareprogram used to build discrete event simulation models is Arena, which combinesthe ease of using high-level templates and the flexibility of using general purposeprogramming languages. The suppleness to mix and match simulation techniqueswithin a model makes Arena the tool of choice when building discrete event simu-lations. There are numerous applications of discrete event simulation in all fieldsof study using Arena and other software packages.

Study 1

One of the advantages of simulating a change versus actually engaging in thechange is that organizational leaders can test various organizational alternativessafely within a simulation with minimal impact on the organization. This wasthe case in our first scenario. This scenario involves an engine repair facilitythat was planning to undergo organizational changes including process changesand structural changes, although the details of these changes were not yet speci-

fied. The authors were asked to create a baseline process model of the enginerepair facility to allow leadership to test various organizational changes(Table 1) Some of the changes being contemplated by the customer included



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analyzing product wait and cycle time based on various organizational structures,and worker utilization rates based on different structures. The customer was con-

templating moving from a functional organizational structure, in which eachworker is responsible for a particular aspect of the engine repair, to a team-based structure, in which members would work together on various aspects ofthe engine throughout its entire manufacturing process. Further, the customerwas interested in exploring worker utilization rates (the inverse of downtime)based on the number of desired engines produced per month.

An engine assembly line is one of the most basic discrete event simulation appli-cations given the stepwise projection through various segmented activities. Theessence of the real system is captured through a high-level view of the engineshop, as shown in Figure 1. In addition to the processes in Figure 1, there aremany sub-processes within each of the higher level processes. Rather thanmaking a common mistake to model fastidiously with excessive fidelity, wechose to keep the model at this level in order to address the organizational levelissues raised above. Attempting to capture every detail within a system canoften times distract from ones objective when that objective is couched in ahigher level organizational issue.

Table 1. Simulation results for functional versus team-based structure

Structure

Functional Team-based

Average overall wait time per engine (days) 139.48 20.35Average total system time (days) 158.16 39.15Maximum number of engines in the system 134 39Average worker utilization rate .99 .95

Note: The total time simulated was held constant at 3 years, and the desired output was set at 13 engines/month.

Figure 1 High-level process model within the engine repair center



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Validation and verification are necessary to ensure a model is accurate andpractical for exploitation (Law, 2008; Sargent, 2008). As such, the currentmodeling required extensive interactions with the customer to ensure that the

model represented a realistic depiction of the organization in question. Subjectmatter experts were interviewed to ascertain an understanding of the taskswithin the center and the time required to complete the tasks. Several task analyseswere conducted throughout the course of this project to ensure that accurate timeparameters were acquired.

Simulation provides the benefit of replicating a system and its processes for longperiods. Simulation modelers are avidly aware of this important quality, however,many others fail to understand the nature of stochastic processes. Some believe anadequate method for testing a specific scenario is to try out the scenario for a shorttime. Assuming to determine the effects of varying levels of throughput, the

engine shop decided to forego simulation for real-world trial and error experimen-tation. Without a healthy understanding of stochastic principles, a shop analystmay attempt to increase the production levels for a 30-day period and examinethe effects on worker utilization, engine wait time (the amount of time theengine waited for someone to be available to work on it) and total number ofengines produced. From this single experiment, an analyst would extrapolateinformation to predict the effects for the next several years. The effects of thismistake are depicted in Table 2, showing the difference between simulating fora short period and for an adequate length of time. In summary, the currentmodel had the following input factors (elements that were manipulated toexplore their impact on outcome variables): total engines produced per month,organizational structure (functional versus team-based), number of model replica-tions (30 versus 1) and total time simulated (30 days versus 3 years). In addition,the following outcome factors were explored: average wait time per step in theprocess, average wait time for the full process, total number of engines in theprocess and worker utilization rates.

Results

As shown in Table 1, the team-based structure had several advantages relative to

the current functional structure. The team-based structure had lower wait times

Table 2. Simulation results for different production goals

Production goal

7 engines/month 13 engines/month

Average overall wait time per engine (days) 1.62 20.35Average total system time (days) 20.39 39.15Number of engines in the system 10 39Average worker utilization rate .52 .95

Note: The team-based organizational structure was used for both examples and the total time simulated was set to

3 years



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for both the average process step and the total cycle time. Furthermore, the team-based structure had fewer engines in the process at any given time and a higherworker utilization rate. Thus, the team-based structure was found to be superior

to the functional structure in all aspects of the current simulation. As shown inTable 2, differing production goals impacted the outcome of the simulation.Moving from 7 to 13 engines per month increased wait times for each processstep as well as for the overall cycle time. This is an indication of the implicationsof adding increased demand to the shops workload, a jump of six engines permonth appears to be manageable. Further, increasing production goals leads to agreater number of engines in the system. However, increasing production goalsalso resulted in higher worker utilization rates. As shown in Table 3, the amountof time simulated also played a role. Simulating 3 years as opposed to simulating30 days resulted in greater wait times for both time indicators, a greater number of

engines in the repair process, and approximately equivalent worker utilizationrates. It is important to consider the consequences of proposed changes in thecontext of an adequate planning period, not simply for the short-term.

Discussion

The current simulation explored the relative impact of two organizational struc-ture options, a team-based versus a functional structure. The results showed thata team-based structure was more efficient than a functional structure. However,the authors acknowledge that although the team-based option appears to be

more efficient in the simulation, it may also carry greater training costs becauseindividuals would need to be proficient in a greater number of processes relativeto those in the functional option. In essence, however, these are some of thetrade-offs that must be considered by senior leadership prior to engaging in organ-izational changes. Simulation, in this case, afforded leadership with an opportu-nity to explore these possible trade-offs. Research has shown that functionalorganization schemes are best for predictable environments because they canpromote efficiency and interactions among key work nodes; whereas divisionalschemes are best for unpredictable environments because they promote advancedskill development and compensatory behaviors among nodes (Hollenbecket al.,

2002; Moon et al., 2004). The current study demonstrated the opposite;

Table 3. Simulation results for different simulation times

Total time simulated

30 days 1,095 days (3 years)

Average overall wait time per engine (days) 7.83 20.35Average total system time (days) 26.30 39.15Number of engines in the system 15 39Average worker utilization rate .93 .95

Note: The team-based organizational structure was used for both examples and the total desired output was set to

13 engines/month



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however, it is notable that the current study was using a highly structured manu-facturing scenario as opposed to the dynamic decision-making tasks used in pastresearch. Future research might attempt to inject turbulence into these simulations

to explore how it impacts the relative benefits of different structures.Study 1 also explored the impact of different production goals. A higher pro-

duction goal (13 engines/month) was found to result in longer wait times,however worker utilization rates were higher relative to the lower productiongoal (seven engines/month). These trade-offs are important for organizationalleaders to recognize and evaluate based on their organizational goals. A lowerworker utilization rate may help support surge capabilities when demandrapidly increases, yet it may foster boredom among employees. By contrast, ahigher worker utilization rate may help to foster employee engagement (Maceyand Schneider, 2008), but may limit surge capabilities. Finally, the current

study explored how different simulation times can impact outcomes. In thiscase, a 30-day comparison was made to a 3-year comparison. The 3-year simu-lation evidenced longer wait times and estimated more than double the enginesin the repair process relative to the 30-day simulation. This is relevant for organ-izational leaders as they must consider things like warehouse capabilities. Insummary, Study 1 demonstrated the benefits of simulation for an exploratorymechanism in organizational change planning. Here, organizational leaders maybe exploring a variety of alternatives and simulation can provide valuable esti-mates to feed their strategic planning.

Study 2

Although organizational leaders need support in exploring different organizationalalternatives, they may also need support in analyzing very specific alternatives;and this was the case in Study 2. The organization for Study 2 was in theprocess of deciding which of two organizational structures to adopt for anongoing organizational change initiative. Organizational structures outline the dis-tribution of power, departmentation, shape and degree of specialization within anorganization (Galbraith, 2002). These factors will impact organizations differentlydepending on their goals and the constraints under which they operate. Similarly,structural contingency theory suggests that there is no one organizational structure

that is best for every situation, rather the structure must match the demands of theenvironment in order to be optimally effective (Carley and Lin, 1997). Otherresearch has focused on the trade-offs between centralized and decentralizedorganizational structures (Gateau et al., 2007; Leweling and Nissen, 2007). Yet,centralized versus decentralized cannot capture the gamut of complexity thatabides within organizational structures of actual organizations. This complexity,however, can be represented using organizational simulation software such asSimVision.

SimVison is an organizational modeling and simulation tool that allows users tobreakdown and analyze projects of all types ranging from bridge building to

course of action planning. SimVision grew out of research conducted by StanfordUniversity that started in the 1980s and has continued through the Virtual DesignTeam (VDT) software which was designed originally as a project management



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tool (Levitt, 2004). The software adopts the perspective that the speed at whichinformation flows through an organization is driven by its structure or decisionhierarchy (Galbraith, 1974). For example, if an employee needs a decision to be

made by their supervisor, that employee must wait until the supervisor processesthe information, makes a decision and communicates that decision back to theemployee. The number of management layers between decision-makers and thetime available for the decision-makers are likely going to have a significantimpact on the time it takes for the system as a whole to process informationand make decisions. Thus, the driving forces that foster speed and breadth of infor-mation flow and decision-making are often rooted in the internal structure andprocess of the organization (Galbraith, 2002).

The users of this tool can construct a virtual rendition of the organizationsstructure and processes to replicate the leadership hierarchies and information pro-

cesses of the organization. Users can then take the baseline design and modifyinputs such as structural changes and or process changes and explore the differen-tial impact these changes have on several outcome measures. The most salientoutcome measures include project risk in terms of estimation of project timelineand identification of information bottlenecks. Again, the software was originallyintended for use as a project management tool and thus the majority of itsoutcome variables are focused on time factors for a given set of activities thatneed to be performed. Similar to the modeling discussed in Study 1, SimVisionmodels activities, time spent on activities and the individual actors who haveresponsibility for those activities. However, SimVision also attempts to accountfor factors such as time spent in meetings, layers of bureaucracy, skill set andexperience of organizational members, and multiple task loadings, all of whichhave implications for time. By et al. (2008) hint at the drawbacks of excessivebureaucracy, The more time spent on administrative tasks and bureaucratic pro-cedures, the less time spent on doing the real job (p. 26). SimVision represents agood mechanism to gauge the time required to complete a set of activities in astepwise fashion given the management hierarchy of an organization, and this iswhere the tool has demonstrated great value to project planners (Levitt, 2004).More recently, this tool has been applied to continuous work (as opposed to aset of finite activities), such as that modeled in the current study (Faas et al., 2009).

The comparative capability of SimVision has great utility for researchers and

practitioners who are engaged in organizational redesign activities. Change isvery common in government organizations and the ability to experiment withchange alternatives before engaging in the actual change would providedecision-makers with better opportunities to discuss various alternatives andtheir inherent limitations or advantages. SimVision offers such a capability and,although it was designed primarily as a project management tool, it has potentialas an organizational change tool as well. The authors have applied this technologyin a DoD organizational change context involving a large command and controlreorganization (Faas et al., 2009). The organization in question was undergoinga merger where several units from other organizations were being brought into

this parent organization. Leaders in this organization were contemplating threealternatives and these organizational design options differed to the extent towhich the individual units were integrated



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The change management team used the results from a SimVision analysis tohelp guide the organizational design for a new command and control center.Specifically, the simulation was used to compare the three alternative organiz-

ational designs (see Figure 2 for an example). The baseline (or current) organiz-ational design was compared with a low integration option in which the unitsshared the same overall leadership but still operated independently, a mediumintegration option in which team members would be matrixed into the variousunits while maintaining the original organizational units, and a fully integratedoption in which the old units would be disbanded and new teams would beformed around the functions of the organization.

Results and Discussion

Unfortunately, detailed results of this organizational change cannot be shown forsecurity reasons, yet high-level findings can be shared. The fully integrated optionwas found to be superior to the other alternatives in terms of total time for taskcompletion (in this case completing a course of action planning activity) andthe low integration option had the highest time for task completion. This

Figure 2 Example organizational model using SimVision



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information was used by the senior leaders in this organization in making strategicdecisions about the new organizational structure. These findings speak to thepotential benefits of collaboration because the fully integrated teams had immedi-

ate access to the necessary team members for completing the team planningactivity. The change management team also noted that the fully integratedoption may pose significant change management challenges because it was themost extreme alternative being considered in terms of training, process and poten-tial cultural differences between the groups. The inherent trade-offs betweenorganizational design options should be given extensive consideration by anychange management team. In summary, Study 2 demonstrated the utility of Sim-Vision as a comparative tool to support high-fidelity organizational changeoptions.

Minimizing Trade-offs In Organizational Change

Various organizational designs often involve different trade-offs in terms of theirinherent costs and benefits (Galbraith, 2002). Researchers have outlined fourapproaches to organizational design/change: mechanistic, motivational, percep-tual and biological, each with their own unique strengths and limitations(Campion et al., 2005). For example, the mechanistic approach focuses on indus-trial engineering principles and emphasizes specialization, simplification and rep-etition with the goal of increasing efficiency. This approach may be mostrepresentative of the simulation approaches discussed above. The drawbacks ofsuch an approach, however, involve decreased job satisfaction and motivation,which are the key goals of the motivational approach. The motivational approachused organizational psychology principles of variety, autonomy and participationfor work design interventions. The limitations of this approach include increasedtraining costs, heightened stress and possibly increased errors. The authors con-clude that work design interventions should use a variety of approaches attendingto the various costs and benefits of each and that the proper combination of workdesign approaches can minimize the trade-offs between the various approaches(Campion et al., 2005). Research has substantiated this claim by showing thatwhen organizations consider multiple approaches and try to minimize the limit-ations of each while maximizing their benefits, they can avoid the trade-offs

between different organizational design alternatives (Morgeson and Campion,2002).

Fostering Effective Change: Simulation and Beyond

Simulation tools may be one way for OD professionals to integrate the rigor ofindustrial design into organizational change initiatives. However, organizationalchange is highly complex and organizational leaders should use multiplemethods to approach organizational change initiatives. Simulations can becoupled with traditional OD interventions that may leverage organizational psy-

chology principles to address the complexities that pervade contemporary organ-izations. In the organizational merger project discussed above, simulation was butone methodology employed to support the change initiative Other methods used



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to complement the simulation included organizational surveys to gauge changereadiness and employee attitudes, focus groups to monitor employee resistanceand engage employees in the change process, process improvement interventions

aiming to reduce non-value-added work activities, strategic communications toshare change-related news and events, as well as success stories for the neworganization, and training/socialization interventions to anchor organizationalchanges into the new organizational culture.

There is a robust literature in the organizational change domain which has pin-pointed several enablers of effective change. These include: establishing a com-pelling and supported vision for the change (Kotter, 1996), mapping employeeresistance factors and taking action to reduce them (Ostroff, 2006), takingaction and following through after employee surveys (Thompson and Surface,2009), engaging in change-oriented leadership (Lyons et al., 2009), tracking

and managing employee change readiness (By, 2007) and fostering change inter-ventions that focus on the specific rational, motivational and emotional needs ofthe organization rather than using one-size-fits-all approaches (Winum et al.,1997). The authors cannot stress enough the fact that while simulation mayoffer a novel tool for supporting organizational change initiatives, it is but onemethod to support change. Successful change initiatives will incorporate abalance of organizational design principles and methods, implement vision-oriented activities, foster leadership support for the change and engage employeesin the change process, as well as understand employee resistance factors andchange readiness among employees.

Conclusion

This article discusses the concept of organizational simulation in the context ofchange while providing two applied examples of how simulation technologieshave been applied to organizational change initiatives within the US governmentas one aspect of a larger change management program. Research has demonstratedthe benefits of attending to the various components of organizational design(people, process, technology and governance) when engaging in organizationalchanges (Garud et al., 2006). Further, case studies have elucidated the notionthat organizational design attempts can be evolutionary and may require constant

modification and tweaking (Madsenet al., 2006). This is consistent with contem-porary perspectives on organizational change which suggest that a continuousapproach is most effective in generating sustained and positive change(By, 2007; Byet al., 2008). Simulations may help organizational leaders to under-stand the incremental steps toward change success. Ultimately, multiple interven-tions are better than one in generating sustained and accepted change (Porras andBerg, 1978). Tools to enable organizational design, such as the simulation toolsdiscussed above, represent just one set of tools that organizational psychologistsmight employ to support organizational change initiatives within the US govern-ment. However, these tools may provide invaluable ROI metrics that can be used

to support decision-making among organizational leaders.When envisioning process change, the tendency to use Lean (i.e. process

improvement) methodologies to determine improved configurations often



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outweighs that of more complicated methods, such as discrete event simulation oranalytical solutions. Lean, however, is not without its deficiencies, as noted inStandridge and Marvel (2006). Detty and Yingling (2000) use discrete event simu-

lation to show the benefits of implementing lean thinking, essentially an ROIstudy. Lean recommendations are initiated in the simulation and compared againstthe baseline operations, thus providing foresight in the form of efficiency-basedpredictions. Any organization that has implemented lean manufacturing andignored simulation should consider a change to their technical approach. Further-more, any organizational consultant who relies solely on simulation shouldconsider the gamut of other drivers of organizational change success. Thus, onceagain, multiple approaches and strategies may be more effective than just onewhen attempting to conduct complex organizational changes. Businesses typicallyhave all the necessary data for an analyst to rapidly construct and validate a

simulation because much of the data used for lean manufacturing is used for simu-lation. To implement one of these techniques devoid of the other is inefficient andwasteful, the two reasons these techniques were fashioned in the first place.

Ultimately, simulation is an underutilized method among organizational con-sultants. This is not surprising given that many such consultants are psychologists,with an inherent bias toward organizational psychology-oriented methods (actionresearch, change management, etc.). However, if used in conjunction with othermore traditional organizational change methods, simulation can be a useful toolfor applied researchers and consultants. Although the term simulation mayappear precarious to some psychologists, many may find that there are availabletools that do not require a computer science background to use effectively.

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