Forecasting
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This document discusses forecasting in manufacturing and mechanical engineering. It defines forecasting as an estimate of uncertain future events used to improve decision-making and planning. Several types of forecasts are described, including short, intermediate, and long term forecasts used for inventory control, production planning, and facility planning. Common forecasting methods like informed opinion, Delphi method, market research, and historical analogy are outlined. The document also covers forecasting techniques like simple and weighted moving averages, exponential smoothing, and seasonal adjustments. It defines forecast error and common metrics to evaluate forecast accuracy like MAD, MAPE, and MSE. In conclusion, the author acknowledges sources for the forecasting information and expresses gratitude to faculties, parents, and friends.
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Measurements Methods of forecasting errors
Forecasting involves making predictions about future events based on historical data. Forecasting errors are measured as the difference between actual and predicted values. This document discusses various methods of measuring forecasting errors over time (calendar errors) and across different products (cross-sectional errors). It defines important error metrics like mean absolute deviation and mean absolute percentage error, and the tracking signal ratio, which are used to evaluate forecast accuracy and consistency over multiple periods. The document also includes an example calculating these metrics based on actual and forecast demand values.
FORECASTING
Quantitative Math - MATH 132
Credits: Group 4 Reporters S.Y. 2015-2016
The ppt has animations, you'll appreciate the presentation if you'll download it. Thank you
ForecastIT 1. Introduction to Forecasting
This lesson begins with background information about forecasting and continues discussing the forecasting process. We concentrate on four main steps: set an objective, build model, evaluate model, and use model.
Forecasting techniques
This document discusses quantitative approaches to forecasting, including time series analysis and forecasting techniques. It covers the components of a time series, including trends, cycles, seasonality, and irregular components. Specific quantitative forecasting approaches covered include smoothing methods like moving averages, weighted moving averages, and exponential smoothing. Examples are provided to demonstrate how to perform moving averages and exponential smoothing on time series data for sales of headache medicine. The document aims to teach readers how to analyze time series data and select appropriate forecasting techniques.
Forecasting
This document discusses various forecasting methods including qualitative methods like panel consensus and quantitative methods like time series analysis. It explains moving averages, weighted moving averages, and exponential smoothing for time series forecasting. Moving averages are simple to calculate but do not respond well to trends while exponential smoothing accounts for trends using smoothing constants. Linear regression can also be used to explore relationships between dependent and independent variables for forecasting. Overall the key points are that forecasting predicts future demand based on past data, different quantitative methods are suited to different situations, and accuracy depends on how well past patterns predict the future.
FORECASTING MODELS
This powerpoint presentation was done as part of the course STAT 591 titled Mater's Seminar during Third semester of MSc. Agricultural Statistics at Agricultural College, Bapatla under ANGRAU, Andhra Pradesh.
Forecating calculations
The document provides information about forecasting methods. It includes a table with historical demand and forecast data for 8 periods. It defines formulas to calculate Mean Absolute Deviation (MAD), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Tracking Signal (TS) based on the data. It then lists common features of forecasts, elements of a good forecast, steps in the forecasting process, types of forecasts including qualitative and quantitative methods, components of time series data, and describes the naive forecasting method.
Forecasting
This document discusses quantitative approaches to forecasting, including time series components and methods. It covers measuring forecast accuracy, smoothing methods like moving averages, and trend projection. Smoothing aims to average out irregular fluctuations, while trend projection uses least squares to determine a linear trend line for future forecasts. An example shows calculating a 3-period moving average forecast.
Recommended
Measurements Methods of forecasting errors
Forecasting involves making predictions about future events based on historical data. Forecasting errors are measured as the difference between actual and predicted values. This document discusses various methods of measuring forecasting errors over time (calendar errors) and across different products (cross-sectional errors). It defines important error metrics like mean absolute deviation and mean absolute percentage error, and the tracking signal ratio, which are used to evaluate forecast accuracy and consistency over multiple periods. The document also includes an example calculating these metrics based on actual and forecast demand values.
FORECASTING
Quantitative Math - MATH 132
Credits: Group 4 Reporters S.Y. 2015-2016
The ppt has animations, you'll appreciate the presentation if you'll download it. Thank you
ForecastIT 1. Introduction to Forecasting
This lesson begins with background information about forecasting and continues discussing the forecasting process. We concentrate on four main steps: set an objective, build model, evaluate model, and use model.
Forecasting techniques
This document discusses quantitative approaches to forecasting, including time series analysis and forecasting techniques. It covers the components of a time series, including trends, cycles, seasonality, and irregular components. Specific quantitative forecasting approaches covered include smoothing methods like moving averages, weighted moving averages, and exponential smoothing. Examples are provided to demonstrate how to perform moving averages and exponential smoothing on time series data for sales of headache medicine. The document aims to teach readers how to analyze time series data and select appropriate forecasting techniques.
Forecasting
This document discusses various forecasting methods including qualitative methods like panel consensus and quantitative methods like time series analysis. It explains moving averages, weighted moving averages, and exponential smoothing for time series forecasting. Moving averages are simple to calculate but do not respond well to trends while exponential smoothing accounts for trends using smoothing constants. Linear regression can also be used to explore relationships between dependent and independent variables for forecasting. Overall the key points are that forecasting predicts future demand based on past data, different quantitative methods are suited to different situations, and accuracy depends on how well past patterns predict the future.
FORECASTING MODELS
This powerpoint presentation was done as part of the course STAT 591 titled Mater's Seminar during Third semester of MSc. Agricultural Statistics at Agricultural College, Bapatla under ANGRAU, Andhra Pradesh.
Forecating calculations
The document provides information about forecasting methods. It includes a table with historical demand and forecast data for 8 periods. It defines formulas to calculate Mean Absolute Deviation (MAD), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Tracking Signal (TS) based on the data. It then lists common features of forecasts, elements of a good forecast, steps in the forecasting process, types of forecasts including qualitative and quantitative methods, components of time series data, and describes the naive forecasting method.
Forecasting
This document discusses quantitative approaches to forecasting, including time series components and methods. It covers measuring forecast accuracy, smoothing methods like moving averages, and trend projection. Smoothing aims to average out irregular fluctuations, while trend projection uses least squares to determine a linear trend line for future forecasts. An example shows calculating a 3-period moving average forecast.
Forecasting
Forecasting uses qualitative and quantitative methods to predict future values of random variables. It aims to aid decision making through accurate predictions by analyzing trends, averages, and external factors. While forecasts provide benefits, they also have costs due to potential inaccuracies from unreliable data, outdated information, and inability to predict external changes. The main approaches to forecasting are time series analysis, econometrics, and combining prior methods with subjective judgement.
What Are Common Forecasting Techniques
This document discusses various forecasting techniques used to predict future events or trends. It outlines both quantitative and qualitative methods. Quantitative methods rely on historical data patterns and time series analysis using tools like moving averages, weighted moving averages, trend projection, naïve forecasting, and multiplicative seasonal forecasting. Qualitative methods are used when data is limited and involve expert opinions through approaches such as sales force composites, field sales management, executive opinion, Delphi technique, and market surveys. The document provides examples and explanations of common quantitative forecasting methods like moving averages, weighted moving averages, and multiplicative seasonal forecasting.
Ops management lecture 2 forecasting
This document discusses forecasting methods. It defines forecasting and identifies different types of forecasts. It discusses time horizons, approaches to forecasting including qualitative and quantitative methods, and steps in the forecasting process. It also describes averaging techniques like moving averages and exponential smoothing that are used to forecast and determine what makes a good forecast.
forecasting methods
- Forecasting helps reduce risk and uncertainty in decision making by predicting future outcomes.
- There are three main types of forecasting methods: qualitative, extrapolative/time series, and causal/explanatory.
- Time series forecasting uses historical data patterns to predict future values, accounting for trends, seasonality, cycles, and randomness. Common time series forecasting techniques include moving averages, weighted moving averages, and exponential smoothing.
step of forecasting
This document outlines the steps for forecasting, including:
1) Determining the use and items to be forecasted, as well as the time horizon, which can be short, medium, or long-term.
2) Selecting either qualitative or quantitative forecasting approaches depending on the situation.
3) Collecting and reducing relevant and reliable data from primary and secondary sources.
4) Exploring patterns in time series data like trends, cycles, and seasonality.
5) Selecting an appropriate forecasting model like exponential smoothing or regression and making a forecast.
Class notes forecasting
Forecasting involves predicting future events and is commonly used to estimate future demand for products and services. There are different time horizons for forecasts, including short-range (up to 1 year), medium-range (3 months to 3 years), and long-range (3 years or more). Forecasting methods can be quantitative, using mathematical techniques and historical data, or qualitative, using intuition and expert opinions. The objectives of demand forecasting include production planning, financial planning, and long-term strategic planning.
Forecasting
- Forecasting involves making predictions about future market conditions and demand. It is an important part of business planning but forecasts will always be imperfect.
- Market size refers to the number of potential buyers and sellers in a market. Understanding market size is important for launching new products or services. Qualitative and quantitative models can be used to forecast market size.
- Qualitative models include expert opinion methods like the Delphi method and jury of executive opinion. Quantitative time series models analyze historical demand patterns using techniques like moving averages, exponential smoothing, and regression analysis. These techniques help minimize forecast errors.
Mba ii pmom_unit-1.3 forecasting a
This document discusses forecasting methods used in production and operations management. It defines forecasting as predicting future values based on historical data. The key types of forecasts discussed are judgmental forecasts using subjective inputs, time series forecasts using historical data patterns, and associative models using explanatory variables. Time series methods covered include simple moving averages, weighted moving averages, and exponential smoothing. Exponential smoothing gives more weight to recent periods to generate forecasts. Quantitative forecasting methods are chosen based on the forecast horizon and data available.
Presentation 2
This document discusses forecasting techniques based on time series analysis. It defines key concepts like extrapolation, time series components, and analytical indicators. Extrapolation involves projecting past trends and patterns into the future, and can be used to forecast trends, cycles, and causal relationships. Time series data has components like trends, seasonality, cycles, and random variations. Analytical indicators like absolute and percentage changes are used to analyze time series data and make forecasts. The document provides an example of forecasting future demand using average absolute increase and average growth rate.
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This document discusses forecasting techniques in time series analysis and causal models. It describes time series models as analyzing a time-ordered sequence of observations over regular intervals to identify trends. These include simple exponential smoothing, which weights older data less and newer data more, and moving averages, which use an average of past periods to forecast the next period. Causal models are based on relationships between dependent and independent variables, assuming past trends will continue influencing future variables. Linear regression is provided as an example causal model that fits a line to measure the effect of a single independent variable.
Forecasting model 15 04-31
1. The document discusses 10 different forecasting models: time series moving average, market research, exponential smoothing, jury of executive opinion, naive method, correlation-regression, sales force composite, Delphi technique, and econometric models.
2. It provides examples and explanations of simple and weighted moving averages as well as exponential smoothing. It also outlines advantages and disadvantages of various qualitative forecasting methods.
3. The document concludes with an application example of forecasting apricot production and distribution using a times series seasonal model.
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The document discusses various quantitative and qualitative forecasting methods. It begins by introducing forecasting and explaining its importance for business planning. It then outlines different types of forecasts based on time horizon (short, medium, long-range). The document proceeds to describe several quantitative time series forecasting models including naive, moving average (simple, weighted), and exponential smoothing (simple, with trend and seasonality components). It also briefly discusses qualitative methods such as executive judgment, sales force composite, market research, and the Delphi method. Examples are provided to illustrate exponential smoothing calculations.
Hr forecasting
Forecasting involves generating numbers or scenarios to predict future occurrences. It is important for short-term and long-term planning as forecasts are based on past data, unlike predictions. Forecasts for shorter time periods like tomorrow or next month will generally be more accurate than forecasts for longer periods like years in the future. It is wise to provide a forecast range rather than a single number since forecasts are seldom totally accurate. Effective forecasts are accurate, reliable, timely, easy to understand, and cost-effective. Forecasting techniques can be qualitative based on opinions or quantitative using mathematical models to analyze past time-series or explanatory variable data.
Forcasting
The document discusses different approaches to time series forecasting and econometric modeling. It recommends that for short-term forecasts covering the next week, month, or year, simple time series models are best. However, for long-term forecasts, causal models are preferable as demand patterns can change over time. The document also notes that a good time series forecast includes the level, trend, seasonal component, business cycle, a predicted value at a specific time point, and an error range.
Quantitative and qualitative forecasting techniques om
Qualitative forecasting uses expert judgment rather than numerical analysis to estimate future outcomes. It relies on the knowledge and insights of experienced employees and consultants. This approach differs from quantitative forecasting, which analyzes historical data to discern future trends. Qualitative forecasting techniques include the Delphi technique, scenario writing, and subjective approaches.
Operations management forecasting
The document summarizes key concepts about forecasting from the 8th edition of the textbook "Operations Management" by William J. Stevenson. It discusses definitions of forecasting, the importance and uses of forecasts in various business functions. Methods of forecasting include qualitative judgmental forecasts, quantitative time series analysis, and associative models using explanatory variables. Specific forecasting techniques covered include naive forecasts, moving averages, exponential smoothing, trend analysis, and regression. The document also addresses evaluating forecast accuracy and controlling forecasts.
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The presentation gives Glimpses of meaning of forecasting and analytical ways to know the forecast demand for future
Forecasting
This document discusses various forecasting techniques. It covers qualitative and quantitative methods as well as different time horizons for forecasting. Specific quantitative techniques discussed include moving averages, exponential smoothing, regression analysis, and double exponential smoothing. Moving averages and exponential smoothing are described as methods for forecasting stationary time series. Exponential smoothing provides a weighted average of past observations with more weight given to recent observations. Double exponential smoothing accounts for trends by smoothing changes in the intercept and slope over time.
Ch 10 forecast
Forecasting involves predicting future events and is essential for business decisions regarding production, inventory, personnel, and facilities. There are qualitative and quantitative forecasting methods, with quantitative relying on mathematical models. Key principles of forecasting are that forecasts are rarely perfect, more accurate for groups than individuals, and more accurate over shorter time horizons. Common patterns in time series data include trends, seasonality, and cycles. Quantitative forecasting models analyze these patterns in historical data to generate forecasts.
Demand forecasting
This document discusses demand forecasting methods. It explains that forecasting involves estimating future demand for products and services. There are different types of forecasts including long-range, medium-range, and short-term forecasts used for strategic, tactical, and operational planning respectively. Qualitative methods rely on judgment while quantitative methods use mathematical models and historical data. Common quantitative methods are linear regression, moving average, and exponential smoothing. Accuracy and characteristics like impulse response and noise dampening ability are used to evaluate forecasting models.
Forecasting Economic Activity using Asset Prices
This dissertation evaluates how well the asset prices and, in particular the term spread, the short rate and the real stock returns, forecast the GDP growth and the Industrial Production. The study is applied with data of seven countries (Canada, France, Germany, Italy, Japan, United Kingdom and United States) and it covers a period of time between 1966 until now. The research finds that the asset prices have forecasting power for one quarter/month but they lose their power when the forecasting horizon increases. Moreover, the paper evaluates that the real stock return is the best predictor of the GDP growth and that the short rate has more predictive content than the term spread.
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William B. Considine studied economics and econometrics as an undergraduate. Econometrics combines statistics, economics, and math to analyze large amounts of data and develop simple relationships to test economic models and predict future trends. These forecasts are often used to drive economic policymaking. Econometrics employs tools like frequency distributions, regression analysis, and statistical inferences to test hypotheses. One area where econometrics informs policymaking is healthcare reform, where it must model costs to inform decisions around issues like provider reimbursement and utilization of services.
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Forecasting
Forecasting uses qualitative and quantitative methods to predict future values of random variables. It aims to aid decision making through accurate predictions by analyzing trends, averages, and external factors. While forecasts provide benefits, they also have costs due to potential inaccuracies from unreliable data, outdated information, and inability to predict external changes. The main approaches to forecasting are time series analysis, econometrics, and combining prior methods with subjective judgement.
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This document discusses various forecasting techniques used to predict future events or trends. It outlines both quantitative and qualitative methods. Quantitative methods rely on historical data patterns and time series analysis using tools like moving averages, weighted moving averages, trend projection, naïve forecasting, and multiplicative seasonal forecasting. Qualitative methods are used when data is limited and involve expert opinions through approaches such as sales force composites, field sales management, executive opinion, Delphi technique, and market surveys. The document provides examples and explanations of common quantitative forecasting methods like moving averages, weighted moving averages, and multiplicative seasonal forecasting.
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This document discusses forecasting methods. It defines forecasting and identifies different types of forecasts. It discusses time horizons, approaches to forecasting including qualitative and quantitative methods, and steps in the forecasting process. It also describes averaging techniques like moving averages and exponential smoothing that are used to forecast and determine what makes a good forecast.
forecasting methods
- Forecasting helps reduce risk and uncertainty in decision making by predicting future outcomes.
- There are three main types of forecasting methods: qualitative, extrapolative/time series, and causal/explanatory.
- Time series forecasting uses historical data patterns to predict future values, accounting for trends, seasonality, cycles, and randomness. Common time series forecasting techniques include moving averages, weighted moving averages, and exponential smoothing.
step of forecasting
This document outlines the steps for forecasting, including:
1) Determining the use and items to be forecasted, as well as the time horizon, which can be short, medium, or long-term.
2) Selecting either qualitative or quantitative forecasting approaches depending on the situation.
3) Collecting and reducing relevant and reliable data from primary and secondary sources.
4) Exploring patterns in time series data like trends, cycles, and seasonality.
5) Selecting an appropriate forecasting model like exponential smoothing or regression and making a forecast.
Class notes forecasting
Forecasting involves predicting future events and is commonly used to estimate future demand for products and services. There are different time horizons for forecasts, including short-range (up to 1 year), medium-range (3 months to 3 years), and long-range (3 years or more). Forecasting methods can be quantitative, using mathematical techniques and historical data, or qualitative, using intuition and expert opinions. The objectives of demand forecasting include production planning, financial planning, and long-term strategic planning.
Forecasting
- Forecasting involves making predictions about future market conditions and demand. It is an important part of business planning but forecasts will always be imperfect.
- Market size refers to the number of potential buyers and sellers in a market. Understanding market size is important for launching new products or services. Qualitative and quantitative models can be used to forecast market size.
- Qualitative models include expert opinion methods like the Delphi method and jury of executive opinion. Quantitative time series models analyze historical demand patterns using techniques like moving averages, exponential smoothing, and regression analysis. These techniques help minimize forecast errors.
Mba ii pmom_unit-1.3 forecasting a
This document discusses forecasting methods used in production and operations management. It defines forecasting as predicting future values based on historical data. The key types of forecasts discussed are judgmental forecasts using subjective inputs, time series forecasts using historical data patterns, and associative models using explanatory variables. Time series methods covered include simple moving averages, weighted moving averages, and exponential smoothing. Exponential smoothing gives more weight to recent periods to generate forecasts. Quantitative forecasting methods are chosen based on the forecast horizon and data available.
Presentation 2
This document discusses forecasting techniques based on time series analysis. It defines key concepts like extrapolation, time series components, and analytical indicators. Extrapolation involves projecting past trends and patterns into the future, and can be used to forecast trends, cycles, and causal relationships. Time series data has components like trends, seasonality, cycles, and random variations. Analytical indicators like absolute and percentage changes are used to analyze time series data and make forecasts. The document provides an example of forecasting future demand using average absolute increase and average growth rate.
Quantitative methods of demand forecasting
This document discusses forecasting techniques in time series analysis and causal models. It describes time series models as analyzing a time-ordered sequence of observations over regular intervals to identify trends. These include simple exponential smoothing, which weights older data less and newer data more, and moving averages, which use an average of past periods to forecast the next period. Causal models are based on relationships between dependent and independent variables, assuming past trends will continue influencing future variables. Linear regression is provided as an example causal model that fits a line to measure the effect of a single independent variable.
Forecasting model 15 04-31
1. The document discusses 10 different forecasting models: time series moving average, market research, exponential smoothing, jury of executive opinion, naive method, correlation-regression, sales force composite, Delphi technique, and econometric models.
2. It provides examples and explanations of simple and weighted moving averages as well as exponential smoothing. It also outlines advantages and disadvantages of various qualitative forecasting methods.
3. The document concludes with an application example of forecasting apricot production and distribution using a times series seasonal model.
Forecasting
The document discusses various quantitative and qualitative forecasting methods. It begins by introducing forecasting and explaining its importance for business planning. It then outlines different types of forecasts based on time horizon (short, medium, long-range). The document proceeds to describe several quantitative time series forecasting models including naive, moving average (simple, weighted), and exponential smoothing (simple, with trend and seasonality components). It also briefly discusses qualitative methods such as executive judgment, sales force composite, market research, and the Delphi method. Examples are provided to illustrate exponential smoothing calculations.
Hr forecasting
Forecasting involves generating numbers or scenarios to predict future occurrences. It is important for short-term and long-term planning as forecasts are based on past data, unlike predictions. Forecasts for shorter time periods like tomorrow or next month will generally be more accurate than forecasts for longer periods like years in the future. It is wise to provide a forecast range rather than a single number since forecasts are seldom totally accurate. Effective forecasts are accurate, reliable, timely, easy to understand, and cost-effective. Forecasting techniques can be qualitative based on opinions or quantitative using mathematical models to analyze past time-series or explanatory variable data.
Forcasting
The document discusses different approaches to time series forecasting and econometric modeling. It recommends that for short-term forecasts covering the next week, month, or year, simple time series models are best. However, for long-term forecasts, causal models are preferable as demand patterns can change over time. The document also notes that a good time series forecast includes the level, trend, seasonal component, business cycle, a predicted value at a specific time point, and an error range.
Quantitative and qualitative forecasting techniques om
Qualitative forecasting uses expert judgment rather than numerical analysis to estimate future outcomes. It relies on the knowledge and insights of experienced employees and consultants. This approach differs from quantitative forecasting, which analyzes historical data to discern future trends. Qualitative forecasting techniques include the Delphi technique, scenario writing, and subjective approaches.
Operations management forecasting
The document summarizes key concepts about forecasting from the 8th edition of the textbook "Operations Management" by William J. Stevenson. It discusses definitions of forecasting, the importance and uses of forecasts in various business functions. Methods of forecasting include qualitative judgmental forecasts, quantitative time series analysis, and associative models using explanatory variables. Specific forecasting techniques covered include naive forecasts, moving averages, exponential smoothing, trend analysis, and regression. The document also addresses evaluating forecast accuracy and controlling forecasts.
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The presentation gives Glimpses of meaning of forecasting and analytical ways to know the forecast demand for future
Forecasting
This document discusses various forecasting techniques. It covers qualitative and quantitative methods as well as different time horizons for forecasting. Specific quantitative techniques discussed include moving averages, exponential smoothing, regression analysis, and double exponential smoothing. Moving averages and exponential smoothing are described as methods for forecasting stationary time series. Exponential smoothing provides a weighted average of past observations with more weight given to recent observations. Double exponential smoothing accounts for trends by smoothing changes in the intercept and slope over time.
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Forecasting involves predicting future events and is essential for business decisions regarding production, inventory, personnel, and facilities. There are qualitative and quantitative forecasting methods, with quantitative relying on mathematical models. Key principles of forecasting are that forecasts are rarely perfect, more accurate for groups than individuals, and more accurate over shorter time horizons. Common patterns in time series data include trends, seasonality, and cycles. Quantitative forecasting models analyze these patterns in historical data to generate forecasts.
Demand forecasting
This document discusses demand forecasting methods. It explains that forecasting involves estimating future demand for products and services. There are different types of forecasts including long-range, medium-range, and short-term forecasts used for strategic, tactical, and operational planning respectively. Qualitative methods rely on judgment while quantitative methods use mathematical models and historical data. Common quantitative methods are linear regression, moving average, and exponential smoothing. Accuracy and characteristics like impulse response and noise dampening ability are used to evaluate forecasting models.
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Various Forecasting Techniques
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Forecasting
Forecasts are done to predict future events for planning
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Key Decisions in Forecasting
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Forecasting Techniques
Qualitative (Judgment) Methods
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Qualitative (Judgment) methods
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Salesforce estimates: Forecasts derived from estimates provided by salesforce.
Executive opinion: Method in which opinions, experience, and technical knowledge of one or more managers are summarized to arrive at a single forecast.
Market research: A scientific study and analysis of data gathered from consumer surveys intended to learn consumer interest in a product or service.
Delphi method: A process of gaining consensus from a group of experts while maintaining their anonymity.
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Case Study
Reference: Krajewski, Ritzman, Malhotra. (2010). Operations Management: Processes and Supply Chains, Ninth Edition. Pearson Prentice Hall. P. 42-43.
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Case study questions
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What does UNILEVER do when statistical information is not useful for forecasting?
What types of qualitative methods are used by UNILEVER?
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Causal methods – Linear Regression
A dependent variable is related to one or more independent variables by a linear equation
The independent variables are assumed to “cause” the results observed in the past
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Causal methods – Linear Regression
Y = a + bX
where
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Causal methods – Linear Regression
Fit of the regression model
Coefficient of determination
Standard error of the estimate
Please go to in-class exercise sheet
Coefficient of determination: Also called r-squared. Measures the amount of variation in the dependent variable about its mean that is explained by the regression line. Range between 0 and 1. In general, larger values are better.
Standard error of the estimate: Measures how closely the data on the dependent variable cluster around the regression line. Smaller values are better.
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IRJET- Overview of Forecasting Techniques
This document provides an overview of different forecasting techniques, including qualitative and quantitative methods. It discusses several qualitative techniques like the Delphi method, consumer market surveys, and jury of executive opinion. It also examines various quantitative techniques such as the moving average method, weighted moving average method, exponential smoothing, and least squares. The document serves to introduce students to common forecasting approaches and provide examples of each type of technique.
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A good forecast should be consistent with other business areas, based on relevant past knowledge, consider economic and political factors, and be timely. The right forecasting technique depends on the item, data availability, time, and how situations interact with method characteristics. Techniques include expert opinion, surveys, projections, econometric models, qualitative judgments, quantitative data analysis, naïve trends, and time series analysis decomposing trends, cycles, seasonality, and randomness.
O M Unit 3 Forecasting
Forecasting is important for businesses to plan activities and meet goals. There are qualitative and quantitative forecasting methods. Qualitative methods include expert opinions, while quantitative methods use past data patterns in time series models. Common time series models are moving averages, which smooth fluctuations, and exponential smoothing, which weights recent data higher. Forecasts are compared to actuals to measure error using metrics like mean absolute deviation and standard error of estimate. Accurate forecasting allows businesses to better allocate resources and serve customers.
Bba 3274 qm week 6 part 2 forecasting
This document discusses various quantitative forecasting techniques including time series models. It provides an overview of moving averages, exponential smoothing, trend projections, and decomposition models. Examples are given to illustrate computing forecasts using a three-month simple moving average and a three-month weighted moving average. Exponential smoothing is also introduced as a type of moving average that requires less data to compute forecasts.
Session 3
This document discusses various forecasting techniques used at Disney World for attendance forecasting. Disney generates daily, weekly, monthly, annual, and 5-year forecasts which are used for labor management, operations, and scheduling. Forecasting models take into account factors like economic conditions, airline prices, school schedules, and previous attendance data. Qualitative techniques like surveys and quantitative techniques like exponential smoothing and regression are used depending on the situation. Accuracy is important, so different forecasting constants and methods are evaluated based on error metrics.
Chapter 3_OM
This document discusses various forecasting techniques used at Disney World for attendance forecasting. Disney generates daily, weekly, monthly, annual, and 5-year forecasts which are used for labor management, operations, and scheduling. Forecasting models take into account factors like economic conditions, airline prices, school schedules, and previous attendance data. Qualitative methods include expert panels, while quantitative methods analyze historical data using techniques like moving averages, exponential smoothing, and regression analysis. Accuracy varies from 0-3% for annual forecasts to 5% for 5-year forecasts.
Demand Forecasting
1. Demand forecasting forms the basis of supply chain planning as it allows managers to plan production, transportation, and other activities in anticipation of or in response to customer demand.
2. Forecasts can use qualitative methods like expert judgment or quantitative methods like time-series analysis of historical data to predict demand trends, levels, and seasonal variations.
3. The appropriate forecasting method depends on the forecast horizon, with short-term forecasts relying more on time-series analysis, medium-term using both time-series and causal models, and long-term relying more on judgment.
18ME56-OM_ Module 2-Forecasting.pdf
Operations Management course develops, among the students, a knowledge and a set of skills to manage operations of a unit, section or an organization in an efficient way. The students will learn how to optimize the resource utilization for the maximum output.
price forecasting of diesel
This document discusses forecasting of diesel fuel prices by a team of students. It provides background on types of diesel fuel and their uses. The document then discusses the purpose and importance of forecasting for businesses. It outlines different qualitative and quantitative forecasting methods that could be used to forecast diesel prices, including executive opinions, Delphi method, time series analysis, exponential smoothing, and linear trend lines. The key factors to consider for price forecasting are also summarized.
Forecasting
This document discusses various forecasting methods used in operations management. It begins by defining forecasting as predicting future events by taking historical data and projecting it using mathematical models adjusted by managerial judgment. There are three types of forecasts: economic, technological, and demand forecasts which project needs for a company's products. Accurate forecasting is important for human resources, capacity, and supply chain planning. The document then outlines quantitative time series and associative forecasting models as well as qualitative methods like Delphi, educated guesses, surveys, and analogy. It concludes by asking questions about forecasting definitions, accuracy, importance for operations, and long-range demand components.
1.3-CHAPTER 13 FORECASTING_BA_UDineshK.pptx
The document discusses various forecasting techniques used in business analytics. It begins by explaining the importance of forecasting and defining time-series data components like trend, seasonality, cyclicality and irregular components. It then covers techniques like moving average, single exponential smoothing, Holt's method, Croston's method and regression models. It also discusses identifying appropriate autoregressive (AR) and moving average (MA) models using autocorrelation functions and model selection techniques like ARIMA.
18ME56 Operations Management- Module 2-Forecasting
The course operations management is designed for 5th semester undergraduate Mechanical Engineering students of Visvesvaraya Technological University
Forecasting Models & Their Applications
This document provides an overview of operations management forecasting models and their applications. It defines forecasting and lists its common uses. The key components of a forecast and the forecasting process are described. Both qualitative and quantitative forecasting approaches are discussed, along with their advantages and disadvantages. Specific forecasting techniques covered include time series methods, regression methods, moving averages, exponential smoothing, and naive forecasts. Examples are provided to illustrate weighted moving averages and exponential smoothing.
A high level overview of all that is Analytics
Predictive analytics uses past data to forecast future outcomes. The document discusses various predictive analytics techniques including simple forecasting methods, decision trees, and regression. Simple forecasting techniques like moving averages are easiest to implement but lack explanatory power, while decision trees and regression provide more accurate predictions at an individual level but require more complex deployment. The key is selecting the right technique based on the problem, data, and ability to implement predictive models in real-world applications.
CHAPTER 5.pptx
1) Profit planning involves developing budgets to achieve a targeted profit level, including sales forecasts and expense estimates.
2) Key elements in predicting profitability are sales forecasts, which can be done through judgment-based or quantitative methods, and expense forecasts, which estimate fixed, variable, and semi-variable costs.
3) Break-even analysis determines the sales volume needed to cover total costs and yield zero profit. It is used to assess the impact of changes in sales, prices, or costs on profit levels.
1 forecasting SHORT NOTES FOR ESE AND GATE
Time horizons in forecasting can be short term (1-3 months), intermediate (2-3 years), or long term (2+ years). Qualitative techniques like market surveys and Delphi method are used for long term forecasting while quantitative techniques like time series analysis, exponential smoothing, and regression analysis are used for short and medium term forecasting. Moving average and exponential smoothing methods smooth random fluctuations in demand data and represent significant variations. Exponential smoothing assigns higher weight to recent demand data compared to older data.
Chap011
This document discusses various qualitative and quantitative forecasting methods including simple and weighted moving averages, exponential smoothing, and simple linear regression. It provides examples of how to calculate forecasts using each of these methods and evaluates forecast accuracy using metrics like MAD and tracking signal.
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Forecasting
- 1. Swami Vivekananda Institute of Science & Technology
- 2. Definition : A forecast is an estimate of uncertain future event. Application: it is used to improve decision-making & planning. Even though forecasts are almost always in error. Importance in Mechanical engineering: In manufacturing we need it.
- 3. They are usually wrong. Aggregate forecast are usually more accurate. A good forecast is more than a single number mean and standard deviation range (high and low) Forecasts should not be used to the exclusion of known information
- 4. 0-6month Short term • Weekly demand (Inventory control) • Operational decision • Frequently made • Low level of responsibility • Individual items 0.5-2years Intermediate • Annual (monthly) demand (Production planning) 2+years Long term • 10 years Demand (facility planning) • Strategic decision • Infrequently made • Top management level • Product line
- 6. Informed opinion & judgment • Subject opinion of one or more individuals. • EXAMPLE: ("grass roots") collection and aggregation of individual sales forecasts to obtain overall sales forecast by product or region Delphi method • An iterative technique for obtaining a consensus forecast from a group of experts, • EXAMPLE: long range forecasting of technological advances Market Research • An interview or set of questions are used to know the opinion of potential customers, current users,& others. • EXAMPLE: voter preference, new car buyer. HISTORICAL LIFE- CYCLE ANALOGY • Demand for a new product can be forecasted by anticipating an s- shaped growth curve similar to the s-curve experience with related product. • EXAMPLE: Product life cycle curve.
- 7. 1. Last period demand 2. Arithmetic average Average all past demand to average out the random fluctuation 3. Simple Moving Average (N- period) Average of the N most recent demands, to "smooth out" the random fluctuations 4. Weighted Moving Average (N- period) The weighted moving average is equivalent to the simple moving average. 5. Simple Exponential Smoothing A simple way of calculating a weighted moving average forecast with exponentially- declining weights 6. Calculate multiplicative seasonal indexes 1. Collect monthly demand data for several past years 2. For each month of past data, calculate the ration of demand. Average the ratios of several years to get the seasonal index for the quarter.
- 9. The forecast error in period t, et, is the difference between the forecast for demand in period t and the actual value of demand in t. For a multiple step ahead forecast: et = Ft - t, t - Dt. For one step ahead forecast: et = Ft - Dt. e1, e2, .. , en forecast errors over n periods MAD = (1/n) S | ei | MAPE = [(1/n) S | ei /Di| ]*100 MSE = (1/n) S ei 2 A bias occurs when the average value of a forecast error tends to be positive or negative (ie, deviation from truth). Mathematically an unbiased forecast is one in which E (e i ) = 0. S e i = 0 Biases in Forecasts Mean Absolute Deviation Mean Absolute Percentage Error Mean Square error
- 10. I have taken the full concept about the forecasting from the internet specially from personal.ashland.edu/dlifer/internal/OMlectureforecasting. pdf & homes.ieu.edu.tr. I am very much thankful to our faculties of our department. I also thankfull to my parents and friends.