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Operations Management

by: Hester Marquardt
Hester Marquardt
CSU Pomona
GPA 3.52


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Class Notes
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This 4 page Class Notes was uploaded by Hester Marquardt on Saturday October 3, 2015. The Class Notes belongs to TOM 301 at California State Polytechnic University taught by Staff in Fall. Since its upload, it has received 29 views. For similar materials see /class/218247/tom-301-california-state-polytechnic-university in Management Sciences And Information Technology at California State Polytechnic University.

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Date Created: 10/03/15
TOM 302 Midterm Exam Reading Notes Ch 3 Reading NotesChapter 3 Forecasting o Error9 difference between the actual value and the value that was predicted for a given period 0 Error Actual Forecast 0 at At Ft 0 Mean Absolute Deviation MAD 9 the average absolute forecast error 0 Easy to compute o Weights errors linearly Actual Forecast 0 M AD 2 2 t ti T 0 Mean Squared Error MSE9 the average of squared forecast errors AVERAGE ABSOLUTE ERROR o Squares error 0 More weight to large errors Actual Forecast 2 O M AD Z x t t 11 1 0 Mean Absolute Percent Error MAPE9 the average absolute percent error ZlActual tiForecast t O Actual t n gtlt100 Types of ForecastingUudgmental Forecasts TimeSeries Forecasts amp Associative Models 0 v Judgmental Forecasts9 use subjective inputs from various sources I Common sources of opinions are consumer surveys sales staff managers executives and experts I Delphi Method9iterative process in which managers and staff complete a series of questionnaires each developed from the previous one to achieve a consensus forecast 0 usually involves experts going several rounds in making and sharing their forecasts with each other until they move toward a consensus o useful for technological forecasting ie assessing changes in technology and their impact on an organization 39239 Timeseries Foreca5159 forecasts that project patterns identified in recent timeseries observations I In other words attempt to project past experience into the future using historical data I Time Series 9 a time ordered sequence of observations taken at regular intervals key term I Based on the assumption that future values of the series can be estimated from past values gtAnalysis of timeseries data usually done by plotting the data and visually examining the plot 0 Patters that Commonly Appear O Trend 9 a longterm upward or downward movement in data TOM 302 Midterm Exam Reading Notes Ch 3 I Population shifts changing incomes and cultural changes usually account for such movements 0 Techniques for Trend on pg 3 of Notes O Seasonality 9 shortterm regular variations related to the calendar or time of day I Ex Restaurants supermarkets and movie theaters usually have weekly and even daily quotseasonalquot variations O Cycles 9wavelike variations lasting more than a year I Often related to economic political and even agricultural conditions 0 Random and Irregular Variations O Irregular Variations9 caused by unsual circumstances not reflective of typical behavior I Including such irregularities in the series can distort the overall picture Should be identified and removed from the data whenever possible Ex Severe weather conditions strikes amp major changes in a productservice O Random Variations9 residual variations after all other behaviors are accounted for Common Time Series Techniques or Forecasting Models 0 00 Na39ive Forecasts9a forecast for any period that equals the previous period s actual value I Uses a single previous value ofa time series as the basis of a forecast I Cannot provide high accuracy unless demand is very stable I Can be used with O a stable series variations around an average O Seasonal variations O Trend gt Formula Ft At1 NOTE If is always a positive integer sometimes t 0 is permissible 39239 Moving Average 9technique that averages a number of recent actual values updated as new values become available A quot39A 2A 1 gt FormulaFt n Wh ere TOM 302 Midterm Exam Reading Notes Ch 3 o v Exponential Smoothing 9weighted averaging method based on previous forecast plus a percentage of the forecast error gt Next Forecast Previous Forecast 0Actual Previous Forecast gt FormulaFt Ft1 XAt1 Ft1 I Where gtLinear Trend Equation9used to develop forecasts when trend is present Technique for Trend I The line obtained from this formula is the best fit line for the given data 0 Formula Ft a 91 O Fsub t Forecast for period t O a Value of Fsub t at t 0 aka the vertical intercept O b Slope of the line O t Specified number of time periods from t O n t t 0 Formula for bquot b nZt Zt b t 0 Formula for aquot a or a y 91 I n number of periods I y value of the time series 0 I I z o I a Ivnuucn 39 g technique that uses explanatory variables to predict future demand need to know what associative forecasting is but do not have to do any problems on it I It suggests a causal relationship 9 ex Such as personal consumption being based on per capita income of households gt Associative Forecasting Techniques 0 Predictor Variables 9 variables that can be used to predict values of the variable of interest 0 Regression 9 technique for fitting a line to a set of points 0 Least Squares Line 9 Minimizes the sum of the squared vertical deviations around the line O Formula yc a bx I y predicted dependent variable I x predicted independent variable I b slope of the line I a yintercept TOM 302 Midterm Exam Reading Notes Sources of Forecast Errors gt Model may be inadequate gt Irregular variations gt Incorrect use of forecasting technique Choosing a Forecasting Technique 0 I Cost v 2 Most Important Factors I Accuracy 0 v Other Factors 0 I Historical data I Computers I Time needed to gather and analyze the data I Forecast horizon o v Good Operations Strategy I Work to Improve shortterm forecasts 0 Important to understand that accurate shortterm forecasts have benefits for the following O Profits O Lower inventory levels O Reduce inventory shortages O Improve customer service levels O Enhance forecasting credibility I Understand that forecasts are the basis for many decisions 39239 Sharing forecasts with suppliers can gt Improve forecast quality in the supply chain gt Lower costs gt Lead to shorter lead times 0 00 o o o 00 Ft Forecast for period t


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