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Forecasting

Forecasting. Chapter 9. Chapter Objectives. Be able to: Discuss the importance of forecasting and identify the most appropriate type of forecasting approach, given different forecasting situations.

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Forecasting

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  1. Forecasting Chapter 9

  2. Chapter Objectives Be able to: • Discuss the importance of forecasting and identify the most appropriate type of forecasting approach, given different forecasting situations. • Apply a variety of time series forecasting models, including moving average, exponential smoothing, and linear regression models. • Develop causal forecasting models using linear regression and multiple regression. • Calculate measures of forecasting accuracy and interpret the results.

  3. 1. Define Forecast.

  4. Forecasting • Forecast – An estimate of the future level of some variable. • Why Forecast? • Assess long-term capacity needs • Develop budgets, hiring plans, etc. • Plan production or order materials

  5. 2. Define Planning.

  6. Operations Management Framework • Planning- Determining what is needed, and making arrangements to get it, in order to achieve objectives.

  7. 3. What are the financial benefits of effective planning?

  8. Financial Benefits of Effective Planning • Increasing Alternatives • Management has more options if it plans ahead. • Profitability Enhancement • Planning can both reduce costs and increase sales. • Uncertain future The further ahead we plan, however, the less we know about future conditions. There is a tradeoff between increasing alternatives and increasing uncertainty.

  9. Looking into the Future:The Planning Horizon • Planning Horizon • The distance into the future one plans.

  10. 4. What is a contingency plan?

  11. Operations Management Framework • Contingency Plans– Alternative or back-up plans to be used if an unexpected event makes the normal plans infeasible.

  12. 5. What are three common types of forecasts?

  13. Types of Forecasts • Demand • Firm-level • Market-level • Supply • Number of current producers and suppliers • Projected aggregate supply levels • Technological and political trends • Price • Cost of supplies and services • Market price for firm’s product or service

  14. 6. What are the four laws of forecasting?

  15. Laws of Forecasting • Forecasts are almost always wrong by some amount (but they are still useful). • Forecasts for the near term tend to be more accurate. • Forecasts for groups of products or services tend to be more accurate. • Forecasts are no substitute for calculated values.

  16. 7. What are the two types of forecasting models?

  17. Forecasting Methods • Qualitative forecasting techniques – Forecasting techniques based on intuition or informed opinion. • Used when data are scarce, not available, or irrelevant. Do not use past data. Usually used when such data is not available (such as planning for a new product). • Customer surveys, expert opinions, etc

  18. Qualitative Forecasting Methods • Market surveys • Build-up forecasts • Life-cycle analogy method • Panel consensus forecasting • Delphi method

  19. Forecasting Methods • Quantitative forecasting models – Forecasting models that use measurable, historical data to generate forecasts. • Time series and causal models

  20. Quantitative Forecasting Methods • Time series forecasting models – Models that use a series of observations in chronological order to develop forecasts. • Causal forecasting models – Models in which forecasts are modeled as a function of something other than time.

  21. Selecting a Forecasting Method Figure 9.2

  22. 7. What are components of a time series?

  23. Demand Forecasting:Components of a Time Series • There are four potential components of a time series: • Cycles • A pattern that repeats over a long period of time (such as 20 years). • Cycles are less important for demand forecasting, since we rarely have 20 years’ worth of data. • Trend • Seasonality • Randomness

  24. Demand Forecasting:Components of a Time Series • Trend – Component of a time series that causes demand to increase or decrease. Exhibit 10.6 Example of a Time Series with Trend

  25. Demand Forecasting:Components of a Time Series • Seasonality – A pattern in a time series that repeats itself at least once a year. Exhibit 10.7 Time Series with Seasonality

  26. Time series with Trend and Seasonality Figure 9.4

  27. Demand Forecasting:Components of a Time Series • Random Fluctuation – Unpredictable variation in demand that is not due to trend, seasonality, or cycle. Exhibit 10.8 Time Series with Random Fluctuation

  28. Time series with randomness Figure 9.3

  29. 9. What are the various time series techniques?

  30. Last Period Model • Last Period Model - The simplest time series model that uses demand for the current period as a forecast for the next period. Ft+1 =Dt where Ft+1= forecast for the next period, t+1 and Dt= demand for the current period, t

  31. Last Period Model Table 9.3 Figure 9.5

  32. Moving Average Model • Moving Average Model – A time series forecasting model that derives a forecast by taking an average of recent demand value.

  33. Moving Average Model 3-period moving average forecast for Period 8: = (14 + 8 + 10) / 3 = 10.67

  34. Weighted Moving Average Model • Weighted Moving Average Model – A form of the moving average model that allows the actual weights applied to past observations to differ.

  35. Weighted Moving Average Model 3-period weighted moving average forecast for Period 8= [(0.5  14) + (0.3  8) + (0.2  10)] / 1 = 11.4

  36. 10. What is the formula for simple exponential smoothing using a sophisticated weighted moving average?

  37. Exponential Smoothing Model • Exponential Smoothing Model – A form of the moving average model in which the forecast for the next period is calculated as the weighted average of the current period’s actual value and forecast.

  38. Exponential Smoothing Model a = .3

  39. Causal Forecasting Models • Linear Regression • Multiple Regression • Examples:

  40. Linear Regression

  41. Linear Regression • How to calculate the a and b

  42. Linear Regression – Example 9.3

  43. Linear Regression – Example 9.3

  44. Linear Regression – Example 9.3 Figure 9.12 The graph shows an upward trend of 7.33 sales per month.

  45. Multiple Regression • Multiple Regression – A generalized form of linear regression that allows for more than one independent variable.

  46. Forecast Accuracy How do we know: • If a forecast model is “best”? • If a forecast model is still working? • What types of errors a particular forecasting model is prone to make? Need measures of forecast accuracy

  47. 11. What are the two kinds of forecasting errors?

  48. Measures of Forecast Accuracy • Forecast error for period (i) = • Mean forecast error (MFE) = • Mean absolute deviation (MAD) =

  49. Measures of Forecast Accuracy • Mean absolute percentage error (MAPE) = • Tracking Signal =

  50. Forecast Accuracy – Example 9.7 Table 9.11

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