University 27 January 2012 Abstract This exercise is the development of a spreadsheet that depicts the calculations of 3- and 5-period moving average. The data for two weeks is given and the calculation for each is presented. Specific analyses are presented given the data. Period Moving Average 1. Calculated forecast for 3- and 5- period moving average. Day | Demand | 3-Period | 5-Period | 1 | 200 | 164 | 167 | 2 | 134 | 152 | 152 | 3 | 157 | 166 | 154 | 4 | 165 | 156 | 153 |
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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 the forecast in Problem 5-13 or the forecast in the section concerning Wallace Garden Supply is more accurate.
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Starbucks. A simple moving average was used for the first two models using 5 weeks past (figure 1) data and 3 weeks past data (Figure 2. The next method used was a exponential smoothing method with .4 alpha and 5 weeks data (Figure 3) and .2 alpha with 3 weeks data (Figure 4). The mean absolute deviation, mean absolute percent error and tracking signal were calculated based off of the total of all segments. Simple Moving Average Looking at Figure 1 and Figure 2 where a moving average forecast was
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Inventory Proposal, Part 1 QRB 501 Inventory Proposal, Part 1 McDonald’s is an organization that offers fast food, which includes hamburgers, French fries, and other items, to the public. McDonald’s restaurant works to provide hot and fresh food to its customers. Maintaining freshness is sometimes difficult due to the inventory problem it faces. “What used to be the case was McDonald's would pre-cook a batch of hamburgers and let them sit under heat lamps. They would keep them for as long
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to just keep the existing one Slide 2- Learning objectives You should be able to: -Explain the role of demand forecasting -Identify the components of a forecast -Be able to calculate the following forecast: -simple moving average forecast -weighted Moving Average Forecast -Understand the principles behind calculating: -exponential smoothing forecast -linear Trend forecast -Simple and Multiple regressions Note: for data forecasting you will use historical data to calculate future
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uses a single previous value of a time series as the basis of a forecast. [pic] Techniques for Averaging • What is the purpose of averaging? • Common Averaging Techniques o Moving Averages o Exponential smoothing Moving Average [pic] Exponential Smoothing [pic] Techniques for Trend Linear Trend Equation [pic] [pic] Curvilinear Trend Equation
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Test Exam Disclaimer: • This test exam consists merely of computational questions, during the actual exam interpretations could be asked as well. • The total number of points including the free points equal 95 for this test exam; in the actual exam the total number of points will be 100. Questions can be answered in English or Dutch. Explain every step in your reasoning. The number of points for a correctly answered question is shown in front of the question; 10 points are free
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| 3 | 4 | 5 | 6 | 7 | 8 | Total | Forecast | 120 | 135 | 140 | 120 | 125 | 125 | 140 | 135 | 1040 | Q3) Given the following information set up the problem in a transportation table and solve for the minimum-cost plan: Q4) Forecasts based on averages. Given the following data: Period | Number of Complaints | 1 | 60 | 2 | 65 | 3 | 55 | 4 | 58 | 5 | 64 | Prepare a forecast for period 6
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FORECASTING FUNDAMENTALS Forecast: A prediction, projection, or estimate of some future activity, event, or occurrence. Types of Forecasts * Economic forecasts * Predict a variety of economic indicators, like money supply, inflation rates, interest rates, etc. * Technological forecasts * Predict rates of technological progress and innovation. * Demand forecasts
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the next 12 months consists of preparing a simple exponential forecast method to determine the method of simple average during the 12 separate indices. The forecast was processed by Running mean Absolute Deviation (RMAD) was computed by taking the average of two one-year-wide averages that are offset by one period relative to each other. Computing the ratio divided by the moving average in each period. Running sum of forecast errors were computed by taking the differences between the actual and the
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