Forecasting Chapter 9

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall Define Forecast.

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 3 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

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall Define Planning.

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall  Planning - Determining what is needed, and making arrangements to get it, in order to achieve objectives. PLANNING

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall What are the financial benefits of effective planning?

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall  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. Financial Benefits of Effective Planning

10 -8 Planning Horizon – The distance into the future one plans. Looking into the Future: The Planning Horizon

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall What is a contingency plan?

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall  Contingency Plans – Alternative or back-up plans to be used if an unexpected event makes the normal plans infeasible. Contingency Plan

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall What are three common types of forecasts?

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall 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

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall What are the four laws of forecasting?

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall 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.

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall What are the two types of forecasting models?

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall Forecasting Models  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

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall Qualitative Forecasting Models  Market surveys  Build-up forecasts  Life-cycle analogy method  Panel consensus forecasting  Delphi method

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall Forecasting Models  Quantitative forecasting models – Forecasting models that use measurable, historical data to generate forecasts.  Time series and causal models

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall 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.

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall Selecting a Forecasting Method Figure 9.2

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall What are components of a time series?

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall  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 Components of a Time Series

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

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

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall Time series with Trend and Seasonality Figure 9.4

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

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall Time series with randomness Figure 9.3

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall What are the various time series techniques?

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall 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. F t+1 = D t where F t+1 = forecast for the next period, t+1 and D t = demand for the current period, t

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall Last Period Model Table 9.3Figure 9.5

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall Moving Average Model  Moving Average Model – A time series forecasting model that derives a forecast by taking an average of recent demand value.

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall Moving Average Model 3-period moving average forecast for Period 8: =( ) / 3 =10.67 PeriodDemand

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall 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.

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall 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 PeriodDemand

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall What is the formula for simple exponential smoothing using a sophisticated weighted moving average?

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall 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.

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall Exponential Smoothing Model a =.3 PeriodDemandForecast * 50 + (1-.3) * 40 = * 46 + (1-.3) * 43 = * 52 + (1-.3) * 43.9 = * 48 + (1-.3) * = * 47 + (1-.3) * = 46.88

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall Causal Forecasting Models  Linear Regression  Multiple Regression  Examples:

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall Causal - Linear Regression 

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall 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

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall What are the two kinds of forecasting errors?

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall Forecast Accuracy  Forecast error is the actual demand minus the forecast demand.  Absolute Error: how far “off” are we, in absolute terms?  Measured by mean absolute deviation (MAD) or mean squared error (MSE)  Forecast Bias: Are we consistently high or low?  A forecast should be unbiased (low forecasts are as frequent as high forecasts)  Bias is measured by mean forecast error (MFE) or running sum of forecast error (RSFE)

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall  The ideal value for both is zero, which would mean there is no forecasting error  The larger the MAD or MSE, the less the accurate the model Forecast Accuracy Two similar approaches are used to measure absolute forecast error MAD is the mean of the absolute values of the forecast errors MSE is the mean of the squared values of the forecast errors

Calculating MAD

Calculating MSE

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall Forecast Bias  Forecast Bias: Tendency of a forecast to be too high or too low.  Mean forecast error (MFE)  The mean of the forecast errors  Running sum of forecast errors (RSFE)  The sum of forecast error, updated as each new error is calculated.  Ideal measure is zero which indicates no bias.  Positive means forecast tends to low  Negative means forecast tends to high

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall Forecast Bias: Calculating MFE and RSFE Mean Forecast Error = 1.00 RSFE (period 8) = 8