...MEASURING FORECASTING ERROR (The students are advised to refer to the book under reference for details.) Because quantitative forecasting techniques frequently involve time series data, a mathematical notation is developed to refer to each specific time period. The letter Y will be used to denote a time series variable unless there is more than one variable involved. The time period associated with an observation is shown as a subscript. Thus Y1 refers to the value of the time series at time period t. The quarterly data for the Outboard Marine Corporation presented in Example 3.5 (see p. 73) would be denoted Y1 = 147.6,Y2 = 251.8, Y3 = 273.1, ... , Y52 = 281.4. Mathematical notation must also be developed for distinguishing between an actual value of time series and the forecast value. A^ (hat) will be placed above a value to indicate that it is being forecast. The forecast value for Yt is Yt^. The accuracy of a forecasting technique is frequently judged by comparing the original series Y1, Y2, ... with the series of forecast values Y^1, Y^ 2, .... Basic Forecasting Notation Basic forecasting notation is summarized as follows. Yt = value of time series at period t t = forecast value of Yt et = Yt - Yt^ = residual, or forecast error Several methods have been devised to summarize the errors generated by a particular forecasting technique. Most of these measures involve averaging some function of the difference between...
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...Introduction to Management Science, 10e (Taylor) Chapter 15 Forecasting 1) A trend is a gradual, long-term, up or down movement of demand. Answer Diff: 1 Page Ref: 682 Main Heading: Forecasting Components Key words: trend, forecasting components 2) A seasonal pattern is an up-and-down repetitive movement within a trend occurring periodically. Answer Diff: 2 Page Ref: 682 Main Heading: Forecasting Components Key words: seasonal pattern, forecasting components 3) Random variations are movements that are not predictable and follow no pattern. Answer Diff: 2 Page Ref: 682 Main Heading: Forecasting Components Key words: random variations, forecasting components 4) The basic types of forecasting methods include time series, regression, and qualitative methods. Answer Diff: 2 Page Ref: 683 Main Heading: Forecasting Components Key words: types of forecasting methods 5) Time series is a category of statistical techniques that uses historical data to predict future behavior. Answer Diff: 1 Page Ref: 683 Main Heading: Forecasting Components Key words: time series analysis 6) Regression methods attempt to develop a mathematical relationship between the item being forecast and factors that cause it to behave the way it does. Answer Diff: 2 Page Ref: 683 Main Heading: Forecasting Components Key words: regression methods 7) Qualitative methods use management judgment, expertise, and opinion...
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...SUBJECT: Forecasting and Sales for our Widget Manufacturing line As we have discussed earlier in the day, we have a 60 % accuracy with our sales forecast rate and we are reviewing ways to close the gap between 60% and 100%. In this memo I have written out what our current outlook looks like from the beginning to end of our production cycle and what forecasting methods I believe we can use to improve forecasting accuracy. Our current process: Components Vendor C On Site in Factory Vendor A Vendor B Steel for Widget takes 4 weeks Widget is made 1 week Plastic cover 3 weeks Card Board shipping box 4 weeks Packaging Packaging Department Shipping Receives the covers and applies them and puts the product in the packaging takes 1 day Shipping takes 3 Weeks Time Stamp: The customer places his order on 01/01/2015 and does not need it to be at his business until 03/31/2015. 01/01/2015 Customer places order 02/02/2015 Steel for the Widgets is ordered 01/09/2015 Ordered Card Board Shipping boxes 01/09/2015 Ordered Plastic covers 03/02/2015 Make the widgets 03/09/2015 • widgets are finished, • plastic covers come in • boxes come in, • apply boxes and plastic covers to the widgets • prepare to ship 03/10/2015 Ship Widgets to customer 03/31/2015 Customer receives the widgets The Steel was ordered in early February and planned to be at the Manufacturing plant in time to complete the widget assembly for the beginning of the second week in March. Packaging and shipping...
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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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...Study Guide for Chapter 5: 1. What does the acronym CPFR represent? a. | Coordinated planning and forecasting relationships | b. | Collaborative planning, forecasting, and replenishment | c. | Centralized purchasing and forecasting relationships | d. | Collaborative purchasing, forecasting, and receivables | 2. Some measures of forecasting accuracy include mean absolute deviation, mean absolute percentage error, and mean squared error. The formula for each is dependent on the forecast error, which is calculated by using the equation: a. | Actual demand for period t divided by the forecasted demand for period t | b. | Actual demand for period t plus the forecasted demand for period t | c. | Actual demand for period t minus the forecasted demand for period t | d. | The average of Actual demand for period t and forecasted demand for period t | Data Set E1 Period | Sales Volume | 1 | 10000 | 2 | 12400 | 3 | 14250 | 4 | 15750 | 5 | 20500 | 6 | 18500 | 7 | 15750 | 8 | 20500 | 9 | 21500 | 10 | 22550 | 3. Using Data Set E1, what would be the forecast for period 7 using a four period moving average: (Choose the closest answer.) a. | 17625 | b. | 15225 | c. | 15300 | d. | 17250 | 4. Using Data Set E1, what would be the forecast for period 6 using a five period weighted moving average? The weights for each period are 0.05, 0.10, 0.20, 0.30, and 0.35 from the oldest period to the most recent period, respectively...
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...CASE Merriwell Bag Company Merriwell Bag Company is a small, family owned corporation located in Seattle, Washington. The stock of the company is equally divided among five members of the Merriwell family (husband, wife, and three sons), but the acknowledged leader is the founder and patriarch, Ed Merriwell. Ed Merriwell formed the company 20 years ago when he resigned as a mill supervisor for a large paper manufacturer. Ironically, the same manufacturer formed a container division five years ago and is presently one of Merriwell’s competitors. Company Strategy The family attributes the success of Merriwell Bag Company to the fact that it has found a market niche and has no “serious” competition. Merriwell supplies stock bags to many small chain stores scattered over a wide geographical area. It ships the bags directly to small regional warehouses or drop ships directly to the individual stores. The family reasons that the large bag manufacturers cannot profitably provide service to accounts on that small of a scale. In fact, Ed Merriwell formed the business with one second-hand bagging machine to provide bags for a small discount store chain and a regional chain of drug stores. These two organizations have grown tremendously over the years, and Ed Merriwell proudly points out that the Bag Company has grown with them. Today, these two original clients are Merriwell’s largest customers. The Merriwell family does not want its business to be too...
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...CHAPTER SEVEN Discussion 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 its supply chain? Collaborative forecasting requires all supply chain partners to share information regarding parameters that might affect demand, such as the timing and magnitude of promotions. Dell could share with their components suppliers all of the promotions, e.g., holiday, back-to-school, etc., they have planned. These suppliers could, in turn, notify their suppliers of discrete components that a spike in demand is anticipated. These demand forecasts for end items determine the demand for components and coupled with knowledge of fabrication times, allows all members of the supply chain to provide the right quantity at the right time to their customers. 3. What role does forecasting play in the supply chain of a mail order firm such as LL Bean? LL Bean has historically operated almost exclusively in a make-to-stock mode and with very few exceptions, stocked products that did not go out of style as rapidly as many other clothing...
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...Forecasting Methods Genius forecasting - This method is based on a combination of intuition, insight, and luck. Psychics and crystal ball readers are the most extreme case of genius forecasting. Their forecasts are based exclusively on intuition. Science fiction writers have sometimes described new technologies with uncanny accuracy. There are many examples where men and women have been remarkable successful at predicting the future. There are also many examples of wrong forecasts. The weakness in genius forecasting is that its impossible to recognize a good forecast until the forecast has come to pass. Some psychic individuals are capable of producing consistently accurate forecasts. Mainstream science generally ignores this fact because the implications are simply to difficult to accept. Our current understanding of reality is not adequate to explain this phenomena. Trend extrapolation - These methods examine trends and cycles in historical data, and then use mathematical techniques to extrapolate to the future. The assumption of all these techniques is that the forces responsible for creating the past, will continue to operate in the future. This is often a valid assumption when forecasting short term horizons, but it falls short when creating medium and long term forecasts. The further out we attempt to forecast, the less certain we become of the forecast. The stability of the environment is the key factor in determining whether trend extrapolation is an appropriate forecasting...
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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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...Discussion Questions: 1,7, and 15 1: (a) What is forecasting? Why is it so important in the management 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: ...
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...DEMAND FORECASTING: EVIDENCE-BASED METHODS Forthcoming in the Oxford Handbook in Managerial Economics Christopher R. Thomas and William F. Shughart II (Eds.) Subject to further revisions File: Demandforecasting-17-August-2011-clean.docx 17 August 2011 J. Scott Armstrong The Wharton School, University of Pennsylvania 747 Huntsman, Philadelphia, PA 19104, U.S.A. T: +1 610 622 6480 F: +1 215 898 2534 armstrong@wharton.upenn.edu Kesten C. Green International Graduate School of Business, University of South Australia City West Campus, North Terrace, Adelaide, SA 5000, Australia T: +61 8 8302 9097 F: +61 8 8302 0709 kesten.green@unisa.edu.au # words in body 10,053 (requested range was 6,000 to 9,000) ABSTRACT We reviewed the evidence-based literature related to the relative accuracy of alternative methods for forecasting demand. The findings yield conclusions that differ substantially from current practice. For problems where there are insufficient data, where one must rely on judgment. The key with judgment is to impose structure with methods such as surveys of intentions or expectations, judgmental bootstrapping, structured analogies, and simulated interaction. Avoid methods that lack evidence on efficacy such as intuition, unstructured meetings, and focus groups. Given ample data, use quantitative methods including extrapolation, quantitative analogies, rule-based forecasting, and causal methods. Among causal methods, econometric methods are useful given good theory, and few key...
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...International Thompson Business Press, 1999, pp. 92-119. Forecasting for Marketing J. Scott Armstrong The Wharton School, University of Pennsylvania Roderick J. Brodie Department of Marketing, University of Auckland Research on forecasting is extensive and includes many studies that have tested alternative methods in order to determine which ones are most effective. We review this evidence in order to provide guidelines for forecasting for marketing. The coverage includes intentions, Delphi, role playing, conjoint analysis, judgmental bootstrapping, analogies, extrapolation, rule-based forecasting, expert systems, and econometric methods. We discuss research about which methods are most appropriate to forecast market size, actions of decision makers, market share, sales, and financial outcomes. In general, there is a need for statistical methods that incorporate the manager's domain knowledge. This includes rule-based forecasting, expert systems, and econometric methods. We describe how to choose a forecasting method and provide guidelines for the effective use of forecasts including such procedures as scenarios. INTRODUCTION Forecasting has long been important to marketing practitioners. For example, Dalrymple (1987), in his survey of 134 U.S. companies, found that 99 percent prepared formal forecasts when they used formal marketing plans. In Dalrymple (1975), 93 percent of the companies sampled indicated that sales forecasting was one of the most critical' aspects, or a ‘very important’...
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...critical role in improving the supply chain of Whirlpool. This is because people nowadays are more demanding due to their busy lifestyle. Thus, oftentimes, people purchase appliances with the expectation that the store will be able to provide or deliver the product to them in just a couple of days, hence “deliver with integrity.” Thirdly, building better expertise in the application of decision support and IT tools such as CPFR and i2 to identify patterns, assess potential performance, and manage newly developed processes was an excellent move on the part of the planners. Benchmarking best practices in forecasting led Whirlpool and some of its key trade partners to believe that CPFR offered a significant opportunity for improvement in the area of forecasting. The end result was that Whirlpool and its trade partners saw a reduction in weekly order variability due to improved forecasting and a less...
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...The Accuracy of Demand Forecasting Between Point of Sale and Order History Supply Chain Management TBS908 Table of Contents 1. Executive Summary 4 2. Company Profile 4 3. Demand 5 3.1 Demand Forecasting 6 3.2 Demand Forecasting Methods 6 3.2.1 Opinion Polling / Qualitative Method (subjective): 6 3.2.2 Statistical Methods/Quantitative Approach (objective): 6 4. Order History Vs. Point-of-sale 8 5. Planning Promotions 8 5.1 Promotion Planning and Supply Chain Contracting in a High-Low Pricing Environment 9 5.1.1 Basic Household Inventory Model: 9 6. Types of demand forecast in GCC and UAE 10 7. Objective 10 8. Methodology 11 Table 3 13 Figure 1 13 9. Result 14 10. Recommendations 14 11. Conclusion: 15 11. References 16 12. Appendixes 17 Appendix I 17 Appendix II 19 1. Executive Summary Demand forecasting is essentially anticipating future prospects by reviewing historical data in the most calculated way in an uncontrollable environment. Foreseeing what and when buyers will purchase has never been a simple procedure for producers or retailers. Troubled by the overwhelming undertaking of correctly coordinating supply with interest, makers are always enhancing procedures to accomplish the most noteworthy estimate exactness that will guarantee when the customer enters a store, the item they are searching for is on the rack. This is getting significantly tricky as the uncertainty level increase. In the below report the demand...
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...1. What are features common to all forecasts, elements of a good forecast, steps in forecasting, and forecasting accuracy? A: A forecast is a statement about the future value of a variable such as demand. That is, forecasts are predictions about the future. The better those predictions, the more informed decisions can be. Some forecasts are long range, covering several years or more. Long-range forecasts are especially important for decisions that will have long-term consequences for an organization or for a town, city, country, state, or nation.(Stevensons- 11e). In simple terms, forecasting is the predicting future results from the present context using forecasting tools such as statistical analysis, regression analysis, moving average, time series, etc. Larger samples and random statistical analysis would lead to better forecasting than casual or convenience method of data collection resulting in more accurate forecasting of the future trend. FEATURES COMMON TO ALL FORECASTS 1. Forecasting techniques generally assume that the same underlying causal system that existed in the past will continue to exist in the future. 2. Forecasts are not perfect and their results usually differ from predicted values. Allowances should be made for forecast errors. 3. Forecasts for groups of items tend to be more accurate than forecasts for individual items because forecasting errors among items in a group usually have a canceling effect. Opportunities for grouping may arise if parts...
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