Chapter 13: Forecasting Assistant Prof. Abed Schokry Operations and Productions Management First Semester 2013-2014 Chapter 13: Learning Outcomes You should be able to: List the elements of a good forecast Outline the steps in the forecasting process Describe qualitative forecasting techniques and their advantages and disadvantages Compare and contrast qualitative and quantitative approaches to forecasting Briefly describe averaging techniques, trend and seasonal techniques, and regression analysis, and solve typical problems Describe three measures of forecast accuracy Describe two ways of evaluating and controlling forecasts Identify the major factors to consider when choosing a forecasting technique ١
Forecast Forecast a statement about the future value of a variable of interest We make forecasts about such things as weather, demand, and resource availability Forecasts are an important element in making informed decisions An Important Input to Decision Making The primary goal operations and supply chain management is to match supply to demand A demand forecast is essential for determining how much supply will be needed to match demand: Budget preparation Capacity decisions (e.g., staff and equipment) Purchasing decisions ٢
Forecast Uses Plan the system Generally involves long-range plans related to: Types of products and services to offer Facility and equipment levels Facility location Plan the use of the system Generally involves short- and medium-range plans related to: Inventory management Workforce levels Purchasing Budgeting Elements of a Good Forecast The forecast should be timely should be accurate should be reliable should be expressed in meaningful units should be in writing technique should be simple to understand and use should be cost effective ٣
Steps in the Forecasting Process 1. Determine the purpose of the forecast 2. Establish a time horizon 3. Select a forecasting technique 4. Obtain, clean, and analyze appropriate data 5. Make the forecast 6. Monitor the forecast 7. Validate and implement results Forecasting Approaches Qualitative Forecasting Qualitative techniques permit the inclusion of soft information such as: Human factors Personal opinions Hunches (feelings, suggestions) These factors are difficult, or impossible, to quantify Quantitative Forecasting Quantitative techniques involve either the projection of historical data or the development of associative methods that attempt to use causal variables to make a forecast These techniques rely on hard data ٤
Forecasting Approaches (cont.) Qualitative Methods (subjective) = people expertise) Used when situation is unclear & little data exist New products New technology Involves intuition, experience e.g., forecasting sales on Internet Quantitative Methods (objective) = math models Used when situation is stable & historical data exist Existing products Current technology Involves mathematical techniques e.g., forecasting sales of colour televisions Time-Series Behaviors ٥
Time-Series Forecasting - Averaging These Techniques work best when a series tends to vary about an average Averaging techniques smooth variations in the data They can handle step changes or gradual changes in the level of a series Techniques Moving average Weighted moving average Exponential smoothing Simple Linear Regression Regression - a technique for fitting a line to a set of data points Simple linear regression - the simplest form of regression that involves a linear relationship between two variables The object of simple linear regression is to obtain an equation of a straight line that minimizes the sum of squared vertical deviations from the line (i.e., the least squares criterion) ٦
Monitoring the Forecast Tracking forecast errors (following) and analyzing them can provide useful insight into whether forecasts are performing satisfactorily or not. Sources of forecast errors The model may be inadequate Irregular variations may have occurred The forecasting technique has been incorrectly applied Random error Control charts are useful for identifying the presence of nonrandom error in forecasts Tracking signals can be used to detect forecast bias Choosing a Forecasting Technique Factors to consider Cost Accuracy Availability of historical data Availability of forecasting software Time needed to gather and analyze data and prepare a forecast Forecast horizon ٧
Using Forecast Information Reactive approach View forecasts as probable future demand React to meet that demand Proactive approach Seeks to actively influence demand Advertising Pricing Product/service modifications Generally requires either and explanatory model or a subjective assessment of the influence on demand Example Sales for over the last 5 weeks are shown below: Week: 1 2 3 4 5 Sales: 150 157 162 166 177 Plot the data and visually check to see if a linear trend line is appropriate. Determine the equation of the trend line Predict sales for weeks 6 and 7. ٨
Line chart Sales Sales 180 175 170 165 160 155 150 145 140 135 1 2 3 4 5 Week Sales Islamic University of Gaza -Palestine Calculating a and b b = n (ty) - t y n t 2 -( t) 2 a = y - b t n ٩
Linear Trend Equation Example t y Week t 2 Sales ty 1 1 150 150 2 4 157 314 3 9 162 486 4 16 166 664 5 25 177 885 Σ t = 15 Σ t 2 = 55 Σ y = 812 Σ ty = 2499 (Σ t) 2 = 225 Linear Trend Calculation b = 5 (2499) -15(812) 5(55) -225 = 12495-12180 275-225 =6.3 a = 812-6.3(15) 5 = 143.5 y = 143.5 + 6.3t ١٠
Linear Trend plot Actual data Linear equation 180 175 170 165 160 155 150 145 140 135 1 2 3 4 5 Forecasting Performance How good is the forecast? Mean Forecast Error (MFE): Measures average deviation of forecast from actual. Mean Absolute Deviation (MAD): Measures average absolute deviation of forecast from actuals. Mean Absolute Percentage Error (MAPE): Measures absolute error as a percentage of the forecast. Mean Standard Squared Error (MSE): Measures variance of forecast error ١١
Operations Strategy The better forecasts are, the more able organizations will be to take advantage of future opportunities and reduce potential risks. A meaningful strategy is to work to improve short-term forecasts Accurate up-to-date information can have a significant effect on forecast accuracy: Prices Demand Other important variables Reduce the time horizon forecasts have to cover Sharing forecasts or demand data through the supply chain can improve forecast quality End of Chapter 11 ١٢