...Forecasting Using Indices Tricia Thedford QRB 501/Quantitative Reasoning for Business March 4, 2012 Vinata Kulkarni, PhD Forecasting using indices Forecasting is an integral part of business. Forecasting allows investors to see anticipated growth/decline in a business. An accurate forecast will also allow a business to respond appropriately should a problem be evident in the desired business plan. Forecasting can be done on all data within a financial statement or can target specific areas, depending on the information desired. Investors as well as business executives have a need to see where a company is headed when making future business decisions. The dictionary defines the word forecast as “to anticipate, calculate, or predict (some future event or condition) usually as a result of rational study and analysis of available pertinent data” (Merriam-Webster, Inc., 2002, para 2). One should review all data available for making an accurate business forecast. In researching Dell, Inc., inventory history the following data was obtained: Dell, Inc. historical inventories data | 2011 | 2010 | Period | Amount | Period | Amount | October 31 | 1.40 billion | October 31 | 1.29 billion | July 31 | 1.35 billion | July 31 | 1.37 billion | April 30 | 1.28 billion | April 30 | 1.18 billion | January 31 | 1.30 billion | January 31 | 1.05 billion | 2009 | 2008 | Period | Amount | Period | Amount | October 31 | 952 million | October 31 | 1.11 billion | July...
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...Forecasting with Indices Forecasting is a tool that a company can use to predict trends or patterns based on the data available from previous years, quarters or periods. Forecasting can assist a company with a wealth of information, such as where cuts need to be made, where increases need to be made. Forecasting can even aid a company with what product to carry or discontinue and when would be a good time for a sale. Calculating Averages Taking the monthly sales for the last four years of Honda’s sales, the author entered this information into Excel, and formulated this chart: | Year | Year | Year | Year | average by month | Month | 1 | 2 | 3 | 4 | monthly index | 1 | 98,511 | 71,031 | 67,479 | 76,269 | 78,323 | 2 | 115,397 | 71,575 | 80,671 | 98,059 | 91,426 | 3 | 138,734 | 88,379 | 108,262 | 133,650 | 117,256 | 4 | 135,180 | 101,129 | 113,697 | 124,799 | 118,701 | 5 | 167,997 | 98,344 | 117,173 | 90,773 | 118,572 | 6 | 142,539 | 100,420 | 106,627 | 83,892 | 108,370 | 7 | 138,744 | 114,690 | 112,437 | 80,502 | 111,593 | 8 | 146,855 | 161,439 | 108,729 | 82,321 | 124,836 | 9 | 96,626 | 77,229 | 97,361 | 89,532 | 90,187 | 10 | 85,864 | 85,502 | 98,811 | 98,333 | 92,128 | 11 | 76,233...
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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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...Briefly describe the steps that are used to develop a forecasting system. Identify the three forecasting time horizons and state an approximate duration for each. Provide a Criminal Justice example for each time horizon. Forecasting is defined as the art and science of predicting future events (Heizer & Render, 2008). When implementing a forecasting system, there are seven basic steps, determine the use of the forecast, select the items to be forecasted, determine the time horizon of the forecast, select the forecasting model(s), gather the data needed to make the forecast, make the forecast, and validate and implement the results. (Heizer & Render, 2008) A forecast is usually classified by the future time horizon that it covers (Heizer & Render, 2008). According to Heizer & Render (2008) “Time horizons fall into three categories: 1. Short-range forecast: This forecast has a time span of up to 1 year but is generally less than 3 months. It used for planning purchasing, job scheduling, workforce levels, job assignments, and production levels. An example would be placing resource in areas known as “hot spots” that have been identified thru crime analysis. 2. Medium-range forecast: A medium-range, or intermediate, forecast generally spans from 3 months to 3 years. It is useful in sales planning, production planning and budgeting, cash budgeting, and analysis of various operating plans. An example would be the planning of manpower for a new...
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...easily, such as how long a trip would last or how many people were coming. Some things, however, needed a forecast. Was it likely to rain? What would we do if it did rain? The best plans had two essential ingredients: First, everyone worked to the same plan; second, all pertinent information was included in that plan. In the business world, there are many methods of forecasting product demand, and they must include all known information. Extrinsic forecasting methods involve factors such as economic conditions, market trends, competition, government regulations, or the sale of related goods. These techniques look for patterns or correlations linking product demand with these outside factors. Qualitative forecasting techniques most often are used for extrinsic forecasting. They are employed by senior managers and involve using good judgment, intuition, and informal opinions. Qualitative forecasting is necessary for products where no previous sales data exist. Intrinsic forecasting, on the other hand, uses data from previous sales, and the forecast is developed using that sales history. This quantitative forecasting is done by most members of a supply chain, especially those near the final consumer. Many factors can be included in the forecast along with traditional methods to improve forecast reliability. Principle 2: Forecasts must include some measure of error. The forecast is going to be wrong—but by how much? Estimates of forecast error can be made by studying past performance...
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...QRB/501 – Quantitative Reasoning for Business University of Phoenix Inventory Proposal This proposal is for a solution for an inventory problem within an organization. The details of the organization’s business operations are established and the problem identified so a potential solution may be formulated and proposed. The expected benefits to the organization are also examined as the motivation for implementation. Additionally, an inventory index is used to compare and contrast historical trends leading to the inventory problem and supporting evidence is presented through a histogram and slope-intercept calculations. Organization Vertical Entertainment (VE) is a fireworks wholesaler headquartered in Austin, Texas. VE has been in business since 2005 and provides fireworks products through business-to-business and business-to-consumer channels. The organization’s key segments are the small-box retailers and fireworks display companies. The small-box retailers account for increased volume during the summer months (e.g. 4th of July) whereas the fireworks display companies purchase goods year-round. In recent years VE has expanded to direct sales via the company website. Inventory Problem VE purchases all firework inventories directly from China and they arrive in roughly 12 weeks. Product backorder usually result in lost sales because most events are time specific. The main inventory issue...
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...some of the many inventory issues that may occur. This paper will present an analysis of the Winter Historical Inventory Data of the University of Phoenix campus. Due to the many concerns that the winter season can bring, a solution to respond to the possible heavy snowfall needs to be presented and executed each year. Patterns need to be reviewed that occur during each month of the year. Forecasting is one way that companies can plan for future needs. Forecasting is a valuable tool to allow for organizations to stay ahead of competitors, prepare budgets, and adapt better to change should one occur in the specific industry. Organization The University of Phoenix campus in Woodbury, Minnesota is located in an area that receives approximately fifty inches of snow on average in a year (CityTopLists, n.d.), which requires winter maintenance to ensure a safe and welcoming environment for students and family. This paper will explore the needs specifically related to the winter maintenance supplies such as salt, the equipment to spread the salt around the campus and staffing. Using seasonal indices to analyze the inventory data, a solution will be presented to respond to the inventory issue of snowfall, a forecast has been added to the histogram to display present and future inventory data, and a forecast is...
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...Inventory Proposal: Analysis of University Of Phoenix Demand for Units QRB501 Inventory Proposal University of Phoenix is a for-profit university initiated in 1976 by John Sperling (Kinser, 2006). The university originated in the Phoenix metropolitan area and started with only eight students (Kinser, 2006). Sperling wrote about the origin of University of Phoenix and refers to it as “Rebel with a Cause” (Kinser, 2006, para. 2). Sperling experienced much opposition in getting UOPX in operation to help underserved and hard working adults further their education. Thanks to his determination and confrontational side, Sperling is 88 and can see UOPX having a net worth of $9 billion (Kinser, 2006). University of Phoenix is top on the list of for-profit universities. They enroll more than 230,000 students and have 170 throughout the United States (Kinser, 2006). It is apparent to past critics that universities who provide opportunities for working adults are growing at a rapid rate. More students are leaning to online schools and universities to advance with higher degrees. One thing plaguing the United States is the economy. The cost of tuition is steadily increasing, making it more difficult for students to afford education without the help of scholarships, grants, and student loans. It is said that for-profit universities depend heavily on federally funded students to stay afloat financially (Field, 2011). There are reports and suits filed from past instructors...
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...Forecasting and Indices Forecasting is a process in which statements or conclusions are made regarding the outcome of events that have not yet happened. Forecasting is predicting what the could look like. There are many examples of forecasting. Estimating or predicting can be referred to as formal statistical methods that employ time series, cross-sectional or longitudinal data. Forecasting can be use to estimate a wide variety of issues, weather related events, the use of resources, sales, profit or loss and even staffing needs for a particular project. Coupling these findings with good planning can give a company a good indication of what the future should look like. Forecasting can use many different methods. Evaluating your objectives and conditions relevant to the individual situations can help in choosing the correct method to use. Another option is to use several methods and compare or combine the results, as many times, there is no one “best” method. “The forecast error is the difference between the actual value and the forecast value for the corresponding period. where E is the forecast error at period t, Y is the actual value at period t, and F is the forecast for period t. Measures of aggregate error: Mean absolute error (MAE) Mean Absolute Percentage Error (MAPE) Mean Absolute Deviation (MAD) Percent Mean Absolute Deviation (PMAD) Mean squared error (MSE) Root Mean squared error (RMSE) Forecast skill (SS) Average of Errors (E)...
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...ForecastX Software (There will be additional ForecastX notes for subsequent chapters) 1. Insert disk and install a. Note: you will have to do this every time you use a campus computer, but the process should only take a minute or two. b. Insert disk. If a McGraw Hill screen pops up, just agree to what it is asking you, and then reduce the window. c. Double click on “My computer” d. Select (click once on) cdrom drive (or whatever the listing is for the drive into which you have inserted your disk), right click over the shaded area, select “open” from the menu, click on “forecast x installer”, click on “setup.exe” (You may find there are two “setup” files, if so, choose the top one, but try the bottom one if the top one doesn’t work.) 2. Add in the forecast x add-in. a. Open excel b. Click on the following: File, options, add-ins. You should see “Forecastx7Toolbar” underneath “Active Application Add-Ins”. To make sure that ForcastX is installed, click on “go” at the bottom of the page, and make sure that there is a check in the check box next to ForecastX in the pop up box that appears, then click on OK. Then close out of Excel, and reopen Excel. You should find ForecastX when you select the Add-Ins bar at the top of the page. c. If you do not see ForecastX in the pop up box, click ok and close Excel. Then close Excel and do step 2b again. If you still do not see ForecastX in the pop up box, Click on...
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...Forecasting with Time Series QRB/501 Quantitative Reasoning for Business February 7, 2012 Forecasting with Time Series For most companies, forecasting is very important. Their future can be determined with forecasting and this also helps pin point the problems of the past. Forecasting can be done in many methods, depending on what exactly is being forecasted. A forecasting tool used to determine demand for various commodities or goods in a given marketplace over the course of a typical year (or a shorter time period). Such an index is based on data from previous years that highlights seasonal differences in consumption. In some industries, the seasonality index experiences huge swings. (Business Dictionary, 2012) This forecasting tool is known as seasonal indexing. Find the seasonal index for summer historical inventory data below. Summer Historical Inventory Data Summer historical inventory data measures monthly figures in units four times a year. Data forecasted helps the company with inventory demands for the following year. Organizations should average a certain amount each month for a four year forecast. Organizations estimate demand for that month for the fifth year. Companies should average demands each year, and display average demands per month for that year. Data helps provide the organization, an overview with demands from one year to another year (brainmass, 2004, 2011). Organizations should identify the sequence of observation...
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...a p p e n d i x C DEMAND FORECASTING IN MARKETING * When you finish this appendix you should • Understand the principles of forecasting. • Know the differences between Time Series and Regression Analyses. • Understand how expert opinion, sales forces and consumer opinions can all contribute to better forecasts. * by Beverley Thompson, The University of Western Sydney, Nepean Demand Forecasting in Mar keting w 689 An important part of the marketing planning process is the setting of goals that are realistic and achievable, given a particular marketing environment and level of marketing commitment. In marketing, such goals are usually based on market share objectives and sales targets, both of which require accurate forecasts of total market size, market size of target segments and likely market share within a targeted segment. W H AT A R E W E F O R E C A S T I N G ? Accurate forecasting requires a clear definition of the market in question. Markets may be differentiated on the basis of the following variables. GEOGRAPHY A market may be defined at world, country, state, region, sales territory, town, store or customer level. When formulating a forecast or other marketing plans, the geographical dimension must be clearly indicated. Planning Coca Cola consumption for the year 2000 Sydney Olympics for example, will necessitate the forecasting of increased consumption for the Sydney sales region, but not necessarily for Brisbane...
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...Part 1: Expected future seminar participants for the next four seasons HR Inc. is considering expansion and intends to base its decision on analysis of historical trend in seminar participant numbers. First and foremost, we shall look at the participants’ data for the previous periods, and identify the general trend in terms of any increases or decreases in number of participants over time. We can use quantitative forecasting methods to forecast the future expected class sizes, since we have past data on class sizes. We shall also assume that the past pattern will continue in the future and therefore use the time series forecasting method. Year | Quarter | Time period | Pax | Moving Total | Moving Average | Seasonal Index (Actual/ MA) | 2003 | Jan - Mar | 1 | 35 | | | | | Apr - Jun | 2 | 44 | | | | | Jul - Sep | 3 | 54 | | | | | Oct - Dec | 4 | 49 | | | | 2004 | Jan - Mar | 5 | 68 | 182 | 45.50 | 1.49 | | Apr - Jun | 6 | 61 | 215 | 53.75 | 1.13 | | Jul - Sep | 7 | 61 | 232 | 58.00 | 1.05 | | Oct - Dec | 8 | 75 | 239 | 59.75 | 1.26 | 2005 | Jan - Mar | 9 | 70 | 265 | 66.25 | 1.06 | | Apr - Jun | 10 | 62 | 267 | 66.75 | 0.93 | | Jul - Sep | 11 | 70 | 268 | 67.00 | 1.04 | | Oct - Dec | 12 | 74 | 277 | 69.25 | 1.07 | 2006 | Jan - Mar | 13 | 64 | 276 | 69.00 | 0.93 | | Apr - Jun | 14 | 72 | 270 | 67.50 | 1.07 | | Jul - Sep | 15 | 76 | 280 | 70.00 | 1.09 | | Oct - Dec | 16 | 72 | 286 | 71.50 | 1.01 | 2007 | Jan - Mar...
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...Forecasting with Indices Andrea Quantitative Reasoning for Business - QRB 501 Kumar Das, PhD January 24, 2011 Forecasting is a tool business use to help plan for the future. Businesses forecast, revenue, overhead, needed inventory, consumers demands, as well as other factors. Forecasting helps to determine the future market value of a company or organization. Forecasting is important because the curse of action business take today often depends on what is going to happen in the future. The winter historical data for the University of Phoenix covers four years of demand in actual units. Each year has been broken down to 12 months of data. The data is presented in figure 1.1 Typical Seasonal Demand for Winter Highs Actual Demands (in units) Month Year 1 Year 2 Year 3 Year 4 Forecast 1 55,200 39,800 32,180 62,300 2 57,350 64,100 38,600 66,500 3 15,400 47,600 25,020 31,400 4 27,700 43,050 51,300 36,500 5 21,400 39,300 31,790 16,800 6 17,100 10,300 31,100 18,900 7 18,000 45,100 59,800 35,500 8 19,800 46,530 30,740 51,250 9 15,700 22,100 47,800 34,400 10 53,600 41,350 73,890 68,000 11 83,200 46,000 60,200 68,100 12 72,900 41,800 55,200 61,100 Avg. Figure 1.1 (University of Phoenix, 2011). The Time-Series Method is an example of quantitative forecasting. This forecasting model uses historical data to try to predict future events .Knowing the data from the last four years, will help to predict how long the demand in units...
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...MBA 643-17 April - May 2015 Problem-Solving Skills Assignment One Texas State sales Tax Forecasting Due Date May 22, 2015 A major source of revenue in Texas is a state sales tax on certain types of goods and services. Data are compiled and the state controller uses them to project future revenues for the state budget. One particular category of goods is classified as Retail Trade. Four years of quarterly data (in millions) for one particular area of southeast Texas follows. Quarter | YEAR 1 | YEAR 2 | YEAR 3 | YEAR 4 | 1 | 218 | 225 | 234 | 250 | 2 | 247 | 254 | 265 | 283 | 3 | 245 | 255 | 264 | 289 | 4 | 292 | 299 | 327 | 356 | 1. Compute seasonal indices for each quarter for year 5 based on CMA. Seasonal indices can be calculated using this formula: (sum of average value for Qx divided by number of data). Therefore seasonal indices for quarters in year five will be: Q1 = (88.148 + 88.51 + 87.98)/3 = 88.21% Q2 = (98.68 + 98.51 + 97.29)/3 = 98.16% Q3 = (97.46 + 98.31 + 96.17)/3 = 97.31% Q4 = (115.35 + 114.17 + 117.30)/3 = 115.6% 2. Deseasonalize the data and develop a trend line of the deseasonalized data. Quarter | Sales | Index | Deseasonalised data | 1 | 218 | 0.8821 | 247.1 | 2 | 247 | 0.9816 | 251.6 | 3 | 245 | 0.9716 | 252.2 | 4 | 292 | 1.1561 | 252.6 | 5 | 225 | 0.8821 | 255.1 | 6 | 254 | 0.9816 | 258.8 | 7 | 255 | 0.9716 | 262.5 | 8 | 299 | 1.1561 | 258.6 | 9 | 234 | 0.8821 | 265.3 | 10 | 265 | 0.9816 | 270...
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