1. 1-1

2. 1-2 Forecasting The art and science of predicting future events. Forecasting is the first step in business planning. Estimates of the future demand for products and services are called SALES FORECAST. SALES FORECASTs are the starting point for all other forecasts in production and operations management.

3. 1-3 Forecasting Two functional areas of company that make use of the forecasting methods most are: 1.Marketing: forecasts sales for new and existing products, plans promotions. 2.Production: uses sales forecasts for operations planning. The other functional areas are:

4. 1-4 Forecasting 1. Personnel: uses sales forecasts for work-force planning. 2. Accounting: forecasts costs and revenues in tax planning. 3. Finance: forecasts cash flows to maintain solvency. It is not uncommon for working people to hear this complaint: “It is not my fault we ran out of those parts. The demand forecast was wrong.”

5. 1-5 Can all events be accurately forecasted? The answer is clearly no. But things are not as bad as in purelly chance events like tossing, stock buying. Because in forecasting, we have chance to use past observations and based on the trends, cycles, seasonal variation observed in data, we predict the future.

6. 1-6 Time Horizon in Forecasting 1. Short-term forecasts (days or weeks) are required for inventory managent, production plans and resource requirements planning. 2. Medium-term forecast (weeks and months) are required for estimating product family sales, resource requirements. 3. Long-term forecasts (Months and years) are required for estimating capacity needs, growth trends.

7. 1-7 Characteristics of Forecasts 1.They are usually wrong. Forecasts should not be treated as known information once determined. Rather, they need need to be contiuously monitored and modified. 2. A good forecast is more than a single number. Given that forecasts are generally wrong, a good forecast should be given in the form of a range or with an error measure such as the distribution of the forecast error.

8. 1-8 Characteristics of Forecasts 3. Aggregate forecasts are more accurate. Remember from statistics that the variance of the sample mean is smaller than the population variance. The same analogy can be applied to forecasting as well. On a percentage basis, the error made in forecasting sales for an entire product line is generally less than the error made in forecasting sales for an individual item.

9. 1-9 Characteristics of Forecasts 4. The longer the forecast horizon, the less accurate the forecast will be. This is quite intuitive. 5. Forecasts taking into consideration all available information will be more accurate. For instance, demand forecasting taking into consideration promotional sales will be more accurate.

10. 1-10 Forecasting and Supply Chain Management The success of supply chain management depends on accuracy of forecasts. Using methods which give accurate forecasts are very important in supply chain management which consists of various flows between suppliers, producers, distributors and consumers. (collaborative forecasting, information sharing) Besides quantitative methods, personal judgments based on practical experience should always play an important role in preparing forecasts.

11. 1-11 ForecastMethod(s) DemandEstimates SalesForecastManagementTeam Inputs:Market,Economic,Other BusinessStrategy Production Resource Forecasts

12. 1-12 Qualitative Approaches Usually based on judgments about causal factors that underlie the demand of particular products or services Do not require a demand history for the product or service, therefore are useful for new products/services Approaches vary in sophistication from scientifically conducted surveys to intuitive hunches about future events

13. 1-13 Qualitative Methods are good for new product forecasting Executive committee consensus (Jury of Executive Opinion) Delphi method Survey of sales force (Sales Force Composites) Survey of customers Market research (See Table 1.1) Disadvantages of these methods are: 1. Biased 2. not consistently accurate 3. requires expertise

14. 1-14 Table 1-1

15. 1-15 NEW PRODUCT FORECASTING Marketing Research The Product Life Cycle Concept Analog Forecasts Test Marketing Product Clinics Diffusion Curves (S-curves) The Bass Model Gompertz Curve Logistics Curve

16. 1-16 NEW PRODUCT FORECASTING The Bass Model (member of product diffusion curves, like in Fig.1.2 pg.26)

17. 1-17 The Bass Model The basic premise of the model is that adopters can be classified as innovators or as imitators and the speed and timing of adoption depends on their degree of innovativeness and the degree of imitation among adopters. The Bass model has been widely used in forecasting, especially new products' sales forecasting and technology forecasting. Mathematically, the basic Bass diffusion is a Riccati equation with constant coefficients. forecastingnew products' sales forecastingtechnology forecasting Riccati equation

18. 1-18 The Bass Model p = is the coefficient of innovation q = is the coefficient of imitation a = pm p= a/m b = q-p q= -mc c = -q / m

19. 1-19 Quantitative Forecasting Approaches The widespread availability of microcomputers had stimulated a lot the use of quantitative methods. Based on the assumption that the “forces” that generated the past demand will generate the future demand, i.e., history will tend to repeat itself Analysis of the past demand pattern provides a good basis for forecasting future demand Majority of quantitative approaches fall in the category of time series analysis

20. 1-20 Time Series Analysis A time series is a set of numbers where the order or sequence of the numbers is important, e.g., historical demand Analysis of the time series identifies patterns Once the patterns are identified, they can be used to develop a forecast

21. 1-21 Data Patterns Trends are noted by an upward or downward sloping line Seasonality is a data pattern that repeats itself over the period of one year or less Cycle is a data pattern that repeats itself over several years Random fluctuation from random variation or unexplained causes

22. 1-22 (a) Trend; (b) Cycle; (c) Seasonal; (d) Trend w/Season Forecasting Components Patterns Copyright © 2010 Pearson Education, Inc Publishing as Prentice Hall

23. 1-23 Length of Time Number of Before Pattern Length of Seasons Is Repeated Season in Pattern YearQuarter 4 Year Month12 Year Week52 Month Week 4 Month Day 28-31 Week Day 7 Seasonality

24. 1-24 Time Span of Forecasts Long-range time spans usually greater than one year necessary to support strategic decisions about planning products, processes, and facilities Short-range time spans ranging from a few days to a few weeks cycles, seasonality, and trend may have little effect random fluctuation is main data pattern

25. 1-25 Quantitative Forecasting Approaches Linear Regression (causal forecasting models) Simple Moving Average Weighted Moving Average Exponential Smoothing (exponentially weighted moving average) Exponential Smoothing with Trend (double smoothing)

26. 1-26 Forecast Accuracy Accuracy is the typical criterion for judging the performance of a forecasting approach Accuracy is how well the forecasted values match the actual values Accuracy of a forecasting approach needs to be monitored to assess the confidence you can have in its forecasts and changes in the market may require reevaluation of the approach

27. 1-27 Monitoring Accuracy Accuracy can be measured in several ways: Mean error (ME) Mean absolute error (MAE) Mean percentage error (MPE) Mean absolute percentage error (MAPE) Mean squared error (MSE) Root-mean squared error (RMSE) Theil’s U

28. 1-28 Forecast Accuracy ME and MPE are not preferred, because large positive errors are offset by large negative errors. However, they are useful for measuring bias: A negative ME or MPE states that forecast is overstated, positive ME or MPE states that forecast is understated. The other five accuracy measures are used for comparing different methods.

29. 1-29 Forecast Accuracy In this course, mostly we will be using MAE and RMSE: RMSE= √∑(A t – F t ) 2 /n (standart deviation) MAE= ∑│ A t – F t │/n To redude the error in forecasting, it is usually a good idea to combine forecasts.

30. 1-30 Two Simple Naive Models For Forecasting Forecasts based only on the most recent observations are called as “naive forecasts.” First Naive Method Assumes that the next period will be identical to the present: F t = A t-1 F t : Forecast value for time period t A t-1 : Observed value one period earlier

31. 1-31 Example Forecasting U.S. Average annual unemployment rate using data for 1999Q1 through 1999Q4. URF t = UR t-1 URF t : Unemployment rate naive forecast UR t-1 : Observed unemployment rate one period earlier. See Table 1.2, Figure 1.1. Graphic forms like Figure 1.1 and Figure 1.2 help to see the fluctuations in most economic and business series.

32. 1-32 Table 1-2

33. 1-33 Figure 1-1 U.S. Average Quartely Unemployment Rate

34. 1-34 Example Note that in Table 1.3, each forecast value simply replicates the actual value for the preceding year. The graphic form in Figure 1.2 also shows the one-period shift between the two series.

35. 1-35 Table 1-3

36. 1-36 Table 1-3 (continued)

37. 1-37 Figure 1-2

38. 1-38 Example The second naive method includes some proportion of the most recently observed rate of change in series: F t = A t-1 + P(A t-1 – A t-2 ) If we apply the second naive model to our example: URF2 t = UR t-1 + P(UR t-1 – UR t-2 ) See Table 1.4, Figure 1.3.

39. 1-39 Table 1-4

40. 1-40 Table 1-4 (continued)

41. 1-41 Figure 1-3

42. 1-42 Evaluating Accuracy Just based on one period, it is easier to say which forecasting method performs better than other (see the table on page 22). But our concern is to identify a forecasting method which works better than others over a number of periods. The Table 1.5 shows that the second naive forecasting method works better than the other in all of the seven performance measures. In most examples throughout the text we will evaluate the forecast methods using a holdout period.

43. 1-43 Table 1-5