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    L L Bean

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    Questions 1. What role does forecasting play in the supply chain of a build-to-order manufacturer such as Dell? Although Dell builds to order‚ they obtain PC components in anticipation of customer orders and therefore they rely on forecasting. This forecast is used to predict future demand‚ which determines the quantity of each component needed to assemble a PC and the plant capacity required to perform the assembly. 2. How could Dell use collaborative forecasting with its suppliers to improve

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    restaurant affect restaurant customer satisfaction. In addition of that quality of both food and service are the most important factors satisfy customer. * Forecasting: By Predicting timing and volume of customer demand‚ restaurant using forecasting for placing purchase orders for food‚ and supplies. For example restaurateurs often use forecasting to estimate the necessary purchase quantities of various food-related items. * Capacity planning: By Matching supply and demand‚ and  how efficient restaurant

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    Answer with 2 paragraphs‚ chart‚ paragraph‚ and company example Minimum 1.5 pages for each question Answer all 5 essays Part 1: power point on blackboard Part I: Essay (worth 50% of the grade) Essay #1: Why is the understanding of sales forecasting critical to the success of business? What method and/or tools (at least two) can one use to forecast sales? #1:Forcasting sales critical to business: setting up a budget for the next 12 months‚ understand the road map of where you intend to go

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    Forecasting Assignment

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    Forecasting Assignment There are many forecasting methods including seasonal‚ Delphi‚ technological and time series. Depending upon the situation‚ one may work better for a company than another. In describing forecasting‚ Amara and Salanik (1972) offer the following: Forecasting is: a statement about the future:‚ a probabilistic statement about the future: a probabilistic‚ reasonably definite statement about the future: a probabilistic‚ reasonably definite statement about the future‚ based

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    TIME SERIES MODELS FOR FORECASTING NEW ONE-FAMILY HOUSES SOLD IN THE U.S. INTRODUCTION The housing market has been weak since its recent peak in 2005. Then‚ the sharp drop in the housing prices in 2007 contributed to the subprime loan crisis [1]. This dramatic change in the housing market not only affects the construction industry‚ but also may have a significant impact on the whole economy [3]. We are still in the midst of the housing problem with the increase in the delinquency rate and

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    To compete in today ’s global markets‚ organizations strive to deliver their products and services in both an efficient and effective manner. A critical component in this effort is the design and coordination of the supply and distribution strategies in supply chain companies and the service quality in the service industry. Based on a site visit to the IGA Distribution Centre‚ below are be comparisons between the IGA National Distribution Centre and the well known popular Hard Rock Café. TRANSFORMATION

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    industry possible. The epidemiology-based forecasting model makes use of epidemiology data gathered from research publications and primary interviews with physicians to establish the target patient population and treatment flow patterns for individual diseases and therapies. Using prevalence and incidence data and diagnosed and treated population‚ the epidemiology-based forecasting model arrives at the final numbers. Capital equipment-based forecasting models are done based on the installed base

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    Chapter 4 Answers

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    Chapter 4_class exercise True/False 1. The forecasting time horizon and the forecasting techniques used tend to vary over the life cycle of a product. Answer: TRUE 2. A time-series model uses a series of past data points to make the forecast. Answer: TRUE 3. Cycles and random variations are both components of time series. Answer: TRUE 4. One advantage of exponential smoothing is the limited amount of record keeping involved. Answer: TRUE 5. If a forecast is consistently greater

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    Month Sales F M A M J J A S 20 0 2. a. b. 1) | | t | Y | tY | From Table 3–1 with n = 7‚ t = 28‚ t2 = 140 | | | 1 | 19 | 19 | | | | 2 | 18 | 36 | | | | 3 | 15 | 45 | | | | 4 | 20 | 80 | | | | 5 | 18 | 90 | | | | 6 | 22 | 132 | | | | 7 | 20 | 140 | | | | 28 | 132 | 542 | | For Sept.‚ t = 8‚ and Yt = 16.86 + .50(8) = 20.86 (000) 2) Solutions (continued) 3) | | Month | Forecast = | F(old) |

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    Regression is always a superior forecasting method to exponential smoothing. 3. T F The 3 categories of forecasting models are time series‚ quantitative‚ and qualitative. 4. T F Time-series models attempt to predict the future by using historical data. 5. T F A moving average forecasting method is a causal forecasting method. 6. T F An exponential forecasting method is a time-series forecasting method. 7. T F The Delphi

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