Journal of Change Management Vol. 11, No. 2, 207- 221, June 2011 Organizational Development Goes Digital: Applying Simulation to Organizational Change JOSEPH B. LYONS , JEREMY JORDAN , PAUL FAAS & STEPHANIE SWINDLER Air Force Research Lab, Wright-Patterson AFB, USA, Air Force Institute of Technology, WrightPatterson AFB, USA ABSTRACT Organizational change initiatives are challenging for both researchers to understand and for practitioners/organizational leaders to execute. This article takes a conceptual approach to describe organizational simulation technologies as one of many approaches for use in organizational development activities while also providing two examples of how simulations have been applied to real-world scenarios. Scenario 1 involved a process re-engineering effort within a manufacturing organization where a manufacturing process was modeled to explore how numerous factors (e.g. product inputs, organizational structure, manpower allocation) influenced the simulated output. Scenario 2 included an organizational change initiative involving organizational design modifications in a command and control center. Here, two organizational design alternatives were compared and contrasted. Simulation technologies may provide organizational development (OD) professionals with the opportunity to experiment with alternative organizational designs, an inherent strength for generating potential return on investment estimates. Researchers from the Air Force Research Laboratory have used simulation technologies as one element of an overall organizational development strategy within two different projects, albeit as one aspect of a larger change management strategy. This article discusses these applied examples in the context of a conceptual discussion on the merits of simulation as a tool to support organizational change. KEY WORDS : Organizational change, simulation, organizational design, process improvement Introduction Owing to increased globalization, the introduction of advanced technologies, wavering markets, a shrinking pool of applicants and omnipresent competition Correspondence Address: Joseph B. Lyons, Air Force Research Lab, 2698 G Street, Bldg 190, Wright-Patterson AFB, 45433-7604, USA. Email: joseph.lyons@wpafb.af.mil 1469-7017 Print/1479-1811 Online/11/020207-15 # 2011 Taylor & Francis DOI: 10.1080/14697017.2010.501022 208 J.B. Lyons et al. at the organizational level, change is ubiquitous in contemporary organizations (By, 2007; Karoly, 2007). Organizations must adapt to their present conditions through 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 operations tempo characteristic of the last decade has been shown to affect soldiers' wellbeing and commitment (Huffman et al., 2005). Complexity in demands must be met with complexity in organizational design (Galbraith, 2002). Yet, this is easier said than done. Recent estimates posit that the majority of change initiatives fail to reach the goals they set out to accomplish (Pellettiere, 2006). This fact is exemplified in the following quote from a prominent organizational change practitioner, 'The typical twentieth-century organization has not operated well in a rapidly changing environment. Structure, systems, practices, and culture often have been more of a drag on change than a facilitator. If environmental volatility continues to increase, as most people now predict, the standard organization of the twentieth century will likely become a dinosaur.' (Kotter, 1996, p. 161). Researchers and practitioners alike must seek novel techniques for use in organizational change initiatives. This article discusses organizational simulation methods as a mechanism to evaluate and support organizational change alternatives. Further, the article briefly discusses the use of simulation in two US Air Force organizational change scenarios. The underlying causes for the shortcoming of organizational change initiatives go well beyond the scope of this article, however what is pertinent is that organizational psychologists use all the tools available to them to help ensure that future organizational change initiatives do not suffer a similar fate. Although this article focuses on how simulation technologies can be applied to the organizational change context, the authors provide more in-depth coverage of other factors (such as change readiness, employee participation, leadership) that impact change initiatives in the latter sections of the article. Organizational development (OD) efforts and or work redesign programs can have a positive impact on organizational outcomes (Hackman and Oldham, 1976; Porras and Berg, 1978; Klinger and Klein, 1999). Classic organizational development literature discusses OD as a response to mismatches between organizational and environmental factors (Porras and Silvers, 1991). To address this mismatch, one can focus on influencing the organizational vision or elements of the work environment (Porras and Silvers, 1991); the present research focuses on the latter, specifically, organizational design issues. The article discusses the application of simulation technologies that allow researchers and practitioners to experiment with different organizational designs to foster effective organizational changes within the US Department of Defense (DoD). Organizational Design Organizational designs are imperative to the success of any organization because they specify how the organization functions at various levels, thus enabling individuals to traverse individual outputs and allowing a collective output to emerge. Organizational Development Goes Digital 209 Researchers consider factors such as structure, process, rewards, personnel and culture 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; Garud et al., 2006). Although traditionally thought of as division of labor, contemporary theorists suggest that organizations can gain strategic advantages by designing themselves in such a way as to foster their internal organizational capabilities (Galbraith, 2002). There are multiple facets that contribute to an effective organizational design, however structure and process are two fundamental aspects of organizations that can be used as inputs for simulation technologies. Ultimately, and unfortunately for organizational consultants, there are no panaceas 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 because different leaders will have unique goals to accomplish through the organizational design, as was the case in the following two examples. In the first scenario, organizational leaders were focused on establishing a baseline process model which could be used to evaluate the impact of a wide range of factors on the productivity of an engine repair center. This scenario was very high-level and was used primarily as an exploratory tool for leadership in strategic planning. The second scenario was intended to provide decision support to organizational leaders in evaluating two very specific organizational structures. This scenario was more detailed and was used for tactical planning for an actual structural change. The authors used these two scenarios to highlight the breadth of how simulation can be used in organizational change initiatives. Again, however, the authors must emphasize that simulation alone is insufficient to execute organizational changes. Rather, a comprehensive approach must be taken to incorporate various elements of organizational 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 their ongoing 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 in digital representations of organizational design, whereas the activities (i.e. processes) of the organization can be animated through organizational simulation technologies. In addition, in the realm of organizational change, there is a significant need to provide return on investment (ROI) estimates for organizational change initiatives (Cascio, 1995). Such estimates can support decision-making among senior leaders and provide a reality check for different organizational design alternatives to help ensure that change is being engaged for the right reasons. ROI estimates for organizational change initiatives may be particularly important for government organizations because government organizational leaders tend to have shorter tenures relative to their industry counterparts (Ostroff, 210 J.B. Lyons et al. 2006). As a result, the potential exists for two scenarios which could both have deleterious effects on government organizations. First, organizational leaders may plan organizational change initiatives but not remain in the organization long enough to see the plans through to completion. This could lead to reduced support for change initiatives as well as a tendency to declare victory prematurely, both of which can stifle the success of an organizational change program (Winum et al. 1997). Second, with a short tenure may come the desire to leave one's 'mark' on the organization. What more assured way to leave a signature than to have planned the design of a new representation of one's organization? The consequences of engaging in change initiatives for the wrong reasons include: wasting resources, increased formalization and centralization, as well as inhibited employee motivation (By et al., 2008). Therefore, it is imperative that organizational leaders have the best information at their disposal when planning and conducting organizational change initiatives. Organizational simulations may be one mechanism to help foster increased awareness of the costs and benefits of various organizational change alternatives and these simulations may focus on either process or structural issues, or both. Simulation As an Organization Modeling Tool Researchers can examine process or structural changes by experimenting with the real organization or with a mathematical model of the organization, the latter being preferred to minimize disruptions to operations. Linear programming, network models, queuing theory and simulation provide mathematically sound solutions for organizational development because they foster real-time experimentation with hypothetical scenarios. Although the former three techniques are mathematically superior to the latter, their fabrication and implementation are quite complicated and require skill sets well beyond those typically found in OD professionals. In addition, the simplifying assumptions needed to fit these types of models to organizations can diminish their value. So, although there are numerous ways to model an organization or system that are beneficial for decision-making throughout organizational change initiatives, simulation remains a practical method for OD consultants. Simulation is a multidisciplinary term used in the fields of physics, chemistry, biology, economics, engineering and the social sciences. A computer simulation may be static or dynamic, that is, a repeated depiction of a system at one point in time or over time, respectively. Systems are broken down into discrete and continuous types and are typically a mix of both. A discrete system changes at definite points over time, whereas a continuous system changes continuously over time. A simulation is further categorized into stochastic and deterministic. A stochastic model incorporates a number of random input variables to produce an overall random output, this being a statistical estimate of the true output of the system. A deterministic model contains constant parameters such as a system of differential equations (Law and Kelton, 2000). Most organizations can be classified as dynamic, discrete and stochastic, thus most can be modeled using discrete event simulation (DES). Organizational Development Goes Digital 211 Discrete Event Simulation Organizations engaged in organizational design initiatives often neglect process issues and proceed straight to structural changes; however, this neglects the details of how information flows and how decisions are made within the bounds of the organization (Neilson et al., 2008). Discrete event simulation is useful in representing an organization for the purpose of studying and analyzing its process and information flows. Organizations are a conglomeration of stochastic relationships so the simplifying assumptions needed to build a mathematical model of a complex organization typically minimizes the value of the analysis. A simulation approach, however, allows great flexibility in representing the real system, and allows the testing of different policies, varying parameters, alternate designs and contingency scenarios. In addition to the benefits of a discrete event simulation tool, an organization that goes through the simulation building process will gain new understanding into the true operation and function of their organization. This alone can provide tremendous value to an organization because it may promote communication about organizational changes and their desired impact while facilitating detailed analyses of key relationships or processes within the organization. Critically attempting to model an organization as a discrete event simulation requires creativity and an open mind. These lead to an increased knowledge of the true system and consequently more creative solutions to the organization's problems. Many options exist for creating a discrete event simulation. They can be constructed by hand or programmed into a general purpose computer language such as FORTRAN or JAVA, which is typically very time-consuming. Specialpurpose simulation languages are designed to provide a framework for specific types of simulation applications, but also require significant time investments. High-level simulators provide an easier method for building models but sacrifice the flexibility of using computer programming languages. A common software program used to build discrete event simulation models is Arena, which combines the ease of using high-level templates and the flexibility of using general purpose programming languages. The suppleness to mix and match simulation techniques within a model makes Arena the tool of choice when building discrete event simulations. There are numerous applications of discrete event simulation in all fields of study using Arena and other software packages. Study 1 One of the advantages of simulating a change versus actually engaging in the change is that organizational leaders can test various organizational alternatives safely within a simulation with minimal impact on the organization. This was the case in our first scenario. This scenario involves an engine repair facility that was planning to undergo organizational changes including process changes and structural changes, although the details of these changes were not yet specified. The authors were asked to create a baseline process model of the engine repair facility to allow leadership to test various organizational changes (Table 1). Some of the changes being contemplated by the customer included 212 J.B. Lyons et al. Table 1. Simulation results for functional versus team-based structure Structure Average overall wait time per engine (days) Average total system time (days) Maximum number of engines in the system Average worker utilization rate Functional Team-based 139.48 158.16 134 .99 20.35 39.15 39 .95 Note: The total time simulated was held constant at 3 years, and the desired output was set at 13 engines/month. analyzing product wait and cycle time based on various organizational structures, and worker utilization rates based on different structures. The customer was contemplating moving from a functional organizational structure, in which each worker is responsible for a particular aspect of the engine repair, to a teambased structure, in which members would work together on various aspects of the engine throughout its entire manufacturing process. Further, the customer was 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 applications given the stepwise projection through various segmented activities. The essence of the real system is captured through a high-level view of the engine shop, as shown in Figure 1. In addition to the processes in Figure 1, there are many sub-processes within each of the higher level processes. Rather than making a common mistake to model fastidiously with excessive fidelity, we chose to keep the model at this level in order to address the organizational level issues raised above. Attempting to capture every detail within a system can often times distract from one's objective when that objective is couched in a higher level organizational issue. Figure 1. High-level process model within the engine repair center. Organizational Development Goes Digital 213 Validation and verification are necessary to ensure a model is accurate and practical for exploitation (Law, 2008; Sargent, 2008). As such, the current modeling required extensive interactions with the customer to ensure that the model represented a realistic depiction of the organization in question. Subject matter experts were interviewed to ascertain an understanding of the tasks within the center and the time required to complete the tasks. Several task analyses were conducted throughout the course of this project to ensure that accurate time parameters were acquired. Simulation provides the benefit of replicating a system and its processes for long periods. Simulation modelers are avidly aware of this important quality, however, many others fail to understand the nature of stochastic processes. Some believe an adequate method for testing a specific scenario is to try out the scenario for a short time. Assuming to determine the effects of varying levels of throughput, the engine shop decided to forego simulation for real-world trial and error experimentation. Without a healthy understanding of stochastic principles, a shop analyst may attempt to increase the production levels for a 30-day period and examine the effects on worker utilization, engine wait time (the amount of time the engine waited for someone to be available to work on it) and total number of engines produced. From this single experiment, an analyst would extrapolate information to predict the effects for the next several years. The effects of this mistake are depicted in Table 2, showing the difference between simulating for a short period and for an adequate length of time. In summary, the current model had the following input factors (elements that were manipulated to explore their impact on outcome variables): total engines produced per month, organizational structure (functional versus team-based), number of model replications (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 the process, average wait time for the full process, total number of engines in the process 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 Average overall wait time per engine (days) Average total system time (days) Number of engines in the system Average worker utilization rate 7 engines/month 13 engines/month 1.62 20.39 10 .52 20.35 39.15 39 .95 Note: The team-based organizational structure was used for both examples and the total time simulated was set to 3 years. 214 J.B. Lyons et al. for both the average process step and the total cycle time. Furthermore, the teambased structure had fewer engines in the process at any given time and a higher worker 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 in Table 2, differing production goals impacted the outcome of the simulation. Moving from 7 to 13 engines per month increased wait times for each process step as well as for the overall cycle time. This is an indication of the implications of adding increased demand to the shop's workload, a jump of six engines per month appears to be manageable. Further, increasing production goals leads to a greater number of engines in the system. However, increasing production goals also resulted in higher worker utilization rates. As shown in Table 3, the amount of time simulated also played a role. Simulating 3 years as opposed to simulating 30 days resulted in greater wait times for both time indicators, a greater number of engines in the repair process, and approximately equivalent worker utilization rates. It is important to consider the consequences of proposed changes in the context of an adequate planning period, not simply for the short-term. Discussion The current simulation explored the relative impact of two organizational structure options, a team-based versus a functional structure. The results showed that a 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 because individuals would need to be proficient in a greater number of processes relative to those in the functional option. In essence, however, these are some of the trade-offs that must be considered by senior leadership prior to engaging in organizational changes. Simulation, in this case, afforded leadership with an opportunity to explore these possible trade-offs. Research has shown that functional organization schemes are best for predictable environments because they can promote efficiency and interactions among key work nodes; whereas divisional schemes are best for unpredictable environments because they promote advanced skill development and compensatory behaviors among nodes (Hollenbeck et al., 2002; Moon et al., 2004). The current study demonstrated the opposite; Table 3. Simulation results for different simulation times Total time simulated Average overall wait time per engine (days) Average total system time (days) Number of engines in the system Average worker utilization rate 30 days 1,095 days (3 years) 7.83 26.30 15 .93 20.35 39.15 39 .95 Note: The team-based organizational structure was used for both examples and the total desired output was set to 13 engines/month. Organizational Development Goes Digital 215 however, it is notable that the current study was using a highly structured manufacturing scenario as opposed to the dynamic decision-making tasks used in past research. 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 production goal (13 engines/month) was found to result in longer wait times, however worker utilization rates were higher relative to the lower production goal (seven engines/month). These trade-offs are important for organizational leaders to recognize and evaluate based on their organizational goals. A lower worker utilization rate may help support surge capabilities when demand rapidly increases, yet it may foster boredom among employees. By contrast, a higher worker utilization rate may help to foster employee engagement (Macey and Schneider, 2008), but may limit surge capabilities. Finally, the current study explored how different simulation times can impact outcomes. In this case, a 30-day comparison was made to a 3-year comparison. The 3-year simulation evidenced longer wait times and estimated more than double the engines in the repair process relative to the 30-day simulation. This is relevant for organizational leaders as they must consider things like warehouse capabilities. In summary, Study 1 demonstrated the benefits of simulation for an exploratory mechanism in organizational change planning. Here, organizational leaders may be exploring a variety of alternatives and simulation can provide valuable estimates to feed their strategic planning. Study 2 Although organizational leaders need support in exploring different organizational alternatives, 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 the process of deciding which of two organizational structures to adopt for an ongoing organizational change initiative. Organizational structures outline the distribution of power, departmentation, shape and degree of specialization within an organization (Galbraith, 2002). These factors will impact organizations differently depending 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 the environment in order to be optimally effective (Carley and Lin, 1997). Other research has focused on the trade-offs between centralized and decentralized organizational structures (Gateau et al., 2007; Leweling and Nissen, 2007). Yet, centralized versus decentralized cannot capture the gamut of complexity that abides within organizational structures of actual organizations. This complexity, however, can be represented using organizational simulation software such as SimVision. SimVison is an organizational modeling and simulation tool that allows users to breakdown and analyze projects of all types ranging from bridge building to course of action planning. SimVision grew out of research conducted by Stanford University that started in the 1980s and has continued through the Virtual Design Team (VDT) software which was designed originally as a project management 216 J.B. Lyons et al. tool (Levitt, 2004). The software adopts the perspective that the speed at which information flows through an organization is driven by its structure or decision hierarchy (Galbraith, 1974). For example, if an employee needs a decision to be made by their supervisor, that employee must wait until the supervisor processes the information, makes a decision and communicates that decision back to the employee. The number of management layers between decision-makers and the time available for the decision-makers are likely going to have a significant impact on the time it takes for the system as a whole to process information and make decisions. Thus, the driving forces that foster speed and breadth of information flow and decision-making are often rooted in the internal structure and process of the organization (Galbraith, 2002). The users of this tool can construct a virtual rendition of the organization's structure and processes to replicate the leadership hierarchies and information processes of the organization. Users can then take the baseline design and modify inputs such as structural changes and or process changes and explore the differential impact these changes have on several outcome measures. The most salient outcome measures include project risk in terms of estimation of project timeline and identification of information bottlenecks. Again, the software was originally intended for use as a project management tool and thus the majority of its outcome variables are focused on time factors for a given set of activities that need to be performed. Similar to the modeling discussed in Study 1, SimVision models activities, time spent on activities and the individual actors who have responsibility for those activities. However, SimVision also attempts to account for factors such as time spent in meetings, layers of bureaucracy, skill set and experience of organizational members, and multiple task loadings, all of which have implications for time. By et al. (2008) hint at the drawbacks of excessive bureaucracy, 'The more time spent on administrative tasks and bureaucratic procedures, the less time spent on doing the 'real' job' (p. 26). SimVision represents a good mechanism to gauge the time required to complete a set of activities in a stepwise fashion given the management hierarchy of an organization, and this is where the tool has demonstrated great value to project planners (Levitt, 2004). More recently, this tool has been applied to continuous work (as opposed to a set 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 is very common in government organizations and the ability to experiment with change alternatives before engaging in the actual change would provide decision-makers with better opportunities to discuss various alternatives and their inherent limitations or advantages. SimVision offers such a capability and, although it was designed primarily as a project management tool, it has potential as an organizational change tool as well. The authors have applied this technology in a DoD organizational change context involving a large command and control reorganization (Faas et al., 2009). The organization in question was undergoing a merger where several units from other organizations were being brought into this parent organization. Leaders in this organization were contemplating three alternatives and these organizational design options differed to the extent to which the individual units were integrated. Organizational Development Goes Digital 217 The change management team used the results from a SimVision analysis to help guide the organizational design for a new command and control center. Specifically, the simulation was used to compare the three alternative organizational designs (see Figure 2 for an example). The baseline (or current) organizational design was compared with a low integration option in which the units shared the same overall leadership but still operated independently, a medium integration option in which team members would be matrixed into the various units while maintaining the original organizational units, and a fully integrated option in which the old units would be disbanded and new teams would be formed around the functions of the organization. Results and Discussion Unfortunately, detailed results of this organizational change cannot be shown for security reasons, yet high-level findings can be shared. The fully integrated option was found to be superior to the other alternatives in terms of total time for task completion (in this case completing a course of action planning activity) and the low integration option had the highest time for task completion. This Figure 2. Example organizational model using SimVision. 218 J.B. Lyons et al. information was used by the senior leaders in this organization in making strategic decisions about the new organizational structure. These findings speak to the potential benefits of collaboration because the fully integrated teams had immediate access to the necessary team members for completing the team planning activity. The change management team also noted that the fully integrated option may pose significant change management challenges because it was the most extreme alternative being considered in terms of training, process and potential cultural differences between the groups. The inherent trade-offs between organizational design options should be given extensive consideration by any change management team. In summary, Study 2 demonstrated the utility of SimVision as a comparative tool to support high-fidelity organizational change options. Minimizing Trade-offs In Organizational Change Various organizational designs often involve different trade-offs in terms of their inherent costs and benefits (Galbraith, 2002). Researchers have outlined four approaches to organizational design/change: mechanistic, motivational, perceptual and biological, each with their own unique strengths and limitations (Campion et al., 2005). For example, the mechanistic approach focuses on industrial engineering principles and emphasizes specialization, simplification and repetition with the goal of increasing efficiency. This approach may be most representative of the simulation approaches discussed above. The drawbacks of such an approach, however, involve decreased job satisfaction and motivation, which are the key goals of the motivational approach. The motivational approach used organizational psychology principles of variety, autonomy and participation for work design interventions. The limitations of this approach include increased training costs, heightened stress and possibly increased errors. The authors conclude that work design interventions should use a variety of approaches attending to the various costs and benefits of each and that the proper combination of work design approaches can minimize the trade-offs between the various approaches (Campion et al., 2005). Research has substantiated this claim by showing that when organizations consider multiple approaches and try to minimize the limitations 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 of industrial design into organizational change initiatives. However, organizational change is highly complex and organizational leaders should use multiple methods to approach organizational change initiatives. Simulations can be coupled with traditional OD interventions that may leverage organizational psychology principles to address the complexities that pervade contemporary organizations. In the organizational merger project discussed above, simulation was but one methodology employed to support the change initiative. Other methods used Organizational Development Goes Digital 219 to complement the simulation included organizational surveys to gauge change readiness and employee attitudes, focus groups to monitor employee resistance and engage employees in the change process, process improvement interventions aiming to reduce non-value-added work activities, strategic communications to share change-related news and events, as well as success stories for the 'new' organization, and training/socialization interventions to anchor organizational changes into the new organizational culture. There is a robust literature in the organizational change domain which has pinpointed several enablers of effective change. These include: establishing a compelling and supported vision for the change (Kotter, 1996), mapping employee resistance factors and taking action to reduce them (Ostroff, 2006), taking action 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 interventions that focus on the specific rational, motivational and emotional needs of the organization rather than using one-size-fits-all approaches (Winum et al., 1997). The authors cannot stress enough the fact that while simulation may offer a novel tool for supporting organizational change initiatives, it is but one method to support change. Successful change initiatives will incorporate a balance of organizational design principles and methods, implement visionoriented activities, foster leadership support for the change and engage employees in the change process, as well as understand employee resistance factors and change readiness among employees. Conclusion This article discusses the concept of organizational simulation in the context of change while providing two applied examples of how simulation technologies have been applied to organizational change initiatives within the US government as one aspect of a larger change management program. Research has demonstrated the benefits of attending to the various components of organizational design (people, process, technology and governance) when engaging in organizational changes (Garud et al., 2006). Further, case studies have elucidated the notion that organizational design attempts can be evolutionary and may require constant modification and tweaking (Madsen et al., 2006). This is consistent with contemporary perspectives on organizational change which suggest that a continuous approach is most effective in generating sustained and positive change (By, 2007; By et al., 2008). Simulations may help organizational leaders to understand the incremental steps toward change success. Ultimately, multiple interventions are better than one in generating sustained and accepted change (Porras and Berg, 1978). Tools to enable organizational design, such as the simulation tools discussed above, represent just one set of tools that organizational psychologists might employ to support organizational change initiatives within the US government. 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 220 J.B. Lyons et al. outweighs that of more complicated methods, such as discrete event simulation or analytical solutions. Lean, however, is not without its deficiencies, as noted in Standridge and Marvel (2006). Detty and Yingling (2000) use discrete event simulation to show the benefits of implementing lean thinking, essentially an ROI study. Lean recommendations are initiated in the simulation and compared against the baseline operations, thus providing foresight in the form of efficiency-based predictions. Any organization that has implemented lean manufacturing and ignored simulation should consider a change to their technical approach. Furthermore, any organizational consultant who relies solely on simulation should consider the gamut of other drivers of organizational change success. Thus, once again, multiple approaches and strategies may be more effective than just one when attempting to conduct complex organizational changes. Businesses typically have 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 simulation. To implement one of these techniques devoid of the other is inefficient and wasteful, the two reasons these techniques were fashioned in the first place. Ultimately, simulation is an underutilized method among organizational consultants. This is not surprising given that many such consultants are psychologists, with an inherent bias toward organizational psychology-oriented methods (action research, change management, etc.). However, if used in conjunction with other more traditional organizational change methods, simulation can be a useful tool for applied researchers and consultants. 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