J0444 OPERATION MANAGEMENT Pert 6 Forecasting Universitas Bina Nusantara

Peramalan $$$ Process of predicting a future event Underlying basis of all business decisions Production Inventory Personnel Facilities $$$

Jenis Peramalan Berdasarkan Horison Waktu Short-range forecast Up to 1 year; usually less than 3 months Job scheduling, worker assignments Medium-range forecast 3 months to 3 years Sales & production planning, budgeting Long-range forecast 3+ years New product planning, facility location At this point, it may be useful to point out the “time horizons” considered by different industries. For example, some colleges and universities look 30 to fifty years ahead, industries engaged in long distance transportation (steam ship, railroad) or provision of basic power (electrical and gas utilities, etc.) also look far ahead (20 to 100 years). Ask them to give examples of industries having much shorter long-range horizons.

Short-term vs. Longer-term Forecasting Medium/long range forecasts deal with more comprehensive issues and support management decisions regarding planning and products, plants and processes. Short-term forecasting usually employs different methodologies than longer-term forecasting Short-term forecasts tend to be more accurate than longer-term forecasts. At this point it may be helpful to discuss the actual variables one might wish to forecast in the various time periods.

Influence of Product Life Cycle Stages of introduction and growth require longer forecasts than maturity and decline Forecasts useful in projecting staffing levels, inventory levels, and factory capacity as product passes through life cycle stages This slide introduces the impact of product life cycle on forecasting The following slide, reproduced from chapter 2, summarizes the changing issues over the product’s lifetime for those faculty who wish to treat the issue in greater depth.

Strategy and Issues During a Product’s Life Introduction Growth Maturity Decline Standardization Less rapid product changes - more minor changes Optimum capacity Increasing stability of process Long production runs Product improvement and cost cutting Little product differentiation Cost minimization Over capacity in the industry Prune line to eliminate items not returning good margin Reduce capacity Forecasting critical Product and process reliability Competitive product improvements and options Increase capacity Shift toward product focused Enhance distribution Product design and development critical Frequent product and process design changes Short production runs High production costs Limited models Attention to quality Best period to increase market share R&D product engineering critical Practical to change price or quality image Strengthen niche Cost control critical Poor time to change image, price, or quality Competitive costs become critical Defend market position OM Strategy/Issues Company Strategy/Issues HDTV CD-ROM Color copiers Drive-thru restaurants Fax machines Station wagons Sales 3 1/2” Floppy disks Internet

Jenis Peramalan Economic forecasts Technological forecasts Address business cycle, e.g., inflation rate, money supply etc. Technological forecasts Predict technological change Predict new product sales Demand forecasts Predict existing product sales One can use an example based upon one’s college or university. Students can be asked why each of these forecast types is important to the college. Once they begin to appreciate the importance, one can then begin to discuss the problems. For example, is predicting “demand” merely as simple as predicting the number of students who will graduate from high school next year (i.e., a simple counting exercise)?

Seven Steps in Forecasting Determine the use of the forecast Select the items to be forecast Determine the time horizon of the forecast Select the forecasting model(s) Gather the data Make the forecast Validate and implement results A point to be made here is that one requires a forecasting “plan,” not merely the selection of a particular forecasting methodology.

Product Demand Charted over 4 Years with Trend and Seasonality 1 2 3 4 Seasonal peaks Trend component Actual demand line Average demand over four years Demand for product or service Random variation This slide illustrates a typical demand curve. You might ask students why it is important to know more than simply the actual demand over time. Why, for example, would one wish to be able to break out a “seasonality” factor?

Actual Demand, Moving Average, Weighted Moving Average Actual sales Moving average This slide illustrates one of the simplest forecasting techniques - the moving average. It may be useful to point out the lag introduced by exponential smoothing - and ask how one can actually make use of the forecast.

Realities of Forecasting Forecasts are seldom perfect Most forecasting methods assume that there is some underlying stability in the system Both product family and aggregated product forecasts are more accurate than individual product forecasts This slide provides a framework for discussing some of the inherent difficulties in developing reliable forecasts. You may wish to include in this discussion the difficulties posed by attempting forecast in a continuously, and rapidly changing environment where product life-times are measured less often in years and more often in months than ever before. One might wish to emphasize the inherent difficulties in developing reliable forecasts.

Overview of Qualitative Methods Jury of executive opinion Pool opinions of high-level executives, sometimes augment by statistical models Sales force composite Estimates from individual salespersons are reviewed for reasonableness, then aggregated Delphi method Panel of experts, queried iteratively Consumer Market Survey Ask the customer This slide outlines several qualitative methods of forecasting. Ask students to give examples of occasions when each might be appropriate. The next several slides elaborate on these qualitative methods.

Jury of Executive Opinion Involves small group of high-level managers Group estimates demand by working together Combines managerial experience with statistical models Relatively quick ‘Group-think’ disadvantage Ask your students to consider other potential disadvantages. (Politics?) © 1995 Corel Corp.

Sales Force Composite Each salesperson projects their sales Combined at district & national levels Sales rep’s know customers’ wants Tends to be overly optimistic Sales © 1995 Corel Corp. You might ask your students to consider what problems might occur when trying to use this method to predict sales of a potential new product.

Delphi Method Decision Makers Staff (What will sales be? survey) (Sales will be 50!) (What will sales be? survey) Iterative group process 3 types of people Decision makers Staff Respondents Reduces ‘group-think’ You might ask your students to consider whether there are special examples where this technique is required. ( Questions of technology transfer or assessment, for example; or other questions where information from many different disciplines is required.) Respondents (Sales will be 45, 50, 55)

Overview of Quantitative Approaches Naïve approach Moving averages Exponential smoothing Trend projection Linear regression Time-series Models Associative models

Quantitative Forecasting Methods (Non-Naive) Time Series Associative Models Models A point you may wish to make here is that only in the case of linear regression are we assuming that we know “why” something happened. General time-series models are based exclusively on “what” happened in the past; not at all on “why.” Does operating in a time of drastic change imply limitations on our ability to use time series models? Moving Exponential Trend Linear Average Smoothing Projection Regression

What is a Time Series? Set of evenly spaced numerical data Obtained by observing response variable at regular time periods Forecast based only on past values Assumes that factors influencing past and present will continue influence in future Example Year: 1993 1994 1995 1996 1997 Sales: 78.7 63.5 89.7 93.2 92.1 This and subsequent slide frame a discussion on time series - and introduce the various components.

Time Series Components Trend Seasonal Cyclical Random

General Time Series Models Any observed value in a time series is the product (or sum) of time series components Multiplicative model Yi = Ti · Si · Ci · Ri (if quarterly or mo. data) Additive model Yi = Ti + Si + Ci + Ri (if quarterly or mo. data) This slide introduces two general forms of time series model. You might provide examples of when one or the other is most appropriate.

Moving Average Method MA is a series of arithmetic means Used if little or no trend Used often for smoothing Provides overall impression of data over time Equation At this point, you might discuss the impact of the number of periods included in the calculation. The more periods you include, the closer you come to the overall average; the fewer, the closer you come to the value in the previous period. What is the tradeoff? MA n   Demand in Previous Periods

Moving Average Example You’re manager of a museum store that sells historical replicas. You want to forecast sales (000) for 1998 using a 3-period moving average. 1993 4 1994 6 1995 5 1996 3 1997 7 © 1995 Corel Corp.

Moving Average Solution

Moving Average Solution

Moving Average Solution

Moving Average Graph 95 96 97 98 99 00 Year Sales 2 4 6 8 Actual Forecast This slide shows the resulting forecast. Students might be asked to comment on the useful ness of this forecast.

Weighted Moving Average Method Used when trend is present Older data usually less important Weights based on intuition Often lay between 0 & 1, & sum to 1.0 Equation This slide introduces the “weighted moving average” method. It is probably most important to discuss choice of the weights. Σ(Weight for period n) (Demand in period n) WMA = ΣWeights

Actual Demand, Moving Average, Weighted Moving Average Actual sales Moving average This slide illustrates one of the simplest forecasting techniques - the moving average. It may be useful to point out the lag introduced by exponential smoothing - and ask how one can actually make use of the forecast.

Disadvantages of Moving Average Methods Increasing n makes forecast less sensitive to changes Do not forecast trend well Require much historical data These points should have been brought out in the example, but can be summarized here.

Exponential Smoothing Method Form of weighted moving average Weights decline exponentially Most recent data weighted most Requires smoothing constant () Ranges from 0 to 1 Subjectively chosen Involves little record keeping of past data This slide introduces the exponential smoothing method of time series forecasting. The following slide contains the equations, and an example follows.

Exponential Smoothing Equations Ft = At - 1 + (1-)At - 2 + (1- )2·At - 3 + (1- )3At - 4 + ... + (1- )t-1·A0 Ft = Forecast value At = Actual value  = Smoothing constant Ft = Ft-1 + (At-1 - Ft-1) Use for computing forecast You may wish to discuss several points: - this is just a moving average wherein every point in included in the forecast, but the weights of the points continuously decrease as they extend further back in time. - the equation actually used to calculate the forecast is convenient for programming on the computer since it requires as data only the actual and forecast values from the previous time point. - we need a formal process and criteria for choosing the “best” smoothing constant.

Exponential Smoothing Example You’re organizing a Kwanza meeting. You want to forecast attendance for 2000 using exponential smoothing ( = .10). The1995 forecast was 175. 1995 180 1996 168 1997 159 1996 175 1999 190 This slide begins an exponential smoothing example. © 1995 Corel Corp.

Exponential Smoothing Solution Ft = Ft-1 + ·(At-1 - Ft-1) 175.00 + Forecast, F t Time Actual ( α = .10) 1995 180 175.00 (Given) 1996 168 1997 159 1998 175 1999 190 2000 NA

Exponential Smoothing Solution Ft = Ft-1 + ·(At-1 - Ft-1) Forecast, F t Time Actual ( α = .10) 1995 180 175.00 (Given) 1996 168 175.00 + .10(180 - 175.00) = 175.50 1997 159 1998 175 1999 190 2000 NA

Exponential Smoothing Solution Ft = Ft-1 + ·(At-1 - Ft-1) Forecast, F t Time Actual ( α = .10) 1995 180 175.00 (Given) 1996 168 175.00 + .10(180 - 175.00) = 175.50 This slide illustrates the result of the steps used to make the forecast desired in the example. In the PowerPoint presentation, there are additional slides to illustrate the individual steps. 1997 159 175.50 + .10(168 - 175.50) = 174.75 1998 175 174.75 + .10(159 - 174.75) = 173.18 1999 190 173.18 + .10(175 - 173.18) = 173.36 2000 NA 173.36 + .10(190 - 173.36) = 175.02

Exponential Smoothing Graph Year Sales 140 150 160 170 180 190 93 94 95 96 97 98 Actual Forecast This slide illustrates graphically the results of the example forecast.

Linear Regression Model Shows linear relationship between dependent & explanatory variables Example: Sales & advertising (not time) Y-intercept Slope ^ This slide introduces the linear regression model. This can be approached as simply a generalization of the linear trend model where the variable is something other than time and the values do not necessarily occur a t equal intervals. Y = a  b X i i Dependent (response) variable Independent (explanatory) variable

Linear Regression Model Y a Y b X i =  Error  i Observed value Y a b X =  Regression line Error ^ i i X