...Mt. Everest Simulation- Personal Reflection Table of Contents 1.0 Introduction to Mount Everest team and Leadership simulation 2 2.0 Our Team Process Efficacy 3 2.1 The role of the leader 3 2.2 Psychological Safety 5 2.3 Group Thinking 7 3.0 Conclusions 8 4.0 Reference 9 1.0 Introduction to Mount Everest team and Leadership simulation Climbing Mount Everest is a dangerous undertaking, a Herculean task fraught with difficulties, danger, complexities and volatile weather conditions. Disaster can strike without warning, leaving the most competent and experienced professional high altitude mountain climber gasping for breath, and desperately fighting to stay alive. Death is not uncommon to mountaineers who ascend Mt. Everest, a simple mistake, faulty judgments, miscommunication, wrong decisions or complacency can lead to disastrous consequences. Indeed, climbing Mt. Everest cannot be accomplished alone, individuals need to work together as a team, cooperate and collaborate with one another, and rely on one another's capabilities to ascend the mountain summit successfully. Everest leadership and team multimedia simulation is an attempt to help the participants experience and understand team dynamics and leadership. It requires participants to work as a unit, a cohesive entity, collaborating and communicating essential information in order to make effective decisions that will benefit individuals and the group in achieving their goals. The interactive...
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...According to Kreitner and Kinicki, a group is defined as “two or more freely interacting individuals who share collective norms and goals and have a common identity” (Kreitner & Kinicki, 2013, p. 269). A team is defined as “a small number of people with complementary skills who are committed to a common purpose, performance goals, and approach for which they hold themselves mutually accountable” (Kreitner & Kinicki, 2013, p.300). The team exercise was a simulated climb of Mt. Everest, with each member having a unique role during the simulation. According to Psychologist Bruce Tuckman, there are five stages in group development including forming, storming, norming, performing, and a later added adjourning (Kreitner & Kinicki, 2013). Not only was the Harvard Publishing Leadership and Team Simulation: Everest V2 a simulation in climbing Mt Everest, it was a lesson in executing a group challenge. The formation of the team began with the team assignments from the professor. There was some confusion as to who was on what team but after a few days; one after the other was present for the forming processes. It took a few days for everyone to collaborate and get their schedules all to work out to complete this task. Communication between team members was initially challenging trying to figure out the best method of communication between group members. Some members communicated more than others in the discussion board. A slower method of communication was utilized...
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...Leadership and Teamwork A Reflective Report on the Everest Simulation The Everest simulation used the dramatic context of a Mount Everest expedition as related to management concepts exploring the role of leadership, effective communication, and team work to achieve success. The simulation required students to work in cohesive teams consisting of five members, where each individual was assigned a specific role and a goal. The roles included the team leader, physician, environmentalist, photographer, and marathoner. Some goals were contradictory in order to assess how the team reacted to complex and sometimes conflicting situations. Before the actual simulation started, the group discussed the general approach and how to deal with situations where personal goals collided with the team’s goals. The distinction between working with a team and working as a team was the main lesson I learned from the simulation. Listed below are objective barriers our team encountered during the simulation, some of which we succeeded in overcoming and some of which we did not. We succeeded at: 1. Planning: Good decision making seldom took place by chance. We fully discussed the decision making process and always took action as a team. 2. Performance: The need for individual high scores did not encourage team members to act in detriment of the team. Although each team member was assigned individual roles and tasks to accomplish, we all aimed for the shared goal first. 3. Cohesion: Even when...
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...Lessons learnt from simulation We as a team performed well as an individual doing their task. This was because team was prepared and understood their own profile very well and 50% of other’s profiles. However, where we failed as a team was to collaborate. We moved from Forming Storming Norming during the prep time, medical challenge and weather challenge respectively, however during oxygen challenge we went back to the Storming stage [Jurgens (Director) (2015, July 12)]. This could be partly attributed to the fact that we had some technical difficulties with remote connections, shortcoming to our psychological safety and communication issues. My profile was that of a ‘leader.’ My main goal was to steer the team to move towards the summit point. As the book by Gladwell (2005) suggests there are at-least seven things that need to wrong, in-order to experience a fatal crash, I did live this on the day of simulation assignment. It was my travel day at work I was working from hotel where the internet connection was bad. As a result, I missed some of the key portions discussed prior to the simulation task. My mind was in panic mode and with constant fear of failure of this exercise. It could cost me some points from my grade [Edmondson & Roloff (2009)].Once I overcame the connectivity issue, I began to actively participate. During the initial 15 min time frame, when we as a team were sharing each other’s profile information and goals, our “observer” asked the group...
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...1. Round 1, Base Camp At first, the team generally discussed about each role’s condition and got to know about the simulation system. One teammate suggested sharing personal goals. After warmly discussion and voting, the team agreed to share personal goals. Soon we realized that there were some conflicts within the team about whether to follow the team goals or personal goals. However, we decided to rely on teamwork to achieve our common goal, Avoid Rescue and furthermore to try our best to reach the summit. Open talk and honestly sharing information helps a team build up trust and deepen personal understanding. However, time is limited when to reach a consensus for most situations. I realized that, my role, the marathoner, is ambitious and energetic, therefore, the character may have strong bias to push the team to the top and willing to take higher risks. In concern of this, I decided to behave the opposite way, listen more to the team and use team wisdom to fairly decide what to do. 2. Round 2, Base Camp 2 At Base Camp 2, we checked team status on heath, weather prediction, frost-bite dangers, supplies, speed and food. We realized that our environmentalist was weak at Camp 2. When asked about what help she needs, she could not analyze (based on information available) about what help she needs. But after a double check round, we found a cure for her. Even though I strongly disagreed to rest just for her, the team decided to take a rest for the further rush, so I had...
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...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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