chapter8.pptx

    Forecasting – Defined

    Predicting future events

    One of the most important business functions as decisions are based on a forecast of the future

    Goal: Generate good forecasts on the average over time and keep errors low

    Forecasting is an ongoing process

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    Principles of Forecasting

    Many types of forecasting models differ in complexity and amount of data & way they generate forecasts.

    Common features include:

    Forecasts are rarely perfect

    Forecasts are more accurate for grouped data than for individual items

    Forecast are more accurate for shorter than longer time periods

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    Types of Forecasting Methods

    Decide what needs to be forecast

    Level of detail, units of analysis & time horizon required

    Evaluate and analyze appropriate data

    Identify needed data & whether it’s available

    Select and test the forecasting model

    Cost, ease of use & accuracy

    Generate the forecast

    Monitor forecast accuracy over time

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    Types of Forecasting Methods – cont’d

    Classified into two groups:

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    Types of Forecasting Models

    Qualitative methods – judgmental methods

    Forecasts generated subjectively by the forecaster

    Educated guesses

    Quantitative methods – based on mathematical modeling:

    Forecasts generated through mathematical modeling

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    Qualitative Methods

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    Quantitative Methods

    Time Series Models:

    Assumes information needed to generate a forecast is contained in a time series of data

    Assumes the future will follow same patterns as the past

    Causal Models or Associative Models

    Explores cause-and-effect relationships

    Uses leading indicators to predict the future

    Housing starts and appliance sales

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    Time Series Models

    Forecaster looks for data patterns as

    Data = historic pattern + random variation

    Historic pattern to be forecasted:

    Level (long-term average) – data fluctuates around a constant mean

    Trend – data exhibits an increasing or decreasing pattern

    Seasonality – any pattern that regularly repeats itself and is of a constant length

    Cycle – patterns created by economic fluctuations

    Random Variation cannot be predicted

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    Time Series Patterns

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    Time Series Models

    Naive:

    The forecast is equal to the actual value observed during the last period – good for level patterns

    Simple Mean:

    The average of all available data – good for level patterns

    Simple Moving Average:

    The average value over a set time period

    (e.g.: the last four weeks)

    Each new forecast drops the oldest data point & adds a new observation

    More responsive to a trend but still lags behind actual data – good for level patterns; trend + level = bad forecast

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    Time Series Models cont'd

    Weighted Moving Average:

    Method in which “n” of the most recent observations are averaged and past observations may be weighted differently

    All weights must add to 100% or 1.00

    e.g. Ct .5, Ct-1 .3, Ct-2 .2 (weights add to 1.0)

    Allows emphasizing one period over others; above indicates more weight on recent data (Ct=.5)

    Differs from the simple moving average that weighs all periods equally – more responsive to trends

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    Time Series Models cont'd

    Exponential Smoothing:

    Most frequently used time series method because of ease of use and minimal amount of data needed

    Need just three pieces of data to start:

    Last period’s forecast (Ft)

    Last periods actual value (At)

    Select value of smoothing coefficient, , between 0 and 1.0

    If no last period forecast is available, average the last few periods or use naive method

    Higher values (e.g. .7 or .8) place a lot of weight on current periods actual demand and influenced by random variation

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    Time Series Problem

    Determine forecast for periods 7 & 8

    2-period moving average

    4-period moving average

    2-period weighted moving average with t-1 weighted 0.6 and t-2 weighted 0.4

    Exponential smoothing with alpha=0.2 and the period 6 forecast being 375

    Period Actual
    1 300
    2 315
    3 290
    4 345
    5 320
    6 360
    7 375
    8

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    Time Series Problem Solution

    Period Actual 2-Period 4-Period 2-Per.Wgted. ExponentialSmoothing
    1 300        
    2 315        
    3 290        
    4 345        
    5 320        
    6 360        
    7 375 340.0 328.8 344.0 372.0
    8   367.5 350.0 369.0 372.6

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    Forecasting Trend

    Basic forecasting models for trends compensate for the lagging that would otherwise occur

    One model, trend-adjusted exponential smoothing uses a three step process

    Step 1 – Smoothing the level of the series

    Step 2 – Smoothing the trend

    Step 3 – Forecast including the trend

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    Forecasting trend problem: a company uses exponential smoothing with trend to forecast usage of its lawn care products. At the end of July the company wishes to forecast sales for August. July demand was 62. The trend through June has been 15 additional gallons of product sold per month. Average sales have been 57 gallons per month. The company uses alpha+0.2 and beta +0.10. Forecast for August.

    Smooth the level of the series:

    Smooth the trend:

    Forecast including trend:

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    Linear Trend Line

    A time series technique that computes a forecast with trend by drawing a straight line through a set of data using this formula:

    Y = a + bx

    where

    Y = forecast for period X

    X = the number of time periods from X = 0

    A = value of y at X = 0 (Y intercept)

    B = slope of the line

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    Forecasting Seasonality

    Remember it is a regularly repeating pattern

    Examples:

    University enrollment varies between quarters or semesters; higher in the fall than in the summer

    Seasonal Index:

    Percentage amount by which data for each season are above or below the mean.

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    Forecasting Seasonality Steps

    Calculate the average demand per season

    E.g.: average quarterly demand

    Calculate a seasonal index for each season of each year:

    Divide the actual demand of each season by the average demand per season for that year

    Average the indexes by season

    E.g.: take the average of all Spring indexes, then of all Summer indexes, …

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    Forecasting Seasonality Steps – cont'd

    Forecast demand for the next year & divide by the number of seasons

    Use regular forecasting method & divide by four for average quarterly demand

    Multiply next year’s average seasonal demand by each average seasonal index

    Result is a forecast of demand for each season of next year

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    Seasonality problem: a university must develop forecasts for the next year’s quarterly enrollments. It has collected quarterly enrollments for the past two years. It has also forecast total enrollment for next year to be 90,000 students. What is the forecast for each quarter of next year?

    Quarter Year 1 Seasonal Index Year 2 Seasonal Index Avg. Index Year3
    Fall 24000 1.2 26000 1.238 1.22 27450
    Winter 23000 22000
    Spring 19000 19000
    Summer 14000 17000
    Total 80000 84000 90000
    Average 20000 21000 22500

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    Causal Models

    Often, leading indicators can help to predict changes in future demand e.g. housing starts

    Causal models establish a cause-and-effect relationship between independent and dependent variables

    A common tool of causal modeling is linear regression:

    Additional related variables may require multiple regression modeling

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    Measuring Forecast Accuracy

    Forecasts are never perfect

    Need to measure over time

    Need to know how much we should rely on our chosen forecasting method

    Measuring forecast error:

    Note that over-forecasts = negative errors and under-forecasts = positive errors

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    Measuring Forecasting Accuracy

    Mean Absolute Deviation (MAD)

    measures the total error in a forecast without regard to sign

    Cumulative Forecast Error (CFE)

    Measures any bias in the forecast

    Mean Square Error (MSE)

    Penalizes larger errors

    Tracking Signal

    Measures if your model is working; quality

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    Accuracy & Tracking Signal Problem: A company is comparing the accuracy of two forecasting methods. Forecasts using both methods are shown below along with the actual values for January through May. The company also uses a tracking signal with ±4 limits to decide when a forecast should be reviewed. Which forecasting method is best?

    Month Actual sales Method A Method B
    F’cast Error Cum. Error Tracking Signal F’cast Error Cum. Error Tracking Signal
    Jan. 30 28 2 2 2 28 2 2 1
    Feb. 26 25 1 3 3 25 1 3 1.5
    March 32 32 0 3 3 29 3 6 3
    April 29 30 -1 2 2 27 2 8 4
    May 31 30 1 3 3 29 2 10 5
    MAD 1 2
    MSE 1.4 4.4

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    Selecting the Right Forecasting Model

    The amount & type of available data

    Some methods require more data than others

    Degree of accuracy required

    Increasing accuracy means more data

    Length of forecast horizon

    Different models for 3 month vs. 10 years

    Presence of data patterns

    Lagging will occur when a forecasting model meant for a level pattern is applied with a trend

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    Predictive Analytics and Forecasting

    Uses statistics, modeling and data mining to analyze currentand historical facts to make predictions on the futureAbility to forecast events before they happen by sensing smallchanges over time

    Forecasting Within OM: How It All Fits Together

    Forecasts impact not only other business functions but all other operations decisions. Operations managers make many forecasts, such as the expected demand for a company’s products.

    These forecasts are then used to determine:

    Product designs that are expected to sell (Ch 2)

    The quantity of product to produce (Chs 5 and 6)

    The amount of needed supplies and materials (Ch 12)

    Future space requirements (Ch 10)

    Capacity and location needs (Ch 9)

    The amount of labor needed (Ch 11)

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    Forecasting within OM – cont'd

    Forecasts drive strategic operations decisions, such as:

    Choice of competitive priorities, changes in processes, and large technology purchases (Ch 3)

    Forecast decisions serve as the basis for tactical planning; developing worker schedules (Ch 11)

    Virtually all operations management decisions are based on a forecast of the future.

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