logging in or signing up production management topic rockwell403 Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINT lite Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: (To copy code, click on the text box) Embed: URL: Thumbnail: WordPress Embed Customize Embed The presentation is successfully added In Your Favorites. Views: 358 Category: Product Traini.. License: All Rights Reserved Like it (1) Dislike it (0) Added: December 26, 2010 This Presentation is Public Favorites: 0 Presentation Description It helps to forecasts the future demands of different products and services Comments Posting comment... Premium member Presentation Transcript Slide 1: William J. Stevenson Operations Management 8th edition Slide 2: FORECAST: A statement about the future value of a variable of interest such as demand. Forecasts affect decisions and activities throughout an organization Accounting, finance Human resources Marketing MIS Operations Product / service design Uses of Forecasts : Uses of Forecasts Slide 4: Assumes causal systempast ==> future Forecasts rarely perfect because of randomness Forecasts more accurate forgroups vs. individuals Forecast accuracy decreases as time horizon increases Elements of a Good Forecast : Elements of a Good Forecast Steps in the Forecasting Process : Steps in the Forecasting Process Types of Forecasts : Types of Forecasts Judgmental - uses subjective inputs Time series - uses historical data assuming the future will be like the past Associative models - uses explanatory variables to predict the future Judgmental Forecasts : Judgmental Forecasts Executive opinions Sales force opinions Consumer surveys Outside opinion Delphi method Opinions of managers and staff Achieves a consensus forecast Time Series Forecasts : Time Series Forecasts Trend - long-term movement in data Seasonality - short-term regular variations in data Cycle – wavelike variations of more than one year’s duration Irregular variations - caused by unusual circumstances Random variations - caused by chance Forecast Variations : Forecast Variations Trend Irregularvariation Seasonal variations 90 89 88 Figure 3.1 Cycles Naive Forecasts : Naive Forecasts The forecast for any period equals the previous period’s actual value. Naïve Forecasts : Simple to use Virtually no cost Quick and easy to prepare Data analysis is nonexistent Easily understandable Cannot provide high accuracy Can be a standard for accuracy Naïve Forecasts Uses for Naïve Forecasts : Stable time series data F(t) = A(t-1) Seasonal variations F(t) = A(t-n) Data with trends F(t) = A(t-1) + (A(t-1) – A(t-2)) Uses for Naïve Forecasts Techniques for Averaging : Techniques for Averaging Moving average Weighted moving average Exponential smoothing Moving Averages : Moving Averages Moving average – A technique that averages a number of recent actual values, updated as new values become available. Weighted moving average – More recent values in a series are given more weight in computing the forecast. Simple Moving Average : Simple Moving Average Actual MA3 MA5 Exponential Smoothing : Exponential Smoothing Premise--The most recent observations might have the highest predictive value. Therefore, we should give more weight to the more recent time periods when forecasting. Ft = Ft-1 + (At-1 - Ft-1) Exponential Smoothing : Exponential Smoothing Weighted averaging method based on previous forecast plus a percentage of the forecast error A-F is the error term, is the % feedback Ft = Ft-1 + (At-1 - Ft-1) Slide 19: Example 3 - Exponential Smoothing Picking a Smoothing Constant : Picking a Smoothing Constant Trend adjusting Exponential Smoothing : Trend adjusting Exponential Smoothing Common Nonlinear Trends : Common Nonlinear Trends Figure 3.5 Linear Trend Equation : Linear Trend Equation Ft = Forecast for period t t = Specified number of time periods a = Value of Ft at t = 0 b = Slope of the line Calculating a and b : Calculating a and b Linear Trend Equation Example : Linear Trend Equation Example Linear Trend Calculation : Linear Trend Calculation SEASONALITY EFFECT : SEASONALITY EFFECT Summarizing Forecast Accuracy : Summarizing Forecast Accuracy Linear Model Seems Reasonable : Linear Model Seems Reasonable A straight line is fitted to a set of sample points. Controlling the Forecast : Controlling the Forecast Control chart A visual tool for monitoring forecast errors Used to detect non-randomness in errors Forecasting errors are in control if All errors are within the control limits No patterns, such as trends or cycles, are present Sources of Forecast errors : Sources of Forecast errors Model may be inadequate Irregular variations Incorrect use of forecasting technique Tracking Signal : Tracking Signal Tracking signal Ratio of cumulative error to MAD Bias – Persistent tendency for forecasts to be Greater or less than actual values. Choosing a Forecasting Technique : Choosing a Forecasting Technique No single technique works in every situation Two most important factors Cost Accuracy Other factors include the availability of: Historical data Computers Time needed to gather and analyze the data Forecast horizon Slide 41: Exponential Smoothing Slide 42: Linear Trend Equation Slide 43: Simple Linear Regression You do not have the permission to view this presentation. In order to view it, please contact the author of the presentation.
production management topic rockwell403 Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINT lite Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: (To copy code, click on the text box) Embed: URL: Thumbnail: WordPress Embed Customize Embed The presentation is successfully added In Your Favorites. Views: 358 Category: Product Traini.. License: All Rights Reserved Like it (1) Dislike it (0) Added: December 26, 2010 This Presentation is Public Favorites: 0 Presentation Description It helps to forecasts the future demands of different products and services Comments Posting comment... Premium member Presentation Transcript Slide 1: William J. Stevenson Operations Management 8th edition Slide 2: FORECAST: A statement about the future value of a variable of interest such as demand. Forecasts affect decisions and activities throughout an organization Accounting, finance Human resources Marketing MIS Operations Product / service design Uses of Forecasts : Uses of Forecasts Slide 4: Assumes causal systempast ==> future Forecasts rarely perfect because of randomness Forecasts more accurate forgroups vs. individuals Forecast accuracy decreases as time horizon increases Elements of a Good Forecast : Elements of a Good Forecast Steps in the Forecasting Process : Steps in the Forecasting Process Types of Forecasts : Types of Forecasts Judgmental - uses subjective inputs Time series - uses historical data assuming the future will be like the past Associative models - uses explanatory variables to predict the future Judgmental Forecasts : Judgmental Forecasts Executive opinions Sales force opinions Consumer surveys Outside opinion Delphi method Opinions of managers and staff Achieves a consensus forecast Time Series Forecasts : Time Series Forecasts Trend - long-term movement in data Seasonality - short-term regular variations in data Cycle – wavelike variations of more than one year’s duration Irregular variations - caused by unusual circumstances Random variations - caused by chance Forecast Variations : Forecast Variations Trend Irregularvariation Seasonal variations 90 89 88 Figure 3.1 Cycles Naive Forecasts : Naive Forecasts The forecast for any period equals the previous period’s actual value. Naïve Forecasts : Simple to use Virtually no cost Quick and easy to prepare Data analysis is nonexistent Easily understandable Cannot provide high accuracy Can be a standard for accuracy Naïve Forecasts Uses for Naïve Forecasts : Stable time series data F(t) = A(t-1) Seasonal variations F(t) = A(t-n) Data with trends F(t) = A(t-1) + (A(t-1) – A(t-2)) Uses for Naïve Forecasts Techniques for Averaging : Techniques for Averaging Moving average Weighted moving average Exponential smoothing Moving Averages : Moving Averages Moving average – A technique that averages a number of recent actual values, updated as new values become available. Weighted moving average – More recent values in a series are given more weight in computing the forecast. Simple Moving Average : Simple Moving Average Actual MA3 MA5 Exponential Smoothing : Exponential Smoothing Premise--The most recent observations might have the highest predictive value. Therefore, we should give more weight to the more recent time periods when forecasting. Ft = Ft-1 + (At-1 - Ft-1) Exponential Smoothing : Exponential Smoothing Weighted averaging method based on previous forecast plus a percentage of the forecast error A-F is the error term, is the % feedback Ft = Ft-1 + (At-1 - Ft-1) Slide 19: Example 3 - Exponential Smoothing Picking a Smoothing Constant : Picking a Smoothing Constant Trend adjusting Exponential Smoothing : Trend adjusting Exponential Smoothing Common Nonlinear Trends : Common Nonlinear Trends Figure 3.5 Linear Trend Equation : Linear Trend Equation Ft = Forecast for period t t = Specified number of time periods a = Value of Ft at t = 0 b = Slope of the line Calculating a and b : Calculating a and b Linear Trend Equation Example : Linear Trend Equation Example Linear Trend Calculation : Linear Trend Calculation SEASONALITY EFFECT : SEASONALITY EFFECT Summarizing Forecast Accuracy : Summarizing Forecast Accuracy Linear Model Seems Reasonable : Linear Model Seems Reasonable A straight line is fitted to a set of sample points. Controlling the Forecast : Controlling the Forecast Control chart A visual tool for monitoring forecast errors Used to detect non-randomness in errors Forecasting errors are in control if All errors are within the control limits No patterns, such as trends or cycles, are present Sources of Forecast errors : Sources of Forecast errors Model may be inadequate Irregular variations Incorrect use of forecasting technique Tracking Signal : Tracking Signal Tracking signal Ratio of cumulative error to MAD Bias – Persistent tendency for forecasts to be Greater or less than actual values. Choosing a Forecasting Technique : Choosing a Forecasting Technique No single technique works in every situation Two most important factors Cost Accuracy Other factors include the availability of: Historical data Computers Time needed to gather and analyze the data Forecast horizon Slide 41: Exponential Smoothing Slide 42: Linear Trend Equation Slide 43: Simple Linear Regression