LittleField (1) What was your initial strategy and how/why did you change your strategy during the game?. (2) Which concepts that you learned in this course did you apply in making your decisions? Explain. (3) What did you learn from the game? General Strategy Getting into the game our strategy was to identify the bottleneck in the process and maximize its utilization. We were aiming to balance our stock so a new stock will arrive every 4 days‚ and the current stock will not run out (but will get
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MGT 9702 – SERVICE OPERATIONS I LITTLEFIELD TECHNOLOGIES – OVERVIEW Littlefield Technologies (LT) produces Digital Satellite System (DSS) receivers. These receivers are assembled from kits of electronic components procured from a single supplier. The assembly process consists of four steps carried out at 3 stations called board stuffing‚ testing and tuning. The machines at these stations cost $90‚000‚ $80‚000
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Littlefield Simulation II Based on our success in the last Littlefield Simulation‚ we tried to utilize the same strategy as last time. Our goals were to minimize lead time by reducing the amount of jobs in queue and ensuring that we had enough machines at each station to handle the capacity. We wanted to keep the lead time between .5 and 1 day in order to get the maximum amount of revenue per job. We utilized data from the first 50 days and put it in an Excel chart to forecast the demand for the
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Littlefield Labs 1 Capacity Management at Littlefield Labs I. Introduction There are 3 stations in the game called sample preparing‚ testing‚ and centrifuging‚ while there are 4 steps to process the jobs. Before the game started‚ we tried to familiarize with the process of the laboratories and calculating the costs (both fixed and variable costs) based on the information on the sheet given. We did not intend to buy any machines too early‚ as we wanted to see the demand fluctuation and the
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Regression Analysis: A Complete Example This section works out an example that includes all the topics we have discussed so far in this chapter. A complete example of regression analysis. PhotoDisc‚ Inc./Getty Images A random sample of eight drivers insured with a company and having similar auto insurance policies was selected. The following table lists their driving experiences (in years) and monthly auto insurance premiums. Driving Experience (years) Monthly Auto Insurance Premium 5 2 12 9
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Executive Summary Our team operated and managed the Littlefield Technologies facility over the span of 1268 simulated days. Our team finished the simulation in 3rd place‚ posting $2‚234‚639 in cash at the end of the game. We did intuitive analysis initially and came up the strategy at the beginning of the game. And then we applied the knowledge we learned in the class‚ did process analysis and modified our strategies according to the performance results dynamically. We have reinforced many of the
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Applied Linear Regression Notes set 1 Jamie DeCoster Department of Psychology University of Alabama 348 Gordon Palmer Hall Box 870348 Tuscaloosa‚ AL 35487-0348 Phone: (205) 348-4431 Fax: (205) 348-8648 September 26‚ 2006 Textbook references refer to Cohen‚ Cohen‚ West‚ & Aiken’s (2003) Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. I would like to thank Angie Maitner and Anne-Marie Leistico for comments made on earlier versions of these notes. If you
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and the number of construction permits issued at present. Example 2: The demand for new house or automobile is very much affected by the interest rates changed by banks. Regression analysis is one such causal method. It is not limited to locating the straight line of best fit. Types:- 1. Simple (or Bivariate) Regression Analysis: Deals with a Single independent variable that determines the value of a dependent variable. Ft+1 = f (x) t Where Ft+1: the forecast for the next period. This
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ittlefield Simulation #1: Capacity Management Team: Computronic When the simulation began‚ we quickly determined that there were three primary inputs to focus on: the forecast demand curve (job arrivals‚) machine utilization‚ and queue size prior to each station. Specifically we were looking for upward trends in job arrivals and queue sizes along with utilizations consistently hitting 100%. Upon initial analysis of the first fifty days of operations‚ the team noticed that Station 1 had reached
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Production Planning and Inventory Control CTPT 310 Littlefield Simulation Executive Report Arlene Myers: 260299905 Rubing Mo: 260367907 Brent Devenne: 260339080 Miyaoka Scenario‚ Re: Littlefield Technology Simulation Game: Inventory Management Executive Summary At the onset of the game‚ we determined there were a few key things that had to be addressed to succeed. The first was to avoid stock outs which had already occurred in the first 50 days. We quickly moved to avoid stock outs
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