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L3 - Introduction to Production and Manufacturing Systems...

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Unformatted text preview: Introduction to Production and Manufacturing Systems 05/12/09 Texas A&M Industrial Engineering 1 ISEN 220 What is Forecasting? Process of predicting a future event Underlying basis of all business decisions Production Inventory Personnel Facilities ?? Forecasting Approaches Forecasting Approaches Overview of Qualitative Methods Overview of Quantitative Methods Forecasting Discussion Focus Timeseries Forecasting Decomposition of a Time Series Nave Approach Moving Averages Exponential Smoothing Exponential Smoothing with Trend Adjustment Trend Projections Seasonal Variations in Data Cyclical Variations in Data Forecasting Discussion Focus Associative Forecasting Methods: Regression And Correlation Analysis Using Regression Analysis to Forecast Forecasting Time Horizons Shortrange forecast Up to 1 year, generally less than 3 months Purchasing, job scheduling, workforce levels, job assignments, production levels 3 months to 3 years Sales and production planning, budgeting 3+ years New product planning, facility location, research and development Mediumrange forecast Longrange forecast Features of time horizons Medium/long range forecasts deal with more comprehensive issues and support management decisions regarding planning and products, plants and processes Shortterm forecasting usually employs different methodologies than longerterm forecasting Shortterm forecasts tend to be accurate than longerterm forecasts Types of Forecasts Economic forecasts Address business cycle inflation rate, money supply, housing starts, etc. Predict rate of technological progress Impacts development of new products Predict sales of existing product Technological forecasts Demand forecasts Seven Steps in Forecasting Determine the use of the forecast Select the items to be forecasted Determine the time horizon of the forecast Select the forecasting model(s) Gather the data Make the forecast Validate and implement results The Realities! Forecasts are seldom perfect Most techniques assume an underlying stability in the system Product family and aggregated forecasts are accurate than individual product forecasts Aggregated Forecasts Forecasting Approaches Qualitative Methods Used when situation is vague and little data exist New products New technology Involves intuition, experience e.g., forecasting sales on Internet Forecasting Approaches Quantitative Methods Used when situation is `stable' and historical data exist Existing products Current technology Involves mathematical techniques e.g., forecasting sales of color televisions Overview of Qualitative Methods Jury of executive opinion Delphi method Pool opinions of highlevel executives, sometimes augment by statistical models Panel of experts, queried iteratively Overview of Qualitative Methods Sales force composite Consumer Market Survey Ask the customer Estimates from individual salespersons are reviewed for reasonableness, then aggregated Jury of Executive Opinion Involves small group of high-level managers Group estimates demand by working together Relatively quick Disadvantage: Sales Force Composite Each salesperson projects his or her sales Combined at district and national levels Sales reps know customers' wants Disadvantage: Delphi Method Iterative group process, continues until consensus is reached 3 types of Staff (Administering participants Decision makers Staff Respondents survey) Decision Makers (Evaluate responses and make decisions) Respondents (People who can make valuable judgments) Consumer Market Survey Ask customers about purchasing plans Sometimes difficult to answer Disadvantage: Overview of Quantitative Approaches 1. Naive approach 2. Moving averages 3. Exponential smoothing 4. Trend projection 5. Linear regression Time-Series Models Associative Model Time Series Forecasting Set of evenly spaced numerical data Forecast based only on past values Obtained by observing response variable at regular time periods Assumes that factors influencing past and present will continue influence in future Time Series Components Trend Cyclical Seasonal Random Components of Demand Demand for product or service Trend component Seasonal peaks Actual demand Average demand over four years | 3 Year | 4 Figure 4.1 Random variation | 1 | 2 Trend Component Persistent, overall upward or downward pattern Changes due to population, technology, age, culture, etc. Typically several years duration Seasonal Component Regular pattern of up and down fluctuations Due to weather, customs, etc. Occurs within a single year Period Week Month Month Year Year Year Length Day Week Day Quarter Month Week Number of Seasons 7 4-4.5 28-31 4 12 52 Cyclical Component Repeating up and down movements Affected by business cycle, political, and economic factors Multiple years duration Often causal or associative relationships 0 5 10 15 20 Random Component Erratic, unsystematic, `residual' fluctuations Due to random variation or unforeseen events Short duration and nonrepeating M T W T F Naive Approach Assumes demand in next period is the same as demand in most recent period e.g., If May sales were 48, then June sales will be 48 Sometimes cost effective and efficient Moving Average Method MA is a series of arithmetic means Used if little or no trend Used often for smoothing Provides overall impression of data over time demand in previous n periods Moving average = n Moving Average Example Month January February March April May June July Actual Shed Sales 10 12 13 16 19 23 26 3-Month Moving Average Graph of Moving Average 30 28 26 24 22 20 18 16 14 12 10 Shed Sales Actual Sales Moving Average Forecast | J | F | M | A | M | J | J | A | S | O | N | D Potential Problems With Moving Average Increasing n smooths the forecast but makes it less sensitive to changes Do not forecast trends well Require extensive historical data Exponential Smoothing Form of weighted moving average Requires smoothing constant () Involves little record keeping of past data Ranges from 0 to 1 Subjectively chosen Weights decline exponentially Most recent data weighted most Exponential Smoothing t = last period's forecast + (last period's actual demand last period's forecast) Ft = Ft 1 + (At 1 - Ft 1) where Ft = new forecast Ft 1 = previous forecast = smoothing (or weighting) constant (0 1) Exponential Smoothing Example Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant = .20 Exponential Smoothing Example Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant = .20 ...
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