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Bina Nusantara Model Ramalan Peretemuan 13: Mata kuliah: K0194-Pemodelan Matematika Terapan Tahun: 2008.

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Presentation on theme: "Bina Nusantara Model Ramalan Peretemuan 13: Mata kuliah: K0194-Pemodelan Matematika Terapan Tahun: 2008."— Presentation transcript:

1 Bina Nusantara Model Ramalan Peretemuan 13: Mata kuliah: K0194-Pemodelan Matematika Terapan Tahun: 2008

2 Bina Nusantara Learning Outcomes Mahasiswa akan dapat menjelaskan definisi, pengertian dan proses model ramalan.

3 Bina Nusantara Outline Materi: Definisi ramalan Pengertian Trend/ramalan Model proses ramalan. Metoda ramalan. Contoh kasus..

4 Bina Nusantara What is Forecasting? Art and science of predicting future events. Underlying basis of all business decisions. –Production & Inventory. –Personnel & Facilities. Focus on forecasting demand. Sales will be $200 Million!

5 Bina Nusantara Examples Predict the next number in the pattern: a) 3.7, 3.7, 3.7, 3.7, 3.7, ? b) 2.5, 4.5, 6.5, 8.5, 10.5, ? c) 5.0, 7.5, 6.0, 4.5, 7.0, 9.5, 8.0, 6.5, ?

6 Bina Nusantara Examples Predict the next number in the pattern: a) 3.7, 3.7, 3.7, 3.7, 3.7, y = 3.7 b) 2.5, 4.5, 6.5, 8.5, 10.5, y = x c) 5.0, 7.5, 6.0, 4.5, 7.0, 9.5, 8.0, 6.5, y = x + c i c 1 = 0; c 2 = 2; c 3 = 0; c 4 = -2; etc

7 Bina Nusantara Short-range forecast: Usually < 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. Types of Forecasts by Time Horizon

8 Bina Nusantara Short- vs. Long-term Forecasting Medium & Long range forecasts: –Long range for design of system. –Deal with comprehensive issues. –Support management decisions regarding planning. Short-term forecasts: –To plan detailed use of system. –Usually use quantitative techniques. –More accurate than longer-term forecasts.

9 Bina Nusantara Forecasting During the Life Cycle Hard to forecast. Need long-range forecasts. Often use qualitative models. IntroductionGrowthMaturityDecline Sales Forecasting critical, both for future magnitude and growth rate. Long-range forecasts still important. Easier to forecast. Use quantitative models. Hard to forecast, but forecasting is less important. Time

10 Bina Nusantara Eight 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. Monitor forecasts and adjust when needed.

11 Bina Nusantara Realities of Forecasting Assumes future will be like the past (causal factors will be the same). Forecasts are imperfect. Forecasts for groups of product are more accurate than forecasts for individual products. Accuracy decreases with length of forecast.

12 Bina Nusantara Forecasting Approaches Used when situation is ‘stable’ & historical data exist. –Existing products & current technology. –No significant changes expected. Involves mathematical techniques. –Example: forecasting sales of color televisions. Quantitative Methods Used when little data or time exist. –New products & technology. –Long time horizon. –Major changes expected. Involves intuition, experience. –Example: forecasting for e- commerce sales. Qualitative Methods

13 Bina Nusantara Overview of Qualitative Methods Jury of executive opinion. –Combine opinions from executives. Sales force composite. –Aggregate estimates from salespersons. Delphi method. –Query experts interatively. Consumer market survey. –Survey current and potential customers.

14 Bina Nusantara Quantitative Forecasting Methods Quantitative Forecasting Linear Regression Associative Models Exponential Smoothing Moving Average Time Series Models Trend Projection

15 Bina Nusantara Set of evenly spaced numerical data. –From observing response variable at regular time periods. Forecast based only on past values. –Assumes that factors influencing past will continue influence in future. Example: Year: Sales: What is a Time Series?

16 Bina Nusantara Trend Seasonal Cyclical Random Time Series Components

17 Bina Nusantara Product Demand over 4 Years Year 1 Year 2 Year 3 Year 4 Demand for product or service

18 Bina Nusantara Product Demand over 4 Years Actual demand line Year 1 Year 2 Year 3 Year 4 Seasonal peaks Trend component Demand for product or service Random variation Cyclic component

19 Bina Nusantara Persistent, overall upward or downward pattern. Due to population, technology etc. Several years duration. Time Trend Component

20 Bina Nusantara Regular pattern of up & down fluctuations. Due to weather, customs etc. Occurs within 1 year. Quarterly, monthly, weekly, etc. Time Demand Summer Seasonal Component

21 Bina Nusantara Repeating up & down movements. Due to interactions of factors influencing economy. Usually 2-10 years duration. Year Demand Cycle Cyclical Component

22 Bina Nusantara Erratic, unsystematic, ‘residual’ fluctuations. Due to random variation or unforeseen events. –Union strike –Tornado Short duration & non-repeating. Random Component

23 Bina Nusantara Any value in a time series is a combination of the trend, seasonal, cyclic, and random components. Multiplicative model: Y i = T i · S i · C i · R i Additive model: Y i = T i + S i + C i + R i General Time Series Models

24 Bina Nusantara


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