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Forecasting

Forecasting. BUS255. Goals. By the end of this chapter, you should know: Importance of Forecasting Various Forecasting Techniques Choosing a Forecasting Method. Forecasting. Forecasts are done to predict future events for planning

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Forecasting

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  1. Forecasting BUS255

  2. Goals By the end of this chapter, you should know: • Importance of Forecasting • Various Forecasting Techniques • Choosing a Forecasting Method

  3. Forecasting • Forecasts are done to predict future events for planning • Finance, human resources, marketing, operations, and supply chain managers need forecasts to plan • Forecasts are made on many different variables • Forecasts are important to managing both processes and managing supply chains

  4. Key Decisions in Forecasting • Deciding what to forecast • Level of aggregation • Units of measurement • Choosing a forecasting system • Choosing a forecasting technique

  5. Qualitative (Judgment) methods • Salesforce estimates • Executive opinion • Market Research • The Delphi Method

  6. Case Study

  7. Case study questions • What information system is used by UNILEVER to manage forecasts? • What does UNILEVER do when statistical information is not useful for forecasting? • What types of qualitative methods are used by UNILEVER? • What were some suggestions provided to improve forecasting?

  8. Causal methods – Linear Regression • A dependent variable is related to one or more independent variables by a linear equation • The independent variables are assumed to “cause” the results observed in the past • Simple linear regression model assumes a straight line relationship

  9. Causal methods – Linear Regression Y = a + bX where Y = dependent variable X = independent variable a = Y-intercept of the line b = slope of the line

  10. Causal methods – Linear Regression • Fit of the regression model • Coefficient of determination • Standard error of the estimate • Please go to in-class exercise sheet

  11. Time Series • A time series is the repeated observations of demand for a service or product in their order of occurrence • There are five basic time series patterns • Horizontal • Trend • Seasonal • Cyclical • Random

  12. Quantity Time Demand Patterns (a) Horizontal: Data cluster about a horizontal line

  13. Quantity Time Demand Patterns (b) Trend: Data consistently increase or decrease

  14. Year 1 Quantity Year 2 | | | | | | | | | | | | J F M A M J J A S O N D Months Demand Patterns (c) Seasonal: Data consistently show peaks and valleys

  15. Quantity | | | | | | 1 2 3 4 5 6 Years Demand Patterns (d) Cyclical: Data reveal gradual increases and decreases over extended periods

  16. Demand Patterns • Four of the patterns – horizontal, trend, seasonal, and cyclical – combine in varying degrees to define the underlying time pattern • Fifth pattern • Random variation: Results from chance causes and cannot be predicted • Random variation is what makes every forecast ultimately wrong

  17. Time-Series methods • Use only historical information rather than independent variables (as used by Regression) • Assumption is that past pattern continues in future • In a naive forecast the forecast for the next period equals the demand for the current period (Forecast = Dt)

  18. Time-Series methods • This section considers time-series methods with demand that has no trend, seasonal, or cyclical patterns • All variation in time series is due to random variation, so the following techniques are appropriate: • Simple moving average • Weighted moving average • Exponential smoothing

  19. Sum of last n demands n Dt+ Dt-1 + Dt-2 + … + Dt-n+1 n Ft+1 = = Simple Moving Average • The forecast for period t + 1 can be calculated at the end of period t (after the actual demand for period t is known) as where Dt = actual demand in periodt n = total number of periods in the average Ft+1 = forecast for period t + 1

  20. Forecast error • For any forecasting method, it is important to measure the accuracy of its forecasts. Forecast error is simply the difference found by subtracting the forecast from actual demand for a given period, or Et = Dt– Ft where Et = forecast error for period t Dt = actual demand in periodt Ft = forecast for period t

  21. Simple Moving Average Please refer to problem in the in-class exercise

  22. Weighted Moving Average Using this method, each historical demand in the average can have its own weight, provided that the sum of the weights equals 1.0. Ft+1 = W1D1 + W2D2 + … + WnDt-n+1 A three-period weighted moving average model with the most recent period weight of 0.50, the second most recent weight of 0.30, and the third most recent might be weight of 0.20 Ft+1 = 0.50Dt+ 0.30Dt–1 + 0.20Dt–2

  23. Weighted Moving Average Please refer to problem in the in-class exercise

  24. Exponential Smoothing • A sophisticated weighted moving average that calculates the average of a time series by giving recent demands more weight than earlier demands • Requires only three items of data • The last period’s forecast • The demand for this period • A smoothing parameter, alpha (α), where 0 ≤ α ≤ 1.0 • The equation for the forecast is Ft+1 = α(Demand this period) + (1 – α)(Forecast calculated last period) = αDt+ (1 – α)Ft or the equivalent Ft+1 = Ft+ α(Dt– Ft)

  25. Exponential Smoothing Please refer to problem in the in-class exercise

  26. Exponential Smoothing • The emphasis given to the most recent demand levels can be adjusted by changing the smoothing parameter • Larger α values emphasize recent levels of demand and result in forecasts more responsive to changes in the underlying average • Smaller α values treat past demand more uniformly and result in more stable forecasts • Exponential smoothing is simple and requires minimal data • When the underlying average is showing some trend, different model is needed

  27. Choosing a Time-Series Method • Forecast performance is determined by forecast errors • Forecast errors detect when something is going wrong with the forecasting system • Forecast errors can be classified as either bias errors or random errors • Bias errors (or systematic errors) are the result of consistent mistakes • Random error results from unpredictable factors that cause the forecast to deviate from the actual demand

  28. So, what do we mean by systematic error?

  29. Measures of Forecast Error • Forecast Error = Demand value – Forecast Value • Mean absolute deviation (MAD) • Mean signed deviation (MSD) • Tracking signal (TS) • Mean squared error (MSE) • Mean absolute percentage error (MAPE) Et = Dt– Ft

  30. |Et| n MAD = Mean Absolute Deviation (MAD) • MAD is the average of the absolute values of the errors. • Stated in the same units as the forecast • Captures the magnitude of the forecasting error • Compute MAD for the example problem in Excel sheet (tab 2) and interpret the results

  31.  Et n MSD = Mean Sign Deviation (MSD) • MSD is the average of the errors • Stated in the same units as the forecast • Signs (+/-) of the error terms tend to cancel each other out • A large value (+/-) indicates systematic forecast error • Compute MSD for the example problem in Excel sheet (tab 2) and interpret the results

  32. MSD MAD TS = Tracking Signal (TS) • Tracking Signal (TS) measures systematic error • TS is unitless and is between -1 and 1 • Think of it as percentage of forecast error that is systematic

  33. Tracking Signal (TS)

  34. Interpreting Tracking Signal (TS)

  35. Interpreting Tracking Signal (TS) • Calculate TS for the example problem and interpret it

  36.  Et2 n MSE = Mean Squared Error(MSE) • MSE is the average of the square errors • It is a measure of dispersion of forecast error • Smaller values indicate that forecast is typically close to actual demand • Compute MSE for the example problem in Excel sheet (tab 2) and interpret the results

  37.  (|Et|/Dt)(100) n MAPE = Mean Absolute Percentage Error(MAPE) • MAPE takes the absolute error of each forecast, and divides it by the value of the demand, multiply that quantity with 100 to get the error as a percentage of the demand, and then averages these percentage errors. • Very useful for comparisons between time series for different SKUs • Compute MAPE for the example problem in Excel sheet (tab 2) and interpret the results

  38. Criteria for Selecting Methods • Criteria to use in making forecast method and parameter choices include • Minimizing bias • Minimizing MAPE, MAD, or MSE • Meeting managerial expectations of changes in the components of demand • Minimizing the forecast error last period • Statistical performance measures can be used • For projections of more stable demand patterns, use lower αvalues or larger n values • For projections of more dynamic demand patterns try higher αor smaller n values

  39. Adjust history file 1 Consensus meetings and collaboration 3 Prepare initial forecasts 2 Review by Operating Committee 5 Finalize and communicate 6 Revise forecasts 4 Forecasting as a Process • Forecasting is not a stand-alone activity, but part of a larger process

  40. Remember, forecasting is like fortune telling… You’re right only by accident!

  41. References • Krajewski, Ritzman, Malhotra. (2010). Operations Management: Processes and Supply Chains, Ninth Edition. Pearson Prentice Hall. • Dr. Gary Mitchell, Class Notes • Dr. Min Yu, Class Notes

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