...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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...A SYSTEM SIMULATION STUDY ON THE THREE FAST MOVING PRODUCTS (MARLBORO, C2, VIVA) OF THE COLLEGE VARIETY SHOPPE USING THE MONTE CARLO SIMULATION IN INVENTORY MANAGEMENT EXECUTIVE SUMMARY This study shows how the selected three fast moving products (Marlboro cigarettes, C2, Viva mineral water) move from the current Inventory Management technique of the College Variety Shoppe from its distributors to its warehouse storage to the end user or customer. An excel program and a simulation model was made to observed its current performance. After the observation, the group performs an experimentation that will improve the current technique of the College Variety Shoppe. After simulating the experimentation, the group then give conclusions and recommendations on how to improve the College Variety Shoppe’s current Inventory Management. TABLE OF CONTENTS Title Page ........................................................ 1 Executive Summary ........................................................ 2 Table of Contents ........................................................ 3 Introduction ........................................................ 4 Methodology ........................................................ 6 Model Development ........................................................ 7 Model Validation ........................................................ 11 Experimentation, Results .......
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...Initial operations strategy Prior to the commencement of the simulation, we examined the 50 days of historical data to glean as much information as we could about the operations. We performed some analysis in Excel and created a dashboard to illustrate various data. Specifically, we regressed the prior 50 days of jobs accepted to forecast demand over the next 2 - 3 months within a 95% confidence interval. The yellow and grey lines represent the maximum and minimum variability, respectively, based on two standard deviations (95%). Exhibit 1: Forecasted and actual demand by Day 50 and Day 270 Our two primary goals at the beginning of the simulation were as follows: 1) Eliminate bottlenecks and increase capacity in order to meet forecasted demand 2) Decrease lead time to 0.25 days in order to satisfy Contract 2 and maximize revenue In order to achieve these goals, we would need to know the capacity and throughput time of the entire system. We used the time required by each machine to process a lot to calculate capacity per station and then capacity of the entire production line (380 kits/day or 6 orders/day). In Exhibit 2 we can observe that the capacity of the production line is given by the station that produces the least number of units per day. Exhibit 2: Capacity of the production line The Decisions We decided to work with the maximum variability of demand because there was a penalty for late jobs and because there was no revenue for orders that took more than “x”...
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...Monte Carlo Statistical Analysis Name Course Instructor Date The Monte Carlo method is a mathematical method used for problem solving through the generation of random numbers and then observing a fraction of these numbers and the properties they obey. It is useful in obtaining numerical solutions to problems that are too complicated for analytical solutions. It is a form of probability used to understand the impact of risk and uncertainty in various areas such as financial and cost forecasting. It involves computation of the likelihood of given events occurring or not occurring, without taking into account the interaction of the elements involved 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...
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...materials such as Ultra/Advanced High Strength Steels (U/AHSS), aluminum alloys, magnesium alloys and boron steels in automotive industry is increasing to reduce vehicle weight and increase crash performance. The use of these relatively new materials requires advanced and reliable techniques to a) obtain data on material properties and flow stress, b) predicting springback and fracture in bending and flanging, c) selecting lubricants and die materials/coatings for stamping and forging and d) designing tools for blanking and shearing. In addition, designing the process and tooling for a) hot stamping of boron steels, b) warm forming of Al and Mg alloys, and c) optimizing the use of servo-drive presses require advanced Finite Element based simulation methods. CPF is conducting R&D in most of these topics and also in many hot and cold forging related topics. This paper gives an overview of this research and discusses how the research results are applied in cooperation with industry. Keywords: Metal Forming, Sheet metal, Forging, FEM 1 INTRODUCTION The Center for Precision Forming (CPF) has been established with funding from the National Science Foundation (NSF) and a number of companies (www.cpforming.org). CPF is an outgrowth of the Engineering Research Center for Net Shape Manufacturing (ERC/NSM – www.ercnsm.org) and conducts research in sheet metal forming while ERC/NSM focuses on cold and hot forging related R&D projects. Both Centers work closely with industry under contract....
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...Introduction This chapter describes our work in evolution of buildable designs using miniature plastic bricks as modular components. Lego 1 bricks are well known for their flexibility when it comes to creating low cost, handy designs of vehicles and structures. Their simple modular concept make toy bricks a good ground for doing evolution of computer simulated structures which can be built and deployed. Instead of incorporating an expert system of engineering knowledge into the program, which would result in more familiar structures, we combined an evolutionary algorithm with a model of the physical reality and a purely utilitarian fitness function, providing measures of feasibility and functionality. Our algorithms integrate a model of the physical properties of Lego structures with an evolutionary process that freely combines bricks of different shape and size into structures that are evaluated by how well they perform a desired function. The evolutionary process runs in an environment that has not been unnecessarily constrained by our own preconceptions on how to solve the problem. The results are encouraging. The evolved structures have a surprisingly alien look: they are not based in common knowledge on how to build with brick toys; instead, the computer found ways of its own through the evolutionary search process. We were able to assemble the final designs manually and confirm that they accomplish the objectives introduced with our fitness functions. This chapter...
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...developed, operated and maintained the Concept Simulator since 1998. The Concept Simulator models the functional operation and inter-operation of key components of train control system architecture and the external systems to which the system interfaces. The simulator is valuable in the development and analysis of operational principles, and in assessing design trade-offs. The SEA-designed facility simulates operation at ERTMS Levels 1, 2, and 3. Components include a Communications Network, a Network Management Centre, a generic Interlocking, a Radio Block Centre, a Track Simulator including both conventional and TCS equipment, a Driver Desk, a European Vital Computer and a Driver MMI. The components are modelled using software-based simulations hosted on networked PCs. The simulator has been valuable in the engineering evaluation and validation of emergent system architectures, and enables system constraints to be explored and defined. ERTMS operational modes and the transitions between them are simulated and ERTMS principles and procedures are followed. Innovative Customer Information System (ICIS) SEA's Innovative Customer Information System (ICIS) is capable of managing and displaying customer information, including real time information, in a visually dynamic manner. The system utilises intelligent screens and wireless technology to distribute data, thus minimising installation and commissioning efforts. The system will be suitable for any transport environment, mobile...
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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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...678-679 of the text. Using simulation estimate the loss of revenue due to copier breakdown for one year, as follows: 1. In Excel, use a suitable method for generating the number of days needed to repair the copier, when it is out of service, according to the discrete distribution shown. 2. In Excel, use a suitable method for simulating the interval between successive breakdowns, according to the continuous distribution shown. 3. In Excel, use a suitable method for simulating the lost revenue for each day the copier is out of service. 4. Put all of this together to simulate the lost revenue due to copier breakdowns over 1 year to answer the question asked in the case study. 5. In a word processing program, write a brief description/explanation of how you implemented each component of the model. Write 1-2 paragraphs for each component of the model (days-to-repair; interval between breakdowns; lost revenue; putting it together). 6. Answer the question posed in the case study. How confident are you that this answer is a good one? What are the limits of the study? Write at least one paragraph. There are two deliverables for this Case Problem, the Excel spreadsheet and the written description/explanation. Please submit both of them electronically via the dropbox. The assignment will be graded using the associated rubric. |Outcome Assessed: |Create statistical analysis of simulation results. ...
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...Jet Copy Simulation Prepared by Joe Miller Prepared for February 7th 2013 TABLE OF CONTENTS INTRODUCTION SUMMARY JET COPY SIMILATION CONCLUSION AND RECOMMENDATIONS APPENDIX INTRODUCTION The purpose of this report is to examine the feasibility to for Jet Copy to purchase a second copy machine. Based on copies produced and lost with only the use of one copier. In this report I will use data from a simulation to assist in the determination of an additional copier for Jet Copy. As a reminder that this is a random simulation and the information in the simulation will only assist between when the test stop and 52 week will not have great affect on the outcome of the in the decision making. The simulation that was run was base on a 52 week scenario. The actual results are based on 51 week trial. The difference between the 51 week test and the actual 52 weeks will not have any great impact on the recommendation. ------------------------------------------------- SUMMARY This report creates a simulation that shows one possible outcome for Jet Copier when there one copier is out for repair for up to 4 days. It will also aid in the decision to ad d or not to add a second copier. This report will answer the following questions: 1. Using Excel to generate the number of days needed to repair the copier. 2. Using Excel to generate the interval between successive breakdowns according to continuous distribution. 3. Using Excel, use a suitable method to...
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...Class: Seminar in Organizational Theory & Behavior Professor :Dr. Dyck's Bio Name: Zhihui Dai Change Management Simulation: Power and Influence Abstract Within this program Change Management Simulation: Power and Influence, I am a Director Product Innovation manager in Spectrum Sunglass Company, which is a private company that designs, manufactures, and sells sunglasses. What I need do is convince people adapt my proposal. I should choose method from 18 change levers that attempt to convince workers to adapt my new proposal that will change their attitude from awareness, interest, trial, to adoption. I must decide the proper method and use the time wisely. Be able to increase credibility and achieve the greatest percentage of adopters. Key words: Convince percentage of adoption credibility limited time This Management Simulation Project from Harvard provides students a virtual experience to convince people in start-up companies. When I started the first test run of the simulation there are various decisions, However, if an administrator make the wrong move, then I lose credibility and set the proposal is a backwards. On the other hand, if I decide on a correct decision, then not only will I get the workers are more interested in my proposal, but it also increases the credibility of the workers. When I was implementing change behavior, building a coalition of support is a important tool to get satisfied from people. When I simulate this behavior, I can have more...
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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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...What is a simulacrum (the singular form of simulacra)? Please explain the examples Baudrillard musters to support his "explanation" of simulacra. What factors or forces have made our age (the postmodern era) an age of simulacra? What benefits and risks does this postmodern, hyperreal age present? When Baudrillard refers to “simulacra,” Baudrillard is talking about simulating, as stated in the text, simulation threatens the difference between “true” and “false,” between “real” and “imaginary”. The examples Baudrillard uses to explain this word is when the text relates to being ill. “A person can go to bed and make believe he is ill. But someone who simulates an illness produces in himself some of the symptoms.” A big source Baudrillard relates to is religion. The belief in a God is considered simulacra, there are many beliefs of different gods and different way of presenting the different gods but which ones are true and which are false. There is not any true way of proving which Gods are true, therefore it becomes a simulacrum. This also plays a role in today’s day in age; there are still many different religions and beliefs that are still believed in. Watergate is a key example when looking at simulaca, as a few people made something look real but at the same time was also fake. and stated in the text, to simulate is to feign to have what one hasn’t. One Implies a presence, the other an absence. But the matter is more complicated, since to simulate is not simply...
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