...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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...Thundercats Team Contract November 4th, 2014 Mission The mission of our team is to complete all aspects of the team assignment on time and to the full requirements set forth by Professor McNickle. Project We will work to the best of our abilities on the Littlefield simulation and will work as a team to make agreed upon manufacturing changes as often as is deemed needed. In addition, we will research and tour Darigold Inc. to evaluate their operations, providing analysis and recommended changes where we deem applicable. Our goal is to function as a reciprocal interdependent team, using each members’ varied skills and time to complete tasks both well and on time. To ensure we are focused and accomplish these set goals, the following guidelines...
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...Littlefield Initial Strategy When the simulation first started we made a couple of adjustments and monitored the performance of the factory for the first few days. Initially we set the lot size to 3x20, attempting to take advantage of what we had learned from the goal about reducing the lead-time and WIP. We also changed the priority of station 2 from FIFO to step 4. After viewing the queues and the capacity utilization at each station and finding all measures to be relatively low, we decided that we could easily move to contract 3 immediately. Except for one night early on in the simulation where we reduced it to contract 2 because we wouldn’t be able to monitor the factory for demand spikes, we operated on contract 3 almost the entire time. This proved to be the most beneficial contract as long as we made sure that we had the machines necessary to accommodate the increasing demand through day 150. Machine Purchases The first time our revenues dropped at all, we found that the capacity utilization at station 2 was much higher than at any of the other stations. So we purchased a machine at station 2 first. Station 2 never required another machine throughout the simulation. After all of our other purchases, utilization capacity and queuing at station 2 were still very manageable. As demand began to rise we saw that capacity utilization was now highest at station 1. We nearly bought a machine there, but this would have been a mistake. A huge spike in demand...
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...Littlefield Technologies Assignment 5 PM on February 22 . Starting at 5 PM on Wednesday, February 27, the simulation will begin The game will end at 9 PM on Sunday, March 3. Accessing your factory http://quick.responsive.net/lt/toronto3/entry.html Littlefield Technologies’ Operations board stuffing testing tuning Operations Policies at Littlefield Purchasing Supplies Processing in Batches Contract Pricing Borrowing from the Bank Cash Balance The winning team is the team with the most cash at the end of the game (cash on hand less debt). Current State of the System and Your Assignment At the end of day 350, the factory will shut down and your final cash position will be determined. Starting at 5 PM on Wednesday, February 27, the simulation will begin The game will end at 9 PM on Sunday, March 3. PLEASE DO NOT WAIT UNTIL THE FINAL SECONDS TO MAKE YOUR CHANGES. SOMETIMES THEY TAKE A FEW MINUTES TO BE PROCESSED. Written Assignment: Analysis of Game 2 of Littlefield Technologies Simulation Due March 14, 8:30 am in eDropbox Your group is going to be evaluated in part on your success in the game and in part on how clear, well structured and thorough your write-up is. The write-up only covers the second round, played from February 27 through March 3. It should not discuss the first round. Your write-up should address the following points: • A brief description of what actions you chose and when. Not a full list of every action, but the major...
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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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...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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...NS2 soft solution: Ns2 soft solution is a software development based company which contain innovative and expertise to facilitate complex projects in an efficient way. We offer various broad solution projects for researchers and students to increase demands among other centers and customer centric solution with high standard. We offer various projects under NS2 simulation based on IEEE papers and non IEEE papers. We deploy various NS2 projects as a virtual one in real time application. Ns2 soft solution is a highly experienced team member of developer professionals providing a wide range of complex projects and network protocol simulation. Our motto: • Advance technology enhancement. • Make everything possible. • Provide service quality for every commitment. Basic aims of Ns2 soft solution are: • Providing guidance for students to select the efficient project based on student interest which ensures a success in their projects. • We train and make students to learn all the concepts from basic to advance such that students can get a clear idea about the project what they do. • Based on...
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...The Mikes Bikes simulation is an exciting and interesting way to gain critical insights into the development of a business. By operating a simulated bicycle manufacturing corporation over a period of ten years was an opportunity to gain insights on a real entrepreneurial experience. It allowed us to expand on the ideas taught in class such as creating a business strategy and using tools like SWOT and Porter’s five forces. We had many assumptions initially regarding the procedures but gradually we could learn the basics by facing enough challenges and by trial and error method. These skills cannot be learned by the usual form of lecturing. Considering our team, this was our first comprehensive exposure to real business environment. Each...
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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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...Simulation technology can have countless benefits to all the eager students out there. It can, "Simulation is a technique, not a technology, to replace or amplify real experiences with guided experiences, often immersive in nature, that evoke or replicate substantial aspects of the real world in a fully interactive fashion," (Aebersold & Tschannen, 2013). It allows the students to be apart of a bigger picture than just the words in their textbooks. The simulation experience can also be recorded, therefore it allows students to backtrack on their prior experience and review it. They can see what they have done well and what could use some slight alterations to improve their skills. "A large body of research shows that simulation is incredibly effective as a teaching methodology and can contribute both to better patient outcomes and a culture of safety among nursing staff, " (American...
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