Simulation Modeling, Essay Example

Abstract

Simulation and modeling techniques allow for businessmen and women to conduct transactions within specific scenarios without the inherent risk or dangers of taking those same actions in the real life business environment. The key purpose behind simulation and modeling is to fully prepare the business team for a specific scenario or business operation prior to actually conducting that business. There are multiple tools and techniques used for simulation and modeling in which two are regression simulation and load testing. Each allows the business users to simulation key transactions with a multitude of variables to provide key information to make informed business decisions. The benefits of these two techniques are focused on implementing solutions using software implementations and how the systems can be fully verified and approved prior to the implementation of key business systems.

Simulation Modeling

Simulations and modeling of particular scenarios allow for a representation of events to occur in order to create a basis of knowledge for the participants in the event. Simulations of particular events allow the users and participants of the scenarios to manipulate inputs to generate results during the scenario run. Simulation modeling is a powerful tool that allows for the planning, design, control and implementation of a project because of the ability to provide insight and information on potential real world scenarios. This type of testing and validation lends itself to problem-solving and a proactive approach for designers, project managers, engineers, entrepreneurs and other developers of new ideas or enhancements to existing products or processes. In the business world, there are situations in which the organization making the change, implementing a new product or engaging in a new market opportunity would like to generate potential results of their initiation. Simulations and modeling allows for the business to generate results through mimicking the business environment and changing different variables to produce different results. Not only does the business generate a plethora of business intelligence through simulations and modeling but they also mitigate risk of implementing something new into the market without fully understanding what their changes make generate in the impacted environment.

There are multiple areas in which simulation and modeling can help predict areas for improvement within the business’ operating model. Examples of areas that utilize simulations include software product launches, supply chain optimization projects, lean management implementations as well as other continual process improvement efforts implemented by the organization to optimize the effectiveness and efficiency of the organization. Through simulations and modeling the organization can illuminate areas for improvement and focus their resources on those particular areas in order to streamline processes and reduce waste within the process. Another key area in which a business will focus on simulations revolves around implementing a software solution that will create more opportunities for improvement and efficiencies. As software and integration opportunities increase there is more reliance on integration, optimization and simulation testing to ensure the solutions that are implemented will actual produce the results required when launched into a production environment.

When developing a simulation model there are four key steps that must be followed to ensure the simulation is established. These areas include identification of the problem, encapsulate the problem’s focus, collect inherent and real-world data, and develop the modeling plan (Maria, 1997). Each of these areas is the prerequisite for the inputs and development of the simulation modeling test. The foundation is built by understanding what the problem is, imparting boundaries on the problem being solved, developing a process framework and taking that framework and inputting variables, changes, and configurations based upon simulation results to drive the appropriate solution to the problem.

Variables and Risk

The variables that the end user or testing agent provides into the simulation are the key focal points that will provide the data and information to the project manager or other implementer of the new product or process. For example, the current business process must reduce the amount of time between the beginning of the waiting queue and the completion of the waiting queue. The key variables in this simulation model include the amount of people lining up to the queue, when they show up, the processing time through the queue, the variation of the processing time, the variation of the entry times, etc. These variables would provide key data points and visibility into where the chokepoints were in the system and it would highlight the areas that could be addressed to solve the queuing issue. These variables play into the simulation schematic for improving the current situation. This type of simulation lends itself to a continual improvement of an existing or new process by identifying key hot points the process engineer can focus upon.

The primary driver for a simulation model is to limit the inherent risks associated with a project. By simulating multiple scenarios and aligning multiple variables within the simulation the end user has the ability to shed light on areas of the project that would not otherwise be understood or known without the simulation testing. Understanding what variables to manipulate and alter within the testing includes the facet of understanding what risk is and how it is managed as inputs to the testing. This will also provide extensive information on creating the risk mitigation plan for the project that is a key output for the simulation modeling testing. Risk is the possibility of a deviation from the expected result. Many people that are monitoring, controlling, reviewing or evaluating risk associate risk as a potential loss or a type of undesirable outcome to an intended plan, project, process or system. The result of a risk can be associated to specific outcomes or costs associated with the risk. This is heavily dependent upon the variables of the risk such as probability or likelihood of occurrence, level of deviation from the intended plan and the breadth or impact of the risk.

Techniques and Benefits

Just as simulation and modeling was used to focus on key areas of improvement, the same key aspects of simulation will be used to ensure the solution is ready for implementation. Two forms of simulation and modeling include regression testing and load testing. Both of these types of simulations are necessary to ensure the software, hardware, integrations and user access meets or exceeds the requirements of the organization, its suppliers and ultimately the customers. The first area to discuss is that of load testing. Load testing is used to ensure the environment the business users are transacting business actions in can handle the amount of transactions, requests and processes that are required given a simulated number of users. This is important in a business simulation because the solution to allow multiple users to transact within one web-based portal could work perfectly in a closed environment with minimal intrusions and transactions but when faced with a heavy load of users it could come to a standstill and potential business opportunities could be lost due to poor planning and implementation.

Load testing allows for a simulation or modeling of transactions based on key transactions that are built by the users internal to the business to generate thousands of transactions as would occur in a real life scenario (Jorgensen, 2008). For example, when a business utilizes a supplier portal to manage their supply base to transmit key information regarding anything from orders to payment, the system must be able to handle multiple concurrent users while also performing key transactions within the appropriate time limits. A poorly planned infrastructure or bugs within the system could be reduced, improved and mitigated with a properly planned load simulation. Load testing is provides a critical benefit to allow a stress tested based on actual levels of use so that the business and retain a proactive approach to mitigate potential risks that could ultimately eliminate opportunities. The load test simulation provides insurance that the system and its capacity will not be an issue for production and would allow critical resources to be utilized in other business ventures. Load testing is used prior to implementation or when a new group of users are engaged within the system. The capacity of the system is tested and validated through the load testing simulations.

As with any great solution there is always room for improvement. As new patches for software becomes available or new functionality is developed there is always a need to manage the change and to ensure the changes are producing positive results without impacting current functionality in a negative way (Naik & Tripathy, 2009). Regression simulations are used when a change occurs within the business’ production environment and the organization wants to ensure that all aspects of the tool remain functional. Regression testing is especially important as software projects are implemented using the agile project management to complete software projects. Agile project management uses best practice project management methodologies based on an iterative release method in which new functionality or requirements are implemented in multiple versions (Highsmith & Highsmith, 2010). Each time a new requirement is put into production the business’ project managers must ensure that current functionality still exists and is not hindered. This is done through simulating regression tasks. Each task is developed by simulating operations or tasks within the system and similar to load testing is simulated multiple times with different variables to ensure full functionality works. This would provide insight into how the system works with the new functionality and how well the integrations handle transactions between the core system and new requirements. The benefits of regression simulations lie inherently with the ability to key in on transactions, run the simulations against these tasks and reap the results without the risk of impacting current business operations in the process. Regression testing allows requirements to be vetted and tested in a simulated environment providing a window into how the production environment will respond.

Overall the benefits include providing a clear and better understanding of the system being developed or enhanced and this clarity is being provided with mitigated risk by separating the actual implementation of ideas into a confined and controlled simulation. This reduces costs of potential failures as well as reduces the burden on the limited resources such as time and funding. Simulation also provides the ability to test core functionality as well as scenarios that would have a low probability but a high impact to the project. Simulation modeling allows for the ability to build a robust and precisely executed project by utilizing key data and information that is only available through this technique based upon the time and cost constraints.

Conclusion

Simulations and modeling are key business tools based on their specific ability to allow operational activities to take place without placing a risk on the actual business transactions that are occurring. These tools allow forecasting and foreshadowing of key business transactions while also allowing the integration of new functionality with core business tools and processes. New enhancements to business tools are occurring at a rapid rate and each enhancement must be fully tested and validated prior to providing that tool to the users to take advantage of. The systems can use the simulation and modeling techniques of regression simulations and load testing to ensure the systems are fully operational and can handle the amount of transactions required by the business requirements.

References

Maria, A. (1997). Introduction to Modeling and Simulation. New York. Department of System Science and Industrial Engineering.

Highsmith, J. A., & Highsmith, J. (2010). Agile project management, creating innovative products. Addison-Wesley Professional.

Jorgensen, P. (2008). Software testing : a craftsman’s approach. (3rd ed.). New Jersey: Auerbach Publications.

Naik, K., & Tripathy, P. (2009). Software testing and quality assurance, theory and practice. New Jersey: Wiley-Spektrum.