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

Session 4 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis July, Shanghai 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

Session 4 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis 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

Session 4 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis 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

Session 4 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis 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

Session 4 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis July, Shanghai 6 Data and short run forecasting Outlier detection and correction  Visual inspection in graphical format

Session 4 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis 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

Session 4 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis July, Shanghai 8 Data and short run forecasting Outlier detection and correction  Mathematical detection Chronological series Time bucket series

Session 4 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis 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( )/2 =  Statistical control (3*3.7) = 115.6

Session 4 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis July, Shanghai 10 Data and short run forecasting Outlier detection and correction  Means of correction Bucket series  Average( )/2 =  Note that we are using not  Statistical control (3*8.0) = 123.6

Session 4 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis 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

Session 4 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis July, Shanghai 12 Data and short run forecasting Short run forecasting  Returning to the original full data set

Session 4 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis 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

Session 4 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis July, Shanghai 14 Data and short run forecasting Short run forecasting  Forecasting one month ahead using naïve model

Session 4 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis 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

Session 4 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis 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)

Session 4 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis 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

Session 4 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis July, Shanghai 18 Data and short run forecasting Short run forecasting  Three-period moving average model

Session 4 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis 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

Session 4 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis 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

Session 4 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis 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

Session 4 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis 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 ( ) 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

Session 4 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis 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

Session 4 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis 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= Upper limit: 93.4+(1.64)*6.99= We are 95% confident (there is a 95% probability) that demand for January 2008 will range from to

Session 4 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis July, Shanghai 25 Data and short run forecasting Short run forecasting  Confidence intervals for one month ahead  95% confidence level

Session 4 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis July, Shanghai 26 Data and short run forecasting Short run forecasting  Confidence intervals for one month ahead  99% confidence level

Session 4 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis July, Shanghai 27 Data and short run forecasting Short run forecasting  Comparison of forecasts for one and two months ahead

Session 4 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis 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

Session 4 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis July, Shanghai 29 Data and short run forecasting For further reading  Armstrong J. Scott, ed Principles of Forecasting: A handbook for researchers and practitioners. Norwell, Mass.: Kluwer Academic Publishers.  Jain, Chaman L. and Jack Malehorn Practical Guide to Business Forecasting (2nd Ed.). Flushing, New York: Graceway Publishing Inc.  Newbold, Paul and Theodore Bos Introductory Business & Economic Forecasting (2nd Ed.). Cincinnati, Ohio: South-Western Publishing Co.