Managing Customer Responsiveness at Littlefield Labs Background Littlefield Laboratories (LL) has opened another lab. The new lab uses the same process as the lab in the assignment “Capacity Management at Littlefield Labs” — neither the process sequence nor the process time distributions at each machine have changed. On day 0‚ the lab began operations with three preparers‚ one tester‚ and one centrifuge‚ and an inventory of 160 test kits. This left the lab with $1‚000‚000 in reserves. Customer
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linear (i.e. if the relationship between y and x is linear). Next perform residual analysis and test for violation of assumptions. (Let y = arterial oxygen and x = blood flow). twoway (scatter y x) (lfit y x) regress y x rvpplot x 2. Since regression diagnostics failed‚ we transform our data. Ratio transformation was used to generate the dependent variable and reciprocal transformation was used to generate the independent variable. 3. Check if the model is adequate by checking the t-statistic
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1. Qeach brand t=β0+β1*PMinute Maid t+β2*PTropicana t+β3*PPrivate label t+ueach brand t Q: quantity P: price By running the above regression model for each brand‚ we got the following elasticity matrix and the figures for “V” and “C.” Note that we used the average price and quantity for P and Q to calculate each brand’s elasticity. Price Elasticity | Tropicana | Minute Maid | Private Label | Tropicana | -3.4620441 | 0.40596537 | 0.392997566 | Minute Maid | 1.8023329 | -4.26820251 | 0.765331803
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Before the simulation started‚ our team created a trend forecast‚ using the first 50 days of data‚ showing us that the bottleneck station was at Station 1. On day 50 of the simulation‚ my team‚ 1teamsf‚ decided to buy a second machine to sustain our $1‚000 revenue per day and met our quoted lead time for producing and shipping receivers. On day 97‚ we changed Station 2’s scheduling rule to priority step 2. We noticed that the bottleneck was not just at station 1‚ but at all stations‚ and that buying
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Operations Management: Process Analysis and Applications Module Changing Sources of Competitive Advantage Targeting Improvement: Operational Measures - Time T‚ Inventory I‚ Throughput rate R » Link through Little’s Law » Link to Financial Measures » Targeting improvement: CRU Computer Rentals Capacity and Flow Time Analysis » Pizza Pazza » Levers for Improvement Multi-product Capacity Management and Investment » Joint Marketing & Production Decisions » Optimal Capacity
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Travis Foglesong Dr. Carter English 111-F23 February 19‚ 2013 The American Regression In Cal Thomas ’ article‚ “Is the American Dream Over?” he expresses a strong‚ anti-liberal opinion that explains why “the American dream” is lost. The article points out that the American society has become too dependent on a dysfunctional government while the key to a successful life in America used to lie in individual initiative. Certain traits‚ that had lead past generations to success‚ have been lost
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Little Field 1 Report Paper Team Bigfield Michael Cambell Donovan Ricks Robert Sorum Van Sy I. Team Dynamics For the Little field 1 project our team‚ Bigfield‚ consisted of four people. First we have Michael Cambell‚ his number is 707-450-9523 and his email is: theonebras@aol.com. Next is Donovan Ricks‚ his number is 916-284-5028 and his email is: Donny.Rix_84@yahoo.com. Next is Robert Sorum‚ his number is 530-219-4173 and his email is: rtsorum@gmail.com. And
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Progressive Regression: A brief analysis of the post-Soviet democratization process of Azerbaijan The demise of the Soviet Union of December of 1991 did not only bring an end of the Cold War; it brought over two-hundred and ninety-three million souls spanning over eleven time zones out of the shadows that lurked behind an Iron Curtain. This unprecedented event in modern history gave birth to an era of transition for the fifteen republics that would emerge‚ providing the world with one of the most
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Littlefield Simulation Write-up December 7‚ 2011 Operations Management 502 Team 9 Littlefield Lab We began our analysis by searching for bottlenecks that existed in the current system. It was easily identified that major issues existed in the ordering process. Without calculations‚ you could tell the reorder point was too low since the historical plots showed inventory levels at zero for two or more days at a time. The number of jobs in customer orders showed correlating spikes at the same
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retailers behavior towards Aircel in selected region. The data is collected directly by visiting outlets through structured interview scheduled. The statistical tools used to analyze the data are: Co-relation analysis‚ Simple Linear Regression and Multiple Linear Regression. The software used to analyze the data is Windostat version 8.6‚ developed by Indostat services‚ is an advanced level statistical software for research and experimental data analysis. The study is carried mainly in the areas like
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