...Sample questions 1. Time-series forecasting models: a. | are useful whenever changes occur rapidly and wildly | b. | are more effective in making long-run forecasts than short-run forecasts | c. | are based solely on historical observations of the values of the variable being forecasted | d. | attempt to explain the underlying causal relationships which produce the observed outcome | e. | none of the above | 2. The forecasting technique which attempts to forecast short-run changes and makes use of economic indicators known as leading, coincident or lagging indicators is known as: a. | econometric technique | b. | time-series forecasting | c. | opinion polling | d. | barometric technique | e. | judgment forecasting | 3. The use of quarterly data to develop the forecasting model Yt = a +bYt1 is an example of which forecasting technique? a. | Barometric forecasting | b. | Time-series forecasting | c. | Survey and opinion | d. | Econometric methods based on an understanding of the underlying economic variables involved | e. | Input-output analysis | 4. The variation in an economic time-series which is caused by major expansions or contractions usually of greater than a year in duration is known as: a. | secular trend | b. | cyclical variation | c. | seasonal effect | d. | unpredictable random factor | e. | none of the above | 5. The type of economic indicator that can best be used for business forecasting is the: a. | leading indicator |...
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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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...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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...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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...Forecasting Assignment Name University of Phoenix Operations Management – MGT 554 Instructor Date Forecasting Assignment Forecasting assists managers (companies) to help predict future demand. Demand management is important because companies can increase value or productivity and reduce costs. Chase, Jacobs, & Aquilano (2005) state, “the purpose of demand management is to coordinate and control all sources of demand so the productive system can be used efficiently and the product delivered on time” (p. 512). When a manager is choosing a forecast method, the manager must analyze the cost of doing the forecast and the opportunity cost of using inaccurate data. In addition, Chase, et al. (2005) state “the manager must look at the following factors: (1) Time horizon to forecast, (2) Data availability, (3) Accuracy required, (4) Size of forecasting budget, and (5) Availability of qualified personnel ( p. 518). This paper will compare and contrast three forecasting methods (Delphi Method, Box Jenkins Technique, and Econometric Models) used by managers to help predict future demand as well as explain how the National Basketball Association (NBA) uses forecasting methods to forecast demand under conditions of uncertainty. The Delphi method is a qualitative technique. Chase, et al. (2005) defines qualitative techniques as “subjective or judgmental and are based on estimates and opinions (p. 513). The Delphi method according to Chase et al., is when a group of experts responds...
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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 to make...
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...PRODUCTION AND OPERATIONS MANAGEMENT BUS3020 COURSE CONTENT WEEK THREE TOPIC: FORECASTING • Different Methods of Forecasting – Formal and Informal, • Qualitative and Quantitative Methods • Causal methods auto projection methods of forecasting. What is forecasting? Forecasting is the art and science of predicting future events. As a science it uses historical data and projects them into the future using mathematical models. As an art it uses intuition or judgment to predict the future. Why forecast? Done to minimize uncertainty and evaluate risk relating to future events caused by dynamism of the environment within which organizations operate. Such dynamisms includes: a) Changes in legislation b) Stiff competition c) Demographic changes. Forecasting is important for planning and control of functional areas such as; marketing, finance, operations e.t.c.In the public sector, forecasts are used to plan on: Health, Education, Social services e.t.c. Types of forecasts • Demand forecasts – projections of demand for a company’s products or services. • Economic forecasts – predicts inflation rates, money supplies e.t.c • Technological forecasts.- concerned with the rate of technological progress. Forecasting Horizons a) Short – range forecasts: Covers from a few days to 6 months. It concerns issues like purchase forecasts, job scheduling, workforce levels, job assignments, production levels e.t.c. b) Medium range forecasts: Covers usually from 3 months...
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...companies on a daily basis. Forecasting allows managers to plan according to future events and be prepared to use the system accordingly. With a prediction of the future managers reduce uncertainty and develop plans. The historical data is put together and analyzed to determine forecast events. All large companies use forecasting to make important strategic business decision. This helps them save costs and manage their resources effectively. A firm that is prepared for future occurences will have a healthy financial position. Forecast is of great use to a company because it affects several departments throughout the company. Some of the departments affected are: accounting and finance, marketing, operations, human resources, and information systems. Budgeting, sales, production, inventory control, capacity planning, and purchasing make use of forecasting. Accounting and finance use the data collected to estimate future costs, predict profit or loss, and identify resources available. The marketing department uses forecasting to predict prices and create promotions. The operations of a company is able to run smoothly because job schedules, production schedules, capacity planning, inventory planning, and detecting outsourcing needs are predicted ahead of time. Human resources also benefits from forecasting because seasonal or cyclical hiring is scheduled ahead of time. The company’s information systems are being monitored and revised to keep up with the forecasting results. There are...
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...Forecasting Methods Assignment University of Phoenix MGT 554: Operations Management Steven Williams August 28, 2006 Introduction Forecasting can be defined as Estimating or predicting future events or conditions. Forecasts may be long-term or short-term. The techniques used may be quantitative (often making sue of computers) or qualitative. Quantitative forecasting models may be classified into (a) causal models in which independent variables are used to forecast dependent variables, and (b) time series models, which produce forecasts by extrapolating the historical values of the variables of interest by, e.g., moving averages. Seasonal Model Seasonality is a pattern that repeats for each period. For example annual seasonal pattern has a cycle that is 12 periods long, if the periods are months, or 4 periods long if the periods are quarters. The seasonal index is required to be found for each month, or other periods, such as quarter, week depending on the data availability (Hossein, 1994-2006). Seasonal Index: Seasonal index represents the extent of seasonal influence for a particular segment of the year. The calculation involves a comparison of the expected values of that period to the grand mean. A seasonal index is how much the average for that particular period tends to be above (or below) the grand average. Therefore, to get an accurate estimate for the seasonal index, compute the average of the first period of the cycle, and the second period...
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...Choose one of the forecasting methods and explain the rationale behind using it in real-life. Describe how a domestic fast food chain with plans for expanding into China would be able to use a forecasting model. Time series forecasting is a category of statistical techniques that uses historical data to predict future behavior. Time series methods assume that what has occurred in the past will continue to occur in the future. Time series methods tend to be most useful for short range forecasting, although they can be used for longer range forecasting. A domestic fast food chain with plans of expanding to China could use the Qualitative method. The Qualitative method uses judgment, expertise, and opinion to make forecasts. They could use surveys and other research techniques to determine if there product is something that customers will want to purchase. By using surveys, you could find out if your product is something, someone would want or need. You could research your competition to find out if people want and are willing to purchase your food. What is the difference between a causal model and a time-series model? Give an example of when each would be used. The time series model forecasts statistical techniques that use historical data. This method is often used in restaurants and retail. The causal method uses mathematical correlation between the forecasted items and factors affecting how the forecasted items behaves. What are some of the problems and drawbacks...
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...FORECASTING - a method for translating past experience into estimates of the future. Forecasting is the process of making statements about events whose actual outcomes (typically) have not yet been observed. A commonplace example might be estimation of the expected value for some variable of interest at some specified future date. Prediction is a similar, but more general term. Both might refer to formal statistical methods employing time series, cross-sectional or longitudinal data, or alternatively to less formal judgemental methods. Usage can differ between areas of application: for example in hydrology, the terms "forecast" and "forecasting" are sometimes reserved for estimates of values at certain specific future times, while the term "prediction" is used for more general estimates, such as the number of times floods will occur over a long period. Risk and uncertainty are central to forecasting and prediction; it is generally considered good practice to indicate the degree of uncertainty attaching to forecasts. The process of climate change and increasing energy prices has led to the usage of Egain Forecasting of buildings. The method uses Forecasting to reduce the energy needed to heat the building, thus reducing the emission of greenhouse gases. Forecasting is used in the practice of Customer Demand Planning in every day business forecasting for manufacturing companies. The discipline of demand planning, also sometimes referred to as supply chain forecasting, embraces...
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...Forecasting Models: Associative and Time Series Forecasting involves using past data to generate a number, set of numbers, or scenario that corresponds to a future occurrence. It is absolutely essential to short-range and long-range planning. Time Series and Associative models are both quantitative forecast techniques are more objective than qualitative techniques such as the Delphi Technique and market research. Time Series Models Based on the assumption that history will repeat itself, there will be identifiable patterns of behaviour that can be used to predict future behaviour. This model is useful when you have a short time requirement (eg days) to analyse products in their growth stages to predict short-term outcomes. To use this model you look at several historical periods and choose a method that minimises a chosen measure of error. Then use that method to predict the future. To do this you use detailed data by SKU's (Stock Keeping Units) which are readily available. In TSM there may be identifiable underlying behaviours to identify as well as the causes of that behaviour. The data may show causal patterns that appear to repeat themselves – the trick is to determine which are true patterns that can be used for analysis and which are merely random variations. The patterns you look for include: Trends – long term movements in either direction Cycles - wavelike variations lasting more than a year usually tied to economic or political conditions...
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...A Time Series Forecasting Analysis on the Monthly Stocks of Rice in the Philippines A Research Paper Presented To Dr. Cesar Rufino Of the Department of Economics School of Economics De La Salle University, Manila In Partial Fulfillment of the Course Requirements in Economic Forecasting (ECOFORE) Term 3 AY 2014-2015 Submitted by: Jayme, Kevin Matthew D. April 24 2015 0 I. Introduction The Philippines has been the accredited as an agricultural nation that provides different types of agricultural related goods, both for the domestic and international market. Rice has been the staple food in the Philippine to 80% of the population as it is customary diet that has been in beaded in the Philippine culture (Drilon Jr., 2012). Despite the strong history of agriculture and the skills and weather condition perfect for growth of rice, decrease of land and increase of total population around the Philippines decrease the opportunity for the population to have access to rice. In addition, neighboring countries, such as Thailand and Vietnam, had been on the rise of rice exportation. Not to mention the implementation of the ASEAN integration is happening in 2015. This means that the Philippines is lagging behind as it is the 8th largest exporters of rice in the world (Tiongco & Francisco, 2011). Institution, such as International Rice Research Institute (IRRI), has gone into research and development of rice growth in different conditions and situation...
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...to the following: * Choose one of the forecasting methods and explain the rationale behind using it in real-life. Adjusted exponential smooth is the exponential smoothing forecast with a trend factor added to it. It can adjust with the trend factor (Beta) with a high beta reflecting changes more than a low beta. In real life example such as forecasting computer sales based on at least one year of sales data helps management. By using exponential smoother factor (alpha) we will react and adjust more slowly as the value reaches 0. As alpha gets higher we will have a more positive effect on demand and the forecast will be for an increase. The initial smoothing constant alpha and the trend factor beta are set by the manager with a value from 0 to 1. It is adjusted accordingly and a comparison of the results is use in the forecast. * Describe how a domestic fast food chain with plans for expanding into China would be able to use a forecasting model. When a fast food chain plans to expand into china the use of forecasting methods will greatly enhance the company’s chances of success. For instance, they include integrated information system and real time exchange of information with its suppliers. Demand can be based on information of its demand for locations with at least 12 months of sales data. A labor management system could also use forecasting methods. What is the difference between a causal model and a time- series model? Give an example of when each would be used...
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...forecasts are qualitative and quantitative. Within each of these types are multiple methods and models. Qualitative forecasts are based upon subjective data. Quantitative forecasts are derived from objective data. Both methods are not suitable for all situations and circumstances. Each has inherent strengths and weaknesses. The forecaster must understand the strengths and shortcomings of each method and choose appropriately. One example of forecasting is the United States Marine Corps use of forecasting techniques, both qualitative and quantitative, to predict ammunition requirements. Forecasting Defined Forecasting is “A statement about the future†(Anonymous, 2005). Operations management is designed to support forecasted performances and events. Specifically, operations managers allocate personnel, time, and resources in order to meet the demands of forecasts. The most successful companies achieve their results by assuming just such a proactive vice reactive posture. While forecasting is widely used, it does not fit into a standard one size fits all model. Multiple proven methods and models exist. In this paper we will examine, compare, and contrast the two most commonly used methods, qualitative and quantitative forecasting. Lastly, as a case study, we will examine how the United States Marine Corps forecasts its fiscal year ammunition requirements. Qualitative Forecasting Qualitative forecasts are the least scientific. They are based exclusively upon subjective data...
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