1 King Abdulaziz University Faculty of Engineering Industrial Engineering Dept. IE 436 Dynamic Forecasting

2 CHAPTER 1 Introduction to Forecasting Some Techniques: In the nineteenth century. Some Techniques: In the nineteenth century. Some Others: only recently. Some Others: only recently. Forecasting Process: Continue to grow. Forecasting Process: Continue to grow. Consumers of forecasts (Managers): Consumers of forecasts (Managers): More attention to Forecasting. More attention to Forecasting. The History of Forecasting

3 Is Forecasting Necessary? All organizations operate in uncertainty. All organizations operate in uncertainty. Decisions affecting the future must be made. Decisions affecting the future must be made. Judgments are not so accurate. Judgments are not so accurate. World has always been changing. World has always been changing. Competition has become more keen. Competition has become more keen.

4 Who needs forecasts ? All types of organizations: (large-small, private-public). All functional lines: (finance, marketing, personnel, production areas)

5 Some Questions that need forecasting Increase advertising by 10% : sales? Increase advertising by 10% : sales? Over the next 2 years : revenues? Over the next 2 years : revenues? How many units might we sell? How many units might we sell? To explain sales variability : To explain sales variability : factors to consider? factors to consider? Loan balance over the next 10 years? Loan balance over the next 10 years?

6 Types of Forecasts Time horizon: (long, intermediate, short). Level of details: (micro-macro). Methods used: (Quantitative-qualitative). Note: judgment must be used along with quantitative forecasting procedures.

7 RangePeriodVariables to be forecasted Short Term 1 day to 1 year, )1 season) Demand forecasting, staffing levels, purchasing, inventory levels Medium Term 1 season to 2 years Aggregate planning, capacity planning, sales forecasts Long Term 5 and more years into the future R&D, plant location, product planning Range of Forecasting

8 1. Problem Formulation and Data collection. Data collection. 2. Data manipulation and cleaning. 3. Model building and evaluation. 4. Model implementation (the actual Forecast). (the actual Forecast). 5. Forecast evaluation. Forecasting Steps

9 Data Collection Model evaluation ? Data Manipulation and cleaning Model implementation (Actual Forecast) Model implementation (Actual Forecast) Forecast evaluation Model Building Forecast Updating Problem Formulation NO Data Reduction Yes