...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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...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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...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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...Report: Everest Simulation Table of Contents | Page # | 1. Executive Summary........................................................................................ | ..........3 | 2. Introduction..................................................................................................... | ..........4 | 3. Method & Results............................................................................................ | ..........4 | 4.1 Experimental Procedure | 4 | 3.2. Findings | 5 | 4. Discussion....................................................................................................... | ..........5 | 5.2 Communication | 5 | 4.2 Types of Leadership & Power | 6 | 4.3 Decision Making | 6 | 4.4 Conflict | 7 | 5. Conclusion..................................................................................................... | ..........8 | 6. References..................................................................................................... | ..........8 | 7. Appendices.................................................................................................... | ..........9 | I. Goals on Track | 9 | II. Team Contract | 10 | 1. Executive Summary During the semester, two Everest Simulation tasks were undertaken by a group of five people. This Everest Simulation was an online task which was to be completed as a team in order to determine...
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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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...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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...Marketing 1120 2014 Marketing Simulation Report LuxPak: Team 6 Michaela Sumpter & Harry Singh Table of Contents Introduction: Summary 3 Overall Result 3 Research and Applied Marketing Knowledge 4 Competition 4 Play by Play 5 Original Strategy 6 Target Market 7 Marketing Mix 7 Overall Performance Evaluation / Changes Made 8 Lessons Learned 9 Conclusion: Summary 9 Introduction: Summary During the Marketing Simulation Game, our team experienced many obstacles and had to work through them from beginning to end. Our turns appeared to be very scattered due to an communication obstacle, but we agreed we had to make a change in our entire strategy when we hit $3,970 in debt. Cohesively, we decided to change our target market, which led to promotional, pricing, product and placement changes as well. These alterations benefitted our team, since we began to bring in profit. Throughout this entire process, we learned many lessons and if we were to do this simulation over, both of us would approach our strategy differently. In this report, our overall result will be evaluated, the lessons we learnt along the way and how we applied marketing knowledge to our turns will be discussed, along with other factors that influenced our team along the way. Overall Result According to the leader board metric, we sold 310 units throughout the game, and earned a total of $15,220.06. Our changes benefitted our overall result, because in the end we ended...
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...Trade Simulation and Report Team B: Kimberly Castillo, Tanya Bell, Elijah B. Gowdy, Derrick Brown ECO/212 June 6, 2012 Instructor, John Holmberg One Advantage and One Limitation of International Trade Advantage and limitation of International Trade, Countries have different quantities, qualities, and cost for resources such as land, labor, capital, and entrepreneurship (University of Phoenix, 2009). International trade is the import and export of these resources between countries. International trade allows countries to distribute their resources more efficiently. Importing and exporting of resources is vital to the economy. A gain from International trading is a price increase or decrease, in the local markets. If it is cheaper to make a good and export the good the importer will gain from trade by getting a good at a better price than what the opportunity cost of it would be. If the market price was higher, a lower price exporter will allow market price to fall and pose a benefit for consumers, in the sense that everyone gains the most with minimal losses in the short run. Four Key Points Emphasized in the Simulation Within the simulation team b has identified four key points that were underlined. First there is what is called dumping. Dumping is the selling of goods and products in other countries at a cost that is lower than the cost of those goods and products in its own country. Another key point identified in the simulation was consumer...
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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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...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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...'smu' to register your team. Please set your team password and keep it secured. Students can access a description of the first assignment at http://sc.responsive.net/sc/low/Assign1 . Students can access a description of the second assignment at http://sc.responsive.net/sc/low/Assign2 . Once the simulation begins, students can access their teams at http://sc.responsive.net/sc/low/entry.html and begin playing. FAQs are available at: http://responsive.net/gameFAQ.html Game 1 The 2-years historical data will be released when the game starts. Every group will start the game @TBA. The game will run continuously from day 730 to day 1460 over 3 real days. i.e. this means approximately 5.9 real minutes per simulated day. Game 2 The 2-years historical data will be released when the game starts. Every group will start the game @TBA. The game will also run continuously from day 730 to day 1460 over 3 real days. Note: 1) Please note that for both games, the game will end on day 1460. Demand will become zero, production capacity will be worthless, and inventory will be obsoleted. 2) Game 1 is only for familiarisation. There will be no scoring and no report required. 3) Each group will submit a memo max 3 pages (all-in) for game 2 by the following lecture 1 week after the game...
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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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...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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...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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