Demand Forecasting:Time Series Models presentation
seminar presentation Active In SP Posts: 582 Joined: Apr 2010 
13052010, 11:57 AM
Demand Forecasting: Time Series Models PResented By: Professor Stephen R. Lawrence College of Business and Administration University of Colorado Forecasting Horizons Long Term 5+ years into the future R&D, plant location, product planning Principally judgementbased Medium Term 1 season to 2 years Aggregate planning, capacity planning, sales forecasts Mixture of quantitative methods and judgement Short Term 1 day to 1 year, less than 1 season Demand forecasting, staffing levels, purchasing, inventory levels Quantitative methods Short Term Forecasting: Needs and Uses Scheduling existing resources How many employees do we need and when? How much product should we make in anticipation of demand? Acquiring additional resources When are we going to run out of capacity? How many more people will we need? How large will our backorders be? Determining what resources are needed What kind of machines will we require? Which services are growing in demand? declining? What kind of people should we be hiring? Types of Forecasting Models Types of Forecasts Qualitative  based on experience, judgement, knowledge; Quantitative  based on data, statistics; Methods of Forecasting Naive Methods  eyeballing the numbers; Formal Methods  systematically reduce forecasting errors; time series models (e.g. exponential smoothing); causal models (e.g. regression). Focus here on Time Series Models Assumptions of Time Series Models There is information about the past; This information can be quantified in the form of data; The pattern of the past will continue into the future. Forecasting Examples Examples from student project and implimentations: Demand for tellers in a bank; Traffic on major communication switch; Demand for liquor in bar; Demand for frozen foods in local grocery warehouse. Example from Industry: American Hospital Supply Corp. 70,000 items; 25 stocking locations; Store 3 years of data (63 million data points); Update forecasts monthly; 21 million forecast updates per year. Simple Moving Average Forecast Ft is average of n previous observations or actuals Dt : Note that the n past observations are equally weighted. Issues with moving average forecasts: All n past observations treated equally; Observations older than n are not included at all; Requires that n past observations be retained; Problem when 1000's of items are being forecast. Simple Moving Average Include n most recent observations Weight equally Ignore older observations Moving Average Example: Moving Average Forecasting Exponential Smoothing I Include all past observations Weight recent observations much more heavily than very old observations: Exponential Smoothing I Include all past observations Weight recent observations much more heavily than very old observations: Exponential Smoothing I Include all past observations Weight recent observations much more heavily than very old observations: Exponential Smoothing I Include all past observations Weight recent observations much more heavily than very old observations: Exponential Smoothing: Concept Include all past observations Weight recent observations much more heavily than very old observations: Exponential Smoothing: Math Exponential Smoothing: Math Exponential Smoothing: Math Thus, new forecast is weighted sum of old forecast and actual demand Notes: Only 2 values (Dt and Ft1 ) are required, compared with n for moving average Parameter a determined empirically (whatever works best) Rule of thumb: a < 0.5 Typically, a = 0.2 or a = 0.3 work well Forecast for k periods into future is: Exponential Smoothing Example: Exponential Smoothing Complicating Factors Simple Exponential Smoothing works well with data that is moving sideways (stationary) Must be adapted for data series which exhibit a definite trend Must be further adapted for data series which exhibit seasonal patterns Holtâ„¢s Method: Double Exponential Smoothing What happens when there is a definite trend? Holtâ„¢s Method: Double Exponential Smoothing Ideas behind smoothing with trend: ``Detrend'' timeseries by separating base from trend effects Smooth base in usual manner using a Smooth trend forecasts in usual manner using b Smooth the base forecast Bt Smooth the trend forecast Tt Forecast k periods into future Ft+k with base and trend ES with Trend Example: Exponential Smoothing with Trend Winterâ„¢s Method: Exponential Smoothing w/ Trend and Seasonality Ideas behind smoothing with trend and seasonality: Detrendâ„¢: and deseasonalizetimeseries by separating base from trend and seasonality effects Smooth base in usual manner using a Smooth trend forecasts in usual manner using b Smooth seasonality forecasts using g Assume m seasons in a cycle 12 months in a year 4 quarters in a month 3 months in a quarter et cetera Winterâ„¢s Method: Exponential Smoothing w/ Trend and Seasonality Smooth the base forecast Bt Smooth the trend forecast Tt Smooth the seasonality forecast St Winterâ„¢s Method: Exponential Smoothing w/ Trend and Seasonality Forecast Ft with trend and seasonality Smooth the trend forecast Tt Smooth the seasonality forecast St ES with Trend and Seasonality Example: Exponential Smoothing with Trend and Seasonality Forecasting Performance Mean Forecast Error (MFE or Bias): Measures average deviation of forecast from actuals. Mean Absolute Deviation (MAD): Measures average absolute deviation of forecast from actuals. Mean Absolute Percentage Error (MAPE): Measures absolute error as a percentage of the forecast. Standard Squared Error (MSE): Measures variance of forecast error Forecasting Performance Measures Mean Forecast Error (MFE or Bias) Want MFE to be as close to zero as possible  minimum bias A large positive (negative) MFE means that the forecast is undershooting (overshooting) the actual observations Note that zero MFE does not imply that forecasts are perfect (no error)  only that mean is on target Also called forecast BIAS Mean Absolute Deviation (MAD) Measures absolute error Positive and negative errors thus do not cancel out (as with MFE) Want MAD to be as small as possible No way to know if MAD error is large or small in relation to the actual data Mean Absolute Percentage Error (MAPE) Same as MAD, except ... Measures deviation as a percentage of actual data Mean Squared Error (MSE) Measures squared forecast error  error variance Recognizes that large errors are disproportionately more expensive than small errors But is not as easily interpreted as MAD, MAPE  not as intuitive Fortunately, there is software... download full presentation leedsfaculty.colorado.edu/lawrence/Tools/FORECAST/forecast.ppt Use Search at http://topicideas.net/search.php wisely To Get Information About Project Topic and Seminar ideas with report/source code along pdf and ppt presenaion



project report helper Active In SP Posts: 2,270 Joined: Sep 2010 
15102010, 02:25 PM
Human Resource Demand Forecasting Lecture 4.ppt (Size: 1.36 MB / Downloads: 82) Human Resource Demand Forecasting By Neha Mhaskar Managerial Judgment In this managers sit together, discuss and arrive at a figure which would be the future demand for labor. Two approaches are there bottomup and topdown. In bottomup approach, line managers submit their departmental proposals to the top managers who finally decide the forecast. In topdown approach, top managers prepare the same. 


