Demand Management and Forecasting


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  • Demand Management and Forecasting

    1. 1. Demand Management and Forecasting Selected Slides from Jacobs et al, 9 th Edition Operations and Supply Management Chapter 15 Edited, Annotated and Supplemented by Peter Jurkat
    2. 2. Independent Demand: What a firm can do to manage it? <ul><li>Can take an active role to influence demand </li></ul><ul><li>Can take a passive role and simply respond to demand </li></ul>15-
    3. 3. Demand Management A Dependent Demand: Raw Materials, Component parts, Sub-assemblies, etc. <ul><li>Independent Demand: </li></ul><ul><li>Product/service you sell </li></ul><ul><li>Might influence but cannot control how much </li></ul>15-
    4. 4. 15- Forecasting Techniques
    5. 5. Historical Analogy: Focus Forecasting <ul><li>Short range (up to one year from 1-2 year’s data) </li></ul><ul><li>From a set of heuristics rules </li></ul><ul><ul><li>Whatever sold last three months will sell next three months (except for strongly seasonal goods) </li></ul></ul><ul><ul><li>Whatever sold same three month last year will sell in same three month this year (seasonal effect) </li></ul></ul><ul><ul><li>Likely 10% more next three month than last three month </li></ul></ul><ul><ul><li>Likely 50% more next three months than same three months last year </li></ul></ul><ul><ul><li>Whatever change last three month from same three month last year will be same for next three months compared to same three months last year </li></ul></ul><ul><li>Not necessary to use all rules in any one forecast but often done </li></ul><ul><li>Can add rules as you find them. </li></ul><ul><li>See FocusForcasting.xls </li></ul>
    6. 6. Delphi Method <ul><li>l. Choose experts representing a variety of knowledgeable people in different areas to participate </li></ul><ul><li>2. Through a questionnaire (or E-mail), obtain forecasts anonymously (and any premises or qualifications for the forecasts) from all participants </li></ul><ul><li>3. Summarize the results and redistribute anonymously along with appropriate new questions/conditions </li></ul><ul><li>4. Repeat Step 3 until consensus or none to be found </li></ul>e.g., German 1997 study Review of Country Studies Staff (Administering survey) Decision Makers (Evaluate responses and make decisions) Respondents (People who can make valuable judgments)
    7. 7. Web-Based Forecasting: CPFR <ul><li>Collaborative Planning, Forecasting, and Replenishment (CPFR) a Web-based tool used to coordinate demand forecasting, production and purchase planning, and inventory replenishment between supply chain trading partners. </li></ul><ul><li>Used to integrate the multi-tier or n -Tier supply chain, including manufacturers, distributors and retailers. </li></ul><ul><li>CPFR’s objective is to exchange selected internal information to provide for a reliable, longer term future views of demand in the supply chain. </li></ul><ul><li>CPFR uses a cyclic and iterative approach to derive consensus forecasts. </li></ul><ul><li>Supported by Voluntary Interindustry Commerce Solutions (VICS) </li></ul>15-
    8. 8. Demand Deconstruction: Time Series and Causal Analysis <ul><li>Average demand for a period of time </li></ul><ul><li>Trend </li></ul><ul><li>Cyclical elements </li></ul><ul><li>Seasonal element (a type of cyclical element with a 4 quarter, 12 month, and 52 week long cycle) </li></ul><ul><li>Random variation </li></ul><ul><li>Autocorrelation </li></ul>15-
    9. 9. Finding Components of Demand Sales Linear Trend 15- X actual demand Average 1 2 3 4 x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x Year Seasonal variation
    10. 10. Time Series Analysis <ul><li>Time series forecasting models try to predict the future based on past data </li></ul><ul><li>You can pick models based on: </li></ul><ul><li>1. Time horizon to forecast </li></ul><ul><li>2. Data availability </li></ul><ul><li>3. Accuracy required </li></ul><ul><li>4. Size of forecasting budget </li></ul><ul><li>5. Availability of qualified personnel </li></ul>15-
    11. 11. Simple Moving Average Formula <ul><li>The simple moving average model assumes an average of some number of past periods is a good estimator of future behavior </li></ul><ul><li>The formula for the simple moving average is: </li></ul>F t = Forecast for the coming period N = Number of periods to be averaged A t-1 = Actual occurrence in the past period for up to “n” periods 15- <ul><li>See Ch15_Forecasting.xlsx! Exhibit 15.5 </li></ul><ul><li>Smoothing assigns the moving average to the middle of the time periods in the average (e.g., average from period s1-3 is assigned to period 2) </li></ul><ul><li>Forecasting assigns moving average to next period( e.g., average from periods 1-3 assigned to period 4) </li></ul><ul><li>Unless n is small compared to the length of the series, moving average forecasting introduces significant lags into forecast </li></ul>
    12. 12. Weighted Moving Average Formula While the moving average formula implies an equal weight being placed on each value that is being averaged, the weighted moving average permits an unequal weighting on prior time periods w t = weight given to time period “t” occurrence (weights must add to one) The formula for the moving average is: 15- <ul><li>See Ch15_Forecasting.xlsx! Exhibit 15.5 </li></ul><ul><li>Increasing weights emphasize most recent time periods </li></ul><ul><li>Decreasing weights emphasize more distant past </li></ul>
    13. 13. Exponential Smoothing Model <ul><li>When series is expanded weights are      </li></ul><ul><li>Since  < 1 it follows that  i decreases as the exponent gets larger, hence exponentially </li></ul><ul><li>Premise: The most recent observations might have the highest predictive value </li></ul><ul><li>Therefore, we should give more weight to the more recent time periods when forecasting </li></ul><ul><li>See Ch15_ForecastingAnnotatedMPJ.xlsx </li></ul><ul><li>Note significant lag </li></ul>F t = F t-1 +  (A t-1 - F t-1 ) 15-
    14. 14. The MAD Statistic to Determine Forecasting Error <ul><li>The ideal MAD and MAPE is zero which would mean there is no forecasting error </li></ul><ul><li>The larger the less the accurate the resulting model </li></ul><ul><li>See Ch15_ForecastingAnnotatedMPJ.xlsx </li></ul>15- <ul><li>Since there are many forecasting techniques, need ways to decide which might be better than others </li></ul><ul><li>Simplest is Mean Absolute Deviation (MAD) – scale dependent </li></ul><ul><li>Another is Mean Absolute Percentage Deviation (MAPE) - scale independent </li></ul>
    15. 15. Tracking Signal Formula <ul><li>The Tracking Signal or TS is a measure that indicates whether the forecast average is keeping pace with any genuine upward or downward changes in demand. </li></ul><ul><li>Depending on the number of MAD’s selected, the TS can be used like a quality control chart indicating when the model is generating too much error in its forecasts. </li></ul><ul><li>The TS formula is: </li></ul>See Ch15_ForecastingAnnotatedMPJ.xlsx 15- Vertical scale is in MADs ≈ number of .8 s.d.
    16. 16. Linear Trend Projections A trend line fitted to historical data points can project into the medium to long-range Linear trends can be found using the least squares technique Moving averages and exponential projection can only forecast one period ahead; often valuable (e.g., inventory replenishment) See Ch15_ForecastingAnnotatedMPJ.xlsx!Excel Regression Exhibit 15.12 for instructions to do Excel regression y = a + bx ^ where y = computed value of the variable to be predicted (dependent variable) a = y-axis intercept b = slope of the regression line x = the independent variable ^
    17. 17. Least Squares Method Figure 4.4 Time period Values of Dependent Variable Deviation 1 (error) Deviation 5 Deviation 7 Deviation 2 Deviation 6 Deviation 4 Deviation 3 Actual observation (y value) Trend line, y = a + bx ^
    18. 18. Least Squares Method Figure 4.4 Least squares method minimizes the sum of the squared errors (deviations) Time period Values of Dependent Variable Deviation 1 Deviation 5 Deviation 7 Deviation 2 Deviation 6 Deviation 4 Deviation 3 Actual observation (y value) Trend line, y = a + bx ^
    19. 19. Nonlinear Trend Projections Non-linear trends can also be found using the least squares technique <ul><li>f usually </li></ul><ul><ul><li>logarithm ln(t)/log(t), </li></ul></ul><ul><ul><li>exponential growth/decay e +/-t , </li></ul></ul><ul><ul><li>low order polynomial, or </li></ul></ul><ul><ul><li>logistic function </li></ul></ul>y = f( t, previous periods, parameters ) ^
    20. 20. Common Trend Functions See CommonFunctions.xls! CommonFunctions for Excel equation forms See CommonFunctions.xls!Trendline for automatic forecast See CommonFunctions.xls!Non-linearFit non-linear (cubic) fit example All except logistic function can by fitted with least square regression.
    21. 21. Logistic Trend <ul><li>So called S-curve - often fits product sales during introduction, growth and maturity phases </li></ul><ul><ul><li>Slow start until “word gets around” </li></ul></ul><ul><ul><li>Rapid rise when product is “hot” </li></ul></ul><ul><ul><li>Slowing growth when </li></ul></ul><ul><ul><ul><li>“ almost everybody has one that wants one” and/or </li></ul></ul></ul><ul><ul><ul><li>competitors enter market unencumbered by development costs </li></ul></ul></ul><ul><li>May not provide useful information for peak and decline </li></ul><ul><li>Cannot use regression - fit by minimizing RMSE </li></ul><ul><ul><li>with Excel use Tools -> Solver </li></ul></ul><ul><ul><li>see CommonFunctions.xls!LogisticFit </li></ul></ul>
    22. 22. Components of Demand <ul><li>Average demand for a period of time </li></ul><ul><li>Trend </li></ul><ul><li>Cyclical elements </li></ul><ul><li>Seasonal element (a type of cyclical element with a 4 quarter, 12 month, and 52 week long cycle) </li></ul><ul><li>Random variation </li></ul><ul><li>Autocorrelation </li></ul>15-
    23. 23. Cyclical Element <ul><li>A approximately repetitive patters in a time series for some number of time periods </li></ul><ul><li>Multiplicative more common since deviation is often a proportion of the trend value </li></ul><ul><li>Cycle length can be found by lagged correlation – see Ch15_ForecastingAnnotatedMPJ.xlsx! Exhibits 15.15 - 16 </li></ul>
    24. 24. Forecasting Trend w/ Cycles <ul><li>One for each period in a cycle </li></ul><ul><li>Determined before trend by ratio of each period’s sales in cycle to average in entire cycle or all the data – then forecast trend and adjust – see Ch15_ForecastingAnnotatedMPJ.xlsx!Exhibits 15.15 - 16 </li></ul><ul><li>Determined after trend by ratio of period’s sales to trend sales in same period or all data </li></ul><ul><li>Determines simultaneously with trend using dummy variables – see Ch15_ForecastingAnnotatedMPJ.xlsx!DummyVariableForCycle </li></ul>
    25. 25. Forecasting w/ Leading Indicators <ul><li>Obvious benefit – if a time series exists that leads a time series of interest it can be used to forecast </li></ul><ul><li>Example: building permits lead construction materials which lead furnishings, births lead demand for baby furniture, … </li></ul><ul><li>Implemented by multivariate regression with leading indicators as independent variables </li></ul>