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Session 4: Data and short run forecasting

Session 4: Data and short run forecasting. Demand Forecasting and Planning in Crisis 30-31 July, Shanghai Joseph Ogrodowczyk, Ph.D. Data and short run forecasting. Session agenda Outlier detection and correction Naïve one-step, moving average, and confidence interval forecasts

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Session 4: Data and short run forecasting

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  1. Session 4: Data and short run forecasting Demand Forecasting and Planning in Crisis 30-31 July, Shanghai Joseph Ogrodowczyk, Ph.D.

  2. Data and short run forecasting • Session agenda • Outlier detection and correction • Naïve one-step, moving average, and confidence interval forecasts • Activity: Produce short run forecasts with different historical data Demand Forecasting and Planning in Crisis 30-31 July, Shanghai

  3. Data and short run forecasting Outlier detection and correction In the previous session, the data set contained data in every month of the year Demand Forecasting and Planning in Crisis 30-31 July, Shanghai

  4. Data and short run forecasting Outlier detection and correction Definition: Outliers are data points that are outside of (greater or less than) the “normal” range for the data set Sometimes outliers can be identified with visual inspection Note: Outliers may also indicate seasonality, advertising jump, or other vital variable Demand Forecasting and Planning in Crisis 30-31 July, Shanghai

  5. Data and short run forecasting Outlier detection and correction Visual inspection in table format Historical data now include years 2000 – 2009 Data in bold below were the data set from previous example. Red values were the missing data points Demand Forecasting and Planning in Crisis 30-31 July, Shanghai

  6. Data and short run forecasting Outlier detection and correction Visual inspection in graphical format Demand Forecasting and Planning in Crisis 30-31 July, Shanghai

  7. Data and short run forecasting Outlier detection and correction Mathematical detection Calculate the mean and standard deviation Based on chronological or time buckets Mean ±3*(standard deviation) With limited data, omit the suspected outlier Also called statistical control Demand Forecasting and Planning in Crisis 30-31 July, Shanghai

  8. Data and short run forecasting Outlier detection and correction Mathematical detection Chronological series Time bucket series Demand Forecasting and Planning in Crisis 30-31 July, Shanghai

  9. Data and short run forecasting Outlier detection and correction Means of correction Two suggested methods Missing data method (average of preceding and following data points) Statistical control limit Data series Average (99.0+103.3)/2 = 101.1 Statistical control 104.5 + (3*3.7) = 115.6 Demand Forecasting and Planning in Crisis 30-31 July, Shanghai

  10. Data and short run forecasting Outlier detection and correction Means of correction Bucket series Average (97.5+105.2)/2 = 101.3 Note that we are using 2000-2008 not 2004-2008 Statistical control 99.6 + (3*8.0) = 123.6 Demand Forecasting and Planning in Crisis 30-31 July, Shanghai

  11. Data and short run forecasting Short run forecasting An executive has asked for a forecast of demand for the next month Three suggested methods to use: Naïve: Using the most recent data point as a forecast Moving average: Using an average of several most recent data points as a forecast Confidence intervals: Using historical data to calculate areas of demand probabilities Demand Forecasting and Planning in Crisis 30-31 July, Shanghai

  12. Data and short run forecasting Short run forecasting Returning to the original full data set Demand Forecasting and Planning in Crisis 30-31 July, Shanghai

  13. Data and short run forecasting Short run forecasting Suppose we wish to forecast January 2008 and we have just completed December 2007 Naïve Using the most recent data point (88.9 in December 2007) to forecast January 2008 Graph is shown on following slide Each forecast is produced for the next month Demand Forecasting and Planning in Crisis 30-31 July, Shanghai

  14. Data and short run forecasting Short run forecasting Forecasting one month ahead using naïve model Demand Forecasting and Planning in Crisis 30-31 July, Shanghai

  15. Data and short run forecasting Short run forecasting Now suppose we wish to forecast February 2008 and we have just completed December 2007 Naïve Using the most recent data point (December 2007) to forecast February 2008 Graph is shown on following slide Note that the first forecast quantity is the same as the previous example Demand Forecasting and Planning in Crisis 30-31 July, Shanghai

  16. Data and short run forecasting Short run forecasting Forecasting two months ahead using naïve model Looks similar to forecasting one month ahead Longer delay to recognize the decline (Nov. and Dec) Demand Forecasting and Planning in Crisis 30-31 July, Shanghai

  17. Data and short run forecasting Short run forecasting Forecasting January 2008 Moving average model: Calculate an average based on a set of previous values Three-period moving average would use December, November and October 2007 data January 2008 forecast = 93.4 This forecast is made for the next month Demand Forecasting and Planning in Crisis 30-31 July, Shanghai

  18. Data and short run forecasting Short run forecasting Three-period moving average model Demand Forecasting and Planning in Crisis 30-31 July, Shanghai

  19. Data and short run forecasting Short run forecasting Forecasting February 2008 Moving average model: Calculate an average based on a set of previous values 3 period moving average would use December, November and October of 2007 data February 2008 forecast = 93.4 This forecast is made for two months ahead Demand Forecasting and Planning in Crisis 30-31 July, Shanghai

  20. Data and short run forecasting Short run forecasting 3 period moving average model two months ahead Again, forecasts take longer to respond to declines in demand Demand Forecasting and Planning in Crisis 30-31 July, Shanghai

  21. Data and short run forecasting Short run forecasting Forecasting January 2008 Confidence interval model Based on the moving average model Constructing forecasting ranges with associated confidence levels What is the likely level of demand in the future? The higher the confidence level, the greater the range of estimated demand Demand Forecasting and Planning in Crisis 30-31 July, Shanghai

  22. Data and short run forecasting Short run forecasting Confidence intervals Calculations can be done according to the month being forecasted Use all previous January data (2000-2007) to construct the confidence interval for January 2008 More statistically involved and uses probabilities taken from the standard normal curve (bell curve) MAF ±(std normal statistic)*Std deviation MAF: moving average forecast Demand Forecasting and Planning in Crisis 30-31 July, Shanghai

  23. Data and short run forecasting Short run forecasting Standard normal statistic A 95% confidence level corresponds to a value of 1.64 A 99% confidence level corresponds to a value of 2.33 Demand Forecasting and Planning in Crisis 30-31 July, Shanghai

  24. Data and short run forecasting Short run forecasting Confidence intervals MAF ±(std normal statistic)*Std deviation Moving average forecast for January = 93.4 Std dev for January = 6.99, 95% confidence = 1.64 Lower limit: 93.4-(1.64)*6.99= 81.93 Upper limit: 93.4+(1.64)*6.99= 104.87 We are 95% confident (there is a 95% probability) that demand for January 2008 will range from 81.93 to 104.87 Demand Forecasting and Planning in Crisis 30-31 July, Shanghai

  25. Data and short run forecasting Short run forecasting Confidence intervals for one month ahead 95% confidence level Demand Forecasting and Planning in Crisis 30-31 July, Shanghai

  26. Data and short run forecasting Short run forecasting Confidence intervals for one month ahead 99% confidence level Demand Forecasting and Planning in Crisis 30-31 July, Shanghai

  27. Data and short run forecasting Short run forecasting Comparison of forecasts for one and two months ahead Demand Forecasting and Planning in Crisis 30-31 July, Shanghai

  28. Data and short run forecasting Short run forecasting Note how each of the models responds to the sudden decrease in demand Naïve one-step responds quickest, moving average shows a slight delay Trend needs to change before the calculations are affected Actual demand dropped in Sept and Oct. Forecasts responded in Nov and Dec. This can be problematic in time of steep decline Confidence intervals assist by suggesting probabilities of demand Higher confidence leads to greater intervals Upper range is the optimistic scenario while lower range is the risk scenario All three models are better suited for short run forecasting For longer run forecasting, we need to use models that produce dynamic forecast quantities Demand Forecasting and Planning in Crisis 30-31 July, Shanghai

  29. Data and short run forecasting • For further reading • Armstrong J. Scott, ed. 2001. Principles of Forecasting: A handbook for researchers and practitioners. Norwell, Mass.: Kluwer Academic Publishers. • Jain, Chaman L. and Jack Malehorn. 2005. Practical Guide to Business Forecasting (2nd Ed.). Flushing, New York: Graceway Publishing Inc. • Newbold, Paul and Theodore Bos. 1994. Introductory Business & Economic Forecasting (2nd Ed.). Cincinnati, Ohio: South-Western Publishing Co. Demand Forecasting and Planning in Crisis 30-31 July, Shanghai

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