FORECASTING

• Strategic Role of Forecasting in Supply Chain Management
• Components of Forecasting Demand
• Time Series Methods
• Forecast Accuracy
• Time Series Forecasting Using Excel
• Regression MethodsForecasting
• Predicting the future
• Qualitative forecast methods:

  • Subjective
• Quantitative forecast methods:

  • Based on mathematical formulas
• Accurate forecasting determines inventory levels in the supply chain
• Continuous replenishment:

  • Supplier & customer share continuously updated data
  • Typically managed by the supplier
  • Reduces inventory for the company
  • Speeds customer delivery
• Variations of continuous replenishment:

  • Quick response
  • JIT (just-in-time)
  • VMI (vendor-managed inventory)
  • Stockless inventory
• Quality Management:

  • Accurately forecasting customer demand is key to providing good quality service
• Successful strategic planning requires accurate forecasts of future products and markets
• Types of Forecasting Methods depend on:

  • Time frame
  • Demand behavior
  • Causes of behavior
• Time Frame indicates how far into the future is forecast:

  • Short- to mid-range forecast typically encompasses the immediate future, daily up to two years
  • Long-range forecast usually encompasses a period longer than two years
• Demand Behavior:

  • Trend: a gradual, long-term up or down movement of demand
  • Random variations: movements in demand that do not follow a pattern
  • Cycle: an up-and-down repetitive movement in demand
  • Seasonal pattern: an up-and-down repetitive movement in demand occurring periodically
• Forecasting Methods:

  • Time series: statistical techniques that use historical demand data to predict future demand
  • Regression methods: attempt to develop a mathematical relationship between demand and factors that cause its behavior
  • Qualitative: use management judgment, expertise, and opinion to predict future demand
• Qualitative Methods:

  • Management, marketing, purchasing, and engineering are sources for internal qualitative forecasts
  • Delphi method involves soliciting forecasts about technological advances from experts
• Forecasting Process:

  1. Identify the purpose of forecast

  2. Collect historical data

  3. Plot data and identify patterns

  4. Select a forecast model that seems appropriate for data

  5. Develop/compute forecast for period of historical data

  6. Check forecast accuracy with one or more measures

  7. Is accuracy of forecast acceptable?

  8a. Forecast over planning horizon

  8b. Select new forecast model or adjust parameters of existing model

  9. Adjust forecast based on additional qualitative information and insight

  10. Monitor results and measure forecast accuracy
• Time Series:

  • Assume that what has occurred in the past will continue to occur in the future
  • Relate the forecast to only one factor - time
  • Include moving average, exponential smoothing, linear trend line
• Moving Average:

  • Naive forecast: demand in current period is used as next period’s forecast
  • Simple moving average: uses average demand for a fixed sequence of periods, stable demand with no pronounced behavioral patterns
  • Weighted moving average: weights are assigned to most recent data
• Exponential Smoothing:

  • Averaging method
  • Weights most recent data more strongly
  • Reacts more to recent changes
  • Forecast based on smoothing constant
• Adjusted Exponential Smoothing:

  • Adjusts moving average method to reflect data fluctuations more closely
  • Involves trend factor and smoothing constant for trend
• Adjusted Exponential Smoothing Forecasts:
• Forecast for period 13 using adjusted exponential smoothing: 57.56 units
• Linear Trend Line:
• Formula for linear trend line: y = a + bx
• Calculation for linear trend line: y = 35.2 + 1.72x
• Least Squares Example:
• Calculation for linear trend line using least squares method: y = 35.2 + 1.72x
• Seasonal Adjustments:
• Seasonal factor calculation for different years
• Seasonal adjustment calculation for different seasonal factors
• Forecast Accuracy:
• Mean Absolute Deviation (MAD) calculation: 4.85
• Mean Absolute Percent Deviation (MAPD) calculation: 9.6%
• Comparison of different forecasts based on MAD, MAPD, and average error
• Forecast Control:
• Tracking signal calculation and interpretation