Essay On Forecasting Flashcards
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Forecasting Definition and Applications
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The art and science of predicting future events and outcomes Applications: -Economic Forecasts: Predicting changes of business cycles -Technological Forecasts: rates of technological change -Business Forecasts: predicting demand
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Forecasting Means
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Anticipating & quantifying what needs to be done to meet the customer's needs/requirements Planning for the resources needed to meet future demand Arming management with the info needed to make the best possible business decisions (to minimize risks inherent in decision making)
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Forecast: Basic Principles
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Forecasts are never accurate Forecasting techniques assume a causal relationship Forecasting aggregate units tend to give more accurate results than forecasting individual units Forecasting accuracy decreases as the time horizon increases **It's more than just predicting future demand
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Forecasting Requirements:
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Organization Defining approach Methodology -Top-down or Bottom-Up -Short term vs. Long Term -Denationalized vs. Raw Data Simulating forecasting performance -Include all factors influencing demand -Historical Evaluation Monitoring accuracy Consistency Easy to Understand
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Choosing a Forecast Model
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-Chose the forecast model that makes best use of available data -Consider the stage of the product's life cycle -Selecting applicable forecast models, use relevant or historical data -Simulating a forecast performance and determine which forecast model gives the best accuracy
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Forecasting Error
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Difference between the actual demand ; its forecasted value *Perfect Forecast is when Actual = Forecast, which is impossible
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Error Magnitude tools
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Mean Absolute Deviation (MAD) Mean Absolute Percentage Error (MAPE) Mean squared error (MSE)
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Mean Absolute Deviation
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Measures absolute deviation of forecast from actual. *Positive ; negative errors do not cancel out *Goal: Seek MAD ratios to be as small as possible
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Mean Absolute Percent Error
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The MAPE ratio is the same as the MAD ratio, except This measures deviation as a percentage of actual data
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Mean Squared Error
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MSE measures squared forecast error **Recognizes that large errors are disproportionately more "expensive" than small errors
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Cumulative Sum of Forecast Errors
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Used to detect biases or tendencies *If the sum is Positive: when the forecast on average is below the actual results Negative: when the forecasts are on average higher than the actual results
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Qualitative Models for Forecasting
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Used when a situation is vague and little relevant data is available. (Based on Experience, intuition, judgement, knowledge) ex. Sales-Force Pooling Market research Customer surveys Historical analogy Delphi Method -Set up a questionarre of industry experts Management's Judgement or consensus***
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Statistical Models for Forecasting
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Used when a situation is "stable" & relevant data is available (based on data and statistical techniques) Assumes that market and industry forces that influenced or generated past demand will continue to generate future demand. *if demand is purely random, statistical models cannot be able to predict what will happen, but if the demand shows some correlation to one ore more explanatory variables, a forecast can be made.
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Role of Analytics on Forecasting
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Analytics is the science of examining raw data with the purpose of drawing conclusions about consumer trends and patterns. *To identify undiscovered patterns and establish hidden relationships, enabling companies to make better evidence-based business decisions and predictions. *Helps a company to be much more agile responding to customers needs and expectations
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Business Forecast: Time Horizons
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Long Term (over 3 years) -New product planning, facility location& capacity, R&D -Predominantly judgmental-based forecasts Medium (1-3 yrs) -Demand forecast, aggregate planning -Mix of judgmental & statistical forecasts Short Term (Less than 1 year) -SKU Forecast, staffing levels, purchasing & inventory levels -Predominantly statistical forecasts
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Time-Series Forecasting Models
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Forward Projection based on historical data or observations *Key Assumption: Historical demand patterns will continue into the foreseeable future How to: By **analyzing/identifying historical demand patterns (looking Secular trends, seasonal variations, cyclical variations, irregular behavior)
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Naive Forecasting Model
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Model based on using most recent data Use the actual demand of the most recent period, to forecast the demand of the following period. *What I did last week is what I do this week is what I will do next week.
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Moving Average Forecasting Model
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Model based on the arithmetic average of a specific number of historical data points. "Moves through time" -The model is updated after every new set of data becomes available and then oldest data period is dropped. * Great for short-term
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Weighted Moving Average Model
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The weighted moving average model assigns weights to each data point -Decreasing weights to older data -Sum of weights must equal 1. *Assumes that the most recent points are the most important indicators of what to expect in the future.
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Exponential Smoothing Model
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The Weighted Average Model between two values (the most recent demand value and its forecasted) The "weight" factor is known as the smoothing constant . Requires: Actual, Forecast, and Weight Used in Short-Term forecasting
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Forecasting Trends
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When trends are present, level forecasting models will lag behind the actual values.
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Trend-Adjusted Exponential Smoothing Model
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Qualitative very similar to the exponential smoothing model except it includes a component to account for the trend. Forecast = Base + Trend Concept behind smoothing with trend is to "detrend" the time series by separating the base from trend effects. *Smooth both the base and the trend
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Linear Regression Forecasting Models
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A time series forecasting technique that fits a trend line to a series of data points: then, projects the results into the future.
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Forecasting Seasonality
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Any regularly repeating patter represents a seasonal demand pattern Seasonality is measured by the extent to which actual values deviate from the average or mean of the data --The most frequent use to analyze seasonality is to use the seasonal indexes -At least two full season of data is required for the computation of a seasonal index
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How to Forecast Seasonal Variation (Independent vs. In Conjunction)
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After the seasonal indexes are determined, the results can be used to: -Forecast demand independently or In conjuction
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Causal Forecasting Models
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Identifies one or more independent variables (explanatory variables) that are related to the dependent variable being predicted Once the predictor variables are identified, a regression analysis model is used to forecast. *Causal forecasting models can be a more powerful technique than using a time-series model
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Business Forecasts: Success Factors
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Success is linked to: -Continuously checking forecast accuracy -Constantly seeking ways to minimize the forecasting error -Comparing the accuracy of your forecasting model to a naive model -If you cannot beat a naive benchmark, forecasting is usually useless
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Reasons for Forecasting failure
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-Not Recognizing that forecasting is integral to business planning -Not accepting that forecasted results are usually wrong -Not forecasting the right parameters -Not selecting an appropriate forecasting model -Not monitoring the accuracy of the forecast model being used -Not having enough experience & common sense -Not Synchronized through all functional ares
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Common Mistakes in Forecasting
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-Forecasting what the boss wants -Not understanding relationships between variables -Misleading graphs/tables -Belief that the more complex & expensive the forecast model the more accurate it is -Good forecasts and bad forecasts will balance out -Overconfidence/Stubbornness/Wishful thinking -Not involving a broad cross section of people
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Forecasting Management Summary
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-Forecasts should be used only when they can impact the decision making process -Develop contingency plans, because the actual results are always different than forecasts -Update models and revise as more information is gathered -Continuously seek for the most appropriate forecasting model -Use different approaches: Gain Confidence by validating results using different methods
