...This report provides an in-depth analysis of the two Everest Simulations conducted by Group 10 of MGMT1001 Thursday Tutorial. This task required students to form teams consisting of five to six members whose goals were to summit Mount Everest. While it provided us with a rich experience in team dynamics and collaboration, it also enabled us to explore key managerial concepts taught in the course, consisting of: • Communication • Groups and Teams • Leadership In this report, we examine the effectiveness of Face to Face Communication (FTFC) versus Computer Mediated Communication (CMC), and the problems encountered through the utilisation of the virtual medium including efficiency of the feedback system, loss of personal focus and other emergent issues. It includes personal reflections on attitudes and perceptions, as well as group performance and strategies adopted in the second Simulation in order to create a more positive team experience. Theories which relate to interpersonal communication have also been integrated in the report to illustrate its relation to certain situations encountered during the Simulation. Additionally, we provide a multifaceted analysis on the notion of team cohesiveness and how it attributes to better performance outcomes. An overview on the different intragroup conflicts encountered in the Simulation has been included, examining the positive and negative impact that conflict had on team experience and performance, and how mutual agreements were reached...
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...is a computer necessary in conducting a realworld simulation? Answer It is important because there are many different types of outcomes that comes in with simulation. Computers are used in daily life activities and it is necessary. 14-11 What is operational gaming? What is systems simulation? Give examples of how each may be applied. Answer Operational gaming is the use of simulation in competitive situations such as military games and business or management. System simulation ls that deal with the dynamics of large organizational or governmental systems. Validation The process of comparing a model to the real system that it represents to make sure that it is accurate. 14-17 (a) Resimulate the number of stockouts incurred over a 20-week period (assuming Higgins maintains a constant supply of 8 heaters). (b) Conduct this 20-week simulation two more times and compare your answers with those in part (a). Did they change significantly? Why or why not? (c) What is the new expected number of sales per week? Answer A. The number of stockouts incurred over a 20 week period is HOT WATER NUMBER OF HEATER SALES WEEKS THIS PER WEEK NUMBER WAS SOLD 3 2 4 9 5 10 6 15 7 25 8 12 9 12 10 10 B, Two more times would give us the value of a multiplied by 2. c. 25 14-18 A. 15 days of barge uploadings and average number of barges delayed B, They both are probabilistic simulations. Chapter 5 HW 5-14 Using MAD, determine whether...
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...in influencing the occurrence. The mathematical method was invented by Stanislaw Ulam in 1946 and named by Nicholas Metropolis after a classy gambling resort in Monaco, where a relative of Ulam frequently gambled [ (Fishman, 1996) ]. Concepts of the Monte Carlo method Uncertainty Being a forecasting model, there are assumptions that need to be made due to the uncertainty of various factors. One therefore needs to be able to make estimations of the expected results as they cannot predict with certainty what the end value will be. Important factors such as historical data and past experiences in the field can be helpful in making an accurate estimate. Estimation In some cases, estimation may be possible but in others it is not. In situations where estimation is possible, it is wise to use a wide range of possible values instead of single values. This ensures that a more realistic outcome is predicted. The actual outcome is likely to lie in the range. It is important to note that this method only assists in forecasting how the actual results are likely to look like, not the actual results of the analysis. Experiment Each calculation using this method is a numerical experiment. Therefore, it is likely to have...
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...Brooklyn Warren Chapter 10 Simulation Modeling What is Simulation? * To try to duplicate the features, appearance, and characteristics of a real system. * Imitate a real-world situation mathematically. * Study its properties and operating characteristics. * Draw conclusions and make action decisions based on the results. Processes of Simulation: 1. Define Problem 2. Introduce Important Variables 3. Construct Simulation Model 4. Specify Values of Variables to Be Tested 5. Conduct the Simulation 6. Examine the Results 7. Select Best Course of Action Advantages of Simulation: * Straightforward and flexible. * Can handle large and complex systems. * Allows "what-if" questions. * Does not interfere with real-world systems. * Study interactions among variable. * "Time comparison" is possible. * Handles complications that other methods can't. Disadvantages of Simulation: * Can be expensive and time consuming. * Does not generate optimal solutions. * Managers must generate all conditions and constraints. * Each model is unique. Monte Carlo Simulation: Can be used with variables that are probabilistic. Steps: * Establish the probability distribution for each random variable. * Use random numbers to generate random values. * Repeat for some number of replications. Probability Distributions: Historical data Goodness-of-fit tests for common distributions: ...
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...Applied mathematics in Engineering, Management and Technology 2 (2) 2014:466-475 www.amiemt-journal.com Using a combined method of hierarchical analysis and Monte Carlo simulation in order to identify and prioritize the target market selection criteria (Case study: Food distribution companies of Mashhad-Iran) Amir kariznoee Ph.D. student of Industrial Management,University of Mazandaran ,Iran (Corresponding Author's E-mail: Amir.kariznoee@yahoo.com) Monireh Bijandi Graduate of Accounting in Ferdowsi University of Mashhad,Iran Mahdi Ghayur Maddah Student of Public Management in Ferdowsi University of Mashhad,Iran Vajihe Mogharabi M.A. Student of Information Technology Management, Shahid beheshti University,Tehran,Iran Abstract The aim of this study is to identify and prioritize the key factors in selecting a target market in the food industry. In order to determine the components and subcomponents of this study, we have used previous researches in this area. In order to match these factors with the food industry situation and create a hierarchical structure, we have obtained the opinions of 323 experts about affecting factors on choosing a market in this industry with the use of questionnaire. Then, using a combination of hierarchal analysis process and Monte Carlo simulation and cooperation with 10 senior executives of distribution companies, the weight of each component and sub-component was determined. In general, four components and ten sub-components were...
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...INTRODUCTION: The purpose of this paper is to bring out the use of Simulation by Managers of various organizations. Under discussion will be: Definition of simulation. Model construction. When and why it becomes handy to use simulation. Steps used in simulation technique. Random selection in simulation. Advantages / benefits derived from use of simulation technique. Disadvantages /Challenges associated with simulation technique. Role of computer in simulation. Conclusion / comments. Definition of simulation: Simulation can be defined as a quantitative technique which describes a process by developing a model of that process and then conduct a series of organized trial and error experiments to predict the behavior of the process through time often with the aid of a computer. It is a method that is used to solve a problem in many areas of management, for example inventory management, queuing problems, profit analysis and project management among others. Model construction: A successful simulation model has to be carefully designed in order to achieve the intended predictive purpose. Therefore important factors have to be considered such as; i. It must be objective oriented. It must be constructed for a purpose and be able to achieve that purpose. ii. Critical variables and relationships. These must be identified and incorporated in model. However it is not essential or indeed desirable to include all variables in the model. iii. Simplicity...
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...Chapter 51. Enhancing Patient Safety in Nursing Education Through Patient Simulation Carol Fowler Durham, Kathryn R. Alden Background The alarming rise in morbidity and mortality among hospitalized patients throughout the United States heightens concerns about professional competency.1 Nurses and other health care professionals are under increased scrutiny to provide safe, effective care. Likewise, nursing education programs are faced with increased pressure to produce graduates who are capable of providing safe patient care. Toward that end, nursing education programs develop curricula, hire qualified faculty, and select learning experiences for students in an effort to train and graduate competent, effective nurses. The instructional strategies utilized in both didactic and clinical components of nursing education courses are highly influential in determining critical thinking and clinical decisionmaking ability as well as in developing the psychomotor skill performance of new graduates. Of course, it is unrealistic to think that graduates of nursing education programs have received all the training they need when they depart the doors of academia. Orientation programs for new graduates and continuing education for nurses are essential tools to help practitioners improve their knowledge, skills, and expertise so that quality patient care is provided and outcomes are optimized while errors are minimized. Ongoing evaluation of nursing competence is necessary...
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...County Hospital Simulation Project Abstract The paper offers a simulation model describing the X-ray departments operations in the county hospital. It can be used for testing various process scenarios, for allocation of resources and also conduct activity based cost analysis. The simulation model is used for demonstrating a new operational method that makes the operations at the X-ray department more effective. The operational method is referred to as Triage team method. This method has been it is been studied from two view points. The results showed that the method enhances the X-ray department if properly implemented and it incorporates all the required tasks. County hospital simulation project Introduction The department of x-ray in the county hospital is facing a challenge in the x-ray department. With the increased amount of patients visiting the x-ray department, the challenge to the health care managers is becoming serious. The process undergone and the resources need to be reconsidered to make the operations more efficient. This will also maintain the quality of care. The objective of the department is to ensure that the patients take minimal time in the process. The resources also need to be allocated in the most effective way and by use of more advanced technology. Since X-ray department and the processes therewith are somewhat complicated, and have dynamic variables and has random features, definitely there is a need for a method that is effective. The...
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...Simulation-based learning is a training method that involves imitating the circumstances regarding a real-life job through the use of representations and models, (Saks, Haccoun, 2013. Pg.178-179). This training method presents trainees with the opportunity to learn in an environment that resembles the real-life job while protecting them from any risks. Furthermore, it helps trainees develop the necessary knowledge and skills to perform the job effectively, (Lateef, 2010). It causes them to master the task, improve their problem-solving skills, and enhance their teamwork abilities, (Lateef, 2010). Simulation training is commonly used in training for dangerous jobs, such as jobs in medicine or the military. Medical training programs often...
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...Crisis Management Simulations Preparing for the unexpected is what most businesses and governments strive to do. They hope to curb the next disaster in their favor. Or in worst-case scenario, businesses and governments hope to at least curb the crisis to be the least damaging to their image. The question people struggle with is how can these organizations prepare for every possible crisis they could encounter? It is simple: they cannot. However, with today’s technology many organizations have implemented crisis management simulations to better prepare for these worst-case scenarios as well as test the organizations’ tactics used in managing crises. Crisis simulations are relatively new and unheard of in some companies. Crisis management’s purpose is to engage “the whole organization in efforts to avert crises that may affect the firm, and to effectively manage those that do occur” (Pearson, n.d.). Organized crises are used in firms to simulate a crisis, such as a PR scandal or terrorist attack, and test the organization’s reaction to the event, how the crisis is handled, and if it is done effectively. These simulations are recently becoming popular to use in organizations because of new technology that gives them a real-time, fast-paced feel. Companies are now realizing that “as the development of new technology outpaces our ability to fully understand its impact, and as global reach relentlessly stretches organizational capabilities, ‘firefighting’ becomes a way of life” (Pearson...
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...the probability distribution of the wait for repair services on JET’s copier. To model the probability of wait times in the JET Copies simulation, the JET partners generated a random number representing the probability of an occurrence of a breakdown. They then programmed a VLOOKUP function to match this breakdown probability to the corresponding “Repair Time in Days” column of the table. The result is the simulated time to get repair service for each breakdown occurrence. Interval between breakdowns The James, Ernie, and Terri purchased a copier just like the one used at their college office. When Ernie talked with someone in the dean’s office at State, he was told that the University’s copier broke down frequently often for 1 to 4 days. The partners became worried that their machine would also frequently break down. Although they could not get an exact probability distribution, James was able to determine that breakdowns occurred between 0 and 6 weeks apart. The probability of a breakdown increased as time passed. To model the time between breakdowns in their simulation, JET created a list of random numbers. Next, they applied the probability function f(x) = 2x/a2 0≤ x ≤ a. For this situation, the formula used is x = a √r. Since James estimated breakdowns occur zero to six weeks apart, the formula he used for this simulation is x = 6√r1. He then generated a list of random numbers and applied the formula to each random number. The result is the simulated time...
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...Simulation is a technique for practice and learning that can be used in many nursing facilities to help trainees to learn different skills in training and communication. It is a technique to replace and amplify real experiences with guided ones that can help the trainee to develop essential skills while at work. Simulation-based learning can be the way to develop health professionals’ knowledge, skills, and attitudes, while protecting patients from high risks situations. Simulation-based medical education can be the basics which provides a valuable information in learning how to solve certain problems and issues in nursing. Simulation-based training techniques, tools, and strategies can be applied learning experiences, as well as be used as...
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...Simulation 2 Write-Up During the simulation there were many different factors leading to varying results. One of the biggest causes for variation was in the performance of the worker. The worker had the impression that they were under a time constraint. The performance of the worker was usually kept constant until the last 5 or 10 drawings of cards, which is when the worker felt the pressure to draw cards faster. Another cause of variation was the shuffling of the worker. If the worker didn’t shuffle the cards thoroughly it would result in a varied outcome from normal results. This variation is considered a normal cause for variation because as the time constraint became more noticeable the workers would shuffle differently. This occurred more on the last few draws each round. One special cause occurred when the professor walked over to our workstation and had a conversation. This distracted the worker and simulated a special cause for variation. In order to improve the process, I would first designate a worker for shuffling the cards so we could eliminate time used passing the cards. This worker would then split the cards into two piles. Then I would assign two other workers the jobs of counting the cards after they have been separated. The workers would then both tell a single recorder the amounts to be recorded. This would be repeated with the designated shuffler. In order to assure that the second supplier is delivering the promised quality, we made a second...
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...Monte Carlo Simulation Tutorial This tutorial shows how to use Microsoft Excel to develop Monte Carlo simulations without the use of add-ins or special software (such as @RISK or Crystal Ball). (Note, however, that there are some important advantages of using these dedicated software packages.) After completing the tutorial you should have a sufficient understanding of Monte Carlo concepts and Excel capabilities to begin building your own Monte Carlo simulations applied to a wide range of business problems, including the construction of short-term profit plans (otherwise referred to as “Cost-Volume-Profit” models, covered in Chapter 9 of the text). Monte Carlo refers to a widely used approach for solving complex problems using computer algorithms to simulate the variables in the model (e.g., a CVP model). Typically, an algorithm is developed to "model" the problem, and then the algorithm is run many times (from a few hundred up to millions) in order to develop a statistical data set for how the model behaves. Simple Example: Tossing a “Fair” Coin—Heads vs. Tails? For the simplest example, consider the basic coin toss. This is a process which has two possible outcomes (heads or tails), each with a 50% probability. In a million coin tosses, roughly half will be “heads” and half will be “tails.” A simple Monte Carlo simulation would support this conclusion. If you were to develop a spreadsheet with a random number generator resulting in 0 for “heads” and 1 for “tails,” then...
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...TOPICS IN AD HOC AND SENSOR NETWORKS Simulations in Wireless Sensor and Ad Hoc Networks: Matching and Advancing Models, Metrics, and Solutions Ivan Stojmenovic, University of Birmingham and University of Ottawa ABSTRACT The objective of this article is to give advice for carrying out a proper and effective simulation activity for protocol design. It challenges some of the existing criticisms of simulation practices that emphasized validation aspects. This article advocates the use of simple models, matching assumptions and metrics in the problem statement and simulation to provide a basic “proof of concept,” and comparison with truly competing solutions, which is possible only after a thorough and critical literature review. Then the complexity of the models can be increased (one parameter at a time), revising the algorithms themselves by adapting them to new assumptions, metrics, and the corresponding simulation environment. Selected independent variables should explain performance under a wide range of scenarios. unclear which protocol will perform well under a wide range of scenarios. It is our view that each article should be judged on its overall contribution, including the assumptions used, theory developed, new algorithms introduced, protocol details, simulation results, and relevance to an ultimate goal of staying on a path toward creating applications. We begin with a literature review of existing criticism for simulation practices, and then discuss what we believe...
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