Forecasting & time series data

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Forecasting & time series data

  1. 1. By: Jane Karla Rosita & Billy Grace Diñ0
  2. 2. Operations management  deals with the design and management of products, processes, services and supply chains. It considers the acquisition, development, and utilization of resources that firms need to deliver the goods and services their clients want.
  3. 3.  Forecasting helps managers and businesses develop meaningful plans and reduce uncertainty of events in the future. Managers want to match supply with demand; therefore, it is essential for them to forecast how much space they need for supply to each demand.
  4. 4.  Predict the next number in the pattern: a) 3.7, 3.7, 3.7, 3.7, 3.7, ? b) 2.5, 4.5, 6.5, 8.5, 10.5, ? c) 5.0, 7.5, 6.0, 4.5, 7.0, 9.5, 8.0, 6.5, ?
  5. 5.  Predict the next number in the pattern: a) 3.7, 3.7, 3.7, 3.7, 3.7, 3.7 b) 2.5, 4.5, 6.5, 8.5, 10.5, 12.5 c) 5.0, 7.5, 6.0, 4.5, 7.0, 9.5, 8.0, 6.5, 9.0
  6. 6.  Process of predicting a future event based on historical data  Educated Guessing  Underlying basis of all business decisions  Production  Inventory  Personnel  Facilities ??
  7. 7.  Short-range forecast  Up to 1 year, generally less than 3 months  Purchasing, job scheduling, workforce levels, job assignments, production levels  Medium-range forecast  3 months to 3 years  Sales and production planning, budgeting  Long-range forecast  3+ years  New product planning, facility location, research and development
  8. 8.  Economic forecasts  Address business cycle – inflation rate, money supply, housing starts, etc.  Technological forecasts  Predict rate of technological progress  Impacts development of new products  Demand forecasts  Predict sales of existing products and services
  9. 9.  Forecasting methods are classified into two groups:
  10. 10.  Qualitative methods – judgmental methods  Forecasts generated subjectively by the forecaster  Educated guesses  Quantitative methods – based on mathematical modeling:  Forecasts generated through mathematical modeling
  11. 11. Type Executive opinion Characteristics Strengths Weaknesses A group of managers Good for strategic or One person's opinion meet & come up with new-product can dominate the a forecast forecasting forecast Market research Uses surveys & Good determinant of It can be difficult to interviews to identify customer preferences develop a good customer preferences questionnaire Delphi method Seeks to develop a consensus among a group of experts Excellent for Time consuming to forecasting long-term develop product demand, technological changes, and
  12. 12.  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
  13. 13. Naive approach 2. Moving averages 3. Exponential smoothing 4. Trend projection 1. 5. Linear regression time-series models associative model
  14. 14.  Set of evenly spaced numerical data  Obtained by observing response variable at regular time periods  Forecast based only on past values, no other variables important  Assumes that factors influencing past and present will continue influence in future
  15. 15. Trend Cyclical Seasonal Random
  16. 16.  MA is a series of arithmetic means  Used if little or no trend  Used often for smoothing  Provides overall impression of data over time Moving average = ∑ demand in previous n periods n
  17. 17.  Form of weighted moving average  Weights decline exponentially  Most recent data weighted most  Requires smoothing constant ( )  Ranges from 0 to 1  Subjectively chosen  Involves little record keeping of past data
  18. 18. New forecast = Last period’s forecast + (Last period’s actual demand – Last period’s forecast) Ft = Ft – 1 + (At – 1 - Ft – 1) where Ft = Ft – 1 = = new forecast previous forecast smoothing (or weighting) constant (0 ≤ ≤ 1)
  19. 19. Mean Absolute Deviation (MAD) MAD = ∑ |Actual - Forecast| n Mean Squared Error (MSE) MSE = ∑ (Forecast Errors)2 n
  20. 20. Mean Absolute Percent Error (MAPE) n MAPE = ∑100|Actuali - Forecasti|/Actuali i=1 n
  21. 21. Fitting a trend line to historical data points to project into the medium to long-range Linear trends can be found using the least squares technique ^ y = a + bx where ^ = computed value of the variable to be y predicted (dependent variable) a = y-axis intercept b = slope of the regression line x = the independent variable

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