D0052 Pengantar Teknik dan Sistem Industri Peramalan D0052 Pengantar Teknik dan Sistem Industri To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved.

Apa Yang Dimaksud dengan Peramalan? Proses untuk memprediksi kejadian mendatang. Mendasari semua keputusan bisnis Produksi Persediaan Personil Fasilitas Metoda Kualitatif Didasarkan pada metoda subyektif Methods Kuantitatif Didasarkan formula matematik Sales will be $200 Million!

Peranan Strategis dari Peramalan Fokus pada supply chain management Peranan jangka pendek dari permintaan produk Peranan jangka panjang dari produk baru, proses, dan teknologi Fokus pada Total Quality Management Memuaskan permintaan konsumen Aliran produk yang terjaga dari kerusakan Diperlukan untuk perencanaan strategis

Tipe Peramalan Didasarkan pada Horison Waktu Short-range forecast Sampai 1 tahun; biasanya kurang 3 bulan Penjadwalan kerja, Penugasan pekerja Medium-range forecast 3 bulan to 3 tahun Perencanaan penjualan dan produksi, budgeting Long-range forecast 3+ tahun Perencanaan produk baru, lokasi fasilitas 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 peramalan yang berhubungan dengan isu komprehensif dan pendukung untuk keputusan manajemen berkenaan dengan perencanaan dan produk, lantai produksi dan proses. Short-term peramalan untuk kurun waktu pendek biasanya menggunakan metoda yang berbeda dengan peramalan dengan kurun waktu panjang. Short-term peramalan untuk kurun waktu pendek lebih akurat dari peramalan dengan kurun waktu panjang. At this point it may be helpful to discuss the actual variables one might wish to forecast in the various time periods.

Tipe Peramalan Peramalan Ekonomi PeramalanTeknologi Ditujukan untuk siklus usaha, contoh : tingkat inflasi, penyediaan uang dll. PeramalanTeknologi Prediksi tingkat kemajuan teknologi Prediksi diterimanya produk baru Peramalan Permintaan Prediksi penjualan produk yang ada sekarang 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)?

Tujuh Langkah Peramalan Tetapkan kegunaan dari peramalan Pilih items yang akan diramal Tetapkan rentang waktu peramalan Pilih model peramalan Kumpulkan data dan plot data pada grafik Buat peramalan Validasi dan terapkan hasil A point to be made here is that one requires a forecasting “plan,” not merely the selection of a particular forecasting methodology.

Komponen dari Permalan Permintaan Jangka Waktu Short-range, medium-range, long-range Pola Permintaan Trends, cycles, seasonal patterns, random

Peta Permintaan Produk dengan Trend and Seasonality Year 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?

Metoda Peramalan Kuantitatif Quantitative Forecasting 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

Metoda Time Series Metoda Statistik menggunakan data historik Moving average Exponential smoothing Linear trend line Diasumsikan pola data berulang Naive forecasts Forecast = data dari perioda terakhir Demand?

Simple Moving Average Rata-rata dari beberapa perioda data Mengurangi perubahan Digunakan bila permintaan stabil tanpa pola trend dan seasonal MAn = n i = 1  Di dimana n = jumlah perioda dalam moving average Di = permintaan pada perioda i

Simple Moving Average ORDERS THREE-MONTH FIVE-MONTH Jan 120 – – Feb 90 – – Mar 100 – – Apr 75 103.3 – May 110 88.3 – June 50 95.0 99.0 July 75 78.3 85.0 Aug 130 78.3 82.0 Sept 110 85.0 88.0 Oct 90 105.0 95.0 Nov – 110.0 91.0 ORDERS THREE-MONTH FIVE-MONTH MONTH PER MONTH MOVING AVERAGE MOVING AVERAGE

Smoothing Effects 150 – 125 – 100 – 5-month 75 – 50 – 25 – 0 – Orders | | | | | | | | | | | Jan Feb Mar Apr May June July Aug Sept Oct Nov 5-month 3-month Actual Orders Month

Weighted Moving Average WMAn = i = 1  Wi Di dimana Wi = bobot untuk perioda i, antara 0 dan 100 persen  Wi = 1.00 Justifikasi metoda moving average untuk lebih mencerminkan fluktuasi data

Weighted Moving Average Example MONTH WEIGHT DATA August 17% 130 September 33% 110 October 50% 90 November forecast WMA3 = 3 i = 1  Wi Di = (0.50)(90) + (0.33)(110) + (0.17)(130) = 103.4 orders

Linear Trend Line y = a + bx where a = intercept (at period 0) b = slope of the line x = the time period y = forecast for demand for period x

Least Squares Example 78 Linear trend line x = = 6.5 12 y = = 49.42 y = 47.21 + 0.34x Forecast for period 13 y = 47.21 + 0.34(13) y = 51.63 units x = = 6.5 y = = 49.42 b = = b = 0.34 a = y - bx = 49.42 - (0.34)(6.5) = 47.21 3903 - (12)(6.5)(49.42) 650 - 12(6.5)2 xy - nxy x2 - nx2 78 12 593 Least Squares Example x(PERIOD) y(DEMAND) xy x2 1 73 73 1 2 40 80 4 3 41 123 9 4 37 148 16 5 45 225 25 6 50 300 36 7 43 301 49 8 47 376 64 9 56 504 81 10 52 520 100 11 55 605 121 12 54 648 144 78 593 3903 650

Linear Trend Line 70 – 60 – 50 – 40 – Actual 30 – 20 – 10 – 0 – Demand | | | | | | | | | | | | | 1 2 3 4 5 6 7 8 9 10 11 12 13 Actual Demand Period Linear trend line

Justifikasi Seasonal Di D Seasonal factor = Si = Kenaikan / penurunan yang berulang dari permintaan Gunakan seasonal factor untuk menjustifikasi peramalan Seasonal factor = Si = Di D

Keakuratan Peramalan Error = Actual - Forecast Dapatkan metoda yang meminimasi error Mean Absolute Deviation (MAD) Cumulative Error (E)

Mean Absolute Deviation (MAD)  Dt - Ft  n MAD = where t = the period number Dt = demand in period t Ft = the forecast for period t n = the total number of periods  = the absolute value

MAD Example PERIOD DEMAND, Dt Ft ( =0.3) (Dt - Ft) |Dt - Ft| 1 37 37.00 – – 2 40 37.00 3.00 3.00 3 41 37.90 3.10 3.10 4 37 38.83 -1.83 1.83 5 45 38.28 6.72 6.72 6 50 40.29 9.69 9.69 7 43 43.20 -0.20 0.20 8 47 43.14 3.86 3.86 9 56 44.30 11.70 11.70 10 52 47.81 4.19 4.19 11 55 49.06 5.94 5.94 12 54 50.84 3.15 3.15 557 49.31 53.39 PERIOD DEMAND, Dt Ft ( =0.3) (Dt - Ft) |Dt - Ft|