...of business firms and other enterprises? (b) What are the different types of forecasting? (c) How can the firm determine the most suitable forecasting method to use? a) Forecasting is used to try and predict the economic activity of a firm’s future. It aims to reduce risk/uncertainty that is faced in the short-term operational decision making. It is also used to plan for the firm’s long-term growth. Forecasting helps make decisions by using macroforecasts of the general economic activity as inputs for their microforecasts of the industry’s and firm’s demand and sales. Forecasting helps decide a firm’s marketing strategy, production needs, sales forecast, and helps predict financial needs such as cash flow, profits, and outside financing. Furthermore, it helps make personal based decisions, as well as assist for the long-term future of the firm (Salvatore, 2012). b) Forcasting types range from expensive to inexpensive, as well as simple to complex. Forecasting techniques can be qualitative, and others can be quantitative. Salvatore focuses on qualitative forecasts. These forecasts include: time-series, smoothing techniques (moving averages), barometric forecasts with leading indicators, econometric forecasts, and input-output forecasts. c) A firm determines the most suitable forecasting method to use by using the following criterion: 1. the cost of preparing the forecast and the benefit that results from its use 2. the...
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...NOVA SOUTHEASTERN UNIVERSITY The Wayne Huizenga Graduate School of Business and Entrepreneurship-Master's ProgramS Assignment for Course: QNT 5040- Business Modeling Submitted to: Dr. Tom Griffin Submitted by: Prince A. Storr ps44@nova.edu Date of Course Meeting: November 18, 2011 Date of Submission: November 18, 2011 Title of Assignment: Greaves Brewery: 10 Month Forecasting CERTIFICATION OF AUTHORSHIP: I certify that I am the author of this paper and any assistance that I received in its preparation is fully acknowledged and disclosed in the paper. I have also cited any sources from which I used data, ideas, or words, either quoted directly or paraphrased. I also certify that this paper was prepared by me specifically for this course. Student Signature: Prince A Storr Instructor(s Grade on Assignment: Instructor(s Comments: Greaves Brewery: Ten Month Sales Forecasting Case Synopsis Alex Benson, purchasing manager for Greaves Brewery in Trinidad was faced with a dilemma in early 2004. He encountered difficulty in forecasting sales for 2004; particularly because of the 2003 slump, government excise taxes and other factors such as decreased numbers in both tourist arrivals to the Caribbean island and beer exports to the U.S. As purchasing manager, Benson’s prime responsibility was maintaining adequate inventory levels for all goods and materials used in the company’s production processes, including the purchase of new bottles and...
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...30100 | 2005-5 Duke | 40500 | 2006-1 Indiana | 42500 | 2006-2 North Texas | 48200 | 2006-3 Texas A&M | 44200 | 2006-4 Southern | 33900 | 2006-5 Oklahoma | 47800 | 2007-1 LSU | 46900 | 2007-2 Texas | 50100 | 2007-3 Prairie View A&M | 45900 | 2007-4 Montana | 36300 | 2007-5 Arizona State | 49900 | An important thing to note is that the homecoming game of every season is the second home game (bold), and is always well attended. Also the forth home game always corresponds with a local festival that always draws from attendance (italics). Summary of Forecasting Methods: Below is a table of the forecasting methods. The correlation coefficient, bias, mean absolute deviation (MAD), mean squared error (MSE), and mean absolute percent error (MAPE) are shown. | Correlation | Bias | MAD | MSE | MAPE | Naïve | -- | 541.38 | 6865.52 | 69,856,200 | .19 | Moving Average (3 periods) |...
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...cost is still an issue with many alternative sources of fuel, according to Chuck Combs, who is the program lead for alternative energy at China Lake and provides the Navy and Marine Corps with technical expertise in solar and fuel cell efforts”. (Hillburn, 2007) Here will be discussed how the alternative fuel automobile company has a first year market forecast of 1000 units by identifying the forecasting model with an explanation of why it is the obvious choice, while during the first three years of operation the automobile company had actual sales of: year one 800 units, year two 1200 units, year three 2000 units and by using a simple three year moving average calculation of the predicted demand for year four with an explanation of reasoning, in addition the sales department expects the growth in year four to closely resemble the average growth experienced in the last two years, with a prediction of the number of units expected in year four along with a discussion on whether or not to recommend this quantity as...
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...branded coffeemaker that is sold by 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 used, the 3 and 5 week data were very similar in the mean absolute deviation, and percent error. The tracking signal is where they were the most different. Since the cumulative deviation (RSFE) was negative, the tracking number for the three week data was negative which show that forecasts are too high. If you look at the -4 data column, you can see that this number is significantly lower than the rest of the prior week’s numbers. This dip in sales is what caused the forecasts for the 3 week data to be higher while the forecasts for the 5 week data were lower causing a positive RSFE and therefore a positive tracking signal. Exponential Smoothing Figures 3 and 4 used the exponential smoothing forecast method. Comparing with the moving average, the 3 week data with the alpha of .2 most signifies the data that was recorded in Figures 1 and 2. The rule of thumb when switching from moving average is that alpha equal 2/(n+1). When using this rule...
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...bags of fertilizer at Wallace Garden Supply are shown in the following table. Year Demand for fertilizer (1,000s of bags) 1 4 2 6 3 4 4 5 5 10 6 9 7 10 8 11 9 15 10 16 11 18 a. Develop a 3-year moving average to forecast sales. b. Then estimate demand again with a weighted moving average in which sales in the most recent year are given a weight of 3 and a weight of 2 for the second past year and sales in the other 2 years are each given a weight of 1. c. Which method do you think is best? In this case, the 3 year moving average is the better method as the Mean Absolute Deviation (MAD) is only 3.042 as compared to 3.347 for the weighted moving average method. What it actually means is that each forecast missed the actual value by 3.042 units instead of 3.347 units. d. Develop a trend line for the demand for fertilizer using any computer software. e. You have now developed three different forecasts. These are a 3-year moving average, a weighted moving average, and a trend line. Which one would you use? Explain your answer. By observing the MAD in part (d), it can be observed that the value is the smallest among the 3 methods, hence I will choose the trend line as the forecast predicted is more accurate. Moreover the demand for the future can also be...
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...Dr. Burns Operations Management PRACTICE EXAM 3 (with answers at the end) This exam consists of 40 multiple choice and 3 discussion questions/problems. The multiple choice questions are worth 40% of the exam grade. The problems are worth 60% of the exam grade. The exam is to be taken closed-book, closed-notes. Formulas are provided on the last page. 1. Aggregate Production Planning (APP) involves all of the following except _________. a. hiring and laying off workers b. subcontracting work out c. building up inventories d. explosion of end-item requirements 2. Which of the following is not an input to the aggregate planning system? a. demand forecasts b. capacity constraints c. strategic objectives d. company policies e. master production schedule 3. Capacity planning is… a. long range planning b. short range planning c. involves facility size, expansion and location decisions d. all of the above e. a and c only 4. In terms of production planning, aggregate production planning produces outputs that are used in the creation of a. a facility expansion plan b. a master production schedule c. an enterprise architecture plan d. all of the above e. a and b only 5. Kaizen is another term for a. continuous improvement. b. JIT. c. visual control. d. defect prevention. 6. Which of the following is an element of lean production? a. flexible resources b. total quality management ...
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...___________________ Answer Page for Selected Problems Provide only your final answers (for example, the final numbers, selection, etc.) to the following problems. Remember to show your work and answers for these problems on the problem pages. Example only: the break-even point is 100,000 units as this is the point where the cost of the two options is equal. Below that level we prefer option A and above that we would use option C #1 #2 #3 #5 #6 #7 #8 #9 #10 1. (22 points) The H&S Motor Company produces small motors at a production cost of $30 per unit. Defective motors can be reworked at a cost of $12 each. The company produces 100 motors per day and averages 80 percent good...
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...GROUP PROJECT Pasquet Ferdinand Ravera Quentin I- Presentation of the company The company was founded by Walter Chrysler (1875–1940) on June 6, 1925. The headquarters are in Auburn Hills in the Michigan. The brand was at the beginning a premium luxury position competing with the brands Cadillac. Chrysler was the top brand in the portfolio of what was then known as Chrysler Corporation. Chrysler's positioning changed several times over the years. Indeed, The Chrysler brand was originally a premium luxury position competing with Cadillac and Lincoln through the cars Imperial.Then, Chrysler's positioning of the Chrysler brand towards a mid-price brand caused Chrysler to kill the brand DeSoto after 1961.After that, in 1975, Chrysler once again became the top brand, with range of products competing with both luxury and near luxury brands like Buick. During the 1980s the Chrysler brand took on a similar role as Oldsmobile or Mercury by offering entry-level luxury cars of various types and sizes. Before the Fiat's acquisition of a 20% stake in Chrysler in 2009, Chrysler had a discontinued line of cars with economy car and sport cars. This strategy ended when the company was threatened with an imminent bankrupt in 2008. That’s why, in December 2008, U.S. President George W. Bush agreed to a $4 billion bailout for Chrysler and another one of $8.5 billion was also given by the President Barack Obama early in his administration. Chrysler LLC (new company emerged from...
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...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 the forecast in Problem 5-13 or the forecast in the section concerning Wallace Garden Supply is more accurate. Answer The more Acurate was the forecast in the section concerning Wallace garden supply. 5-15 Data collected on the yearly demand for 50-pound bags of fertilizer at Wallace Garden Supply are shown in the following table. Develop a 3-year moving...
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...Download http://www.hwspeed.com/Devry-GSCM-520-Week-2-Quiz-849803.htm?categoryId=-1 If You Face Any Problem E- Mail Us At JOHNMATE1122@Gmail.Com Page 1 Question 1.1. (TCO 5) In most cases, demand for products or services can be broken down into several components. Which of the following is not considered a component of demand? (Points : 3) Average demand for a period A trend Seasonal elements Past data Autocorrelation Question 2.2. (TCO 5) In most cases, demand for products or services can be broken into several components. Which of the following is considered a component of demand? (Points : 3) Forecast error Autocorrelation Previous demand Consistent demand Repeat demand Question 3.3. (TCO 5) Which of the following forecasting methodologies is considered a causal forecasting technique? (Points : 3) Exponential smoothing Weighted moving average Linear regression Historical analogy Market research Question 4.4. (TCO 5) Which of the following forecasting methods uses executive judgment as its primary component for forecasting? (Points : 3) Historical analogy Time series analysis Panel consensus Market research Linear regression Question 5.5. (TCO 5) In business forecasting, what is usually considered a long-term time period? (Points : 3) Three months or longer ...
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...Forecasting Methods A time series is a sequence of observations which are ordered in time. Inherent in the collection of data taken over time is some form of random variation. There exist methods for reducing of canceling the effect due to random variation. Widely used techniques are "smoothing". These techniques, when properly applied, reveals more clearly the underlying trends. Enter the time series Row-wise in sequence, starting from the left-upper corner, and the parameter(s), then click the Calculate button for obtaining one-period-ahead forecasting. Blank boxes are not included in the calculations but zeros are. In entering your data to move from cell to cell in the data-matrix use the Tab key not arrow or enter keys. Features of time series, which might be revealed by examining its graph, with the forecasted values, and the residuals behavior, condition forecasting modeling. Moving Averages: Moving averages rank among the most popular techniques for the preprocessing of time series. They are used to filter random "white noise" from the data, to make the time series smoother or even to emphasize certain informational components contained in the time series. Exponential Smoothing: This is a very popular scheme to produce a smoothed Time Series. Whereas in Moving Averages the past observations are weighted equally, Exponential Smoothing assigns exponentially decreasing weights as the observation get older. In other words, recent observations are given relatively more...
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...Operation Strategy: Specifies the means by which operations implements corporate strategy * Corporate strategy: Provides an overall direction and framework for carrying out all the organization’s function (Environmental scanning, core competencies, core processes, and global strategy) * Competitive capabilities: current performance along those dimensions that a process or supply chain actually has * Order Winner: a criterion the customers use to differentiate the services / products of a firm from those of others * Price (low prices), Flexibility (large variety), Quality (in-stock merchandise availability, customer service), Time * Order Qualifier: the minimum level of performance of an order winner that is required for a firm todo business in a market segment. Quality (Products and services. Focuses on quality) Process Strategy * Process Decision: 1) Process Structure (Customer-contact position [services]), 2) Customer Involvement (low involvement, high involvement), 3) Resource Flexibility (Specialized, enlarged), 4) Capital Intensity (low and high automation) * Process Architecture: 1) Job process: low volume, high customized to specific needs of the customers. The process has highly flexible flow, with considerable divergence in production steps (flexible resources. Resources are organized by resource type, but not by product type. Each order is treated as a single unit, a job). Ex: Nike workshop for sponsored athletic shoes 2) Batch process:...
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...Forecasting with Indices Jamie Prather QRB/501 April 23rd, 2012 Measuring the winter historical inventory data for 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 forecast demand for the periods being evaluated. | | | | | | Alpha | 0.2 | | | | Month | 1 | 2 | 3 | 4 | | Forecast | ABS Value | RMAD | RSFE | TS | 1 | | 39800 | 32180 | 62300 | 189,480 | 189,480 | | | | | 2 | 57350 | 64100 | 38600 | 66500 | 226,550 | 189480 | 37070 | 37070 | 37,070 | 1 | 3 | 15400 | 47600 | 25020 | 31400 | 119,420 | 196894 | 77474 | 114544 | -40,404 | -0.35274 | 4 | 27700 | 43050 | 51300 | 36400 | 158,450 | 181399.2 | 22949.2 | 137493.2 | -63,353 | -0.46077 | 5 | 21400 | 39300 | 31790 | 16800 | 109,290 | 176809.4 | 67519.36 | 205012.6 | -130,873 | -0.63836 | 6 | 17100 | 10300 | 31100 | 18900 | 77,400 | 163305.5 | 85905.49 | 290918 | -216,778 | -0.74515 | 7 | 18000 | 45100 | 59800 | 35500 | 158,400 | 146124.4 | 12275.61 | 303193.7 | -204,502 | -0.67449 | 8 | 19800 | 46530 | 30740...
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...Week 3 DQ 2 Forecasting Methods Read Problem 6 in Chapter 6 of your textbook. Calculate and answer parts a through d. Include all calculations and spreadsheets in your post. Explain why the moving average method was used instead of another forecasting method. What might be another forecasting method that could prove to be just as useful? Your initial post should be 200-250 words. Below see the number of mergers that took place over a 12-year period in the savings and loan industry. |Year | |Mergers | |Year | |2005 |61 |52.6 |8.4 |70.56 | |2006 |83 |55.6 |27.4 |750.76 | |2007 |123 |63 |60 |3,600 | |2008 |97 |75.2 |21.8 |475.24 | |2009 |186 |85.6 |100.4 |10,080.16 | |2010 |225 |110 |115 |13,225 | |2011 |240 |142.8 ...
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