...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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...Choose one of the forecasting methods and explain the rationale behind using it in real life. I would choose to use the exponential smoothing forecast method because it weighs the most recent past data more strongly than more distant past data. This makes it so that the forecast will react more strongly to immediate changes in the data. This is good to examine when dealing with seasonal patterns and trends that may be taking place. I would find this information very useful when examining the increased production of a product that appears to be in higher demand in recent times than past. Describe how a domestic fast food chain with plans for expanding into China would be able to use a forecasting model. By looking at the data of other companies the fast food chain would be able to put together a forecast to determine if their business venture was viable. They could examine the sales data and determine through a exponential smoothing forecast if it made sense for them to enter into the market. This would show the trends and changes in the data more recently rather than in past time. 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 is based on using historical data to predict future behavior. This method could be used by a retail store, fast food restaurant or clothing manufacturer to predict sales for an upcoming season change. The causal model uses a mathematical correlation...
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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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...Forecasting can be broadly considered as a method or a technique for estimating many future aspects of a business or other operation. There are numerous techniques that can be used to accomplish the goal of forecasting. For example, a retailing firm that has been in business for 25 years can forecast its volume of sales in the coming year based on its experience over the 25-year period—such a forecasting technique bases the future forecast on the past data. While the term "forecasting" may appear to be rather technical, planning for the future is a critical aspect of managing any organization—business, nonprofit, or other. In fact, the long-term success of any organization is closely tied to how well the management of the organization is able to foresee its future and to develop appropriate strategies to deal with likely future scenarios. Intuition, good judgment, and an awareness of how well the economy is doing may give the manager of a business firm a rough idea (or "feeling") of what is likely to happen in the future. Nevertheless, it is not easy to convert a feeling about the future into a precise and useful number, such as next year's sales volume or the raw material cost per unit of output. Forecasting methods can help estimate many such future aspects of a business operation. Suppose that a forecast expert has been asked to provide estimates of the sales volume for a particular product for the next four quarters. One can easily see that a number of other decisions will...
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...Forecasting can be broadly considered as a method or a technique for estimating many future aspects of a business or other operation. There are numerous techniques that can be used to accomplish the goal of forecasting. While the term "forecasting" may appear to be rather technical, planning for the future is a critical aspect of managing any organization;business, nonprofit, or other. In fact, the long-term success of any organization is closely tied to how well the management of the organization is able to foresee its future and to develop appropriate strategies to deal with likely future scenarios. Intuition, good judgment, and an awareness of how well the economy is doing may give the manager of a business firm a rough idea of what is likely to happen in the future. Nevertheless, it is not easy to convert a feeling about the future into a precise and useful number, such as next year's sales volume or the raw material cost per unit of output. Forecasting methods can help estimate many such future aspects of a business operation. All forecasting methods can be divided into two broad categories: qualitative and quantitative. Many forecasting techniques use past or historical data in the form of time series. A time series is simply a set of observations measured at successive points in time or over successive periods of time. Forecasts essentially provide future values of the time series on a specific variable such as sales volume. Division of forecasting methods into qualitative...
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...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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...Chapter 13 Chapter 13 • Forecasting Forecasting TRUE/FALSE 1. The repeated observations of demand for a product or service in their order of occurrence form a pattern known as a time series. Answer: True Reference: Demand Patterns Difficulty: Easy Keywords: time series, repeated observations 2. One of the basic time series patterns is trend. Answer: True Reference: Demand Patterns Difficulty: Easy Keywords: time series, pattern, trend 3. One of the basic time series patterns is random. Answer: True Reference: Demand Patterns Difficulty: Easy Keywords: time series, pattern, random 4. Random variation is an aspect of demand that increases the accuracy of the forecast. Answer: False Reference: Demand Patterns Difficulty: Easy Keywords: random variation, forecast accuracy 5. Aggregation is the act of clustering several similar products or services. Answer: True Reference: Key Decisions on Making Forecasts Difficulty: Moderate Keywords: aggregation, clustering 6. Aggregating products or services together generally decreases the forecast accuracy. Answer: False Reference: Key Decisions on Making Forecasts Difficulty: Moderate Keywords: aggregation, forecast accuracy 54 Copyright ©2010 Pearson Education, Inc. Publishing as Prentice Hall Chapter 13 • Forecasting 7. Judgment methods of forecasting are quantitative methods that use historical data on independent variables to predict demand. Answer: False ...
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...What is Forecasting? Forecasting is the process of making statements about events whose actual outcomes have not yet been observed. Forecasting can be seen as a planning tool for managers to attempt to cope with the uncertainty of the future. Managers are constantly trying to predict the future, making decisions in the present that will ensure the continued success of their firms. Managers use forecasts for budgeting purposes. A forecast aids in determining volume of production, inventory needs, labor hours required, cash requirements, and financing needs. A variety of forecasting methods are available. However, consideration has to be given to cost, preparation time, accuracy, and time period. The manager must understand clearly the assumptions on which a particular forecast method is based to obtain maximum benefit. 1 Types of Forecasts Short Term Short-range forecasts typically encompass the immediate future and concern the day to day operations of a firm. A short-term forecast usually only covers a period of a few months and can be considered an “operating” forecast. Medium Term Medium-range forecasts typically span a few months up to a year. A forecast of this length can be considered “tactical” in nature. Long Term Long-range forecasts typically encompass a period longer than 1 to 2 years. These forecasts are considered strategic and are generally related to management’s attempt to plan new products or build new facilities. 2 Forecasting Methods Time Series ...
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...What is Forecasting? Forecasting is the process of making statements about events whose actual outcomes have not yet been observed. Forecasting can be seen as a planning tool for managers to attempt to cope with the uncertainty of the future. Managers are constantly trying to predict the future, making decisions in the present that will ensure the continued success of their firms. Managers use forecasts for budgeting purposes. A forecast aids in determining volume of production, inventory needs, labor hours required, cash requirements, and financing needs. A variety of forecasting methods are available. However, consideration has to be given to cost, preparation time, accuracy, and time period. The manager must understand clearly the assumptions on which a particular forecast method is based to obtain maximum benefit. 1 Types of Forecasts Short Term Short-range forecasts typically encompass the immediate future and concern the day to day operations of a firm. A short-term forecast usually only covers a period of a few months and can be considered an “operating” forecast. Medium Term Medium-range forecasts typically span a few months up to a year. A forecast of this length can be considered “tactical” in nature. Long Term Long-range forecasts typically encompass a period longer than 1 to 2 years. These forecasts are considered strategic and are generally related to management’s attempt to plan new products or build new facilities. 2 Forecasting Methods Time Series ...
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...Forecasting Methods La Nesha H.Tyler MTH 540: Strayer University Scientific management is based on the applying systematic approaches to solving problems and making decisions. This guide to decision making provides a number of mathematical techniques derived from varied sources such as natural science, mathematics, engineering and statistics (Taylor, 2010). One such technique used in scientific management is the Forecasting method. A Forecast provides a reasonable prediction for a future event. Being able to predict the future can provide a valuable asset for any organization. Predictions will not always be one hundred percent accurate, but they can be a reasonable guide to making decisions based on systematic data. Taylor, 2010, discusses two widely used forecasting methods; time series analysis and regression. This project will present information on forecasting in the form of a storyboard. This project will provide an overview of forecast methods, how forecasts are measured, and identify different types of forecasting methods. According to author Bernard W. Taylor, 2010, “a variety of forecasting exist, and their applicability is dependent on the time frame of the forecast, the existence of patterns in the forecast, and the number of variables to which the forecast is related” (p.682). Time frame classifies forecast into three different categories, short –range, medium-range, and long-range forecast. Short-range forecast are forecast such as Weather forecast in that...
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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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...An Initial Study on the Comparison of Forecast Model for Electricity Consumption in Malaysia. Abstract The purpose of this article is to compare and determine the most suitable technique for forecasting the Electricity Consumption Malaysia. The data was obtained from Statistical Department from January 2008 until December 2012. Five univariate modeling techniques were used include Naïve with Trend Model, Average Percent Change Model, Single Exponential Smoothing, Holt’s Method Model and Holt-winter’s. The data are divided into two parts which are model estimation (fitted) and model evaluation. The selection of the most suitable model was indicated by the smallest value of mean square error (MSE) and Mean Absolute Percentage Error (MAPE.) Based on the analysis, Holt’s Method Model is the most suitable model for forecasting electricity consumption since it has the smallest value of MSE and MAPE. Keywords: Univariate Modelling Techniques; Forecast Model; Mean Absolute Percentage Error; Mean Square Error. Introduction Electricity is one of the most important and used form of energy. Nowadays, electricity is essential for economic development especially for industrial sector. Malaysia, as a developing country, the important of electricity cannot be denied especially in industrial sector. Malaysia’s National electricity utility company (TNB) is the largest in the industry, serving over six million customers throughout the country. TNB is responsible for transmission...
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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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...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, such as opinions and estimates (Aquilano, Chase & Jacobs, 2005). Within the realm of qualitative forecasting are multiple techniques and measures. These are: grass roots, market research, panel consensus, historical analogy, and the Delphi method...
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