Forecasting PaperJoin now to read essay Forecasting PaperAbstractForecasts are extensively used to support business decisions and direct the work of operations managers. The two major types of 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.
< p>The Forecast of U.S. Naval Forces
An introductory section introduces the fundamentals of forecast models, both quantitative and principal-based. In this section, we will explain how forecasters, analysts, administrators, and others might best understand and employ a series of forecasting techniques. Forecasters are trained in the following fields:
• Information security (IRS),
• Statistical techniques (CS/P and other CS/P models),
• Technical expertise, including statistical science and computer science.
• Forecasters learn how to forecast, analyze, and estimate trends in a large set of data and trends for an entire field of data (for example, in real life and online data structures). Forecasters are also taught a skill-based approach in the field of analysis, such as forecasting, estimation, and validation.
• Forecasters obtain information on any trends in the population, the U.S. military, industry and the global environment (for example, trends for a particular city, region or country). Forecasters can be highly motivated to improve their methods and practices on a specific target area to help facilitate efficient action by the U.S. government.
• Forecasters include analysts, analysts, and non-technical practitioners who can prepare and produce forecasts. Forecasters may include a portfolio of professional or personal knowledge in their specialty.
• Forecasters are asked to perform analyses of real-world data that is relevant only to a specific geographic area or to a particular demographic group.
• Forecasters study and develop a research agenda to be relevant to a specific problem or problem area.
• Forecasters can analyze and forecast trends over time, in a variety of environments, as expected by the U.S. government. Forecasters have limited knowledge of information security and can not understand or understand statistics that they gather from real-world data or from sources they use within their field.
Forecasters can also employ complex tools such as forecast summaries, to gather more data quickly and rapidly.
• Forecasters can make judgments and judgments based on observations or assumptions based on observations obtained from many available sources. Forecasters are also asked to include data from a research project under the supervision of various scientists, analysts, and non-technical practitioners.
• Forecasters understand the importance of economic policy and are familiarizing themselves with financial markets of many different financial instruments.
• Forecasters are trained in statistical analysis, including a statistical methodology.
• Forecasters are exposed to models such as computer model simulations, data manipulation, probability analysis, regression, and regression to arrive at predictive models. Forecasters learn how to build and maintain models quickly and accurately. Forecasters are also encouraged to learn programming in the field so that they can leverage their extensive experience developing complex programs from scratch.
• Forecasters have extensive experience in applying new techniques into traditional market modeling. Forecasters are encouraged to apply them to natural and applied statistics.
• Forecasters are familiar with the economics and economics of markets as they work to predict and analyze large amounts of economic data.
• Forecasters can utilize forecasting techniques to guide, assess, and provide advice to the public in
< p>The Forecast of U.S. Naval Forces
An introductory section introduces the fundamentals of forecast models, both quantitative and principal-based. In this section, we will explain how forecasters, analysts, administrators, and others might best understand and employ a series of forecasting techniques. Forecasters are trained in the following fields:
• Information security (IRS),
• Statistical techniques (CS/P and other CS/P models),
• Technical expertise, including statistical science and computer science.
• Forecasters learn how to forecast, analyze, and estimate trends in a large set of data and trends for an entire field of data (for example, in real life and online data structures). Forecasters are also taught a skill-based approach in the field of analysis, such as forecasting, estimation, and validation.
• Forecasters obtain information on any trends in the population, the U.S. military, industry and the global environment (for example, trends for a particular city, region or country). Forecasters can be highly motivated to improve their methods and practices on a specific target area to help facilitate efficient action by the U.S. government.
• Forecasters include analysts, analysts, and non-technical practitioners who can prepare and produce forecasts. Forecasters may include a portfolio of professional or personal knowledge in their specialty.
• Forecasters are asked to perform analyses of real-world data that is relevant only to a specific geographic area or to a particular demographic group.
• Forecasters study and develop a research agenda to be relevant to a specific problem or problem area.
• Forecasters can analyze and forecast trends over time, in a variety of environments, as expected by the U.S. government. Forecasters have limited knowledge of information security and can not understand or understand statistics that they gather from real-world data or from sources they use within their field.
Forecasters can also employ complex tools such as forecast summaries, to gather more data quickly and rapidly.
• Forecasters can make judgments and judgments based on observations or assumptions based on observations obtained from many available sources. Forecasters are also asked to include data from a research project under the supervision of various scientists, analysts, and non-technical practitioners.
• Forecasters understand the importance of economic policy and are familiarizing themselves with financial markets of many different financial instruments.
• Forecasters are trained in statistical analysis, including a statistical methodology.
• Forecasters are exposed to models such as computer model simulations, data manipulation, probability analysis, regression, and regression to arrive at predictive models. Forecasters learn how to build and maintain models quickly and accurately. Forecasters are also encouraged to learn programming in the field so that they can leverage their extensive experience developing complex programs from scratch.
• Forecasters have extensive experience in applying new techniques into traditional market modeling. Forecasters are encouraged to apply them to natural and applied statistics.
• Forecasters are familiar with the economics and economics of markets as they work to predict and analyze large amounts of economic data.
• Forecasters can utilize forecasting techniques to guide, assess, and provide advice to the public in
Forecasting DefinedForecasting 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 ForecastingQualitative 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 (Aquilano, Chase & Jacobs, 2005).
Grass RootsGrass roots can best be described as a bottom-up process. This method is predicated on the assumption that employees who closely interact with customers are best aware of the customers’ desires (Aquilano, Chase & Jacobs, 2005). Inputs from the lowest level are progressively staffed to the highest level where the decision is ultimately made (Aquilano, Chase & Jacobs, 2005).
Market ResearchMarket research is performed by specialized companies who collect data on customer likes and dislikes regarding existing or proposed products (Aquilano, Chase & Jacobs, 2005). This data is then used to create forecasts (Aquilano, Chase & Jacobs, 2005).
Panel ConsensusPanel consensus forecasting employs a panel of individuals with varying degrees of experience, training, and seniority in order to produce a diverse, broad estimate of the future (Aquilano, Chase & Jacobs, 2005). The purpose of the diverse panel is to eliminate group think (Aquilano, Chase & Jacobs, 2005). Too often, however, group think results as the junior members feel pressured to endorse the senior members’ views.
Historical AnalogyThe historical analogy technique uses the past performance of similar products to forecast sales for new products (Aquilano, Chase & Jacobs, 2005). An example would be Mercedes Benz using sales data related to the BMW X5 sports utility vehicle (SUV) to forecast sales of their own ML-350 SUV.
Delphi MethodThe Delphi method is a variation of the panel consensus. In order to avoid the intimidation of lower-level members, Delphi activity masks the identities of participants and grants equal weight to the inputs of all members (Aquilano, Chase & Jacobs, 2005). The Delphi method is a series of inputs. After each series, the panel reviews the inputs and each member updates his/her contributions. The process repeats itself until a consensus is reached (Aquilano, Chase & Jacobs, 2005).
Quantitative MethodsQualitative forecasts are based upon calculations and are more accurate for long-term use. They are based exclusively upon objective data, such as past performance (Aquilano, Chase & Jacobs, 2005). Time series analysis is the most widely used quantitative forecasting method.
Time Series Analysis ForecastingTime series analysis is a series of observations taken at regular intervals over a specified period of time (Anonymous, n. d.). The following are techniques of time series analysis: simple moving average, weighted moving average and simple exponential smoothing, exponential smoothing with trend,