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LESSON 3 - CBME

Manabat Jensel Marie H. English 74 cards

LESSON 3 - CBME

Cards in this deck (74)

  1. 1FORECASTA statement about the future value of a
    variable of interest.
  2. 2Two Important Aspects of Forecast1. Expected level of demand - The level of demand may be a function of some structural variation.
    2. Accuracy - Potential size of forecast error.
  3. 3Plan the systemGenerally involves long-range plans related to:
    • Types of product and services to offer
    • Facility and equipment levels
    • Facility location
  4. 4Plan the use of the systemGenerally involves short and medium range plans related to:
    • Inventory management
    • Workforce levels
    • Purchasing
    • Production
    • Budgeting
    • Scheduling
  5. 5Elements of a Good Forecast✓ timely
    ✓ accurate
    ✓ reliable
    ✓ expressed in meaningful units
    ✓ in writing
    ✓ technique is simple to understand and use
    ✓ cost-effective
  6. 6Steps in the Forecasting Process1. Determine the purpose of the forecast.
    2. Establish a time horizon.
    3. Obtain, clean, and analyze appropriate data.
    4. Select a forecasting technique.
    5. Make the forecast.
    6. Monitor the forecast errors.
  7. 7Forecast Accuracy and ControlAllowances should be made for forecast errors. Forecast errors should be monitored.
    Error = Actual – Forecast
  8. 8Forecast Accuracy MetricsForecast Accuracy Metrics
  9. 9Forecast Error CalculationForecast Error Calculation
  10. 10STEPS in Forecast Error Calculation (STEPS 1-3)Step 1. Find the forecast error for each period: Error= (Actual- Forecast)
    Step 2. The error for each period are computed as follows; Period 1 Formula:
    Error = (Actual- Forecast)
    Solution: = (217 – 215) = 2
    Step 3. Follow the given equation on step 1 to compute the ERROR for period 2 to 8.
  11. 11STEPS in Forecast Error Calculation (continuation, STEPS 4-5)Step 4. ABSOLUTE VALUE (/ERROR/) of the Error of Period 1 to Period 8.
    the negative sign of the number will be ignored.
    Step 5. For Period 1, the absolute value of the (Error) 2 is 2, the positive sign
    of the number remains.
    Whereas, for Period 2 the absolute value of the (Error) -3 is 3, the negative sign of the
    number was IGNORED.
  12. 12STEPS in Forecast Error Calculation (continuation, STEP 6)Step 6: Find the square value of each Error following the rule on computing square value. To illustrate, the square value of (Error) 2 for Period 1 is 4. Apply the same equation for square value of Errors of Period 2 to Period 8.
  13. 13STEPS in Forecast Error Calculation (continuation, STEP 7-8)Step 7: Compute for the last absolute percentage error value of Period 1 to Period 8 using the formula; (I Error I / Actual) x 10
    Step 8: To illustrate, for Period 1; Period 1
    Formula : (I Error I / Actual) x 100
    Solution : (2 / 217) x 100 = .92%
  14. 14STEPS in Forecast Error Calculation (continuation, STEP 9)Step 9: Using the figures shown in the table, compute for MAD, MASE, and MAPE.
    Step 10: To compute for MAD (Mean Absolute Deviation), compute for the summation of the ABSOLUTE ERROR VALUES (/ERROR/) from Period 1 to Period 8 by using the formula;    STEPS in Forecast Error Calculation (continuation, STEP 9)
  15. 15STEPS in Forecast Error Calculation (continuation, STEP 11)Step 11: To illustrate, the solution to compute for the MAD on the given problem is;
    Solution:
    MAD = (2 + 3 + 1 + 4 + 2 + 5 + 1 +4) / 8
    = 22 / 8
    MAD = 2.75
  16. 16STEPS in Forecast Error Calculation (continuation, STEP 12)Step 12: MSE (Mean Squared Error), compute for the summation of the SQUARED ERROR VALUES (ERROR 2) from Period 1 to Period 8 by using the formula;STEPS in Forecast Error Calculation (continuation, STEP 12)
  17. 17STEPS in Forecast Error Calculation (continuation, STEP 13)Step 13: The solution to compute for the MSE on the given problem is;
    MSE = (4 + 9 + 1 + 16 + 4 + 25 + 1 + 16)/ 8 -1
    = 76 / 7
    MSE = 10.86
  18. 18STEPS in Forecast Error Calculation (continuation, STEP 14)Step 14: MAPE (Mean Absolute Percent Error), compute for the summation of the (I Error I / Actual) x 100 VALUES from Period 1 to Period 8 by using the formula:STEPS in Forecast Error Calculation (continuation, STEP 14)
  19. 19STEPS in Forecast Error Calculation (continuation, STEP 15)Step 15: The solution to compute for the MAPE on the given problem is
    Solution:
    MAPE = (.92% + 1.41% + .46% + 1.90% + .94% + 2.28% .46% + 1.89%) / 8
    MAPE = 1.28%
  20. 20YOUR FORESCAST ERROR CALCULATION SHOULD LOOK LIKE THIS:YOUR FORESCAST ERROR CALCULATION SHOULD LOOK LIKE THIS:
  21. 21Qualitative Forecasting- Soft information such as: Human Factors, Personal opinions & Hunches.
    - Use subjective inputs such as opinions from consumer surveys, sales staff, managers, executives, and experts.
  22. 22Executive OpinionsA small group of upper-level managers may meet and collectively develop a forecast.
  23. 23Sales Force OpinionsCan be good sources of information due to their direct contact with customer and may be aware of plans that the customers may be considering for the future.
  24. 24Consumer Surveys- Since consumer ultimately determine demand, it makes sense to solicit input from them.
    - Typically represent a sample of consumers opinions.
  25. 25Others ApproachesManager may solicit opinions from other managers of staff people/outside experts to help with developing forecast.
    Delphi method - iterative process intended to achieve a consensus.
  26. 26Quantitative ForecastingHard data, involves projection of historical data/development of associative methods that attempt to use causal variables to make a forecast.
  27. 27Time-Series ForecastsForecast that project patterns in recent time-series observations.
  28. 28Time-series- A time-ordered sequence of observations taken at regular time intervals.
    - Assume that future values of the time-series can be estimated from past value of the time series.
  29. 29Trends- A long-term upward or downward movement in data.
    - Population shifts
    - Changing income
  30. 30Seasonality- Short-term, fairly regular variations related to the calendar or time of day.
    - Restaurants, services call centers, and theaters all experience seasonal demand.
  31. 31Cycles- wavelike variations of more than one year
    - often related to a variety of economic, political, and even agricultural conditions.
  32. 32Irregular variations- due to unusual circumstances that do not reflect typical behavior.
    • Labor strike
    • Weather event
  33. 33Random Variation- Residual variations that remain after all other behaviors have been accounted for.
  34. 34Cycles and Variations IllustrationCycles and Variations Illustration
  35. 35Naive Forecast- Uses a single previous value of a time series as the basis of a forecast.
    - The forecast for a time period is equal to the previous time period’s value .
    Can be used with:
    • A stable time series
    • Seasonal variations
    • trend
  36. 36EXAMPLE 1: Naive Forecast CalculationTo illustrate use NAÏVE method to forecast given the following data on the table below and compute for the ERROR for each period.
  37. 37EXAMPLE 1: Naive Forecast Calculation (Continuation, STEP 1)To compute for the forecast using Naïve Method, simply used the actual demand of the previous period as the basis of the forecast on the next period. Following the rule on Naïve Method, the forecast of Period 2 is the DEMAND of Period 1;
  38. 38EXAMPLE 1: Naive Forecast Calculation (Continuation) It will look like this:EXAMPLE 1: Naive Forecast Calculation (Continuation) It will look like this:
  39. 39EXAMPLE 1: Naive Forecast Calculation (Continuation, STEP 2)To get the error, subtract the demand to the forecast. Same with the following periods.
  40. 40Positive and Negative Errors on PeriodsImportant to analyze because they potentially provides with information about seasonality trends that can help to understand the performance of a product during particular times of the year.
  41. 41YOUR NAIVE FORECAST CALCULATION SHOULD LOOK LIKE THIS:YOUR NAIVE FORECAST CALCULATION SHOULD LOOK LIKE THIS:
  42. 42Time-Series Forecasting – Averaging- Work best when a series tends to vary about an average. Smooth variations in the data.
    - They can handle step changes/gradual changes in the level of a series.
  43. 43Techniques1. Moving Average
    2. Weighted Moving Average
    3. Exponential Smoothing
  44. 44DEFINITION of Moving Average (MA)Techniques that averages number of the most recent actual values in generating a forecast.DEFINITION of Moving Average (MA)
  45. 45Moving Average (MA) : EXAMPLE 1Compute a 3-period Moving Average forecast given demand for shopping carts for the last eleven periods.Moving Average (MA) : EXAMPLE 1
  46. 46Moving Average (MA) : STEP 1 & SOLUTIONUse the three most recent demand on the given table, to compute for the forecast on MA6;
    Solution:
    MA6 = 43 + 40 + 41/3
    = 124/3
    MA6 = 41.33
  47. 47Moving Average (MA) : STEP 2 & SOLUTIONIf actual demand in period 6 turns out to be 38, the moving average forecast for period 7 would be;
    Solution:
    MA7 = 40 + 41 + 38/3
    = 119/3
    MA7 = 39.67
  48. 48DEFINITION of Weighted Moving AverageSimilar to a (MA), except that it typically assigns more weight to the most recent values in a time series.
  49. 49Weighted Moving Average: EXAMPLE 1 STEP 1 & SOLUTIONa. Compute a weighted average forecast using a weight of .40 for the most recent period, .30 for the next most recent, .20 for the next, and .10 for the next.
    b. If the actual demand for period 6 is 39, forecast demand for period 7 using the
    same weights as in part A.
  50. 50YOUR ASSIGNED WEIGHT SHOULD LOOK LIKE THIS:YOUR ASSIGNED WEIGHT SHOULD LOOK LIKE THIS:
  51. 51Weighted Moving Average FormulaWeighted Moving Average Formula
  52. 52Weighted Moving Average STEP 2 & SOLUTIONSolution:
    F6 = 0.40 (41) + 0.30 (40) + 0.20 (43) + 0.10 (40) = 41.0    Weighted Moving Average STEP 2 & SOLUTION
  53. 53Weighted Moving Average STEP 3 & SOLUTIONSolution:
    F6 = 0.40 (39) + 0.30 (41) + 0.20 (40) + 0.10 (43) = 40.2    Weighted Moving Average STEP 3 & SOLUTION
  54. 54DEFINITION of Exponential SmoothingSophisticated weighted averaging method that is still relatively easy to use and understand.
  55. 55Exponential Smoothing FormulaFt = Ft-1 + α (At-1 - Ft-1)
    Where:
    Ft = Forecast for period t
    Ft-1 = Forecast for period t – 1
    α = smoothing constant
    At-1 = Actual demand or sales for period t – 1
  56. 56Smoothing ConstantRepresents a percentage of a forecast error. Each new forecast is equal to the previous forecast plus a percentage of the previous
    error.
  57. 57Exponential Smoothing ExampleUse exponential smoothing to develop a series of forecasts for the following data.
    a. Use a smoothing factor of 0.10
    b. Use a smoothing factor of 0.40
  58. 58Exponential Smoothing Solution & Step 1Formula: F3 = F2+ α (A2 – F2)
    Solution:
    F3 = 42 + .10 (40 – 42) = 42 + .10 (-2) = 42 + -.2
    F3 = 41.80
    Solution:
    F4 = 41.80 + .10 (43 – 41.80) = 41.80 + .10 (1.2)
    = 41.80 + .12
    F4 = 41.92
  59. 59Exponential Smoothing Solution & Step 2Formula:
    F3 = F2+ α (A2 – F2)
    Solution:
    F3 = 42 + .40 (40 – 42) = 42 + .40 (-2)
    = 42 + -.8
    F3 = 41.20
    Solution:
    F4 = 41.20 + .40 (43 – 41.20) = 41.20 + .40 (1.8) = 41.20 + .72
    F4 = 41.92
  60. 60Your Exponential Smoothing Calculation should look like this:Your Exponential Smoothing Calculation should look like this:
  61. 61Techniques for SeasonalitySeasonality – regularly repeating movements in series values that can tied to recurring events.
    - Expressed in terms of the amount that actual values deviate from the average value of a series.
  62. 62Models of SeasonalityADDITIVE
    - a quantity that gets added to or subtracted from the time series average in order to incorporate seasonality.
    MULTIPLICATIVE
    - a percentage of the average (or trend) amount which is used to multiply the value of series in order to incorporate seasonality.
  63. 63Techniques for Seasonality: Example 1Calculate the seasonal indices for the rain fall (in mm) data in Tamil Nadu given below by simple average method.
  64. 64Techniques for Seasonality: Example 1 IllustrationTechniques for Seasonality: Example 1 Illustration
  65. 65Techniques for Seasonality: SolutionGeneral Average = Total of seasonal average/4
    = 169.2 + 289.8 + 497.8 + 23.8 /4
    = 980.6/ 4
    = 245.15
  66. 66Techniques for SeasonalityTechniques for Seasonality
  67. 67Monitoring the ForecastTracking forecast errors and analyzing them can provide useful insight whether forecast are performing satisfactorily.
  68. 68Sources of Forecast Errors- The model may be inadequate due to
    a. Omission of an important variable
    b. A change or shift in the variable the model cannot handle.
    c. The appearance of a new variable
    - Irregular variations may have occurred
    - Random variation
  69. 69Control ChartsUseful for identifying the presence of non-random error in forecast/
  70. 70Tracking SignalsCan be used to detect forecast bias
  71. 71Factors to consider in Choosing a Forecasting Technique- Cost
    - Accuracy
    - Availability of Historical data
    - Availability of forecasting software
    - Time needed to gather & analyze data and prepare a forecast.
    - Forecast horizon
  72. 72Operations StrategyThe better forecast are the more able organizations will be to take advantages of future opportunities and reduce potential risks.
  73. 73A Worthwhile Strategyis to work to improve short-term forecasts
  74. 74Accurate up-to-date information can have a significant effect on forecast accuracy:- Price
    - Demand
    - Other important variable
    - Reduce the time horizon forecast have to cover.
    - Sharing forecast or demand data through the supply chain can improve
    forecast quality.

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Flashcards on LESSON 3 - CBME, created by Manabat Jensel Marie H. on 18/11/2021.
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