To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna Forecasting

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna Introduction Everyday, managers make decisions without knowing what will happen in the future. Inventory is ordered though no one knows what sales will be, new equipment is purchased though no one knows the demand for products, and investments are made though no one knows what profits will be. Managers are always trying to reduce this uncertainty and to make better estimates of what will happen in the future. Accomplishing this is the main purpose of forecasting. There are many ways to forecast the future. Regardless of the method that is used to make the forecast, the same eight overall procedures that follow are used.

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna Introduction Eight Steps to Forecasting 1.Determine the use of the forecast-what objective are we trying to obtain? 2.Select the items or quantities that are to be forecasted. 3.Determine the time horizon of the forecast-is it 1 to 30 days (short term), one month to one year (medium term), or more than one year (long term)? 4.Select the forecasting model or models. 5.Gather the data needed to make the forecast. 6.Validate the forecasting model. 7.Make the forecast. 8.Implement the results. These steps present a systematic way of initiating, designing, and implementing a forecasting system.

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna Introduction There is seldom a single superior forecasting method. One organization may find regression effective, another firm may use several approaches, and a third may combine both quantitative and subjective techniques. Whatever tool works best for a firm is the one that should be used.

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna Types of Forecasts Forecasting models can be classified into one of the three categories. These categories, shown in the next figure, are time- series models, Causal models, and qualitative models.

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna Moving Average Exponential Smoothing Trend Projections Time-Series Methods: include historical data over a time interval Forecasting Techniques No single method is superior Delphi Methods Jury of Executive Opinion Sales Force Composite Consumer Market Survey Qualitative Models : attempt to include subjective factors Causal Methods: include a variety of factors Regression Analysis Multiple Regression Decomposition

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna Time-Series Models Time-series models attempt to predict the future by using historical data. These models make the assumption that what happens in the future is a function of what has happened in the past. In other words, time-series models look at what has happened over a period of time and use a series of past data to make a forecast. Thus, if we are forecasting weekly sales for lawn mowers, we use the past weekly sales for lawn mowers in making the forecast.

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna Causal Models Causal models incorporate the variables or factors that might influence the quantity being forecasted into the forecasting model. For example, daily sales of a cola drink might depend on the season, the average temperature, the average humidity, whether it is a weekend or a weekday, and so on. Thus, a causal model would attempt to include factors for temperature, humidity, season, day of the week, and so on. Causal models may also include past sales data as time- series models do.

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna Qualitative Models Whereas time-series and causal models rely on quantitative data, qualitative models attempt to incorporate judgment or subjective factors into the forecasting mode. Opinions by experts, individual experiences and judgments, and other subjective factors may be considered. Qualitative models are especially useful when subjective factors are expected to be very important or when accurate quantitative data are difficult to obtain.

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna Qualitative Models\continued Here is a brief overview of four different qualitative forecasting techniques: 1.Delphi method. This iterative group process allows experts, who may be located in different places, to make forecasts. There are three different types of participants in the Delphi process: decision makers, staff personnel, and respondents. The decision making group usually consists of five to ten experts who will be making the actual forecast. The staff personnel assist the decision makers by preparing, distributing, collecting, and summarizing a series of questionnaires and survey results. The respondents are a group of people whose judgments are valued and are being sought. This group provides inputs to the decision makers before the forecast is made.

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna Qualitative Models\continued 2.Jury of executive opinion. This method takes the opinions of a small group of high level managers, often in combination with statistical models, and results in a group estimate of demand. 3.Sales force composite. In this approach, each salesperson estimates what sales will be in his or her region; these forecasts are reviewed to ensure that they are realistic and are then combined at the district and national levels to reach an overall forecast. 4.Consumer market survey. This method solicits input from customers or potential customers regarding their future purchasing plans. It can help not only in preparing a forecast but also in improving product design and planning for new products.

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna Time-Series Forecasting Models A time series is based on a sequence of evenly spaced (weekly, monthly, quarterly, and so on) data points. Examples include weekly sales of IBM personal computers, quarterly earnings reports of Microsoft Corp. stock, daily shipments of Everyday batteries, and annual U.S. consumer price indices. Forecasting time-series data implies that future values are predicted only from past values and that other values, no matter how potentially valuable, are ignored. In the next slides, we will study two models of the time- series models, Exponential Smoothing and Trend Projections

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna Moving Averages n Simple moving average =  demand in previous n periods Moving average methods consist of computing an average of the most recent n data values for the time series and using this average for the forecast of the next period.

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna Wallace Garden Supply’s Three-Month Moving Average Month Actual Sales Three-Month Moving Average January10 February12 March13 April16 May19 June23 July26 ( )/3 = 11 2 / 3 ( )/3 = 13 2 / 3 ( )/3 = 16 ( )/3 = 19 1 / 3

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna Weighted Moving Averages Weighted moving averages use weights to put more emphasis on recent periods.  (weight for period n) (demand in period n) ∑ weights Weighted moving average =

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna Calculating Weighted Moving Averages Weights Applied Period 3 Last month 2 Two months ago 1 Three months ago 3*Sales last month + 2*Sales two months ago + 1*Sales three months ago 6 Sum of weights

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna Wallace Garden’s Weighted Three- Month Moving Average MonthActual Shed Sales Three-Month Weighted Moving Average January February March April May June July26 [3*13+2*12+1*10]/6 = 12 1 / 6 [3*16+2*13+1*12]/6 =14 1 / 3 [3*19+2*16+1*13]/6 = 17 [3*23+2*19+1*16]/6 = 20 1 / 2